7 ms·

Tacit knowledge is knowledge that results from adapting to experience, like learning to tie shoelaces with practice, rather than something like finding the deri

by SiVal 6y ago

Tacit knowledge is knowledge that results from adapting to experience, like learning to tie shoelaces with practice, rather than something like finding the derivative of sin(x) by the usual mathematical proof method (reasoning step by formal step). Every deep learning system has tacit knowledge: it knows a chair when it sees one but can't explain how it knows. It just adapted its connections to training until it got it right most of the time. So computers are provably capable of what, in humans, is defined as tacit knowledge and can be given sensors and actuators to learn from. A car can learn to parallel park with practice. It can't explain how it does it, but you can copy the trained system into a new car. I don't see why you couldn't produce a combination of sensors and actuators that vastly exceeds what any human is capable of. But AGI isn't that. It's a variety of information processing techniques (algorithms) deployed as a toolbox managed by meta-techniques (more algorithms) that know how to deploy the others in various combinations. We don't yet know much about the management algorithms, but I don't see any reason in principle why we couldn't eventually find some and invent others.

yehbut99 6y ago
Computers can’t heal themselves. Our bodies do that on their own without conscious prompting. Humans evolve the complexity of a computers electron states, not the computers own inherent properties. My use doesn’t force transistors to evolve into better transistors. It has no self regeneration. You don’t see the issue in principle but here you have an article by an expert pointing them out. Perhaps be a better listener? A computer has observable, literal limitations relative to a humans mechanical functionality. You can’t scrape away literal reality to arrive at some reductionist idea of what a consciousness is. Our only known good model for a machine that can create our consciousness is us. It took billions of years of the universe churning at random to accidentally generate us. We have no clue how to replicate that scale. A computer literally lacks a whole lot of literal information that’s embedded in the hardware and software of a person. Watch this and tell me the last time your computer reconfigured it’s literal shape when you altered its electric field properties: https://youtu.be/RjD1aLm4Thg https://youtu.be/RjD1aLm4Thg There’s something to “life” we’ll never be able to jam into silicon.
- simonh 6y ago
  This is exactly the sort of conflation of completely unrelated concepts as I found in the article. What on Earth has healing got to do with reasoning? You even say our bodies do it without conscious prompting, in other words it’s a completely irrelevant issue. Yes computers aren’t biological, they aren’t life, but so what? Why does that constrain their ability to interact with and learn from the world? AGI is defined as human like intelligence, human go to the toilet, computers don’t go to the toilet, therefore AGI is impossible. See? It’s easy to “prove” AGI is impossible. That’s really all their argument boils down to. I kept reading the article expecting to hit some essential argument, not to find one. Very disappointing.
  - engineeringwoke 6y ago
    For all the pontificating about souls as a means to discredit OP's beliefs, you all seem to avoid this statement: > Our only known good model for a machine that can create our consciousness is us. It took billions of years of the universe churning at random to accidentally generate us. We have no clue how to replicate that scale. Would you please provide a counterargument? Do you understand how difficult this really is? We can't even comprehend the physical constraints.
    - cygx 6y ago
      Technological progress is many orders of magnitudes faster than evolutionary change; assuming the 'magic' of the brain is indeed in the neural circuity, hardware is expected to be powerful enough to simulate that on a timescale of O(100) years in the future instead of O(10e9). Of course, it's not a given that simulating connected neurons with action potentials or whatever is sufficient to capture the relevant features of the brain (perhaps long-distance em interactions are relevant? quantum magic? do we need to drop to the molecular level?) - but without proof to the contrary, we'll just have to wait and see.
      - engineeringwoke 6y ago
        This is a well-balanced take. Maybe we will see emergent properties at that scale. If that were the case, then we could catch enough of a glimpse of what is really happening... But another part of me says that is silly. We had an expectation of what the Higgs was before we found it. Here, we are shooting in the dark.
baddox 6y ago
> Every deep learning system has tacit knowledge: it knows a chair when it sees one but can't explain how it knows. A traditional computer program that can find the derivative of sin(x) also can not explain how it knows.
- dreamcompiler 6y ago
  > A traditional computer program that can find the derivative of sin(x) also can not explain how it knows. Oh but it could, and that's the point. Some computer differentiation techniques just follow the same rules you learned when you took calculus. They typically don't show you which rules they followed, but they easily could. Other differentiation techniques are more exotic but there's no reason they couldn't show you the chain of computations and/or deductions they went through to arrive at that derivative. Such programs can easily justify their results and even teach humans calculus if configured properly. Contrast that with the chair example. It is impossible right now to write a program that can show a human the chain of reasoning it went through to decide some image is a chair, because no such chain exists. There's a giant iterated polynomial with nonlinear threshold functions and a million coefficients, but there's no chain of reasoning.
