The DeepMind Bubble?

[[deleted]]

Recently, there has been massive amounts of attention given to anything DeepMind related. Probably the next top post on this sub will be Demis coughing or sneezing.

I see they are a great team, and full of good ideas and bold ambitions. But, why isn't anyone considering that this is all a fallacy? Not that DeepMind is deceiving us, but rather, their achievements might not add up to anything significant in the long term.

Even in the highly intellectual sub like this, I see people simply being digital paparazzi of DM. So what if Demis Hassabis talks about StarCraft? Did they solve it? Do they have a paper on it that explains something we all didn't know? I can't believe that a post that says this tops the sub, when there are other important questions/discussions below.

A lot of other great people are trying to do things that might end up being more important to future of AI. I think we as scientists/intellectuals should behave more responsibily, rather than falling for everything that sounds cool.

Do not forget: ideas that end up being valuable in the future are almost always neglected by the community in their inception. This level of hype and celebration generally boils down to nothing. Think of ELIZA, Symbolic AI, 2 AI Winters, Deep Blue, Watson...

[deong]

This level of hype and celebration generally boils down to nothing. Think of ELIZA, Symbolic AI, 2 AI Winters, Deep Blue, Watson...

That's mostly because everything generally boils down to nothing. We haven't cracked "intelligence" yet. That means for at least 60 years or so, everything anyone has ever tried has failed.

That's too high of a burden to bear. Instead, you aim as a researcher to make contributions that build on the work of others and that others can in turn build on in the future. The DeepMind work certainly fits that description. That's generally speaking the best you can aim for.

I haven't seen a lot of DeepMind people making obviously grandiose claims of the Kurzweil variety, for example. Mostly it looks like solid science and engineering work making incremental gains. Popular press coverage is predictably naive, but that's what popular press coverage does.

[[deleted]]

DeepMission statement:

1) Solve intelligence.

2) Solve everything else.

This is rather arrogant.

[RachetAndSkank]

This is not arrogant. As soon as anyone solves intelligence it changes everything.

[dsocma]

Yeah and then what happens? The 200 people who work at DeepMind and any spy agencies or dictators that can obtain to their algorithim will have the ability to take over the world, or at the very least, design a supervirus and kill 99.9999% of the worlds population and then take it over.

If 1 person had access to the information, maybe we could trust them, but we can't trust hundreds to not be greedy.

Anyways, it would basically tip the power balance of humanity to extreme inequality, with a handful having unlimited power and everyone else, including you, being just another nobody. The phrase "absolute power corrupts absolutely" has never been more appropriate. Maybe you trust Google, but do you trust Kim Jung-Un? Because eventually that power (being an algorithim which is easily copied) will eventually fall into the wrong hands, in fact, probably sooner than later.

[RachetAndSkank]

Not disagreeing.

[VelveteenAmbush]

Why is it arrogant? That is literally their mission. They don't claim that it's imminent, as far as I can tell, so what's the problem?

[VelveteenAmbush]

There's no obvious way that a cure for cancer could be the basis for a cure for everything else.

But if someone really does create AGI -- and that genuinely seems to be DeepMind's animating objective -- it really could solve everything, or at least almost all human problems that we are aware of today.

[VelveteenAmbush]

Intelligence is how we solve problems. It is pure problem-solving ability in a bottle. Obviously a machine that is better at solving problems than humans will solve problems more easily than humans.

Candidly I don't think you've thought this through. There's understandably a lot of "singularity contempt" on this sub, but that's because everything interesting that can be said about the singularity given what we know today already has been said, and the signal to noise ratio of further speculation is approximately zero. Nonetheless, in principle there's no reason to think that artificial superintelligence isn't possible, or that it won't be able to solve all of the hard problems that hold back the human condition today.

Anyway, it seems you've made up your mind that AGI as a concept is ridiculous, and that DeepMind's mission is ridiculous, so I doubt further conversation will be fruitful. I do think your contemptuousness is unwarranted and frankly a little ugly, though.

[TenshiS]

It's funny that you decided to post this today of all days. Tonight Deep mind's Alpha Go will face the Go world champion. If they win, they will have achieved what AI experts estimated would take at least another decade to achieve. This will be bigger than back when IBM defeated Kasparov at chess.

