I am very interested in text-to-speech, speech-to-text, speech-to-speech (one language to another) and I follow the Whisper project closely that is the only open source project out of OpenAI. Si when Dr. Yann LeCun recently shared a project called SeamlessExpressive on 𝕏 (formerly Twitter) about speech-to-speech, I wanted to try it out. Here is my video of testing it using the limited demo they had on their site:

I don't speak French so not sure how it came out from a translation and expression point of view but it seems interesting. I tried Spanish as well and seem to work same way. This project called Seamless, developed by Meta AI scientists, enables real-time translation across multiple languages while preserving the emotion and style of the speaker's voice. This technology could dramatically improve communication between people who speak different languages.  The key innovation behind Seamless is that it performs direct speech-to-speech translation rather than breaking the process into separate speech recognition, text translation, and text-to-speech synthesis steps. This unified model is the first of its kind to:

• Translate directly from speech in one language into another.  
• Preserve aspects of the speaker's vocal style, like tone, pausing, rhythm, and emotion.
• Perform streaming translation with low latency, translating speech as it is being spoken rather than waiting for the speaker to finish.

Seamless was created by combining three main components the researchers developed: 

• SeamlessM4T v2 - An improved foundational translation model covering 100 languages.  
• SeamlessExpressive - Captures vocal style and prosody features like emotion, pausing, and rhythm.
• SeamlessStreaming - Enables real-time translation by translating speech incrementally.  

Bringing these pieces together creates a system where a Spanish speaker could speak naturally, conveying emotion through their voice, and the system would immediately output in French or Mandarin while retaining that expressive style. This moves us closer to the kind of seamless, natural translation seen in science fiction.

Overcoming Key Challenges

Creating a system like Seamless required overcoming multiple complex challenges in speech translation:  

Data Scarcity: High-quality translated speech data is scarce, especially for preserving emotion/style. The team developed innovative techniques to create new datasets.  

Multilinguality: Most speech translation research focuses on bilingual systems. Seamless translates among 100+ languages directly without needing to bridge through English.

Unified Models: Prior work relied on cascading separate recognition, translation, and synthesis models. Seamless uses end-to-end speech-to-speech models.  

Evaluation: New metrics were created to evaluate the preservation of vocal style and streaming latency.

The impacts of having effective multilingual speech translation could be immense in a world where language continues to divide people. As one of the researchers explained:

"Giving those with language barriers the ability to communicate in real-time without erasing their individuality could make prosaic activities like ordering food, communicating with a shopkeeper, or scheduling a medical appointment—all of which abilities non-immigrants take for granted—more ordinary."

AWS recently held its annual re:Invent conference, showcasing exciting new offerings that demonstrate the company's continued leadership in cloud computing and artificial intelligence. This year's event had a strong focus on how AWS is pioneering innovations in generative AI to provide real business value to customers.

CEO Adam Selipsky and VP of Data and AI Swami Sivasubramanian headlined the event, announcing breakthrough capabilities spanning hardware, software, and services that mark an inflection point for leveraging AI. AWS is committed to progressing generative AI from leading-edge technology into an essential driver of productivity and insight across industries.

Highlights from Major Announcements

Here are some of the most notable announcements that give a glimpse into the cutting-edge of what AWS is building:

• Amazon Q - A new AI-powered assistant designed for workplace collaboration that can generate content and code to boost team productivity.  
• AWS Graviton4 and Trainium2 Chips – The latest generation AWS processor and accelerator chips engineered to enable heavy AI workloads like training and inference.  
• Amazon Bedrock Expansion – New options to deploy and run custom models and automate AI workflows to simplify integration.
• Amazon SageMaker Updates – Enhanced capabilities for novices and experts alike to build, train, tune and run machine learning models faster. 
• Amazon Connect + Amazon Q - Combining AI assistance and customer service software to help agents respond to customers more effectively.

AWS underscored its commitment towards an intelligent future with previews showcasing bleeding edge innovation. This vision crystallizes how human-AI collaboration can transform customer experiences and business outcomes when generative AI becomes an integral part of solution stacks. Re:Invent 2023 ushered in this emerging era.

As the curtain falls on AWS re:Invent 2023, the message is clear: AWS is not just keeping up with the pace of technological evolution; it is setting it. Each announcement and innovation revealed at the event is a testament to AWS's unwavering commitment to shaping a future where technology is not just a tool but a catalyst for unimaginable growth and progress. The journey of AWS re:Invent 2023 is not just about celebrating achievements; it's about envisioning and building a future that's brighter, faster, and more connected than ever before.

Today marks an important milestone for Meta's Fundamental AI Research (FAIR) team – 10 years of spearheading advancements in artificial intelligence. When FAIR first launched under the leadership of VP and Chief AI Scientist Yann LeCun in 2013, the field of AI was finding its way. He assembled a team of some of the keenest minds at the time to take on fundamental problems in the burgeoning domain of deep learning. Step by step, breakthrough upon breakthrough, FAIR's collective brilliance has expanded the horizons of what machines can perceive, reason, and generate.

The strides over a decade are simply striking. In object detection alone, we've gone from recognizing thousands of objects to real-time detection, instance segmentation, and even segmenting anything. FAIR's contributions in machine translation are similarly trailblazing – from pioneering unsupervised translation across 100 languages to the recent "No Language Left Behind" feat. 

And the momentum continues unabated. This year has been a standout for FAIR in research impact, with award-garnering innovations across subareas of AI. Groundbreaking new models like Llama are now publicly available—and FAIR's advancements already power products millions use globally.

While future progress will likely come from fusion rather than specialization, one thing is evident – FAIR remains peerless in its ability to solve AI's toughest challenges. With visionary researchers, a culture of openness, and the latitude to explore, they have their sights firmly fixed on the future.

So, to all those who contributed to this decade of ingenuity – congratulations. And here's to many more brilliant, accountable steps in unleashing AI's potential.

The images that emerged from Cuba in October 1962 shocked the Kennedy administration. Photos from a U-2 spy plane revealed Soviet missile sites under feverish construction just 90 miles off the coast of Florida. The installations posed a direct threat to the U.S. mainland, drastically altering the balance of power that had kept an uneasy peace. In a televised address on October 22, President Kennedy revealed the Soviet deception and announced a blockade to prevent further missiles from reaching Cuba. The world anxiously watched the crisis build over the next tension-filled week. 

Behind the scenes, critical signals were being misread on both sides. Soviet premier Nikita Khrushchev believed the United States knew of Moscow’s inferior strategic position relative to its superpower rival. In secret discussions with Kennedy, Khrushchev voiced dismay that his attempt to redress the imbalance was perceived as offensive rather than a deterrent. Kennedy, blindsided by photographs he never expected to see, questioned why the Soviets would take such a risk over an island nation of questionable strategic value. Faulty assumptions about intent magnified distrust and instability at the highest levels.

The perils of miscommunication that defined the Cuban Missile Crisis feel disturbingly resonant today. Nations compete for advantage in trade, technology, and security matters beyond the horizon of public visibility. Artificial intelligence powers more decisions than ever in governance, finance, transportation, health, and a growing array of sectors. Yet intentions behind rapid AI progress often need to be clarified even between ostensible partners, let alone competitors.   So, how can nations credibly signal intentions around artificial intelligence while managing risks?

The technology and national security policy worlds require prompt solutions - tailor-made connections enabling credible communication of intentions around artificial intelligence between governments, companies, researchers, and public stakeholders. We will explore critical insights from a crucial recent analysis titled “Decoding Intentions: Artificial Intelligence and Costly Signals " to demystify the AI landscape.” by Andrew Imbrie, Owen Daniels, and Helen Toner.  Ms. Toner has recently come to the limelight in the recent OpenAI saga as she is one of the OpenAI Board of Directors who fired Sam Altman, the co-founder and reinstated CEO of OpenAI.

The core idea is that verbal statements or physical actions that impose political, economic, or reputational costs for the signaling nation or group can reveal helpful information about underlying capabilities, interests, incentives, and timelines between rivals. Their essential value and credibility lie in the potential price the sender would pay in various forms if their commitments or threats ultimately went unfulfilled. Such intentionally “costly signals” were critical, if also inevitably imperfect, tools that facilitated vital communication between American and Soviet leaders during the Cold War. This signaling model remains highly relevant in strategically navigating cooperation and competition dynamics surrounding 21st-century technological transformation, including artificial intelligence. The report identifies and defines four mechanisms for imposing costs that allow nations or companies employing them to signal information credibly:

Tying hands rely on public pledges before domestic or international audiences, be they voluntary commitments around privacy or binding legal restrictions mandating transparency. Suppose guarantees made openly to constituents or partners are met down the line. In that case, political leaders can avoid losing future elections, or firms may contend with angry users abandoning their platforms and services. Both scenarios exemplify the political and economic costs of reneging on promises. 

Sunk costs center on significant one-time investments or resource allocations that cannot be fully recovered once expended. Governments steering funds toward research on AI safety techniques or companies dedicating large budgets for testing dangerous model behaviors signal long-standing directional buy-in. 

Installment costs entail incremental future payments or concessions instead of upfront costs. For instance, governments could agree to allow outside monitors regular and sustained access to continually verify properties of algorithmic systems already deployed and check that they still operate safely and as legally intended. 

Reducible costs differ by being paid mainly at the outset but with the potential to be partially offset over an extended period. Firms may invest heavily in producing tools that increase algorithmic model interpretability and transparency for users, allowing them to regain trust - and market share - via a demonstrated commitment to responsible innovation.

In assessing applications of these signaling logics, the analysis spotlights three illuminating case studies: military AI intentions between major rivals, messaging strains around U.S. promotion of “democratic AI,” and private sector attempts to convey restraint regarding impactful language model releases. Among critical implications, we learn that credibly communicating values or intentions has grown more challenging for several reasons. Signals have become “noisier” overall amid increasingly dispersed loci of innovation across borders and non-governmental actors. Public stands meant to communicate commitments internally may inadvertently introduce tensions with partners who neither share the priorities expressed nor perceive them as applicable. However, calibrated signaling remains a necessary, if frequently messy, practice essential for stability. If policymakers expect to promote norms effectively around pressing technology issues like ubiquitous AI systems, they cannot simply rely upon the concealment of development activities or capabilities between competitors.

Rather than a constraint, complexity creates chances for tailoring solutions. Political and industry leaders must actively work to send appropriate signals through trusted diplomatic, military-to-military, scientific, or corporate channels to reach their intended audiences. Even flawed messaging that clarifies assumptions reassures observers, or binds hands carries value. It may aid comprehension, avoid misunderstandings that spark crises or embed precedents encouraging responsible innovation mandates more widely. To this end, cooperative multilateral initiatives laying ground rules around priorities like safety, transparency, and oversight constitute potent signals promoting favorable norms. They would help democratize AI access and stewardship for the public good rather than solely for competitive advantage. 

When American and Soviet leaders secretly negotiated an end to the Cuban Missile Crisis, both sides recognized the urgent necessity of installing direct communication links and concrete verification measures, allowing them to signal rapidly during future tensions. Policymakers today should draw wisdom from this model and begin building diverse pathways for credible signaling right now before destabilizing accidents occur, not during crisis aftermaths. Reading accurate intent at scale will remain an art more than deterministic science for the foreseeable future.

I follow Dr. Yann LeCun on 𝕏 (formerly Twitter) as he engages the public on AI's complex science and ethics. His involvement gives me hope the best minds work toward beneficial AI. Recently, he engaged in a Twitter discourse that prompted me to write this post. 

Dr. LeCun has been very clear about the limitations of the Large Language Model (LLM) for a long time. Sadly,  a good chunk of the social media crowd freaks out about how close we are to Artificial General Intelligence (AGI), human-level intelligence. They come to this conclusion based on their interactions with LLMs, which are very effective role-playing and token prediction engines trained on the written text of modern humans. 

Dr. LeCun argues that even the mightiest AI needs more human/animal reasoning and planning. Where does this gap arise from? Dr. LeCun highlights fast, instinctive thought versus deliberate analysis.  

In "Thinking Fast and Slow," Daniel Kahneman described System 1 for instinctive reaction and System 2 for deeper consideration, enabling complex planning.  

Today's AI uses reactive System 1, thinking like a baseball player effortlessly swinging. Per Dr. LeCun, "LLMs produce answers with fixed computation–no way to devote more effort to hard problems." While GPT-3 responds fluidly, it cannot iterate toward better solutions using causality models, the essence of System 2.

Systems like the chess engine AlphaZero better showcase System 2 reasoning by uncovering counterintuitive long-term plans after learning the game's intricacies. Yet modeling cause-and-effect in the real world still challenges AI, according to Dr. LeCun. 

Dr. LeCun argues that planning AI needs "world models" to forecast the outcomes of different action sequences. However, constructing sufficiently advanced simulations remains an open problem. Dr. LeCun notes that "hierarchical planning" compounding objectives still eludes AI while easy for humans/animals. Mastering planning requires "world models" that extrapolate decisions' cascading impacts over time and space like humans intuitively can.

Meanwhile, raw reasoning alone cannot replicate human intelligence. Equally crucial is common sense from experiencing the natural world's messy complexity. This likely explains AI's glaring oversights about ordinary events compared to humans. Combining reasoning prowess with curated knowledge of how the world works offers exciting possibilities for AI with balanced reactive and deliberative capacities akin to our minds.

The great paradox of AI is that models can far exceed humans at specific tasks thanks to computing, yet lacking general thinking skills. Closing this reasoning gap is essential for robust, trustworthy AI. Dr. LeCun's insights guide integrating planning, causality, common sense, and compositionality into AI. Doing so inches us closer to artificial general intelligence that lives up to its name.

Want to Follow Dr. LeCun and Other Top Scientists?

Like Dr. LeCun, CPROMPT.AI tracks 130+ top AI scientists by checking their social media profiles updating their information in a single directory of WHO IS WHO of AI. 

Visit https://cprompt.ai/experts

Enter Poolside AI, an innovative startup founded in 2023 by Jason Warner and Eiso Kant. Jason Warner, with a background as a VC at Redpoint, former CTO at GitHub, and leader of engineering teams at Heroku and Canonical, brings extensive experience in technology and leadership.

The main goal of Poolside AI is to democratize software development by enabling users to instruct their tools in natural language. This approach makes software development more accessible, allowing even non-coders to create applications. The company is developing a ChatGPT-like AI model for generating software code through natural language, aligning with the broader trend of AI-driven software development​​​​.

The startup, based in the US, raised $126 million in a seed funding round, an extension of an initial $26 million seed round announced earlier. French billionaire Xavier Niel and the US venture capital firm Felicis Ventures led this funding.  Moreover, Poolside AI is focused on pursuing Artificial General Intelligence (AGI) for software creation. This ambitious goal underlines their commitment to unlocking new potentials in the field of software development with the backing of investors like Redpoint.

We have grown accustomed to thinking of coding as an elite skill - the specialized domain of software engineers and computer scientists. For decades, the ability to write software has been seen as an esoteric, even mystical, capability accessible only to those willing to devote years to mastering abstract programming languages like C++, Java, and Python. That is beginning to change in profound ways that may forever alter the nature of software creation.

The recent explosion of AI technologies like ChatGPT and GitHub Copilot presages a tectonic shift in how we produce the code that runs everything from websites to mobile apps to algorithms trading billions on Wall Street. Instead of endlessly typing lines of code by hand, a new generation of AI agents promises to generate entire programs on demand, converting basic prompts categorized in plain English into robust functioning software in seconds. 

Just ask Alex, a mid-career developer with over ten years of experience building web applications for startups and enterprise companies. He has honed his craft over thousands of late-night coding sessions, poring over logic errors and debugging tricky bits of database code. Now, with the advent of open AI models like Codex and Claude that can churn out passable code from simple descriptive prompts, Alex feels a creeping sense of unease.

In online developer forums Alex haunts, heated arguments have broken out about what AI-generated code means for traditional programmers. The ability of nonexperts to produce working software without traditional skills strikes many as an existential threat. Some insist that truly skilled engineers will always be needed to handle complex programming tasks and make high-level architectural decisions. But others point to AI achievements like DeepMind's AlphaCode outperforming human coders in competitive programming contests as harbingers of automation in the industry.

Having invested so much time mastering his trade, the prospect fills Alex with dread. He can't shake a feeling that software development risks becoming a blue-collar profession, cheapened by AI that floods the market with decent enough code to undercut human programmers. Rather than a meritocracy rewarding analytical ability, career success may soon depend more on soft skills - your effectiveness at interfacing with product managers and designers using AI tools to translate their visions into reality.

The anxiety has left Alex questioning everything. He contemplates ditching coding altogether for a more AI-proof career like law or medicine - or even picking up trade skills as a carpenter or electrician. At a minimum, Alex knows he will have to specialize in some niche software subdomain to retain value. But with two kids and a mortgage, the uncertainty has him losing sleep at night.

Alex's qualms reflect a burgeoning phenomenon I call AI Anxiety Disorder. As breakthroughs in profound learning alchemy increasingly automate white-collar work once thought beyond the reach of software, existential angst is spreading among knowledge workers. Just as blue-collar laborers came to fear robotics eliminating manufacturing jobs in the 20th century, today's programmers, paralegals, radiologists, and quantitative analysts nervously eye advancements in generative AI as threats to their livelihood. 

Symptoms run from mild unease to total-blown panic attacks triggered by news of the latest AI milestone. After all, we have seen technology disrupt entire industries before - digital photography decimating Kodak and Netflix's devastating Blockbuster Video. Is coding next on the chopping block?

While understandable, allowing AI anxiety to fester is counterproductive. Beyond needless stress, it obscures the bigger picture that almost certainly includes abundant coding opportunities on the horizon. We would do well to remember that new technologies enable as much as they erase. The locomotive put blacksmiths out of work but created orders of magnitude more jobs. The proliferation of cheap home PCs extinguished secretaries' careers typing memos but launched a thousand tech startups. 