  - baddox 6y ago
    That lack of a chain of reasoning just doesn’t feel any more significant to me than the fact that a human can also not endlessly regress upwards explaining every bit of knowledge they have or every reason they made a decision. Likewise the computer algebra software can only answer “how did you know that?” so many times in a sequence.
  - strken 6y ago
    I'm not sure a human can explain how they know a chair is a chair, either. They can come up with a post-hoc rationalisation, but that's not guaranteed to really represent the decision-making process they went through. At best you get an answer that describes one or more conscious decisions and leaves the unconscious decisions out, such as "it looks a lot like a stool because it's low to the ground and has three legs, but it has a back, so I think it's a chair"; when the real answer is that they have a bunch of pattern-matching visual neurons, and those neurons feed into other neurons that detect more complicated patterns, and the concept of a chair eventually emerges.
nl 6y ago
The article specifically says "I have earlier said that neural networks need not be programmed, and therefore can handle tacit knowledge." The OP comments here is just wrong: it misunderstands what tacit means, and mischaracterizes the argument in the article.
mbrock 6y ago
Hubert Dreyfus in his 1986 book "Mind Over Machine": > The digital computer, when programmed to operate by taking a problem apart into features and combining them step by step according to inference rules, operates as a machine—a logic machine. However, the computer is so versatile it can also be used to model a holistic system. Indeed, recently, as the problems confronting the AI approach remained unsolved for more than a decade, a new generation of researchers have actually begun using computers to simulate such systems. It is too early to say whether the first steps in the direction of holistic similarity recognition will eventually lead to devices that can discern the similarity between whole real-world situations. We discuss the development here for the simple reason that it is the only alternative to the information processing approach that computer science has devised. [...] Remarkably, such devices are the subject of active research. When used to realize a distributed associative memory, computers are no longer functioning as symbol-manipulating systems in which the symbols represent features of the world and computations express relationship among the symbols as in conventional AI. Instead, the computer simulates a holistic system. Further down, this is quite a good summary of Dreyfus general argument: > Thanks to AI research, Plato's and Kant's speculation that the mind works according to rules has finally found its empirical test in the attempt to use logic machines to produce humanlike understanding. And, after two thousand years of refinement, the traditional view of mind has shown itself to be inadequate. Indeed, conventional AI as information processing looks like a perfect example of what Imre Lakatos would call a degenerating research program. [...] Current AI is based on the idea, prominent in philosophy since Descartes, that all understanding consists in forming and using appropriate representations. Given the nature of inference engines, AI's representations must be formal ones, and so commonsense understanding must be understood as some vast body of precise propositions, beliefs, rules, facts, and procedures. Thus formulated, the problem has so far resisted solution. We predict it will continue to do so.
- glenstein 6y ago
  I think that Dreyfus has unfortunately set back the cultural understanding of computers by decades, by confidently declaring certain tasks impossible for computers to do, because minds have "insight" or "tacit knowledge" or are "holistic", each of which functionally lets a mind be a ghost in the machine. A lot of the rhetorical momentum comes from pointing at the progress of technology at various stages in human history, especially the fits and starts of AI/Language research in the mid 20th century, and remarking at how little progress has been made. And the terms used to define how computers were are also vague. >Given the nature of inference engines, AI's representations must be formal ones When AI trained on images of dog faces "dreams" on an image, and progressively twists flowers and purses into dog faces and noses, is the connection made between patterns and dog faces "formal" ? Are the images generated by ThisPersonDoesNotExist informal? The ways computers work on data now deals with abstractions & fuzziness in a way that I think Dreyfus did not imagine to be possible. I think Dreyfus wanted to say that the higher-level methods that we now employ to generate images, human-like language, transpose art styles and create nearly photorealistic faces are on a foundation of principles that are new and distinct from the characteristic principles that he understood to be central to computing. But all of our new progress is implemented on a foundation of silicon and bits, too, which simulate neural networks, meaning those are just as computational as the desktop calculator app. I think Dreyfus just couldn't imagine that 'computing' could include all this extra stuff, and, to take a term from Dennet, Dreyfus mistook his failure of imagination for an insight into necessity.
dragonwriter 6y ago
> But AGI isn't that. We haven't got much of a clue about what AGI is or isn't because we don't have much of a clue about GI is or isn't without the A.

