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Comment author: bogus 31 January 2016 12:11:54AM *  1 point [-]

Well, in this case of Go and for an increasing number of domains, it can do far more than any brain - learns far faster.

This might actually be the most interesting thing about AlphaGo. Domain experts who have looked at its games have marveled most at how truly "book-smart" it is. Even though it has not shown a lot of creativity or surprising moves (indeed, it was comparatively weak at the start of Game 1), it has fully internalized its training and can always come up with the "standard" play.

Right, and they use a small fraction of the energy budget, and thus can't contribute much to the computational power.

Not necessarily - there might be a speed vs. energy-per-op tradeoff, where neurons specialize in quick but energy-intensive computation, while neuroglia just chug along in the background. We definitely see such a tradeoff in silicon devices.

Comment author: Kaj_Sotala 31 January 2016 11:25:26AM *  0 points [-]

Domain experts who have looked at its games have marveled most at how truly "book-smart" it is. Even though it has not shown a lot of creativity or surprising moves (indeed, it was comparatively weak at the start of Game 1), it has fully internalized its training and can always come up with the "standard" play.

Do you have links to such analyses? I'd be interested in reading them.

EDIT: Ah, I guess you were referring to this: https://www.reddit.com/r/MachineLearning/comments/43fl90/synopsis_of_top_go_professionals_analysis_of/

Comment author: jacob_cannell 30 January 2016 12:22:49AM *  9 points [-]

This is a big deal, and it is another sign that AGI is near.

Intelligence boils down to inference. Go is an interesting case because good play for both humans and bots like AlphaGo requires two specialized types of inference operating over very different timescales:

  • rapid combinatoric inference over move sequences during a game(planning). AlphaGo uses MCT search for this, whereas the human brain uses a complex network of modules involving the basal ganglia, hippocampus, and PFC.
  • slow deep inference over a huge amount of experience to develop strong pattern recognition and intuitions (deep learning). AlphaGo uses deep supervised and reinforcement learning via SGD over a CNN for this. The human brain uses the cortex.

Machines have been strong in planning/search style inference for a while. It is only recently that the slower learning component (2nd order inference over circuit/program structure) is starting to approach and surpass human level.

Critics like to point out that DL requires tons of data, but so does the human brain. A more accurate comparison requires quantifying the dataset human pro go players train on.

A 30 year old asian pro will have perhaps 40,000 hours of playing experience (20 years * 50 * 40 hrs/week). The average game duration is perhaps an hour and consists of 200 moves. In addition, pros (and even fans) study published games. Reading a game takes less time, perhaps as little as 5 minutes or so.

So we can estimate very roughly that a top pro will have absorbed between 100,000 games to 1 million games, and between 20 to 200 million individual positions (around 200 moves per game) .

AlphaGo was trained on the KGS dataset: 160,00 games and 29 million positions. So it did not train on significantly more data than a human pro. The data quantities are actually very similar.

Furthermore, the human's dataset is perhaps of better quality for a pro, as they will be familiar with mainly pro level games, whereas the AlphaGo dataset is mostly amateur level.

The main difference is speed. The human brain's 'clockrate' or equivalent is about 100 hz, whereas AlphaGo's various CNNs can run at roughly 1000hz during training on a single machine, and perhaps 10,000 hz equivalent distributed across hundreds of machines. 40,000 hours - a lifetime of experience - can be compressed 100x or more into just a couple of weeks for a machine. This is the key lesson here.

The classification CNN trained on KGS was run for 340 million steps, which is about 10 iterations per unique position in the database.

The ANNs that AlphaGo uses are much much smaller than a human brain, but the brain has to do a huge number of other tasks, and also has to solve complex vision and motor problems just to play the game. AlphaGO's ANNs get to focus purely on Go.

A few hundred TitanX's can muster up perhaps a petaflop of compute. The high end estimate of the brain is 10 petaflops (100 trillion synapses * 100 hz max firing rate). The more realistic estimate is 100 teraflops (100 trillion synapes * 1 hz avg firing rate), and the lower end is 1/10 that or less.

So why is this a big deal? Because it suggests that training a DL AI to master more economically key tasks, such as becoming an expert level programmer, could be much closer than people think.

The techniques used here are nowhere near their optimal form yet in terms of efficiency. When Deep Blue beat Kasparov in 1996, it required a specialized supercomputer and a huge team. 10 years later chess bots written by individual programmers running on modest PC's soared past Deep Blue - thanks to more efficient algorithms and implementations.

Comment author: Kaj_Sotala 31 January 2016 11:20:28AM *  1 point [-]

A 30 year old asian pro will have perhaps 40,000 hours of playing experience (20 years * 50 * 40 hrs/week). The average game duration is perhaps an hour and consists of 200 moves. In addition, pros (and even fans) study published games. Reading a game takes less time, perhaps as little as 5 minutes or so.

So we can estimate very roughly that a top pro will have absorbed between 100,000 games to 1 million games, and between 20 to 200 million individual positions (around 200 moves per game) .

I asked a pro player I know whether these numbers sounded reasonable. He replied:

At least the order of magnitude should be more or less right. Hours of playing weekly is probably somewhat lower on average (say 20-30 hours), and I'd also use 10-15 minutes to read a game instead of five. Just 300 seconds to place 200 stones sounds pretty tough. Still, I'd imagine that a 30-year-old professional has seen at least 50 000 games, and possibly many more.