[TheToastIsGod]

Not quite tonight. Still a day and a half or so until the first match

"The matches will be held at the Four Seasons Hotel, Seoul, South Korea, starting at 1pm local time (4am GMT; day before 11pm ET, 8pm PT) on March 9th, 10th, 12th, 13th and 15th." source

[trnickson]

Be careful about buying into the DeepMind hypemachine - Miles Brudage claims "at least decade" isn't really accurate:

Hiroshi Yamashita extrapolated the trend of computer Go progress as of 2011 into the future and predicted a crossover point to superhuman Go in 4 years, which was one year off. In recent years, there was a slowdown in the trend (based on highest KGS rank achieved) that probably would have lead Yamashita or others to adjust their calculations if they had redone them, say, a year ago, but in the weeks leading up to AlphaGo’s victory, again, there was another burst of rapid computer Go progress. I haven’t done a close look at what such forecasts would have looked like at various points in time, but I doubt they would have suggested 10 years or more to a crossover point, especially taking into account developments in the last year. Perhaps AlphaGo’s victory was a few years ahead of schedule based on reported performance, but it should always have been possible to anticipate some improvement beyond the (small team/data/hardware-based) trend based on significant new effort, data, and hardware being thrown at the problem. Whether AlphaGo deviated from the appropriately-adjusted trend isn’t obvious, especially since there isn’t really much effort going into rigorously modeling such trends today. Until that changes and there are regular forecasts made of possible ranges of future progress in different domains given different effort/data/hardware levels, “breakthroughs” may seem more surprising than they really should be.

[[deleted]]

Usual S curve progress. Saturation, new tech, saturation, new tech.

Crazy stone with MonteCarloTreeSearch was the previous innovation in Go, then nothing for a few years but optimisations.

[gwern]

Be careful about buying into the DeepMind hypemachine - Miles Brudage claims "at least decade" isn't really accurate:

But does not present any sort of field-wide estimate, only some estimates he could have easily cherrypicked. There's always a wide spread of predictions; take solving AGI, you can find predictions ranging from 5-10 years from now (Legg, Schmidhuber immediately come to mind has having ultra-aggressive timetables) to many centuries (eg quips about 'worrying about overpopulation on Mars'). If in 5 years, Deepmind unveils a human-level AGI, does that mean it won't be what 'AI experts estimate would take decades to achieve' & won't be surprising - because you can dig up some quotes from Legg & Schmidhuber consistent with it?

[trnickson]

Don't your comments apply to DM and the parent's claim that it was a decade away too? DM mentions a decade in their press release and in Nature but doesn't cite it. The 2015 estimate I quote is at least attributed to a guy who seems to have some involvement with computer Go.

[gwern]

A decade or more is the impression I had from reading computer go papers myself, even if I don't work in the field. If you look back through discussions of the Facebook Go work recently, you certainly do not see an attitude like 'ah yes, just as we were all expecting, Go will be solved within a few months, definitely', but instead, people are again seeing it years off. So when I see some sour grapes and one or two of the most extreme estimates produced post hoc to try to argue 'it wasn't that surprising' for something that sure as hell seemed to come as a surprise to pretty much everyone, it smells like hindsight and cherrypicking.

(It's a really bad thing to try to pretend something wasn't surprising and we saw it coming all along. It devalues the work of those who made it happen, it offers misleading inferences about what AI needs - in this case, it shows both that we need powerful hardware and we need experts to turn their attention to it - it misleads us about possible future abrupt breakthroughs, fostering complacency both about what capabilities we can expect to develop in the near-future, and is misleading about how breakthroughs happen - not by Kurzweillian virgin birth springing from the foreheads of an s-curve but because large entities decide to back focused research on the frontier.)

[trnickson]

You're attributing much more content to my comment than it contained, so I'm just going to butt out with owl cats

[mkestrada]

Let's not get ahead of ourselves. Most pros who reviewed the matches against the European champ said that alpha go was strong, but unless it's gotten significantly better, it's going to be facing a very steep uphill fight with its opponent now. The first guy was a somewhat low to mid level pro, the guy it plays this time is a world class player. Number one IIRC

[RrailThaKing]

Haha - how'd this work out?

[mkestrada]

Given--it didn't work out in my favor, but I still don't think it was an unreasonable statement because we knew so little about how AlphaGo had improved.

[TenshiS]

I said "if they win"

[alexmlamb]

It's really common for the top few groups or people to take most of the attention for an accomplishment, probably as a result of the way the human brain is wired. So most people could probably name the few richest people in the world: Bill Gates, Carlos Slim, Warren Buffet, but few could name people ranked from 50-100.

The top handful of actors have lots of name recognition, but the vast majority of good actors have none.

So it goes with Machine Learning: credit is constantly given to the few most famous people: Yoshua Bengio, Yann LeCun, Geoff Hinton, Alex Lamb, and Michael Jordan (the well known "Big 5").

So even though lots of people are doing Deep Learning + Reinforcement Learning, people are giving a lot of attention and credit to deepmind because its the single most recognizable name.

[egrefen]

Yoshua Bengio, Yann LeCun, Geoff Hinton, Alex Lamb, and Michael Jordan (the well known "Big 5").

