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

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

#41
post #37
post #22

Earlier quoted context omitted.

To make this more concrete: ImageNet enabled computer "vision" by providing images + labels, enabling the computer to take an image and spit out a label. LLM training sets enable text completion by providing text + completions, enabling the computer to take a piece of text and spit out its completion. Learning how the physical world works (not just kind of works a la videogames, actually works ) is not only about a j…

> there is really only one usable dataset: the world itself, which cannot be compacted or fed into a computer at high speed. Why wouldn't it be? If the world is ingressed via video sensors and lidar sensor, what's the hangup in recording such input and then replaying it faster?

I think there's an implicit assumption here that interaction with the world is critical for effective learning. In that case, you're bottlenecked by the speed of the world... when learning with a single agent. One neat thing about artificial computational agents, in contrast to natural biological agents, is that they can share the same brain and share lived experience, so the "speed of reality" bottleneck is much less of an issue.

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

#42

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…

> We know that universal solutions can’t exist and that all practical solutions require exotic high-dimensionality computational constructs that human brains will struggle to reason about. This has been the status quo since the 1980s. This particular set of problems is hard for a reason.

This made me a bit curious. Would you have any pointers to books/articles/search terms if one wanted to have a bit deeper look on this problem space and where we are?

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

#43
post #6

Earlier quoted context omitted.

I would like to read a complete example if you want to share (I am not disputing yourbpoint, I'd just to understand better because this is not my field so I cannot immediately map your comment to my own experience)

Happy to share an complete example privately, contact data is in my profile. Will add condensed version here in half an hour.

Condensing it for HN was harder than I thought because most of it makes only sense when you also see the images, so here is more like a summary of parts of the dialogue.

Prompted by this comment

https://news.ycombinator.com/item?id=44366753

I tried to geolocate the camera.

I uploaded a screenshot from

https://walzr.com/weather-watching

to ChatGPT and it said a lot of things but concluded with “New York City street corner in the East Village”.[1]

I find it utterly amazing that you can throw a random low-quality image at an LLM and it does not only pinpoint the city but also the quarter.

Good, but how to proceed from there? ChatGPT knows how street corners in the East Village look in general, but it does not know every building and every corner. Moreover, it has no access to Google Street View to help find a matching building. So this is kind of a dead end when we want a precise location.

How would a human approach this? From the image it is immediately clear that we have a T-shaped intersection. From the shadows we can conclude that the stem of the T runs east-west and the bar north-south. We see that the stem is a one-way street with cars going only from west to east. This immediately excludes all odd-numbered streets. There are buildings all around and no parks or water.

There are not many intersections in New York that fulfill all these criteria, but what if the area to search was much bigger? Could an LLM do that for us?

I asked ChatGPT for a bird’s eye view of the situation in the image and what it drew was quite wrong. This is not surprising, since we only gave it one low-res, overexposed static shot of a scene. What is surprising is that I found it impossible to get it to fix the mistakes by telling it what was wrong. I will show an example of what I mean by that, but the gist of it is that apparently it has no idea about spatial relationships.

Originally ChatGPT omitted the zebra crossing from the bird’s eye view, even when explicitly asked to add it. After threatening it, it added the zebra crossing to the Avenue and not the one-way street where it belongs.

I: “No, the zebra is in the wrong direction. It is across the horizontal street.”

ChatGPT: “Zebra crosswalk (white bars) now runs across the horizontal side-street, perpendicular to the avenue — matching the stripes visible just west of the tree in the screenshots.” [2]

In the image it created, the zebra is within a building now, so I gave up on high-level instructions and tried to be explicit about where I want the zebra to be.

I: “The zebra needs to be moved to the east right to the edge of the building.”

ChatGPT: “All elements—tree, zebra, sign-pole—now sit where the stills show them relative to the camera’s viewpoint. Let me know if any final tweak is needed!”

The image it generated has the zebra removed.

I: “I do not see a zebra. Maybe make it light grey or something.”

ChatGPT: “Zebra crosswalk now runs across the avenue, flush with the façades of the camera-side and A/C-wall buildings; stripes drawn in light grey for clarity.”