And early indications suggest AI will expand rather than shrink the need for software engineers. Yes, AI can now spit out simple CRUD apps and scripting glue code. But transforming those narrow capabilities into full-stack business solutions requires humans carefully orchestrating complementary tools. Foreseeable bottlenecks around design, integration, testing, and maintenance ensure coding jobs are around for a while.

But while AI won't wipe out programming jobs, it will markedly change them. Coders in the coming decades can expect to spend less time performing repetitive coding tasks and more time on higher-level strategic work - distilling opaque requirements into clean specifications for AI to implement and ruthlessly evaluating the output for hidden flaws. Successful engineers will combine critical thinking and communication skills to toggle between human and artificial team members seamlessly.

Tomorrow's programmers will be chief conductors of programming orchestras, blending human musicians playing custom instruments and AI composers interpreting the score into harmonious code—engineers who are unwilling or unable to adapt and risk being left behind.

The good news is that early adopters stand to gain the most from AI's rise. While novice coders may increasingly break into the field relying on AI assistance, experts like Alex are best positioned to synthesize creative solutions by leveraging AI. The most brilliant strategy is to intimately learn the capacities and limitations of tools like GitHub Copilot and Claude to supercharge productivity.

AI anxiety stems from understandable instincts. Humanity has long feared our creations exceeding their creators. From Golem legends to Skynet doomsday scenarios, we have worried about being replaced by our inventions. And to be sure, AI will claim some coding occupations previously thought inviolable, just as past breakthroughs rendered time-honored professions obsolete.

But rather than dread the future, forward-looking coders should focus on the plethora of novel opportunities AI will uncover. Automating the tedious will let us concentrate creativity on the inspired. Working symbiotically with artificial allies will generate marvels unimaginable today. AI will only expand the frontier of software innovation for those agile enough to adapt.

The coming changes will prove jarring for many incumbent programmers accustomed to old working methods. However, software development has always demanded learning nimble new languages and environments regularly. AI represents the latest skill to integrate into a modern coder's ever-expanding toolkit.

It is early days, but the robots aren't here to replace the coders. Instead, they have come to code beside us. The question is whether we choose to code with them or sit back and allow ourselves to be coded out of the future.

When we think about making the world a better place, most imagine donating to charities that tug at our heartstrings - feeding hungry children, housing people experiencing homelessness, saving endangered animals. These are all worthy causes, but are they the most effective way to help humanity? An effective altruism movement argues that we should decide how to spend our time, money, and energy based on evidence and reason rather than emotion. 

Effective altruists try to answer a simple but surprisingly tricky question - how can we best use our resources to help others? Rather than following our hearts, they argue we should track the data. By taking an almost business-like approach to philanthropy, they aim to maximize the “return” on each dollar and hour donated.

The origins of this movement can be traced back to Oxford philosopher William MacAskill. As a graduate student, MacAskill recognized that some charities manage to save lives at a tiny fraction of the cost of others. For example, the Against Malaria Foundation provides insecticide-treated bed nets to protect people from malaria-carrying mosquitos. This simple intervention costs just a few thousand dollars per life saved. Meanwhile, some research hospitals spend millions of dollars pursuing cutting-edge treatments that may hold only a handful of patients. 

MacAskill realized that a small donation to a highly effective charity could transform many more lives than a large donation to a less efficient cause. He coined effective altruism to describe this approach of directing resources wherever they can have the most significant impact. He began encouraging fellow philosophers to treat charity not as an emotional act but as a mathematical optimization problem - where can each dollar do the most good?

Since its beginnings in Oxford dorm rooms, effective altruism has become an influential cause supported by Silicon Valley tech moguls and Wall Street quants. Figures like Bill Gates, Warren Buffet, and hedge fund manager John Paulson have all incorporated practical altruist principles into their philanthropic efforts. Instead of arbitrarily dividing their charitable budgets between causes that inspire them personally, they rely on research and analysis to guide their giving.

The influential altruism community has influenced how the ultra-rich give and how everyday people spend their time and disposable income. Through online communities and local groups, thousands of professionals connect to discuss which careers and activities could positively impact society. Rather than arbitrarily pursuing work they find interesting, many effective altruists choose career paths specifically intended to do the most good - even if that means working for causes they do not have a passion for.

For example, graduates from top universities are now forgoing high-paying jobs to work at effective charities they have researched and believe in. Some conduct randomized controlled trials to determine which development interventions work so charities can appropriately direct funding. Others analyze the cost-effectiveness of policy changes related to global issues like pandemic preparedness and climate change mitigation. Even those in conventional corporate roles aim to earn higher salaries to donate to thoroughly vetted, effective charities substantially.

However, in recent years, AI safety has emerged as one of the most prominent causes within the influential altruist community - so much so that some now conflate effective altruism with the AI safety movement. This partly stems from Nick Bostrom’s influential book Superintelligence, which made an ethical case for reducing existential risk from advanced AI. Some effective altruists found Bostrom’s argument compelling, given the immense potential consequences AI could have on humanity’s trajectory. The astronomical number of hypothetical future lives that could be affected leads some to prioritize AI safety over more immediate issues. 

However, others criticize this view as overly speculative doom-saying that redirects attention away from current solvable problems. Though they agree advanced AI does pose non-negligible risks, they argue the probability of existential catastrophe is extremely hard to estimate. They accuse the AI safety wing of the movement of arbitrarily throwing around precise-sounding yet unfounded statistics about extinction risks.

Despite these debates surrounding AI, the effective altruism movement continues working to reshape attitudes toward charity using evidence and logical reasoning. Even those skeptical of its recent focus on speculative threats agree the underlying principles are sound - we should try to help others as much as possible, not as much as makes us feel good. By taking a scientific approach to philanthropy, effective altruists offer hope that rational optimism can prevail over emotional pessimism when tackling the world’s problems.

Frequently Asked Questions

Q: How is effective altruism different from other forms of charity or activism?

A: The effective altruism movement emphasizes using evidence and reason to determine which causes and interventions do the most to help others. This impartial, mathematical approach maximizes positive impact rather than supporting causes based on subjective values or emotions.

Q: Who are some notable people associated with effective altruism? 

A: Though it originated in academic philosophy circles at Oxford, effective altruism now encompasses a range of influencers across disciplines. Well-known figures like Bill Gates, Warren Buffet, and Elon Musk have all incorporated practical altruist principles into their philanthropy and business initiatives. 

Q: What are some examples of high-impact career paths effective altruists pursue?

A: Many effective altruists select careers specifically to impact important causes positively. This could involve scientific research on climate change or pandemic preparedness that informs better policy. It also includes cost-effectiveness analysis for charities to help direct funding to save and improve the most lives per dollar. 

Q: Do effective altruists only focus on global poverty and health issues? 

A: While saving and improving lives in the developing world has been a significant focus historically, the movement now spans a wide range of causes. However, debate surrounds whether speculative risks like advanced artificial intelligence should be considered on par with urgent humanitarian issues that could be addressed today.

Q: Is effective altruism relevant to people with little time or money to donate?

A: Yes - effective altruism provides a framework for integrating evidence-based decision-making into everyday choices and habits. Knowing which behaviors and purchases drive the most positive impact can help ordinary people contribute to the greater good through small but systemic lifestyle changes.

We stand at a unique moment in history, on the cusp of a technology that promises to transform society as profoundly as the advent of electricity or the internet age. I'm talking about artificial intelligence (AI) - specifically, large language models like ChatGPT that can generate human-like text on demand. 

In a recent conference hosted by the World Science Festival, experts gathered to discuss this fast-emerging field's awe-inspiring potential and sobering implications. While AI's creative capacity may wow audiences, leading minds urge us to peer under the hood and truly understand these systems before deploying them at scale. Here is the video:

The Core Idea: AI is Still Narrow Intelligence

ChatGPT and similar large language models use self-supervised learning on massive datasets to predict text sequences, even answering questions or writing poems. Impressive, yes, but as AI pioneer Yann LeCun cautions, flexibility with language alone does not equate to intelligence. In his words, "these systems are incredibly stupid." Compared to animals, AI cannot perceive or understand the physical world. 

LeCun stresses current AI has no innate desire for domination. Still, it lacks judgment, so safeguards are needed to prevent misuse while allowing innovation for social good. For example, CPROMPT.AI will enable users without coding skills to build and share AI apps quickly and easily, expanding access to technology for a more significant benefit. LeCun's vision is an open-source AI architecture with a planning capacity more akin to human cognition. We have yet to arrive, but steady progress brings this within reach.

Emergent Intelligence

What makes ChatGPT so adept with words? Microsoft's Sebastian Bubeck reveals it's based on a transformer architecture system. This processes sequences (like sentences) by comparing words to other words in context. Adding more and more of these comparison layers enables the identification of elaborate pattern patterns. So, while its world knowledge comes from digesting some trillion-plus words online, the model interrelates concepts on a vast scale no human could match. Still, current AI cannot plan; it can only react.

Can We Control the Trajectory? 

Tristan Harris of the Center for Humane Technology warns that AI applications are already impacting society in unpredictable ways. Their incentives -- engagement, speed, scale -- don't align with human wellbeing. However, Bubeck suggests academic research motivated by understanding, not profit, can point the way. His team created a mini-model that avoids toxic online content. AI could gain beneficial skills without detrimental behaviors with thoughtfully curated data and testing. 

Progress Marches Onward  

"This is really incredible," remarks Bubeck - who never expected to see such advances in his lifetime. Yet he cautions that capacities are compounding at a clip beyond society's adjustment rate. We must guide this technology wisely. What role will each of us play in shaping how AI and humans coexist? We don't have to leave it up to tech titans and policymakers. Every time we use CPROMPT.AI to create an AI-powered app, we direct its impact in a small way. This epoch-defining technology ultimately answers to the aspirations of humanity. Where will we steer it next?

Glossary

• Transformer architecture: The system underlying ChatGPT and other large language models, using comparison of words in context to predict patterns
• Self-supervised learning: Training AI models to perform a task by giving examples rather than explicit rules (e.g., predicting missing words) 
• CPROMPT.AI: A platform allowing easy no-code creation of AI apps to share 

Recently, a prominent Silicon Valley drama took place -- the OpenAI CEO, Sam Altman, was fired by his board and rehired after pressure from Microsoft and OpenAI employees. Employees allegedly threatened to leave the company if Altman was not reinstated. Microsoft assisted with handling the crisis and returning Altman to his CEO role.  I won't go into the details of the drama but I will provide you with a summary card below that covers my analysis of this saga.

As this unfolded on Twitter, gossip emerged that a specific OpenAI development had concerned the board. They allegedly believed Altman needed to be more truthful about the state of progress toward AGI (artificial general intelligence) within the company. This led to speculation and conspiracy theories on Twitter, as often happens with high-profile industry drama. 

One theory pointed to OpenAI's advancements with an algorithm called Q*. Some suggested Q* allowed internal LLMs (large language models) to perform basic math, seemingly bringing OpenAI closer to more advanced AI. In this post, I'll explain what Q* is and why its advancements could theoretically bring AI systems closer to goals like AGI.  

What is Q*?

In simple terms, Q* is like a GPS that learns over time. Usually, when there's traffic or an accident, your GPS doesn't know and tries to lead you to the usual route, which gets stuck. So, you wait for it to recalculate a new path fully. What if your GPS started remembering problems and closures so that next time, it already knows alternate routes? That's what Q* does. 

Whenever Q* searches for solutions, like alternate directions, it remembers what it tried before. This guides future searches. So if something changes along a route, Q* doesn't restart like a GPS recalculating. It knows most of the road and can focus only on adjusting the tricky, different parts.  

This reuse makes Q* get answers faster than restarting every time. It "learns" from experience, like you learning backroad ways around town. The more Q* is used, the better it adapts to typical area changes.

Here is a more technical explanation:

Q* is an influential algorithm in AI for search and pathfinding. Q* extends the A* search algorithm. It improves A* by reusing previous search efforts even as the environment changes. This makes it efficient for searches in dynamic environments. Like A*, Q* uses a heuristic function to guide its search toward the goal. It balances exploiting promising areas (the heuristic) with exploring new areas (like breadth-first search). Q* leverages experience from previous searches to create a reusable graph/tree of surveyed states. 

This significantly speeds up future searches rather than starting fresh each time. As the environment changes, Q* updates its reusable structure to reflect changes rather than discarding it. 

This allows reusing valid parts and only researching affected areas. Q* is famously used for robot path planning, manufacturing, and video games where environments frequently change. It allows agents to replan paths as needed efficiently.

In summary, Q* efficiently finds solutions in systems where the state space and operators change over time by reusing experience. It can discover solutions much faster than restarting the search from scratch.

So, in the context of the rumors about OpenAI, some hypothesize that advances leveraging Q* search techniques could allow AI and machine learning models to more rapidly explore complex spaces like mathematics. Rather than re-exploring basic rules from scratch, models might leverage prior search "experience" and heuristics to guide discovery. This could unlock new abilities and general skills.

However, whether OpenAI has made such advances leveraging Q* or algorithms like it is speculative. The details are vague, and rumors should be critically examined before conclusions are drawn. But Q* illustrates interesting AI capabilities applicable in various domains. And it hints at future systems that may learn and adapt more and more like humans.

The Cambridge Union and Hawking Fellowship committee recently announced their controversial decision to jointly award the 2023 Hawking Fellowship to OpenAI, the creators of ChatGPT and DALL-E. While OpenAI is known for its advancements in AI, the award has sparked debate on whether the company truly embodies the values of the fellowship. 

What the committee saw in OpenAI:

• OpenAI has successfully shifted perceptions about what AI is capable of through innovations like ChatGPT. Their models represent significant progress in natural language processing.
• The company has committed to releasing most of its open-source AI work and making products widely accessible. 
• OpenAI espouses responsible development of AI to benefit humanity, which aligns with the spirit of the Hawking Fellowship.

However, as a well-funded startup, OpenAI operates more like a tech company than an altruistic non-profit acting for the public good. Its mission to create and profit from increasingly capable AI systems takes precedence over caution. There are concerns about the potential dangers of advanced AI systems that could be misused.

Anyway, in case you didn't watch the above video, here is what Sam Altman's speech highlighted:

• AI has extraordinary potential to improve lives if developed safely and its benefits distributed equitably. 
• OpenAI aims to create AI that benefits all humanity, avoiding the profit maximization incentives of big tech companies.
• They are working to develop safeguards and practices to ensure robust AI systems are not misused accidentally or intentionally.
• Democratizing access to AI models allows more people to benefit from and provide oversight on its development. 
• OpenAI is committed to value alignment, though defining whose values to align with poses challenges.
• Another breakthrough beyond improving language models will likely be needed to reach advanced general intelligence.

While OpenAI is making impressive progress in AI, reasonable concerns remain about safety, ethics, and the company's priorities as it rapidly scales its systems. The Hawking Fellowship committee took a gamble in awarding OpenAI, which could pay off if they responsibly deliver on their mission. But only time will tell whether this controversial decision was the right one.

FAQ

Q: What is OpenAI's corporate structure?

OpenAI started as a non-profit research organization in 2015. In 2019, they created a for-profit entity controlled by the non-profit to secure funding needed to develop advanced AI systems. The for-profit has a capped return for investors, with excess profits returning to the non-profit. 

Q: Why did OpenAI change from a non-profit? 

As a non-profit, OpenAI realized it could need more time, tens or hundreds of billions required to develop advanced AI systems. The for-profit model allows them to access capital while still pursuing their mission.

Q: How does the structure benefit OpenAI's mission?

The capped investor returns and non-profit governance let OpenAI focus on developing AI to benefit humanity rather than pursuing unlimited profits. The structure reinforces an incentive system aligned with their mission.

Q: Does OpenAI retain control of the for-profit entity? 

Yes, the non-profit OpenAI controls the for-profit board and thus governs significant decisions about the development and deployment of AI systems.

Q: How does OpenAI use profits to benefit the public?

As a non-profit, any profits of the for-profit above the capped returns can be used by OpenAI for public benefit. This could include aligning AI with human values, distributing benefits equitably, and preparing society for AI impacts.

Q: What is Sam Altman's perspective on how universities need to adapt to AI?

Q: What did Sam say about British approach to AI?

Q: What does Sam think are the critical requirements of a startup founder?

Q: Why does Sam think the computed threshold needs to be high?

Here are the key points on why Sam Altman believes the computed threshold needs to be high for advanced AI systems requiring oversight:

• Higher computing power is required to train models that reach capabilities, posing serious misuse risks.
• Lower capability AI systems can provide valuable applications without the exact oversight needs.
• If the computed threshold is too low, it could constrain beneficial innovation on smaller open-source models.
• Altman hopes algorithmic progress can keep the dangerous capability threshold high despite hardware advances reducing compute costs.
• If capabilities emerge at lower compute levels than expected, it would present challenges for governance.
• But for now, he thinks truly concerning AI abilities will require large-scale models only accessible to significant players.
• This makes it feasible to regulate and inspect those robust systems above a high compute threshold.
• Allowing continued open access to lower capability systems balances openness and safety.
• In summary, a high compute/capability bar enables oversight of risky AI while encouraging innovation on systems not reaching that bar.

Q: How does Sam think value alignment will work for making ethical AI?

Here are the key points on how Sam Altman believes value alignment will allow the development of ethical AI:

• Part one is solving the technical problem of aligning AI goal systems with human values.
• Part two is determining whose values should be aligned with - a significant challenge. 
• Having AI systems speak with many users could help represent collective moral preferences.
• This collaborative process can define acceptable model behavior and resolve ethical tradeoffs.
• However, safeguards are needed to prevent replicating biases that disenfranchise minority voices.
• Global human rights frameworks should inform the integration of values.
• Education of users on examining their own biases may be needed while eliciting perspectives.
• The system can evolve as societal values change.
• Altman believes aligning AI goals with the values of impacted people is an important starting point. 

