Google to Buy Artificial Intelligence Startup DeepMind for $400M
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Deep Mind has a whole bunch of talented and serious people so this is an exciting acquisition.
The only real "deep" learning here is that they used a GPU library and stochastic gradient descent to perform Q-learning updates on a network with 3 large hidden layers. It was an interesting application paper, but I suspect that the Google acquisition is for something more novel than this work.
I hazard it's not very impressed with your Space Invader score, either.
So, I was not impressed by their results on Space Invaders.
Overall, we struggled to learn long-term strategies (finding pure reactive strategies is easy) and to learn to avoid bullets. They did too: "The games Q*bert, Seaquest, Space Invaders, on which we are far from human performance, are more challenging because they require the network to find a strategy that extends over long time scales."
=> that's the real challenge...
[1] http://workinstartups.com/job-board/jobs-at/deepmind-technol...
- Some top ML talent: Geoffrey Hinton, Sebastian Thrun, Peter Norvig, Jeff Dean, Andrew Ng.
- One of D-Wave's quantum computers to establish their 'Quantum Artificial Intelligence Lab'.
- Creepy robot maker Boston Dynamics.
- A stack of other robotics companies: Schaft.inc, Industrial Perception, Redwood Robotics, Meka Robotics, Molomni, Bot & Dolly, Autofuss.
- DeepMind, obviously.
Am I missing any?
I found that book to be one of the most unsettlingly plausible premises for an AI. In particular I liked that it didn't go the typical Hollywood idea of AI as being a "soul/homunculus conjured into a machine" but rather a very effective decision-making agent.
2) Presumes our intelligence is deemed valuable enough to devote those resources to.
[Thanks to Messrs Banks, Stross and Vinge for that particular cheery thought].
I find the 'upload your brain to a hard drive' thing silly unless they figure out how to have your biological consciousness seamlessly move to the VR, like it was taking some bus ride and ended up in this new place.
I think something similar is the only way you're going to get people to want to do this. Though whether or not that is possible is way beyond me.
I would still have a similar problem with "teleporters" (of the Star Trek kind), though.
I'd put it in the same ball park as "what would that grurple (kind of a greenish purple) colour look like if we removed blue from it?". Sure you could say orange but the nature of grurple is undefined enough that it would be really hard to conclude that you're right.
So let's hope they haven't gone quite as nuts as they seem to have gone.
We can sweep this problem under the rug in case of pets; it's not as if they could start making weapons and organizing an army. With human-level intelligence, we already have a friendliness problem with fellow humans, and in case of potential superhuman minds we need to be damn sure that it doesn't do something stupid (in our opinion) like taking all the resources of our planet and using it to tile the solar system with paperclips.
As the saying goes, "The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else."
See also http://wiki.lesswrong.com/wiki/Paperclip_maximizer.
A lot of the singularity people think that superhuman AI in inevitable whether they work on it or not, but if the first AI isn't a friendly one (as opposed to a paperclip-optimizer that happily turns the planet in paperclips) then you don't get a second chance.
It's also a smart move for your career: You can create your own job and attract lots of funding for your research, or sell your business for a lot.
> "The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else."
I suppose AI will one day be able to explain this to us?
Keep in mind that creating more advance intelligence than us, while aphoristically wouldn't be difficult, is a REALLY HARD task. I doubt anything will happen within our lifetimes unless it is by some major fluke.
But AI definitely has a lot of benefits. Start from self driving cars to automation in manufacturing industries. And that is just the very beginning. Google's line of businesses can benefit immensely from such work. There is endless money to be made. And it is better you grab it when it is a low hanging fruit.
Most of Kurzweil has to say about robotics, nanotechnology and AI. Has a direct benefit to humanity in the immediate future. Humans can have longer lives, the world can have a smaller population, many diseases we know can be eradicated. Problems like hunger, pollution, disease etc can be solved. The list is endless. Why wouldn't any one want a share of that business?
And yes the whole AI taking over the world and making us extinct thing- It won't happen anything like just turning on a switch. I believe even a run away super intelligent will still need biological life forms for their own very survival.
Like some one mentioned in this thread, copies of your self will continue live in the cloud and such copies will provide a great wealth of insight for the machines themselves to survive.
I think Google has a proven track record of working on whatever they find interesting/beneficial to humanity, not necessarily minding shareholders - cf. self-driving cars, Project Loon, 10^100, whatever was that stuff they did in clean energy business (I recall them working on wind energy or sth?).
If anyone is going to pull off something like an real AI, I'd bet it will be Google - it's the only company I know that has the size, manpower, minds, money, know-how and a healthy attitude toward profits (bettering human kind > short term gains) all in one place.
Living forever / getting rid of death, fixing world problems, countless amazing technological achievements, colonizing the galaxy. In probable order of happening.
> Wouldn't a drastically more advanced intelligence just dwarf and dissolve any lesser intelligence that tried to meld with it?
