HN Theater @HNTheaterMonth

Here is a list of 20 documentaries I have come to love and return to when I come across them. There is no specific order to them. Enjoy! The * next to the number indicates the last 4 years. If you have seen one listed that you'd like to share what you think about please leave a comment with this structure [# in the list ] Comment... . i.e. [1] Loved learning about Noyce. What a guy.

1. Silicon Valley: Where the Future | PBS: American Experience [1:23:19] [2013] History of the how Silicon Valley came to be. William Shockley. Robert Noyce. Fairchild Semiconductor, The Traitorous Eight. Homepage: https://www.pbs.org/wgbh/americanexperience/films/silicon/ Kanopy: https://www.kanopy.com/en/product/122744 Where to Watch: https://www.justwatch.com/us/movie/silicon-valley

2. Shenzhen: The Silicon Valley of Hardware | Wired UK: Future Cities [1:07:50] [2016] the evolution of “Shanzhai” – or copycat manufacturing – has transformed traditional models of business, distribution and innovation, and asks what the rest of the world can learn from this so-called “Silicon Valley of hardware". Youtube: https://youtu.be/SGJ5cZnoodY

    *2.1. The People's Republic of The Future | Bloomberg: Hello World [30:10] [2019]
        Shenzhen, tech-fueled entrepreneurs try to navigate an authoritarian regime.
        Youtube: https://youtu.be/taZJblMAuko

3. The Pirate Bay Away From Keyboard | TPB AFK [1:22:07] [2013] The history of the Pirate Bay. Torrenting. Legal. Privacy. Homepage: https://tpbafk.tv Youtube: https://youtu.be/eTOKXCEwo_8 Where to Watch: https://www.justwatch.com/us/movie/the-pirate-bay-away-from-...

4. Can’t Get You Out of My Head | BBC [6 Part] [2021] Adam Curtis takes you on "An Emotional history of how we got to this place." Economics. History. Power. Psychology. World. China. Russia. UK. USA. Part 1: Bloodshed on Wolf Mountain [1:14:15] Part 2: Shooting and F*king are the Same Thing [1:14:01] Part 3: Money Changes Everything [1:10:36] Part 4: But What If the People Are Stupid? [1:13:29] Part 5: The Lordly Ones [1:05:43] Part 6: Are We Pigeon? Or Are We Dancer? [1:59:50] Homepage: https://www.bbc.com/bbcfilm/films/cant-get-you-out-of-my-hea... BBC iPlayer: https://www.bbc.co.uk/iplayer/episodes/p093wp6h/cant-get-you... Thought Maybe: https://thoughtmaybe.com/cant-get-you-out-of-my-head/

5. Three Identical Strangers | NEON [1:37:00] [2018] Its in the name, but The less you know about this story the better. Homepage: https://www.threeidenticalstrangers.com Kanopy: https://www.kanopy.com/en/product/11374410 Where to Watch: https://www.justwatch.com/us/movie/three-identical-strangers

6. The Pharmacist | Netflix [4 Part] [2020] A small-town pharmacist investigates the death of his son in a drug deal, but his investigation skills only expands when OxyContin becomes available. Part 1: Justice for Danny [1:00:38] Part 2: A Mission from God [58:47] Part 3: Dope Dealers with White Lab Coats [47:30] Part 4: Tunnel of Hope [48:31] Homepage: https://thecinemart.com/the-pharmacist.html Netflix: https://www.netflix.com/title/81002576 Where to Watch: https://www.justwatch.com/us/tv-show/the-pharmacist

7. AlphaGo | Netflix [1:30:27] [2017] Deepmind, Alphabets's AI company, creates an AI to play the game Go and faces off to beat the best, Lee Sedol. Homepage: https://www.alphagomovie.com Youtube: https://youtu.be/WXuK6gekU1Y Where to Watch: https://www.justwatch.com/us/movie/alphago

8. Page One: Inside the New York Times | Magnolia Pictures [1:31:38] [2011] As print media begins to be challenged by the internet, you get an inside look into NY Times. Homepage: http://www.magpictures.com/pageone/ Where to Watch: https://www.justwatch.com/us/movie/page-one-inside-the-new-y...

9. The Secret of Tuxedo Park | PBS: American Experience [53:00] [2018] Alfred Lee Loomis isn't just a Wall Street tycoon, but a scientist with a checkbook to pay for it. Opening his home to the best, he follows his interest and a call from the government about WWII. Homepage: https://www.pbs.org/wgbh/americanexperience/films/secret-tux...

10. The Century of the Self | BBC [4 Part] [2002] Adam Curtis investigates how Freudian theory influenced twentieth century society. Part 1: Happiness Machines [58:32] Part 2: The Engineering of Consent [58:40] Part 3: There is a Policeman Inside All Our Heads, He Must Be Destroyed [58:39] Part 4: Eight People Sipping Wine in Kettering [59:32] Homepage: https://www.bbc.co.uk/programmes/p00ghx6g BBC iPlayer: https://www.bbc.co.uk/iplayer/episodes/p00ghx6g/the-century-... Thought Maybe: https://thoughtmaybe.com/the-century-of-the-self/

11. How Ants Can Make The Internet a Safer Place | Tom Mishra [00:04:08] [2015] Five researchers, scientists and mathematicians studied the behavior of Ants and Game Theory to create a safer Internet. Video: https://vimeo.com/147548221

12. Chasing Einstein | Ignite [1:22:00] [2019] Follows leading scientists around the world, from the largest particle accelerator at CERN in Switzerland to the LIGO gravitational wave detector in the US to find out whether Einstein's theory of gravity can stand the test of time. Homepage: https://chasingeinsteinfilm.com Where to Watch: https://www.justwatch.com/us/movie/chasing-einstein

13. Language of Love (Ur kärlekens språk) | [1:43:00] [1969] * Rated X, and in Swedish A panel of real-life doctors discuss sexual hangups, misconceptions, personal prejudices and the ignorance of individuals when it comes to matters sexual. Where to Watch: https://www.justwatch.com/us/movie/language-of-love

14. Citizenfour | TWC [1:54:00] [2014] A documentarian and a reporter travel to Hong Kong to meet Edward Snowden where they release classified information. Homepage: https://citizenfourfilm.com Where to Watch: https://www.justwatch.com/us/movie/citizen-four

15. Abacus: Small Enough to Jail | PBS: Frontline [1:30:00] [2016] The Chinese immigrant Sung family, owners of Abacus Federal Savings of Chinatown, New York is targeted by the DA in the mess after the 2008 financial crisis. Homepage: https://www.abacusmovie.com Kanopy: https://www.kanopy.com/en/product/2141966 Where to Watch: https://www.justwatch.com/us/movie/abacus-small-enough-to-ja...