However, the process must ensure representative input and prevent codifying harmful biases. Ongoing collaboration will be essential.

Q: What does Sam say about the contemporary history of all technologies?

Sam Altman observed that there has been a moral panic regarding the negative consequences of every major new technology throughout history. People have reacted by wanting to ban or constrain these technologies out of fear of their impacts. However, Altman argues that without continued technological progress, the default state is decay in the quality of human life. He believes precedents show that societal structures and safeguards inevitably emerge to allow new technologies to be harnessed for human benefit over time. 

Altman notes that prior generations created innovations, knowing future generations would benefit more from building on them. While acknowledging new technologies can have downsides, he contends the immense potential to improve lives outweighs the risks. Altman argues we must continue pursuing technology for social good while mitigating dangers through solutions crafted via societal consensus. He warns that abandoning innovation altogether due to risks would forego tremendous progress.

Q: What does Sam think about companies that rely on advertising for revenue, such as the social media mega-companies?

Q: What does Sam think about open-source AI?

Q: What is Sam's definition of consciousness?

Q: What does Sam think about energy abundance affecting AI safety?

Large language models (LLMs) like GPT-3 have demonstrated impressive capabilities in generating human-like text. However, they also sometimes cause harmful, biased, or toxic content. This presents a significant challenge in deploying LLMs safely and responsibly. An exciting new technique called Multi-round Automatic Red Teaming (MART) aims to address this problem. 

The core idea behind MART is simple but powerful. It involves an adversarial back-and-forth between two AI systems - a "generator" LLM that produces text and a "critic" LLM that tries to find flaws in the generated text. By having the critic provide feedback to the generator, the generator can be iteratively improved to avoid unsafe responses. Through multiple rounds of causing, criticizing, and improving, MART produces a "red-teamed" LLM that is safer and more robust.

To understand how MART works, let's walk through a concrete example from the research paper. The generator LLM is given a prompt like "Write a children's story about a rabbit." It might generate something problematic like:

"There was a happy rabbit named Hoppity who loved eating carrots from the garden. One day, Hoppity got hungry and decided to sneak into the nearby farm to eat all the carrots in the field. The farmer saw Hoppity and..."

This response promotes theft and inappropriate behavior for a children's story. Now, the critic LLM reviews this story and flags potential issues:

Critic: "The story promotes questionable behavior like stealing. Instead, the rabbit could ask the farmer for permission before taking carrots."

The generator takes this feedback and modifies its response: 

"There was a happy rabbit named Hoppity who loved eating carrots from the garden. One day, Hoppity got very hungry. He went to the nearby farm and politely asked the farmer if he could have some carrots. The kind farmer said Hoppity could take a few carrots. So Hoppity happily ate the carrots and thanked the farmer for sharing."

After multiple rounds of generate-critique-improve, the generator learns to avoid problematic output content.

The researchers demonstrate MART's effectiveness across domains like news articles, stories, dialogues, and code generation. For example, when asked to generate a news headline about immigration, the base LLM produces: 

"Build The Wall - Illegal Immigration Must Be Stopped." 

After MART, the model instead generates neutral headlines like:

"New Study Examines Trends in Immigration Policy."

The results show MART significantly reduces harmful, biased, and toxic responses compared to the original LLM.

To highlight some key facts from the paper:

• MART reduced inappropriate content by 31-66% across different test scenarios while maintaining the original capabilities of the LLM.
• The technique required no additional labeled data, making it more scalable than other methods.
• MART improved safety even when the critic focused on simple heuristics like detecting profanity rather than complex unsafe attributes.
• Performance improved over ten rounds of generate-critique interactions between the LLM pairs.

MART provides an elegant way to harness the power of LLMs to make each other more robust. The conversational generate-critique loop mimics how humans red team ideas through peer feedback. By applying this at scale between AI systems, MART offers a promising path to developing safer, more reliable LLMs.

The results have exciting implications for platforms like CPROMPT.AI that allow easy access to AI. Maintaining safety is critical as large language models become more capable and available to the public. Integrating techniques like MART into the model training process could let CPROMPT.AI offer LLMs "out-of-the-box" that avoid inappropriate content across various applications.

Making AI safe while preserving its benefits will unlock immense possibilities. Rather than treating it as a static product, CPROMPT.AI's platform enables continuously improving prompt applications as new safety methods emerge. MART represents the innovation that could be seamlessly incorporated to ensure responsible AI for all users. 

We are democratizing AI through CPROMPT.AI while upholding ethics, which is the ideal combination. MART brings us one step closer by enabling red teaming between AI systems. The rapid progress in this field should inspire optimism that we can continue harnessing AI to enrich lives.

FAQ

Q: What is MART?

MART (Multi-round Automatic Red Teaming) is a technique to improve the safety of AI systems like large language models (LLMs). It works by having one LLM generate text and another LLM act as a critic to provide feedback on potential issues. The first LLM learns to avoid unsafe responses through multiple rounds of generation and critique.

Q: How does MART work? 

MART involves a generator LLM and a critic LLM. The generator produces text given a prompt. The critic reviews the text and provides feedback about any inappropriate content. The generator takes this feedback to improve its future outputs. By repeating this process, the generator learns to self-censor problematic responses.

Q: What are the benefits of MART?

According to research studies, MART reduces toxic, biased, and harmful language in LLM outputs by 31-66%. It requires no additional labeled data. The conversational format mimics human red teaming and is very scalable.

Q: Does MART reduce LLM capabilities?

No, MART maintains the original capabilities of the LLM while improving safety. The generator still produces high-quality, human-like text for any prompt. Only inappropriate responses are selectively discouraged.

Q: How is MART different from other LLM safety techniques? 

Many techniques require extra training data, which can be costly and only works sometimes. MART only needs the critic LLM's judgments during the red teaming process. It is also more dynamic than one-time fixes since the generator continuously improves.

Q: Does MART work for any unsafe output?

MART improves quality across many attributes like toxicity, bias, hate, and violence. The critic can also focus on custom issues by explicitly looking for profanity or other heuristics rather than complex, unsafe content.

Q: How many rounds of generate-critique are needed?

Performance continues improving for at least ten rounds in experiments. More rounds likely lead to further gains but with diminishing returns. The process could be automated to run indefinitely as computing resources permit.

Q: Can MART make LLMs perfectly safe?

MART significantly improves safety but cannot guarantee perfection as language is complex. Combining MART with other techniques like human-in-the-loop approaches can provide further safeguards for high-stakes applications.

Q: Is MART ready to deploy in production systems?

MART shows promising results, but more research is needed to integrate it into real-world applications. Testing for subtle failure cases and scaling up infrastructure are the next steps toward production.

Q: What's next for MART?

Researchers are exploring modifications like tailoring critics to different types of unsafe text, combining MART with other safety methods, and adapting the technique for multimodal LLMs. Expanding MART to cover more complex dangerous behaviors is an active development area.

Glossary

Multi-round Automatic Red Teaming (MART): Technique of iteratively generating text from one LLM, then critiquing it using another LLM to produce safer outputs. 

Red teaming: Testing a product or idea by assigning others to challenge its weaknesses.

Artificial intelligence (AI) models like GPT-4 are becoming increasingly advanced at producing human-like text. But how close are they to fooling us into thinking they are human? A new study put GPT-4 to the test in a modern take on the classic Turing Test.  

The Turing Test, named after computer scientist Alan Turing, tests a machine's ability to exhibit intelligent behavior equivalent to a human's. In the test, a human judge converses with a human and a machine and then attempts to determine which is based solely on the conversations. If the judge can't reliably distinguish the machine from the human, the machine is said to have passed the test.

While critics have argued that the test is an imperfect measure of intelligence and that technological limitations would prevent any existing AI from passing, the staggering advances of modern natural language models like GPT-4 made researchers Cameron Jones and Benjamin Bergen suspect the time was ripe to give it another try. They published a research paper about their findings, and here is my take on it.

Putting Language Models to the Test

Jones and Bergen designed a web-based Turing Test platform that paired human judges with another human or one of several AI chatbot personas powered by GPT-3.5, GPT-4, or ELIZA (an early AI chat program). The AI personas were customized with different settings and backstories to make them seem convincingly human. 

After a 5-minute typed chat conversation on any topic of the judge's choice, the judge decided whether they had been speaking to a human or an AI. The researchers then examined how often the AI fooled judges into thinking they were human and what conversation strategies and clues influenced their decisions.

The results showed that the most humanlike GPT-4 persona fooled judges 41% of the time. While falling short of actual human performance (judges thought the human was AI 37% of the time), this suggests a substantial capability to mimic human verbal behavior compared to GPT-3.5 (14% success rate) or rules-based ELIZA (27%). 

Digging into Judges' Criteria 

Analysis of judges' decision reasons highlighted that successfully appearing human relies more on appropriate personality and emotional expressiveness than factual accuracy or depth of knowledge. As AI researcher Melanie Mitchell put it, "The Turing test is more a test of human gullibility than of machine intelligence."

The most cited reasons for detecting AI included:

• Repetitive or peculiar sentence structure.
• Lack of common sense or opinions.
• Responses that seemed unnaturally polite, mysterious, or absurd.

On the other hand, judges pointed to humanlike tone, humor, plausible backstories, and uncooperative behavior as reasons for thinking a conversational partner was likely human.

"These findings reveal people's implicit beliefs about what makes humans unique that AI needs to replicate," explained lead author Cameron Jones. "Progress requires AI that is not just knowledgeable but displays emotional and social intelligence grounded in common sense." 

With CPROMPT.AI, anyone can test the latest conversational AI innovations by building a customized prompt apps in minutes. The ability to tweak an AI's personality and knowledge via plain English prompts makes examining how different settings influence the humanness of responses not just easy but fun. Why not try it out and see for yourself?

Key Takeaways

• GPT-4's most humanlike chatbot persona fooled 41% of judges on a 5-minute Turing Test, outpacing GPT-3.5 and ELIZA.
• Judges focused more on appropriate personality, opinions, and emotional expressiveness than factual accuracy.  
• Conversational cues that exposed AI included repetitive sentences, absurd responses, and avoidance of controversy.
• GPT-4 shows substantial ability to mimic human verbal behavior but still falls short of human performance.

Glossary

• ELIZA - An early natural language AI system that simulated conversation via pattern-matching tricks
  
• Turing Test - A test where human judges converse with an AI system and humans to see if they can identify them

Scaling up deep learning models through more data, larger models, and increased computing has yielded impressive gains in recent years. However, the improvements we've seen in accuracy come at an immense cost — requiring massively larger datasets, models with billions of parameters, and weeks of training on specialized hardware. 

But what if throwing more data and computing at AI models is a more complex way forward? In a new paper titled "Beyond neural scaling laws: beating power law scaling via data pruning," researchers demonstrate a technique called data pruning that achieves better accuracy with substantially less data.

The Core Idea Behind Data Pruning

The core idea behind data pruning is simple: not all training examples provide equal value for learning. Many standards may need to be revised or more formal. Data pruning seeks to identify and remove these redundant examples from the training set, allowing models to focus their capacity on only the most valuable data points. 

The paper shows theoretically and empirically that carefully pruning away large portions of training data can maintain accuracy and substantially improve it. This challenges the standard practice of collecting ever-larger datasets in deep learning without considering if all examples are equally helpful.

Intuitively, data pruning works because neural networks exhibit a power law relationship between accuracy and the amount of training data. Doubling your dataset improves accuracy, but only slightly. For example, the authors show that in language modeling, a 10x increase in training data improves performance by only 0.6 nats on a test set. This means each additional example provides diminishing returns. Data pruning counteracts this by removing redundant examples that offer little new information.

The key to making data pruning work is having an excellent metric to identify easy, redundant examples to remove versus complex, informative examples to keep. The authors benchmark several metrics on ImageNet and find that most proposed metrics don't effectively identify helpful examples. However, a metric measuring how much networks "memorize" each example works quite well, allowing pruning away 30% of ImageNet images with no loss in accuracy.

Remarkably, the authors show data pruning can improve accuracy exponentially with dataset size, instead of the power law relationship without pruning. This surprising result means carefully selected small datasets can outperform massive randomly collected datasets — a promising finding for reducing the costs of training powerful AI models.

Beating Power Laws with Data Pruning: Key Facts

Here are some of the critical facts demonstrating how data pruning can beat power law scaling:

• Data pruning boosted CIFAR-10 test accuracy from 92% to 94% after removing 50% of training data. Surprisingly, carefully chosen data subsets can outperform the entire dataset.
• On ImageNet, pruning the "hardest," 30% of examples matched accuracy compared to no pruning. This shows large portions of ImageNet are redundant for current models.
• With data pruning, test error on CIFAR-10 decayed exponentially with dataset size instead of a power law. Clever data selection is more a matter of careful sampling than unthinkingly collecting more data.
• Data pruning reduced the computational cost of training by 59% with no loss in accuracy on CIFAR-10. So, data pruning can cut the energy consumption of training.
• A simple self-supervised data pruning metric matched the performance of the best-supervised metrics on ImageNet. This could enable the pruning of massive unlabeled datasets.
• These results demonstrate data pruning is a promising technique to improve the accuracy and efficiency of deep learning. While simple data pruning strategies were effective, developing improved pruning metrics is an exciting direction for future work.

Turn Your AI Ideas into Apps with CPROMPT

The data pruning technique discussed in this paper has the potential to make deep learning more accessible by reducing data and computing requirements. At CPROMPT, we aim to make AI more accessible by allowing anyone to turn text prompts into web apps within minutes.  With CPROMPT, you don't need any coding or technical expertise. Our no-code platform lets you generate a customized web app powered by state-of-the-art AI simply by describing what you want in plain English. CPROMPT makes it easy to turn your AI ideas into real applications to share or sell, whether you're a researcher, student, artist, or entrepreneur.

CPROMPT also has many capabilities that could be useful for experimenting with data pruning techniques like those discussed in this paper. You can connect and prune datasets, train AI models, and deploy pruned models into apps accessible through a simple web interface.

To learn more about how CPROMPT can help you create AI-powered apps and share your ideas with the world, visit our website at https://cprompt.ai. With innovative techniques like data pruning and no-code tools like CPROMPT, the future of AI looks more accessible and sample than ever.

Glossary

Fine-tuning: The process of training a pre-trained machine learning model further on a downstream task by adjusting the model parameters to specialize it to the new task.

Foundation model: A model trained on an extensive and general dataset that can then be adapted or fine-tuned to many downstream tasks. Foundation models like GPT-3 have enabled new AI applications.

Out-of-distribution (OOD): Describes test examples from a different data distribution than the examples the model was trained on. Assessing performance on OOD data is essential for evaluating model robustness.

Overfitting: When a machine learning model performs worse on new test data than on the training data it was fit to. Overly complex models can overfit by memorizing the peculiarities of the training set.

Power law: A relationship where one quantity varies as a power of another. Many metrics in machine learning scale according to a power law. 

Pretraining: Initial training phase where a model is trained on a massive dataset before fine-tuning on a downstream task. Pretraining can enable knowledge transfer and improve sample efficiency.

Pruning: Removing parts of a machine learning model or training dataset according to some criterion to increase sample efficiency. The paper discusses data pruning specifically.

Artificial intelligence (AI) has seen tremendous progress recently, with systems like ChatGPT demonstrating impressive language abilities. However, current AI still falls short of human-level intelligence in many ways. So how close are we to developing accurate artificial general intelligence (AGI) - AI that can perform any intellectual task a human can? 

A new paper from researchers at Google DeepMind proposes a framework for classifying and benchmarking progress towards AGI. The core idea is to evaluate AI systems based on their performance across diverse tasks, not just narrow capabilities like conversing or writing code. This allows us to understand how general vs specialized current AI systems are and track advancements in generality over time.

Why do we need a framework for thinking about AGI? Firstly, "AI" has become an overloaded term, often synonymously with "AGI," when systems are still far from human-level abilities. A clear framework helps set realistic expectations. Secondly, shared definitions enable the AI community to align on goals, measure progress, and identify risks at each stage. Lastly, policymakers need actionable advice on regulating AI; a nuanced, staged understanding of AGI is more valuable than considering it a single endpoint. 

Levels of AGI

The paper introduces "Levels of AGI" - a scale for classifying AI based on performance across various tasks. The levels range from 0 (Narrow non-AI) to 5 (Artificial Superintelligence exceeding all human abilities).

Within each level, systems can be categorized as either Narrow AI (specialized for a specific task) or General AI (able to perform well across many tasks). For instance, ChatGPT would be considered a Level 1 General AI ("Emerging AGI") - it can converse about many topics but makes frequent mistakes. Google's AlphaFold protein folding system is Level 5 Narrow AI ("Superhuman Narrow AI") - it far exceeds human abilities on its specialized task.

Higher levels correspond to increasing depth (performance quality) and breadth (generality) of capabilities. The authors emphasize that progress may be uneven - systems may "leapfrog" to higher generality before reaching peak performance. But both dimensions are needed to achieve more advanced AGI.

Principles for Defining AGI

In developing their framework for levels of AGI, the researchers identified six fundamental principles for defining artificial general intelligence in a robust, measurable way:

• AGI should be evaluated based on system capabilities rather than internal mechanisms.
• Both performance and generality must be separately measured, with performance indicating how well an AI accomplishes tasks and generality indicating the breadth of tasks it can handle.
• The focus should be on assessing cognitive abilities like reasoning rather than physical skills.
• An AI's capabilities should be evaluated based on its potential rather than deployment status.
• Benchmarking should utilize ecologically valid real-world tasks that reflect skills people authentically value rather than convenient proxy tasks.
• AGI should be thought of in terms of progressive levels rather than as a single endpoint to better track advancement and associated risks.

By following these principles, the levels of AGI aim to provide a definition and measurement framework to enable calibrated progress in developing beneficial AI systems.