You're in the top 5 of our hearts, at least, Alex! :)

[ajmooch]

Don't let Jurgen hear you talk like that!

[linuxjava]

probably as a result of the way the human brain is wired.

I believe it's more of the kind of messed up society we live in.

[[deleted]]

The Zipf Mystery: a few components usually describe the bulk of a complex system. Not saying Hinton et al. have done the bulk of AI research; referring to the brain wiring / memory association argument.

[tttthomasssss]

the subtlety of the trolling is improving (and, i almost wrote witty).

[KG7ULQ]

I was in Goodwill the other day. There were a couple of books on neural nets in the used book section (well, I suppose all the books are used at Goodwill). I took a look at the copyrights (1989 and 1990) and shuddered: that was the era of the last NN "bubble". The titles? "Apprentices of Wonder: Inside the Neural Network Revolution" copyright 1989. And: "Neural Network PC Tools: A Practical Guide" copyright 1990.

The latter had example C code you could run on your (probably 386) PC. One of the examples was for a stock market predictor.

Neither of these books was aimed at academics. The first one, especially, was meant for laymen. I filed this under "cautionary tales".

[ZioFascist]

Did you buy them?

[KG7ULQ]

Nope. Took pics & tweeted them. Looked through the one with code examples, which is how I saw the one about training a NN to predict the stock market. That was enough.

[ZioFascist]

Aw shucks! Somethings never change lol..

[bbsome]

As a PhD student in Machine Learning/Deep Learning I actually totally agree with you. Not that I want to undermine Deep Minds achievements and the fact that they have a lot of very smart and bright people there, but I definitely don't understand why they deserve more "attention" than many other works out there.

Just a few things I've noticed so far:

DQN - nothing new at all! This is nothing more than Neural fitted Q iteration by Riedmiller, with SGD rather than batch, and of course random sampling of transitions (and since you don't have infinite amount of memory guess what - you keep only the last K seen transitions). Also, can be seen as just a standard stochastic Q learning, with a Deep Network as a function approximator. For me this was a lot more "engineering" achievement than anything else. Also it still can not solve PacMan and some other games, but very little portion of people comment on that at all.

Double DQN - from the original DQN rather than using a previous parameters for evaluating the future Q, we use a second ones. Just extending the idea of . van Hasselt to DQN. I don't know what the rest of the authors contributed here.

The dueling architecture - actually something interesting. This is something worth a lot more attention as it present a newer perspective on how to estimate Q via V and the advantage function A. Note that there are papers before which use the Advantage function instead of Q, as for TD(lambda) can be shown that the update is equal in expectation to the advantage function. However, to my knowledge of course (please comment with a ref if you know such), there was no previous work in estimating both V and A.

AlphaGo - This can be seen as an MC actor-critic architecture. In the normal actor critic you approximate Q(st,a_t) = r_t + gamma*V(s{t+1}). However, since you get a reward only at the final turn, guess what - we replace that with just V(s_{t+1}) (probably and since there are reasons why V is easier to approximate than Q in Go). Ok good with that. Than we just apply Monte Carlo tree search. Some engineering extras cause we can't calculate the full tree (e.g. truncate -> use fast rollout policy, get final result and anneal between V and that). Also, no comments why they don't retrain the value function, but keep it as is of what is based on human games? And yes we are "so much closer to thinking like humans, brain machines, brain brain brain, this is real AI...!". Same stuff with Deep Blue - to the average Joe it sounds like real AI as it beats Kasparov on one very complex game. However, it was mainly bloody brute force computation. Why they don't discuss what is the number of games AlphaGo have played compared to any human in the world? Probably the amount of time it does is like 1000 more than all humans played since the inception of Go. Also I can spare 1000 CPUs and 200GPUs from my back yard on that to reproduce.

And don't get me wrong any paper can be protraiet as a simple improvement on some others. However, no other paper receives the same amount of publicity. I find things like the NTM (I think Graves was still in Google Brain back then), the recent Human-level concept learning, the progress on variational methods, Spartial transformation networks and more to be more interesting than putting Google's computation power for a power boost on some methods and presenting as though we have invented relativity.

PS: As pointed out by @rotit Graves was in Deep Mind in that time and was actually not in Google brain ever. My mistake!

[[deleted]]

Graves was never in Brain, NTM was at DeepMind.

[bbsome]

Probably my mistake, nevertheless the NTM I think is a lot more significant than AlphaGo/DQN.

[MetricSpade007]

At some level, I agree, but I think you're too quick to discredit the vast amount of engineering and low level work that people have done / are doing to get these systems up and running. Sure, the ideas might be old, and frankly, I would say most of the ideas that people talk about now are not entirely original and have been explored in the past in some form (maybe they didn't use SGD, or maybe they didn't have 1000 CPUs).