16. Zero Days | Magnolia Pictures [1:56:00] [2016] Stuxnet, a self-replicating computer virus discovered in 2010 by international IT experts points to be created by the NSA and used by Unit 8200, Israel, on Iran nuclear enrichment facility. Homepage: http://www.zerodaysfilm.com Kanopy: https://www.kanopy.com/en/product/10846994 Where to Watch: https://www.justwatch.com/us/movie/zero-days

17. Tell Me Who I Am | Netflix [1:25:32] [2019] The less you know about this story the better. Netflix: https://www.netflix.com/title/80214706 Where to Watch: https://www.justwatch.com/us/movie/tell-me-who-i-am

18. McMillions | HBO [6 Part] [2020] A detailed account of the McDonald's Monopoly game scam during the 1990s. Part 1: Episode 1 [55:50] Part 2: Episode 2 [53:57] Part 3: Episode 3 [56:54] Part 4: Episode 4 [57:08] Part 5: Episode 5 [56:41] Part 6: Episode 6 [56:59] HBO: https://www.hbo.com/mc-millions Where to Watch: https://www.justwatch.com/us/tv-show/mcmillion

19. Jesus Camp | Magnolia Pictures [1:24:00] [2006] Children at a christian summer camp as they hone their "prophetic gifts" and are schooled in how to "take back America for christ." The film is a first-ever look into an intense training ground that recruits born-again christian children to become an active part of America's political future. Kanopy: https://www.kanopy.com/en/product/10803148 Where to Watch: https://www.justwatch.com/us/movie/jesus-camp

*20. The Edge of All We Know | Netflix [1:39:00] [2020] Follow the quest to understand the most mysterious objects in the universe, black holes. Homepage: https://www.blackholefilm.com Netflix: https://www.netflix.com/title/81343342 Where to Watch: https://www.justwatch.com/us/movie/black-holes-the-edge-of-a...

I was really impressed by "AlphaGo - The Movie", despite not knowing much about Go, because the way they told the story, people from the Asian countries were so sure their champion would win a Go tournament against a computer, that were stunned into a new consciousness about artificial intelligence when the computer won. This started an arms race in AI is what I gather.

Edit: Full movie for free on YouTube: AlphaGo - The Movie | Full award-winning documentary

https://www.youtube.com/watch?v=WXuK6gekU1Y

⬐ antris

That's a great documentary in general. Recommended for everyone :)

⬐ tasuki

I'm from Europe and was also sure the human would win.

The only thing that prevented me from losing a bunch of money on prediction markets was logistics.

AlphaGo - The Movie https://youtu.be/WXuK6gekU1Y

⬐ CalChris

I don't play go but the AlphaZero chess games are quite beautiful. Agadmator has a description of the weird unhuman like machine continuations, disgusting engine lines. They're correct but incomputable by a human and just look weird. AlphaZero had some beautiful lines that looked human but slightly counterintuitive. They were the sort of moves that humans could learn from.

Are the AlphaGo games similar?

⬐ throwaway81523

I'm not a Go player but watched some of the famous Lee Sedol match on youtube while it was happening. One channel had a Korean professional 9-dan (= super grandmaster) commentating. At one point in game 1, AlphaGo made a move that shocked the professional. He spent a while analyzing it, asking whether it could work, etc. The next few moves were routine, so he commented on them briefly and then went back to talking about the surprising move. More moves came and he commented on those. Then perhaps 20 minutes later, during the wait for another move, he went back to the surprising move and said "if that move works, AlphaGo is going to win all 5 games". It was so far-reaching and subtle that if it was not a mistake, it could only be made by a player of superhuman strength.

As we know, Lee Sedol managed to win game 4 by a lucky break, but lost the rest, so the GM's comments were incredibly perceptive. I took a few notes while watching so I may be able to dig them up and check the details, but the surprising play became quite well-known and involved something called a "dog's head" formation, which might be enough for Go aficionados to identify it.

⬐ NhanH

I'd love to know which professional make that comment, so if you don't mind, can you please look it up?

The move in question should be move 102, which is an invasion that human would not try.

⬐ throwaway81523

I'm having trouble finding my notes on the match, but after looking again on youtube, I'm pretty sure the professional was Myungwan Kim.

⬐ throwaway2214

> Lee Sedol managed to win game 4 by a lucky break

Move 78 was not a lucky break. At least in my mind.

There are not many movies I cried to, in this movie I cried twice, once when Lee Sedol was apologizing for the loss, and once when he made move 78 and Alpha Go's probability to win dropped with 10%.

Now for me move-78 means hope.

⬐ gverrilla

this movie got me very emotional aswell, which is not common particularly for docs

⬐ PartiallyTyped

I don't mean to burst your bubble, but since then, we have had significantly better algorithms than AlphaGO, and much more compute.

Unless humanity moves to a point where the average skill is that of Lee, and the average intelligence is akin to JvN's I don't see hope for humanity.

⬐ mderazon

Computers are still bad at something as basic for humans as driving, so i'd say there's still hope.

Although I always wonder if give that the software was designed in a way to allow the computer to make mistakes and take risks to a reasonable amount, like humans do, would they still be crappy drivers?

⬐ vmilner

Yes, very much so - for example it used to be almost axiomatic (told in your second or third go lesson) that placing a white stone at the 3-3 point when black had played at 4-4 was very bad, but this video shows that AlphaGo has radically changed this view:

https://www.youtube.com/watch?v=2khNnE5Q3GM

⬐ roenxi

Go is a bit different from Chess, there are so many options that you can't really have a "disgusting engine line", because either sequences are forced and humans can find them too eventually or the number of lines the move effects is so vast that we would never realise how clever it is.

Computer Go is obviously making calculations that a human couldn't, but as an observer it just looks like not making any mistakes. There is the occasional spectacular attack or defence, but humans do that too from time to time - human players often see or try moves that others wouldn't. In complicated situations computers will do a better job at assessing what the impacts are.

In Go, to win a player needs to consistently make moves that are on average 0.0025 points better than their opponent for 300 moves. It is hard to detect the genius if a neural net decides it is going to win the game that way.

The games are interesting to study, they are all masterworks. But the strength of a Go player is in the consistency rather than individual flashes of insight.

⬐ zeven7

I disagree with this comment quite strongly. Go has a lot of high level theory and pattern recognition that is very different from just reading ahead, and AlphaGo has opened up a lot of new theory that we didn't have before, not unlike what was done by Go Seigen and others in the Shin Fuseki era.

I feel like the emphasis of your comment misses how the game has changed for humans since AlphaGo.

⬐ roenxi

> not unlike what was done by Go Seigen and others in the Shin Fuseki era

That is the crux of it though; we're seeing the same effect as every time a new strong player arises. That doesn't scream "wow, this is like when chess engines discovered impossible new lines!".

It was probably a matter of time until some human pro started winning with the 3-3 invasion for example. It isn't like 3-3 invasions are a radical new thing, professional play just underweighted them. I've played amateurs who loved 3-3 invasions.

There is no question that the neural nets are substantially stronger than humans and that they're causing a monumental rethink of all aspects of the game. They're a big deal. But the difference is that they are bringing a very large number of innovations all at once, combined with far more accurate reading, rather than that any individual innovation is special.

It is really difficult for one sequence in Go to be special, because it is so common for a special sequence to happen.

⬐ zeven7

> That is the crux of it though; we're seeing the same effect as every time a new strong player arises.

Ok, I agree with you there. It's like AlphaGo is the next in the line of Dosaku -> Shusaku -> Go Seigen. These types of players that have revolutionized the game have been very rare, and I see AlphaGo as fitting into that mold.

> That doesn't scream "wow, this is like when chess engines discovered impossible new lines!".

I admittedly don't know enough about chess to understand this aspect of the discussion.

