Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

youtube.com

Post reply on HN
Print view View original

151–160 of 163 posts

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#151

Post by vrighter »

jandrewrogers wrote: post #8

I appreciate the video and generally agree with Fei-Fei but I think it almost understates how different the problem of reasoning about the physical world actually is. Most dynamics of the physical world are sparse, non-linear systems at every level of resolution. Most ways of constructing accurate models mathematically don’t actually work. LLMs, for better or worse, are pretty classic (in an algorithmic information t…

ah but neural networks are universal function approximators! (proceeds to ignore the size of network needed for an adequate approximation and/or how much data would be required to train it)

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#152

Post by mistersquid »

dopadelic wrote: post #104
mistersquid wrote: post #67

Earlier quoted context omitted.

> An immaterial side note: funny how obsessed she seems to be with her age. Given her intellectual stature, Professor Li likely was one of the strongest minds in any room she found herself in and, for the first half of her life, also one of the youngest voices. Now that she’s entering mid-life, she’s still one of the most powerful minds, but no longer one of the youngest. It’s something middle-aged thinkers can’t hel…

It's interesting how figures get idolized. Fei-Fei Li is known for the creation of ImageNet, which is certainly transformative in the field of computer vision. But the crux of it is painstaking grunt work to create the vast labeled dataset. Fei-Fei Li is a leader who mobilized vast resources and people hours to create this vast dataset. Certainly worth a ton of acclaim. But to claim she's the most brilliant mind in a…

> Fei-Fei Li is known for the creation of ImageNet[…] But to claim she's the most brilliant mind in an entire room is a stretch.

You reduce Professor Li’s massive intellect to her leading the ImageNet project. You also misrepresent my observation that “Professor Li likely was one of the strongest minds in any room she found herself in [....]”.

That’s intellectually dishonest.

Watch the video linked in the OP, listen to her assessments of the direction of artificial intelligence, the state and future of the computing industry, the ways one might make a strong impact as a scholarly researcher, etc.

To do so is to recognize Professor Li is not only one of the most brilliant minds in that particular room, but also one of the sharpest minds in the history of Silicon Valley.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#153

Post by fennecfoxy »

IdealeZahlen wrote: post #20

I've always wondered how spatial reasoning appears to be operating quite differently from other cognitive abilities, with significant individual variations. Some people effortlessly parallel park while others struggle with these tasks despite excelling at other forms of pattern recognition. What was particularly intriguing for me is that some people with aphantasia have no difficulty with spatial reasoning tasks, so…

I mean just walking down the street or through a supermarket, it seems to me like 95% of people have no spatial awareness at all. Walking forward while looking to the side or backward.

Either that or they're perfectly capable, they just don't care.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#154

Post by DennisP »

jandrewrogers wrote: post #142
jedharris wrote: post #133

Earlier quoted context omitted.

some pointers to the research program please?

It was a national security program with no public face. I was recruited into it because I solved a fundamental computer science problem they were deeply interested in. I did not get my extensive supercomputing experience in academia. It was a great experience if you just wanted to do hardcore computer science research, which at the time I did. There are several VCs with knowledge of the program. It is obscure but has…

I'm not surprised that the government does great research, but I wonder how much good does that research does, if it's unpublished and disappears after budget cuts.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#155

Post by HappMacDonald »

loa_in_ wrote: post #62
HappMacDonald wrote: post #55

Earlier quoted context omitted.

Yeah I'm envisioning putting a thousand simplistic robotic "infants" into a vast "playpen" to gather sensor data about their environment, for some (probably smaller) number of deep learning models to ingest the input and guess at output strategies (move this servo, rotate this camshaft this far in that direction, etc) and make predictions about resulting changes to input. In principle a thousand different deep learni…

But the playpen will contain objects that are inherently breakable. You cannot rough handle the glass vessel and have it too.

Basically everything applicable to the playpen of a human baby is applicable to the playpen of an AI robot baby in this setup, to at least some degree.

Perhaps the least applicable part is that "robot hurting itself" has the liability of some cost to replace the broken robot part, vs the potentially immeasurable cost of a human infant injuring themselves.