Comment author: Risto_Saarelma 31 January 2016 08:41:56AM 2 points [-]

One problem is that the community has few people actually engaged enough with cutting edge AI / machine learning / whatever-the-respectable-people-call-it-this-decade research to have opinions that are grounded in where the actual research is right now. So a lot of the discussion is going to consist of people either staying quiet or giving uninformed opinions to keep the conversation going. And what incentive structures there are here mostly work for a social club, so there aren't really that many checks and balances that keep things from drifting further away from being grounded in actual reality instead of the local social reality.

Ilya actually is working with cutting edge machine learning, so I pay attention to his expressions of frustration and appreciate that he persists in hanging out here.

Comment author: Kaj_Sotala 31 January 2016 10:12:04AM 0 points [-]

Agreed both with this being a real risk, and it being good that Ilya hangs out here.

Comment author: Kaj_Sotala 30 January 2016 07:46:59PM *  7 points [-]

Interestingly, Brienne just posted a rather similar story, though for her the environment's effect on her behavior is much more immediately obvious than in Ferrett's case.

Comment author: IlyaShpitser 29 January 2016 11:13:19PM 1 point [-]

Say I am worried about this tribal thing happening a lot -- what would put my mind more at ease?

Comment author: Kaj_Sotala 30 January 2016 07:07:01PM *  0 points [-]

I don't know your mind, you tell me? What exactly is it that you find worrying?

My possibly-incorrect guess is that you're worried about something like "the community turning into an echo chamber that only promotes Eliezer's views and makes its members totally ignore expert opinion when forming their views". But if that was your worry, the presence of highly upvoted criticisms of Eliezer's views should do a lot to help, since it shows that the community does still take into account (and even actively reward!) well-reasoned opinions that show dissent from the tribal leaders.

So since you still seem to be worried despite the presence of those comments, I'm assuming that your worry is something slightly different, but I'm not entirely sure of what.

Comment author: IlyaShpitser 29 January 2016 05:54:10AM *  1 point [-]

Sure, but it's not just about what experts say on a survey about human level AI. It's also about what info a good Go program has for this question, and whether MIRI's program makes any sense (and whether it should take people's money). People here didn't say "oh experts said X, I am updating," they said "EY said X on facebook, time for me to change my opinion."

Comment author: Kaj_Sotala 29 January 2016 11:11:58AM 6 points [-]

People here didn't say "oh experts said X, I am updating," they said "EY said X on facebook, time for me to change my opinion."

My reaction was more "oh, EY made a good argument about why this is a big deal, so I'll take that argument into account".

Presumably a lot of others felt the same way; attributing the change in opinion to just a deference for tribal authority seems uncharitable.

Comment author: IlyaShpitser 29 January 2016 06:03:31AM *  1 point [-]

"Well, what a relief!"

I think you misunderstood me (but that's my fault for being opaque, cadence is hard to convey in text). I was being sarcastic. In other words, I don't need EY's opinion, I can just look at the problem myself (as you guys say "argument screens authority.")


I feel like you're wrong about how he's wrong.

Look, I met EY and chatted with him. I don't think EY is "evil," exactly, in a way that L. Ron Hubbard was. I think he mostly believes his line (but humans are great at self-deception). I think he's a flawed person, like everyone else. It's just that he has an enormous influence on the rationalist community that immensely magnify the damage his normal human flaws and biases can do.

I always said that the way to repair human frailty issues is to treat rationality as a job (rather than a social club), and fellow rationalists as coworkers (rather than tribe members). I also think MIRI should stop hitting people up for money and get a normal funding stream going. You know, let their ideas of how to avoid UFAI compete in the normal marketplace of ideas.

Comment author: Kaj_Sotala 29 January 2016 11:09:39AM 7 points [-]

I also think MIRI should stop hitting people up for money and get a normal funding stream going. You know, let their ideas of how to avoid UFAI compete in the normal marketplace of ideas.

Currently MIRI gets their funding by 1) donations 2) grants. Isn't that exactly what the normal funding stream for non-profits is?

Comment author: Gleb_Tsipursky 27 January 2016 09:58:52PM 0 points [-]

Ah, I see. So for those people, it's not about the post, it's about the author. Gotcha.

Comment author: Kaj_Sotala 28 January 2016 01:13:35PM *  3 points [-]

Judging from the amount of perfectly innocuous comments from you that are downvoted at the moment (including the one that I'm responding to; or rather was downvoted until I upvoted it back to 0), this seems like it's indeed the case.

Comment author: CellBioGuy 27 January 2016 07:34:30AM 5 points [-]

Cities are where they are because of actual reasons of geography, not just people plopping things down randomly on a map. You need to get stuff into them, stuff out of them, have the requisite power and water infrastructure to get to them (ESPECIALLY in California)... they aren't something you plop down randomly on a whim.

Comment author: Kaj_Sotala 27 January 2016 11:24:35AM 3 points [-]

Also, previous attempts at doing exactly this have only had modest success:

California City had its origins in 1958 when real estate developer and sociology professor Nat Mendelsohn purchased 80,000 acres (320 km2) of Mojave Desert land with the aim of master-planning California's next great city. He designed his model city, which he hoped would one day rival Los Angeles in size, around a Central Park with a 26-acre (11 ha) artificial lake. Growth did not happen anywhere close to what he expected. To this day a vast grid of crumbling paved roads, intended to lay out residential blocks, extends well beyond the developed area of the city.

Comment author: fubarobfusco 26 January 2016 08:52:25PM 6 points [-]

There's a whole -osphere full of blogs out there, many of them political. Any of those would be better places to talk about it than LW.

Comment author: Kaj_Sotala 27 January 2016 11:22:34AM 5 points [-]

What's wrong with LW?

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