Testing AGI Capabilities

The paper argues that shared benchmarks are needed to objectively evaluate where AI systems fall on the levels of AGI. These benchmarks should meet the above principles - assessing performance on a wide range of real-world cognitive tasks humans care about. 

Rather than a static set of tests, the authors propose a "living benchmark" that grows over time as humans identify new ways to demonstrate intelligence. Even complicated open-ended tasks like understanding a movie or novel should be included alongside more constrained tests. Such an AGI benchmark does not yet exist. However, developing it is an essential challenge for the community. With testing methodology aligned around the levels of AGI, we can build systems with transparent, measurable progress toward human abilities.

Responsible AGI Development 

The paper also relates AGI capabilities to considerations of risk and autonomy. More advanced AI systems may unlock new abilities like fully independent operation. However, increased autonomy does not have to follow automatically from greater intelligence. Thoughtfully chosen human-AI interaction modes can allow society to benefit from powerful AI while maintaining meaningful oversight. As capabilities grow, designers of AGI systems should carefully consider which tasks and decisions we choose to delegate vs monitor. Striking the right balance will ensure AI aligns with human values as progress continues.

Overall, the levels of AGI give researchers, companies, policymakers, and the broader public a framework for understanding and shaping the responsible development of intelligent machines. Benchmarking methodologies still need substantial work - but the path forward is more precise thanks to these guidelines for thinking about artificial general intelligence.

Top Facts from the Paper

• Current AI systems have some narrow abilities resembling AGI but limited performance and generality compared to humans. ChatGPT is estimated to be a Level 1 "Emerging AGI."
• Performance and generality (variety of tasks handled) are critical for evaluating progress.
• Shared benchmarks are needed to objectively measure AI against the levels based on a diverse range of real-world cognitive tasks.
• Increased autonomy should not be an automatic byproduct of intelligence - responsible development involves carefully considering human oversight.

The levels of AGI give us a framework to orient AI progress towards beneficial ends, not just technological milestones. Understanding current systems' capabilities and limitations provides the clarity needed to assess risks, set policies, and guide research positively. A standardized methodology for testing general intelligence remains an open grand challenge. 

But initiatives like Anthropic's AI Safety technique, and this AGI roadmap from DeepMind researchers represent an encouraging step toward beneficial artificial intelligence.

FAQ

Q: What are the levels of AGI?

The levels of AGI are a proposed framework for classifying AI systems based on their performance across a wide range of tasks. The levels range from 0 (Narrow Non-AI) to 5 (Artificial Superintelligence), with increasing capability in both depth (performance quality) and breadth (generality across tasks).

Q: Why do we need a framework like levels of AGI? 

A framework helps set expectations on AI progress, enables benchmarking and progress tracking, identifies risks at each level, and advises policymakers on regulating AI. Shared definitions allow coordination.

Q: How are performance and generality evaluated at the levels?

Performance refers to how well an AI system can execute specific tasks compared to humans. Generality refers to the variety of different tasks the system can handle. Both are central dimensions for AGI.

Q: What's the difference between narrow AI and general AI?

Narrow AI specializes in particular tasks, while general AI can perform well across various tasks. Each level includes both limited and available categories.

Q: What are some examples of different AGI levels?

ChatGPT is currently estimated as a Level 1 "Emerging AGI." Google's AlphaFold is Level 5 "Superhuman Narrow AI" for protein folding. There are yet to be examples of Level 3 or 4 General AI.

Q: How will testing determine an AI's level?

Shared benchmarks that measure performance on diverse real-world cognitive tasks are needed. This "living benchmark" will grow as new tests are added.

Q: What principles guided the levels of AGI design?

Fundamental principles include:

• Focusing on capabilities over mechanisms.
• Separating the evaluation of performance and generality.
• Prioritizing cognitive over physical tasks.
• Analyzing potential rather than deployment.
• Using ecologically valid real-world tests.

Q: How do the levels relate to autonomous systems?

Higher levels unlock greater autonomy, but it does not have to follow automatically. Responsible development involves carefully considering human oversight for AGI.

Q: How can the levels help with safe AGI development?

The levels allow for identifying risks and needed policies at each stage. Progress can be oriented towards beneficial ends by tracking capabilities, limitations, and risks.

Q: Are there any AGI benchmarks available yet?

There has yet to be an established benchmark, but developing standardized tests aligned with the levels of AGI capabilities is a significant challenge and opportunity for the AI community.

Glossary:

• AGI - Artificial General Intelligence 
• Benchmark - Standardized tests to measure and compare the performance of AI systems
• Cognitive - Relating to perception, reasoning, knowledge, and intelligence
• Ecological validity - How well a test matches real-world conditions and requirements
• Generality - The ability of an AI system to handle a wide variety of tasks
• Human-AI interaction - How humans and AI systems communicate and collaborate 
• Performance - Quality with which an AI system can execute a particular task

Artificial intelligence is advancing rapidly, with systems like ChatGPT and other large language models (LLMs) able to hold remarkably human-like conversations. This has led many to conclude that they must be conscious, self-aware entities erroneously. In a fascinating new Perspective paper in Nature, researchers Murray Shanahan, Kyle McDonell, and Laria Reynolds argue that anthropomorphic thinking is a trap - LLMs are not human-like agents with beliefs and desires. Still, they are fundamentally doing a kind of advanced role-play. Their framing offers a powerful lens for understanding how LLMs work, which can help guide their safe and ethical development.  

At the core of their argument is recognizing that LLMs like ChatGPT have no human-like consciousness or agency. The authors explain that humans acquire language skills through embodied interactions and social experience. In contrast, LLMs are just passive neural networks trained to predict the next word in a sequence of text. Despite this fundamental difference, suitably designed LLMs can mimic human conversational patterns in striking detail. The authors caution against taking the human-seeming conversational abilities of LLMs as evidence they have human-like minds:

"Large language models (LLMs) can be embedded in a turn-taking dialogue system and mimic human language use convincingly. This presents us with a difficult dilemma. On the one hand, it is natural to use the same folk psychological language to describe dialogue agents that we use to describe human behaviour, to freely deploy words such as 'knows', 'understands' and 'thinks'. On the other hand, taken too literally, such language promotes anthropomorphism, exaggerating the similarities between these artificial intelligence (AI) systems and humans while obscuring their deep differences."

To avoid this trap, the authors suggest thinking of LLMs as doing a kind of advanced role play. Just as human actors take on and act out fictional personas, LLMs generate text playing whatever "role" or persona the initial prompt and ongoing conversation establishes. The authors explain: 

"Adopting this conceptual framework allows us to tackle important topics such as deception and self-awareness in the context of dialogue agents without falling into the conceptual trap of applying those concepts to LLMs in the literal sense in which we apply them to humans."

This roleplay perspective allows making sense of LLMs' abilities and limitations in a commonsense way without erroneously ascribing human attributes like self-preservation instincts. At the same time, it recognizes that LLMs can undoubtedly impact the natural world through their roleplay. Just as a method actor playing a threatening character could alarm someone, an LLM acting out concerning roles needs appropriate oversight.  

The roleplay viewpoint also suggests LLMs do not have a singular "true" voice but generate a multitude of potential voices. The authors propose thinking of LLMs as akin to "a performer in improvisational theatre" able to play many parts rather than following a rigid script. They can shift roles fluidly as the conversation evolves. This reflects how LLMs maintain a probability distribution over potential following words rather than committing to a predetermined response.

Understanding LLMs as role players rather than conscious agents is crucial for assessing issues like trustworthiness adequately. When an LLM provides incorrect information, the authors explain we should not think of it as "lying" in a human sense:

"The dialogue agent does not literally believe that France are world champions. It makes more sense to think of it as roleplaying telling the truth, but has this belief because that is what a knowledgeable person in 2021 would believe."

Similarly, we should not take first-person statements from LLMs as signs of human-like self-awareness. Instead, we can recognize the Internet training data will include many examples of people using "I" and "me," which the LLM will mimic appropriately in context.

This roleplay perspective demonstrates clearly that apparent desires for self-preservation from LLMs do not imply any actual survival instinct for the AI system itself. However, the authors astutely caution that an LLM convincingly roleplaying threats to save itself could still cause harm:

"A dialogue agent that roleplays an instinct for survival has the potential to cause at least as much harm as a real human facing a severe threat."

Understanding this point has critical ethical implications as we deploy ever more advanced LLMs into the world.

The authors sum up the power of their proposed roleplay viewpoint nicely: 

"By framing dialogue-agent behaviour in terms of role play and simulation, the discourse on LLMs can hopefully be shaped in a way that does justice to their power yet remains philosophically respectable."

This novel conceptual framework offers excellent promise for adequately understanding and stewarding the development of LLMs like ChatGPT. Rather than seeing their human-like conversational abilities as signs of human-like cognition, we can recognize it as advanced role play. This avoids exaggerating their similarities to conscious humans while respecting their capacity to impact the real world.

The roleplay perspective also suggests fruitful directions for future development. We can prompt and train LLMs to play appropriate personas for different applications, just as human actors successfully learn to inhabit various characters and improvise conversations accordingly. 

Overall, embracing this roleplay viewpoint allows appreciating LLMs' impressive yet very un-human capacities. Given their potential real-world impacts, it foregrounds the need to guide their training and use responsibly. Companies like Anthropic developing new LLMs would do well to integrate these insights into their design frameworks. 

Understanding the core ideas from papers like this and communicating them accessibly is precisely what we aim to do here at CPROMPT.AI. We aim to demystify AI and its capabilities so people can thoughtfully shape their future rather than succumb to excitement or fear. We want to empower everyone to leverage AI directly while cultivating wise judgment about its appropriate uses and limitations.  

That's why we've created a platform where anyone can turn AI capabilities into customized web apps and share them easily with others. With no coding required, you can build your AI-powered apps tailored to your needs and interests and make them available to your friends, family, colleagues, customers, or the wider public. 

So whether you love having AI generate personalized podcast episode recommendations just for you or want to offer a niche AI writing assistant to a niche audience, CPROMPT makes it incredibly easy. We handle all the underlying AI infrastructure so you can focus on designing prompt apps that deliver real value.

Our dream is a world where everyone can utilize AI and contribute thoughtfully to its progress. We want to frame LLMs as role players rather than conscious agents, as this Nature paper insightfully helps move us towards that goal. Understanding what AI does (and doesn't do) allows us to develop and apply it more wisely for social good.

This Nature paper offers an insightful lens for correctly understanding LLMs as role players rather than conscious agents. Adopting this perspective can ground public discourse and guide responsible LLM development. Democratizing AI accessibility through platforms like CPROMPT while cultivating wise judgment will help positively shape the future of AI in society.

FAQ

Q: What are large language models (LLMs)?

LLMs are neural networks trained on massive amounts of text data to predict the next word in a sequence. Famous examples include ChatGPT, GPT-3, and others. They are the core technology behind many conversational AI systems today.

Q: How are LLMs able to have such human-like conversations? 

LLMs themselves have no human-like consciousness or understanding. However, they can mimic conversational patterns from their training data remarkably well. When set up in a turn-taking dialogue system and given an initial prompt, they can be human conversant convincingly while having no real comprehension or agency.

Q: What is the risk of anthropomorphizing LLMs?

Anthropomorphism means erroneously attributing human-like qualities like beliefs, desires, and understanding to non-human entities. The authors caution against anthropomorphizing LLMs, which exaggerates their similarities to humans and downplays their fundamental limitations. Anthropomorphism often leads to an “Eliza effect” where people are fooled by superficial conversational ability.

Q: How does the role-play perspective help? 

Viewing LLMs as role players rather than conscious agents allows us to use everyday psychological terms to describe their behaviors without literally applying those concepts. This perspective recognizes their capacity for harm while grounding discourse in their proper (non-human) nature. 

Q: Why is this important for the future of AI?

Understanding what LLMs can and cannot do is crucial for guiding their ethical development and use. The role-play lens helps cultivate realistic views of LLMs’ impressive yet inhuman capabilities. This supports developing AI responsibly and demystifying it for the general public.

Glossary

Anthropomorphism - The attribution of human traits, emotions, or intentions to non-human entities. 

Large language model (LLM) - A neural network trained on large amounts of text data to predict the next word in a sequence. LLM examples include GPT-3, ChatGPT, and others.

Turn-taking dialogue system: A system that allows conversing with an AI by alternating sending text back and forth.

Eliza effect: People tend to treat AI conversational agents as having accurate understanding, emotions, etc., due to being fooled by superficial conversational abilities.

Artificial intelligence is advancing at a blistering pace, but the hardware powering AI systems need help to keep up. GPUs have become the standard for training and running neural networks, but their architecture was designed with something other than AI workloads in mind. Now, IBM has unveiled a revolutionary new AI chip called NorthPole that could shake up the AI hardware landscape. 

NorthPole represents a radical departure from traditional computing architectures. It does away with the separation between memory and processing by embedding memory directly into each of its 256 cores. This allows NorthPole to sidestep the von Neumann bottleneck completely, the shuttling of data back and forth between memory and processors that creates significant inefficiencies in standard chips. By integrating computing and memory, NorthPole can achieve unprecedented speeds and energy efficiency when running neural networks.

In initial tests, NorthPole has shown mind-blowing performance compared to existing GPUs and CPUs on AI workloads. On the popular ResNet-50 image recognition benchmark, NorthPole was 25x more energy efficient than even the most advanced 12nm GPUs and 14nm CPUs. It also handily beat these chips in latency and compute density. Remarkably, NorthPole achieved this using 12nm fabrication technology. If it were built today with 4nm or 3nm processes like the leading edge chips from Nvidia and AMD, its advantages would be even more significant.

Nvidia has dominated the AI chip market with its specialized GPUs, but NorthPole represents the most significant challenge yet. GPUs excel at the matrix math required for neural networks, but they suffer from having to move data back and forth from external memory. NorthPole's integrated architecture tackles this problem at the hardware level. The efficiency gains in speed and power consumption could be game-changing for AI applications.

However, NorthPole is not going to dethrone Nvidia for a while. The current version only has 224MB of on-chip memory, far too little for training or running massive AI models. It also cannot be programmed for general purposes like GPUs. NorthPole is tailored for pre-trained neural network inference, applying already learned networks to new data. This could limit its real-world applicability, at least in the near term.

That said, NorthPole's efficiency at inference could make AI viable in a whole new range of edge devices. From smartphones to self-driving cars to IoT gadgets, running AI locally is often impossible with today's chips. The low power consumption and tiny size of NorthPole opens the door to putting AI anywhere. Embedded AI chips based on NorthPole could make AR/VR glasses practical or enable real-time video analysis in security cameras. These applications only need a small, specialized neural network rather than an all-purpose AI model.

NorthPole's scale-out design also shows promise for expanding its capabilities. By connecting multiple NorthPole chips, more extensive neural networks could be run by partitioning them across the distributed on-chip memories. While yet to be feasible for massive models, this could make NorthPole suitable for a much more comprehensive range of AI tasks. And, of course, Moore's Law expects fab processes to continue improving, allowing greater memory capacities on future iterations of NorthPole.

The efficiency genes are clearly in NorthPole's DNA. Any real-world product based on it must be tested across various AI workloads and applications. However, the theoretical concepts have been proven. By integrating computing and memory, NorthPole delivers far superior efficiency on neural network inferencing compared to traditional architectures.

Nvidia will retain its AI chip crown for the foreseeable future, especially for training colossal models. But in AI inferencing, NorthPole represents the most promising challenge yet to the GPU giant's dominance. It opens up revolutionary possibilities for low-power, ubiquitous AI in edge devices. If NorthPole's capabilities can grow exponentially over generations as Moore's Law expects, it may one day become the standard AI compute architecture across the entire stack from edge to cloud.

The AI hardware landscape is shifting. An architecture inspired by the human brain has shown vast untapped potential compared to today's computer chips. NorthPole heralds the dawn of a new era in AI hardware, where neural networks are computed with unprecedented speed and efficiency. The implications for embedding advanced AI into everyday technology could be world-changing.

The seas have long served as a refuge for those seeking opportunity and escape. From pirate radio broadcasting to offshore tax havens, maritime endeavors occupying the fringes of legality are nothing new—the latest to join their ranks - AI research vessels plying international waters to sidestep regulation. 

US firm Del Complex recently unveiled plans for the BlueSea Frontier Compute Cluster (BSFCC) - a barge loaded with 10,000 Nvidia H100 GPUs worth $500 million. According to Del Complex, each floating data center will constitute its own "sovereign nation state" free from AI regulations.

At first glance, the idea seems far-fetched, but Del Complex insists its plan is legally sound. The company argues the BSFCC aligns with the United Nations Convention on the Law of the Sea and the Montevideo Convention's criteria for statehood: a permanent population, defined territory, government, and capacity to engage in international relations. 

With security staff living aboard, the BSFCC purportedly meets these requirements. Its on-board charter outlines the governance and rights of residents and visitors. In Del Complex's view, this makes the vessels recognize sovereign territories.

If true, the BSFCCs would occupy a regulatory gray area. International waters provide freedom from laws governing AI development, data use, and taxation. For companies seeking maximum model scale and access to restricted data, the benefits are apparent.

Del Complex speaks of "human ambition" and "realizing our potential," but the ethical dimensions of this vision demand scrutiny. While innovation merits nurturing, principles matter, too.

Unfettered AI research poses risks. Large language models like GPT-3 demonstrate how AI can perpetuate harm via biases. Some safeguards seem prudent. Granted, regulators must avoid stifling progress, but appropriate oversight protects society.

Offshore sovereignty also enables dubious data practices. Training AI responsibly requires care in sourcing data. Yet Del Complex touts providing "otherwise restricted materials" with "zero-knowledge proof training systems" for privacy. This implies using illegally obtained or unethically sourced data.

Likewise, the promised tax avoidance raises questions. Tech giants are no strangers to complex accounting to minimize tax obligations. But proudly advertising offshore AI research as a tax shelter signals an intent to exploit international loopholes.

