5 ms·

Interestingly, not much about ML. Surprinsing for lisp which, if I understand correctly, has roots in AI...

by wiz21c 6y ago

jascii 6y ago
The focus of "AI" has shifted over time from symbolic processing (something lisps star at) to neural network machine learning, which requires more brute-force power.
- jefft255 6y ago
  I think the issue isn't "brute force power" (which python doesn't really have compared to CL if you look at the language itself), but rather the quality and completeness of numerical routines and GPU support. Matlab was very popular for early ML because it had the former.
  - hajile 6y ago
    Python is doing the actual lifting with C or Fortran, so I'd say brute force still applies quite a bit.
    - junke 6y ago
      Same for the Lisp libraries which rely on CUDA, OpenBlas, etc.
pjmlp 6y ago
The AI of the first Winter had nothing to do with ML, rather expert systems and symbolic processing. Python is starting to look like the Lisp of the second Winter. https://norvig.com/python-lisp.html https://norvig.com/python-lisp.html
- waynecochran 6y ago
  Are we not heading for a 3rd AI Winter? The first being in the 1970's and the second in the 1980's and 1990's (which I experienced)?
  - evgen 6y ago
    I don't think there was a post-70s winter. There was just not enough 'there' to over-hype; it was toy problems that did not even try to masquerade as real solutions other than in sci-fi and popular culture. Luminaries in the field definitely made a name by pumping out speculative paper after speculative paper, but IMHO there was more of an ember waiting to spark than there was a fire consuming all of its fuel. By the late-80s and early-90s you have venture-backed companies, big institutional efforts, grifters who had honed their pitch in academic tenure-track positions prior to moving to richer waters, and the first real claims being made regarding just-around-the-corner deliverables that would change everything. Maybe I am jaded from experiencing that same winter, but from what I recall the prior decades were more consumed with people making broad claims to try to establish intellectual primacy more than making claims about what could be delivered.
    - Jtsummers 6y ago
      The term showed up in the 80s, but there was an earlier (70s) failure in AI. There were a lot of grand ideas and promises, people really did anticipate AI moving much faster in the 60s and early 70s than it did. Since a lot of ideas (both regarding AI and CS in general) were in their infancy, the limits of computers (fundamental limits) weren't yet fully recognized, but also the hardware was itself creating limits (non-fundamental to the field) that weren't escaped until the 80s and 90s. See chess AIs of the 90s finally "solving" the problem, which would've been technically conceivable (how to do it) in the 70s but totally unrealizable (unless, maybe, you hooked every computer of the time together).
  - vram22 6y ago
    By 1980's and 1990's, are you referring to the Japanese 5th Generation project?
- KineticLensman 6y ago
  > The AI of the first Winter had nothing to do with ML, rather expert systems and symbolic processing Between 1988 and 1992 I worked for a UK company participating in a multinational project to use Common Lisp to build an expert system building tool. After creating the thing, we worked with clients (internal and external) to try to solve real customer projects. Our conclusions matched others, and contributed to the AI winter: * the rule-based expert systems were extremely brittle in their reasoning * once you got beyond toy problems with small rule sets, you needed some programming skills in addition to the domain expertise that you were supposedly encoding * you sometimes spotted the actual algorithms that could be coded in a conventional language rather than rules + inference engine. We eventually abandoned the AI side and kept going with the underlying Lisp, and started to get real leverage in the company, rapidly becoming a prototyping / risk reduction group who delivered usable functionality. [Edit] We were using Lisp processors embedded in Macintosh hardware, with outstanding (for the time) IDEs and thanks to the Mac interface, we could create some really slick apps for the end users. One of our Lisp systems that got rave reviews internally was a drag-and-drop network modelling tool that replaced a frightening mass of Fortran and data entry spreadsheets. No AI/ML at all, but it really improved the throughput of our network modelling group. As we were a comms company, this got positive reaction from senior management, offsetting the non-return on investment in the rule system.
  - ane 6y ago
    Are you referring to the NuBus boards mentioned here[0], the Symbolic MicroExplorer or MacIvory? Those look interesting! https://en.m.wikipedia.org/wiki/NuBus https://en.m.wikipedia.org/wiki/NuBus
    - KineticLensman 6y ago
      It was the TI MicroExplorer. We also used Symbolics machines. By the time I left we had switched to conventional Macs running Procyon Common Lisp [0], which had a stunningly good native IDE. [0] http://www.edm2.com/index.php/Procyon_Common_Lisp http://www.edm2.com/index.php/Procyon_Common_Lisp
- 7thaccount 6y ago
  I don't think Python has the same issues. It was very popular as a scripting language long before it became good at numerical and data science work. It has always been free/open source and never required special hardware to run. Even if ML tanks, the impact to Python would be minimal.
bumbada 6y ago
I would say AI roots and research are lisp, as 99% of all early research work was done on lisp. Lisp was the language people like Richard Stallman or John McCarthy(the inventor of Lisp) used at MIT AI laboratory: https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artificial_Intelligence_Laboratory https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artif... Everybody used Lisp there and young people that learned from the masters learned lisp too. But that was AI 1.0. Then came the AI winter and 2.0 spring with GPUs that were programmed in C dialects and gave incredible levels of raw power. So, as a high level access to low level C code, python was picked by most people.
- blackrock 6y ago
  It would be great if Python had a PEP to automatically compile down to a binary executable file, and resolve all dependencies for it.

