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Yes. The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't

Yes. The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't Do (1972, 1979), or even better the updated reprint What Computers Still Can't Do (1992). He's a Heideggarian Philosopher but all you need to know is that modern AI is ignorant of vast swathes of 20th Century investigation into the human mind, and state of being. Hence, I think the big idea you're talking about is in AI that takes Hubert Dreyfus's critiques seriously. Luckily for anyone reading about this for the first time, that process has already started. Dreyfus wrote a 2007 paper on the successes and failures of the first few steps of what he called Heideggarian AI with the snappy name of "Why Heideggerian AI failed and how fixing it would require making it more Heideggerian". The "fixing" refers to work of a Neuroanatomist, with a suitable philosophical background, called Walter Freeman III, and is broadly described in the paper, but properly investigated in Freeman's (also eminently readable) book How Brains Make Up Their Minds (2000). A note of caution, you'll be introduced to concepts that blur the line between body and environment, subject and object, intention and influence, and eventually things like relinquising your belief in causality and an objective "out there" universe (or at least any value in such a belief), all whilst staying perfectly scientific and evidence based. Finally, bear in mind that if we do create an intelligence worthy of the name, we have reason to believe it will take about 18 years of "raising" by two adult humans, after which it will want to do its own thing, and not your dumb image classification tasks.

ilaksh 10y ago
I just read a review of "How Brains Make Up Their Minds. Thanks for mentioning that. It looks like a good book with mostly correct information (based on the review). From the summary, I can tell you that researchers in fields such as AGI, deep learning, robotics, etc. have absolutely been working from many if not all of Freeman's assumptions for years and almost all (if not all) of that has been integrated into various research programs and systems. Freeman's pragmatist view is now the most popular. Of course, all of Freeman's ideas aren't _usually_ together in every one of these systems or the conceptualization of them, but there are at least a few that have most of them. Certainly AGI researchers are aware of the concept of higher-level abstractions being formed at root on the basis of sensory input and action output. And most of the recent serious AI research such as pretty much any NN for example demonstrates the idea of meaning from global patterns. These are some interesting AGI videos in case people haven't seen them. https://www.youtube.com/channel/UCCwJ8AV1zMM4j9FTicGimqA/playlists https://www.youtube.com/channel/UCCwJ8AV1zMM4j9FTicGimqA/pla...
- AndrewOMartin 10y ago
  I strongly object to the ideas in that book bring described with the terms such as "higher-level abstractions", "sensory input and action output", and "meaning from global patterns". As I read it, they were precisely the notions Freeman was arguing against. There is no "input" when the history of a being, it's current sensitivities and orientation in the environment are all inseparable and mutually defining. There are no higher level abstractions, and none needed, when you're so poised in a situation with your whole brain and body that any appropriate stimulation is already meaningful. Freeman, for me turned thought upside down. I.e. we don't go, "I've detected that as being food, so I may eat it", rather we go "I've noticed I interact with all these things in a similar way (e.g. eating) that's how I recognise them as related".
  - ilaksh 10y ago
    Your conclusion aligns with my statements. So I think you are interpreting what I wrote incorrectly.
Animats 10y ago
"The critiques of AI from Hubert Dreyfus have stood the test of time..." This is the guy who in 1965 said "no computer can play even amateur chess". He was right. It took a lot longer than expected for computers to get good at chess. But they did. Now they're better, a lot better. Chess.com now says "any decent chess program could easily beat the world's top humans". No need for a supercomputer; Komodo costs $59.96 and running on a laptop will trounce any human. Dreyfus is too much into "humans are special snowflakes", and he writes book-length arguments which assume you agree with that.
Animats 10y ago
If you're going to obsess over the nature/nuture thing, you have to consider the counterexample - animals that are born ready to go. These are called precocial species. Most of the large grazing mammals, including all the equines, are precocial. You can see this by watching the first day of life of a horse. Within the first hour, the foal stands up by itself. This is a complex coordinated operation for an animal with such long legs. The sequence for doing this is dynamic (not statically stable and can't be done slowly), and clearly built-in, but requires tuning. The foal may fail the first few times, but eventually gets up on the long spindly legs and wobbles. The first few steps are tiny and cautious, but the nervous system calibrates rapidly. Stable walking is achieved quickly. Trotting appears after a few more hours. Within a day or two, a newborn foal can run with the herd. At that point, all the locomotion functions are working - balance, coordination, visual foot placement, obstacle detection, and collision avoidance. That's a lot of capability. Having worked on both automatic driving and legged robot balance, I know how hard that is. This demonstrates that mammal brains don't come up blank and learn. There's a lot of hard-wired capability. (I sometimes comment on mobile robotics that the main thing is getting through the next 15 seconds of life without screwing up. If you can do that, you can then add task-oriented back-seat driving to get something done, and that's the easy part.)