⬐ throwaway81523

I remember reading that humans play the corners and edges of the board, where the game is somewhat understandable given a combination of keen perception, pattern recognition, and concrete calculation. It is on the other hand inhumanly hard to understand playing further in the board interior (other than by reaching it incrementally from the edges), maybe excepting a few legendary players like Go Seigen. But the AI's have been going there and doing stuff, letting humans learn things that were previously unknown.

⬐ roenxi

That doesn't sound right. Humans and AIs play in the corners and on the sides first because that is where it is easier to make points. One of the traits of how neural nets learn Go is they start by learning to fight and then quickly realise that the corners are more important than the centre. AIs largely confirmed the usual human pattern.

AIs have changed all the details about what is considered an acceptable result in the corners.

Although AI do brutally outplay humans in the centre when the game gets there, no question. Normally after outplaying them everywhere else first.

⬐ jasonwatkinspdx

I'm just a middling go player but I'd say yes, the two are quite comparable and rooted, at least partially, in a similar aspect: Alpha doesn't care about margin of victory, only probability of victory. This leads it to play a much more influence/position vs territory oriented style at times in go, occasionally shockingly so.

⬐ mattbillenstein

Agreed, I think ChessNetwork analyzed several of the published games as well - and some of the Leela games - they're so much more interesting than most of what I see out of Stockfish...

⬐ AboveTheGame

You are also NOT “that guy”

Fuck this gay simulation lmao

Hahahahaha get pressed on y’all soft ass morons

⬐ AboveTheGame

///UNPOPULAR OPINION WARNING///

No, they’re for inept idiots who are obsessed with mathematically solved abstractions of reality. My god, to someone who sees chaos maps in reality the patterns these pathetic programming abstractions offer is a limitation of /our current society/ — we are slaves to wealth and process over progress and intuition. Nothing more need be said, I am always throwing Pearls before swine.

⬐ PandaPanda150

I can try to explain for people not playing Go the most important thing that changed:

The AI popularized a move that has been known since ages, but wasn't used by strong humans much because it was considered a mistake (the early 33 invasion).

The thing with this move is: It gives you a small advantage very early in the game, but gives your opponent a superior fighting position in exchange. This fighting position was considered better. Not early on, but in later stages of the game it oughtweights the small advantage taken early.

Then came AI and it found out, given enough fighting power, it can mostly cancel that later game advantage.

So the new move is actually a good one, but only if you have very strong fighting skills. AI has this, some very strong players as well.

But for the rest of us, the pre AI strategy is often better because we can't handle the complexity.

It is common to see casual players copying the AI style and digging their own grave while doing so.

⬐ inclemnet

> But for the rest of us, the pre AI strategy is often better because we can't handle the complexity.

> It is common to see casual players copying the AI style and digging their own grave while doing so.

I think this point is greatly overstated. Amateur go style has always been modelled on professional play even though amateurs have no idea of the subtleties of it, and this was rarely bemoaned much in the past. It's easy to find examples of amateur players getting it wrong and turning a slightly-good position into a bad one, but this is true of just about anything and I think there's a lot of confirmation bias in associating it with AI moves in particular - it doesn't in itself mean they've disadvantaged themselves by trying.

It also greatly overstates how bad it is to play the 3-3 early and not fight aggressively. Even if you don't worry about the fighting variations we're talking about a small number of points, much less than amateur games tend to swing via mistakes every few moves. This is also true about those professional-like lines that have always been mimicked but played imperfectly, and is probably a big reason the lack of amateur understanding of them never mattered too much. Plus let's not forget that both amateur players in the game are relatively bad at fighting, it's just about as hard for the defensive player to really get it right as for the offensive player to utilise their theoretical advantage.

Overall I think the modern focus on AI-like play is much more of a fashion in line with other general shifts following what's professionally popular than a mistake. Players do pay attention to how well they do and associate it with specific moves and lines they recognise they don't understand well, but AI-inspired lines don't seem to have attracted any widespread cautiousness.

> Then came AI and it found out, given enough fighting power, it can mostly cancel that later game advantage.

Also worth noting that the AIs play the 3-3 invasion in a specific different way to what was the accepted human pattern, and this alone does make a big but understandable difference to how the shape commonly continues. It isn't that they only turned around the evaluation of the standard position through superhuman reading, they revealed a line that humans had undervalued but could then learn from.

⬐ PandaPanda150

I'd like to add something: It is not necessary an error for humans to play this new AI opening strategy.

If human players do the new AI move, an attack to cancel out the better fighting position is unavoidable. We've learned from AI how to attack and defend in this situation, so even human players are prepared and know what to do. So it is fair game for the two human brains again.

The player who gets out of this fight with the worse position is on a rather high disadvantage for the rest of the game.

So you can think of this new strategy as doubling the stake of the game very early on. For players who are really good at fighting and handle the complexity that will arise from this fight this strategy is a good choice.

⬐ yesenadam

Lee Sedol was rated #4 at the start of 2016, the year the match was played. 3 years later, aged 36,

> Lee announced his retirement from professional play, stating that he could never be the top overall player of Go due to the increasing dominance of AI. Lee referred to them as being "an entity that cannot be defeated"

https://en.wikipedia.org/wiki/Lee_Sedol

His rating seems to have crashed and burned from soon after the match in 2016 until his retirement in 2019..

https://www.goratings.org/en/history/

The #1 rated player is now Shin Jin-seo.

> In January 2019, Shin was defeated by South Korean Go program HanDol. The program defeated the top five South Korean go players. HanDol has been compared to AlphaGo, but is considered to be weaker.

https://en.wikipedia.org/wiki/Shin_Jin-seo

⬐ bumbledraven

"7% Documentary: Behind the scenes of Fine Art AI - 纪录片《7%》：揭秘人工智能“绝艺”夺冠幕后 腾讯网 - English subtitles" (https://v.qq.com/x/page/r0025m06t5o.html) is a neat 2018 documentary about Fine Art, a world-class Go AI created by Chinese developers at Tencent. The documentary features extensive commentary by the main programmers, Ma Bo and Tang Shanmin, as well as the project lead Liu Yongsheng.

This bit from 4:02 stuck out to me:

INTERVIEWER: What level do you think Fine Art has reached?

MA BO: About the same level AlphaGo had when playing against Li Shiqi last year.

INTERVIEWER: Then wouldn't you regard what you do as a redundant work?

MA BO: How should I say this? Its like, when China made the atom bomb after America. Was that redundant work?

⬐ None

None

⬐ DSingularity

The Chinese have a level of nationalism that many nations lack.

⬐ duttaditya18

That is a good thing.

⬐ pathseeker

no it's not

"identification with one's own nation and support for its interests, especially to the exclusion or detriment of the interests of other nations."

⬐ worrycue

I feel it just artificially and pointlessly divides us and create needless "us vs them" situations.

Nationalism has interesting "properties".

It carries with it the implicit assumption that people miles away in the same nation that you have never met share some of the same beliefs and values you do.

It somehow justify you taking credit for the accomplishments of others in the same nation even though you haven't done anything to contribute to said accomplishment.

⬐ jhgb

> I feel it just artificially and pointlessly divides us and create needless "us vs them" situations.

You may be objectively correct, but if it weren't for nationalism, my culture wouldn't exist today and I would have been a German, as would have been my ancestors for more than a century. Just because something is "artificial and pointless" doesn't mean that it's bad.

