I would say be flexible as some topics are much more complex than others. I’ve found that most summaries on this list have a good length.
RaelwayScot
Happy Longevity Day!
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/
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.
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.
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?
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?
Ok, so the motivation is to learn templates to do correlation at each image location with. But where would you get the idea from to do the same with the correlation map again? That seems non-obvious to me. Or do you mean biological vision?
Do Bayesianists strongly believe that the Bayes’ theorem accurately describes how the brain changes its latent variables in face of new data? It seems very unlikely to me that the brain keeps track of probability distributions and that they sum up to one. How do Bayesianists believe this works at the neuronal level?
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.
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.
Is that actually the ‘strange loop’ that Hofstadter writes about?
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)?
I mean a moral terminal goal. But I guess we would be a large step closer to a solution of the control problem if we could specify such a goal.
What I had in mind is something like this: Evolution has provided us with a state which everyone prefers who is healthy (who can survive in a typical situation in which humans have evolved with high probability) and who has an accurate mental representation of reality. That state includes being surrounded by other healthy humans, so by induction everyone must reach this state (and also help others to reach it). I haven’t carefully thought this through, but I just want to give an idea for what I’m looking for.
What is the motivation behind maximizing QUALY? Does it require certain incentives to be present in the culture (endorsement of altruism) or is it rooted elsewhere?
More why doing it is desirable at all. Is it a matter of the culture that currently exists? I mean, is it ‘right’ to eradicate a certain ethnic group if the majority endorses it?
Because then it would argue from features that are built into us. If we can prove the existence of these features with high certainty, then it could perhaps serve as guidance for our decisions.
On the other hand, it is reasonable that evolution does not create such goals because it is an undirected process. Our actions are unrestricted in this regard, and we must only bear the consequences of the system that our species has come up with. What is good is thus decided by consensus. Still, the values we have converged to are shaped by the way we have evolved to behave (e.g. empathy and pain avoidance).
What are the implications of that on how we decide what is are the right things to do?
Moral philosophy is a huge topic and it’s discourse is not dominated by looking at DNA.
Everyone can choose their preferred state then, at least to the extent it is not indoctrinated or biologically determined. It is rational to invest energy into maintaining or achieving this state (because the state presumably provides you with a steady source of reward), which might involve convincing others of your preferred state or prevent them from threating it (e.g. by putting them into jail). There is likely an absolute truth (to the extent physics is consistent from our point of view), but no absolute morale (because it’s all memes in an undirected process). Terrorists do nothing wrong from their point of view, but from mine it threatens my preferred state, so I will try to prevent terrorism. We may seem lucky that many preferred states converge to the same goals which are even fairly sustainable, but that is just an evolutionary necessity and perhaps mostly a result of empathy and the will to survive (otherwise our species wouldn’t have survived in paleolithic groups of hunters and gatherers).
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/