ilaksh 10y ago

I just read a review of "How Brains Make Up Their Minds. Thanks for mentioning that. It looks like a good book with mostly correct information (based on the review). From the summary, I can tell you that researchers in fields such as AGI, deep learning, robotics, etc. have absolutely been working from many if not all of Freeman's assumptions for years and almost all (if not all) of that has been integrated into various research programs and systems. Freeman's pragmatist view is now the most popular. Of course, all of Freeman's ideas aren't _usually_ together in every one of these systems or the conceptualization of them, but there are at least a few that have most of them. Certainly AGI researchers are aware of the concept of higher-level abstractions being formed at root on the basis of sensory input and action output. And most of the recent serious AI research such as pretty much any NN for example demonstrates the idea of meaning from global patterns. These are some interesting AGI videos in case people haven't seen them. https://www.youtube.com/channel/UCCwJ8AV1zMM4j9FTicGimqA/playlists https://www.youtube.com/channel/UCCwJ8AV1zMM4j9FTicGimqA/pla...

AndrewOMartin 10y ago
I strongly object to the ideas in that book bring described with the terms such as "higher-level abstractions", "sensory input and action output", and "meaning from global patterns". As I read it, they were precisely the notions Freeman was arguing against. There is no "input" when the history of a being, it's current sensitivities and orientation in the environment are all inseparable and mutually defining. There are no higher level abstractions, and none needed, when you're so poised in a situation with your whole brain and body that any appropriate stimulation is already meaningful. Freeman, for me turned thought upside down. I.e. we don't go, "I've detected that as being food, so I may eat it", rather we go "I've noticed I interact with all these things in a similar way (e.g. eating) that's how I recognise them as related".
- ilaksh 10y ago
  Your conclusion aligns with my statements. So I think you are interpreting what I wrote incorrectly.

Animats 10y ago

"The critiques of AI from Hubert Dreyfus have stood the test of time..." This is the guy who in 1965 said "no computer can play even amateur chess". He was right. It took a lot longer than expected for computers to get good at chess. But they did. Now they're better, a lot better. Chess.com now says "any decent chess program could easily beat the world's top humans". No need for a supercomputer; Komodo costs $59.96 and running on a laptop will trounce any human. Dreyfus is too much into "humans are special snowflakes", and he writes book-length arguments which assume you agree with that.

Animats 10y ago

If you're going to obsess over the nature/nuture thing, you have to consider the counterexample - animals that are born ready to go. These are called precocial species. Most of the large grazing mammals, including all the equines, are precocial. You can see this by watching the first day of life of a horse. Within the first hour, the foal stands up by itself. This is a complex coordinated operation for an animal with such long legs. The sequence for doing this is dynamic (not statically stable and can't be done slowly), and clearly built-in, but requires tuning. The foal may fail the first few times, but eventually gets up on the long spindly legs and wobbles. The first few steps are tiny and cautious, but the nervous system calibrates rapidly. Stable walking is achieved quickly. Trotting appears after a few more hours. Within a day or two, a newborn foal can run with the herd. At that point, all the locomotion functions are working - balance, coordination, visual foot placement, obstacle detection, and collision avoidance. That's a lot of capability. Having worked on both automatic driving and legged robot balance, I know how hard that is. This demonstrates that mammal brains don't come up blank and learn. There's a lot of hard-wired capability. (I sometimes comment on mobile robotics that the main thing is getting through the next 15 seconds of life without screwing up. If you can do that, you can then add task-oriented back-seat driving to get something done, and that's the easy part.)