HN user
There is a huge memory leak in Preview since El Capitan and the display of PDFs sometimes fails when you zoom in. I have no idea how many users are affected by this, but it seems strange that Apple hasn't fixed it yet. Perhaps it is just a tricky bug.
I somehow doubt that this is just a photo stylized by a computer. Aren't the eyes way out of proportion? http://turing.deepart.io/f/0.png
Wait, so Steve Jobs wasn't a visionary. He was just a salesman exploring unexploited markets as any salesman does? Albert Einstein wasn't a visionary. He was just a physicist that explored unconsidered theories as any physicist does?
It seems to me that in philosophy there is just as much "groupthink" as in almost any human endeavor—there is a maybe little less in the hard sciences where the systems give you feedback about whether an idea is correct or not.
In a recent estimate of the bits per synapse they found it to be an order of magnitude higher than previous estimates: http://www.eurekalert.org/pub_releases/2016-01/si-mco012016....
Skim.app is very suitable for Latex PDF preview (it even supports syncing given you have an Editor that supports it too, e.g. Emacs AUCTex or TexShop).
Care to list those reasons. Genuinely interested, and not sure which reasons you are referring to.
The difference isn't easy to describe, but one such difference would be that a single extra stone can change a Go position value much more than a single pixel changes an image classification.
A CNN can still distinguish extremely subtle differences of various animal breeds, exceeding human performance in such tasks. Why was that advance not a warning sign? The rotational-translational invariance prior of the convolutional neural network probably helps because, by default, local changes of the patterns can massively change the output value without the need to train that subtle change for all translations. Also, AlphaGo does a tree search all the way to the games end, which can probably easily detect such dramatic changes of single extra stones. Reality is likely much too unconstrained to to able to efficiently simulate such things.
Yes, the latter number was perhaps too conservative; no doubt about deeper predictions being easily possible, but I doubt even expert players consider many alternative paths in the search tree. They might recognize overall strategies which reach many moves into the future, but extensive consideration of what will happen in the upcoming moves is probably constrained to a only few steps; at least relative to the number and depths of paths that AlphaGo considers.
It's because they have a much larger stack size than a human brain (which does not have a stack at all, but just various kinds of short term memories). An expert Go player can realistically maybe consider 2-3 moves into the future and can have a rough idea about what will happen in the coming 10 moves, while this method does tree search all the way to the end of the game on multiple alternative paths for each move.
1. The value network is trained with gradient descent to minimize the difference between predicted outcome of a certain board position and the final outcome of the game. Actually they use the refined policy network for this training; but the original policy turns out to perform better during simulation (they conjecture it is because it contains more creative moves which are kind of averaged out in the refined one). I'm wondering why the value network can be better trained with the refined policy network.
2. They just run a certain number of simulations, i.e. they compute n different branches all the way to the end of the game with various heuristics.
Great achievement.
To summarize, I believe what they do is roughly this: First, they take a large collection of Go moves from expert players and learn a mapping from position to moves (a policy) using a convolutional neural network that simply takes the 19 x 19 board as input. Then they refine a copy of this mapping using reinforcement learning by letting the program play against other instances of the same program: For that they additionally train a mapping from the position to a probability of how how likely it will result in winning the game (the value of that state). With these two networks they navigate through state-space: First they produce a couple of learned expert moves given the current state of the board with the first neural network. Then they check the values of these moves and branch out over the best ones (among other heuristics). When some termination criterion is met, they pick the first move of the best branch and then it's the other player's turn.
I can't recommend the Tree Style Tabs FF add-on enough for these kinds of "large sessions that diverge into many sub-sessions". If you ctrl-click a link, this add-on creates a new child-tab of the current tab and loads the link there. It kind of builds little spanning trees of the WWW graph, which seems to be a very natural way of browsing the web, but you can also organize these tab trees completely freely with drag & drop.
Exactly. The divide between address bar and search bar is a much under-appreciated privacy feature.
Perhaps give Tree Style Tabs on FF a try.
How is FF ugly? I don't find the UI very different from Chrome and Safari.
How come this is still on YouTube? Isn't this copyright infringement?
