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I appreciate this article; I'm impressed when intelligent people can take complex things and explain them in laymen's terms. I buy the idea that AI will be lik

I appreciate this article; I'm impressed when intelligent people can take complex things and explain them in laymen's terms. I buy the idea that AI will be like RDBMS. Except ... RDMBS is tangible, straight forward. Easily applicable. AI is indirect, soft. So while I agree AI will find it's way into most things - and - will be a critical feature of some things (i.e. it will enable self driving cars) ... I still think it's over hyped. It's a new and interesting field that is just too vague and 'non-parameterizeable' to provide value in so many ways. Remember 'Big Data' - it was mostly an optimization. Most businesses simply don't depend on data in such quantities, and when they can make use of it, it's often just a tweak to their business, not a deep strategic insight. If we see an explosion in GPU type computing, wherein AI experiments are able to grow maximally, perhaps we can dream a little bigger ... But in the meantime 'there be a lot of hype' around this subject. Kudos for the article, though.

hadsed 8y ago
I think it is soft to the extent that you need an expert to define precisely the bounds of your problem and the AI solution. However, consider the following situation today: you have millions of images that are fairly related (perhaps on a real estate listing site) and tags. You can now straightforwardly build a product for predicting hashtags and describing the photos without knowing hardly anything about AI. There are plenty of products to help engineers do this already, one of which is Google cloud's ML products. Now imagine that at some point a lot of the AI problems get to a point where they can figure out the correct training procedures automatically. People are working on this with varying amounts of progress but I think the future looks like we'll be able to do enough of this to sell/open source something as well-defined as a SQL database.

hadsed 8y ago

I think it is soft to the extent that you need an expert to define precisely the bounds of your problem and the AI solution. However, consider the following situation today: you have millions of images that are fairly related (perhaps on a real estate listing site) and tags. You can now straightforwardly build a product for predicting hashtags and describing the photos without knowing hardly anything about AI. There are plenty of products to help engineers do this already, one of which is Google cloud's ML products. Now imagine that at some point a lot of the AI problems get to a point where they can figure out the correct training procedures automatically. People are working on this with varying amounts of progress but I think the future looks like we'll be able to do enough of this to sell/open source something as well-defined as a SQL database.