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Part I
The state of ML

Part II
The challenges of AI

Part III
The causal viewpoint

Part IV
The algebraic viewpoint

From Machine Learning to Artificial Intelligence

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There is a big gap between our Machine Learning techniques and our ambition to make a dent in Artificial Intelligence. The purpose of these pages is to explain the nature of the gap, that is, clarify some of the problem we must solve, and describe conceptual tools to reason about them and maybe lead to solutions.

I am planning to add new pages every so often. The most recent additions are listed below in blog style. Since I expect that all these pages will form a coherent ensemble, the sidebar list them as a table of contents, in the order they are expected to be read in the end.

Michael Littman's AI landscape “The Future of AI Symposium”
Michael Littman, 2016


AI for the open world

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. McCarthy, Minsky, Rochester, and Shannon, 1955

It is well known that things did not go that easily.

→ Read more...

2018/05/24 11:41 · leonb
feuilleton/start.txt · Last modified: 2018/05/26 16:37 by leonb

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