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New 'Deep Learning' Technique Lets Robots Learn Through Trial-and-Error
jan_jes writes: UC Berkeley researchers turned to a branch of artificial intelligence known as deep learning for developing algorithms that enable robots to learn motor tasks through trial and error. It's a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. Their demonstration robot completes tasks such as "putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more" without pre-programmed details about its surroundings. The challenge of putting robots into real-life settings (e.g. homes or offices) is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings, so this type of learning is an important step.
This seems more like basic-level stuff... learning from your mistakes. That strikes me as the sort of thing that would be "hardwired" in everything from nematodes to primates. Why is this news?
Because you haven't learned what is news yet. But by trial and error, you'll catch on
The shepherds did so well protecting the flock that the sheep no longer believed that wolves existed.
It is a good question, and there are several answers...
Artificial Intelligence has been seen as a goal since Ada Lovelace was a lass. In the fifties, it was hoped that computers fed with parallel translations could learn the rules of languages, and provide fought translations of (say) technical documents on aeronautics from Russian to English, where sufficiently skilled and positively vetted engineers were rare. There were later attempts in the sixties and seventies to learn to walk, recognise objects, or solve puzzles. There was the constant hope that the next hardware would be a bit more powerful, and you could throw problems at it, and intelligence would somehow boot up. After all, that is how it must have started last time. However, intelligence failed to boot up, or maybe it always lost out to other brute force techniques which regular computers are good at.
The nematode has a simple. pre-programmed brain. It is good for being a nematode, but it doesn't really learn. Our brains have a lot of structure when they are formed, which means that our language centres, our vision centres, the parts that are active when we are solving spatial problems, or composing music, turn up in the same places most of the time; but we don't seem to run an actual program as such. We are born with very little instinct when compared to most other complex animals, but I suspect even they are not really running a program either.
The trick seems to be to provide the robot with enough plastic design to nudge it in the general direction of intelligence: too little design and it never gets its act together, while too much design means it is just doing what you programmed it to do. There are interesting times where computers are getting the complexity and the connectivity and plastic re-programmability to rival animal brains; but the spontaneous self-evolving problem solving spark just isn't there yet. But I hope we may see it in our lifetimes.
Hmm... sorry, this has been happening for many years already... back to the 80s... This is nothing new.
No, it was not happening in the 1980s. The fundamental algorithm behind deep learning networks was worked out by Geoffrey Hinton in 2006. Before that, training a NN more than 2 layers deep was intractable.