Yep. I joined Google X Robotics (which became Everyday Robots, which got canceled) just as the org was winding down a big R&D push and moving to product development. Engineers were palpably hurt by their various projects being canceled, and in my opinion they never had a viable product strategy beyond “let’s see if we can find a consumer use for this robot”. This strategy ultimately failed and they canceled the project, let go a bunch of the people, and now the robots are being used for AI research. So the whole shift from R&D to product development was a failure. They could have saved a lot of grief and money if they just continued as an R&D org, and in my opinion they would have left open some important doors which would have really helped with AI research.
I'm afraid this is going to be another Everyday Robots or Boston Dynamics. Google is ruled by managers, not visionaries. Strategy is 'try and see if it works in 4 years. if not cancel'. They followed it in many cases. So, DeepMind's cancellation is long overdue. A couple of years back one of Google's top managers talking about AGI said it's most likely to happen in DM. But current LLM boom happened elsewhere. Likely managers are disappointed in DM.
Just because a company is 99% one thing, doesn’t mean that the 1% remaining don’t have moments of real genius. What it does mean however is that when those real genius moments happen, the company isn’t in a good position to capitalize on it (ala Xerox or Kodak mentioned previously, but there are so many more).
Because that's the hallmark of business-minded people being at the helm and not engineers, which has been an issue for Google for a long time: the greatest inventions ever seen will be squandered in terms of the org itself because management can't see beyond the next few quarters and won't invest properly in it.
??? Xerox and Kodak are immortal household names that dominated their niches for decades, what level of "success" would satisfy you? what are you lamenting, that they didn't completely enshittify their products while they were on top and torch their brands to the ground for a little more revenue?
Well, strictly speaking modern LLMs are transformers (not counting state space models like Mamba, for the moment). Not sure what hidden Markov chains have to do with modern LLMs.
Well, LLMs are not AGI. They have serious limitations [1] and honestly my fear is that it’s too soon. I agree Google is ruled by managers (that’s why I hated it), and my fear is that the managers have FOMO and want to push to productize asap even tho the tech isn’t ready yet.
Look what happened when Google tried to throw an LLM in to search. Absolute shitshow. That’s not ready to become any kind of product!
If they kill R&D now to focus on productizing something that is half baked, they will fail to develop those new inventions which might get us to AGI. When I worked at Google X Robotics I was hired on to the remnants of the last research team, which was dissolved six months after I started (I was moved to hardware test engineer). Our subteam really wanted to research multi-finger grippers but we got overruled, so the robot had to do everything with a two finger pinch gripper. Which is fine for research but absolutely unsuitable for real world tasks. It couldn’t even operate a spray bottle without special attachments and they thought it was going to clean people’s homes!
[1] I am sharing this one a lot lately but I’m very moved by Yann LeCun’s arguments about the limits of autoregressive approaches here. As a robotics engineer I have been dismayed at all the attention LLMs are getting despite serious limitations that make them generally unsuitable to solve some of the most important problems in robotics. https://youtu.be/1lHFUR-yD6I
> my fear is that the managers have FOMO and want to push to productize asap even tho the tech isn’t ready yet
This is exactly it. With the limitations ChatGPT is encountering around safety and hallucination, Google probably should've just said "we're working on something awesome - hold on" and kept plugging away before releasing, instead of ex-Product CEO making them release something now, even if half of the demo video is fake.
I'm using chatGPT to Google something for around a year (or whenever bing browsing became available), and I'm yet to set a single hallucination based on web search results. May be Google is just not very good at this.
Neither is Yann (who has since proposed a different architecture / vision which is yet to take off), but my comment was meant to highlight a recent claim from another accomplished researcher in the field.
Right but I’m not talking about claims, I’m talking about arguments. As a robotics engineer running a farming robot project, I am all too aware of the serious differences in computational challenges and dataset availability between text and image data on the web and the kinds of problems that once faces in robotics. LeCun doesn’t just claim LLMs aren’t up to the task, he provides a detailed list of provable shortcomings which I feel overall make for a compelling argument. I’m hopeful that his JEPA may shed some light on possible solutions, but it’s also a fact that one can find issues with proposed engineering solutions even if they don’t have their own better solution. It you say you have a faster than light space engine designed, one wouldn’t need to have their own functional design to show why yours didn’t work (although such expertise is always helpful). And, well, he did invent convolutional neural networks, tho as I say such expertise is not required to raise valid arguments against some proposed solution.
fwiw, I agree with Yann & you (and many others!) on the shortcomings of LLMs (not from a position of authority, but as a matter of opinion).
I meant to counter-balance OP's point in that there are other equally accomplished individuals who aren't swayed by Yann's (and others accelerationists like Andrew Ng) arguments or claims.
Same experience at EDR. It was a great research platform, but nowhere near ready for the real world. Compute, power, functional safety... The "real" working robots of today are completely different. A successful product from EDR would have looked completely different.
