The Bitter Lesson (2019)
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The Bitter Lesson (2019)
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Re: The Bitter Lesson (2019)
#2You have to let you chAIld learn for itself.
Re: The Bitter Lesson (2019)
#3This is a philosophical and epistemological matter that is often undiscussed. Cognitive philosophers are still hung up on questions from the 70s and their offshoots ("hard problem") etc.
On the other hand i am not sure if the "computational" people often know what they are doing. Looking at something like the deep Transformer models, one has to ask if there is any rhyme or reason there, or the thing just works because it's too big and deep. Same even with gradient descent methods, are we sure there aren't closed form solutions instead?
There s an even more pessimistic view of this: that the brain and its creations (language, formal systems etc) are resting on the chaos of spiking cells, and are not as ideal as I 'd like them to be.
Re: The Bitter Lesson (2019)
#4https://en.wikipedia.org/wiki/The_Lifecycle_of_Software_Obje...
Fiction, but closer to reality now than it was when it was written.
Re: The Bitter Lesson (2019)
#5Four very interesting concepts in a short space:
- "general methods that leverage computation are ultimately the most effective, and by a large margin"
- "[search and learning are] methods that continue to scale with increased computation"
- "We should stop trying to find simple ways to think about the contents of minds"
- "We want AI agents that can discover like we can, not which contain what we have discovered"
In other words, computer programs should stop trying be something they are not. They are not AI. Computers are expensive machines that can (very efficiently, and with economies of scale) calculate and present anything that the author of the software desires. It takes actual human intelligence, economics, and ethics to translate that into action.
Re: The Bitter Lesson (2019)
#6> [the critics of brute force] said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess.
No expert in AI or chess, but I assume that this observation is wrong. Chess players do indeed construct moves based on a search of an interior mental library... a sort of pattern recognition. Any chess players here agree/disagree?
As an artist who is amazed at some of the AI art coming out, I can tell you that this is how painters make their paintings. AI is just emulating this. Of course there are differences: AI is not aware of culture, society, new technologies etc. etc. It is also in a closed loop of reference.
My colleagues and I are now envisioning a future where AI image rule the roost, but is calling upon the same 'bucket' of human-made existing art. Eventually AI will start using other AI art as reference, and will dissolve into a sort of 'Lorem ipsum' state: an impression of sense without sense. Indeed, it might be argued that to a degree this has already happened.
Re: The Bitter Lesson (2019)
#7I think this bitter lesson needs to be taken for a several grains of salt.
Number one, the progress in a particular AI field tends to go, at first, from custom to more general algorithms, exactly as Professor Richard Sutton described. However, there is a second part to this progress, where, once we "understood" (which we never really do) the new level of general algorithms (say Transformers in NLP), we begin to put back in the all the things we learned before (say, from Linguistics experience, we put the bias towards compositionality and corresponding tree structures back into the Transformers).
Number two, the computationally scalable algorithms always win in the environments where you have unlimited access to the computation and the data, i.e. if you working for Google, Facebook, Alibaba, etc... In other companies, you have limited computational budget and limited data. You could end up putting back-in a lot of sophisticated inductive biases back into your DL algorithms.
Re: The Bitter Lesson (2019)
#8>The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation.
That doesn't sound right.
If there's an "ultimate reason" for computation AI success, is that the problem, of thought, signal processing, pattern matching etc, is not "rational" or based on semantic manipulation, but computational in its nature/substrate in humans too.
Computation being increasingly cheaper wouldn't guarantee a success in AI, if those problems weren't inherently solvable by mere throwing computation at them.
That is, it's not like we solve it with a brute force over a "proper semantic" approach because brute force got cheaper. It's more like the proper approach is more like our computation one, than a semantic one, and we wrongly assumed that this is not the case in the 70s and 80s, because we only considered the higher levels of conscious/semantic processing in our brains, and not the deep computational processing underneath them.
>They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess.
I'd think that most chess playing in humans happens at an uncoscious level, after the player has "trained" their brain, than actual conscious semantic arguing about the next move. The conscious manipulation comes only after huge swaths of moves have been pruned through pattern matching/computation going on in the background.
Re: The Bitter Lesson (2019)
#9> [the critics of brute force] said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. No expert in AI or chess, but I assume that this observation is wrong. Chess players do indeed construct moves based on a search of an interior mental library... a sort of pattern recognition. Any chess players here agree/disagree? As an artist who is…
Pattern recognition and brute force search are not the same thing. Deep Blue was literally trying every possible move many moves deep, with some heuristics to avoid wasting time on obviously bad options. Humans definitely don't do that.
Re: The Bitter Lesson (2019)
#10> [the critics of brute force] said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. No expert in AI or chess, but I assume that this observation is wrong. Chess players do indeed construct moves based on a search of an interior mental library... a sort of pattern recognition. Any chess players here agree/disagree? As an artist who is…
Pattern recognition and brute force search are not the same thing. Deep Blue was literally trying every possible move many moves deep, with some heuristics to avoid wasting time on obviously bad options. Humans definitely don't do that.
Noted. I appreciate the distinction that you are trying to make.
I guess that my point is that humans are not true problem solvers. Rather they are 'solution apply-ers', with the solutions being permutations of existing material.