by SiVal 6y ago

yehbut99 6y ago

Computers can’t heal themselves. Our bodies do that on their own without conscious prompting. Humans evolve the complexity of a computers electron states, not the computers own inherent properties. My use doesn’t force transistors to evolve into better transistors. It has no self regeneration. You don’t see the issue in principle but here you have an article by an expert pointing them out. Perhaps be a better listener? A computer has observable, literal limitations relative to a humans mechanical functionality. You can’t scrape away literal reality to arrive at some reductionist idea of what a consciousness is. Our only known good model for a machine that can create our consciousness is us. It took billions of years of the universe churning at random to accidentally generate us. We have no clue how to replicate that scale. A computer literally lacks a whole lot of literal information that’s embedded in the hardware and software of a person. Watch this and tell me the last time your computer reconfigured it’s literal shape when you altered its electric field properties: https://youtu.be/RjD1aLm4Thg https://youtu.be/RjD1aLm4Thg There’s something to “life” we’ll never be able to jam into silicon.

simonh 6y ago
This is exactly the sort of conflation of completely unrelated concepts as I found in the article. What on Earth has healing got to do with reasoning? You even say our bodies do it without conscious prompting, in other words it’s a completely irrelevant issue. Yes computers aren’t biological, they aren’t life, but so what? Why does that constrain their ability to interact with and learn from the world? AGI is defined as human like intelligence, human go to the toilet, computers don’t go to the toilet, therefore AGI is impossible. See? It’s easy to “prove” AGI is impossible. That’s really all their argument boils down to. I kept reading the article expecting to hit some essential argument, not to find one. Very disappointing.
- engineeringwoke 6y ago
  For all the pontificating about souls as a means to discredit OP's beliefs, you all seem to avoid this statement: > Our only known good model for a machine that can create our consciousness is us. It took billions of years of the universe churning at random to accidentally generate us. We have no clue how to replicate that scale. Would you please provide a counterargument? Do you understand how difficult this really is? We can't even comprehend the physical constraints.
  - cygx 6y ago
    Technological progress is many orders of magnitudes faster than evolutionary change; assuming the 'magic' of the brain is indeed in the neural circuity, hardware is expected to be powerful enough to simulate that on a timescale of O(100) years in the future instead of O(10e9). Of course, it's not a given that simulating connected neurons with action potentials or whatever is sufficient to capture the relevant features of the brain (perhaps long-distance em interactions are relevant? quantum magic? do we need to drop to the molecular level?) - but without proof to the contrary, we'll just have to wait and see.
    - engineeringwoke 6y ago
      This is a well-balanced take. Maybe we will see emergent properties at that scale. If that were the case, then we could catch enough of a glimpse of what is really happening... But another part of me says that is silly. We had an expectation of what the Higgs was before we found it. Here, we are shooting in the dark.

baddox 6y ago

> Every deep learning system has tacit knowledge: it knows a chair when it sees one but can't explain how it knows. A traditional computer program that can find the derivative of sin(x) also can not explain how it knows.

