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Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. cognitive psychology You are overselling th

Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. cognitive psychology You are overselling the insights of this discipline. Has cognitive psychology solved its replication problems? Where is the world-beating AI that is based on "concept spaces", "sparse representations", "small-world networks" and "learning and memory" neuroscience? [1] https://arxiv.org/abs/1505.00521 https://arxiv.org/abs/1505.00521

ForHackernews 6y ago
> Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. This seems like overstating your point. Nobody has been able to rigorously define "AI" yet, so there's no way of saying whether it's possible with a Turing machine architecture. The human brain, at least, doesn't seem that similar to a Turing architecture. Neurons don't carry out anything like discrete operations. Maybe it's possible to run AGI on a Turing machine, maybe it's not, but there are more options than simply "possible with a Turing machine" or "completely impossible".
- GregarianChild 6y ago
  While I agree that we don't have an agreed upon definition of AI, the problem is firmly in the "I" part of AI! The "A" part is taken to mean implementable by a computer, i.e. a Turing machine. This is the content of the Church–Turing thesis [1]. [1] https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis
  - ForHackernews 6y ago
    > It states that a function on the natural numbers can be calculated by an effective method if and only if it is computable by a Turing machine. The article at the top of this thread is specifically about properties ("The Generalized Archimedean Property") that real numbers do not possess. There's also a little bit of slipperiness around the use of "AI" vs. "AGI" - you could easily argue (and people do!) that we've already achieved "AI" for many specialized domains. It's the General bit that seems to be the sticking point, and that this article focuses on.
- joe_the_user 6y ago
  Maybe it's possible to run AGI on a Turing machine, maybe it's not, Arguing from ignorance, of course nothing is knowable for certain. However there has been a lot of work on the universality of Turing machines, showing that a Turing machine can simulate any conceivable concept of finite computation and can approximate any conventional physical system. I think a more useful way to express your intuition is to note that if human-built AGI comes into existence, it might be runnable on a Turing machine but quite possibly not efficiently so.
fsuuttg 6y ago
Why does it matter? Turing completeness is a low bar. Both exp(exp(N)) and log(log(N)) algorithms solve the AI problem in a finite number of steps, but one of them is a useless abstraction, while the other one really works.
YeGoblynQueenne 6y ago
Note that Turing complete means undecidable. An algorithm that can learn any program that can be computed by a Universal Turing Machine must, in the worst case, search an infinite program space. So, even if a Neural Turing Machine can learn arbitrary programs (I haven't read the paper so I can't say) it might need to consume infinite resources before learning any particular progarm. In short- Turing completeness is no guaranteed path to AGI. Assuming an "AGI program" exists, it is hidden away in an infinity of almost identical, but not quite, programs.
deleted 6y ago
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bra-ket 6y ago
>Where is .. AI it's always 5 years away because mainstream AI researches are stuck with yak shaving their gradient descents. I mean you can't just throw things at the wall and hope they stick, but it's literally the state of the art, if you follow ML conferences and their world-beating toy benchmarks results, with a lot of pseudo-rigorous handwaving for theory. The reason physics has been so successful is that their theory closely followed empirical data and constraints imposed by nature. I think the only hope to achieve common sense in AI is to align it with hard constraints living organisms have, using those constraints as a guide. A few terms I mentioned are coming from that POV, if you dig a bit deeper they all have direct physical manifestation in natural learning systems.
- YeGoblynQueenne 6y ago
  >> it's always 5 years away because mainstream AI researches are stuck with yak shaving their gradient descents. A small correction: that's deep learning researches, not AI researchers and not all machine learning researchers even. To be charitable, it's not even all deep learning researchers. It's just that the field of deep learning research has been inundated with new entrants who are sufficiently skilled to grok the practicalities but lack understanding of AI scholarship and produce unfortunately shoddy work that does not advance the field (any field, any of the aforementioned ones). As a personal example, my current PhD studies are in Inductive Logic Programming which is, in short, machine-learning of logic programs (you know, Prolog etc). I would not be able to publish any papers without a theoretical section with actual theoretical results (i.e. theorems and their proofs - and it better be a theorem other than "more parameters beget better accuracy", which is not really a theorem). Reviewers would just reject such a paper without second thought, regardless of how many leaderboards I beat in my empirical results section. And of course there are all the other fields of AI were work continues - search, classical planning, constraint satisfaction, automated theorem proving, knowledge engineering and so on and so forth. Bottom line- the shoddy scholarship you flag up does not characterise the field of AI research, as a whole, it only afflicts a majority of modern deep learning research.
eli_gottlieb 6y ago
>Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. Brainfuck is also Turing complete, so logically if we just do, for instance, Markov chain Monte Carlo for Bayesian program learning in Brainfuck, we can realize AGI that way. "Everything is possible, but nothing is easy." The Turing tarpit.