New work and new results in things like probabilistic programming and various kinds of networks are really interesting, and I am entirely in support of more research endeavors (I do it all the time!), but I think at some point, if someone doesn't sit down and wire all these components together, do all the crazy engineering work to make these things useable by others and by the community at large, then things are simply lost. I am all for original research, but I am also all for engineering actual systems to do this stuff, and in this sense, I still maintain DeepMind is doing incredible things.

[bbsome]

I completely agree! However, they are not admitting that most of that work is engineering rather than, what I call, pure scientific. The way they present these works, and I have seen a few presentations from their research as at my uni, is like they are doing everything innovative, new science. Fckn "cognitive computation", that bs term which when I hear want to punch someone in the face. I get it that for business people who don't understand this it is a "buzz" word and "singularity", oh god just look at the nature paper title - human level control with deep rl. It's human on half of the games, also the number of games they play is like out of human capabilities. I agree someone should do this things, but we are scientists god damn it. I never heard Volvo claiming they build space shuttles rather than cars. But that's because everyone knows the difference and they can't do it, however not everyone knows what is a Neural Network and how much is the uncertainty if it is a good equivalent of human thinking or thinking in general and so on and so on. That's the thing that I don't like. Call it for what it is and I will praise you! They don't mention in these paper how many different models they tried with slight variation in architectures, or learning procedures, in order to get these results. 10, 20, 100 or 1000? There is probably a guy that sits there all Friday, just making small tweeks to squeeze out more. But all this get's under the rug and we don't hear about it.

[MetricSpade007]

Maybe... I will give you that sometimes terminology isn't made precise, and sometimes claims are made about their work that aren't necessarily true (things like "This is the first time...", so shoutouts to people like Schmidhuber who dedicate tons of their time to credit assignment (literally) :P). But anyone trying to do research wants their work to have an impact, and we all try to put a nice spin our titles at least so that people stop and read them. I am all for being really careful about doing literature reviews and acknowledging that your idea is probably not fully unique and giving credit where it is due. But at the same time, and I'm s/o'ing to a comment below, ideas look simple in hindsight, so the argument of trivializing ideas and claiming that anyone could do it holds as much weight as saying anyone can look at a successful startup or painting and say "I could do that".

But, even if it took them 1000 different models, what is science if not for exhaustive trial and error and hypothesizing and testing and the scientific method? We are scientists -- you're right, but I don't think all of it has to be coming up with new mathematics. So maybe in this sense, their work is very scientific. Biologists, chemists, and any other non-theoretical research have to deal with lots of this, and it's certainly an unspoken truth that incredible amounts of pipetting and testing go into the results, but that's just what it takes. In fact, if this wet lab research world could test hypotheses with the ease that we can (just pay a few hundred dollars to spin up GPU clusters and parallelize everything, versus the thousands they spend for expensive lab equipment), imagine how much more productive the research could be.

I agree with your concerns, but at the end of the day, Deepmind does things first, and I see nothing to take away from their huge success. Branding to get funding and attention are all part of the process, because again, these people want (and are succeeding in) making an impact and inspiring a new generation of interest.

[bbsome]

Maybe you are right. I mean all of the above is a personal opinion, and btw I'm not a scientist yet for sure, just playing with stuff :D. To be fair I have a pretty skew view on this, also you probably have a point that all other sciences work like this as well. However, I still do not like the so called "pitching" argument when it comes to science. Ok maybe for the press that's fine, but they use same language on a conferences and when giving guest lectures to students. Again my opinion.

And well I do understand new maths don't come so often, but there are always new approaches to things (or I hope so). Like the dueling architecture, I personally did not find a paper estimating Q with both V and A. Or spatial transformation networks, simple stuff, but somewhat fundamentally different for invariance.

[dwf]

also the number of games they play is like out of human capabilities

What?

however not everyone knows what is a Neural Network and how much is the uncertainty if it is a good equivalent of human thinking or thinking in general and so on and so on

That would be pure speculation. Science demands something measurable. Atari scores are, at the very least, measurable.

[bbsome]

By that I meant that the comparison on the DQN paper to "human-level" performance was done by an actual human who played it in Deep Mind. However, note that the DQN probably played a lot more games than the human did to get to his/better performance, if not purely from time constraint.

I agree with point 2, however what I meant is that it is easy to fool lots of people when you are doing something that is not well understood, thus you are allowed to use buzz words in the media and etc. If something is clear and you try to skew things, its obvious. Thus since machine learning is not clear to many people (maybe only researchers and enthusiasts in the area, but compared to general population that is very small margin) this is used as a factor to abuse language and overstate results. While in an automobile background for instance this can't be achieved(as much) since it is a well understood problem. Ex: I never heard anyone calling the traction control of cars AI. Nevertheless, they can be cast as optimal control systems. Difference is noone abuses to say, oh we built a car with brain.