dreamcompiler 6y ago
> A traditional computer program that can find the derivative of sin(x) also can not explain how it knows. Oh but it could, and that's the point. Some computer differentiation techniques just follow the same rules you learned when you took calculus. They typically don't show you which rules they followed, but they easily could. Other differentiation techniques are more exotic but there's no reason they couldn't show you the chain of computations and/or deductions they went through to arrive at that derivative. Such programs can easily justify their results and even teach humans calculus if configured properly. Contrast that with the chair example. It is impossible right now to write a program that can show a human the chain of reasoning it went through to decide some image is a chair, because no such chain exists. There's a giant iterated polynomial with nonlinear threshold functions and a million coefficients, but there's no chain of reasoning.
- baddox 6y ago
  That lack of a chain of reasoning just doesn’t feel any more significant to me than the fact that a human can also not endlessly regress upwards explaining every bit of knowledge they have or every reason they made a decision. Likewise the computer algebra software can only answer “how did you know that?” so many times in a sequence.
- strken 6y ago
  I'm not sure a human can explain how they know a chair is a chair, either. They can come up with a post-hoc rationalisation, but that's not guaranteed to really represent the decision-making process they went through. At best you get an answer that describes one or more conscious decisions and leaves the unconscious decisions out, such as "it looks a lot like a stool because it's low to the ground and has three legs, but it has a back, so I think it's a chair"; when the real answer is that they have a bunch of pattern-matching visual neurons, and those neurons feed into other neurons that detect more complicated patterns, and the concept of a chair eventually emerges.

nl 6y ago

The article specifically says "I have earlier said that neural networks need not be programmed, and therefore can handle tacit knowledge." The OP comments here is just wrong: it misunderstands what tacit means, and mischaracterizes the argument in the article.

mbrock 6y ago

Hubert Dreyfus in his 1986 book "Mind Over Machine": > The digital computer, when programmed to operate by taking a problem apart into features and combining them step by step according to inference rules, operates as a machine—a logic machine. However, the computer is so versatile it can also be used to model a holistic system. Indeed, recently, as the problems confronting the AI approach remained unsolved for more than a decade, a new generation of researchers have actually begun using computers to simulate such systems. It is too early to say whether the first steps in the direction of holistic similarity recognition will eventually lead to devices that can discern the similarity between whole real-world situations. We discuss the development here for the simple reason that it is the only alternative to the information processing approach that computer science has devised. [...] Remarkably, such devices are the subject of active research. When used to realize a distributed associative memory, computers are no longer functioning as symbol-manipulating systems in which the symbols represent features of the world and computations express relationship among the symbols as in conventional AI. Instead, the computer simulates a holistic system. Further down, this is quite a good summary of Dreyfus general argument: > Thanks to AI research, Plato's and Kant's speculation that the mind works according to rules has finally found its empirical test in the attempt to use logic machines to produce humanlike understanding. And, after two thousand years of refinement, the traditional view of mind has shown itself to be inadequate. Indeed, conventional AI as information processing looks like a perfect example of what Imre Lakatos would call a degenerating research program. [...] Current AI is based on the idea, prominent in philosophy since Descartes, that all understanding consists in forming and using appropriate representations. Given the nature of inference engines, AI's representations must be formal ones, and so commonsense understanding must be understood as some vast body of precise propositions, beliefs, rules, facts, and procedures. Thus formulated, the problem has so far resisted solution. We predict it will continue to do so.

glenstein 6y ago
I think that Dreyfus has unfortunately set back the cultural understanding of computers by decades, by confidently declaring certain tasks impossible for computers to do, because minds have "insight" or "tacit knowledge" or are "holistic", each of which functionally lets a mind be a ghost in the machine. A lot of the rhetorical momentum comes from pointing at the progress of technology at various stages in human history, especially the fits and starts of AI/Language research in the mid 20th century, and remarking at how little progress has been made. And the terms used to define how computers were are also vague. >Given the nature of inference engines, AI's representations must be formal ones When AI trained on images of dog faces "dreams" on an image, and progressively twists flowers and purses into dog faces and noses, is the connection made between patterns and dog faces "formal" ? Are the images generated by ThisPersonDoesNotExist informal? The ways computers work on data now deals with abstractions & fuzziness in a way that I think Dreyfus did not imagine to be possible. I think Dreyfus wanted to say that the higher-level methods that we now employ to generate images, human-like language, transpose art styles and create nearly photorealistic faces are on a foundation of principles that are new and distinct from the characteristic principles that he understood to be central to computing. But all of our new progress is implemented on a foundation of silicon and bits, too, which simulate neural networks, meaning those are just as computational as the desktop calculator app. I think Dreyfus just couldn't imagine that 'computing' could include all this extra stuff, and, to take a term from Dennet, Dreyfus mistook his failure of imagination for an insight into necessity.

dragonwriter 6y ago

> But AGI isn't that. We haven't got much of a clue about what AGI is or isn't because we don't have much of a clue about GI is or isn't without the A.