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> In the book [Dreyfus] argued that an important part of human knowledge is tacit. Therefore, it cannot be articulated and implemented in a computer program. [.

> In the book [Dreyfus] argued that an important part of human knowledge is tacit. Therefore, it cannot be articulated and implemented in a computer program. [...] [...] [...] These skills cannot just be learned from textbooks. They are acquired by instruction from someone who knows the trade. This is a classic example of the Mind Projection Fallacy [1], where a property of how you think is assumed to be a property of reality. It's true, for humans, that it is simply not possible to be told how to ride a bike and then be good at riding a bike. No matter how carefully and completely you explain to a human what they will have to do, when you put them on a bike for the first time they will struggle. The mistake is assuming that this "have-to-really-do-it" effect is a limitation intrinsic to bike-riding-knowledge instead of a limitation in human learning and communication mechanisms. The mistake is assuming this property will generalize to all bike riding systems. In a computer system, what would be tacit knowledge for a human is no longer tacit. If you create a computer program that can successfully control one bike riding robot, that program can be copied to a freshly built bike riding robot of the same make. The new robot will then successfully ride a bike the first time it is placed on it, without any hint of the human "have-to-really-do-it" struggling phase. It can be intuitively useful to imagine computers as having the ability to "super communicate" in a way that humans simply can't. That has its advantages, and its disadvantages. If you had super communication you could super-explain to a blind person what it was like to see and if they ever did gain their sight there would be no "Oh so that's what you meant" moment. On the other hand, a heroin addict could super-explain being addicted to heroin to you. 1: https://en.wikipedia.org/wiki/Mind_projection_fallacy https://en.wikipedia.org/wiki/Mind_projection_fallacy

mekkkkkk 6y ago
Very true. It seems closely related to the problem with qualia, which I've always thought of similarly. Instinctively it seems impossible to describe the color green to someone who has never seen it. Seeing it seems to add "something" that is impossible to acquire in any other way. But this is because of our physical limitations, rather than the existence of some ephemeral quality. If we would have total knowledge and full ability of introspection, I don't see why we wouldn't be able to accurately predict the subjective experience of any input, including colors.
- goatlover 6y ago
  Maybe, but since we don’t have that knowledge, we can’t program it.
  - analog31 6y ago
    A friend of mine was the project manager for a project to develop one of the first color matching systems for paint stores. My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. I've gotten color matching done at Home Depot, to get paint for repairing my house. It's uncanny.
    - dragonwriter 6y ago
      > My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships.
      - analog31 6y ago
        True. For instance there's knowledge that can't be directly perceived by any one individual, given variations in our sensory apparatus. Even among so called "normal" sighted people, the sensation of red varies from person to person. I have a normal sighted colleague, and there are "red" LED wavelengths that seem very bright to me, but he can barely see them. The color matching equipment is partly based on an accommodation of those variations.
      - glenstein 6y ago
        >Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships. Yeah, I think that was their point. Whatever 'knowledge' we think we have with qualia is actually something that, functionally, we seem to be able to get along without and do just fine. Which makes you wonder what functional purpose we don't have by not having 'qualia'.
mrmonkeyman 6y ago
This is not the same at all and you misunderstood the argument. Your biker example would be the same if one could show a software entity completely mastering bike dynamics in its first try by reason, pure deduction from first principles. On the other hand, if you 'train' a bike program - eg let it fail million times - first, that would be a perfect example of tacit knowledge. Nothing to do with reason, just pure experience building on itself. That is actually what a human would do although we lack the ability to be copied.
TheOtherHobbes 6y ago
The point isn't bike riding. Bike riding is an example of a class of problems, and your solution begs the question. The point is that humans and computers operate differently. The human approach is based on adaptive experiential heuristics. The computer approach is based on explicit formalism. (Even in neural networks, there's still a formal model. It's just made of weightings instead of logic paths.) The epistemology of these approaches is completely different. The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. Why would anyone do this? Because adaptive experiential heuristics are far more flexible and generalisable than explicit formalisms. And - it suggests here - you can't have real AGI without them. So the problem then becomes unpicking what "adaptive" and "experiential" really mean. Both rely on huge accumulations of tacit knowledge and tacit motivations. If this isn't obvious, consider that a human child will learn how to ride a bike and then go and have a lot of fun with it. An ideal bike-riding computer doesn't even have a concept of fun. The human experience of fun is a complex system of experimentation, exploration, reward, and challenge, combined with physical, emotional, and mental correlates. This matters because play in childhood helps develop the heuristics that adults use for problem solving, and for personal motivation and satisfaction. Even more simply, the problem is the difference between building a workable but dumb bike riding machine and building a machine that will improvise bike riding as a goal for itself, will "enjoy" the experience, and will generalise from that to mastery of other domains.
- Strilanc 6y ago
  > The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.
  - neatze 6y ago
    I could not agree more there is no evidence of any kind that there is some process that is human-intrinsic from perspective of neuroscience and computer science. Furthermore learning is just subset of intelligence, it seems to me most arguments are about concept of intelligence where such concept has different meaning for each participant.
htfu 6y ago
> It's true, for humans, that it is simply not possible to be told how to ride a bike and then be good at riding a bike. No matter how carefully and completely you explain to a human what they will have to do, when you put them on a bike for the first time they will struggle. I'm not sure that's automatically true. The way we learn riding bikes, as young children, yes of course. But someone who was already an expert skateboarder, inliner, equestrian, fighter pilot etc, somehow without ever having ridden a bike, likely wouldn't struggle. Balance, lack of fear and general trust in your instruments definitely transfers.