ForHackernews 6y ago

> Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. This seems like overstating your point. Nobody has been able to rigorously define "AI" yet, so there's no way of saying whether it's possible with a Turing machine architecture. The human brain, at least, doesn't seem that similar to a Turing architecture. Neurons don't carry out anything like discrete operations. Maybe it's possible to run AGI on a Turing machine, maybe it's not, but there are more options than simply "possible with a Turing machine" or "completely impossible".

GregarianChild 6y ago
While I agree that we don't have an agreed upon definition of AI, the problem is firmly in the "I" part of AI! The "A" part is taken to mean implementable by a computer, i.e. a Turing machine. This is the content of the Church–Turing thesis [1]. [1] https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis
- ForHackernews 6y ago
  > It states that a function on the natural numbers can be calculated by an effective method if and only if it is computable by a Turing machine. The article at the top of this thread is specifically about properties ("The Generalized Archimedean Property") that real numbers do not possess. There's also a little bit of slipperiness around the use of "AI" vs. "AGI" - you could easily argue (and people do!) that we've already achieved "AI" for many specialized domains. It's the General bit that seems to be the sticking point, and that this article focuses on.
joe_the_user 6y ago
Maybe it's possible to run AGI on a Turing machine, maybe it's not, Arguing from ignorance, of course nothing is knowable for certain. However there has been a lot of work on the universality of Turing machines, showing that a Turing machine can simulate any conceivable concept of finite computation and can approximate any conventional physical system. I think a more useful way to express your intuition is to note that if human-built AGI comes into existence, it might be runnable on a Turing machine but quite possibly not efficiently so.

fsuuttg 6y ago

Why does it matter? Turing completeness is a low bar. Both exp(exp(N)) and log(log(N)) algorithms solve the AI problem in a finite number of steps, but one of them is a useless abstraction, while the other one really works.

YeGoblynQueenne 6y ago

Note that Turing complete means undecidable. An algorithm that can learn any program that can be computed by a Universal Turing Machine must, in the worst case, search an infinite program space. So, even if a Neural Turing Machine can learn arbitrary programs (I haven't read the paper so I can't say) it might need to consume infinite resources before learning any particular progarm. In short- Turing completeness is no guaranteed path to AGI. Assuming an "AGI program" exists, it is hidden away in an infinity of almost identical, but not quite, programs.

deleted 6y ago

[deleted]

bra-ket 6y ago

>Where is .. AI it's always 5 years away because mainstream AI researches are stuck with yak shaving their gradient descents. I mean you can't just throw things at the wall and hope they stick, but it's literally the state of the art, if you follow ML conferences and their world-beating toy benchmarks results, with a lot of pseudo-rigorous handwaving for theory. The reason physics has been so successful is that their theory closely followed empirical data and constraints imposed by nature. I think the only hope to achieve common sense in AI is to align it with hard constraints living organisms have, using those constraints as a guide. A few terms I mentioned are coming from that POV, if you dig a bit deeper they all have direct physical manifestation in natural learning systems.

YeGoblynQueenne 6y ago
>> it's always 5 years away because mainstream AI researches are stuck with yak shaving their gradient descents. A small correction: that's deep learning researches, not AI researchers and not all machine learning researchers even. To be charitable, it's not even all deep learning researchers. It's just that the field of deep learning research has been inundated with new entrants who are sufficiently skilled to grok the practicalities but lack understanding of AI scholarship and produce unfortunately shoddy work that does not advance the field (any field, any of the aforementioned ones). As a personal example, my current PhD studies are in Inductive Logic Programming which is, in short, machine-learning of logic programs (you know, Prolog etc). I would not be able to publish any papers without a theoretical section with actual theoretical results (i.e. theorems and their proofs - and it better be a theorem other than "more parameters beget better accuracy", which is not really a theorem). Reviewers would just reject such a paper without second thought, regardless of how many leaderboards I beat in my empirical results section. And of course there are all the other fields of AI were work continues - search, classical planning, constraint satisfaction, automated theorem proving, knowledge engineering and so on and so forth. Bottom line- the shoddy scholarship you flag up does not characterise the field of AI research, as a whole, it only afflicts a majority of modern deep learning research.

eli_gottlieb 6y ago

>Reinforcement learning is Turing complete [1], so if AI is possible at all, then it can be realised through RL. Brainfuck is also Turing complete, so logically if we just do, for instance, Markov chain Monte Carlo for Bayesian program learning in Brainfuck, we can realize AGI that way. "Everything is possible, but nothing is easy." The Turing tarpit.