Sources detail the pressure inside Meta and Google to move faster with AI amid the surge of attention around ChatGPT, potentially sweeping safety concerns aside
Google, Facebook and Microsoft helped build the scaffolding of AI. Smaller companies are taking it to the masses, forcing Big Tech to react.
Washington Post
Context & Ripple Effects
Google had already faced a release dilemma: its capable LaMDA bot was held back over reputational concerns even as ChatGPT threatened to capture the market, according to coverage of Google's LaMDA hesitation. The new reporting places Meta and Google at the point where competitive urgency is challenging that restraint.
The later arc shows that the pressure was not confined to product launches: Google moved toward sharing research only after it had been productized, a shift documented in Google's tighter approach to AI research sharing.
First-order effects
Meta and Google teams face stronger incentives to accelerate AI releases and development, with internal safety concerns at risk of receiving less weight in launch decisions.
Smaller companies bringing AI products to mass audiences force Big Tech's established AI groups to compete on product speed rather than research leadership alone.
Google's research organization becomes more tightly coupled to commercialization, as the competitive value of publishing work declines when rivals can turn comparable capabilities into consumer products first.
Third-order effects
If the pattern persists, frontier-AI competition shifts governance from a pre-release gate toward a deployment-speed trade-off, with safety teams operating under stronger product-market pressure.
Large platforms' distribution becomes more consequential than their prior research output: the firms that can rapidly place AI in existing products gain a structural advantage over labs focused chiefly on model development.
The trend:Generative AI is turning frontier-model research into a product-distribution race, compressing the time available for internal safety review.
LLMs have no physical intuition as they are trained exclusively on text. They may correctly answer physical intuition questions *if* they can retrieve answers to similar questions from their vast associative memory. But they may get the answer *completely* wrong 1/ https://twitte…
For years, tech giants like Google and Facebook have been developing the underlying tech behind tools like ChatGPT and Stable Diffusion—but had been cautious about releasing them due to concerns over safety, bias, and misinfo. That's changing now. https://www.washingtonpost.com/ …
I think we can all agree that what's been holding us back lately is tech giants being being cautious and beholden to ethicists. Glad we're getting more headlong charges into murky waters without regard for risk, now. I'm sure it will be fine. https://twitter.com/...
MAV3D (Make-A-Video3D) - a method for generating three-dimensional dynamic scenes from text descriptions. Using a 4D dynamic Neural Radiance Field (NeRF). project page: https://make-a-video3d.github.io/ arXiv: https://arxiv.org/... https://twitter.com/...
I came up with the example above during my interview with Alex Kantrowitz for his podcast. ChatGPT's answer sounds good, but it's exactly backwards. Funny thing is that Alex read the response quickly to me during the interview and didn't immediately realize it was wrong! 2/
@parismarx I don't know if it was in your podcast or mentioned by someone else, but the ChatGPT and recent resurgence of desperate AI hype cycle feels like it stems from L after L after L for the “next big thing in tech”. They no longer want a win, they NEED a win.
@parismarx It's amazing how freaked-out tech companies get over demos that (1) don't make any money and (2) are jury-rigged or conceptually limited in some way that makes them totally unmarketable.
@DataChaz ... You are making my point. Ask yourself why Galactica was pilloried and crucified while chatGPT (which has similar flaws) was welcome as the second coming of the Messiah?
@ylecun ... Ask Meta to spin-off a semi-independent section of FAIR and release under a non-profit setup with exclusive deal to Meta (profit) That's how Microsoft is working around it. Maybe name it OpenAIR 😉
@parismarx Foretold by Sam Harris in one of his Ted Talks, he noted that, once we saw successful examples of AI, it would become an arms race. And in the race, big mistakes will be made. (He was talking about countries, similar to the space race, but theory applies to tech compan…
@powerbottomdad1 Yeah that meta chatbot sucks. It's going down a different route than how chatGPT is built though. But lambda is kinda good, wish that big G would release it
More generative AI innovations. Watch as Meta and Google roll out everything they've been working on over the next 6 weeks, as a response to ChatGPT. It's going to be wild. https://twitter.com/...
yann lecun yesterday: open ai is simply just not ahead of google and meta. they can easily make a product like chatgpt a conversation with metas new chatbot today: [Screenshots of answers from Meta's chatbot]
🚨 Correcting @ylecun here: @Meta/@facebook *did* release a #ChatGPT-like thing, the highly-anticipated #Galactica, but it was withdrawn just three days after its release, following a deluge of trolling and accusations of bias! The internet remembers: https://techunwrapped.com/...…
Race to the bottom indeed. I think this is good reporting, though it seems to be very eager to take the perspective of engineers who are “frustrated” with their employers for being cautious. https://www.washingtonpost.com/ ... #AIhype #MathyMath #ChatGPT
Educated skeptics are so important. Yann highlights a limitation of current LLMs and tools like ChatGPT. Some might consider this as being a hater, but I consider this as the only way to understand how this stuff actually works. https://twitter.com/...