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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.
Human beings, who are almost unique in having the ability to learn from the experience of others, are also remarkable for their apparent disinclination to do so.
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...Why is this news?
Because they couldn't do it before....
/. zen: Imagine a Beowulf cluster of Beowulf clusters...
That strikes me as the sort of thing that would be "hardwired" in everything from nematodes to primates. Why is this news?
Because it isn't a nematode or a primate. It is a robot. A living thing that can learn and adapt is not news, because that's what living things do. A non-living think that can learn and adapt is news because that's what living things do.
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.
An automaton can be neither benevolent nor have free agency.
Why not? Unless you believe that brains are magic, or created by the intervention of a deity, there is no reason to believe that computers have any inherent limitation that living things do not have.
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.
I think we are doing much the same, it's just that computers have caught up to theory and are able to perform now. Now it is no longer a question of theory, but one of technique, and what is described in the article is a new technique--one that will likely have many, many applications in the near future.
In the late 80's/early 90's, they were able to use some of their theory, but it just wasn't super-robust because things just took too darn long. You couldn't have your system analyze at a million images in a minute, hence allowing them to go through hundreds of generations in a day.
At least that is my view, as an educated layperson.
Here are the papers: http://rll.berkeley.edu/deeple...
You can't experience the experience of others (paraphrase) J.D. Lang.
OTOH when I read the OP, I immediately thought of 'Deep Thought' and a couple of philosophers who were too highly trained to be useful.
Don't be apathetic. Procrastinate!
"Deep learning" refers a family of machine learning techniques (such as neural-networks, convolutional neural-networks, stacked-autoencoders, etc.) that have a multi-layer architechture, typically allowing the system to learn highly non-linear functions of many variables. Each layer can be thought of as a simple learned function whose output is fed into the next layer. Such systems can often have thousands or millions of parameters to learn and thus require a LOT of training data and a fair bit of computing power/ runtime to train. But if you look at some area (e.g. object reccognition in computer vision), deep networks are currently the top techniques by a fair margin.
This seems more like basic-level stuff...
The devil is in the details. How do you best represent learning mathematically and computationally? What are mistakes and or what are the objectives? How do you encode these and how to you penalize making these mistakes in the future? These are all challenging questions.
That strikes me as the sort of thing that would be "hardwired" in everything from nematodes to primates.
Machine learning approaches have often taken inspiration from biology, however the exact neurological mechanisms of learning are not yet entirely understood. Its difficult to replicate nature. Its even more difficult when you don't yet understand nature.
If you believe evolution your argument makes no sense. Random mutations accomplished already what you claim is unlikely to occur until the theorized heat death of the universe. How likely is that?
Is the universe infinite?
PS: Evolution does not rely on random mutations.
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