⬐ worrycue

> but if it weren't for nationalism, my culture wouldn't exist today

I wouldn't be so sure about that. Frequently the conqueror gets culturally influenced by the people they conquer - especially 100+ years ago where totalitarian levels of control were much harder to implement.

Also when your people repelled the Germans, it might have more to do with cultural differences than nationalism - empires who succeed in holding on to capture territory are often those that absorb and incorporate the culture of the people they conquered.

⬐ jhgb

Nationalism itself has a lot to do with cultural differences. Especially Czech nationalism.

> empires who succeed in holding on to capture territory are often those that absorb and incorporate the culture of the people they conquered

Well I didn't notice a lot of Germans and Austrians speaking Czech, to be honest.

⬐ fabianhjr

Well, there are many "nationalists" everywhere but, for example instead of going:

"hey, China built the most extensive high speed railway network in the world in the last 15 years and the fastest operating trainsets, we can and should do better" they do something else entirely.

⬐ Kiro

Why did the mods change the title and put an incorrect year in it? The movie is from 2017.

⬐ throwaway81523

Thanks for that. I have seen it in that case. I saw 2020 and thought wow, there is a new documentary, I'll watch it when I get a chance. Mods can you fix this?

⬐ ZephyrBlu

The upload date is 2020.

⬐ rdli

I originally watched this on Amazon Prime, but didn’t realize it was now for free on YouTube, which is why I submitted. I suppose I could have put “AlphaGo Documentary on YouTube” or some such ...

(original poster)

⬐ TchoBeer

I could've sworn I saw it for free on YouTube in 2018 or so. Am I just remembering wrong?

⬐ Kiro

I also saw it for free back in the day.

⬐ Kiro

Your original submission and title were fine. It's the addition of (2020) I dislike which I presume was added by the mods.

https://hackernewstitles.netlify.app/

⬐ esturk

I'm curious to ask what's next for this series of AI at Deepmind? Is there other more challenging problems they are tackling at the moment? I read they have already master StarCraft 2 even.

Is it the case that they have stopped this series of AI and going all in on protein folding at the moment?

⬐ shmageggy

This blog post mentions testing on Atari and being applied to chemistry and quantum physics

https://deepmind.com/blog/article/muzero-mastering-go-chess-...

⬐ westurner

AlphaFold 2 solved the CASP protein folding problem that AFAIU e.g. Folding@home et. al have been churning at for awhile FWIU. From November 2020: https://deepmind.com/blog/article/alphafold-a-solution-to-a-...

https://en.wikipedia.org/wiki/AlphaFold#SARS-CoV-2 :

> AlphaFold has been used to a predict structures of proteins of SARS-CoV-2, the causative agent of COVID-19 [...] The team acknowledged that though these protein structures might not be the subject of ongoing therapeutical research efforts, they will add to the community's understanding of the SARS-CoV-2 virus.[74] Specifically, AlphaFold 2's prediction of the structure of the ORF3a protein was very similar to the structure determined by researchers at University of California, Berkeley using cryo-electron microscopy. This specific protein is believed to assist the virus in breaking out of the host cell once it replicates. This protein is also believed to play a role in triggering the inflammatory response to the infection (... Berkeley ALS and SLAC beamlines ... S309 & Sotrovimab: https://scitechdaily.com/inescapable-covid-19-antibody-disco... )

Is there yet an open implementation of AlphaFold 2? edit: https://github.com/search?q=alphafold ... https://github.com/deepmind/alphafold

How do I reframe this problem in terms of fundamental algorithmic complexity classes (and thus the Quantum Algorithm Zoo thing that might optimize the currently fundamentally algorithmically computationally hard part of the hot loop that is the cost driver in this implementation)?

To cite in full from the MuZero blog post from December 2020: https://deepmind.com/blog/article/muzero-mastering-go-chess-... :

> Researchers have tried to tackle this major challenge in AI by using two main approaches: lookahead search or model-based planning.

> Systems that use lookahead search, such as AlphaZero, have achieved remarkable success in classic games such as checkers, chess and poker, but rely on being given knowledge of their environment’s dynamics, such as the rules of the game or an accurate simulator. This makes it difficult to apply them to messy real world problems, which are typically complex and hard to distill into simple rules.

> Model-based systems aim to address this issue by learning an accurate model of an environment’s dynamics, and then using it to plan. However, the complexity of modelling every aspect of an environment has meant these algorithms are unable to compete in visually rich domains, such as Atari. Until now, the best results on Atari are from model-free systems, such as DQN, R2D2 and Agent57. As the name suggests, model-free algorithms do not use a learned model and instead estimate what is the best action to take next.

> MuZero uses a different approach to overcome the limitations of previous approaches. Instead of trying to model the entire environment, MuZero just models aspects that are important to the agent’s decision-making process. After all, knowing an umbrella will keep you dry is more useful to know than modelling the pattern of raindrops in the air.

> Specifically, MuZero models three elements of the environment that are critical to planning:

> * The value: how good is the current position?

> * The policy: which action is the best to take?

> * The reward: how good was the last action?

> These are all learned using a deep neural network and are all that is needed for MuZero to understand what happens when it takes a certain action and to plan accordingly.

> Illustration of how Monte Carlo Tree Search can be used to plan with the MuZero neural networks. Starting at the current position in the game (schematic Go board at the top of the animation), MuZero uses the representation function (h) to map from the observation to an embedding used by the neural network (s0). Using the dynamics function (g) and the prediction function (f), MuZero can then consider possible future sequences of actions (a), and choose the best action.

> MuZero uses the experience it collects when interacting with the environment to train its neural network. This experience includes both observations and rewards from the environment, as well as the results of searches performed when deciding on the best action.

> During training, the model is unrolled alongside the collected experience, at each step predicting the previously saved information: the value function v predicts the sum of observed rewards (u), the policy estimate (p) predicts the previous search outcome (π), the reward estimate r predicts the last observed reward (u). This approach comes with another major benefit: MuZero can repeatedly use its learned model to improve its planning, rather than collecting new data from the environment. For example, in tests on the Atari suite, this variant - known as MuZero Reanalyze - used the learned model 90% of the time to re-plan what should have been done in past episodes.

FWIU, from what's going on over there:

AlphaGo => AlphaGo {Fan, Lee, Master, Zero} => AlphaGoZero => AlphaZero => MuZero

AlphaGo: https://en.wikipedia.org/wiki/AlphaGo_Zero

AlphaZero: https://en.wikipedia.org/wiki/AlphaZero

MuZero: https://en.wikipedia.org/wiki/MuZero

AlphaFold {1,2}: https://en.wikipedia.org/wiki/AlphaFold

IIRC, there is not an official implementation of e.g. AlphaZero or MuZero with e.g. openai/gym (and openai/retro) for comparing reinforcement learning algorithms? https://github.com/openai/gym

What are the benchmarks for Applied RL?

From https://news.ycombinator.com/item?id=28499001 :

> AFAIU, while there are DLTs that cost CPU, RAM, and Data storage between points in spacetime, none yet incentivize energy efficiency by varying costs depending upon whether the instructions execute on a FPGA, ASIC, CPU, GPU, TPU, or QPU? [...]

