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Why “fundamentally unsuited”? Neural networks have solved tons of problems previously thought to be “too hard” for ML, e.g. playing Go.
by halpmeh 4y ago
Why “fundamentally unsuited”? Neural networks have solved tons of problems previously thought to be “too hard” for ML, e.g. playing Go.
- b4je7d7wb 4y agoGo is not solved. The AI doesn't know the best move. It just knows a good move.
- FeepingCreature 4y agoAnd yet, Go AIs are now unbeatable by humans. This demonstrates that "solved" is unreasonable and unnecessary.
- BurningFrog 4y agoCars are much faster than humans. That doesn't mean transportation is solved.
- deleted 4y ago[deleted]
- pessimizer 4y ago> That doesn't mean transportation is solved. What are you even talking about? edit: if we call a cab to get us to the restaurant, and it arrives successfully and takes us to the restaurant, transportation was solved.
- halpmeh 4y ago“Hmm how should I get to work tomorrow? Normally I’d take the car, but after adopting a stance of distractive pedantism I realized that a car isn’t an acceptable solution to my transportation problem.” Like please explain what definition of solved you are using. It’s not one most people would be familiar with.
- remram 4y agoSolved means you have the best solution.
- halpmeh 4y agoWhat is better than a car for people who don't have access to good public transportation?
- remram 4y agoThe fact that we don't have a better solution now is why it's worth researching. We only have modern cars because we didn't consider transportation "solved" when we had horse-pulled carriages. Many people are looking for better solutions right now, because we believe they certainly exist.
- halpmeh 4y agoIsn’t that true of everything? There may always be something better in the future. It’s ridiculous to say that we can’t have solutions to anything in the present because there may be better options in the future.
- remram 4y agoNo, that is not true of solved problems and solved games in the mathematical sense, which is what we're talking about. E.g. tic-tac-toe is a solved game, no better solution can be devised.
- halpmeh 4y agoOk, then please prove that cars are not the solution to transportation. Obviously, you can't, because transportation is not a game with well-defined rules. Detecting the truth is also not a game with well-defined rules. So obviously we're using a different definition of "solved" than the one used in game theory. QED
- seanhunter 4y agoSolved in game theory has a very specific, strong definition. Transportation isn't a game in the game theoretic sense.
- pessimizer 4y agoYou're equivocating on "solved." Solved as in performing as well as humans, not solved in the mathematical sense which is both 1) not necessarily possible, and 2) nothing anybody has ever named as a test for AI.
- Animats 4y agoNo, that's correct. Checkers is solved; there is an algorithmic solution. Chess and Go have computer systems that exceed human performance, but are not solved.
- halpmeh 4y ago"Solved" means having a solution to a problem. In context, we're talking bout whether or not neural networks can detect truth better than "decades of work by experts to reach consensus." So, in this case, solving would be detecting truth better than the status quo, not detecting truth 100% of the time. In the example of Go, the problem was "playing Go better than the best humans." So in that sense, the problem was solved. Adding your own, unfavorable definition of "solved" to the discussion is unwarranted.
- skybrian 4y agoFundamentally unsuited because of how they train it using "fill in the blank." Training a large model to guess when it doesn't know the answer results in fiction. They need to do something else to get nonfiction. By contrast, for Go the model was trained not to make illegal moves, because checking for that as part of the training is easy and cheap.
- halpmeh 4y agoWe have models that accurate classify things, e.g. whether or not an email is spam. There isn’t a fundamental limitation into building something like a truth classifier into a generative model so that it optimized for outputting “true” statements. The hardest part is probably identifying what is truth and what is falsehood. That’s a fundamental problem with humanity, not neural networks.
- skybrian 4y agoWell, we could quibble about what "fundamental" means but my point is that the way they train large language models doesn't work for this. Something different needs to happen.
- nextaccountic 4y ago> There isn’t a fundamental limitation into building something like a truth classifier into a generative model so that it optimized for outputting “true” statements. Problem is, they didn't do that
- imtringued 4y agoTruth has nothing to do with humanity unless you mean the specific way humans construct belief systems. Anyway I already told you the answer. The AI will need a series of trainable belief systems to verify whether statements are internally consistent. The strange part about this is that the AI would need to have a way to obtain validation and each prompt would have to derive a new belief system which you must use in the next prompt. In other words, the model must be able to learn continuously. That is something that these single shot AI models are not capable of.
- mannykannot 4y agoNote that we are not talking about neural networks in general, but specifically the sort of generative autoregressive language model that Galactica is. What reason do we have to think that such a model is more likely to produce a true statement than a false one? - especially as just one misplaced truth-valued function or operator is likely to turn a true proposition into a false one. Truthfulness (not to be confused with truthiness) of their productions does not seem to be something we should expect from how they work, and the empirical evidence from Galactica supports this view.