3 ms·

> The problem is that we are nowhere near such a thing. How are you certain of this?

toss1 4y ago
We can be certain of this by 1) looking at the structure of these engines, 2) looking at the kinds of errors that they make, and 3) looking at their learning methods. The engines are basically indexes of common associations, maps of frequency of occurrence. Regurgitating a bunch of stuff that has a high correlation to your input is NOT intelligence, it is the result of having an insanely large map. This can often produce impressive and useful results, but it is not intelligence or wielding concepts. For errors, the image generators provide some of the best illustrations. They produce images most associated with the inputs. One error illustrates this very well, asked to produce an image of a woman sitting on a sailboat, the bikini-clad woman looks great, until you see it — her face and torso are facing mostly towards the camera, but also, her buttocks are facing the camera and legs sitting pointing away from us. No intelligent person or concept-wielding "AI" would produce such an error - it'd know the relationships with head, torso, buttocks and legs. These don't. Another telling type of error is when asked to produce an image of Person X on a new background, when the training set had only a handful of images of Person X. It cannot do it - it returns essentially one of the full training images, with no new background. There is obviously zero concept of what a person is, or what the boundaries of a human shape would be. They can only produce these results with hundreds of thousands of images, so what is built up is the set of things that match or don't match the label (e.g., "astronaut" or "Barack Obama".), so that the actual images are statistically separated from the thousands of backgrounds. Which brings us to how they learn. Intelligent beings from worms to humans learn and abstract on incredibly small data sets. By the time a child can use a crayon, having seen only hundreds of humans, s/he can separate out what is a human from the background (might not make a good drawing yet, but knows the difference). Show a child a single new thing, and s/he will separate it from the background immediately. In contrast, these LLMs and GANs require input of nearly the entire corpus of human knowledge, and can only some of the time output something resembling the right thing. It is entirely different from intelligence (which is not to say it isn't often useful). But the more I learn about how they work and are built, the less I'm worried about this entire generation of machines. It is no more cause for worry than an observation 25 years ago that Google could do the work of 10000 librarian person-hours in 0.83 seconds. Great stuff, changes values of some types of work, but not an existential threat.
- mitthrowaway2 4y ago
  I agree that we can conclude that AlphaGo, GPT, and stable diffusion are geographically far from an AGI in program-design-space, just like we could conclude that an airship, an airplane, and a rocket are all far apart from each other in aircraft-design-space. But I don't think this offers certainty that AGI won't be developed for a long time (temporal distance). Nor that there are a large number of fundamental breakthroughs needed or new hardware, rather than just one or two key software architecture insights. With the eager investment and frantic pace of research competition, it seems like there will only be increasing pressure to explore AI-design-space for the near future, which might mean that even radically different and improved designs might be discovered in a short time.
  - toss1 4y ago
    >>radically different and improved designs That, right there, is the key - radically different and improved; i.e., not an extension of the current stuff. I fully agree that the enthusiasm generated by the impressive stunts of ALphaGO/GPT/SD, etc. does bring enthusiasm, investment, and activity to the field which will shorten any search. The catch for me is that these technologies, as impressive as they are, 1) not themselves a direct step towards AGI (beyond generating enthusiasm/investment), 2) tell us nothing about how much further we will need to search. That radical improvement may be right under our nose, or a millenium away. This reminds me of Hero's aeolipile, a steam engine invented over 2000 years ago. It could be said that we almost got the industrial revolution right then. Yet it took another 1800+ years for the other breakthroughs and getting back around to it. Plus, Hero's engine was exactly using the correct principles, whereas these AG/GPT/SD are clearly NOT onto the correct principles. So, how much will this enthusiasm, investment, and activity speed the search? If its just an order of magnitude, we're still 180 years away. If it's three orders of magnitude, it'll be late next year, and if it's five, it'll be here next weekend. So, I guess, in short, we've both read Bostrom's book, agree on that the AGI runaway scenario is a serious concern, but that these aren't any form of AGI, but might, as an secondary effect of their generated enthusiasm and genuine (albeit flaky) usefulness, accelerate the runaway AGI scenario? EDIT: considering your "airship/airplane/rocket distances in aircraft-design-space" analogy. It seems we don't even know if what we've got with AG/GPT/SD is an airship, and need a rocket, or if we've got an airplane, but actually need a warp drive. So, we know we're accelerating the search in the problem/design space. But, how can we answer the question of how big a space we'll need to search, and how big is our investment relative to the search volume?