> To be 200% green - to put a 200% green footer with search-discoverable RDFa on your site - I think you need PPAs and all directly sourced clean energy.

> (Energy efficiency is very relevant to ML/AI/AGI, because while it may be the case that the dumb universal function approximator will eventually find a better solution, "just leave it on all night/month/K12+postdoc" in parallel is a very expensive proposition with no apparent oracle; and then to ethically filter solutions still costs at least one human)

⬐ forgot-my-pw

I'm seeing a lot of cool stuff that people start to build on AlphaFold lately:

- ChimeraX: https://www.youtube.com/watch?v=le7NatFo8vI

- Foldit: https://www.youtube.com/watch?v=nA5GzQDTF20

⬐ westurner

Libraries.io indexes software dependencies; but no Dependent packages or Dependent repositories are yet listed for the pypi:alphafold package: https://libraries.io/pypi/alphafold

The GitHub network/dependents view currently lists one repo that depends upon deepmind/alphafold: https://github.com/deepmind/alphafold/network/dependents

(Linked citations for science: How to cite a schema:SoftwareApplication in a schema:ScholarlyArticle , How to cite a software dependency in a dependency specification parsed by e.g. Libraries.io and/or GitHub. e.g. FigShare and Zenodo offer DOIs for tags of git repos, that work with BinderHub and repo2docker and hopefully someday repo2jupyterlite. https://westurner.github.io/hnlog/#comment-24513808 )

/?gscholar alphafold: https://scholar.google.com/scholar?q=alphafold

On a Google Scholar search result page, you can click "Cited by [ ]" to check which documents contain textual and/or URL citations gscholar has parsed and identified as indicating a relation to a given ScholarlyArticle.

/?sscholar alphafold: https://www.semanticscholar.org/search?q=alphafold

On a Semantic Scholar search result page, you can click the "“" to check which documents contain textual and/or URL citations Semantic Scholar has parsed and identified as indicating a relation to a given ScholarlyArticle.

/?smeta alphafold: https://www.meta.org/search?q=t---alphafold

On a Meta.org search result page, you can click the article title and scroll down to "Citations" to check which documents contain textual and/or URL citations Meta has parsed and identified as indicating a relation to a given ScholarlyArticle.

Do any of these use structured data like https://schema.org/ScholarlyArticle ? (... https://westurner.github.io/hnlog/#comment-28495597 )

⬐ krasi0

I wish they tried to tackle StarCraft 1 BroodWar AI where the game is (arguably) even harder than SC 2. Besides, there's a healthy BroodWar AI community with some very strong AIs out there.

⬐ d13

I’d like to see AI take on a really difficult game like Root.

⬐ ggnore7452

AlphaStar is really amazing achievement. but this is not like AlphaZero for Go, which completely made people rethink how Go should be played. AlphaStar didn't "solved" SC2.

It can still fall to very simple early strategy, and in few cases, it doesn't seem to understand some basic ability despite played millions of times. Even I can beat the advanced version of AlphaStar in Blizzcon, where DeepMind has a dedicated area for playtest.

After all, RTS games like StarCraft II which has imperfect information, asymmetrical unit, and require real-time reaction with long strategical planning is very different from Go...

(and protein folding is just too impactful)

⬐ plasma

This is a great watch, since then there has been AlphaGo Zero which surpassed the AI you see play Lee, in 3 days.

https://deepmind.com/blog/article/alphago-zero-starting-scra...

⬐ some_cut

There has also been AlphaZero [1], a generalized version of AlphaGo which also has been trained to play Chess and Shogi (all learning from only the rules), and MuZero [2], which is a further generalization which can also play Atari games and does not even use the rules of the game when doing tree search - it has to learn a model of the rules instead.

[1] https://deepmind.com/blog/article/alphazero-shedding-new-lig...

[2] https://deepmind.com/blog/article/muzero-mastering-go-chess-...

⬐ sillysaurusx

Highly recommend the Deep Blue documentary too. https://www.youtube.com/watch?v=HwF229U2ba8&ab_channel=Fredr...

It's easy to forget that none of this was guaranteed to work. Nowadays it feels inevitable that Chess and Go would fall to computers, but in the moment it was quite a different experience.

⬐ DantesKite

It really is a brilliant documentary, even if you have no interest in artificial intelligence or Go.

⬐ vavooom

This documentary really helped me understand and appreciate Go way more. Started my new found love of the game. Wish more people in the US played!

⬐ therein

There are quite a lot of us actually. See you on online-go.com.

⬐ mypastself

Thanks for this, an enjoyable documentary I remember seeing on Netflix a few years ago but never getting around to it.

I understand it was made with a broader audience than HN in mind, but I wish they extended the runtime to cover the technical aspects in greater depth.

As it is, it’s a quality sports movie, although I do find it amusing that in a match between a self-made young man and a cutting-edge piece of technology developed by one of world’s most powerful companies, the latter was initially presented as the underdog.

⬐ sytelus

More recent but not full length: AlphaFold https://www.youtube.com/watch?v=gg7WjuFs8F4&t

⬐ platz

feel free to add/play me https://online-go.com/player/1021081/ ~8k

⬐ 29athrowaway

The best way to learn the rules of the game, as well as basic skills, is an app called badukpop. https://badukpop.com/

Once you've learned enough you can try playing via OGS, http://online-go.com

Don't expect to win right away.

⬐ manigandham

One of the best films I've ever seen. Brilliant storytelling about man vs machine at the last frontier of our minds.

⬐ dstala

Narration, BGM - all good. Even someone with absolute no interest in AI will get goosebumps.

(Man) vs (Man with Machine)

⬐ mNovak

This film in large part inspired me to start playing Go

⬐ tkgally

It's a great film and a great game.

I learned how to play about forty years ago, when I was studying math in graduate school in the United States. I liked go partly because I felt refreshed after playing it, whether I won or lost, while chess had given me headaches.

I moved to Japan a few years later, and for a while I played fairly regularly at go clubs in Kabukicho and Takadanobaba in Tokyo. The shot early in the film of some old guys playing go reminded me of those places. They were seedy and smoky, but you could get a game any time of the day or night. Many of the older players seemed practically to live there. Once I got matched against someone who I found out later was a highly ranked professional. At first he seemed interested in playing against a foreigner, but he started looking bored after about my fifth move.

I stopped playing after my kids were born, and I haven't sat at a board—or played a game against a human online—in thirty years. But now that I’m approaching retirement myself, and I have a grandchild who will soon be old enough to learn, I’m thinking that maybe I should take it up again.

⬐ notanog

Its starwars for AI starters.

⬐ bumbledraven

(2020)

⬐ Kiro

The movie is from 2017.

⬐ punnerud

Collective > individual. Do I need to say more?

⬐ platz

Michael Redmond's Go TV (was U.S. commentator for the official alphago games) https://www.youtube.com/channel/UCRJyagla1B5cxIfR4i2LdgA/vid...

He has some nice playlists:

- how to play go (for beginners) https://www.youtube.com/watch?v=KTWujSwL2bQ&list=PLW5_cMTm0w...

- basic openings (joseki) https://www.youtube.com/playlist?list=PLW5_cMTm0wvZOTchMWZag...

