Posts for Tag: Sam Altman

Turkey-Shoot Clusterfuck: Open AI @Sama Saga and Lessons Learned

The drama surrounding artificial intelligence startup OpenAI and its partnership with Microsoft has all the hallmarks of a Silicon Valley soap opera. OpenAI's board abruptly fired CEO and co-founder Sam Altman last month, setting off a behind-the-scenes crisis at Microsoft, which has invested billions in the AI firm's technology.  

OpenAI has been at the leading edge of AI innovation, captivating the public last year with the launch of ChatGPT. This conversational bot can generate essays, poems, and computer code. Microsoft saw integrating OpenAI's technology into its software as key to upgrading its products and competing with rivals Google and Amazon in the red-hot AI race.  

The two companies forged an extensive partnership, with Microsoft investing over $10 billion into OpenAI. This collaboration led Microsoft to launch one of its most ambitious new products in years – a suite of AI "copilots" embedded into Word, Excel, and other Microsoft productivity tools. 

Dubbed Office Copilots, these AI assistants can write documents, analyze spreadsheets, and complete other tasks by having natural conversations with users. Microsoft planned a slow, phased introduction of this potentially transformative technology, first to select business customers and then gradually to millions of consumers worldwide.

Behind the scenes, however, tensions mounted between Altman and OpenAI's board. Altman is a classic Silicon Valley leader – visionary, ambitious, controlling. OpenAI's academic and non-profit-minded directors eventually clashed with Altman's hard-driving style.   So, without warning, OpenAI's board fired Altman. Stunned Microsoft CEO Satya Nadella learned of the move just minutes before the public announcement. Despite owning 49% of OpenAI, Microsoft had yet to be consulted on leadership changes at its AI partner.

The news set off unrest behind the scenes. Blindsided Microsoft executives urgently met to chart a response. OpenAI employees threatened mass resignations, with its chief technology officer quitting immediately. Recriminations flew externally over what one journalist called "idiocy" and "cloddery" by OpenAI's directors.  Microsoft swiftly developed contingency plans to navigate the crisis. It first supported OpenAI's interim CEO while seeking Altman's reinstatement. But the silent board refused to provide details or reverse course. 

Microsoft then leveraged its power to reinstall Altman or rebuild OpenAI directly within Microsoft. As leadership paralysis worsened at OpenAI, Microsoft made its boldest play – inviting Altman to lead a lavishly-funded new AI lab as part of Microsoft.  OpenAI's entire staff essentially revolted, signing a petition threatening to join Altman at Microsoft unless OpenAI's board resigned and Altman was restored as CEO. Within 48 hours, Microsoft's nuclear option worked – humbled OpenAI directors relented and reinstated Altman.

The saga illuminated challenging issues around developing AI responsibly. What's the right balance between unleashing progress and imposing caution? Can startups govern unprecedented technologies prudently? Does public transparency help or heighten risks? Behind Microsoft's response was executive Kevin Scott, the company's chief technology officer. Having grown up poor in rural Virginia, Scott knew firsthand how technology could empower or polarize. He became determined to make AI "level the playing field" by making it accessible to ordinary people through natural conversation.

Scott quickly aligned with OpenAI's mission to ensure AI broadly benefits humanity. He respected OpenAI's talented staff, including optimistic chief scientist Ilya Sutskever. Sutskever fervently believes AI will soon solve humanity's most significant problems.   Scott also connected with OpenAI chief technology officer Mira Murati over similarly humble backgrounds. Raised amid chaos in war-torn Albania, Murati's childhood taught perseverance despite long odds. This instilled balanced optimism – hopeful progress is possible but only with thoughtful safeguards in place.   Such optimism needed tempering, though, as early experiments revealed AI's potential dangers. Systems hallucinated facts or gave harmful advice if not properly constrained. So Microsoft and OpenAI collaborated extensively on frameworks and guardrails, allowing ambitious innovation within cautious boundaries.  Their formula:

  • Release useful but imperfect AI to real-world users.
  • Gather feedback.
  • Refine safeguards based on public testing.

This transparency around AI's strengths and limitations builds trust, Scott argues. Enlisting regular users to examine new technologies also teaches more about capabilities and shortcomings revealed in actual daily applications.  

Gradually, this measured strategy succeeded, powering new products like GitHub Copilot, which could automatically complete code. Despite some objections, Copilot won over skeptics as public testing demonstrated benefits while showcasing constraints around the technology.  

Encouraged by successes like Copilot, Microsoft stealthily developed its new AI assistants for Word, Excel, and other ubiquitous programs used by over a billion people worldwide. The stakes were far higher here, given the massive scale and sensitivity. So Microsoft tapped its specialized Responsible AI division with hundreds of technologists, ethicists, and policy experts.  

This cross-disciplinary team exhaustively stress-tested Copilot prototypes with a process called "red teaming." They relentlessly tried making AI systems fail safely in simulated scenarios by feeding offensive comments or dangerous advice and monitoring responses. 

With human guidance around preferred reactions, the models learned to incorporate ethical safeguards and self-governing instructions when answering user questions. After extensive adjustments, Microsoft rolled out the Office Copilot pilots to select business clients before a gradual public debut.

But product rollout had barely started when OpenAI erupted into leadership chaos. Altman's firing threatened to derail Microsoft's measured approach just as Office Copilots prepared for mass adoption. 

In the aftermath, hard questions loom around developing AI responsibly. What's the right balance between unfettering progress and imposing caution? Can startups wisely govern unprecedented technologies? Do public testing and transparency help or heighten risks?

Microsoft shows one possible path – collaborating across sectors on frameworks and safeguards while enlisting users to examine new technologies. Critics argue this may not be safe or transparent enough. Others believe it found the proper equilibrium so far. 

As AI progresses, its scope for both benefit and damage keeps increasing. The stakes around guiding its trajectory responsibly couldn't be higher. This astonishing age of intelligent machines raises difficult questions about opportunities, obligations, and an uncertain future potentially shaped by today's decisions.

What lessons can be drawn from this saga for companies navigating the rise of transformative technologies like artificial intelligence? Perspectives vary across Microsoft, OpenAI's former board, and the broader AI community.

Microsoft believes it identified an essential blueprint for developing AI responsibly and exiting the crisis with an even more robust capacity to lead. Its hard-won formula:

  • Build guardrails collaboratively.
  • Test transparently by engaging users.
  • Move cautiously but steadily to deployment.AI's

Benefits and risks will become more apparent through practice across societies, functions, and industries.

For OpenAI's former directors, centralized control and publicly airing disputes seem risky, given AI's pivotal emergence. They sought more discretion by ousting Altman. However, the board learned its unilateral surprise move wrongly ignored critical constituents like partners and staff. However, vital independent oversight procedural prudence matters too.

Parts of the broader technology universe still clamor for more public deliberation around AI's collective impacts or slower adoption to digest societal implications. Some argue models like Microsoft's need to be more opaque about internal testing or panels forming policies. Others counter this incremental approach found balance so far – ambitious innovation tempered with gathering feedback.

If anything is clear, governing globe-spanning technologies evolving daily confounds. Multi-stakeholder collaboration helps check tendencies like short-termism, insularity, and marginalizing public interests. But cooperation gets messy between startups disrupting, corporations scaling, and academia deliberating.

Technical systems centralizing power or limiting accountability also risk compounding historic inequities. So, in this vast transition, one lesson may be prudence around the certainty that anyone has all the answers. With technology's complexity and pace of change, humility itself may be the wisest path forward.


Q* | OpenAI | 𝕏

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.