An AGI prediction on the bold end (5 years, because Deep Learning carries us all the way), by @IntuitMachinehttps://medium.com/intuitionmachine/near-term-agi-should-be-considered-as-a-possibility-9bcf276f9b16 …
-
-
Replying to @Plinz @IntuitMachine
It seems to imply that we’re just missing the compute power. An optimistic prediction but the cynical in me says there is more to it than brute force. When true AGI is developed, I suspect it will be through novel techniques/algorithms we may or may not have discovered yet.
2 replies 0 retweets 1 like -
Replying to @FieryPhoenix7 @IntuitMachine
The argument is based on the insight that neural networks can represent any computable function, and the hypothesis that RL can approximate all the functions we need. Even if our RL algorithms are not very efficient, we may possibly derive better ones with RL.
1 reply 0 retweets 0 likes -
Replying to @Plinz @IntuitMachine
I understand that now. Was not my original takeaway. Apologies for the confusion.
1 reply 0 retweets 0 likes -
Replying to @FieryPhoenix7 @IntuitMachine
I think that your objection is possibly valid, and it is not clear to me if current RL can efficiently reach every solution we need, even though it seems not impossible
1 reply 0 retweets 0 likes -
Replying to @Plinz @FieryPhoenix7
Isn't that the crux of the argument? That is that DRL is similarly scalable as DL and therefore there are no more obstacles other than compute? I believe there is a conceptual obstacle, but I don't think it's a big hurdle!
1 reply 0 retweets 0 likes -
Replying to @IntuitMachine @Plinz
There is a good chance we're witnessing a second incarnation of the Church-Turing thesis. My main concern is unsupervised learning: would extra compute solve that particular problem? I don't know, but from the outset it would appear as a primarily conceptual obstacle.
2 replies 0 retweets 0 likes -
Replying to @FieryPhoenix7 @Plinz
This isn't a problem that relates to undecidability. This is learning what is knowable and the problem is that we don't know an efficient algorithm for learning. This brute force can potentially discover this algorithm.https://medium.com/intuitionmachine/the-explosive-ramifications-of-combining-intuition-with-logic-in-deep-learning-610f6f3477da …
1 reply 0 retweets 0 likes -
Replying to @IntuitMachine @FieryPhoenix7
Undecidability is a big red herring caused by the timeless definition of truth semantics in classical mathematics. In a fully computational perspective, it resolves to incompressibility: math attempts a lossless compression to axioms, but ignores realizability of the compression
1 reply 0 retweets 2 likes -
Replying to @Plinz @FieryPhoenix7
I am indeed curious as to why so many researchers latch on to undecidability as a problem that relates to general intelligence? Perhaps you may have a good explanation.
3 replies 1 retweet 1 like
I think it stems from the intuition that people can do classical mathematics, and the proof that classical mathematics can do more than computation, without recognizing the undecidability theorem as proof that these parts of classical mathematics don't actually work.
-
-
Replying to @Plinz @FieryPhoenix7
A rational delusion indeed. In this regards, what's your impression of Hutter's AIXI work?
0 replies 0 retweets 0 likesThanks. Twitter will use this to make your timeline better. UndoUndo
-
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.