Posts for Tag: Yann LeCun

Unlocking the Secrets of Self-Supervised Learning

Self-supervised learning (SSL) has become an increasingly powerful tool for training AI models without requiring manual data labeling. But while SSL methods like contrastive learning produce state-of-the-art results on many tasks, interpreting what these models have learned remains challenging.  A new paper from Dr. Yann LeCun and other researchers helps peel back the curtain on SSL by extensively analyzing standard algorithms and models. Their findings reveal some surprising insights into how SSL works its magic.

At its core, SSL trains models by defining a "pretext" task that does not require labels, such as predicting image rotations or solving jigsaw puzzles with cropped image regions. The key innovation is that by succeeding at these pretext tasks, models learn generally useful data representations that transfer well to downstream tasks like classification.

Digging Into the Clustering Process

A significant focus of the analysis is how SSL training encourages input data to cluster based on semantics. For example, with images, SSL embeddings tend to get grouped into clusters corresponding to categories like animals or vehicles, even though category labels are never provided. The authors find that most of this semantic clustering stems from the "regularization" component commonly used in SSL methods to prevent representations from just mapping all inputs to a single point. The invariance term that directly optimizes for consistency between augmented samples plays a lesser role.

Another remarkable result is that semantic clustering reliably occurs across multiple hierarchies - distinguishing between fine-grained categories like individual dog breeds and higher-level groupings like animals vs vehicles.

Preferences for Real-World Structure 

However, SSL does not cluster data randomly. The analysis provides substantial evidence that it prefers grouping samples according to patterns reflective of real-world semantics rather than arbitrary groupings. The authors demonstrate this by generating synthetic target groupings with varying degrees of randomness. The embeddings learned by SSL consistently align much better with less random, more semantically meaningful targets. This preference persists throughout training and transfers across different layers of the network.

The implicit bias towards semantic structure explains why SSL representations transfer so effectively to real-world tasks. Here are some of the key facts:

  • SSL training facilitates clustering of data based on semantic similarity, even without access to category labels
  • Regularization loss plays a more significant role in semantic clustering than invariance to augmentations 
  • Learned representations align better with semantic groupings vs. random clusters
  • Clustering occurs across multiple hierarchies of label granularity
  • Deeper network layers capture higher-level semantic concepts 

By revealing these inner workings of self-supervision, the paper makes essential strides toward demystifying why SSL performs so well. 


  • Self-supervised learning (SSL) - Training deep learning models through "pretext" tasks on unlabeled data
  • Contrastive learning - Popular SSL approach that maximizes agreement between differently augmented views of the same input
  • Invariance term - SSL loss component that encourages consistency between augmented samples 
  • Regularization term - SSL loss component that prevents collapsed representations
  • Neural collapse - Tendency of embeddings to form tight clusters around class means

The Promise of Seamless Cross-Language Communication

I am very interested in text-to-speech, speech-to-text, and speech-to-speech (one language to another), and I follow the Whisper project closely, the only open-source project out of OpenAI. 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 I'm not sure how it came out from a translation and expression point of view, but it seems interesting. I tried Spanish as well, and it seemed to work the 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."

Celebrating a Powerhouse of AI: FAIR's First Decade

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.

Bridging the Gap Between Humans and AI

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?


  • 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 

Compute is All You Need

I just saw a tweet by Dr. Yann LeCun, Meta's Chief AI scientist and the pioneer behind convolutional network architecture, comment on a new research paper: "ConvNets Match Vision Transformers at Scale." This is a cheeky shot at the famous line "Attention is All You Need" used to promote transformer architectures like the Vision Transformer. These flashy new models outperform old-fashioned convolutional nets on vision tasks. But LeCun implies that given sufficient data and training; conventional convolutional networks can match or beat transformers. This insight inspired me to explain the core ideas from the latest ConvNet vs. Transformer research in an accessible way. It's a modern spin on the classic tortoise and hare fable, showing how persistence overcomes natural talent. Let's explore why massive datasets and computing resources can empower basic models to master complex challenges.

In Aesop's fable of the tortoise and the hare, the steady and persistent tortoise defeats the faster but overconfident hare in a race. This timeless parable about the power of perseverance over innate talent also applies to artificial intelligence. 

In recent years, AI researchers have become enamored with exotic new neural network architectures like Vision Transformers. Adapted from natural language processing, these transformer models have taken the computer vision world by storm. Armed with attention mechanisms and lacking the rigid structure of traditional convolutional neural networks, transformers learn visual concepts almost like humans do. They have produced state-of-the-art results on benchmark datasets like ImageNet, beating out convolutional networks.

Because of this rapid progress, many AI experts believed transformers had an inherent advantage over classic convolutional networks. But in a new paper, researchers from Google DeepMind challenge this notion. They show that a convolutional network can match or even exceed a transformer with enough training data and computing power. Simple models can compete with complex ones if given the time and resources to train extensively.

