Fei-Fei Li

Their words

I think for me, let's go back to the first principle of intelligence. Intelligence is not sitting there stuck and just seeing something or interpreting something when it comes to space and physical space, right? It's really this uh closing the loop between seeing and experiencing and interaction.

Their words

cuz we don't yet have a a frontier foundation model that's robust enough for for uh robotics.

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There is also a very important step called randomization. Is that you have to take the same environment and then randomize the conditions. So, the cable doesn't literally only, you know, uh bend this way. It can bend a different way or the box can have different sizes, colors, different lids, and all that.

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We should zoom out and recognize the biggest problem right now in robotics is actually data. One day it'll be chips, but for now it's data.

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I think three of us have total conviction about the scaling law. That that I think we do. I do think the exact architecture choices and data mixtures is where the the devils are in the details.

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And I do believe Atlas is a significant step forward because now with every single frame, you have a you can generate an estimate a important piece of information, which is the the view viewpoint, the camera pose. And that is the most critical information one needs about the geometry of the of the space.

Their words

So in the on the path to spatial intelligence, generating pixels is definitely a a early step, which we have seen with what you call it gazillions of models. But generating pixels that are truly spatially contextualized and grounded is absolutely another major step. And that is the very hard step that Atlas has taken.

Their words

But to do that, a fundamental problem to solve is to understand the geometry and structure and the physics of the space.

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Well, spatial intelligence eventually must enable us to both generate what the space is, reason within it, and being able to edit and interact within it.

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It's the first time we have a unification of pixel generation and pixel reconstruction. In the world of computer vision this field has been around for more than half a century.

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The next frontier of AI as I have been saying is beyond language because again humans develop first preverbally. evolution took, you know, 500 million years without verbal communication

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This is why public education is so important. This is why education is so important. I would love to see our schools K12 teaching prompting.

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Learning fundamentally respecting the hardware you're talking about takes time, takes effort, sometimes takes some pain. That is just how our brain is.

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The other kind of danger is in the name of agency and and uh and motivation the tools are denied to our students because we're worried you cheat or worry you only got your answer from Chad GBT. That is very bad as well

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The absolute bad outcome is that our young generation, their agency and human level motivation of learning and living is taken away by tools. So doom scrolling, passive watching of shorts, all this are not helping agency, human agency.

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What I would not like to see is one person or or a few people coming from industry and telling everybody what to do. I think that would be dangerous because market forces are different from uh societal norms and culture and heritage are different from uh education and ethics and and these are multistakeholder problems to solve together.

Their words

humans are capable of doing a lot of things with our tools, but we don't do all of them. For example, today any car manufacturer can say every Friday the brake doesn't work. This is a trivial technology. There's a clock in the car's computer and it just turns off the brake every Friday. But we don't do that because it has deeply bad implications to our human society.

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So this speaks of a very important fact that um AI learns from patterns. When the patterns are not abundant, then we have to be careful. We have to know how to use AI or how not to use AI.

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I think we're we're ready for complete re rewriting of how scientific discovery can be done because for ages, I don't even know how long, it relies on smart humans retaining what they have learned from other smart humans

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the rhetoric around AI right now is getting really skewed because people who know what this is tend to talk down at the public.

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we need to think about AI as a tool that helps us in our agency. It does it should not take away our agency and people who lead in today's AI should not try to talk like that this this work will take away agency from people.

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My current conjecture is hybrid is that humans working alongside AI would help us to solve these problems whose solutions have yet to be invented.

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that thought is not captured therefore it's not on the internet therefore AI has not seen it so that is where humans still remain so unique

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Internet is not some random thing. Internet is the biggest collection of human behavior in multimodal forms.

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that child who learns about what you say kitty cat would not have the chance to download the internet of images of cat. They likely have seen three cats, 10 cats at most, but yet they're able to identify that tail as a cattail instead of a fox tail through a different kind of learning pathway.

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What happened there is actually not as revolutionary as you might think because the bottom line is it's still data.

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So we conjectured that the lack of data was a huge part of the reason that's the lack of progress in AI. So we took a departure from everybody else who are really focusing only on algorithm and said that we need data. we need data to drive these algorithms.

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But the first photoreceptive cells created a evolutionary force that propelled animals to evolve because sensing the external world changes your self-perception changes the way your relationship with the external world.

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The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data.