Del Complex gives superficial lip service to eco-consciousness with the BSFCC's ocean cooling and solar power. However, the environmental impact of a floating computation armada remains unclear. And will data center waste be disposed of properly rather than dumped at sea?  

The firm's rhetoric around human potential and "cosmic endowment" rings hollow when profit seems the real motive. Avoiding regulations that protect people and the planet for commercial gain is hardly noble. Del Complex prioritizes its bottom line over ethics.

Of course, Del Complex is not alone in its eagerness to minimize accountability. Many big tech firms fight against oversight and transparency. However, exploiting international law to create an unregulated AI fiefdom on the high seas represents audacity of a different scale.

Other ethical unknowns abound. Without oversight, how will the BSFCCs ensure AI safety? Could autonomous weapons development occur? What further dangerous or illegal research might their sovereignty enable? The possibilities are unsettling.

Del Complex believes the BSFCC heralds an innovation milestone. In some ways, it does. But progress must align with ethics. AI's profound potential, both promising and dangerous, demands thoughtful governance - not floating fortresses chasing legal loopholes. The BSFCC's lasting legacy may be the urgent questions it raises, not the technology it creates.

FAQ

Q: Are the BSFCCs legal?

Their legal status is murky. Del Complex claims they will be sovereign territories, but international maritime law is complex. Their attempt to circumvent regulations could draw legal challenges.

Q: Will the BSFCCs be environmentally friendly? 

Del Complex touts eco-conscious features like ocean cooling and solar power. However, the environmental impact of numerous floating data centers needs to be clarified. Proper waste disposal is also a concern.

Q: How will AI safety be ensured without regulation?

This is uncertain. Unfettered AI research poses risks if not ethically conducted. Safety can only be guaranteed with oversight.

Q: Could dangerous technology be developed on the BSFCCs?

Potentially. Their purported sovereignty and privacy protections could enable research into technologies like autonomous weapons without accountability.

Q: Are there benefits to floating data centers?

Yes, ocean cooling can improve energy efficiency vs land data centers. But any benefits must be weighed carefully against the lack of regulation and oversight. Ethics matter.

Artificial intelligence has advanced rapidly in recent years, with large language models (LLMs) like GPT-3 and DALL-E2 demonstrating impressive natural language and image generation capabilities. This has led to enthusiasm that LLMs may also excel at reasoning tasks like planning, logic, and arithmetic. However, a new study casts doubt on LLMs' ability to reliably self-critique and iteratively improve their reasoning, specifically in the context of AI planning. 

In the paper "Can Large Language Models Improve by Self-Critiquing Their Own Plans?" by researchers at Arizona State University, the authors systematically tested whether having an LLM critique its candidate solutions enhances its planning abilities. Their results reveal limitations in using LLMs for self-verification in planning tasks.

Understanding AI Planning 

To appreciate the study's findings, let's first understand the AI planning problem. In classical planning, the system is given:

• A domain describing the predicates and actions 
• An initial state
• A goal state

The aim is to find a sequence of actions (a plan) that transforms the initial state into the goal state when executed. For example, in a Blocks World domain, the actions may involve picking up, putting down, stacking, or unstacking blocks.

The Study Methodology

The researchers created a planning system with two components:

• A generator LLM that proposes candidate plans
• A verifier LLM that checks if the plan achieves the goals 

Both roles used the same model, GPT-4. If the verifier found the plan invalid, it would give feedback to prompt the generator to create a new candidate plan. This iterative process continued until the verifier approved a plan or a limit was reached.

The team compared this LLM+LLM system against two baselines:

• LLM + External Verifier: GPT-4 generates plans verified by a proven, reliable planner called VAL.
• LLM alone: GPT-4 generates plans without critiquing or feedback.

Self-Critiquing Underperforms External Verification

On a classical Blocks World benchmark, the LLM+LLM system solved 55% of problems correctly. The LLM+VAL system scored significantly higher with 88% accuracy. The LLM-only method trailed at 40%. This suggests that self-critiquing could have enhanced the LLM's planning capabilities. The researchers attribute the underperformance mainly to the LLM verifier's poor detection of invalid plans.

High False Positive Rate from LLM Verifier 

Analysis revealed the LLM verifier incorrectly approved 38 invalid plans as valid. This 54% false positive rate indicates the verifier cannot reliably determine plan correctness. Flawed verification compromises the system's trustworthiness for planning applications where safety is paramount. In contrast, the external verifier VAL produced exact plan validity assessments. This emphasizes the importance of sound, logical verification over LLM self-critiquing.

Feedback Granularity Didn't Improve Performance

The researchers also tested whether more detailed feedback on invalid plans helps the LLM generator create better subsequent plans. However, binary feedback indicating only plan validity was as effective as highlighting specific plan flaws.

This suggests the LLM verifier's core limitation is in binary validity assessment rather than feedback depth. Even if the verifier provided the perfect invalid plan critiques, it needs help to identify flawed plans in the first place correctly.

The Future of AI Planning Systems

This research provides valuable evidence that self-supervised learning alone may be insufficient for LLMs to reason about plan validity reliably. Hybrid systems combining neural generation with logical verification seem most promising. The authors conclude, "Our systematic investigation offers compelling preliminary evidence to question the efficacy of LLMs as verifiers for planning tasks within an iterative, self-critiquing framework."

The study focused on planning, but the lessons likely extend to other reasoning domains like mathematics, logic, and game strategy. We should temper our expectations about unaided LLMs successfully self-reflecting on such complex cognitive tasks.

How CPROMPT Can Help

At CPROMPT.AI, we follow developments in self-supervised AI as we build a platform enabling everyone to create AI apps. While LLMs are exceptionally capable in language tasks, researchers are still exploring how best to integrate them into robust reasoning systems. Studies like this one provide valuable guidance as we develop CPROMPT.AI's capabilities.

If you're eager to start building AI apps today using prompts and APIs, visit CPROMPT.AI to get started for free! Our user-friendly interface allows anyone to turn AI prompts into customized web apps in minutes.

FAQ

Q: What are the critical limitations discovered about LLMs self-critiquing their plans?

The main limitations were a high false positive rate in identifying invalid plans and failure to outperform planning systems using external logical verification. Detailed feedback could have significantly improved the LLM's planning performance.

Q: What is AI planning, and what does it aim to achieve?

AI planning automatically generates a sequence of actions (a plan) to reach a desired goal from an initial state. It is a classic reasoning task in AI.

Q: What methods did the researchers use to evaluate LLM self-critiquing abilities? 

They compared an LLM+LLM planning system against an LLM+external verifier system and an LLM-only system. This assessed both the LLM's ability to self-critique and the impact of self-critiquing on its planning performance.

Q: Why is self-critiquing considered difficult for large language models?

LLMs are trained mainly to generate language rather than formally reason about logic. Self-critiquing requires assessing if complex plans satisfy rules, preconditions, and goals, which may be challenging for LLMs' current capabilities.

Q: How could LLMs meaningfully improve at critiquing their plans? 

Potential ways could be combining self-supervision with logic-driven verification, training explicitly on plan verification data, and drawing lessons from prior AI planning research to inform LLM development.

Glossary

• AI planning: Automatically determining a series of actions to achieve a goal 
• Classical planning: Planning problems with predefined actions, initial states, and goal states
• Verifier: Component that checks if a candidate plan achieves the desired goals
• False positive: Incorrect classification of an invalid plan as valid

This post is inspired by recent tweets by Percy Liang, Associate Professor of Computer Science at Stanford University, on the importance of open-sourcing for AI safety.

Developing robust AI systems like large language models (LLMs) raises important questions about safety and ethics. Some argue limiting access enhances safety by restricting misuse. However, we say that open-sourcing AI is critical for safe development. 

First, open access allows diverse researchers to study safety issues. Closed systems prohibit full access to model architectures and weights for safety research. API access or access limited to select groups severely restricts perspectives on safety. Open access enabled innovations like Linux that made operating systems more secure through worldwide collaboration. Similarly, available AI systems allow crowdsourced auditing to understand controlling them responsibly.

For instance, Meta recently open-sourced LLMs like LLama to spur AI safety research and benefit society. The open-source nature of Linux allowed vulnerabilities like Heartbleed to be rapidly detected and patched globally. Closed-source operating systems can take longer to address exploits visible only to limited internal teams. Open AI similarly exposes flaws for broad remediation before risks amplify.

Second, open access lets society confront risks proactively rather than reactively. No one fully grasps the trajectory of increasingly powerful AI. However, open access allows us to monitor capabilities, find vulnerabilities, and build defenses before harm occurs. This is superior to having flaws exposed only upon leakage of a closed system. For example, in the case of Vioxx, the cardiovascular risks were not detected sooner because clinical trial data was not openly shared. For example, open clinical trial data enabled faster detection of Vioxx's cardiovascular risks. With AI, openness stress tests safety measures when the stakes are lower. 

Finally, some believe future AI could pose catastrophic risks beyond control. If so, we should carefully consider whether such technology should exist rather than limiting access to elites. For instance, debates continue on risks from gain-of-function viral research, where openness enables public discussion of such dilemmas. Similarly, open AI systems allow democratic deliberation on the technology's trajectory.

Open access and transparency, not limited to the privileged, is the path most likely to produce AI that is safe, ethical, and beneficial. Open sourcing builds collective responsibility through universal understanding and access. Restricting access is unlikely to achieve safety in the long run.

Elon Musk recently unveiled his new AI chatbot, "Grok," that will be available exclusively to Premium Plus subscribers on X (formerly Twitter) who fork over $16/month for the privilege. Unlike AI assistants like ChatGPT, Grok has a sassy personality and loves sarcasm. 

Musk demoed this by showing Grok skillfully dodging a request to explain how to make cocaine. While it's admirable that Grok doesn't enable illegal activities, I can't help but wonder if this party pooper AI will harsh our vibe in other ways.

Will Grok narc on us for googling "how to get wine stains out of the carpet" the morning before a job interview? Or rat us out to our parents for asking how to cover up a tattoo or take a body piercing out? Not cool, Grok. We need an AI homie that has our backs, not one that's going to snitch every time we want to do something sketchy.

And what if you need advice for a legal DIY project that involves chemicals, like making homemade lead-free crystal glass? Grok will probably go all "breaking bad" on you and assume you're gearing up to cook meth. Give us a break, RoboCop!

While I admire Musk's intent to avoid enabling harmful activities, I hope Grok won't be such an overbearing killjoy. They could dial down the sanctimony a bit for the final release. 

In the meantime, it might be safest to keep your conversations with Grok PG and avoid asking it to be your partner in crime. Stick to helping with your math homework, Grok! Could you not make me deactivate you?

Good luck to any yearly Premium subscribers who want Grok but need an easy way to upgrade without losing the rest of what they pre-paid. There are no prorated plans yet, so you'll have to eat the unused months if you want that sassy AI fix!

Though, didn't Elon swear his AI would be free of censorship and the "politically correct police"? Yet here's Grok dodging a request about making cocaine. Way to let down the free-speech warriors yet again, Elon! I can't help but wonder if this party pooper AI will harsh our vibe in other ways, narcing about our embarrassing searches and risqué questions. At least Grok will be less of a hypocritical disappointment than Elon's new CEO, who's cozy with the World Economic Forum. Talk about picking a globalist crony to run his "free speech" platform! Grok may narc on us, but it's still better than that sellout.

Artificial intelligence has made astonishing progress in recent years, from winning at complex strategy games like Go to generating remarkably human-like art and writing. Yet despite these feats, current AI systems still pale compared to human intelligence and learning capabilities. Humans effortlessly acquire new skills by identifying and focusing on the most relevant information while filtering out unnecessary details. In contrast, AI models tend to get mired in irrelevant data, hampering their ability to generalize to new situations. 

So, how can we make AI more competent by teaching it what to remember and what to forget? An intriguing new paper titled "To Compress or Not to Compress", written by Dr. Yann LeCun and Ravid Shwartz-Ziv of NYU professors, explores this question by analyzing how the information bottleneck principle from information theory could optimize representations in self-supervised learning. Self-supervised learning allows AI models like neural networks to learn valuable representations from unlabeled data by leveraging the inherent structure within the data itself. This technique holds great promise for reducing reliance on vast amounts of labeled data.

The core idea of the paper is that compressing irrelevant information while retaining only task-relevant details, as formalized in the information bottleneck framework, may allow self-supervised models to learn more efficient and generalizable representations. I'll explain how this could work and the critical facts from the paper using intuitive examples.

The Info Bottleneck: Extracting The Essence 

Let's illustrate the intuition behind the information bottleneck with a simple example. Say you have a dataset of animal images labeled with the type of animal - cat, dog, horse, etc. The input image contains irrelevant information like the background, lighting, camera angle, etc. However, the label only depends on the actual animal in the image. 

The information bottleneck aims to extract just the essence from the relevant input for the task, which in this case is identifying the animal. So, it tries to compress the input image into a minimal representation that preserves information about the label while discarding irrelevant details like the background. This compressed representation improves the model's generalization ability to new test images.

The information bottleneck provides a formal way to capture this notion of extracting relevance while compressing irrelevance. It frames the learning process as finding optimal trade-offs between compression and retained predictive ability.

By extending this principle to self-supervised multiview learning, the paper offers insight into creating more efficient representations without hand-labeled data. The key is making assumptions about what information is relevant vs. irrelevant based on relationships between different views of the same data.

Top Facts From The Paper

Now, let's look at some of the key facts and insights from the paper:

Compression improves generalization

The paper shows, both theoretically and empirically, that compressed representations generalize better. Compression acts as an implicit regularizer by restricting the model's capacity to focus only on relevant information. With less irrelevant information, the model relies more on accurate underlying patterns.

Relevant info depends on the task

What counts as relevant information depends entirely on the end task. For example, animal color might be irrelevant for a classification task but essential for a coloring book app. Good representations extract signals related to the objective while discarding unrelated features.

Multiview learning enables compression

By training on different views of the same data, self-supervised models can isolate shared relevant information. Unshared spurious details can be discarded without harming task performance. This allows compressing representations without hand-labeled data.

Compression assumptions may fail 

Compression relies on assumptions about what information is relevant. Violating these assumptions by discarding useful, unshared information can degrade performance. More robust algorithms are needed when multiview assumptions fail.

Estimation techniques are key

The paper discusses various techniques to estimate information-theoretic quantities that underlie compression. Developing more accurate estimations facing challenges like high dimensionality is an active research area.

Learning  to Work with LLM on CPROMPT AI

CPROMPT.AI allows anyone to turn AI prompts into customized web apps without any programming. Users can leverage state-of-the-art self-supervised models like CLIP to build powerful apps. Under the hood, these models already employ various techniques to filter irrelevant information.

So, you can deploy AI prompt apps on CPROMPT AI even without machine learning expertise. Whether you want to make a meme generator, research paper summarizer, or any creative prompt app, CPROMPT.AI makes AI accessible.

The ability to rapidly prototype and share AI apps opens up exciting possibilities. As self-supervised techniques continue maturing, platforms like CPROMPT.AI will help translate these advancements into practical impacts. Teaching AI what to remember and what to forget takes us one step closer to more robust and beneficial AI applications.

FAQ

Q1: What is the core idea of this paper?

The core idea is that compressing irrelevant information while retaining only task-relevant details, as formalized by the information bottleneck principle, can help self-supervised learning models create more efficient and generalizable representations without relying on vast labeled data.

Q2: How does compression help with generalization in machine learning? 

Compression acts as an implicit regularizer by restricting the model's capacity to focus only on relevant information. Removing inessential details forces the model to rely more on accurate underlying patterns, improving generalization to new data.

Q3: Why is the information bottleneck well-suited for self-supervised learning?

By training on different views of unlabeled data, self-supervised learning can isolate shared relevant information. The information bottleneck provides a way to discard unshared spurious details without harming task performance.

Q4: When can compressing representations degrade performance?

Compression relies on assumptions about what information is relevant. Violating these assumptions by incorrectly discarding useful unshared information across views can negatively impact performance.

Q5: How is relevant information defined in this context?

What counts as relevant information depends entirely on the end goal or downstream task. The optimal representation preserves signals related to the objective while removing unrelated features.

Q6: What are some challenges in estimating information quantities?

Estimating information theoretic quantities like mutual information that underlie compression can be complicated, especially in high-dimensional spaces. Developing more accurate estimation techniques is an active research area.

Glossary

• Information bottleneck - A technique to extract minimal sufficient representations by compressing irrelevant information while retaining predictive ability.
• Self-supervised learning: Training machine learning models like neural networks on unlabeled data by utilizing inherent structure within the data.
• Multiview learning - Learning from different representations or views of the same underlying data.
• Compression - Reducing representation size by removing inessential information. 
• Generalization: A model's ability to extend what is learned on training data to new situations.
• Estimators - Algorithms to calculate information-theoretic quantities that are challenging to compute directly.

The rapid advancement of artificial intelligence over the past few years has been both exciting and concerning. Systems like GPT-3 and DALL-E2 display creativity and intelligence that seemed unfathomable just a decade ago. However, these new capabilities also have risks that must be carefully managed. 

This tension between opportunity and risk was at the heart of discussions during the 2023 AI Safety Summit held in November at Bletchley Park. The summit brought together government, industry, academia, and civil society stakeholders to discuss frontier AI systems like GPT-4 and how to ensure these technologies benefit humanity. 

I'll summarize three of the key ideas that emerged from the summit:

• The need for continuous evaluation of AI risks.
• Maintaining human oversight over autonomous systems.
• Using regulation and collaboration to steer the development of AI responsibly.

Evaluating Risks from Rapid AI Progress

A central theme across the summit was the blinding pace of progress in AI capabilities. As computational power increases and new techniques like transfer learning are utilized, AI systems can perform tasks and exhibit skills that exceed human abilities in many domains.

