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EDIT: I am watching the video now, and made my comment below before I started watching the video. I realize now that my comment is somewhat outside the scope of

EDIT: I am watching the video now, and made my comment below before I started watching the video. I realize now that my comment is somewhat outside the scope of the video itself, but I'll leave the comment up anyway, because I think it reveals a similar conclusion as the video. I'll take a stab at what seems to me to be an insurmountable problem for AI: vision. And not just vision, but "seeing". Pretend I am standing next to a table, and the AI successfully identifies me, and identifies a table next to me (which, in itself, is a very difficult, if not impossible, thing for AI to do right now). Now, let's say I sit on the table. Now, we ask the AI, "Am I sitting on a table, or sitting on a chair?" Further to the point (and a less "human" scenario): Musk has been attempting for years to make a lights-out facility for building cars. As of yet, there are still many things that a robot cannot do, or a human can do faster and more reliably, in spite of throwing billions of dollars and millions of work-hours at the problem. Another example: shoe manufacturing robots. Another example: brick laying robots. Another example: pipe welding robots. None of those can even come close the a human's ability to adapt on the fly to small variations, or to learn new behaviors. AI sees the world in a very low-resolution way, and humans are generally unaware of how much "constructing of the world" our brains do compared to the input data it receives from our senses. Replicating this is going to take more than a GPT3, for example.

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worldsayshi 5y ago
Feels like what you're referring to is just "understanding". GPT3 fails at this as well. It seems that gpt3 can string together sentences and whole paragraphs that seem to sort of make sense together but it seems that it never really puts hard constraints on any concepts. It doesn't try to build a comprehensive world with rules. Everything is just a sort of a fuzzy continuum. If you train it on enough data this fuzzyness generate a lot of plausibly sounding output but it will have a hard time coming up with the right answer in a new situation. I feel the missing ingredient is to be able to combine this fuzzy or intuitive understanding with some kind of rule engine. It's like in the book "Thinking Fast and Slow". It feels like GPT and the best image recognition algorithms we have today are good at the fast kind of thinking but doesn't have the slow and logical part.
abecedarius 5y ago
> very difficult, if not impossible, thing for AI to do right now Is that right? I thought computer vision was getting pretty good at object detection in the past few years.
- simonh 5y ago
  Those are just image classifiers though. They associate this pixel patters with the character string "table". They might be able to recognise this image as relating to both table and person, but most likely as competing possible classifications. They generally don't have a concept of relatedness between classifications. They have no spacial conceptual model relating concepts like 'above', beside', 'below', distance, etc. They also have no idea what a table is, no general concept of furniture, or even of a table as a physical or 3 dimensional object. They certainly have no idea how it relates to the human form or what "sitting" on something means. It's literally just pixel pattern => string("table"). That's it. There may be some experimental models that attempt such things, but the image classifiers that get all the press these days are very good at classification but nothing else.
  - abecedarius 5y ago
    Image classifiers aren't what I said. BabyTalk in 2013 could take a photo and write out "This is a photo of one person and one brown sofa and one dog. The person is against the brown sofa. And the dog is near the person, and beside the brown sofa." (All correct.) That's almost prehistoric in neural-network years.
    - heyitsguay 5y ago
      The problem is going from results on curated datasets - usually with at least a little implicit overfitting, since even if you reserve test data, it's typically chosen in the dataset creation process - to data in the wild. BabyTalk had that cool result in 2013, and there have been great papers following up in that area, but there's still not, e.g., a website where you can upload a photo and reliably get an accurate description. It's not because nobody's tried, it's because it's hard - neural network vision methods are still generally quite brittle to unexpected variations in data, and "accurate description" is actually a very loaded term that typically needs some sort of in-the-world contextualization to unpack.

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worldsayshi 5y ago

Feels like what you're referring to is just "understanding". GPT3 fails at this as well. It seems that gpt3 can string together sentences and whole paragraphs that seem to sort of make sense together but it seems that it never really puts hard constraints on any concepts. It doesn't try to build a comprehensive world with rules. Everything is just a sort of a fuzzy continuum. If you train it on enough data this fuzzyness generate a lot of plausibly sounding output but it will have a hard time coming up with the right answer in a new situation. I feel the missing ingredient is to be able to combine this fuzzy or intuitive understanding with some kind of rule engine. It's like in the book "Thinking Fast and Slow". It feels like GPT and the best image recognition algorithms we have today are good at the fast kind of thinking but doesn't have the slow and logical part.

abecedarius 5y ago

> very difficult, if not impossible, thing for AI to do right now Is that right? I thought computer vision was getting pretty good at object detection in the past few years.

simonh 5y ago
Those are just image classifiers though. They associate this pixel patters with the character string "table". They might be able to recognise this image as relating to both table and person, but most likely as competing possible classifications. They generally don't have a concept of relatedness between classifications. They have no spacial conceptual model relating concepts like 'above', beside', 'below', distance, etc. They also have no idea what a table is, no general concept of furniture, or even of a table as a physical or 3 dimensional object. They certainly have no idea how it relates to the human form or what "sitting" on something means. It's literally just pixel pattern => string("table"). That's it. There may be some experimental models that attempt such things, but the image classifiers that get all the press these days are very good at classification but nothing else.
- abecedarius 5y ago
  Image classifiers aren't what I said. BabyTalk in 2013 could take a photo and write out "This is a photo of one person and one brown sofa and one dog. The person is against the brown sofa. And the dog is near the person, and beside the brown sofa." (All correct.) That's almost prehistoric in neural-network years.
  - heyitsguay 5y ago
    The problem is going from results on curated datasets - usually with at least a little implicit overfitting, since even if you reserve test data, it's typically chosen in the dataset creation process - to data in the wild. BabyTalk had that cool result in 2013, and there have been great papers following up in that area, but there's still not, e.g., a website where you can upload a photo and reliably get an accurate description. It's not because nobody's tried, it's because it's hard - neural network vision methods are still generally quite brittle to unexpected variations in data, and "accurate description" is actually a very loaded term that typically needs some sort of in-the-world contextualization to unpack.