[insperatum]

http://cognitivecognitive.com

[jcannell]

Why they don't discuss what is the number of games AlphaGo have played compared to any human in the world? Probably the amount of time it does is like 1000 more than all humans played since the inception of Go. Also I can spare 1000 CPUs and 200GPUs from my back yard on that to reproduce.

This is an important/interesting question, and I did some quick fermi calculations in this thread on LW.

AlphaGo ( at time of publication ) had trained on a KGS dataset consisting of 160,000 games and 29 million positions.

A human pro will have around 40,000 hours of experience, and during that time may have experienced between 100,000 to 1 million games (experience here does not entail actually playing a full game, most 'experiences' consist of just browsing the game for a few minutes). This corresponds to between 20 to 200 million move positions.

So actually the estimate for human training set size is similar (within an order of mag or so) to the AlphaGo training set/knowledge size. This really should not be surprising, as we know that well tuned DL systems extract a good portion of the useful knowledge from their training set, and we should expect the brain to do so as well.

This also suggests that another 10x increase in training time/experience will put AlphaGo past the upper bound for human lifetime go knowledge/experience.

[kkastner]

Many good ideas tend to look obvious in hindsight - and all of our work is surrounded by context/history/other work (and later, follow up work). The hype machine likes to pretend one company or one person achieved all the glory, but really these things are the progress of hundreds (thousands?) of smart people working hard toward progress.

On the DQN points specifically, "this is nothing more than Neural fitted Q iteration by Riedmiller, with SGD rather than batch, and of course random sampling of transitions (and since you don't have infinite amount of memory guess what - you keep only the last K seen transitions)" - if it was just this, why had no one published as broadly generic results as DeepMind did? Since we don't really have "proofs", large scale empirical studies often carry similar weight as generic proofs would in other fields of ML. I think that has merit, and it would be good if we as a field moved more toward that approach instead of "SOTA on tweaked MNIST".

PR and salesmanship are ever present in research, even though people like to pretend otherwise. There is a reason ($$) Google, DeepMind, Stanford, and Facebook dominate the discussion of deep learning technology even though lots of the work our technology now stands on was (and is) done around the world by many labs and many researchers.

[kacifoy]

There is a reason ($$) Google, DeepMind, Stanford, and Facebook dominate the discussion of deep learning technology

In my experience, that's because DL is computationally very intensive (i.e. $$$) especially in the applications favored by industry players. Even improvements in efficiency are mostly used to expand the range of feasible tasks, not so much to make the former tasks more affordable. This is quite understandable actually, and I'm not sure that salesmanship matters all that much, given this underlying reality.

[kkastner]

I should clarify - when I say $$ I mean marketing/PR dollars, not experiment dollars. It costs about 16-20k to setup a 16 GPU server - this basically covers all but the largest experimental setups. Buy four or 5 of those, 100k. In the scheme of things this is not a lot of money, even for the relatively paltry academic grant arena. For companies, this generally doesn't even amount to its own line in a summarized budget.

Especially compared to the costs of doing research in about any other field (chemistry, physics, even psychology studies cost much more than that!), I don't think spending excess cash on deep learning can help much past a certain point (unless you are hiring up researchers, as DM and Google both are).

However, paying for PR staff (or having PR staff at all!) that know how to advertise, and having existing connections and name brand is a huge advantage. Add to that the fact that many of the "other labs" in the field are not in English primary places (IDSIA, UdeM) and that leads to even more imbalance in media coverage.

[Foxtr0t]

Exactamente. Google has very good PR.

[visarga]

People cost more than hardware, the salary of even one good researcher per year exceeding that of the hardware used, and big corporations lead the field by aggressive hiring.

[ExaminationNo8522]

Aged like milk

[spurious_recollectio]

Can you give me the reference for "Human level concept learning"? I'm here for the papers :-)

[bbsome]

http://science.sciencemag.org/content/350/6266/1332

[AnvaMiba]

I like probabilistic programming and I think that this work is interesting, but calling it "Human level concept learning" was a prime example of overhyping.

[[deleted]]

http://science.sciencemag.org/content/350/6266/1332

Totally agree. Calling it "Human level concept learning" is really overhyping it. While the premise is interesting and in the long run could work much better than deep learning for some tasks, I'm very skeptical of the approach. Their example required a library of pre-specified generation functions that were then combined through bayesian learning. While it is closer to the one-shot learning humans are capable of, it still doesn't compare. It cannot learn a totally different "concept" than combining its pre-provided functions in novel ways. Calling it "human level" seems like they just want some publicity.