We can be certain of this by 1) looking at the structure of these engines, 2) looking at the kinds of errors that they make, and 3) looking at their learning methods. The engines are basically indexes of common associations, maps of frequency of occurrence. Regurgitating a bunch of stuff that has a high correlation to your input is NOT intelligence, it is the result of having an insanely large map. This can often produce impressive and useful results, but it is not intelligence or wielding concepts. For errors, the image generators provide some of the best illustrations. They produce images most associated with the inputs. One error illustrates this very well, asked to produce an image of a woman sitting on a sailboat, the bikini-clad woman looks great, until you see it — her face and torso are facing mostly towards the camera, but also, her buttocks are facing the camera and legs sitting pointing away from us. No intelligent person or concept-wielding "AI" would produce such an error - it'd know the relationships with head, torso, buttocks and legs. These don't. Another telling type of error is when asked to produce an image of Person X on a new background, when the training set had only a handful of images of Person X. It cannot do it - it returns essentially one of the full training images, with no new background. There is obviously zero concept of what a person is, or what the boundaries of a human shape would be. They can only produce these results with hundreds of thousands of images, so what is built up is the set of things that match or don't match the label (e.g., "astronaut" or "Barack Obama".), so that the actual images are statistically separated from the thousands of backgrounds. Which brings us to how they learn. Intelligent beings from worms to humans learn and abstract on incredibly small data sets. By the time a child can use a crayon, having seen only hundreds of humans, s/he can separate out what is a human from the background (might not make a good drawing yet, but knows the difference). Show a child a single new thing, and s/he will separate it from the background immediately. In contrast, these LLMs and GANs require input of nearly the entire corpus of human knowledge, and can only some of the time output something resembling the right thing. It is entirely different from intelligence (which is not to say it isn't often useful). But the more I learn about how they work and are built, the less I'm worried about this entire generation of machines. It is no more cause for worry than an observation 25 years ago that Google could do the work of 10000 librarian person-hours in 0.83 seconds. Great stuff, changes values of some types of work, but not an existential threat.

mitthrowaway2 4y ago
I agree that we can conclude that AlphaGo, GPT, and stable diffusion are geographically far from an AGI in program-design-space, just like we could conclude that an airship, an airplane, and a rocket are all far apart from each other in aircraft-design-space. But I don't think this offers certainty that AGI won't be developed for a long time (temporal distance). Nor that there are a large number of fundamental breakthroughs needed or new hardware, rather than just one or two key software architecture insights. With the eager investment and frantic pace of research competition, it seems like there will only be increasing pressure to explore AI-design-space for the near future, which might mean that even radically different and improved designs might be discovered in a short time.
- toss1 4y ago
  >>radically different and improved designs That, right there, is the key - radically different and improved; i.e., not an extension of the current stuff. I fully agree that the enthusiasm generated by the impressive stunts of ALphaGO/GPT/SD, etc. does bring enthusiasm, investment, and activity to the field which will shorten any search. The catch for me is that these technologies, as impressive as they are, 1) not themselves a direct step towards AGI (beyond generating enthusiasm/investment), 2) tell us nothing about how much further we will need to search. That radical improvement may be right under our nose, or a millenium away. This reminds me of Hero's aeolipile, a steam engine invented over 2000 years ago. It could be said that we almost got the industrial revolution right then. Yet it took another 1800+ years for the other breakthroughs and getting back around to it. Plus, Hero's engine was exactly using the correct principles, whereas these AG/GPT/SD are clearly NOT onto the correct principles. So, how much will this enthusiasm, investment, and activity speed the search? If its just an order of magnitude, we're still 180 years away. If it's three orders of magnitude, it'll be late next year, and if it's five, it'll be here next weekend. So, I guess, in short, we've both read Bostrom's book, agree on that the AGI runaway scenario is a serious concern, but that these aren't any form of AGI, but might, as an secondary effect of their generated enthusiasm and genuine (albeit flaky) usefulness, accelerate the runaway AGI scenario? EDIT: considering your "airship/airplane/rocket distances in aircraft-design-space" analogy. It seems we don't even know if what we've got with AG/GPT/SD is an airship, and need a rocket, or if we've got an airplane, but actually need a warp drive. So, we know we're accelerating the search in the problem/design space. But, how can we answer the question of how big a space we'll need to search, and how big is our investment relative to the search volume?