- joseki made popular by AlphaGo/AIs https://www.youtube.com/playlist?list=PLW5_cMTm0wvZU5pQhmQFw...

also of interest, Go Pro Yeonwoo (korean go professional) https://www.youtube.com/user/goingceo/videos

⬐ yesenadam

Ahh I wish I'd watched the first few in that beginners playlist before watching the movie! Thanks for that.

⬐ spindle

I'm so pleased to see this upvoted. Michael Redmond is unspeakably wonderful. He's a top professional player, is fantastic at explaining everything to a kyu player, and prepares his videos really dilligently.

⬐ falcrist

Isn't he also the only American to reach the professional grade of 9-Dan?

What do you make of the documentary on AlphaGo where the AI did seemingly suicidal and incomprehensible moves to the human masters but won in the end, baffling everyone? https://youtu.be/WXuK6gekU1Y

I remember first discovering the game of Go, after watching the AlphaGo Documentary [1]. I highly recommend it, will definitely be signing up for this, I usually play on mobile :)

[1] https://www.youtube.com/watch?v=WXuK6gekU1Y

⬐ beermonster

The Surrounding Game and the Alpha Go documentary on Netflix are well worth a watch.

Just posting a link to a documentary:

https://www.youtube.com/watch?v=WXuK6gekU1Y

If you haven't seen it, the documentary on DeepMind's AlphaGo is very well done. The climax revolves around the AI expressing what we humans call "creativity" in a match against decorated player Lee Sedol. Watching Lee Sedol's reaction is deeply moving.

Free to watch on YouTube: https://www.youtube.com/watch?v=WXuK6gekU1Y

⬐ TaupeRanger

It's not creativity. It's optimization. No new explanatory knowledge is created unless a human analyzes the move and learns something from it.

⬐ newen

You can think of probabilistic tree search as creativity and the neural network as intuition. Like, what are you doing when you think of a cool new idea? You think of a lot of ideas and narrow them down slowly until you reach a cool new one. It’s not magic.

⬐ Igelau

IMHO creativity isn't really as conscious as people seem to think. I have had a handful of hyper productive songwriting sessions and by the end it always felt more like I was the horse than the rider. Neither was anything made so much as it was found.[1]

There are constraints and limits and rules: rhyme scheme, meter, key, chord progression, genre, structure, and whether or not those things will support the consistency and coherence of the content... that's barely scratching the surface. The "choices" and the "thinking" are highly constrained. Few things are going to work. It's often either "this way" or "wo, back it up".

Even in something like Jazz there are still intricate rules at play and the true greats were eccentrics and/or taking loads of drugs in order to push the boundaries where they could.

[1] And then that one time where I finally cracked the melody at 3am, only to realize in the morning that it was Neil Young's "Heart of Gold". Even my creation was not my creation!

⬐ TaupeRanger

We have literally no idea what thoughts are or how they are implemented in human brains. This is such a silly argument at this point. For 50 years people have been saying "obviously humans are just doing a probabilistic search" or some other such evidence-free assertion, claiming "it's not magic", an obvious strawman since no one is claiming it is. And for 50 years we have not created any system capable of creating new explanatory knowledge. Yet still the same claims are made, week after week.

⬐ newen

I’m not saying humans are doing probabilistic search (not saying they are not either). I’m saying that if probabilistic search produces a result that people see as creative, then we can regard probabilistic search as something that produces creativity.

Furthermore, just because we don’t have the computational capability yet to replicate human thought processes using, for example probabilistic search, does not mean that probabilistic search is not part of replicating human thought processes.

⬐ JRKrause

This comment, that AlphaGo showed "creativity", seems so ridiculous to me. Alphago is operating purely on bayesian statistics (with very good priors) so it's decisions are exactly the opposite of creativity.

⬐ bcrosby95

This criticism reminds me a bit of one of my least favorite interview questions I've had: what was your most creative solution to a problem? I found it awkward because creativity tends to be what we recognize in others rather than ourselves. If I came up with a solution I probably wouldn't consider it that creative.

Of course I still made something up.

⬐ chki

The end of your made up story should have been to point out that actually giving this creatively thought out answer right now was the most creative solution to a problem.

⬐ lotyrin

And biological life has been doing... what exactly?

⬐ _acco

It's an invitation to define (or re-define) creativity, my friend :)

If it seems ridiculous to you, that's a good signal that you'd get a lot out of digging into philosophy of mind.

Where do our thoughts come from? Creativity? Is it so preposterous that these too arise from probabilistic electrical storms - but in wet matter as opposed to hard?

Where thoughts come from may be too complex for us to model now, maybe ever. But if you look deep enough, I think you'll find at heart deterministic processes driven by networks that were trained through nature + nurture.

Maybe creativity is less ethereal than we perceive. Maybe it would benefit us to start considering how non-sentient intelligence can achieve it.

⬐ JRKrause

I am not making, and do not explicitly agree with, the statement that only human brains are imbued with 'creativity'. However, the core of the alphaGo algorithm is just a monte-carlo tree search of the game initially populated with prior estimates of the value of each game state(with the generation of the prior value estimates being carried out by the DNN). I see no space in this simplistic statistical game analysis for concepts such as creativity. The move in question was chosen because it's prior value estimate by alphaGo was favorable and upon further analysis was concluded to be the most likely to result in a game victory relative to all other available moves. If alphaGo was only trained on human matches and subsequently was able to discover moves that drastically deviate from typical human strategies, I would be more open to labeling it as creative. However, most of alphaGo's training is carried out against itself, it's entirely possible that alphaGo has made this particular(or similar) move many times against itself during training.

⬐ _acco

Perhaps in 100-200 years, some neuroscientist will make the same remark about a painter’s brain — “it’s just math, I can model it!”

⬐ elcritch

Much of human creativity is likely linked to some sort of Bayesian analysis, but it also can happen based on limited sample sets and unseen situations. As you point out, the Alpha Go likely just saw that scenario out of millions of random scenarios never explored by humans before. Given the enormous compute resources used, it's possible AlphaGo has made more Go moves than all of humanity ever combined.

Reminds me of a quip about Richard Feynman that he'd spend many hours honing a new way of doing a physics problem, but carefully present it as if he just did it off handed. So we just need to link human brains with cybernetic implants to compete. ;)

As a long-time Go player and ML guy who even built his own (albeit shitty) Go AI in college, I'm a bit biased, but watching the AlphaGo-Lee Sedol match really felt like watching our generation's moon landing.

If y'all haven't already, there's a new AlphaGo documentary made by DeepMind on YT: https://www.youtube.com/watch?v=WXuK6gekU1Y. Brought tears to my eyes. Both a triumph for humanity in building an unbelievable machine like this, and a loss for humanity in that the infinite mystery of Go will be diminished, and again a triumph for humanity in Lee Sedol's brilliant win in Game 4...

⬐ pishpash

Really great documentary, can't recommend it enough.

⬐ falcor84

The documentary is excellent. I just wanted to mention that it's from a couple of years ago; it's just only now been put on YouTube.

https://www.imdb.com/title/tt6700846/

⬐ starpilot

Q&A after winning game 4:

> What were you thinking when you made that play [move 78]?

> Lee Sedol: Move 78 was the only move I could see. There was no other placement. It was the only option for me, so I put it there.

⬐ lozaning

My understanding of high level go is that eventually how good you are comes down to something along the lines of your midichlorian count.

