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DARPA Tackles Machine Learning
coondoggie writes "Researchers at DARPA want to take the science of machine learning — teaching computers to automatically understand data, manage results and surmise insights — up a couple notches. Machine learning, DARPA says, is already at the heart of many cutting edge technologies today, like email spam filters, smartphone personal assistants and self-driving cars. 'Unfortunately, even as the demand for these capabilities is accelerating, every new application requires a Herculean effort. Even a team of specially-trained machine learning experts makes only painfully slow progress due to the lack of tools to build these systems,' DARPA says."
you must be a machine then
Even a team of specially-trained machine learning experts makes only painfully slow progress due to the lack of tools to build these systems
Why not just teach a machine to do it?
sudo ergo sum
Defense agency investing in Machine Learning technology? What could possibly go wrong?!
a headline for future 2030.
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They've been trying it since the 50s without it has to be said, too much success given the amount of effort thats been put in. I suspect until we REALLY understand how boligical brains do it (not , "meh, some sort of neural back propagation", yeah , we know that , but what propagation and how exactly?) then machine learning will still remain at the bottom rung of the intelligence ladder.
Personally I think at the moment pre programmed intelligence is still a more successful route to go down. Though hopefully that will change.
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Waiting for you by the bridge
They're hard coded and use massively parallel depth searching. The brute force approach has been the best for chess computers for decades.
And google search and translate isn't really learning, they're just statistical systems that given the best result based on the data they've gathered. They don't "think" about it in any meaningful way.
Didn't IBM do this when they created a computer to play on that quiz show? (the name escapes me)
There are a ton of off-the-shelf machine learning toolkits that are sufficient for 90% of possible use cases. The problem is getting annotated data to feed into these tools so they can learn the appropriate patterns. But all that requires is a host of annotators (i.e. undergrads and interns), not machine learning experts.
They just can't !!
Carbon machines can. Why couldn't silicon ones?
Actually, scratch that. With graphene circuits coming around in a few years it'll be carbon machines all the way.
Conservatism: (n.) love of the existing evils. Liberalism: (n.) desire to substitute new evils for the existing ones.
Sounds like the 1990s fetish for making programming languages so simple that even your boss could make reports and do other stuff for himself. Unfortunately, programming language syntax wasn't the primary hurdle: I've had bosses request reports that would add pounds of product and shipping costs.
For ML, it takes a good bit of training just to know what kinds of problems you can apply it to. A cookbook toolkit isn't going to reduce the need for expertise very much.
Sheesh, evil *and* a jerk. -- Jade
I was just talking with someone about this the other day. Machine learning is going to be the SQL database of the next generation. In 15 years it will be hard to find basic apps that don't use it. The tools will reach a point that it's so easy to include them in your program, people will assume to include them even though they may not really be the most appropriate method to solve the problem. This is how SQL is today. Go to any SMB and try to find a non-trivial application that doesn't use a SQL database. It's difficult.
.net or Java, they won't use it. Weka can be used for Java, but it's a difficult library for a machine learning novice to use. The developer has to know some internals of machine learning to know which algorithm to use and their pros and cons. Meta learners complicate the issue even more. Modern RDBMS have been sugar coated so much a developer can use a RAD IDE and not understand a single line of SQL. I'm not saying that's really a good thing, but it definitely has made SQL databases very common and improved the state of the industry for everyone.
However, the state of current tools is not good. We currently have really good algorithms for machine learning. The gap is in actually getting a developer to use them. If it's not branded and blessed by Oracle or Microsoft, many businesses won't use it. If you search for implementations on the internet you can usually find an implementation of R or Matlab. However, people are weary of including R and Matlab in their programs to begin with. If it's not in
inb4 skynet
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Do you want to play a game?
They say soon it will be fish - lizard - hampster - chimp - neanderthal - human - roomba basically. And that these new "machine life forms" will entirely surpass us in so many ways - longevity, freedom from depression and the faults of our "blind watch maker" brains, strength, speed, the ability to withstand the stresses of heat, pressure, interstellar radiation, the claustrophobia and isolation of intergalactic travel, etc.
I know our efforts towards cultivating AI and facilitating this now seem clumsy and awkward, but I'm with Kurzweil in thinking this only picks up steam more and more quickly from here.
