skysight-inc/HackerNews huggingface dataset:
I know it doesn't help the "working 2 jobs to feed 3 kids" people at all, but part of my lifestyle choice is not to have 3 kids. It's also not part of my lifestyle to have a partner who is demanding about having stuff. These are choices and sometimes they are choices that hurt (just like choosing not to eat even though I have money in the bank).

I understand that there are people who are already in a situation where they are in trouble. That's pretty much my point. It doesn't matter if you have 3 kids and can't afford to feed them or if you have $20K in debt and can't make your payments. That's real poverty. Even without an income, I was never poor because I never had those responsibilities.

I feel sad for people in those predicaments. Sometimes it comes through poor choices. Sometimes it comes from unfortunate circumstances that are outside of their control. It doesn't really matter, because you are where you are and there is nothing that will magically transport you to a better place. But you do have choices left in the future and I think it is worth considering the impact that money in the bank can have on your life.
Could machines have become self aware without our knowing it?
I interviewed Ratan Tata on entrepreneurship. Here’s what I took away
I am sure they will. People will have to rival perfect mannered AIs for other people's feels.
There is a great comparison between Apple's old and new protocols: https://dblog.laulkar.com/http2-protocol-for-apns.html
AlphaGo beats Lee Seoul in game one
I was really expecting Lee Sedol to win here.
I'm very excited, and congratulations to the DeepMind team, but I'm a bit sad about the result, as a go player and as a human.
So has AlphaGo raised its level so far just by continuing with the games against itself? Or did they just throw their entire server farm at it? (Or both, probably.)
You actually can use pythonish decorators in Rust, I wrote a proof of concept library for that: https://github.com/manishearth/rust-adorn (it's not really intended to be used, but if folks want to use it I'm willing to clean it up)
The Singolarity is here!
Stack Overflow community doesn't welcome beginners
No, the Higgs boson was detected a few years ago, but was named and predicted in the 1960s. As far as I know, there's no more room in the Standard Model for additional particles.
I, Cringely What's happening at IBM (it's dying)
Hyper's async IO is being actively worked on or done IIRC.
That's fair, but they're really advertising a specific feature. That is, statically generated sites hosted based on a specific branch in a repository. Nowhere do they advertise themselves as a CDN in the way CocoaPods is using them now.
Worrying about the effect of strong AI on sexual relationships is like worrying about the effect on US-Chinese trade patterns if the Moon crashes into the Earth.
But now every amateur will have access to unlimited play against Lee Sedol-level opponents.
Here's one: how long until a computer can beat a human assisted by a computer?
Thank you! This discussion seems to be full of experts (everyone attended school, after all) making sweeping, baseless generalizations.

I experienced what you describe, and it was never even called homework. It was learning spelling, times tables, or reading a chapter of a book. Probably from age 8-11, with no homework before age 8.
DeepMind is an algorithm which clearly improves a lot on traditional tree search. But how will a better tree search algorithm lead to AI taking over? Why does winning at Go mean AI is closer to taking over the world than when it won at Chess?
What I mean is that it doesn't have particular heuristics programmed into it by observing chess experts. It seems to me that the general technique of having policy and value networks combined with monte carlo search scales to other domains and doesn't require the amount of expertise that it took to get Deep Blue to a winning level.

Another way to look at is, is how fast they were able to make it a lot better in a few months.
The reverse actually...
It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
Can anyone tell me if there are any practical applications for this, or it is 'just' a further understanding of the world around us (albeit an important one)?
Will humans be able to keep up with the depth of analysis these AIs will have, or will it become a problem for the AI to dumb down its thinking in order for us to grasp it?

More generally, scientists using AI for research will probably have to do research on the research, to understand what the AI discoveries mean. Maybe they mean something we can't grasp at all, in which case they go completely over our heads, like ants trying to learn about the finer points of financial markets. We will probably have to learn new concepts and even new languages designed by the AI to convey the meaning.
Ah yes, I believe in the machine learning community that ensemble learning technique is called "meat bagging"
Many of the people saying not to worry ALSO predicted AlphaGo. So let's not get ahead of ourselves.
Java evangelist leaves Oracle to save Java
Lonely comment is lonely.

Literally halfs of dozens of research projects and ones of promotional installations served! Nearly threes of dozens attended conferences, at which twos of booths were no doubt tabled, perhaps both by you, one of the only persons who apparently used Plan 9 commercially.

I'm feeling nostalgic enough to go launch an inferno instance now just on principle.
The issue is that we don't take the threat seriously, since we believe we have plenty of time to solve it. Some don't even believe it's a threat at all (will unplug it, right?)