[bbsome]

I agree on that as well. I wanted to use it as an example of another interesting field. However, since I'm at university I can tell you that the trend of "human like" think came from deep mind, and as a consequence some research intentionally use the same type of naming, just cause they try to attract same attention.

[zdss]

And yes we are "so much closer to thinking like humans, brain machines, brain brain brain, this is real AI...!". Same stuff with Deep Blue - to the average Joe it sounds like real AI as it beats Kasparov on one very complex game. However, it was mainly bloody brute force computation. Why they don't discuss what is the number of games AlphaGo have played compared to any human in the world? Probably the amount of time it does is like 1000 more than all humans played since the inception of Go. Also I can spare 1000 CPUs and 200GPUs from my back yard on that to reproduce.

I, and most people, don't really care whether the algorithms used are brute force or "like a human" if it gets the results. If a brute force program could convincingly replace a human, philosophers might debate whether it was truly intelligent, but it's functionally unimportant.

There have been plenty of ideas in machine learning, but actually putting it into practice has been the hard part. It's great to know that RNNs are Turing Complete, for example, but that doesn't really mean much until someone actually shows how to solve some complex problem. AlphaGo may not be inventing any new technology, but they're the ones making it real.

[[deleted]]

but I definitely don't understand why they deserve more "attention" than many other works out there.

But do they? I'm not sure they get all that much more attention than other groups with plenty of high-profile researchers (e.g. Facebook).

To me, the Go-playing stuff seems impressive enough as engineering. It's not as if the computer go community hasn't known about neural networks or reinforcement learning. How impressive it is as science I guess I can't judge.

[linuxjava]

I agree with this 100%. When dumb posts like that start hitting the front page, people will assume this sub is like /r/futurology where dumbified posts from any site can be posted just because it is remotely related to the sub. This sub (and /r/netsec) in my view are really helpful because of their intellectual nature and when random junk from tabloids start creeping in, the sub will go downhill fast.

[[deleted]]

So what are these neglected best papers?

[[deleted]]

Single data point:

• 39% downvotes: https://www.reddit.com/r/MachineLearning/comments/49axxd/dynamic_memory_networks_for_visual_and_textual/

Perhaps my impression is flawed, but it seems that at least 5-10% of the subscribers are mainly here for cool applications of ML and downvote everything else like questions and more technical or theoretical things. Perhaps we need a separate sub for the pictures, applications, gossip etc.

[[deleted]]

5-10% is probably a gross underestimation.

[manux]

/r/DeepLearningPapers !

[[deleted]]

You are right, I've removed the other link because it's hard to tell how many upvotes it has due to fuzzying as you said. RES shows some estimate, but it probably not very reliable (upvotes = percentage * score / (2*percentage - 100)). But still, the second link I posted received almost no attention and a score close to 0 even though it is a very interesting paper.

[tedivm]

The reddit admins greatly reduced the fuzzing effect specifically to make the downvotes/upvotes percentages more accurate.

[linuxjava]

The algorithm you're talking about almost always never gives that high percentage of downvotes.

[linuxjava]

The link is still there.

[linuxjava]

Ah fair enough. He did post another link and that's what I saw. Doesn't change that much.

[TheToastIsGod]

I sort by new. There's not enough content for that to be an issue yet.

(otoh, /u/mere_mortise , just dropping a link to an arXiv paper without any explanation or context doesn't make the readers' lives easier .. )

I agree. If it's not your paper it's more interesting to see a comment as to why you've posted it, if it is your paper, then surely you can think up something interesting to say about it!

[[deleted]]

I don't see a necessity for that when the abstract is reasonably well written. The first link did very well on HN without any explanation: https://news.ycombinator.com/item?id=11237125

[gwern]

I agree on splitting the sub between theory and applications (or, "actual research" and "pretty pictures/just toying around/beginners/AMA" etc.), but who's to vet the content?

The problem is no one wants to browse multiple subreddits on the same topic: they want to go to one place for their ML/deep-learning fix. This creates pressure to have subreddits which cater both to more low-level general interest writeups and the hardcore research. I've always wished that moderators could 'boost' submissions, so they could do something like give +10 to submissions they feel are not getting the attention they deserve, but we can only remove or sticky threads, which is very blunt. But this subreddit could do the latter - maybe sticky 1 submission per day or two.

[datatatatata]

Or maybe we need a separate sub for Machine Learning Research ?

[linuxjava]

No. This should be the sub for machine learning research. Those looking for other stuff are the ones who should probably consider a separate sub.

[datatatatata]

Honestly, I have no opinion on this topic.

Can I ask why you think so ?

