4 ms·

Au contraire, AlphaGo made several “counterintuitive” moves that professional Go players thought were mistakes during the play, but turned out to be great strat

Au contraire, AlphaGo made several “counterintuitive” moves that professional Go players thought were mistakes during the play, but turned out to be great strategic moves in hindsight. The (in)ability to recognize a strange move’s brilliance might depend on the complexity of the game. The real world is much more complex than any board game. https://en.m.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol https://en.m.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol

tonyhart7 1y ago
I think its doable tbh, if you pour enough resources (smart people,energy,compute power etc) like the entire planet resources of course we can have AGI (damned if we don't) because we put so much, it better works but the problem we cant do that right because its so expensive, AGI is not matter of if but when but even then it always about the cost
- jsrozner 1y ago
  There may be philosophical (i.e. fundamental) challenges to AGI. Consider, e.g., Godel's Incompleteness Theorem. Though Scott Aaronson argues this does not matter (see e.g., youtube video, "How Much Math Is Knowable?"). There would also seem to be limits to the computation of potentially chaotic systems. And in general, verifying physical theories has required the carrying out of actual physical experiment. Even if we were to build a fully reasoning model, "pondering" is not always sufficient.
  - tonyhart7 1y ago
    Yeah but its robotic industry part of works not this company they just need to "MCP" it to robot body and it works (also part of reason why OpenAI buys a robotic company)
  - gizajob 1y ago
    It’s also easy to forget that “reason is the slave of the passions” (Hume) - a lot of what we regard as intelligence is explicitly tied to other, baser (or more elevated) parts of the human experience.
neltnerb 1y ago
That's great, but AlphaGo used artificial and constrained training materials. It's a lot easier to optimize things when you can actually define an objective score, and especially when your system is able to generate valid training materials on its own.
- nopinsight 1y ago
  There are quite a few relatively objective criteria in the real world: real estate holdings, money and material possessions, power to influence people and events, etc. The complexity of achieving those might result in the "Centaur Era", when humans+computers are superior to either alone, lasting longer than the Centaur chess era, which spanned only 1-2 decades before engines like Stockfish made humans superfluous. However, in well-defined domains, like medical diagnostics, it seems reasoning models alone are already superior to primary care physicians, according to at least 6 studies. Ref: When Doctors With A.I. Are Outperformed by A.I. Alone by Dr. Eric Topol https://substack.com/@erictopol/p-156304196 https://substack.com/@erictopol/p-156304196
  - jimbo808 1y ago
    It makes sense. People said software engineers would be easy to replace with AI, because our work can be run on a computer and easily tested, but the disconnect is that the primary strength of LLMs is that they can draw on huge bodies of information, and that's not the primary skill programmers are paid for. It does help programmers when you're doing trivial CRUD work or writing boilerplate, but every programmer will eventually have to be able to actually truly reason about code, and LLMs fundamentally cannot do that (not even the "reasoning" models). Medical diagnosis relies heavily on knowledge, pattern recognition, a bunch of heuristics, educated guesses, luck, etc. These are all things LLMs do very well. They don't need a high degree of accuracy, because humans are already doing this work with a pretty low degree of accuracy. They just have to be a little more accurate.
    - Davidzheng 1y ago
      I think at this point it's an absurd take that they aren't reasoning. I don't think without reasoning about code (& math) you can get to such high scores on competitive coding and IMO scores. Alphazero also doesn't need training data as input--it's generated by game-play. The information fed in is just game rules. Theoretically should also be possible in research math. Less so in programming b/c we care about less rigid things like style. But if you rigorously defined the objective, training data should also be not necessary.
melagonster 1y ago
I want to indicate that the time length of "during the play" is only 5 moves in the game.
- Davidzheng 1y ago
  No? some of the opening moves took experts thorough analysis to figure out were not mistakes. even in game 1 for example. not just the move 37 thing. Also thematic ideas like 3x3 invasions.
cmenge 1y ago
That's a good point, but I doubt that Sonnet adding a very contrived bug that crashes my app is some genius move that I fail to understand. Unless it's a MUCH bigger play where through some butterfly effect it wants me to fail at something so I can succeed at something else. My real name is John Connor by the way ;)
- philipwhiuk 1y ago
  ASI is here and it's just pretending it can't count the b's in blueberry :D
- futasuki 1y ago
  Thanks, this made my day :-D

