This AI Boom Will Also Bust

Post by Aeolos » Mon, Dec 05, 2016, 9:36 AM UTC

bunderbunder wrote: post #237

ryoshu wrote: post #186

Earlier quoted context omitted.

A lot of revolutionary technologies begin as toys. In the 9th century two brothers created a machine called, "the instrument which plays by itself." It uses a rotating cylinder with "teeth" that triggers keys which were encoded to play music. One can say it's a programmable music machine. Fast forward to the 1700s and artisan engineers in France were creating similar devices that automatically played music. Toys for…

I think your story does more to reinforce the parent post's point. The chain of events you outline spans an entire millennium. Over that time, yes, a lot of technology developed, and eventually we got to computers, and there was even the movement that gives us the term Luddite. But it was still one long chain of gradual, evolutionary developments over the course of a very long expanse of time. At no point was there a…

Post by orasis » Mon, Dec 05, 2016, 2:34 PM UTC

YeGoblynQueenne wrote: post #307

vonnik wrote: post #123

[Disclosure: I work for a deep-learning company.] Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites. He is skeptical that deep learning and other forms of advanced AI 1)…

>> One-shot learning, or learning from a few examples, is a field where we're making rapid progress, which means that in the near future, we'll obtain much higher accuracy on smaller datasets. I'm really not convinced by one-shot learning, or rather I really don't see how it is possible to show that any technique used generalises well to unseen data, when you're supposed to have access to only very little data during…

Post by dwaltrip » Mon, Dec 05, 2016, 8:05 PM UTC

Jach wrote: post #208

dwaltrip wrote: post #166

Earlier quoted context omitted.

Brain emulation? I didn't realize people seriously thought that could be good for anything other than research and investigation. If we can successfully emulate the brain, it seems we would have necessarily acquired the knowledge needed to build models that are very powerful without having to exactly mimic the brain.

See Hanson's http://ageofem.com/

Post by HemanHeartYou » Mon, Dec 05, 2016, 8:32 PM UTC

ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

Post by visarga » Mon, Dec 05, 2016, 9:28 PM UTC

logicallee wrote: post #299

visarga wrote: post #256

Earlier quoted context omitted.

I read your consciousness post and will reply here because that thread is already locked. I think the problem of consciousness comes from its definition. The word consciousness is too large, encloses too many aspects of cognition, religion and philosophy to be useful. It's a suitcase word, reifying various meanings into one name. What we need instead of "consciousness" is a set of concepts: - "perception" - means rep…

>Consciousness is a balancing system for the colony of cells that just want to pass their genes into the next generation. is there anything in that which requires that it be a physical biological substrate rather than a model? Can't computer models follow the same behavior as long as that's how their environment / system is set up? (More specifically, can't they also have brains made up of cells connected to each oth…