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I don't deny that AI from the lisp days is out of favour today but I recall Neural Networks sat in a niche corner of maths mostly dormant for quite a while too.

I don't deny that AI from the lisp days is out of favour today but I recall Neural Networks sat in a niche corner of maths mostly dormant for quite a while too... I'm not an expert in either but am confident that progress is non linear. Are there any ideas that you think are definitely bad (or even possibly good) from the lisp days?

brabel 3y ago
Try using neural networks on a computer from 30 years ago, or even 10 years ago, and you'll quickly realize why they were not feasible for almost any problem when this book was written.
- eggy 3y ago
  I was studying evolutionary computation back in the 80s. Yes, you did not have the large ANNs you have today, but you also broke down your problem into manageable, computable bits (no pun intended). Working through Koza's book on Genetic Programming in the 90s, or Mark Watson's book on ANNS, chaos theory, and a bunch of other stuff, was very illuminating. I particularly liked the artificial life stuff out of the Santa Fe Institute. I still have a VHS from the 2nd proceedings. I could have rode the ML/DL wave to a high salary job, but I took another path building real-world objects and machines, and I am very happy I did. I remember when WordPerfect was popular and people spoke of careers in wordprocessing. I always saw it as evolution of the cube farm, and of course, it is. Now I have friends who work in industry with ML/DL, and they joke that it's the same but with a lot more things to track and keep running. I like GOFAI for the simple matter that sometimes 90% is good enough, and 125% is a waste of time, water in the blender. When robotics was taking off I saw the two schools of thought: Rodney Brooke's subsumption architecture making it simple to create emergent behavior, and Mark Tilden's analog circuits and discrete components. I found Tilden's work more interesting. It spoke to me about continuous or biological-like processes. I am now into researching neurocomputing and following the developments in designing neurocomputing hardware. I was so happy to have received free tickets to watch Terminator 2 back in the day, but now I feel like I have a front-row seat to it!
  - myth_drannon 3y ago
    Do you remember the names of topics from Sant Fe's "artifical life stuff"? Was it agent-based modeling? They have a bunch of courses on https://www.complexityexplorer.org https://www.complexityexplorer.org, but it's mostly Game Theory, Chaos, Complexity...
    - eggy 3y ago
      Look at the table of contents here: https://www.amazon.com/Artificial-INSTITUTE-SCIENCES-COMPLEXITY-PROCEEDINGS/dp/0201525712?asin=0201525712&revisionId=&format=4&depth=1 https://www.amazon.com/Artificial-INSTITUTE-SCIENCES-COMPLEX...
    - mistrial9 3y ago
      plenty of game programming has ingested "agents with actions" kind of programming directions; I believe that procedural code with some understanding of state-machines, is all that "artificial life" content was.. minus some details.
kmavm 3y ago
It's "out of favor" because it completely failed as a research program. Let's not equivocate about this; it's nice to understand heuristic search, and there was a time when things like compilation were poorly understood enough to seem like AI. But as a path towards machines that succeed at cognitive tasks, these approaches are like climbing taller and taller trees in the hopes of getting to the moon.

brabel 3y ago

Try using neural networks on a computer from 30 years ago, or even 10 years ago, and you'll quickly realize why they were not feasible for almost any problem when this book was written.

eggy 3y ago
I was studying evolutionary computation back in the 80s. Yes, you did not have the large ANNs you have today, but you also broke down your problem into manageable, computable bits (no pun intended). Working through Koza's book on Genetic Programming in the 90s, or Mark Watson's book on ANNS, chaos theory, and a bunch of other stuff, was very illuminating. I particularly liked the artificial life stuff out of the Santa Fe Institute. I still have a VHS from the 2nd proceedings. I could have rode the ML/DL wave to a high salary job, but I took another path building real-world objects and machines, and I am very happy I did. I remember when WordPerfect was popular and people spoke of careers in wordprocessing. I always saw it as evolution of the cube farm, and of course, it is. Now I have friends who work in industry with ML/DL, and they joke that it's the same but with a lot more things to track and keep running. I like GOFAI for the simple matter that sometimes 90% is good enough, and 125% is a waste of time, water in the blender. When robotics was taking off I saw the two schools of thought: Rodney Brooke's subsumption architecture making it simple to create emergent behavior, and Mark Tilden's analog circuits and discrete components. I found Tilden's work more interesting. It spoke to me about continuous or biological-like processes. I am now into researching neurocomputing and following the developments in designing neurocomputing hardware. I was so happy to have received free tickets to watch Terminator 2 back in the day, but now I feel like I have a front-row seat to it!
- myth_drannon 3y ago
  Do you remember the names of topics from Sant Fe's "artifical life stuff"? Was it agent-based modeling? They have a bunch of courses on https://www.complexityexplorer.org https://www.complexityexplorer.org, but it's mostly Game Theory, Chaos, Complexity...
  - eggy 3y ago
    Look at the table of contents here: https://www.amazon.com/Artificial-INSTITUTE-SCIENCES-COMPLEXITY-PROCEEDINGS/dp/0201525712?asin=0201525712&revisionId=&format=4&depth=1 https://www.amazon.com/Artificial-INSTITUTE-SCIENCES-COMPLEX...
  - mistrial9 3y ago
    plenty of game programming has ingested "agents with actions" kind of programming directions; I believe that procedural code with some understanding of state-machines, is all that "artificial life" content was.. minus some details.

kmavm 3y ago

It's "out of favor" because it completely failed as a research program. Let's not equivocate about this; it's nice to understand heuristic search, and there was a time when things like compilation were poorly understood enough to seem like AI. But as a path towards machines that succeed at cognitive tasks, these approaches are like climbing taller and taller trees in the hopes of getting to the moon.