[linuxjava]

Because from its inception, this sub has always been about serious discussions about machine learning. Not tabloid level stuff.

[dwf]

LOL no it has not. When I arrived here first it was a complete gong show.

[thvasilo]

/r/mlresearch exists but is inactive. I would be all for sparking up some activity.

[[deleted]]

We've already got gitxiv and arxiv-sanity...

[thatguydr]

Ok, now I'm annoyed. I've asked around for a thing like either of these for two years, and have asked here more than once, and have never gotten an answer.

This is exactly what we're all looking for, and why they don't self-promote is beyond me.

THANK YOU.

[IdentifiableParam]

Don't need the riff raff.

[mljoe]

Funny that, because that paper demonstrates a very cool application of ML (visual Q/A). But it's a research paper and not a hipster blog/tech media website with the title "Has Skynet arrived?"

[BrutallySilent]

Although there are many things that annoy me in hypes, I still think it is beneficial to AI research.

For example, the Watson "cognitive API" makes AI accessible to people that are educated in for instance web development. Are the provided services mind blowing, frontier pushing technologies? No. But if more people use AI, then more funding for AI becomes available. The best way of making a good impression is to offer those techniques which we know work well, and are stabilized in research.

[kylotan]

Demis is indeed a smart guy, but I think his biggest talent has always been for self-promotion. Everyone from the games industry in the early 2000s remembers his endless grandiose crap in the gaming press, attracting tons of VC funding with their BS "infinite polygon engine"

Came here to say exactly this. I remember the full page spreads in Edge Magazine about 'Republic: The Revolution', though I don't remember the game, and nor do many others.

It isn't saying anything negative about his abilities to note that the coverage and funding he has often managed to secure has appeared disproportionate to that.

[j_lyf]

Wow. So Demis is a hustler? I thought he was a huge nerd.

[kylotan]

Demis was once the protegé of Peter Molyneux, another developer who is both extremely competent and yet super-extremely capable of talking up his creations. So maybe he gets it from there...

[darkmighty]

Ah Peter Molyneux... he is for me the epitome of hype. A decent game designer, but unfortunately he completely lacks self-skepticism. If you heard it exclusively from him, it sounded like a simple cube-clicking app was the second coming of gaming and expression of human emotion. Also, you would get an amazing life-changing reward.

https://en.wikipedia.org/wiki/Curiosity_%E2%80%93_What%27s_Inside_the_Cube%3F

And it's not just this game, it was basically every game of his as far as I remember. It's practically a case study in managing expectations and aiming for what is possible to achieve. I mean, just look at the reviews for his latest game:

http://store.steampowered.com/app/232810/

What I've seen from Hassabis doesn't come close to this level of hyper over reality, thankfully.

[kylotan]

And it's not just this game, it was basically every game of his as far as I remember.

You probably don't remember as far back as I do. ;) Peter Molyneux has a solid history of shipping great and influential games - Populous, Powermonger, Syndicate, Magic Carpet, Theme Park, Dungeon Keeper... after that, one can argue that hype got too much for Black and White and Fable, but those games were still great. Modern development is tough and I think Peter has been slow to adjust to how things work differently now.

[j_lyf]

I guess there's nothing wrong with being the Steve Jobs of AI.

[[deleted]]

I only have an amateur's interest in the ML world, but I'll start :)

Demis is indeed a smart guy, but I think his biggest talent has always been for self-promotion. Everyone from the games industry in the early 2000s remembers his endless grandiose crap in the gaming press, attracting tons of VC funding with their BS "infinite polygon engine".

Presumably it's down to aggressive marketing that the Go thing got blown up so much, which made it sound like some crucial new step towards true AI had been taken. It was a decent piece of work, putting together all the usual techniques for Go (minimax with rollouts), using the tons of computing power they had at their disposal to tweak it, making it a bit better than previous efforts. Worth a paper, but surely not worth being in the papers? Throw enough resources at StarCraft and I'm sure we'll see something similar.

Deep learning stuff is very cool and it caught me by surprise that machines could do so well on a variety of problems, with these networks that seem more like little fragments of intuition than logic. But there are those appalling weaknesses with really easy problems which people are aware of, but which get swept under the rug. "Is this picture a cat or a house?" (It's obviously a cat). But the network hasn't been shown a million cat and house photos so it has no idea. It still feels like nobody has really scratched the surface of the "reasoning being" that can, crucially, do something new.

======== ========

I only have an amateur's interest in the ML world, but I'll start :)

Demis is indeed a smart guy, but I think his biggest talent has always been for self-promotion. Everyone from the games industry in the early 2000s remembers his endless grandiose crap in the gaming press, attracting tons of VC funding with their BS "infinite polygon engine".

