Edit: It seems I am shadow banned. I am going to take this as a deep insult.
190 karma · joined May 13, 2019
Edit: It seems I am shadow banned. I am going to take this as a deep insult.
HC's role in episodic memory and consolidation via dreams seems kinda plausible, though I would not put much weight on it. I think dreams are a way of training a GAN-like discrimination between reality and imagination:
http://gershmanlab.webfactional.com/pubs/GenerativeAdversari...
Repetition of any kind likely does improve the model, even if it's merely simulation/dreaming.
> AlphaGo's Monte Carlo search + neural network
I think, in effect, MCTS amounts to something like bagging/boosting/mixture of experts, as it computes a weighted average of the predictions when exploring different branches. But sure, the search mechanism implements a function which a recurrent neural network could probably not discover as it hides behind substantial discontinuities in fitness landscape (it's not a structure which you can uncover step by step, but you immediately need tree structure, a search recursion etc.). The RNN would likely need to conceptualize the search process (subvocally but) linguistically like humans do, which requires structure for the sequential composition of stable prototypes (symbols) which likely requires a one-shot sequential memory. I think even the human mind does not literally do MCTS (would require an overhead that the brain is just not capable of), but some heuristic approximation thereof. The brain can simulate MCTS by linguistic means, though, even if it's just words of wisdom like "take counsel with your pillow", which literally means explore the hypothesis space some more and let the temporal differences backup better value estimates.
So you're only using something like i3, XMonad or dwm?
The most striking result in this regard is that one can get backprop with random backprojections. It works regardless because, on average, the error vector will be less than 90° from the true error vector (which is good enough for hillclimbing) and the dynamics play out in a way that the learned weights adjust to the random projections:
https://www.nature.com/articles/ncomms13276
That being said, it does seem to be the case that the brain simply memorizes an awful lot which must work by a different mechanism besides backprop because backprop cannot do one-shot learning. I think one-shot learning is how the brain gets past large discontinuities in the model fitness landscape: It can learn linguistic, logical rules, fragments of general computations and facts that are discovered by exploration (which includes learning about whether it was Bob or Mike) and passed on culturally. The brain basically outsources the problem of tunneling through large discontinuities, to cultural/individual trial-and-error and episodic memory. The greatest consequence is that these bodies of knowledge can concern the improvement of the organization of knowledge itself, resulting in a positive feedback loop in model fitness (especially science/Bayesian updating). Though such bodies of knowledge evolve respecting a learnability-by-hillclimbing soft constraint, which implies they often form a neat latent space where similar codes are organized to belong to similar meanings/representations, as this can easily be learned by stochastic hillclimbing (repetition) because each time the brain processes related information, it is nudged towards the latent space that is meant to be learned. Many parts of the world happen to be learnable in this way because everything is kinda smooth and continuous. Small causes tend to have small effects as everything consists of a myriad of small particles that affect each other in smooth ways if you squint at them. Though obviously not everything can be learned this way (implying large discontinuities) which is where a brute memorization based on reward and punishment comes in handy.
The entire fiasco was mainly caused by the obsession of the media to put women at the forefront.
https://www.bitchute.com/video/WycgzQE8t4LS/
... rules out (2). Technology is not advanced enough for (3). So (1) is currently the most likely: https://www.reddit.com/r/13or30/
Though you've missed the less likely option 4), an extraordinarily gifted 13-yo girl spending too much time on the internet. Some kids are amazingly smart.
Also interesting that there are seemingly no solutions without loose circles beyond 91.
https://pbs.twimg.com/media/CA7zXLWUIAAaSfS?format=png&name=...
We'd need to drive way less, stop sending products overseas, build high speed underground trains. Even more importantly we'd need to make sure that India and Africa are going to follow the same principles because they're going to exceed the consumption of the West in future. That being said, I do not care because I do not plan to have children.
https://dzone.com/articles/protocols-and-standards-behind-io...
1. There is confidential information that the damage is irreversible at this point (e.g. permafrost is melting three times faster and 70 years earlier than expected) and any attempts to break the layers upon layers of prisoner's dilemmas are completely futile.
2. The political caste does not care about more than 20 years into the future because most of them will have died by then (avg. age of the conservative parties is 60(!) [1]).
3. The dangers of a manmade climate catastrophe are being overstated.
[1] https://de.statista.com/statistik/daten/studie/192255/umfrag...