Novelty seeking as reward function has been studied before in AI. See for example this:
http://people.idsia.ch/~juergen/interest.html
IIRC, DeepMind is also working on such goal functions to get their Atari playing RL-based AI to seek more data about the world even when it does not immediately help achieving the main goal function (achieving a high score).
Novelty seeking behavior probably has evolved because there are just not enough immediate rewards in our world to teach us everything that is nesessarey to reproduce [0]. Thus the brain rewards itself for exploring new things which has a collateral effect that we are interested in art and can find intrinsic motivation in all kinds of things (science, work, hobbies etc.).
[0] which does not mean that we are here to maximize the number of our babies. We aren't fitness maximizers ourselves, but we are just adaptation executors of genetic code that has necessarily been shaped by such goals (since the alternative to reproduction is to not reproduce, i.e. going extinct). In other words: We are free do whatever we want!
I thought this would be about text-to-speech applications, while this seems more like an encoder-decoder problem (make the network learn a pattern and then let it reproduce it). I'm wondering how long it is until we see working TTS based on LSTM RNNs.
Perhaps they use the heat very efficiently?
... and biology's.
Yes, the HC likely plays a role in working memory for associative, declarative and spatial information. Most of the working memory is believed to reside in the (prefrontal) cortex, though; in patterns of synaptic depletion (less than 15-30 seconds) and based on the 'hot potato' principle in which processes in different modules mutually re-activate previous states to manipulate them.
Also related, but not yet confirmed: "Time cells", which are also located in the hippocampus: http://www.nature.com/nrn/journal/v15/n11/abs/nrn3827.html
One can possibly think of the hippocampus as a 'space-time integrator' that continuously indices high-level activation patterns from the cortex and stores them much like a log book. Memories in the hippocampus last minutes to months, after which they are either transferred to long-term memory in the cortex or lost.
Paper:
http://elifesciences.org/content/4/e11386
Abstract:
The striatum is an input structure of the basal ganglia implicated in several time-dependent functions including reinforcement learning, decision making, and interval timing. To determine whether striatal ensembles drive subjects' judgments of duration, we manipulated and recorded from striatal neurons in rats performing a duration categorization psychophysical task. We found that the dynamics of striatal neurons predicted duration judgments, and that simultaneously recorded ensembles could judge duration as well as the animal. Furthermore, striatal neurons were necessary for duration judgments, as muscimol infusions produced a specific impairment in animals' duration sensitivity. Lastly, we show that time as encoded by striatal populations ran faster or slower when rats judged a duration as longer or shorter, respectively. These results demonstrate that the speed with which striatal population state changes supports the fundamental ability of animals to judge the passage of time.
And they should employ the developer of the SwipeSelection tweak and build it into iOS:
Static random-access memory (static RAM or SRAM) is a type of semiconductor memory that uses bistable latching circuitry (flip-flop) to store each bit. SRAM exhibits data remanence,[1] but it is still volatile in the conventional sense that data is eventually lost when the memory is not powered.
The term static differentiates SRAM from DRAM (dynamic random-access memory) which must be periodically refreshed. SRAM is faster and more expensive than DRAM; it is typically used for CPU cache while DRAM is used for a computer's main memory.
WP
However, we mostly only arm brains that have an IQ of <120 and some human values baked in (e.g. empathy to other humans and living beings; a family to protect, or dependence on other social structures that demand pro-social attitudes).
How about introducing a simple convention for indicating your degree of belief in similar manner to the sarcasm indicator /s: One could write for example /p1 .. /p9 to refer to the probabilities 0.01% .. 99.9%.
That could resolve a lot of issues that occur in text communication. /p7
What about gamma ray bursts? I've read that our position more at the outer rim of the galaxy is beneficial because there are fewer gamma ray bursts compared to more dense regions.
Lol. I guess we don't need college education as well then, there's education happening in the comments to some extent. We don't need traditional means of news, there's news happening on Twitter to some extent. I could go on with analogous line of reasoning.