This 10 years to beat go prediction shows that our time estimates are wildly ignorant.
It's not so conspiranoic; the agri-lobby managed to get cereals and grains (+pasta) in the base of the food pyramid, where it should be at the top, since they're essentialy refined carbs.

Granted, in the 50s we didn't have the same scientific knowledge as we do today, but the food pyramid was "invented" by them for their own benefit, make no mistake. Remember when butter was good for you because it lubricated your arteries?[1] Or when doctors were associated with smoking?[2]

This sort of propaganda easily permeates into popular culture and can cause a lot of harm. Refined carbs are 90% guilty of the worldwide obesity pandemic.

[1] https://s-media-cache-ak0.pinimg.com/736x/78/d4/5b/78d45b7a6...

[2] http://editorial.designtaxi.com/news-smoke1804/3.jpg
Here's the Nature article: http://www.nature.com/news/google-ai-algorithm-masters-ancie... (it has a link to the free paper, as well)

The position evaluation heuristic was developed using machine learning, but it was also combined with more 'traditional' algorithms (meaning the monte-carlo algorithm). So it was built specifically to play go (in the same way deep blue used tree searching specifically to play chess.....though tree searching is applicable in other domains).
Amen to that. Fortunately the Swift Package manager (Which is also OSS) is ran by their open source swift team who so far is doing a great job of being open and delivering the best solutions for the job.

TestFlight on the other hand....
Chinese gravitational-wave hunt hits crunch time
I believe that is normally the case, but this wouldn't be the first time a particle was discovered before being predicted.

The muon was discovered before an accurate prediction for it was made. There were some known gaps that people tried to fill with particles related to muons, but no one quite predicted that there was a particle identical to an electron but with a larger mass.

The tau neutrino was discovered without any prior reason to believe it existed. We knew of neutrinos and they seemed to fill the only related gaps we had. It wasn't until the tau neutrino was experimentally detected that we even realized there was a gap for it.
ah, but it's not just tree search. It uses neural nets, which makes it different than the chess algorithms.
Lee Sedol should have played that top left 3,3 move earlier (at least before white covered it) WTF. Humanity is not longer at the top of the intelligence pyramid...
It's not an improved tree search. Deepmind was almost pro level purely using the deep learning network before doing any Monte Carlo search.

One way of looking at the significance of this is that it might tell us that relatively simple machine learning algorithms can capture key aspects of the versatile human cortical capacity to learn things like Go using sheer pattern recognition. (It's amazing that human visual cortex can do that.) If the human brain were more mystical in its power, then human-level ability to recognize Go patterns wouldn't have been penetrable at all to a comparatively simple neural algorithm like deep learning.

From another standpoint, this could show that we're reaching the point where, when an AI algorithm starts to reach interesting levels of performance, a much-encouraged Google dumps 100,000 tons of GPU time and 5 months of a few dozen researchers' time to improve the algorithm right past the human performance level. In N years from now when it's a more interesting AI system doing more interesting cognition, we could see a more interesting result materialize when a big organization, encouraged by some key milestone, invests 5 months of time and massively more computing power to further improve an AI system.
I assume you're wondering about the black market? Traditionally, Russian and Eastern European carder forums. In the golden age of Western Union and Moneygram... More recently, dark web markets over TOR with multi-sig cryptocurrency escrow.
Given the number of vim users I know that install a dozen plugins, I'd wager you're right. Surely, I can't be the only person who uses stock vim, though.
If a child says they already know their elementary school homework, which the GP said should be learning spelling or times tables, it's very easy for a parent to test this. "What is 6 x 9?" "Umm."
It'll be interesting to see what new things we learn about Go itself from DeepMind. The game is very deep, and apparently we haven't found the bottom yet!
It isn't a tree search algorithm.
"holding back failing kids"

Worth pondering this phrasing, I think.
My main concern that's stopping me from using Python 3 for all new projects is the risk of being in the middle of the project, and finding yourself needing a library only to find out that it only works on Python 2.

Couple that with 2.7 being good enough just makes the situation "worse" for 3.
Not exactly true. The detection of neutrino oscillations was concrete evidence of physics beyond the Standard Model. Though the consensus has been that only slight modifications of the Standard Model are needed to allow for such oscillations (and neutrino masses).
Something tells me that no amount of "reliable and consistent data" will convince some people, because they already know what they want to know.

Despite the fact that these things are illegal, there are many scientific studies published, books written and a myriad of personal accounts and interpretations given within literature, art, music, film, design, etc.
Just ask google.

But do you want to really know the "truth" ?

It's very easy to discover it yourself. No cherry picking. Just go experience it.

That's all there is to it.
I posted in the earlier thread because this one wasn't up yet[1].