mekkkkkk 6y ago

Very true. It seems closely related to the problem with qualia, which I've always thought of similarly. Instinctively it seems impossible to describe the color green to someone who has never seen it. Seeing it seems to add "something" that is impossible to acquire in any other way. But this is because of our physical limitations, rather than the existence of some ephemeral quality. If we would have total knowledge and full ability of introspection, I don't see why we wouldn't be able to accurately predict the subjective experience of any input, including colors.

goatlover 6y ago
Maybe, but since we don’t have that knowledge, we can’t program it.
- analog31 6y ago
  A friend of mine was the project manager for a project to develop one of the first color matching systems for paint stores. My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. I've gotten color matching done at Home Depot, to get paint for repairing my house. It's uncanny.
  - dragonwriter 6y ago
    > My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships.
    - analog31 6y ago
      True. For instance there's knowledge that can't be directly perceived by any one individual, given variations in our sensory apparatus. Even among so called "normal" sighted people, the sensation of red varies from person to person. I have a normal sighted colleague, and there are "red" LED wavelengths that seem very bright to me, but he can barely see them. The color matching equipment is partly based on an accommodation of those variations.
    - glenstein 6y ago
      >Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships. Yeah, I think that was their point. Whatever 'knowledge' we think we have with qualia is actually something that, functionally, we seem to be able to get along without and do just fine. Which makes you wonder what functional purpose we don't have by not having 'qualia'.

mrmonkeyman 6y ago

This is not the same at all and you misunderstood the argument. Your biker example would be the same if one could show a software entity completely mastering bike dynamics in its first try by reason, pure deduction from first principles. On the other hand, if you 'train' a bike program - eg let it fail million times - first, that would be a perfect example of tacit knowledge. Nothing to do with reason, just pure experience building on itself. That is actually what a human would do although we lack the ability to be copied.

TheOtherHobbes 6y ago

The point isn't bike riding. Bike riding is an example of a class of problems, and your solution begs the question. The point is that humans and computers operate differently. The human approach is based on adaptive experiential heuristics. The computer approach is based on explicit formalism. (Even in neural networks, there's still a formal model. It's just made of weightings instead of logic paths.) The epistemology of these approaches is completely different. The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. Why would anyone do this? Because adaptive experiential heuristics are far more flexible and generalisable than explicit formalisms. And - it suggests here - you can't have real AGI without them. So the problem then becomes unpicking what "adaptive" and "experiential" really mean. Both rely on huge accumulations of tacit knowledge and tacit motivations. If this isn't obvious, consider that a human child will learn how to ride a bike and then go and have a lot of fun with it. An ideal bike-riding computer doesn't even have a concept of fun. The human experience of fun is a complex system of experimentation, exploration, reward, and challenge, combined with physical, emotional, and mental correlates. This matters because play in childhood helps develop the heuristics that adults use for problem solving, and for personal motivation and satisfaction. Even more simply, the problem is the difference between building a workable but dumb bike riding machine and building a machine that will improvise bike riding as a goal for itself, will "enjoy" the experience, and will generalise from that to mastery of other domains.

Strilanc 6y ago
> The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.
- neatze 6y ago
  I could not agree more there is no evidence of any kind that there is some process that is human-intrinsic from perspective of neuroscience and computer science. Furthermore learning is just subset of intelligence, it seems to me most arguments are about concept of intelligence where such concept has different meaning for each participant.

htfu 6y ago

> It's true, for humans, that it is simply not possible to be told how to ride a bike and then be good at riding a bike. No matter how carefully and completely you explain to a human what they will have to do, when you put them on a bike for the first time they will struggle. I'm not sure that's automatically true. The way we learn riding bikes, as young children, yes of course. But someone who was already an expert skateboarder, inliner, equestrian, fighter pilot etc, somehow without ever having ridden a bike, likely wouldn't struggle. Balance, lack of fear and general trust in your instruments definitely transfers.