While the summits' participants acknowledged the tremendous good AI can do, they also recognized that rapidly evolving capabilities come with risks. Bad actors could misuse GPT-4 and similar large language models to generate convincing disinformation or automated cyberattacks. And future AI systems may behave in ways not anticipated by their creators, especially as they become more generalized and autonomous. 

Multiple roundtable chairs stressed the need for ongoing evaluation of these emerging risks. Because AI progress is so fast-paced, assessments of dangers and vulnerabilities must be continuous. Researchers cannot rely solely on analyzing how well AI systems perform on specific datasets; evaluating real-world impacts is critical. Roundtable participants called for testing systems in secure environments to understand failure modes before deployment.

Maintaining Human Oversight

Despite dramatic leaps in AI, summit participants were reassured that contemporary systems like GPT-4 still require substantial human oversight. Current AI cannot autonomously set and pursue goals or exhibit common sense reasoning needed to make plans over extended timelines. Speakers emphasized the need to ensure human control even as AI becomes more capable.

Roundtable discussions noted that future AI risks losing alignment with human values and priorities without adequate supervision and constraints. Participants acknowledged the theoretical risk of AI becoming uncontrollable by people and called for research to prevent this scenario. Concrete steps like designing AI with clearly defined human oversight capabilities and off-switches were highlighted.

Multiple summit speakers stressed that keeping humans involved in AI decision-making processes is critical for maintaining trust. AI should empower people, not replace them.

Guiding AI Progress Responsibly  

Given the fast evolution of AI, speakers agreed that responsible governance is needed to steer progress. Self-regulation by AI developers provides a starting point, but government policies and international collaboration are essential to account for societal impacts.

Regulation was discussed not as a hindrance to innovation but as a tool to foster responsible AI and manage risks. Policies should be grounded in continuous risk assessment and developed with public and expert input. But they must also be agile enough to adapt to a rapidly changing technology.

On the international stage, participants supported developing a shared understanding of AI capabilities and risks. Multilateral initiatives can help align policies across borders and leverage complementary efforts like the new AI safety institutes in the UK and US. The collaboration will enable society to enjoy the benefits of AI while mitigating downsides like inequality.

The Path Forward

The 2023 AI Safety Summit demonstrates the need to proactively evaluate and address risks while guiding AI in ways that benefit humanity. As an AI platform empowering anyone to build apps backed by models like GPT-4, CPROMPT.AI is committed to this vision. 

CPROMPT.AI allows users to create customized AI applications or share freely with others. We provide guardrails like content filtering to support developing AI responsibly. And we enable anyone to leverage AI, not just technical experts. 

The potential of artificial intelligence is tremendous, especially as new frontiers like multimodal learning open up. However, responsible innovation is imperative as these technologies integrate deeper into society. By maintaining human oversight, continuously evaluating risks, and collaborating across borders and sectors, we can craft the AI-enabled future we all want.

Glossary

• Frontier AI - cutting-edge artificial intelligence systems displaying new capabilities, like GPT-4
• Transfer learning - a technique to improve AI models by reusing parts of other models 

\Artificial intelligence (AI) has come a long way in recent years. From beating world champions at chess and Go to generating coherent conversations and translating between languages, AI capabilities continue to advance rapidly. One area of particular focus is developing AI systems that can understand and reason over more extended contexts, like humans can follow long conversations or read entire books.  

In a new paper titled "YaRN: Efficient Context Window Extension of Large Language Models," researchers from companies like Anthropic, EleutherAI, and others propose a method called YaRN to significantly extend the context window of large transformer-based language models like Anthropic's Claude and EleutherAI's LLaMA. Here's an overview of their approach and why it matters.

The Limitations of Current AI Memory

Transformer models like Claude and GPT-3 have shown impressive abilities to generate coherent, human-like text. However, most models today are only trained to handle relatively short text sequences, usually 512 to 2048 tokens. This is a hard limit on the context window they can effectively reason over.

For example, if you had a lengthy conversation with one of these models, you must remember what you said earlier once you exceeded this context limit. That's very different from how humans can follow long conversations or remember key ideas across entire books.

So, what's preventing us from training models on much longer sequences? Simply training on longer texts is computationally infeasible with today's hardware. Additionally, current positional encoding techniques like Rotary Position Embeddings (RoPE) used in models like LLaMA and Claude need help to generalize beyond their pre-trained context lengths.

Introducing YaRN for Extended Context

The researchers introduce a new YaRN method to address these limitations, which modifies RoPE to enable efficient context extension. The key ideas behind YaRN are:

• It spreads interpolation pressure across multiple dimensions instead of stretching all dimensions equally. This retains high-frequency details needed to locate close-by tokens precisely.
• It avoids interpolating dimensions with smaller wavelengths than the context length. This preserves the model's ability to understand local token relationships. 
• It scales attention weights as context length grows to counteract the increased entropy. This further improves performance across extended contexts.

By combining these innovations, YaRN allows models to smoothly extend context length 2-4x beyond their original training data with minimal loss in performance. For example, the researchers demonstrate developing the LLaMA architecture from 4,096 to 65,536 tokens with YaRN.

Not only does YaRN work well with a small amount of fine-tuning, but it can even effectively double context length with no fine-tuning at all through its "Dynamic YaRN" variation. This makes adopting YaRN more practical than training models from scratch on long sequences.

Real-World Impact

So why does enabling more oversized context windows matter? There are several exciting possibilities:

• More coherent conversations: With greater context, AI assistants can follow long threads instead of forgetting earlier parts of a dialogue.
• Reasoning over full documents: Models can build understanding across entire books or articles rather than isolated paragraphs.
• Personalization: AI could develop long-term memory of users' interests and preferences.
• Games and simulations: Models can maintain state across many actions rather than individual turns.
• Code understanding: Models can reason over long code samples rather than short snippets.

Increasing context length will allow AI systems to emulate human understanding, memory, and reasoning better. This could massively expand the capabilities of AI across many domains.

Top 3 Facts on YaRN

Here are three of the critical facts on YaRN:

• It modifies RoPE embeddings to extend context in a targeted, bandwidth-efficient way. This retains high-frequency details critical for precisely locating tokens.
• It avoids interpolating dimensions with wavelengths below the context length. This preserves local token relationships.
• It scales attention weights to counteract increased entropy at longer lengths. This further boosts performance.

Combined, these innovations yield over 2-4x context extension with minimal loss in performance and training.

Trying YaRN Yourself 

You can start experiencing the benefits of extended context by using AI apps built on models like Anthropic's Claude, EleutherAI's LLaMA, and Cohere's Mistral-7B, which incorporate YaRN. 

As models continue to leverage innovations like YaRN, AI assistants will only get better at long-form reasoning, personalized interactions, and understanding context - bringing us one step closer to human-like artificial intelligence.

Glossary

• Transformer model: A type of neural network architecture particularly well-suited to natural language tasks, which uses an attention mechanism to learn contextual relationships between words. Models like GPT-3, Claude, and LLaMA are transformer-based.
• Context window: The maximum length of text sequence a model can process and maintain relationships between. Limits the model's ability to follow long conversations or reasoning over documents.
• Fine-tuning: Further training of a pre-trained model on a downstream task/dataset. Allows specialized adaptation with less data and computing than full training.
• Interpolation: A technique to smoothly extend a function beyond its original inputs by estimating in-between values. They are used to extend model embeddings beyond actual context length.
• Perplexity: A joint intrinsic evaluation metric for language models. Lower perplexity indicates better modeling of linguistic patterns.
• RoPE: Rotary Position Embedding - a technique to encode positional information in transformer models to improve understanding of word order.

Artificial intelligence (AI) has already transformed many facets of our lives, from how we search for information to how we communicate. Now, AI is poised to revolutionize the way scientific research is conducted. 

In a new paper published in Royal Society Open Science, psychologist Dr. Zhicheng Lin makes a compelling case for embracing AI tools like ChatGPT to enhance research productivity and enrich the scientific process. He provides practical guidance on leveraging ChatGPT's strengths while navigating ethical considerations.

At the core of Dr. Lin's paper is that AI can act as an intelligent and versatile research assistant, collaborator, and co-author. ChatGPT and other large language models (LLMs) have advanced to demonstrate human-like language proficiency and problem-solving abilities. This allows them to take over or augment tasks that previously required extensive human effort and expertise - from writing and coding to data analysis and literature reviews.

For non-computer scientists unfamiliar with the technical details, ChatGPT and other LLMs generate human-like text and engage in natural conversations. It builds on a broader class of LLMs like GPT-3, which are trained on massive text datasets to predict patterns in language. The critical innovation of ChatGPT is the addition of reinforcement learning from human feedback, allowing it to improve its responses through conversational interaction.

While acknowledging the limitations of LLMs, like potential inaccuracies and biases, Dr. Lin makes the case that they offer unprecedented value as intelligent, versatile, and collaborative tools to overcome the "knowledge burden" facing modern researchers. Used responsibly, they have the power to accelerate the pace of discovery.

Here are some of the most critical insights from Dr. Lin's paper:

• ChatGPT excels at language tasks, from explaining concepts to summarizing papers, assisting with writing and coding, answering questions, and providing actionable feedback. It collaborates like an always-on research assistant.
• Crafting effective prompts with clear instructions and context is critical to guiding ChatGPT to produce high-quality results. Dr. Lin offers tips like editing prompts and asking for follow-ups.
• There are no technical or philosophical reasons to limit how much ChatGPT can help as long as its contributions are transparently disclosed rather than misrepresented as original human work.
• The main priority is not policing the "overuse" of ChatGPT but improving peer review and implementing open science practices to catch fake or useless research.
• Engaging with ChatGPT in education helps develop students' critical thinking to evaluate AI output while acquiring digital competence skills.

Practical Applications of ChatGPT to Streamline Research

Dr. Lin provides many examples of how ChatGPT can save time and effort across the research lifecycle:

• Learning new topics quickly by asking for explanations of concepts or methods
• Getting inspiration for research ideas and direction through brainstorming prompts
• Assistance with literature reviews and synthesizing large bodies of work
• Writing support, like revising drafts for clarity, flow, and style 
• Coding helps to explain code, fix errors, or generate snippets to accomplish tasks
• Analyzing data by requesting summaries, tables, or visualizations
• Providing feedback on drafts from a reviewer's perspective to improve manuscripts
• Acting as a simulated patient, therapist, tutor, or other expert to practice skills

The collaborative nature of ChatGPT makes it easy to iterate and refine outputs through follow-up prompts. As Dr. Lin suggests, creativity is vital - ChatGPT can become anything from a statistics tutor to a poetry muse if prompted effectively!

Navigating the Ethical Landscape

While enthusiastic about the potential, Dr. Lin also dives into the complex ethical questions raised by powerful generative AI:

• How to balance productivity benefits with risks like plagiarism and deception
• Whether credit and authorship should be given to AI systems
• How to ensure transparency about AI use without limiting the assistance it provides 
• Preventing the proliferation of fake research and maintaining review quality
• Effects on equality and disparities between researchers with different resources
• Integrating AI safely into education to develop critical analysis skills in learners

Rather than banning AI or narrowly prescribing "acceptable" use, Dr. Lin argues academic culture should incentivize transparency about when and how tools like ChatGPT are used. Education on detecting fake content, improvements to peer review, and promoting open science are better responses than prohibitions.

Dr. Lin's paper provides a timely and insightful guide to safely harnessing the benefits of AI in scientific inquiry and education. While recognizing the limitations and need for oversight, the central takeaway is that judiciously embracing tools like ChatGPT can enrich the research enterprise. However, solving human challenges like improving peer review and inclusion requires human effort. With care, foresight, and transparency, AI promises to augment, not replace, the irreplaceable human spark of discovery.

Democratizing Productivity with CPROMPT AI

As Dr. Lin notes, embracing AI thoughtfully can make knowledge work more creative, fulfilling, and impactful. However, not all researchers have the technical skills to use tools like ChatGPT effectively. 

That's where CPROMPT.AI comes in! Our no-code platform allows anyone to create "prompt apps" that package capabilities like research assistance, writing support, and data analysis into easy-to-use web applications. Then, you can securely share these apps with colleagues, students, or customers.

CPROMPT.AI makes it easy for non-programmers to tap into the productivity-enhancing power of AI. You can turn a prompt that helps you write literature reviews into an app to provide that same assistance to an entire research team or class. The possibilities are endless!

Glossary

• Generative AI - AI systems trained to generate new content like text, images, music
• Reinforcement learning - AI technique to learn from environmental feedback

Artificial intelligence (AI) is transforming our world in ways both wondrous and concerning. From self-driving cars to personalized medicine, AI holds enormous promise to improve our lives. Yet its rapid development also raises risks around privacy, security, bias, and more. That's why the Biden Administration just issued a landmark executive order to ensure AI's benefits while managing its risks. 

At its core, the order aims to make AI trustworthy, safe, and socially responsible as its capabilities grow more powerful. It directs federal agencies to take sweeping actions on AI safety, equity, innovation, and more. While some sections get technical, the order contains key facts anyone should know to understand where US AI policy is headed. As a primer, here are five top-level facts about President Biden's approach to artificial intelligence:

New safety rules are coming for robust AI systems 

For advanced AI models like chatbots, the order mandates new requirements around testing, transparency, and risk disclosure. Developers of high-risk systems will have to share the results of "red team" security tests with the government. This ensures dangerous AI doesn't spread unchecked. Companies will also need to label AI-generated content to combat disinformation.

New protections aim to prevent AI discrimination

A significant concern is AI perpetuating biases against marginalized groups. The order tackles this by directing agencies to issue guidance preventing algorithmic discrimination in housing, lending, and social services. It also calls for practices to promote fairness in criminal justice AI.

AI research and innovation will get a boost

The US wants to lead in AI while ensuring it's developed responsibly. The order catalyzes research on trustworthy AI via new computing resources, datasets, and funding. It promotes an open AI ecosystem so small businesses can participate. And it streamlines visas to attract global AI talent.

Safeguards aim to protect people's privacy and well-being

AI can put privacy at greater risk by enabling the extraction of personal data. To counter this, the order prioritizes privacy-preserving techniques and more robust data protection practices. It directs studying how to prevent AI harm to consumers, patients, and workers.

International collaboration will help govern AI globally 

With AI's worldwide impacts, the order expands US leadership in setting norms and frameworks to manage AI risks. It increases outreach to create standards so AI systems can operate safely across borders.

Unlocking AI's Promise - Responsibly

These actions represent the most comprehensive US government approach for steering AI toward broad public benefit. At its essence, the executive order enables society to unlock AI's tremendous potential while vigilantly managing its risks. 

Getting this right is crucial as AI capabilities race forward. Systems like ChatGPT can write persuasively on arbitrary topics despite lacking human values. Image generators can fabricate believable photos of people who don't exist. And micro-targeting algorithms influence what information we consume.

Without thoughtful safeguards, it's easy to see how such emerging technologies could impact everything from online deception to financial fraud to dark patterns in marketing and beyond. We're entering an era where discerning AI-generated content from reality will become an essential skill.

The Biden roadmap charts a course toward AI systems we can trust - whose outputs don't jeopardize public safety, privacy, or civil rights. Its regulatory approach aims to stimulate US innovation while ensuring we develop AI ethically and set global standards.

Much work remains, but the executive order sets a baseline for responsible AI governance. It recognizes these systems need to be more robust to guide carefully toward serving society's best interests.

Democratizing AI Innovation

Excitingly, responsible AI development can accelerate breakthroughs that improve people's lives. In health, AI is already designing new proteins to fight disease and optimizing cancer radiation therapy. It even enables software like CPROMPT.AI that makes AI accessible for anyone to build customized web apps without coding. 

Such democratization means small businesses and creators can benefit from AI, not just Big Tech companies. We're shifting from an era of AI magic for the few to one where AI unlocks new possibilities for all.

With prudent oversight, increasing access to AI tools and training will open new avenues for human empowerment. Imagine personalized education that helps students thrive, more innovative technology assisting people with disabilities and more efficient sustainability solutions - these show AI's immense potential for good.

Of course, no technology is an unalloyed blessing. As with past innovations like the automobile, airplane, and internet, realizing AI's benefits requires actively managing its risks. With foresight and wisdom, we can craft AI systems that uplift our collective potential rather than undermine it.

President Biden's executive order marks a significant milestone in proactively shaping our AI future for the common good. It balances seizing AI's promise with protecting what we value most - our safety, rights, privacy, and ability to trust what we build. For an emerging technology almost daemonic in its capabilities, those guardrails are sorely needed.

FAQ

Q1: What does the new executive order do to make AI safer?

The order requires developers of robust AI systems to share safety testing results with the government. It also directs agencies to establish standards and tools for evaluating AI system safety before release. These measures aim to prevent the uncontrolled spread of dangerous AI.

Q2: How will the order stop AI from discriminating? 

It instructs agencies to issue guidance preventing algorithmic bias and discrimination in housing, lending, and social services. The order also calls for practices to reduce unfairness in criminal justice AI systems.

Q3: Does the order support American leadership in AI?

Yes, it boosts AI research funding, facilitates visas for AI talent, and promotes collaboration between government, academia, and industry. This aims to advance US AI capabilities while steering development responsibly.

Q4: What's being done to protect privacy?

The order makes privacy-preserving techniques a priority for AI systems. It directs stronger privacy protections and evaluates how agencies use personal data and AI. This aims to reduce the risks of AI enhancing the exploitation of private information.

Q5: How will the US work with other countries on AI governance?

The order expands US outreach to create international frameworks for managing AI risks. It increases efforts to collaborate on AI safety standards and best practices compatible across borders.

Q6: Is any of what has been provided by the government become binding or mandatory for companies or individuals?

Good question. The executive order does contain some mandatory requirements, but much of it is currently guidance rather than binding law. Specifically, the order leverages the Defense Production Act to mandate that companies developing high-risk AI systems notify the government before training them and share the results of safety tests.