Some quick observations

1. AlphaGo underwent a substantial amount of improvement since October, apparently. The idea that it could go from mid-level professional to world class in a matter of months is kinda shocking. Once you find an approach that works, progress is fairly rapid.

2. I don't play Go, and so it was perhaps unsurprising that I didn't really appreciate the intricacies of the match, but even being familiar with deep reinforcement learning didn't help either.
You can write a program that will crush humans at chess with tree-search + position evaluation in a weekend, and maybe build some intuition for how your agent "thinks" from that, plus maybe playing a few games.
Can you get that same level of insight into how AlphaGo makes its decisions?
Even evaluating the forward prop of the value network for a single move is likely to require a substantial amount of time if you did it by hand.

3. These sorts of results are amazing, but expect more of the same, more often, over the coming years. More people are getting into machine learning, better algorithms are being developed, and now that "deep learning research" constitutes a market segment for GPU manufacturers, the complexity of the networks we can implement and the datasets we can tackle will expand significantly.

4. It's still early in the series, but I can imagine it's an amazing feeling for David Silver of DeepMind.
I read Hamid Maei's thesis from 2009 a while back, and some of the results presented mentioned Silver's implementation of the algorithms for use in Go[2].
Seven years between trying some things and seeing how well they work and beating one of the best human Go players. Surreal stuff.

---

1. https://news.ycombinator.com/reply?id=11251526&goto=item%3Fi...

2. https://webdocs.cs.ualberta.ca/~sutton/papers/maei-thesis-20... (pages 49-51 or so)

3. Since I'm linking papers, why not peruse the one in Nature that describes AlphaGo? http://www.nature.com/nature/journal/v529/n7587/full/nature1...
@panic I'm sorry but you don't understand go.
The only problem with her/his approach is that she/he said it took 16 hours where they would've liked to only spend 4 and be done (as 4 hours was what was expected). 4 hours may be unrealistic for what was delivered in the end but it would make me feel at least slightly awkward putting 16 hours into a 4 hour assignment as it indicates my performance is not where it should be (I'm off x4 which would be a lot to me) or my priorities are not at all aligned with the company's regarding this assignment.

Don't get me wrong everyone takes whatever time she/he needs and comparing results and time spent is hard unless you only look at "does it work" which in many cases does not do the work justice (and may not even be the most important metric in the long run). For that reason take-home assignments may be better than the almost comical interview often described on HN but they have their flaws that make them far from ideal as well!

I think it is hard to judge the true performance of a potential employee in a company team without actually having the candidate be part of the team (and even then it'll take a good amount time before someone settles in). Some folk may not be the best programmers but are good catalysts in a team, smoothing relations between other colleagues and increasing team output overall. Or they might have a habit of happily taking up tasks that are wildly unpopular and thereby, even if they are not the most performant, solving problems colleagues or perhaps a faster candidate wouldn't have solved. I could go on about this but I think it's clear what I mean.

There is a lot more to a role as programmer than just programming and that is often completely neglected in these discussions.
Regardless of how good the deep learning network is on its own, the algorithm described in the DeepMind paper is an improved tree search.
Instead of the prize money, if I were Lee Sedol I'd request unlimited play time against the latest AlphaGo.
Orchestrator: MySQL Replication Topology Manager
Google's AI wins first game in historic match with GO champion
I watched the commentary that Michael Redmond gave (9-dan-professional) and he didn't point out one obvious mistake that Lee Sedol made the entire match. Just really high quality play by AlphaGo.

Really amazing moment to see Lee Sedol resign by putting one of his opponent's stones on the board.
A top smartphone chess program can beat pretty much all but the best few players in the world. Do you think it's fair to pit a 150 gram device against a 70 kg human?
I'm really torn on this topic. On one hand, the human brain ingrains knowledge by repetition. So this "mindless busywork" is exactly that. They already know how to do it, they just have to work to make it stick. I had an old math professor, he used to say: "repetition, repetition, repetition".

On the other hand, I believe that the time can be far more wisely spend on actual interesting things, incorporating this "mindless busywork"/repetition - it's just not simple or easy to figure out what the activities and problems should be, and in many cases the children need some guidance as well, and/or should be in groups doing this. Given that many parents are unable to help their children because they lack the knowledge to do so, the teachers do the next best thing they know.
We do actually do that: `brew update`, `brew fetch` and `brew install` commands are separate. If you haven't already fetched the sources `brew install` will do them for you. What I'm considering is also making it do `brew update` for you too.
And thank you for taking the time to share some kind words.
Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out.

As hard as writing AI for a problem space like "how to win at Go" is, it's several orders of magnitude easier than creating a general AI with the self-awareness required to see us as a threat.
HashiCorp Atlas loses free tier
Tutorial: Building a Real-Time Retrospective Board with Video Chat
The Real Source of Public Speaking Anxiety
The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.
Elementary schools assign pass fail grades to classes? What's the fallout if you fail?