Not necessarily; that's an actual research field.
http://intelligence.org/research/, also Omohundro's and Bostrom's works.
I can really recommend the book by him, On Intelligence, where he explains it quite understandable.
It explains why the brain has developed the different hierarchical layers of the visual system and how the same principle works everywhere in the neocortex. It's basically all predictions of time series at different abstraction levels.
You can discern some more about their general direction by looking at courses they've been involved with, talks they've given or sponsored, etc., but as far as I know (and I tried to probe a few months ago through a friend who knew someone there) their actual product / business / etc. hasn't really been leaked, or at least not leaked widely enough that I could find out about it.
Edit: peaked --> piqued, thanks to spiderPig!
1: http://venturebeat.com/2014/01/14/where-nest-ranks-among-goo...
There is a difference between Snapchat being worth $3 billion and Nest being worth $3 billion. The former gets the valuation based on users, the latter on talent and intellectual property.
Ditto here: $400 million is not buying you users, it's buying you raw talent and IP. Users can go off to another service in a blink of an eye - IP can't (talent can, but you can often structure the deal so that it won't for some time).
This could still be a terrible deal (I'm sure there are some people at Google still a little sore over Motorola, where the IP was valued far more than it ended up being worth), but for very different reasons.
Not to mention the enormous number of innovations they'll likely be able to churn out. Hopefully it's like an AI focused PARC, but with a competent tech company at the helm :-)
Though if they're expected to do wonderful things, and they've been doing things for years... it seems a certainty that they have already done some of those wonderful things. And hence have something concrete worth acquiring. Which would explain the valuation.
But the real challenge is to make the knowledge graph update in real time and take meaning from something as unstructured as a blog post or an email. And to do something like that requires some really unique AI.
--mjn - I totally agree!
Google's Deep Learning team were the people who developed the alogithm that discovered cats on YouTube (without training). Presumably this team had something that impressed them.
The weakness to knowledge engineering approaches is that they tend to be fragile - they break badly with small holes in recorded knowledge. The IBM Watson team has a great video that showed how the different definitions of "fluid" and "liquid" meant a correct answer would have been missed if evidence collected in the answer verification phase of the DeepQA pipeline (no relation to Deep Learning) hadn't overridden it.
Edit: Your(?) paper on your (?) relevancy engine is interesting. It seems like an application of skip-grams (which, ironically enough are heavily used by the DeepQA answer verification phase mentioned above).
https://www.facebook.com/yann.lecun/posts/10151812982157143?...
If they're hiring his students, they probably have a high level of talent (speaking as a former -- and present, starting tomorrow -- student).
Damn, if DeepMind had to 'push' for an ethics board then that is a fairly bad sign. I am getting more worried.
[0]https://www.theinformation.com/Google-beat-Facebook-For-Deep...
I also quote, from the preface, "This research was funded by the Swiss National Science Foundation under grants 2100-67712.0 and 200020-107616. Many funding agencies are not willing to support such blue-sky research. Their backing has been greatly appreciated."
The title is a bit much but it does say "The title of this thesis is deliberately provocative".
A little off topic, but I suspect that one reason to sell themselves to Google is Google's infrastructure, both in ability to easily run very large jobs and their very nice development environment.
[1]http://en.wikipedia.org/w/index.php?title=Demis_Hassabis&dir...
Deep Mind on the other hand...
I wonder if they managed to pay for DeepMind out of funds that are "stuck" offshore (i.e. earnings from outside the US that can't be repatriated without incurring a big tax bill).
Companies with excess cash are supposed to pay dividends and/or buy back stock.
My own speculation. One of the key concepts to come out of the experience of translation is "A billion is more than a million". When they thought they processed enough data, it still wasn't enough. They may be scaling that concept even larger. At the same time, quantum computers SHOULD be getting to the point where they pass classical computers, and its generally known that Google has had access to them.
If Roses law is true, I'd speculate that Google is ramping up to take advantage.
Where exactly would they pass classical computer? Legitimate question.
Because I've read this (http://www.scottaaronson.com/blog/?p=1643) today, which shows quantum computers probably will not be that much better at NP problems than classical ones.
https://twitter.com/om/status/427653907766972416
> A $400 million talent acquisition with little talent. That's how Google rolls now!
There's also word that it was more than $400m, perhaps as high as $500m. That's a lot for talent any way you slice it.
With a lot of money to spend.. this was a good move.
Acquisitions can also serve to kill the competition
Google to Buy
Google Buy
GooBuy ~ GoodbyeSome exist independently, others are absorbed.
Or imagine what Yahoo would have done to it post acquisition.
I know Apple would have called at iTube & put a price tag of $0.99 on all decent ones :)
It's is good.
This is the Internet, so I'm sure this random opinion is both well-informed and valid.
Nearly every human's job is at stake here.
I lost interest in DeepMind when I could not figure out what their business was after a couple of interviews. (The startup that I was previously working for had just run out of money). More fool me. Heh.
