OpenAI, Google and Anthropic are struggling to build more advanced AI

The best engineering minds have been focused on scaling transformer pre and post training for the last three years because they had good reason to believe it would work, and it has up until now.

Progress has been measured against benchmarks which are / were largely solvable with scale.

There is another emerging paradigm which is still small(er) scale but showing remarkable results. That's full multi-modal training with embodied agents (aka robots). 1x, Figure, Physical Intelligence, Tesla are all making rapid progress on functionality which is definitely beyond frontier LLMs because it is distinctly different.

OpenAI/Google/Anthropic are not ignorant of this trend and are also reviving or investing in robots or robot-like research.

So while Orion and Claude 3.5 opus may not be another shocking giant leap forward, that does not mean that there arn't giant shocking leaps forward coming from slightly different directions.

Sure, that's tautologically true but that doesn't imply that beyondness will lead to significant leaps that offer notable utility like LLMs. Deep Learning overall has been a way around the problem that intelligent behavior is very hard to code and no wants to hire many, many coders needed to do this (and no one actually how to get a mass of programmers to actually be useful beyond a certain of project complexity, to boot). People take the "bitter lesson" to mean data can do anything but I'd say a second bitter lesson is that data-things are the low hanging fruit.

Moreover, robot behavior is especially to fake. Impressive robot demos have been happening for decades without said robots getting the ability to act effectively in the complex, ad-hoc environment that human live in, IE, work with people or even cheaply emulate human behavior (but they can do choreographed/puppeteered kung fu on stage).

The best minds don't follow the herd.

Nor does it mean that there are! We've gotten into this habit of assuming that we're owed giant shocking leaps forward every year or so, and this wave of AI startups raised money accordingly, but that's never how any innovation has worked. We've always followed the same pattern: there's a breakthrough which causes a major shift in what's possible, followed by a few years of rapid growth as engineers pick up where the scientists left off, followed by a plateau while we all get used to the new normal.

We ought to be expecting a plateau, but Sam Altman and company have done their work well and have convinced many of us that this time it's different. This time it's the singularity, and we're going to see exponential growth from here on out. People want to believe it, so they do, and Altman is milking that belief for all it's worth.

But make no mistake: Altman has been telegraphing that he's eyeing the exit, and you don't eye the exit when you own a company that's set to continue exponentially increasing in value.

https://en.m.wikipedia.org/wiki/Five_Years_(David_Bowie_song...

Cool, but we already have robots doing this in 2d space (aka self driving cars) that struggle not to kill people. How is adding a third dimension going to help? People are just refusing to accept the fact that machine learning is not intelligence.

Can you think of any specific examples? Not trying to express disbelief, just curious given that this is obviously not what he's intending to communicate so it would be interesting to examine what seemed to communicate it.

Even assuming the recent robot demo was entirely AI, the only single thing they demonstrated that would have been noteworthy was isolating one voice in a noisy crowd well enough to respond; everything else I saw Optimus do, has already been demonstrated by others.

What makes the uncertainty extra sad, is that a remote controllable humanoid robot is already directly useful for work in hazardous environments, and we know they've got at least that… but Musk would rather it be about the AI.

Tesla is selling this view for almost a decade now in self-driving - how their car fleet feeding training data is going to make them leaders in the area. I don't find it convincing anymore

If we have robots that operate in 3D, they'll be able to kill you not only from behind or from the side, but also from above. So that's progress!

However interpolation isn't reasoning. If we want to understand the motion of planets, we would start with a dataset of (x, y, z, t) coordinates and try to derive the law of motion. Imagine if someone simply interpolated the dataset and presented the law of gravity as an array of million coefficients (aka weights)? Our minds have to work with a very small operating memory that can hardly fit 10 coefficients. This constraint forces us to develop intelligence that compacts the entire dataset into one small differential equation. Btw, English grammar is the differential equation of English in a lot of ways: it tells what the local rules are of valid trajectories of words that we call sentences.

Or because the people running companies who have fooled investors into believing it will work can afford to pay said engineers life-changing amounts of money.

The lack of progress with self driving seems to indicate that Tesla has a serious problem with scaling. The investment in enormous compute resources is another red flag (if you run out of ideas, just use brute force). This points to a fundamental flaw in model architecture.

It's almost like saying "we've already visited every place on Earth, surely Mars is just around the corner now"

At CoRL last week, the progress has noticeably plateaued. Roboticists notably were pessimistic that scaling laws will apply to robotics because of the embodiment issues.

Of course Waymo needs money but if the car made fewer trips compared to Uber/Taxi, it is not suffering the same consequences.

We need to consider human factor and the severe lacking of that in these robot/self driving/LLM and drawing parallels is not a direction I am feeling comfortable.

End of the day, Tesla also sold half baked self drive that killed people, we should not forget.