Added 2020-04-27 : Originally 2018-02-03
Added 2018-10-08 : Originally 2018-09-06
Added 2018-10-08 : Originally 2018-09-05
- Semantic Networks : http://www.jfsowa.com/pubs/semnet.htm
- [AIhttp://www.cs.bilkent.edu.tr/~akman/conf-papers/fss97/node1.htmlAI and semiotics]
Added 2018-07-18 : Originally Added 2018-01-23 : https://digiday.com/social/artificial-intelligence-informing-fashion-designers-create/ AI Fashion design
Added 2018-07-17 : Originally Added 2018-07-01 : https://www.theguardian.com/commentisfree/2018/jun/24/machines-may-beat-us-in-debate-will-they-ever-have-the-human-touch
Added 2018-07-17 : Originally Added 2017-08-09 : https://medium.com/@libbykinsey/iclr2017-deep-thought-vs-exaflops-9f653354737b
Quora Answer : Artificial Intelligence has been around for a long time with Lisp and Prologue but did not have widespread adoption. What has changed recently in the last decade to make AI more useful?
Basically two things :
faster and more powerful hardware
larger data-sets for training.
The second of these is probably more important than the first.
I was in academia with people doing neural network research in the 90s. My first job using AI in 1991 used neural networks with a couple of hundred nodes and a few dozen training examples. We didn't have more training examples, and you couldn't run many more nodes than that on our off-the-shelf PCs.
Today people would find that ludicrous. How can you pick up any patterns with that?
Well you can't. (Just toy, very well prepared, examples.)
And today people use thousands of nodes and millions of examples.
That data is largely available thanks to the last 20 years of the internet, where we've been gathering massive collections of human writing, photos, and other media types which we can now build frighteningly plausible models from.
We have so much data today. The internet collects it. And the business models of the internet giants like Facebook and Google and Amazon means that there's a mature and sophisticated market of hard and software to manage enormous collections of data that be fed to the deep learners.
There are also two approaches to AI.
The "symbolic" which Lisp and Prolog used to focus on. And the "statistic" or "connectionist" ie. data-driven AI.
I don't think either is inherently superior to the other. Both have been around since the dawn of computing in the 1950s. And there have been plenty of religious wars.
But they are usefully complementary.
I suspect today we are just throwing more resources at machine learning / connectionist AI. I wonder how powerful our Prolog systems might be if we gave them equivalent computational resources and programmer time and attention. Possibly equally amazing.
But the nice thing about connectionism is that you don't really need smart programmers. You just need a lot of data, and can already get a long way with off-the-shelf free-software packages.
EDIT : See LanguagesForAI
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