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AI is mostly about curve fitting (2018)

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Re: AI is mostly about curve fitting (2018)

#21

Post by bitL »

ML is about function approximation where function can be anything and mostly something that is practically intractable by usual analytical or numerical methods, such as observing real-time processes with millions of variables.

In math, via a reduction, human life is just a function of time with many inputs and many outputs. I.e. you can do curve fitting there as well...

Re: AI is mostly about curve fitting (2018)

#23

Post by jgalt212 »

huffmsa wrote: post #17

I'd say this is outdated. Since its publication, transfer learning has really started to pick up speed. Taking a model trained in one subject, and reapplying it to build out new vertical is pretty much how children learn new things. We'll have models using models to train new models sooner rather than later. If a program can create it's own curve functions and decide which one to use to evaluate a problem, is it that…

Isn't transfer learning just a faster way to fit a curve by starting with a curve that's partially fit instead of a random vector. So under this interpretation, transfer learning is also "curve fitting."

Re: AI is mostly about curve fitting (2018)

#26

Post by huffmsa »

jgalt212 wrote: post #23
huffmsa wrote: post #17

I'd say this is outdated. Since its publication, transfer learning has really started to pick up speed. Taking a model trained in one subject, and reapplying it to build out new vertical is pretty much how children learn new things. We'll have models using models to train new models sooner rather than later. If a program can create it's own curve functions and decide which one to use to evaluate a problem, is it that…

Isn't transfer learning just a faster way to fit a curve by starting with a curve that's partially fit instead of a random vector. So under this interpretation, transfer learning is also "curve fitting."

It is. But progress has been made taking models fit on pretty different topics and using them for something tangentially related. And that's exactly what humans do.

You don't start from zero when you learn to paint if you already know how to draw. You take another model of behavior and use it as a baseline to develop your painting behavior.

The fundamental thing that you can do that the computer can't right now is decide when you need a new model, when to use an existing one and when to transfer off of something.

Re: AI is mostly about curve fitting (2018)

#27

Post by wayoutthere »

dr_dshiv wrote: post #15

So, one view of AI is that is is machine learning. Another view is that it consists of automated process. So, if expert systems count as AI, or if production rule systems count as AI, then pretty much any handcrafted if-then statement counts as AI. And, so too might any automated process.. This is has the advantage of recognizing how human intelligence can be automated and aggregated into system processes. And, the d…

I too hold the view that artificial intelligence is not reliant on computers. Sufficiently complex business logic is impossible for a single person -- even the CEO -- to have an end-to-end view (much less have a significant influence on). The emergent behavior of large companies fits a definition of an AI today; human "decision makers" are increasingly just reviewing and approving the output of algorithms.

I think the end state for large corporations will be to automate away so much of the human input that they end up looking like what we think of as "AI" in a broader cultural context. But we already live in a world controlled by AI, and we have for 3+ decades.

Re: AI is mostly about curve fitting (2018)

#28

Post by bitL »

streetcat1 wrote: post #22

So RL is true AI. Alpha GO did make moves inconceivable to the best human mind on the subject. There was no curve to fit into.

The learning from game itself was curve fitting, the Deep in Deep Reinforcement Learning usually means some difficult function is replaced by a deep neural network, approximating optimal values (for moves) trained on gameplay samples, usually in sense of rewards/punishments for reaching certain states; in games they could rank e.g. good/bad moves, winning states, losing states etc.
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