This is all very foreign to me. When I was in primary school in thr UK 20 years ago we never received a single piece of homework and the only time we got graded was a national standardised test which was positioned as being more about the school than about us.
As a person who knew only the basic rules beforehand, I wouldn't imagine it any better. Any more complicated and I'd get lost.

I'd love to see one day a live commentary, with an extra window showing what computer is thinking at the moment.
Had a number of their dishes as a beta tester. They have nice quality ingredients. The Chicken Pozole was my favorite.

They mentioned that commercial kitchens use this type of tech. I wonder how many restaurants in SF cook their food this way? Maybe this is the next sous vide or at that price point, it could be your next microwave replacement.
There's a program called gucharmap which does similar on linux, hotkeys probably vary though.

http://paste.click/MYRVrF
iOS stock app for breakouts and realtime alerts
Maybe someday Bitcoin will grow up to be a currency
I watched the whole ~4 hours and somehow missed Issa intervention.
Thank you.
The standard model is not a complete model of all elementary particles. It is a model of all known elementary particles.

There are reasonable outstanding predictions for:

- More Higgs bosons

- A Graviton

- Another 17 super symmetrical pairs to the existing 17 known particles.

- One or more dark matter particles

We don't know which (if any) of those will end up coming true, but they could all add new particles to the standard model. It is also entirely reasonable that new particles outside of the ones I listed could be added and incorporated into the model.
I don't think it's accurate to call AlphaGo 'improved tree search' the way that Deep Blue was improved tree search. You could with equal justice call it an improved neural net.
The announcement blog post doesn't give much detail; here is what they said in the email:

"It is important to note that any agreements in place as of March 1st, 2016 will remain unaffected by pricing changes for six months per your original agreement. We are committed to honoring these agreements and hope this alleviates any concerns you may have. If your monthly bill decreases under the new pricing plan, your bill will be converted during the next billing cycle to this new pricing model. If your monthly bill increases, you will remain grandfathered under your current plan for six months. Importantly, after the six month grace period, the 10 node free tier will be removed.

Old pricing: $40/month per aggregate managed nodes with 10 nodes free

New pricing: $20/month per Terraform node and $20/month per Consul node with no free tier. Packer features are included with a minimum Terraform account ($20/month). All Vagrant features will remain free, except for hosting private boxes which will be $5/month per box. If private Vagrant boxes are not transitioned at the end of the six month grace period, those resources will be locked."
I agree, but UPX is magic.
Considering the complexity of the human brain, it seems only fair to balance out a competitor's handicap in some way. Your idea seems to anticipate the logical progression of these tests: "Nature made this mind inside this small object, the brain, why don't we do that?" Regardless, the trend of course is toward miniaturization. I see news like this recent story: "Glass Disc Can Store 360 TB" http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-...
to back up imagined futures like the film Her: https://youtu.be/WzV6mXIOVl4 (and that film doesn't even address whether the OSes are connected through a wireless network).

This stuff is happening fast, and we might have found ourselves, historically, in a place of unintelligible amounts of change. And possibly undreamt of amounts of self-progression.
Steve Jobs, Bill Gates Musical-Comedy Calls Off Broadway Debut
“the most amount of water” is a really funny grammatical concept. Who writes that stuff?
Paper notes: ‘The impact of syntax colouring on program comprehension’
This is such a risky game to play. Yes, I took a similar approach to HS with similar results. But I remember competing with another student to see who could do the best in a class without opening their book. I passed. I didn't do as well as I could have, but I passed. He wasn't so fortunate. By the time he accepted that he was behind, it was too late for him to save his grade. What I'm saying is, students should be taught which homework to do, not to just to skip it.
If it's any consolation, there are still tons of things humans are far better at than machines.
The 3 most important factors in naming your business
That's an expensive lottery ticket to a tour.
I don't think a computer could ever beat me at Calvinball
Has Google talked about the amount of computing resources they're throwing at this match? I'd be very interested to know.
Perhaps the smaller/scrappier startups are trying to have more rigorous processes because small startups don't have as much resources to train new hires when compared to 'big 4' companies, so they really need engineers that can do it all themselves, quickly and under-pressure. They also may be more risk-averse when it comes to false-positives, because if your entire engineering team is 5-10 people, you're much less likely to be able to afford to get it wrong and hire someone that won't end up working out.
The correct characterization is that it's a hybrid of deep learning and tree search. It's also a hybrid in the feature representation for the algorithm. There are raw features (board state), but there are also handcrafted features for the tree search.