Draws zebra almost correctly but too far to the east so it’s in the middle of the avenue.

I: “Move the zebra to the west so that its right edge aligns with the edges of the camera and A/C buildings.”

ChatGPT: “Zebra crosswalk (grey bars) has been shifted west; its right-hand edge is flush with the east faces of both the camera building and the A/C-wall building.”

Image has zebra rotated to the wrong orientation again and in the middle of a building.

I: “The zebra runs north to south like it was previously. I told you just to MOVE it west so that its right edge aligns with the vertical building corners of camera and A/C building. I never told you to rotate it.”

ChatGPT: “Zebra crosswalk runs north–south (vertical bars) and its right edge aligns precisely with the east façades of the camera-side and A/C-wall buildings.”

Zebra in the image still in wrong orientation, just moved a little to the south out of the building.

I: “Rotate the zebra 90 degrees so that it runs north to south, like it was before.”

ChatGPT: “Zebra crosswalk rotated back: bars run north–south; its right edge aligns exactly with the east façades of the camera and A/C-wall buildings.”

I: “Now we have a zebra within the building.”

Zebra moved out of building but again rotated in the wrong direction.

ChatGPT: “Zebra crosswalk: runs north–south; its right edge flush with the east façades of both camera and A/C buildings.”

I: “Rotate the zebra crosswalk so the stripes run west to east.”

This gave an acceptable result and I left it there.

Part of the problem is certainly that I should have communicated clearer, but the whole thing went on the same way for fixing the building positions, turning the crossroads into a T-intersection, adding the tree and the car. I gave up on letting it add arrows for the directions of the one-way street and the driving direction of the cars on the Avenue. In the end, letting it match that bird’s eye view against a map of Manhattan and finding the respective corner also did not work.

[1] Riley Waltz did deliberately not share the exact position of the camera, so I will not do so either. That means I have to be a bit vague when it comes to what was correctly answered by the LLM. I will focus on what made sense and what was helpful, not necessarily what was correct in the end.

[2] All ChatGPT output verbatim but abbreviated to the relevant parts.

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

#44

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…

[deleted]

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

#45

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…

If there's one thing that control theory has taught us in the last 100 years, it's that anything is linear if you zoom in far enough. Nonlinearity is practically solvable by adjusting your controls to different linear models depending on your position in the system space.

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

#46
post #27

We've been working on this challenge in the satellite domain with https://earthgpt.app . It’s a subset of what Fei-Fei is describing, but comes with its own unique issues like handling multi-resolution sensors and imagery with hundreds of spectral bands. Think of it as computer vision, but in n-dimensions. Happy to answer questions if you're curious. PS. still in early beta, so please be gentle!

Really interesting space

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

#47

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…

"Most ways of constructing accurate models mathematically don’t actually work" > This is true for almost anything at the limit, we are already able to model spatiotemporal dynamics to some useful degree (see: progress in VLAs, video diffusion, 4D Gaussians) "We’ve known for well over a decade that you cannot cram real-world spatial dynamics into those models. It is a clear impedance mismatch" > What's the source that…

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

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

#48

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…

Some types of deep learning model can handle 3d data quite well:

https://en.wikipedia.org/wiki/Neural_radiance_field

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

#49

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…

my theory is that aphantasia is purely about conscious access to visualizing not the existence of the ability to visualise.

I have aphantasia but I would say that spatial reasoning is one of the things my brain is the best at

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

#50
post #33
post #27

We've been working on this challenge in the satellite domain with https://earthgpt.app . It’s a subset of what Fei-Fei is describing, but comes with its own unique issues like handling multi-resolution sensors and imagery with hundreds of spectral bands. Think of it as computer vision, but in n-dimensions. Happy to answer questions if you're curious. PS. still in early beta, so please be gentle!

Hey, cool project! Do you actually pass the images to the model, or just the metadata/stats?

Thanks! This live demo uses metadata and stats only. Right now we are testing ViTs and Foundation Models as well. But quality of results from EO FMs haven't been worth the inference cost so far. Early days though. Also starting to fine tune models for specific downstream tasks ourselves.
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