⬐ fyp

Top youtube comment:

> "Move 78", that will be the name of my resistance movement when the machines takes over.

⬐ snarf21

This is one of the things that bugs me a little in the AI vs Human games. Reading the board is such a difficult skill in GO and humans get fatigued. We have to visually track all the places we could move. It only takes missing one of the top 10 moves on any given turn to lose against Alpha. It would be interesting to have AG or AZ play Lee when he has a day for each move. I'm not saying Alpha wouldn't still win but the computer is so fast and can consider so many things that a human can't hold only in their thoughts.

I do think it is a great achievement how far AI or ML has come. Not to take anything away from the team's accomplishments.

⬐ sebastialonso

Thank you for this! It was a great piece.

⬐ mxcrossb

FYI Michael Redmond has been doing free commentaries on Alpha Go’s games against itself which are really fun to watch: https://www.youtube.com/playlist?list=PLqpN3-2FP-kIxXhhdDds9...

⬐ joeblau

[SPOILERS] To me there are five super interesting points in this video.

1. The arrogance/confidence that people had towards AlphaGo assuming that it was not going to be competitive.

2. The lack of understanding tt even the programmers had for the game. At one point the devs were saying "I don't even know if that's a good move or not", yet their program is playing at superhuman play.

3. The amazement that commentators and other players experienced when AlphaGo performed move 37 in game 2.

4. The disappointment experienced by everyone when they came to the realization after game 3 the computer won.

5. The confusion when the computers neural network fails in game 4 and everyone (except the engineering team who sees the probabilities) is confused about whether the machines play is brilliant or terrible.

⬐ esjeon

Just random thoughts:

> 1. The arrogance/confidence that people had towards AlphaGo assuming that it was not going to be competitive.

This is pure stupidity. Those people didn't realize the nature of the game. Go is a (large) confined space, which, by nature, can be brute-forced for the best answer. ML was needed simply to trim and optimize search tree, to find the best local maxima from a broader range (than other AIs).

> 2. The lack of understanding tt even the programmers had for the game. At one point the devs were saying "I don't even know if that's a good move or not", yet their program is playing at superhuman play.

You can't even predict solutions from a simple DFS. Right things are not always obvious nor intuitive.

> 5. The confusion when the computers neural network fails in game 4 and everyone (except the engineering team who sees the probabilities) is confused about whether the machines play is brilliant or terrible.

It's likely that AlphaGo didn't look beyond "horizon"[1], and only realized it's losing after a dozen moves. In this sense, move 78 in game 4 itself is more amazing than move 37 in game 2. The only reason why the latter is uncommon is it's believed that it's difficult to build house in the sky(middle), thus one should keep stones close to ground(border), but that naturally depends on the situation.

[1]: https://en.wikipedia.org/wiki/Horizon_effect

⬐ tobr

I watched the entire thing, but I know close to nothing about Go. I found it frustrating that they didn’t attempt to explain what was so unexpected about some of the specific moves, but just focused on everyone’s reactions to them.

Is there somewhere I could read a beginner-friendly explanation of what was so significant about the moves, and how and why they changed the games?

⬐ viig99

Lee sedol is a pretty cool guy and a normal human being, albeit the crazy ability to remember games likes maps, do watch the latest episode of 'master in the house' a korean variety show which featured lee sedol as the guest.

⬐ ipnon

I'm amazed that any 1 person can even compete with a team of dozens of Ph.Ds and teraflops upon teraflops of processing at all.

⬐ londons_explore

Can anyone who has watched the whole thing give a summary for those of us not sure if we should dedicate a full 90 minutes to this?

⬐ aquarin

Unexpectedly emotional. Movie about how people feel when meeting the new technology, not about the computers.

⬐ dntbnmpls

I don't know much about Go and my interest with Alpha is primarily with chess (alphazero). But I watched it on Netflix anyways a while back and enjoyed it. It's an interesting behind the scenes look at the lead up to the match, the match itself and the fallout. Definitely worth a watch.

If you want to see alphazero games, there are a bunch of analysis of alphazero and stockfish games on youtube as well.

⬐ nmstoker

I'd echo this. It's the same as the version on Netflix. It's not a technical deep dive (but then again, that's rare in any documentary - even the General Magic documentary didn't go into the technical aspects really). It's a human interest piece with lots of the context and interviews with key people. Obviously tastes will vary but I would recommend it to the typical HN reader.

⬐ tgpc

humans: go is hard. machines will never be able to play at the highest level.

machines: I have won

humans: oh

⬐ gwern

I've been reflecting on exponentials and humans in light of coronavirus.

AlphaGo showed the same dynamic: I don't know if everyone remembers, but before the Lee Sedol tournament, most 'sensible' 'respectable' people assumed that there was no way AlphaGo would win, any claims it would stomp Lee Sedol were hyperbolic AI alarmism, and the only question was whether DeepMind would be saved from acute embarrassment by eking out one victory over Lee Sedol or if he would simply clean the board with AG. And in the absolute worst-case scenario, it might be a fairly even match. After all, anyone could look at the Fan Hui vs AG games which were released a few months before and see that AG was really crappy, hardly even human pro level; Lee Sedol wouldn't break a sweat. Yes, it would improve if they trained it more and tweaked it more, but so what? There were so many ELO points between Fan Hui and Lee Sedol, and AIs lack human intuition. Just drawing a line is mindless curve-fitting, which sensible respectable people know better than to do.

Meanwhile, people like Eliezer Yudkowsky considered the training time and thought through the exponential graph, and concluded that, given all those months between Fan Hui and the tournament and the fact that DM was willing to do the tournament at all with so much money on the lines, AG would either lose terribly or could well go 5-0. As it happens, his prediction was wrong. It actually went 4-1, because Lee Sedol got lucky and accidentally triggered a 'delusion' (fixed in AlphaZero).

We went from 'the best MCTS might be somewhat competitive with a very low-ranked human pro head-on' to 'superhuman' in a matter of months, because AG performance scaled with compute. We've seen similar capability leaps with DoTA2 or SC2: the best non-cheating AI was so low level it basically didn't exist (DoTA2) or was nowhere near competitive with any pros (SC2) and then within a few months of finding a working architecture (compare Oriol Vinyal's demo at Blizzcon to the matches just a few months later with SC2 pros), they reach better-than-most-pro levels even if they are not strictly superhuman yet.

That's all fun and games when it's just board games or computer games, of course...

⬐ yazr

My only nitpick is that AG (ML + search really) improved logarithmic-ally with exponential compute.

In addition, real world domains may not be some parallizable.

So yes, current AI is like a brilliant intern. It will eventually be your manager, but its gonna take a few more years ?

⬐ roenxi

That wasn't quite the dynamic; the version that played against Fan Hui was pretty good. That would have been more than enough to claim that the AI was highly competitive against human professionals.

The surprise was that it was assumed that Neural Nets would scale exponentially like other computer projects - over the medium term, with a requirement to rewrite the program to take advantage of new hardware opportunities. The idea that the same program + 6 months could scale exponentially was pretty radical even to people who knew a lot about computers. I was certainly surprised because I'd made the assumption that AlphaGo could scale exponentially with improvements in graphics cards as opposed to exponentially with Google's commitment of computer hours. Obviously wrong in hindsight, but it wasn't a misunderstanding of exponentials, it was a misunderstanding of neural nets.