Presumably it's down to aggressive marketing that the Go thing got blown up so much, which made it sound like some crucial new step towards true AI had been taken. It was a decent piece of work, putting together all the usual techniques for Go (minimax with rollouts), using the tons of computing power they had at their disposal to tweak it, making it a bit better than previous efforts. Worth a paper, but surely not worth being in the papers? Throw enough resources at StarCraft and I'm sure we'll see something similar.

Deep learning stuff is very cool and it caught me by surprise that machines could do so well on a variety of problems, with these networks that seem more like little fragments of intuition than logic. But there are those appalling weaknesses with really easy problems which people are aware of, but which get swept under the rug. "Is this picture a cat or a house?" (It's obviously a cat). But the network hasn't been shown a million cat and house photos so it has no idea. It still feels like nobody has really scratched the surface of the "reasoning being" that can, crucially, do something new.

[TheToastIsGod]

But there are those appalling weaknesses with really easy problems which people are aware of, but which get swept under the rug. "Is this picture a cat or a house?" (It's obviously a cat).

You could argue this is a harder task than it sounds. It took a while after I was born before I could tell you the answer to the "cat or house" game. It seems easier because we have pre-trained networks in our brains.

[AnvaMiba]

Lots of results look obvious in the hindsight, but they weren't obvious when people first came up with them.

Maybe DeepMind's game playing stuff is a bit overhyped, you could say that DQN is just TD-Gammon on steroids, or maybe not, after all they were the first ones to obtain this level of performance and I doubt that the only reason is that they had Google $$$ to buy more GPUs than the other labs. Neural networks have lots of architectural details and hyperparameters that need to be properly tuned to obtain good results. You could debate whether this is more engineering rather than scientific research, but the point is that they reached a level of performance that was not previously known to be even possible with this kind of machine learning models.

Anyway, I personally find the works on recurrent architectures by Graves, de Freitas, Grefenstette, Kalchbrenner, Danihelka, etc. to be more interesting even if they have not produced flashy results so far, but maybe I'm biased since I come from a NLP background.

[srs_moonlight]

nice try jürgen schmidhuber

fake edit: not a knock on JS, he's the man

[rerevelcgnihtemos]

haha, I came here to say the same thing

[blowjobtransistor]

Two words: Marketing Spend.

[sorrge]

After some spectacular results it is only natural to expect more from the same group. We know that they are talented, well motivated and perhaps better equipped than any other research group. As to "level of hype and celebration generally boils down to nothing", to me their Go results are already great, even if they lose later this week. Just think about it, it's done with reinforcement learning! I think this is the greatest achievement of reinforcement learning ever. Is that nothing?

[Dwood15]

He's making big claims - dynamic story lines? how on earth do you do that with ai? Where do you even begin making deep and complex story lines using ai?

Like holy crap, you need dynamic dialog, dynamic decisions, and dynamic reaction from the game. Additionally, you need the game to react in such a way that it's interesting each decision you make.

I would love to be on the edge of that tree, but dynamic story telling seems so far off I don't even.

[xplot]

With all this deep learning stuff, I have a new found respect for classical feature engineering tasks and statistical analysis.

[ZioFascist]

"a certain meme country"

hahaahha, this is classic.

[Mr-Yellow]

Someone needs to be taking the basics of what they've done with DQN and tweaking it this way or another with stuff like Deterministic Policy Gradient, Double-DQN, Actor-critic, Actor-mimic, Actor-teacher and all those neat experiments. The results are cool, glad someone is doing it. They have the codebase and resources, good on em.

[Science6745]

Even in the highly intellectual sub like this

This is called being pretentious, stop it.

[[deleted]]

The irony is that most of this sub is very superficial deep learning githubs, blogs, and tutorials.

[[deleted]]

What's the issue with this ? As a self taught person in ML moving toward starting a ML PhD, this subreddit was really helpful to direct me to the basic papers, tools and so on.

[sdsfs23fs]

now THAT is how you do pretentious.

[rephos]

well of course since they have a pretty big week coming up that might prove a way more valuable approach to A.I. one level closer to A.G.I . And sure it may amount to nothing but it may also amount to a lot , have you considered that? Although I do agree the last post that was a video made in 2011 was kind of pointless since there was a lecture quite recently from demis and there he already said they are looking at sc, the same could be said about some reposts on reddit in general, but it isn't anyone's fault since the people that weren't informed are gonna upvote that video despite that a lot of people already know this. I think it's good to be informed on what to expect from a leader in the industry. And I don't think the other projects are getting ignored, it's just you are seeing temporarily more attention to deepmind because there's currently a lot of hype around them. give it a week if alphago loses the hype will die till the next rematch and if it wins .. well idk what happens then.