Why does E. Yudkowsky voice such strong priors e.g. wrt. the laws of physics (many worlds interpretation), when much weaker priors seem sufficient for most of his beliefs (e.g. weak computationalism/computational monism) and wouldn’t make him so vulnerable? (With vulnerable I mean that his work often gets ripped apart as cultish pseudoscience.)
RaelwayScot
Here they found dopamine to encode some superposed error signals about actual and counterfactual reward:
http://www.pnas.org/content/early/2015/11/18/1513619112.abstract
Could that be related to priors and likelihoods?
Significance
There is an abundance of circumstantial evidence (primarily work in nonhuman animal models) suggesting that dopamine transients serve as experience-dependent learning signals. This report establishes, to our knowledge, the first direct demonstration that subsecond fluctuations in dopamine concentration in the human striatum combine two distinct prediction error signals: (i) an experience-dependent reward prediction error term and (ii) a counterfactual prediction error term. These data are surprising because there is no prior evidence that fluctuations in dopamine should superpose actual and counterfactual information in humans. The observed compositional encoding of “actual” and “possible” is consistent with how one should “feel” and may be one example of how the human brain translates computations over experience to embodied states of subjective feeling.
Abstract
In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.
If we are in a simulation, why isn’t the simulation more streamlined? I have a couple of examples for that:
Classical physics and basic chemistry would likely be sufficient for life to exist.
There are seven uninhabitable planets in our solar system.
99.9…% of everything performs extremely boring computations (dirt, large bodies of fluids and gas etc.).
The universe is extremely hostile towards intelligent life (GRBs, supernovae, scarcity of resources, large distances between celestial body).
It seems that our simulation hosts would need to have access to vast or unlimited resources. (In that case it would be interesting to consider whether life is sustainable in a world with unlimited resources at all. Perhaps scarcity is somehow required for ethical behavior to develop; malice would perhaps spread too easily.)
I’m a big fan of these infographics by the way.
I meant that for AI we will possibly require high-level credit assignment, e.g. experiences of regret like “I should be more careful in these kinds of situations”, or the realization that one particular strategy out of the entire sequence of moves worked out really nicely. Instead it penalizes/enforces all moves of one game equally, which is potentially a much slower learning process. It turns out playing Go can be solved without much structure for the credit assignment processes, hence I said the problem is non-existent, i.e. there wasn’t even need to consider it and further our understanding of RL techniques.
Then which blogs do you agree with on the matter of the refugee crisis? (My intent is just to crowd-source some well-founded opinions because I’m lacking one.)
Some helpful links I’ve collected over the years:
https://imgur.com/gallery/pHUdq (Actual poor student cookbook)
https://news.ycombinator.com/item?id=7673628 (A free cookbook for people living on $4/day)
https://news.ycombinator.com/item?id=8181101 (The most common errors in undergraduate mathematics)
If you do something related to computer science:
https://news.ycombinator.com/item?id=8085148 (work on some side projects, for example program an economy simulator, invent a simple layout/markup language, implement a LISP-machine in C)
Get familiar with the UNIX command line, learn VIM and use Spacemacs as editor. Use org-mode for notes and git/magit for version control of all your projects and notes. Make use of a cloud service to keep all your files accessible from all your devices.
As someone who has developed RSI during their studies: If you feel that you don’t have enough time to exercise, ignore that voice in your head and get a minimum amount of 7 minutes intense workout and 1 hour of very light exercise (e.g. walking, cycling or swimming) each day (and consider two sessions of longer intense workout per week, e.g. 60 min. swimming lessons). After each hour of sitting do a 5 minute break to stretch or drink a tea (Awareness.app for OS X is a nice software solution that can help you with that). A bad physical condition will affect your mood and mental performance negatively.
Quite good Omega Tau interview on failure modes of mega projects: http://omegataupodcast.net/2015/09/181-why-megaprojects-fail-and-what-to-do-about-it/
Happy Longevity Day!
[Link] Audio recording of Stephen Wolfram discussing AI and the Singularity
What is your preferred backup strategy for your digital life?
I agree. I don’t find this result to be any more or less indicative of near-term AI than Google’s success on ImageNet in 2012. The algorithm learns to map positions to moves and values using CNNs, just as CNNs can be used to learn mappings from images to 350 classes of dog breeds and more. It turns out that Go really is a game about pattern recognition and that with a lot of data you can replicate the pattern detection for good moves in very supervised ways (one could call their reinforcement learning actually supervised because the nature of the problem gives you credit assignment for free).
What are your thoughts on the refugee crisis?
Just speaking of weaknesses of the paperclip maximizer though experiment. I’ve seen this misunderstanding at least 4 out of 10 times that the thought experiment was brought up.
I would love to seem some hard data about correlation between the public interest in science and it’s degree of ‘cult status’ vs. ‘open science’.
Is there a biological basis that explains that utilitarianism and preservation of our species should motivate our actions? Or is it a purely selfish consideration: I feel well when others feel well in my social environment (and therefore even dependent on consensus)?
Perhaps the conditions that cause the Fermi paradox are actually crucial for life. If spaceflight was easy, all resources would have been exhausted by exponential growth pretty quickly. This would invalidate the ‘big distances’ point as evidence for a non-streamlined universe, though.
Perhaps you can revive one of these study groups: https://www.reddit.com/subreddits/search?q=spivak
Cross-posting to all of them might reach some people who are interested.
This Baby Rudin group is currently active: https://www.reddit.com/r/babyrudin/
I find CNNs a lot less intuitive than RNNs. In which context was training many filters and successively apply pooling and again filters to smaller versions of the output an intuitive idea?
Could one say that the human brain works best if it is slightly optimistically biased, just enough to have benefits of the neuromodulation accompanied with positive thinking, but not so much that false expectations have a significant potential to severely disappoint you? Are there some recommended sequences/articles/papers on this matter?
Demis Hassabis has already announced that they’ll be working on a Starcraft bot in some interview.