If it's not a good idea to put a "glass vessel" in a human crib (strictly from an "I don't want the glass vessel to be damaged" sense) then it's not a good idea to put that in the robot-infant crib either.

Give them something less expensive to repair, like a stack of blocks instead. :P

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#157

Post by calf »

dopadelic wrote: post #116
calf wrote: post #112

Earlier quoted context omitted.

How do we prove a trained LLM has no inductive bias for space, causality, etc.? We can't assume this is true by construction, can we?

Why would we need to prove such a thing? Human vision has strong inductive biases, which is why you can perceive objects in abstract patterns. This is why you can lay down at a park and see a duck in a cloud. It's also why we can create abstracted representations of things with graphics. Having inductive biases makes it more relatable to the way we work. And again, you're using the term LLMs again when vision based t…

You said that classic LLMs have no inductive bias for causality. So I am simply asking if any computer scientist has actually proved that. Otherwise it is just a fancy way of saying "LLMs can't reason, they are just stochastic parrots". AFAIK not every computer scientist shares that consensus. So to use that claim is to potentially smuggle in an assumption that is not scientifically settled. That's why I specifically asked about this claim which you made a few paragraphs into your response to the parent commenter.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#158

Post by calf »

jandrewrogers wrote: post #144
calf wrote: post #47

Earlier quoted context omitted.

Above commenter also asserts "highly developed research but no public literature" shrug ...

It was a national security program that plenty of people are familiar with and has been used across several countries. None of those programs publish. As much as the literature doesn’t exist, the tech has been used in production for over a decade. That’s just my word of course but a lot of people know. :shrug:

This is as rhetorically valid as knowing about UFOs, it smells of crackpot science.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#159

Post by calf »

jandrewrogers wrote: post #121
calf wrote: post #114

Earlier quoted context omitted.

But then that sounds more like that person explained it wrong. They didn't explain why it is necessary to reduce to GRAPHCUT, it seems to me to beg the question. We should not assume this is true based on some vague anthropomorphic appeal to spatial locality, surely?

It isn’t a graph cutting problem, graph cutting is just a simpler, special case of this more general cutting problem (h/t IBM Research). If you can solve the general problem you effectively get efficient graph cutting for free. This is obviously attractive to the extent you can do both complex spatial and graph computation at scale on the same data structure instead of specializing for one or the other. The challenge…

Well, previously you said that it (presumably "it" broadly refers to spatial reasoning AI) is a "high dimensional complex type cutting problem".

You said this is obvious once explained. I don't see this as obvious, rather, I see this as begging the question--the research program you were secretly involved in wanted to parallelize the engineering of it so obviously they needed some fancy "cutting algorithm" to make it possible.

The problem is that this conflated the scientific statement of what "spatial reasoning" is. There's no obvious explanation why spatial reasoning should intuitively be some kind of cutting problem however you wish to define or generalize a cutting problem. That's not how good CS research is done or explained.

In fact I could (mimicking your broad assertions) go so far as to claim, the project was doomed to fail because they weren't really trying to understand something, they want to make something without understanding it as the priority. So they were constrained by the parallel technology that they had at the time, and when the computational power available didn't pan out they reached a natural dead end.

Re: Fei-Fei Li: Spatial intelligence is the next frontier in AI [video]

#160

Post by RaftPeople »

jandrewrogers wrote: post #108
andoando wrote: post #103

Earlier quoted context omitted.

Ive spent years trying to tackle spatial representations on my own, so Im extremely curious here. How does the cutting problem relate to intelligence in the first place?

Indexing is a special case of AI. At the limit, optimal cutting and learning are equivalent problems. Non-trivial spatial representations push these two things much closer together than is normally desirable for e.g. indexing algorithms. Tractability becomes a real issue. Practically, scalable indexing of complex spatial relationships requires what is essentially a type of learned indexing, albeit not neural network…

> is essentially a type of learned indexing, albeit not neural network based.

NN is just function approximation, why do you think that could not be a valuable part of the solution?

It seems like a dynamically adjusted/learned function approximator is a good general tool to most of these hard problems.

Post reply on HN