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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."
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.
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.
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.
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.
Jeopardy, and the machine is called Watson
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
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.