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> Now it's just a "what is the next frame of this 2D image" model plus some textual context.
This is incorrect. Sora is not an autoregressive model like GPT, but a diffusion transformer. From the technical report[1], it is clear that it predicts the entire sequence of spatiotemporal patches at once.
[1]: https://openai.com/research/video-generation-models-as-world...
But, even there it says:
> Sora currently exhibits numerous limitations as a simulator. For example, it does not accurately model the physics of many basic interactions, like glass shattering. Other interactions, like eating food, do not always yield correct changes in object states
Regardless whether all the frames are generated at once, or one by one, you can see in their examples it's still just pixel based. See the first example with the dog with blue hat, the woman has a blue thing suddenly spawn into her hand because her hand went over another blue area of the image.
I learned to understand reality by interpreting photons and various sensory inputs. Does that make my model of reality fundamentally flawed? In the sense that I only have a partial intuitive understanding of it, yes. But I don't need to know Maxwell's equations to get a sense of what happens when I open the blinds or turn on my phone.
I think many of the limitations we are seeing here - poor glass physics, flawed object permanence - will be overcome given enough training data and compute.
We will most likely need to incorporate exploration, but we can get really far with astute observation.
Unfortunately we are flawed. We do know how physics work intuitively and can somewhat predict them, but not perfectly. We can imagine how a ball will move, but the image is blurry and trajectory only partially correct. This is why we invented math and physics studies, to be able to accurately calculate, predict and reproduce those events.
We are far off from creating something as efficient as the human brain. It will take insane amounts of compute power to simply match our basic innacurate brains, imagine how much will be needed to create something that is factually accurate.
Additionally, most of the energy cost comes from pretraining, but once we have the resulting weights, downstream fine-tuning or inference are comparatively quite cheap. So even if the energy cost is high, it may be worth it if we get powerful generalist models that we can specialize in many different ways.
> This is why we invented math and physics studies, to be able to accurately calculate, predict and reproduce those events.
We won't do away without those, but an intuitive understanding of the world can go a long way towards knowing when and how to use precise quantitative methods.
Heck a super AI might not even be possible, what if we're peak intelligence with our millions of years of evolution?
Just adding compute speed will not help much -- say the goal of an intelligence is to win a war. If you're tasked with it then it doesn't matter if you have a month or a decade (assume that time is.frozen while you do your research), its a too complex problem and simply cannot be solved, and the same goes for an AI.
Or it will be like with chess solvers, machines will be more intelligent than us simply because they can load much more context to solve a problem than us in their "working memory"
As someone working in the field, the vast majority of AI research isn't concerned with copying the brain, simply with building solutions that work better than what came before. Biomimetism is actually quite limited in practice.
The idea of observing the world in motion in order to internalize some of its properties is a very general one. There are countless ways to concretize it; child development is but one of them.
> If you're tasked with it then it doesn't matter if you have a month or a decade (assume that time is.frozen while you do your research), its a too complex problem and simply cannot be solved, and the same goes for an AI.
I highly disagree.
Let's assume a superintelligent AI can break down a problem into subproblems recursively, find patterns and loopholes in absurd amounts of data, run simulations of the potential consequences of its actions while estimating the likelihood of various scenarios, and do so much faster than humans ever could.
To take your example of winning a war, the task is clearly not unsolvable. In some capacity, military commanders are tasked with it on a regular basis (with varying degrees of success).
With the capabilities described above, why couldn't the AI find and exploit weaknesses in the enemy's key infrastructure (digital and real-world) and people? Why couldn't it strategically sow dissent, confuse, corrupt, and efficiently acquire intelligence to update its model of the situation minute-by-minute?
I don't think it's reasonable to think of a would-be superintelligence as an oracle that gives you perfect solutions. It will still be bound by the constraints of reality, but it might be able to work within them with incredible efficiency.
Sora is not autoregressive anyway but there's nothing "just" and next frame/token prediction.
And if Terence Tao finds some use for GPT-4 as well as Khan Academy employing it as a Math tutor then I don't think I have some wild opinion either.
Now Math isn't just Arithmetic but do you know easy it is to go out of training for say Arithmetic ?
In order to combat Authority you need to both appeal to a higher authority, and that has been lost. One follows AI. Another follows Old Men from long ago who's words populated the AI.
Mind you, it's not brilliant at arithmetic either...
It just computes next frame based on current one and what it learned before, it's a plausible continuation.
In the same way, ChatGPT struggles with math without code interpreter, Sora won't have accurate physics without a physics engine and rendering 3d objects.
Now it's just a "what is the next frame of this 2D image" model plus some textual context.