That's a straw man. I did say it's the fringe and it needs to be tested. I didn't say one should replace the other. Peer-review is essentially just mutual corrections, and there are mutual corrections happening in the comments, just not as thoroughly as when it's institutionalized. Most of it is not new anyway, but just summarizes research results and draws logical conclusions from it (for example this [1]). If it wasn't all brought together on LW, I possibly wouldn't have found out about the wealth of knowledge for a long time.
[1] http://papers.nips.cc/paper/2716-bayesian-inference-in-spiki...
a) Keywords: "If", "Seems" b) Tons of assumptions in that scenario you laid out. If you can't see it, I'm sorry but you're already too far gone. c) Basically, what you've done is precisely the kind of utter crap that LW perpetuates. "If x keeps happening" without providing any reason as to why that would be true.
It's very logical. My certainty referred to the implication, but it is hard, of course, to come up with a prior for that 'if': Exponential progress could continue in various ways, e.g. by invention of more energy efficient chips and by scaling them up, by 3D circuitry, molecular assemblers, memristors, or perhaps quantum computing. There are contradicting studies, so one should put P(Moore's law continues for another 10-20 yrs) at perhaps 50%. So, of course, this is all hedged behind this prior (which I think many people get confused by). The discussion is always concerned with implications which can be made with fairly solid reasoning, by assuming that P(..) above to be 100%.
Make some ridiculous simplifications "complexity is limited by ___", nature often does __ because ___. You basically don't provide any rational reason for why you think AI will be super intelligent and even if it were, why that would be risky.
That's just a basic assumptions which I find plausible, and which some respectable and knowledgeable persons find plausible too (for example Stephen Wolfram and Mark Tegmark; I am aware that appeal to authority is difficult to argue from, but both have publications which I could also refer to). I agree that mentioning the complexity limitations didn't provide any information because they don't tell us whether it makes it simple enough for us to understand, it merely says that the complexity is not infinite, so I should have left it out entirely. But this is not at all representative for the best contents on LW, it was poor reasoning on my behalf. Bostrom's book Superintelligence gives a pretty good summary about why it is thought to be plausible.
You pick numbers out of a hat (10-40 years to come).
That's based on estimates of the processing power required for brain simulations by IBM researchers and Ray Kurzweil. Simple extrapolation of Moore's law shows us that we will reach that point roughly between 2019 and 2025. 40 years is just my bet based on what I know about brain models and current obstacles in AI.
I will look into Farnham Street's Blog, thanks.
What he's preaching isn't science.
I don't buy into the necessity that everything has to be peer-reviewed in the old fashioned way. There is peer-review happening in the comments to some extent. I don't take a fancy to dismissing any radical ideas as pseudoscience. It's just the outer fringe of hypotheses that need to be tested against reality, and as long they are approximately humanist, enlightened and don't contradict existing physics without depending on mathematics (or disclaimers), I cannot see anything wrong with it. As a naturalist, I pretty much agree with everything I've read on LW so far, except for the parts I cannot judge (like hypotheses about physics), which I allocate a weaker priors for and a few unconvincing pieces.
Not to mention, and this is a bit of a pet peeve, I find that most LW people are too self-absorbed, I've literally seen a blog where the person who runs it "warns" the readers that what he writes is too complicated for people to follow.
I have not yet experienced that, but there are also a lot of people on reddit and HN that I don't like, yet I differentiate within these communities between what is valuable and what is not.
Most of what Yudkowsky says is extremely sci-fi, no real basis in scientific fact, but stretching the current technological progress to the point where his opinions on things (stuff like transhumanism, singularity) can be justified.
At the risk of seeming indoctrinated to you, this is what I believe with high certainty: If Moore's law continues another one or two decades, I think the singularity is a very real possibility. The human brain seems to be nothing more than a learning and prediction machine, nothing what transcends what we can understand in principle. Evolution did come up with complex organisms, but the complexity is limited by biochemical mechanisms and availability of energy. In addition, nature often approximates very simple things in overly complicated ways because evolution is based on incremental changes, not on an ultimate goal that prescribes a design of low complexity. I also think that AI will very likely be superintelligent and that poses a tremendous risk in the 10-40 years to come (on the order of atomic warfare and runaway climate change). By the time someone implements an approximately human-level intelligence, we better have a good idea about how to control such a machine.