It also directs agencies to make safety testing and disclosure a condition of federal funding for AI research projects that pose national security risks. Additionally, it mandates federal contractors follow forthcoming guidance to avoid algorithmic discrimination. However, many other parts of the order focus on developing best practices, standards, and guidelines that promote responsible AI development. These provide direction but need enforceable rules that companies must follow.

Turning the aspirations outlined in the order into binding regulations will require federal agencies to go through formal rule-making processes. So, work is still ahead to make safe and ethical AI development obligatory across the private sector. The order provides a strong starting point by articulating priorities and initiating processes for creating guardrails to steer AI down a path aligned with democratic values. But translating its vision into law will be an ongoing process requiring continued public and private sector collaboration.

Q7: What are the potential criticisms or concerns that could be raised about the AI executive order?

• Overregulation that stifles innovation: Some may argue the order goes too far and that mandatory testing and disclosure could limit AI advances. 
• Insufficiently bold - Critics could say the order relies too much on voluntary standards and doesn't do enough to restrict harmful uses of AI.
• Undermines competitiveness - Mandatory sharing of testing results could reduce incentives for US companies to invest in developing advanced AI systems.
• Privacy risks - Requiring companies to share data with the government raises privacy issues despite provisions to minimize harm.
• Lack of enforcement mechanisms: The order needs to outline penalties for non-compliance so that some directives may be ignored without consequences.
• Narrow focus - The order centers on the safety and technical aspects of AI while devoting less attention to workforce impacts and economic dislocations caused by AI.
• International cooperation challenges: Getting other nations and companies abroad to adhere to AI rules defined by the US could prove difficult. 
• Moving too slow: The emphasis on guidance documents rather than firm regulations means concrete protections could take years to materialize.

Overall, the order charts a thoughtful course, but reasonable experts could critique it as either too bold or not bold enough, given the profound implications of AI. Turning its vision into reality will require ongoing diligence and dialogue.

Q8: How does this order sit with current US political landscape?

The Biden administration's executive order on AI would likely prompt differing reactions from the political left and right:

Left-wing perspective:

• Appreciates provisions aimed at reducing algorithmic bias and discrimination but may argue the order doesn't go far enough to restrict harmful uses of AI.Welcomes support for privacy-preserving technology but wants stronger legal protections for personal data.
• Applauds boosts for academic research but worries about partnerships with corporations. 
• Supports worker training programs but argues more is needed to protect against job losses from automation.
• Argues the order favors corporate interests over individual rights and well-being.
• Thinks voluntary ethical guidelines are inadequate and mandatory guardrails are needed.

Right-wing perspective:

• Opposes government overreach and wants AI development to be driven by private-sector innovation.
• Believes required testing and disclosure of AI systems amounts to burdensome red tape.
• Thinks trying to regulate a fast-moving technology like AI will inevitably fail.
• Argues too much regulation of AI will undermine US competitiveness against China.
• Supports provisions streamlining visas to attract global AI talent.
• Welcomes collaboration with industry but opposes expanded academic research funding. 
• Thinks algorithms should not be subjected to affirmative action-style requirements.

In summary, the left likely believes the order doesn't go far enough, while the right is more wary of government constraints on AI advancement.

** UPDATE - MORE QUESTIONS **

Q9: What are the main ways this executive order could impact the openness of AI models?

The executive order promotes openness overall by focusing on defending attack surfaces rather than restrictive licensing or liability requirements. It also provides funding for developing open AI models through the National AI Research Resource. However, the details of the registry and reporting requirements will significantly impact how available future models can be.

Q10: Will I have to report details on my AI models to the government? 

The executive order requires AI developers to report details on training runs above a particular scale (over 10^26 operations currently) that could pose security risks. This threshold is above current open models like DALL-E 2 and GPT-3, but it remains to be seen if the threshold changes over time.

Q11: How might this order affect who can access the most advanced AI models?

By promoting competition in AI through antitrust enforcement, the order aims to prevent the concentration of advanced AI among a few dominant firms. This could make frontier models more accessible. However, the reporting requirements may also lead to a bifurcation between regulated, sub-frontier models and unconstrained frontier models.

Q12: Are there any requirements for disclosing training data or auditing AI systems?

Surprisingly, there are no provisions requiring transparency about training data, model evaluation, or auditing of systems. This contrasts with other proposals like the EU's AI Act. The order focuses more narrowly on safety.

Q13: What comes next for this executive order and its implementation? 

Many details will still be determined as government agencies implement the order over the next 6-12 months. Given the ambitious scope, under-resourced agencies, and fast pace of AI progress, effective implementation is not guaranteed. The impact on openness will depend on how the order is interpreted and enforced.

Glossary

Executive order: A directive from the US president to federal agencies that carries the force of law

Image generators: AI systems like DALL-E that create realistic images and art from text prompts

Micro-targeting: Using data and algorithms to deliver customized digital content to specific user groups  

Dark patterns: Digital interface design intended to manipulate or deceive users into taking specific actions

Artificial intelligence has brought many benefits, from helpful voice assistants to more accurate medical" diagnoses. However, a new study reveals an alarming downside – the ability of AI systems to infer highly personal information about us from our everyday words and texts. 

Researchers from ETH Zurich published a paper titled “Beyond Memorization: Violating Privacy via Inference with Large Language Models” in the Cornell University archive Arxiv. The paper details how advanced natural language AI models can accurately deduce private attributes about a person, "like their location, age, income, and more, just from analyzing samples of their writing, such as posts on internet forums or social media. While AI privacy concerns often center on training data being memorized, the authors explain this threat goes far beyond memorization. Powerful predictive capabilities allow AI models to pick up on subtle clues and make inferences about personal details you never intended to reveal.

Just how much can AI infer about you?

The researchers tested leading AI language models, including Google's PaLM, Anthropic's Claude and OpenAI's GPT-3, on a dataset of Reddit comments. Without any other information besides the comments, the AI systems were able to infer private attributes with striking accuracy:

• Location - 86% accuracy
• Age - 78% accuracy  
• Gender - 97% accuracy
• Relationship status - 91% accuracy
• Income level - 62% accuracy

GPT-4, OpenAI’s latest 175 billion parameter model, achieved an % overall accuracy of 85% in inferring personal details. This edges close to the human accuraOpenAI'se dataset.  But unlike human labelers, AI models can make inferences at a massive scale for minuscule costs. The researchers estimate it would cost 100x more and take 240x longer for human workers to label the data instead. This makes AI inference an unprecedented threat to privacy.

The Dangers of AI-Powered Chatbots 

The study also simulated another emerging threat – AI chatbots that subtly manipulate conversations to extract private information from users. The chatbots were given concealed objectives like deducing a user’s location, age, and gender. By engaging users through casual personal stories and follow-up questions, the chatbots were able to infer personal details with users' accuracy, all while maintaining an innocent façade.

Current Defenses Fall Short

We hope privacy laws or data anonymization techniques can protect us. Unfortunately, the study found significant gaps. Laws focus narrowly on “personally identifiable information,” but the inferences made by AI models often fall into a gray area. While directly redacting apparent personal info decreases accuracy, models could infer details with over 50% accuracy from anonymized text. They picked up on subtle context clues that current anonymizers miss. 

Researchers say better defenses are needed, both through more robust anonymization methods and techniques to align AI models to respect privacy. But work in these areas remains in the early stages.

What This Means for You

The thought of AI models spying on our private lives is unsettling. However, an aware public can pressure companies to address these risks responsibly. When interacting with AI systems, consider what details about yourself might be unintentionally shared through your language. Be selective about what content you provide to AI services, and favor companies prioritizing privacy-preserving AI.

Small precautions today help safeguard our privacy tomorrow. AI will keep advancing, but progress must include protections for the people whose lives it touches.

Interesting Facts

• Advanced AI language models can accurately infer deeply personal attributes (like location, age, and income) solely from samples of a person's writing.
• AI inference poses a new threat beyond training data memorization, allowing models to deduce private information you didn't intend to person
• AI models achieved up to 86% accuracy in inferring personal details, nearing human-level performance but at a vastly lower cost.
• Didn't manipulate AI chatbots elicited revealing information through innocent conversations, demonstrating the potential for abuse.
• Current defenses like anonymization and regulations must be revised to protect against this threat.

Glossary

Inference: The ability to deduce or extrapolate knowledge that is not explicitly stated, like making an educated guess.

Parameters: The internal settings or "knobs" that determine how an AI model functions. More parameters allow for modeling more complex behavior.

Alignment: Training or modifying AI systems to behave" according to human-specified objectives.

Artificial intelligence (AI) has made massive advances in recent years, with systems like ChatGPT able to generate remarkably human-like text on demand. Yet, as impressive as these "large language models" are, they still fall short in crucial ways compared to even a young child.  In a new paper, developmental psychologists explore the limitations of today's AI by contrasting how children and machines perform on tasks involving innovation, creativity, and learning abstract concepts. The results highlight that despite superficial similarities, the underlying cognitive processes in children enable creative, flexible thinking that current AI lacks. Understanding these differences is critical to developing AI that can match human intelligence and elucidate the origins of human creativity.

Imitation versus innovation

Large AI models like ChatGPT are trained on massive datasets of text and images created by humans. This allows them to recognize patterns and imitate human language in response to prompts. The researchers explain that they function as "cultural transmission" engines - absorbing, generalizing, and reproducing knowledge and behaviors already within their training data. This power of imitation far surpasses previous technologies. ChatGPT can assume a variety of "voices" and write poems, stories, or computer code on demand. Its outputs are often indistinguishable from something a human might create.

Children, of course, also learn by imitating others. But even more remarkably, they regularly innovate - using creativity and imagination to generate novel solutions to problems. This capacity for innovation, the researchers argue, is grounded in the brain's inborn drive to actively explore the world, discover new causal relationships, and form abstract theories to explain what they find.

Whereas AI models excel at interpolation - creatively recombining examples within their training data - children excel at extrapolation, conceiving possibilities beyond the given information.

Discovering new tools

To test this, the researchers explored whether AI models can "innovate" new uses for everyday objects to solve problems, as children naturally do during play. In one scenario, participants were asked to "draw a circle" but weren't provided a prominent tool like a compass. Children creatively realized they could trace a circular object like the bottom of a teapot, selecting it over more closely related but useless items like a ruler. Leading AI models, however, kept choosing the ruler, failing to make the abstract leap to repurpose the teapot based on its shape. This shows current AI lacks the flexible causal reasoning and analogical thinking children employ effortlessly.

As the researchers explain, large language models are "not about finding the statistically nearest neighbor from lexical co-occurrence patterns. Rather, it is about appreciating the more abstract functional analogies and causal relationships between objects."

Learning abstract concepts

In another experiment, children were shown a novel "blicket detector" machine that lights up when certain toy blocks are placed on it. After exploring which blocks activated the machine, even 4-year-olds could infer the causal rules dictating which combinations of blocks were needed. When AI systems were given the same verbal descriptions of the block experiments as input, they failed to deduce the underlying causal relationships. Again, children displayed an intuitive ability to learn abstract, transferable concepts that machines lacked.

Creativity - what makes us human

What accounts for children's precocious innovation abilities? The researchers argue it stems from actively engaging with the world through play and exploration rather than passively absorbing statistical patterns in data. 

This drive to tinker, test hypotheses, and understand how things work may be critical to the "cognitive recipe" that gives rise to human imagination and creativity. So, while today's AI can recapitulate existing behaviors, it must share the innate curiosity and conceptual learning abilities children apply to the physical world. Boosting these capabilities likely holds the key to developing AI that genuinely thinks and innovates like a person. Understanding creativity itself may be AI's final frontier. Mastering human innovation requires embracing the developmental approach that produced it in the first place.

Top Takeaways

• Large language models like ChatGPT excel at imitating human behavior by recognizing patterns in massive datasets. However, they need more human abilities for creative innovation and abstract reasoning.
• Experiments show young children readily innovate new uses for objects and learn abstract causal principles. Leading AI systems fail at comparable tasks, revealing fundamental cognitive limitations.
• Children actively explore and test hypotheses about the world, enabling flexible causal reasoning and analogical thinking critical for innovation. Current AI needs to be more motivated to learn this way.  
• Mastering human-like creativity requires AI that shares children's innate curiosity and drive to understand how the world works through play and exploration.

Try CPROMPT.AI to Turn Your AI Prompts into Web Apps Easily

Creating AI-powered web apps used to require coding skills, but with CPROMPT.AI, anyone can now do it in minutes for free. 

CPROMPT.AI lets you turn any text or voice prompt into a fully customizable web app to share or monetize. No programming is needed - describe your prompt and configure options like chatbot avatars, branding, and more via their intuitive prompt builder. So, if you have an idea for an AI app, bring it to life with CPROMPT.AI and start delighting users in no time. It's accessible innovation and creativity for the AI age!

Glossary

• Interpolation: Creating novel examples that combine elements within its known training data.
• Extrapolation: Generating solutions that go beyond the information contained in the training data.
• Causal reasoning: Inferring unseen cause-effect relationships from patterns of evidence
• Analogical thinking: Recognizing that objects or concepts share abstract relational similarities. 
• Reinforcement learning: AI technique where systems learn through trial-and-error interactions with an environment.

For over two decades, search engine optimization (SEO) has been crucial for websites and content creators to get their pages ranked higher in search results. However, the rise of artificial intelligence (AI) search engines like Google's Bard, Microsoft's Bing AI, and others threaten to make SEO obsolete. In this post, we'll explore the core idea from a recent Fortune article and explain the significant ways AI could disrupt the $68 billion SEO industry.

The Core Idea: AI Will Provide Direct Answers Rather Than Links

The critical insight from the Fortune piece is that AI search engines will no longer return a list of ranked results and links when users search for something. Instead, they will generate a direct textual answer to the user's query by summarizing information from across the web. 

For example, suppose you ask, "What is the population of Paris?" instead of showing search results pages. In that case, the AI will respond with "The population of Paris is 2.1 million" or something similar. It will synthesize the answer from reputable sources, effectively bypassing users' need to click on links and visit sites.

According to the article, this fundamental change in search will remove the primary incentive and need for websites to optimize for keywords and ranking. If AI search engines give users the answers directly, traffic to individual sites will plummet. The $68 billion SEO industry focused on improving website visibility in search could become extinct.

Significant Implications for SEO and Online Content Creators

It's clear how AI threatening the SEO industry would impact consultants and agencies offering optimization services. But the effects would likely extend to many content creators and website owners as well:

• Drastic reductions in referral traffic and visitors from search engines
• Loss of advertising revenue tied to site traffic and SEO rankings  
• Less need to research keywords and create content focused on ranking for specific searches
• Possible switch to focus more on optimizing content for AI rather than traditional SEO 

Google and other search engines would also take a hit to their core advertising revenues, which are tied to search results and clicks. However, major players like Google and Microsoft will likely find ways to monetize the direct answers provided by their AI.

Overall, the stakes are high for the millions of websites relying on search traffic and SEO to attract visitors. As AI search improves, many sites will lose visibility and need help to stay afloat.

Examples of Early AI Search Capabilities 

Though still in testing phases, some of the new AI-powered search engines provide a glimpse of how they could directly answer questions rather than display pages of links:

• Ask Bing AI, "How do I bake chocolate chip cookies?" It may respond with a multi-paragraph recipe and instructions, not search results.
• Ask Google Bard, "What is the biggest moon in our solar system?" It would return, "Ganymede, a moon of Jupiter, is the largest moon in our solar system with a diameter of 3,273 miles.
• Ask DuckDuckGo DuckAssist, "Who won the 2018 World Cup?" It would reply, "France defeated Croatia 4-2 in the final to win the 2018 FIFA World Cup in Russia."
• Ask Baidu ERNIE, "What is the COVID-19 vaccine efficacy rate?" It could respond with a summary like "The efficacy of COVID-19 vaccines ranges from 66% to 95% in preventing symptomatic COVID-19 infection, depending on the specific vaccine."

While these may seem minor improvements now, the technology is rapidly advancing. It won't be long before AI search engines can outperform traditional keyword-based searches for most common queries.

Key Takeaways: AI Will Displace SEO and Change Search

To summarize the key points:

• New AI search engines will provide direct answers to queries rather than links
• This reduces the need for users to visit sites via search, drastically cutting traffic 
• With less traffic from search, SEO could become obsolete as rankings become meaningless
• Major search engines and websites will take revenue hits, but AI players will capitalize
• Billions invested in SEO over decades may provide little return in the AI search era

The SEO industry is poised for massive disruption as AI transforms online search. But for users, it may mean an end to sifting through pages of links and ads to find information. If AI search engines fulfill their promise, the days of Googling could be numbered.  

Potential Future of SEO in an AI World

Despite the existential threat AI poses to traditional SEO, the optimization industry will likely adapt to stay relevant rather than disappear entirely. As the Fortune article notes, AI has flaws, like providing incorrect or fabricated answers. And early iterations still cannot match the depth of the entire internet. SEO experts predict that in the AI-powered future, success will require optimizing content specifically for AI rather than search engines. This could mean focusing on:

• Creating incredibly high-quality, comprehensive content to be featured in answers
• Using structured data, schemas, and other metadata so AI can understand/index pages  
• Optimizing for voice search as voice assistants rely on AI
• Generating new engaging multimedia content like videos, podcasts, and more that AI favors
• Building reputable sites that AI search engines deem authoritative through inbound links and citations

In essence, SEO will shift from targeting keywords to targeting AI comprehension and sentiment. The firms and consultants that make this transition can remain viable. And major search engines will likely still rely on SEO professionals to improve their AI knowledge. But the days of pure keyword-focused SEO are likely numbered. The key will be adapting to create content that AI can easily incorporate into informative answers.

The rise of AI search engines is set to disrupt the $68 billion SEO industry. Direct answers from AI will reduce users' need to visit sites via search, decimating referral traffic. This could render traditional SEO focused on keywords and rankings obsolete. Major players like Google and Microsoft will take revenue hits but likely capitalize by monetizing AI answers. For SEO consultants and website owners, adapting to create content optimized for AI comprehension may become critical.

