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I spend a fair amount of time thinking about this stuff... Currently, we've got machine learning--I think there's a market for it, but it's early. I think the

I spend a fair amount of time thinking about this stuff... Currently, we've got machine learning--I think there's a market for it, but it's early. I think the near term is about automating data science tasks--we won't have a shortage of data scientists because we can automate much of what they do. Even that's a hard problem. Personally, I think deep learning (specifically relatively recent neural net related research) will play an important part in this--as far as automation goes, it will help automate feature engineering (currently a huge cost). In general, I agree with the sentiment in the comments that "we are a long way from AI". Objectively, we are. However, I think we were similarly a long way from flight when the Wright brothers figured out how to keep a plane in the air. Or towards the end of the human genome project--although at least in that case, we knew what we were doing, whereas AI still doesn't really have a good theory behind it. The first software that seems awfully "AI like" though--I'm thinking that it could very well appear by accident while working on more general machine learning tasks. I don't think we're as far off as most people think, but I certainly don't have a crystal ball (and neither does anyone else)

VLM 13y ago
Maybe in your second paragraph you're discussing science vs engineering? For example from a science perspective the Wright Bros got to check mark "flight" pretty decisively, mostly because of their windtunnel work, although the engineering has mostly failed for most of the species and most of the species has never flown and due to massive resource constraints probably never will, so defining "flight" as a success is iffy. The genome project is another science to engineering transition problem, where the science check mark is done, but as far as I know there are no widespread engineering applications for the data at all, although someday there might be in the generic sense that all research is potentially useful. Something like this could happen in AI. OK the science proves conclusively that it could work, its just human engineering might not be up to it for a few centuries (like the Babbage experience WRT computation) or even the engineering is possible but the economics and politics make it impossible (like building a massive power generation dam across the straits of Gibraltar) We could prove the science of AI works, and then nothing happens for the next 500 years. Or maybe not.
Houshalter 13y ago
That's actually a really good idea, automating data science. Currently it takes a lot of technical knowledge and time/skill. There are a lot of even everyday things where just putting your data into a spreadsheet and asking it for predictions would be useful.

VLM 13y ago

Maybe in your second paragraph you're discussing science vs engineering? For example from a science perspective the Wright Bros got to check mark "flight" pretty decisively, mostly because of their windtunnel work, although the engineering has mostly failed for most of the species and most of the species has never flown and due to massive resource constraints probably never will, so defining "flight" as a success is iffy. The genome project is another science to engineering transition problem, where the science check mark is done, but as far as I know there are no widespread engineering applications for the data at all, although someday there might be in the generic sense that all research is potentially useful. Something like this could happen in AI. OK the science proves conclusively that it could work, its just human engineering might not be up to it for a few centuries (like the Babbage experience WRT computation) or even the engineering is possible but the economics and politics make it impossible (like building a massive power generation dam across the straits of Gibraltar) We could prove the science of AI works, and then nothing happens for the next 500 years. Or maybe not.

Houshalter 13y ago

That's actually a really good idea, automating data science. Currently it takes a lot of technical knowledge and time/skill. There are a lot of even everyday things where just putting your data into a spreadsheet and asking it for predictions would be useful.