So here's the real nutjob part: If the vast majority of this research is being done by the worlds' militaries, isn't the likelihood of these new uberspecies being aggressive towards humans quite high? And, if cats breed out of control wiping all the birds, and humans breed out of control devastating all sentient beings weaker than themselves, even enslaving, exploiting, and killing their own kind, is it not likely that evolution just kind of favors ruthless dominance? And if so, even in the best of cases, does the evolution of these new uber-species bode well for us basically??? : )
Intelligence is dangerous it seems! I feel like we are witnessing a race between various possible means of causing the 7th (?) mass extinction in geological time - Will we eradicate the current biosphere, ourselves included, by our own hands first by: a) nuclear weapons, b) genetically engineered viruses, c) polluting the environment to the point of mass ecological collapse, or now d) purposely creating species more dominant than our own and allowing nature to run its course?
Maybe it was bird flu, asteroid, or sun unexpectedly blowing up all along, but my money's on us! Just not sure which way???
What they really want is the classic "Computer that Gives a Shit". Instead of the usual passive-aggressive taunting, using your own dumb SQL statement, it fixes it for you instead!
"Deja vu all over again" for lon-undersolved computer problems.
because at the moment it's more of an art than a science for many applications.
My God can beat up your God. Just kidding...don't take offense. I know there's no God.
Raw data need to be cleaned up and organized to feed into the ML algorithm.
The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.
No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.
The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.
All of the above need to work together in concert.
Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.
Oh come on, you know in your heart SkyNet is the only feasible solution to the spam problem.
All narrative is driven by conflict. If our glorious utopian future entailed having all our needs and wants attended by robowetnurses, nobody would be making movies about that stagnant society. ex - "Zardoz" with its Eternals, Niven's "Safe at any speed", the society on Aurora in Asimov's robot stories.
the preceding comment is my own and in no way reflects the opinion of the Joint Chiefs of Staff
Unless you work in the advertising industry, I'm with you...that's a bit crazy. Apparently they like you getting dangly-carroted to death on a daily basis by flashing neon poker chips and penis enlargement pills...that doesn't distract from your work day at all! (I'm assuming you work in an industry where you utilize the internet for reference regularly of course.)
Maybe it's so wasting time on the internet at work sucks.
You may be on to something there...*checks over shoulder for boss-man*. Actually I work from home so that should be *checks over shoulder for boss-woman*.
In my ML class, we used WEKA. Of course, there is also Matlab. Problem is, neither of these are free, and they are both slow as hell. I would not use either one outside of class/prototyping.
Ideally there would be a free, open source toolkit written in a compiled language. The toolkit should have a variety of ML techniques that can be switched around with little pain. Only toolkit I know of like this is the ML part of OpenCV, and the documentation for OpenCV is... lacking.
Another poster linked to mloss.org. I hadn't seen that site.. Looks like a great resource, but it also looks like 400+ fragmented tools that do not play well together, and are probably mostly dead projects by now.
Not so loud! ORACLE is listening. If they take over this domain, imagine the size of the drivers you have to install on your "smart" client.
Just want to point out that this is about machine learning, not AI so need to worry yet about Skynet although the ability to understand data and learn from out is the first step, or at least one part of the jigsaw to achieve Artificial Intelligence.
From what I can gather, this is trying to standardize how machine learns. It sounds like what we have at the moment where we have in t he education system where there are numerous systems on how to teach children to read and write. Rather than having numerous systems on machine learning, why not combine resources and have one method? This has good points and bad points. What happens if the method that DARPA approves isn't the right one? I think a better way, as pointed out by another poster, is to first to standardize how data is stored which will then make it easier for software with machine learning functions to process it. It may even be a case of changing the way we communicate with machine has to change. Languages, particular English, has constantly evolved throughout centuries so why should it not evolve to take account of communicating with machines?
is not building machines capable to learn or teaching computers to automatically understand data.
The real breakthrough will be when we build machines able to teach what they learnt to other machines, in their own terms. Even if they don't do it perfectly.
... if they didn't kill AI research in the mid-eighties, they wouldn't have had to fund research today when it's more expensive? Thanks, DARPA...
That is all.
they wont ask but you should know
this is something we all should be supporting...
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