Looks like PayPal (tm) mafia extended family.
What do you mean by symbolic or sub-symbolic?
Edit: and search is an AI problem. You have a user entering a string and you have to work out what they expect to see returned.
I think there might be feedback loops in Google's current system, so maybe better AI would help.
It's not because something is difficult to measure that it does not exist(1). Even if you don't believe in IQ tests, you gotta admit that those people Google just hired are more intelligent than the average Joe. They sure are more intelligent than me anyway. Probably more intelligent than you as well.
In the same way, the idea that one day a machine could be more intelligent than any human being would be very real the day a machine will write scientific papers, program its own code, design high-tech devices, win a Nobel price and stuff like that.
This machine would be more intelligent than Demis Hassabis (the founder of DeepMind) for exactly the same reasons that I can say that Demis Hassabis is more intelligent than me: he does more intelligent things.
1: for example, the famous conundrum "how long is the coast of Britain?" does not suggest that Britain has no coast.
No, I don't say that it doesn't exists. What I say is that intelligence is not something to be expressed in one number, it doesn't work that way. Intelligence is more like NxN matrix of numbers, where each number in a matrix represent individual skill in some specific task. As for your example of Google employee and average Joe, what you mean by more intelligent is that the sum of all of that NxN matrix number for that employee is larger than of the average Joe. However if you take some particular numbers, Joe might still have them higher. For the simplest example the average Joe will know his house better than the Google employee who hasn't even been at Joe's house. And the same is for AI, just the number of that supposed intelligence matrix will be completely different than that of human. AI without human body, human body needs and without hormones to control his behavior will never be anything like human to be compared to them.
So in the end what you say I also think is true, especially about machines writing scientific papers and generally doing science already out of grasp for human mind. My problem I guess is the measurement problem.
If someone would say "singularity will be when machines will manipulate mathematical concepts and invent/discover and prove theorems that no human mathematician alive can understand" I would agree. But if they say "machine is more intelligent than any human" I can't agree. That statement makes as much sense as statement "singularity will be when apples will be more fruit than any banana".
But nobody in the singularity field says that intelligence is one-dimensional quality! It's a strawman.
It reminds me of a common accusation that "computer people" have subpar worldview because they "reason in 0s and 1s, and the world is not binary", to which I say that actually "computer people"'s view is superior because they figured that out long ago and developed proper methods to quantify and deal with uncertainty.
> Intelligence is more like NxN matrix of numbers, where each number in a matrix represent individual skill in some specific task.
This is also not a good model, because what we usually mean by intelligence are reasoning capabilities, not e.g. motor skills. You don't say about a surgeon that he is smart, because he can manipulate a blade with great precision; we say he's exceptionally skilled.
> For the simplest example the average Joe will know his house better than the Google employee who hasn't even been at Joe's house.
Put Average Joe and Google Employee a house they have never seen before and see which one will learn how to navigate faster - that's a way to measure intelligence. Not the knowledge, but the ability to process and use it.
> If someone would say "singularity will be when machines will manipulate mathematical concepts and invent/discover and prove theorems that no human mathematician alive can understand" I would agree. But if they say "machine is more intelligent than any human" I can't agree.
Saying "machine more intelligent than human" is just a shortcut for saying "machine that is able to reason about the world faster, better, with less biases than human; which will manipulate mental concepts and prove theorems out of reach for humans, as well as invent better technology, tackle human social problems better than humans do, etc. etd.".
Yes, the model is not good, I agree. But your understanding of intelligence incorrectly. You think that intelligence is a rate of learning. Or in neuroscience terms, brain plasticity. So, first, plasticity is largest at birth and gradually decreases as brain matures. This means that newborn would be "more intelligent" than 50yo man. Secondly, a fast rate of learning is not necessarily a good thing. If you ever worked with neural networks, you'd know that when training it, you can adjust at what rate the weights in artificial neurons would change. If you make fast rate, for one, network quickly overlearns, meaning he becomes too specialized and adjusted to exact cases he experienced, and secondly, it can quickly "forget" what he has learned. Slow learning rate makes it longer to learn, but also is more "stubborn" and doesn't give of on old beliefs so easily either.
On the other hand, as you said, knowledge also does not mean intelligence. If it cannot learn from it's mistakes, it is surely not intelligent. So I'd say intelligence is a combination of experience and plasticity.
> Saying "machine more intelligent than human" is just a shortcut for saying "machine that is able to reason about the world faster, better, with less biases than human; which will manipulate mental concepts and prove theorems out of reach for humans, as well as invent better technology, tackle human social problems better than humans do, etc. etd.".
So in the other words machine becoming more proficient in some very specific skill or multiple skills. Yes, that is common sense.
It's not that I disagree with the core idea of singularity, I just find it pointless and unnecessary. Some might say "it brings attention to the field", but I'm not really sure it helps AI research, since it attracts the wrong kind of people that would make actual research.