Dr. Mark Humphrys

School of Computing. Dublin City University.

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History of AI



Classic AI

Concentrated on:


Archetypal problem - Chess



The history of AI: A repeated cycle of excitement and disillusion

Slower than expected progress led to biologically-inspired movement in AI, since mid-1980s.
This itself has also now made slower than expected progress!
Conclusion: AI is hard!

Will probably need both approaches but no one knows how to combine them.




Biologically-inspired AI


Since mid-1980s - Biologically-inspired movement in AI.

AI probably now split 50-50 between "Bio-" and "Non-bio-"
(to use crude stereotypes that do not necessarily fit either)




Themes of Biologically-inspired AI





Analysis v. Synthesis


Whole point of AI is division between:

  1. Analysis (biology, neuroscience, psychology) - reverse engineering with no manual, very difficult.

    and:

  2. Synthesis (AI as part of cognitive science) - try out models of mind by building them! We know what the component parts are (we can label them!). Much easier to study the whole system.
Ironically, the more you allow machines learn and self-adapt (especially evolution of neural networks or program code) the more you run into analysis problems of the final result - just like nature again!



Reading


The Classic-AI v. Bio-AI debate

AI is possible .. but AI won't happen: The future of Artificial Intelligence, Mark Humphrys, 1997. - Online. - My simple, short, popular-science summary of the history of AI, emphasising the recent biologically-inspired trends.

Intelligence without Reason, Rodney Brooks, 1991. - Online. - A lengthy history of AI from the biologically-inspired angle.

Today the earwig, tomorrow man?, David Kirsh, 1991. - Online. - A reply to Brooks.


Bio-AI

Why not the whole iguana?, Dennett, 1978. - See Full reference. - A classic call to build whole creatures.

The animat path to AI, Stewart Wilson, 1990. - Online. - Dennett's call taken up by a whole new movement.

Out of Control: The New Biology of Machines, Kevin Kelly, 1994. - Online. - A mind-bending description of the type of machines we're trying to build.



The failure of bio-AI



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