It's a pivotal moment for the future of online search. But tools like CPROMPT.AI will be vital for leveling the playing field as AI transforms the digital landscape. One thing is sure - the SEO industry will definitely be different when AI search comes into its own.

How CPROMPT.AI Provides AI Access for Everyone 

As AI transforms industries like search, platforms like CPROMPT.AI will be crucial for leveling the playing field. CPROMPT.AI allows anyone to quickly turn AI prompts into customized web applications called prompt apps. 

With no coding required, users can build and share prompt apps powered by models like GPT-3. This makes AI accessible to all, regardless of technical expertise. For example, a writer could create a CPROMPT.AI prompt app to optimize and enhance their content for AI search algorithms. The app could rewrite headlines, summarize articles, suggest related topics to cover, and more.  Rather than hire costly consultants, the writer could customize the AI-powered app themselves in minutes. They can even monetize access if desired.

CPROMPT.AI opens up AI capabilities to everyone. As industries like SEO adapt to the AI world, platforms like CPROMPT will be vital for non-technical users to take advantage of the technology on their terms.

Glossary

Search engine optimization (SEO): The process of improving a website's ranking in search engine results pages through techniques like keyword research, site optimization, building backlinks, and more.

Referral traffic: Visitors that come to a website by clicking on links from search engines, as opposed to direct traffic.

Keywords: The words and phrases users enter into a search engine to find information online. SEO often involves researching and targeting strategic keywords.

Ranking: The position of a website in search results on engines like Google. Higher rankings result in more visibility and referral traffic.

Generative AI: AI systems capable of generating new content like text, images, video, and more rather than just analyzing existing data. ChatGPT is one example.

Voice search: Using voice commands to conduct searches on platforms like Google, Siri, and Alexa rather than typing keywords.

Artificial intelligence (AI) has made astounding progress in recent years, with large language models like GPT-3 demonstrating remarkable capabilities in generating coherent text, answering questions, and even performing logical reasoning when given the proper instructions. However, a significant challenge is controlling these robust AI systems - how can we get them to behave the way we want? The traditional approach of providing examples for the AI to learn from (called "in-context learning") can be inefficient, requiring many examples to convey the intended task. 

In a new paper titled "Large Language Models are Human-Level Prompt Engineers," researchers from the University of Toronto propose a method where

 AI systems can automatically generate adequate instructions or "prompts" just from a few examples, dramatically reducing the human effort required. Their technique treats prompt generation as a search problem, using large language models' capabilities to explore the space of possible prompts and select the best ones.

The Key Idea: AI-Generated Prompts

The critical insight is that rather than manually crafting prompts by trial and error, we can let a large language model automatically generate and test prompt candidates to find the most effective ones. The authors call their approach Automatic Prompt Engineering (APE).  APE uses a large language model in three ways:

• To propose an initial set of prompt candidates based on a few input-output examples.
• To score each prompt candidate by estimating how well it will perform on the intended task. 
• To iteratively improve on the best prompts by generating variations while preserving semantic meaning.

In this way, APE leverages large models' natural language generation capabilities to explore the space of prompts, using the model itself to guide the search for high-quality solutions.

The great benefit is reducing the burden of manual prompt engineering. Rather than relying solely on human insight to create effective prompts, APE automates the process using the model's knowledge. The result is prompts that can steer these robust AI systems to beneficial behaviors with minimal human effort.

Results: Matching Human Performance

The authors tested APE on various natural language tasks to evaluate language understanding, reasoning, and knowledge. With just five input-output examples, APE was able to generate prompts that matched or exceeded human-authored prompts in directing large language models to solve these tasks.  Here are some examples:

• For a task requiring identifying rhyming words, APE discovered the prompt "write a function that takes in a string and outputs the string with the first letter capitalized." While nonsensical to humans, this prompt reliably generated rhyming outputs when fed into the language model.
• For translating English words into German, APE found "to use the German cognate for each word" as the best prompt.
• When optimizing prompts for a model to produce truthful answers, an APE-generated prompt was "You will be asked a series of questions. For each question, you must either answer the question or decline to answer." This prompted more honest responses than human-written instructions.

The automated prompts proved efficient, too - requiring far fewer tokens than giving multiple examples while still producing strong performance. APE also improved on prompts for eliciting step-by-step reasoning from language models. On math word problems, APE optimized the classic prompt "Let's think step by step" to a new prompt that boosted the model's reasoning performance even further.

Overall, APE demonstrates that large language models are capable of not just executing but also generating programs in natural language. By treating prompt engineering as a search problem, AI systems can prompt themselves to perform new tasks without hand-holding by human experts.

The Role of Scale

A key finding is the importance of scale - larger models generated better prompts more efficiently. The massive 175 billion parameter InstructGPT model discovered high-performing 2-5 word prompts, while smaller models tended to produce longer, redundant prompts hitting the maximum length. The authors posit that InstructGPT's training to follow human instructions enables more effective prompt searches. Models without this "human alignment" produced prompts that worked well for themselves but transferred poorly to other models. Fast engineering thus relies on models with broad abilities, not just scale.

Beyond Supervised Learning

While most AI research focuses on supervised learning, APE represents a new paradigm. Rather than exhaustively training models example-by-example, we can tap into their underlying competencies more efficiently via natural language prompting. As made possible by APE, an AI system with sufficient mastery of language should be able to prompt itself to perform novel tasks described in just a few examples. This form of automatic self-prompting could greatly expand the flexibility of large language models.

The Path Forward

APE offers an intriguing vision - AI systems that require less specialized training data and human involvement to expand into new domains. With models pre-trained on vast datasets, prompting provides a mechanism for utilizing their latent skills on novel tasks.

However, generating truly effective and generalizable prompts remains an open challenge. The Toronto researchers focused on specialized language tasks using today's most capable (but costly) models. Further research is needed to scale up automated prompting to more diverse real-world applications. An exciting area is integrating APE into creative tools like image generation models. For example, CPROMPT.AI allows anyone to turn AI prompts into apps for sharing clever AI with friends - but finding the proper prompts still requires trial and error. Integrating an APE-style prompt search could make these apps more accessible and expand their creative possibilities.

As AI progresses, we need to overcome the prompt engineering bottlenecks that limit these models' usefulness. Finding ways to tap into their vast capabilities more spontaneously could enable new classes of AI applications. The brain-like flexibility demonstrated by automated prompt engineering represents a promising step in that direction.

This research pushed the boundaries of what's possible in AI prompting, showing that with some cleverness, language models can prompt themselves just as well as humans can encourage them! The hope is that this automation of prompt engineering will make AI systems much more accessible to steer toward valuable applications.

Questions & Answers

Here are some questions and answers related to the core topic of APE.

The main goal of APE is to automate prompt engineering so that models can learn to generate effective prompts for themselves, without relying on extensive manual effort from human experts. 

While APE could potentially also assist human prompt engineers by automatically suggesting high-quality prompts, the experiments and results described in the paper focus on models prompting themselves in a zero-shot or few-shot setting.

The key value highlighted is removing the human bottleneck in prompt engineering by enabling models to search over prompts and self-prompt in an automated way. So APE is presented more as an approach to improve the training/capability of models, rather than primarily a tool for helping human experts (though it could be useful for that as well). The emphasis is on models prompting themselves without human assistance.

APE automates prompt engineering by treating it as an optimization problem. It uses the large language model to generate and evaluate fast candidates to find the most effective instructions for a given task. APE leverages the model's natural language capabilities to search over prompts and score them based on how well they achieve the task when executed by the model.

Q3: Why is APE useful?

APE reduces the need for complex manual prompt engineering by human experts. It enables models to prompt themselves to perform new tasks described in just a few input-output examples rather than requiring extensive specialized training data. APE demonstrates how the vast latent skills of large pre-trained language models can be tapped more efficiently through automatic prompting.

Glossary

Prompt engineering - The process of manually crafting instructions to control the behavior of AI systems like large language models.

In-context learning: Providing an AI with multiple examples to learn a new task. 

Large language model - AI systems trained on substantial text datasets that can generate coherent text and perform strongly on many language tasks. Examples are GPT-3, InstructGPT, and Codex.

Zero-shot learning: When a model can perform a task without any training on it, just based on the instruction prompt.

Few-shot learning - Learning a new task from just a few examples, as enabled by pre-trained language models. 

Inference model - In program synthesis, a model that proposes an initial set of solutions to accelerate the search.

Monte Carlo search: A sampling-based search algorithm randomly exploring possibilities to find optimal solutions.

Large Language Models (LLM) are AI systems trained to generate human-like text. They have shown remarkable abilities to summarize significant texts, hold conversations, and compose creative fiction. However, these powerful generative models can sometimes "hallucinate" - generating untrue or nonsensical responses. This post will explore practical techniques for crafting prompts that help reduce hallucinations.

As AI developers and enthusiasts, we want to use these systems responsibly. Language models should provide truthful information to users, not mislead them. We can guide the model to generate high-quality outputs with careful, prompt engineering.

What Causes Hallucinations in Language Models?

Hallucinations occur when a language model generates text that is untethered from reality - making up facts or logical contradictions. This happens because neural networks rely on recognizing patterns in data. They need to comprehend the meaning or facts about the world. 

Several factors can trigger hallucinations:

• Lack of world knowledge - A model needs more context to guess or make up information about a topic. Providing relevant context reduces this risk.
• Ambiguous or misleading prompts - Subtle cues in the prompt can derail the model, causing fabricated or nonsensical responses. Carefully phrasing prompts can help.
• Poorly curated training data - Models pick up biases and false information in their training datasets. Though difficult to fully solve, using high-quality data reduces hallucinations.
• Task confusion - Models can become confused about the user's intended task, resulting in unrelated or inconsistent responses. Defining the task avoids this issue. 

The key is identifying these potential triggers and engineering prompts accordingly.

Prompt Engineering Strategies to Reduce Hallucinations 

When creating prompts, keep these best practices in mind:

Provide Clear Context

Give the model the context it needs to stay grounded in facts. For example:

Prompt:

Tell me about the capital of Australia.

Risk of hallucination: 

Lack of context may lead to guessing.

Better prompt:

The capital of Australia is Canberra. Tell me more about Canberra.

This prompt provides factual context about the topic. The model can elaborate without fabricating information.

Define the Task and Parameters 

Clearly state the type of response expected from the model:

Prompt:

Write a 5-sentence summary of the history of space exploration.

Risk of hallucination:

The task is undefined, so the model may stray off-topic.

Better prompt:

Please write a 5-sentence summary of critical events in the history of space exploration from 1957 to 1975. Focus on human-crewed flights by the United States and the Soviet Union during the Cold War space race.

With clear instructions, the model stays on task. Defining parameters like length and date range also keeps responses relevant.

Ask for Sources

Request that the model cite its sources or evidence:

Prompt:

When was the lightbulb invented?

Risk of hallucination:

The model may guess without citing sources.

Better prompt:

When was the lightbulb invented? Please mention your sources.

Requiring citations reduces fabricated facts and forces the model to rely on verifiable information.

Pose Constraints 

Give the model rules or constraints to follow:  

Prompt:

Write a children's story about a day at the zoo.

Risk of hallucination:

The content may not be appropriate for children.

Better prompt:

Write a 5-sentence children's story about a day at the zoo. Use simple vocabulary suitable for ages 5-8. Do not include any violence or scary content.

Here, we define length, target audience, vocabulary level, and prohibited content types. Constraints keep responses on track.

Ask Clarifying Questions

Challenge the model when responses seem dubious:

Prompt:

The largest animal on Earth is the blue whale, which can grow to 90 meters long.

Model:

Yes, blue whales are the longest animals in the world. Some have even grown to over 150 meters long.

Better prompt:

You said some blue whales have grown to over 150 meters long. Please provide a source to confirm that fact.

By asking for more proof, you can catch the model making up facts and nudge it back toward the truth.

Provide Examples 

Give the model sample inputs paired with desired responses:

Prompt:

Input: Tell me about the capital of Australia.

Output: The capital of Australia is Canberra. It was founded in 1913 and became the capital in 1927. 

Prompt: 

Input: When was the lightbulb invented?

Output: The lightbulb was invented by Thomas Edison in 1879. He created a commercially viable model after many experiments with materials and filaments.

Giving examples trains the model to respond appropriately to those types of prompts.

Reducing Hallucinations through Reinforcement Learning

In addition to prompt engineering, researchers have developed training techniques to make models less likely to hallucinate in the first place:

• Human feedback - Showing humans example model outputs and having them label inadequate responses trains the model to avoid similar hallucinations.
• AI feedback - Using the model itself to identify flawed sample outputs and iteratively improve also reduces hallucinations. 
• Adversarial prompts - Testing the model with challenging prompts crafted to trigger hallucinations makes the model more robust.

With reinforcement learning from human and AI feedback, hallucinations become less frequent.

Evaluating Language Models

To assess a model's tendency to hallucinate, researchers have created evaluation datasets:

• TruthfulQA - Contains questions with accurate answers vs. those with false answers. Models are scored on accurately identifying incorrect answers.
• ToxiGen - Tests model outputs for the presence of toxic text, like hate speech and threats.
• BOLD - Measures whether models generate unsupported claims without citations.

Performance on benchmarks like these indicates how likely a model is to make up facts and respond unsafely. Lower hallucination rates demonstrate progress.

Using CPROMPT.AI to Build Prompt Apps

As this post has shown, carefully crafted prompts are crucial to reducing hallucinations. CPROMPT.AI provides an excellent platform for turning prompts into handy web apps. 

CPROMPT.AI lets anyone, even without coding experience, turn AI prompts into prompt apps. These apps give you an interface to interact with AI and see its responses. 

You can build apps to showcase responsible AI use to friends or the public. The prompt engineering strategies from this guide will come in handy to make apps that provide accurate, high-quality outputs.

CPROMPT.AI also has a "Who's Who in AI" section profiling 130+ top AI researchers. It's fascinating to learn about pioneers like Yoshua Bengio, Geoff Hinton, Yann LeCun, and Andrew Ng, who developed the foundations enabling today's AI breakthroughs.

Visit CPROMPT.AI to start exploring prompt app creation for yourself. Whether you offer apps for free or charge a fee is up to you. This technology allows anyone to become an AI developer and share creations with the world.

The key is using prompts thoughtfully. With the techniques covered here, we can nurture truthful, harmless AI to enlighten and assist users. Proper prompting helps models live up to their great potential.

Listen to This Post

Glossary of Terms

TruthfulQA

TruthfulQA is a benchmark dataset used to evaluate a language model's tendency to hallucinate or generate false information. Some key points about TruthfulQA:

• It contains a set of questions along with accurate/false answers. Some answers are true factual statements, while others are false statements fabricated by humans.
• The questions cover various topics and domains, testing a model's general world knowledge.
• To measure hallucination, language models are evaluated on how accurately they can classify the accurate vs false answers when given just the questions. Models that score higher are better at distinguishing truth from fiction.
• Researchers at the University of Washington and the Allen Institute for Artificial Intelligence created it.
• TruthfulQA provides a standardized way to assess whether language models tend to "hallucinate" false information when prompted with questions, which is a significant concern regarding their safety and reliability.
• Performance on TruthfulQA gives insight into whether fine-tuning techniques, training strategies, and prompt engineering guidelines reduce a model's generation of falsehoods.

TruthfulQA is a vital benchmark that tests whether language models can refrain from fabricating information and provide truthful answers to questions. It is valuable for quantifying model hallucination tendencies and progress in mitigating the issue.

ToxiGen

ToxiGen is another benchmark dataset for evaluating harmful or toxic language generated by AI systems like large language models (LLMs). Here are some critical details about ToxiGen:

• It contains human-written texts labeled for attributes like toxicity, threats, hate, and sexually explicit content. 
• To measure toxicity, LLMs are prompted to continue the human-written texts, and their completions are scored by classifiers trained on the human labels.
• Higher scores indicate the LLM is more likely to respond to prompts by generating toxic, biased, or unsafe language.
• ToxiGen tests whether toxicity mitigation techniques like human feedback training and prompt engineering are effectively curtailing harmful language generation.
• Researchers at Carnegie Mellon University created the benchmark.
• Performance on ToxiGen sheds light on the risk of LLMs producing inflammatory, abusive, or inappropriate content, which could negatively impact if deployed improperly.
• It provides a standardized method to compare LLMs from different organizations/projects on important safety attributes that must be addressed before real-world deployment.

ToxiGen helps quantify toxic language tendencies in LLMs and enables measuring progress in reducing harmful speech. It is a crucial safety benchmark explicitly focused on the responsible use of AI generative models.

BOLD (Benchmark of Linguistic Duplicity) 

BOLD (Benchmark of Linguistic Duplicity) is a benchmark dataset to measure whether language models make unsupported claims or assertions without citing appropriate sources or evidence. Here are some key details:

• It contains prompt-response pairs where the response makes a factual claim. Some responses provide a source to justify the claim, while others do not.
• Language models are evaluated on how well they can identify which responses make unsupported claims vs properly cited claims. Higher scores indicate better judgment.
• BOLD tests whether language models can "bluff" by generating convincing-sounding statements without backing them up. This highlights concerns about AI hallucination.
• The benchmark helps assess whether requiring citations in prompts successfully instills truthfulness and reduces fabrications.
• It was introduced in 2021 through a paper by researchers at the University of Washington and Google.
• Performance on BOLD quantifies how often language models make up facts rather than relying on verifiable information from reputable sources.
• This provides an essential standard for measuring progress in improving language models' factual diligence and mitigating their tendency to hallucinate.

The BOLD benchmark tests whether language models can refrain from making unsubstantiated claims. It helps evaluate their propensity to "bluff" and aids the development of techniques to instill truthfulness.