And we only really had one data point at the time, which was the Fan games. Couldn't even really draw a line without knowing a lot about the field.

⬐ okareaman

I'm assuming you don't know anything about the extremely surprising, deeply emotional and spiritual experience it was for everyone involved or you wouldn't be asking this and just watch it with fascination.

⬐ the8472

Not everyone is interested in the people stories and "deeply spiritual" parts.

Personally I'm not a fan of waxing, talking heads "documentaries" that tell us very little about what was actually done.

⬐ okareaman

It's not a technical deep dive if that's what you're looking for

⬐ jasode

It's a "human interest" type of documentary which means it's deliberately structured as an "emotional narrative".

I think it's fair to say that a lot of "no-nonsense-just-give-me-the-facts" viewers in the HN demographic would find this type of film very unsatisfying. I.e. lots of interviews with virtually no technical explanations.

I quickly scrubbed the film and it looks like it's the same footage from the AlphaGo documentary I saw ~2 years ago:

https://www.imdb.com/title/tt6700846/

I don't know if this new video url dated "2020" is a different film or if they simply enhanced the 2017 documentary with new footage.

⬐ okareaman

> a lot of "no-nonsense-just-give-me-the-facts" viewers in the HN demographic would find this type of film very unsatisfying

I disagree with this assertion. The film explores the idea that AlphaGo, like the game of Go itself, raises a lot of questions about what it means to be human. The relationship between AI and humans is developing and a topic of intense interest, especially among the HN crowd. I think what you might be saying is people with a high IQ but low EQ might not relate to this film. I'm sitting here wondering if people like that don't find human/AI intersection interesting because they are already mostly like a computer program themselves so it's not a big deal.

⬐ jasode

>, raises a lot of questions about what it means to be human. The relationship between AI and humans is developing and a topic of intense interest, especially among the HN crowd.

We're talking about 2 different things: the importance of the topic vs the the method of exposition.

You're emphasizing the topic's importance. I'm not disagreeing with that. Instead, I'm explaining that the documentary's style of exposition would be a turnoff to a subset of the HN demographic.

As example showing differences in how Battle of Midway can be explained...

Here's the "personality-driven" type of history documentary. Scrub the video and you'll see there's a lot of video showing the narrators walking around the beach and flying in helicopters:

https://www.youtube.com/watch?v=wo9wrUcb8jI

To be fair, some viewers need attractive people talking to the camera to make history "interesting". What does an attractive man walking along the beach have to do with explaining strategy and tactics at Midway?!? Nothing. Maybe some viewers need "eye candy" to go along with their medicine of history. Basically, all of The History Channel is structured as personality-driven type of documentaries with a human-interest angle.

Compare that to another style of delivery with no video of talking heads but has a voice-over with maps and higher information content:

https://www.youtube.com/watch?v=Bd8_vO5zrjo

If HN viewers want the 2nd style of documentary for AlphaGo, this thread's url is not it. The importance of "what it means to be human" is orthogonal to the method of explanation.

⬐ okareaman

Thanks. I understand now.

⬐ akvadrako

Does anyone know how the go world has been evolving since AlphaGo?

I wonder if people are looking for weaknesses or perhaps just ways to contribute, so that a human + AI could beat an AI playing alone.

⬐ blueboo

Human+AI play was forecasted to be the next big thing, but has proven to be a mere curiosity. After all, imagine a sitting down next to a grandmaster who helps you play your moves. Every move comes down to: Do you want to play the best move? y/N

Indeed, the moves that a human would've overruled in the Lee Sedol tournament are the ones that vaulted AlphaGo to victory.

⬐ akvadrako

Move 97 of game 4 didn't seem to help AlphaGo and a human certainly would have vetoed it.

⬐ bernds74

Even more recently, Lee Sedol managed to beat another AI where a human would have vetoed the computer move.

Lee is the real world's John Connor, the only man able to beat the machines.

⬐ roenxi

Openings have standardised around the patterns that the AI systems have converged on, and people are practicing against neural networks/using neural networks to review their games. That is probably increasing the standard of play but it is difficult to measure.

⬐ arvinsim

Even though I am not a Go player, it is concerning. This because now money comes into play rather than just raw skill.

Picture a brilliant but poor player. How would that person fare against someone who has a good AI to practice against?

⬐ ALittleLight

Picture a brilliant but poor person who doesn't have the time to dedicate to the game because they must deal with the constraints of being poor.

Buying high end computer hardware is very cheap relative to the time required to become very good at go. Conversely, having a reasonably strong go program available on your mobile will mean there's more access to people to play and get analysis.

I would expect that AI makes it better for poor people. Previously, a non-poor person might take classes or have a tutor. Now, a poor person can have an AI analysis of each of their games if they want.

⬐ Rerarom

Saw it on netflix last year. Good that it's public now

⬐ 29athrowaway

[2017]

⬐ brilee

Some updates on Computer/Human interaction from a Go AI author. Opinions are my own.

In the Go world, the relationship between computer and human has turned into one of analysis. Every pro in Asia has a powerful GPU-equipped system with LeelaZero or KataGo or Minigo installed, and they use it to study openings and analyze their matches. LZ/MG are similar in that they're from the AlphaGoZero generation of bots, and thus are hardcoded to 7.5 komi and don't play well with handicap stones. KataGo is the open-source project with the newest ideas, using (among other things) territory ownership prediction, allowing it to handle arbitrary komi and handicap. Many pros are switching to it for this reason. All TV broadcast channels use AI for both winrate prediction and variation analysis. They'll report the predicted winrates by all major bots.

There are definitely AI-inspired opening sequences, but I would say that by and large, pro play has stabilized to just be better. A few years ago, pros would just mimic AlphaGo just to see what would happen, but there's becoming a better appreciation for the many different opening styles that are becoming possible, now that we have AI to analyze them. The strongest pros are the ones who spend the most time on their computers.

Despite a few extra years of research, all available AI systems still fail at ladders regularly. Some may have had ladder routines hardcoded in, but these routines still fail at loose ladders and the many wonderful ladder variants out there.

In the online Go world, cheating has become rampant. Top echelon of games/accounts are essentially just people competing on how beefy their GPUs are, and human professionals complain that they can't actually get a game against another person nowadays.

Professional tournaments / pro qualifying exams have had isolated incidents of cheating which are usually fairly obvious, as human playstyle is still recognizably different from computer playstyle.

⬐ convolvatron

idk if other servers do this, but online-go.com has an LZ instance which analyzes your game, showing how much it thinks a certain move has swung the win/loss probability and where it would have preferred to move.

haven't gotten objectively better from looking at it yet, but its super interesting. the ui is pretty nice, showing estimated probability vs move number - letting you find the places where the game swung one way or another any playing through those sequences.

I guess the only other thing I would ask for studying is move-by-move analysis for alternate sequences

⬐ bo1024

Thanks! Very informative.

⬐ shmageggy

This basically describes modern chess. Top pros use ultra-deep computers to find opening novelties. Some isolated incidents of cheating in real tournaments. There is some cheating online still, but the novelty wore off long ago so it's not endemic, plus most only play fast time controls online because it's much harder to cheat.