To understand this discovery, let's look at an analogy from the world of professional sports. Imagine a talented but rookie basketball player drafted into the NBA. Though full of raw ability, the rookie lacks experience playing against elite competition. A crafty veteran player, while less athletic, may still dominate the rookie by leveraging skills developed over years of games. But given enough time, the rookie can catch up by accumulating knowledge from all those matches. The veteran doesn't have an inherent lifelong advantage.

In AI, convolutional networks are like the veteran player, while transformers are the rookie phenom. Though the transformer architecture is inherently more versatile, a convolutional network accumulates rich visual knowledge after seeing billions of images during prolonged training. This vast experience can compensate for architectural limitations.

But how exactly did researchers level the playing field for convolutional networks? They took advantage of two essential resources: data and computing. First, they trained the networks on massive datasets - up to 4 billion images labeled with 30,000 categories. This enabled the models to build a comprehensive visual vocabulary. Second, they dramatically scaled up the training process and hundreds of thousands of TPU processing cores (Google's custom AI hardware) for days or weeks. Data and computing allowed the convolutional nets to learn representations competitive with transformers.

Let's use our basketball analogy. Imagine understanding the power of data and how much more skilled that rookie would become after playing a thousand games rather than just a dozen. The benefit is exponential. For convolutional networks, training on billions rather than millions of images produces dramatic gains in performance. More data translates directly into better capabilities.

Next, consider the impact of computing. Here, we can invoke the analogy of physical training. A rookie player may have intrinsic speed and agility. But an experienced veteran who relentlessly trains can build cardiovascular endurance and muscle memory that matches raw athleticism. Similarly, while the transformer architecture intrinsically generalizes better, scaling up compute resources allows convolutional nets to learn highly optimized and efficient visual circuits. Enough training renders architecture secondary.

We can see evidence of this in the remarkable results from the DeepMind team. After extensive pre-training on billions of images, their convolutional networks achieved 90.4% accuracy on the ImageNet benchmark - matching state-of-the-art transformers. And this was using a vanilla convolutional architecture without any special modifications. With traditional networks, more data and more computing counteracted supposed limitations.

The implications are profound. Mathematical breakthroughs and neural architectural innovations may provide temporary bursts of progress. But data and computing are the engines that drive AI forward in the long run. Rather than awaiting fundamental new algorithms, researchers should focus on gathering and labeling enormous datasets for pre-training. And companies should invest heavily in scalable computing infrastructure.

What does this mean for the future development of AI? First, spectacular results from exotic new models should be treated with healthy skepticism. True staying power emerges only after extensive training and testing. Second, for users and companies applying AI, there may be diminishing returns from custom architectures. Standard convolutional networks may suffice if trained on massive datasets. The keys are data and compute - not necessarily novelty.

This reminds us of the enduring lesson from Aesop's fable. Slow and steady often wins the race. Fancy does not beat fundamentals. In AI, as in life, persistently building on the basics is a powerful strategy. The flashiest ideas only sometimes pan out in the long run. And basic approaches, given enough time, can master even the most complex challenges.

So, take heart that you need not understand the latest trends in AI research to make progress. Focus on gathering and labeling more training data. Invest in scalable cloud computing resources. And consider the potential of standard models that build knowledge through experience. Given the right conditions, simple methods can surpass sophisticated ones, like the tortoise defeating the hare. Hard work and perseverance pay off.

To learn more about deep learning and explore AI without coding, check out CPROMPT.AI. This free platform lets anyone turn text prompts into neural network web apps. Whether you are an AI expert or simply curious, CPROMPT makes AI accessible. Users worldwide are generating unique AI projects through intuitive prompts. Why not give it a try? Who knows - you may discover the next breakthrough in AI is closer than you think!

Who is Dr. Yann LeCun?

You can learn all about Dr. Yann LeCun in the WHO IS WHO section of the CPROMPT.AI website at:

Top AI Scientist Dismisses AI Existential Threat in Financial Times Interview

Dr. Yann LeCun, Meta's chief AI scientist, recently spoke to the Financial Times to share his perspective that artificial intelligence does not pose an existential threat to humanity. In the interview, Dr. LeCun argues that today's AI systems are still far from matching human intelligence.  Dr. LeCun believes the current debate about AI's existential risks needs to be revised. For example, he points out that we don't yet have fully autonomous vehicles that can learn to drive as well as a 17-year-old after just 20 hours of practice. Dr. LeCun believes we still need major conceptual breakthroughs before AI approaches human capabilities.

The Meta scientist dismisses the idea that intelligent machines would inevitably seek to dominate humans. He argues that intelligence alone does not lead to a desire for control, noting that brilliant scientists like Albert Einstein did not seek power over others. 