tonyhart7 1y ago

I think its doable tbh, if you pour enough resources (smart people,energy,compute power etc) like the entire planet resources of course we can have AGI (damned if we don't) because we put so much, it better works but the problem we cant do that right because its so expensive, AGI is not matter of if but when but even then it always about the cost

jsrozner 1y ago
There may be philosophical (i.e. fundamental) challenges to AGI. Consider, e.g., Godel's Incompleteness Theorem. Though Scott Aaronson argues this does not matter (see e.g., youtube video, "How Much Math Is Knowable?"). There would also seem to be limits to the computation of potentially chaotic systems. And in general, verifying physical theories has required the carrying out of actual physical experiment. Even if we were to build a fully reasoning model, "pondering" is not always sufficient.
- tonyhart7 1y ago
  Yeah but its robotic industry part of works not this company they just need to "MCP" it to robot body and it works (also part of reason why OpenAI buys a robotic company)
- gizajob 1y ago
  It’s also easy to forget that “reason is the slave of the passions” (Hume) - a lot of what we regard as intelligence is explicitly tied to other, baser (or more elevated) parts of the human experience.

neltnerb 1y ago

That's great, but AlphaGo used artificial and constrained training materials. It's a lot easier to optimize things when you can actually define an objective score, and especially when your system is able to generate valid training materials on its own.

nopinsight 1y ago
There are quite a few relatively objective criteria in the real world: real estate holdings, money and material possessions, power to influence people and events, etc. The complexity of achieving those might result in the "Centaur Era", when humans+computers are superior to either alone, lasting longer than the Centaur chess era, which spanned only 1-2 decades before engines like Stockfish made humans superfluous. However, in well-defined domains, like medical diagnostics, it seems reasoning models alone are already superior to primary care physicians, according to at least 6 studies. Ref: When Doctors With A.I. Are Outperformed by A.I. Alone by Dr. Eric Topol https://substack.com/@erictopol/p-156304196 https://substack.com/@erictopol/p-156304196
- jimbo808 1y ago
  It makes sense. People said software engineers would be easy to replace with AI, because our work can be run on a computer and easily tested, but the disconnect is that the primary strength of LLMs is that they can draw on huge bodies of information, and that's not the primary skill programmers are paid for. It does help programmers when you're doing trivial CRUD work or writing boilerplate, but every programmer will eventually have to be able to actually truly reason about code, and LLMs fundamentally cannot do that (not even the "reasoning" models). Medical diagnosis relies heavily on knowledge, pattern recognition, a bunch of heuristics, educated guesses, luck, etc. These are all things LLMs do very well. They don't need a high degree of accuracy, because humans are already doing this work with a pretty low degree of accuracy. They just have to be a little more accurate.
  - Davidzheng 1y ago
    I think at this point it's an absurd take that they aren't reasoning. I don't think without reasoning about code (& math) you can get to such high scores on competitive coding and IMO scores. Alphazero also doesn't need training data as input--it's generated by game-play. The information fed in is just game rules. Theoretically should also be possible in research math. Less so in programming b/c we care about less rigid things like style. But if you rigorously defined the objective, training data should also be not necessary.

melagonster 1y ago

I want to indicate that the time length of "during the play" is only 5 moves in the game.

Davidzheng 1y ago
No? some of the opening moves took experts thorough analysis to figure out were not mistakes. even in game 1 for example. not just the move 37 thing. Also thematic ideas like 3x3 invasions.

cmenge 1y ago

That's a good point, but I doubt that Sonnet adding a very contrived bug that crashes my app is some genius move that I fail to understand. Unless it's a MUCH bigger play where through some butterfly effect it wants me to fail at something so I can succeed at something else. My real name is John Connor by the way ;)

philipwhiuk 1y ago
ASI is here and it's just pretending it can't count the b's in blueberry :D
futasuki 1y ago
Thanks, this made my day :-D