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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."
A.I. is a classic case of moving goal posts --- there's an assumption a hard problem requires it, the problem gets solved using ever-more sophisticated analysis/pattern-matching/data-processing --- the problem domain is no longer considered A.I.
Sphinx of black quartz, judge my vow.
As somebody who is currently taking an advanced class in machine learning *shutter, no more prob stat, no more vector calculus, no more linear algebra, please!*, I'm not going to claim to be an expert by any means, but I will point out that as far as I can tell machine learning is more about classifiers, i.e., is this a square peg, or a round peg, and is that a square hole or a round hole. In other words, take a piece of data, figure out if it belongs in a particular class, or decide if a new class should be created. I can't see any form of sentience coming from anything happening right now.
exactly.
the research field of AI already considered the idea of "artificial intelligence" to be more "solutions based on imitating intelligence", and it has long been postulated, that while the dream is still the real thing, it probably will not be possible with electronics (which do great in calculus, but still have problems with parallelism).
the results in the last decades were OOP, neuronal networks, or the good known Spamchecking algorithms.
But the approach to learning in all these cases is still very different each time. I am e.g. not sure, if spam filters really use neuronal algorithms - it mostly concentrates on the relations of words in a text, or the alterations of a word in a text, and how to use the statistical data about these relations to flag content which is probably spam.
Since humans (or any intelligent mammals) learn to learn by playing, both establishing recognition of rules, and the usage of data, I wonder if it will be ever possible to have an abstract learning machine, which not just "learns", but also learn "what to learn", and "why to learn" on its own. But each respective problem is getting addressed.
Oh yes, and the latest implications, like gamification in industry, and the revelations of the true meaning of "playing", researched more in social and psychological sciences is maybe also an indirectly linked to the field of AI. Which still has a long way to go in a society, where "playing" is associated with "kids", and a waste of time.
Then you haven't seen my spam filter!
Seriously, I am an AI PhD student/researcher. We get this kind of crap all of the time.
"you are working on robots, when is SkyNet? Hahaha"
"...so... the robot is lost and can't figure out where it is... I'm trying to make it so it can figure it out by how many steps its taken and looking around"
"SkyNet!"
"you are working on a program to control a controller for a video game, when is SkyNet? Hahaha"
"...so... I'm trying to figure out how the computer can make Mario jump over the bad guys without telling him that the bad guys are 'bad'"
"SkyNet!"
"you are working on a program to figure out emotional states of students, how long before you unemploy all the nation's teachers?"
"...so... I'm trying to figure out how to teach a computer to recognize when people are bored..."
"Why do you hate your teachers?!"
Seriously, the idea that we will be able to classify spam, or map a room, of jump over an obstacle, or recognize boredom so well that it gets sentience (and decides to kill all of us) is laughable.
Posting Anon from work.