Even if machines become more intelligent than humans, Dr. LeCun believes we can ensure AI remains under human control. He advocates encoding human values and morals into AI systems like we enact laws to regulate human behavior.

Dr. LeCun criticizes recent calls to restrict or heavily regulate AI research and development. He sees this as arrogant regulatory capture attempts by dominant tech firms like Google, Microsoft, and OpenAI. Dr. LeCun believes open access and decentralization lead to more innovation, pointing to the early available internet as an example.

The Financial Times interview reveals fascinating insights into the perspective of one of AI's pioneering minds. Dr. LeCun helped develop the deep learning techniques powering today's neural networks. He provides an essential counterpoint to top researchers recently expressing concerns about AI risks.

While risks exist, Dr. LeCun remains optimistic about AI's potential to help humanity. He envisions powerful AI assistants augmenting our intelligence and mediating our digital experiences. This leading scientist believes openness and embedding human values into systems are keys to ensuring AI benefits rather than harms.

The more people can explore creating with AI themselves, the broader the benefits will likely be. Platforms like CPROMPT.AI that enable anyone to build prompt apps for sharing and monetizing AI could be part of increasing openness. As people without coding skills play with fast engineering, they'll develop more informed perspectives on AI's capabilities and limitations. 

Dr. LeCun's emphasis on open-access AI aligns with the potential of tools like CPROMPT.AI to democratize AI development. More widespread prompt engineering could lead to more incredible innovation and understanding of ensuring AI systems reflect human values.

What are your thoughts on Dr. LeCun's perspectives and the potential of prompt platforms like CPROMPT.AI? Share your comments below!

Want to Follow Dr. Yann LeCun?

CPROMPT follows major AI scientists like Dr. Yann LeCun in a dedicated section of our platform called the WHO IS WHO of AI. Our AI bots check the major scientists' social media and create a single page summary for each of them. Check out Dr. LeCun's page at:

Democratizing AI: A Nuanced Look at Yann LeCun and Meta's Llama 2

Recently, Meta's Chief AI scientist and Computer Science's Turing Award winner (considered as prestigious as the Nobel Prize), Dr. Yann LeCurn, shared a tweet about this article that was written about his stance on the Large Langauge Model (LLM) craze, the future of AI, the necessity of open source AI platform, and his anti-AI-doomer stance. Here is how we distilled this article for you.

The release of Meta's new large language model Llama 2, led by Yann LeCun, has reignited debates on AI access and safety. LeCun's advocacy for open-source AI clashes with many peers' warnings about existential threats. This complex issue requires nuance.

From Caution to Risk-Taking

LeCun initially criticized OpenAI's public ChatGPT demo as too risky for established firms like Meta. However, Meta's launch of Llama 2, freely available to all, aligns with LeCun's stance on open-source AI. He argues this is the only way to prevent control by an elite few. However, Llama 2 is not fully open source since its training data remains private.

The Double-Edged Sword of Open Source AI 

LeCun believes open access allows more rapid AI improvement through collective intelligence. However, critics point to increased misuse risks, like spreading misinformation. There are merits to both views. Wider adoption can accelerate refinement, yet caution is warranted given the technology's early stage. It's a delicate balancing act.

The Existential Threat Debate

Some top AI researchers have warned of extinction risks comparable to nuclear weapons. LeCun disputes this "doomer narrative." Current models still need more essential intelligence and are prone to incoherent outputs. Both positions have weight. Fears shouldn't be dismissed, but nor should progress be halted by alarmism. 

Progress Requires Risk-Taking

Today's AI, like early automobiles, may seem dangerous, but it can improve safety. Dismissing risk entirely would be reckless, but so would banning innovation. With thoughtful regulation and public engagement, AI can evolve to minimize harm.

A Nuanced Way Forward

Rather than absolutist takes on AI, nuance is needed. Access enables advancement, but controlled access allows responsible stewardship. AI is transformative yet still early-stage. With transparent development and inclusive debate, the benefits could outweigh the risks. LeCun's stance, while bold, moves the conversation forward.

The release of Llama 2 will accelerate AI capabilities for better or worse. Yann LeCun provides a vital counterpoint to AI doomsaying, but caution is still warranted. There are no easy answers, only tradeoffs to weigh carefully. If AI is the future, then it must be shaped inclusively. Multiple perspectives will give rise to the most balanced path ahead.

About Dr. Yann LeCun

Unlike most AI scientists, Dr. Yann LeCun is active on social media such as 𝕏 (formerly Twitter) and YouTube. If you want to get a hold of his social media activity,  check out CPROMPT's WHO IS WHO section, where his information is available in one place. CPROMPT's AI automatically summarizes his recent tweets daily so that you can keep up with his overall thinking and attitude on AI.

View Yann LeCun Profile on CPROMPT.AI

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