Interestingly, not much about ML. Surprinsing for lisp which, if I understand correctly, has roots in AI...

by wiz21c 6y ago

Interestingly, not much about ML. Surprinsing for lisp which, if I understand correctly, has roots in AI...

The focus of "AI" has shifted over time from symbolic processing (something lisps star at) to neural network machine learning, which requires more brute-force power.

jefft255 6y ago
I think the issue isn't "brute force power" (which python doesn't really have compared to CL if you look at the language itself), but rather the quality and completeness of numerical routines and GPU support. Matlab was very popular for early ML because it had the former.
- hajile 6y ago
  Python is doing the actual lifting with C or Fortran, so I'd say brute force still applies quite a bit.
  - junke 6y ago
    Same for the Lisp libraries which rely on CUDA, OpenBlas, etc.

pjmlp 6y ago

The AI of the first Winter had nothing to do with ML, rather expert systems and symbolic processing. Python is starting to look like the Lisp of the second Winter. https://norvig.com/python-lisp.html https://norvig.com/python-lisp.html

waynecochran 6y ago
Are we not heading for a 3rd AI Winter? The first being in the 1970's and the second in the 1980's and 1990's (which I experienced)?
- evgen 6y ago
  I don't think there was a post-70s winter. There was just not enough 'there' to over-hype; it was toy problems that did not even try to masquerade as real solutions other than in sci-fi and popular culture. Luminaries in the field definitely made a name by pumping out speculative paper after speculative paper, but IMHO there was more of an ember waiting to spark than there was a fire consuming all of its fuel. By the late-80s and early-90s you have venture-backed companies, big institutional efforts, grifters who had honed their pitch in academic tenure-track positions prior to moving to richer waters, and the first real claims being made regarding just-around-the-corner deliverables that would change everything. Maybe I am jaded from experiencing that same winter, but from what I recall the prior decades were more consumed with people making broad claims to try to establish intellectual primacy more than making claims about what could be delivered.
  - Jtsummers 6y ago
    The term showed up in the 80s, but there was an earlier (70s) failure in AI. There were a lot of grand ideas and promises, people really did anticipate AI moving much faster in the 60s and early 70s than it did. Since a lot of ideas (both regarding AI and CS in general) were in their infancy, the limits of computers (fundamental limits) weren't yet fully recognized, but also the hardware was itself creating limits (non-fundamental to the field) that weren't escaped until the 80s and 90s. See chess AIs of the 90s finally "solving" the problem, which would've been technically conceivable (how to do it) in the 70s but totally unrealizable (unless, maybe, you hooked every computer of the time together).
- vram22 6y ago
  By 1980's and 1990's, are you referring to the Japanese 5th Generation project?
KineticLensman 6y ago
> The AI of the first Winter had nothing to do with ML, rather expert systems and symbolic processing Between 1988 and 1992 I worked for a UK company participating in a multinational project to use Common Lisp to build an expert system building tool. After creating the thing, we worked with clients (internal and external) to try to solve real customer projects. Our conclusions matched others, and contributed to the AI winter: * the rule-based expert systems were extremely brittle in their reasoning * once you got beyond toy problems with small rule sets, you needed some programming skills in addition to the domain expertise that you were supposedly encoding * you sometimes spotted the actual algorithms that could be coded in a conventional language rather than rules + inference engine. We eventually abandoned the AI side and kept going with the underlying Lisp, and started to get real leverage in the company, rapidly becoming a prototyping / risk reduction group who delivered usable functionality. [Edit] We were using Lisp processors embedded in Macintosh hardware, with outstanding (for the time) IDEs and thanks to the Mac interface, we could create some really slick apps for the end users. One of our Lisp systems that got rave reviews internally was a drag-and-drop network modelling tool that replaced a frightening mass of Fortran and data entry spreadsheets. No AI/ML at all, but it really improved the throughput of our network modelling group. As we were a comms company, this got positive reaction from senior management, offsetting the non-return on investment in the rule system.
- ane 6y ago
  Are you referring to the NuBus boards mentioned here[0], the Symbolic MicroExplorer or MacIvory? Those look interesting! https://en.m.wikipedia.org/wiki/NuBus https://en.m.wikipedia.org/wiki/NuBus
  - KineticLensman 6y ago
    It was the TI MicroExplorer. We also used Symbolics machines. By the time I left we had switched to conventional Macs running Procyon Common Lisp [0], which had a stunningly good native IDE. [0] http://www.edm2.com/index.php/Procyon_Common_Lisp http://www.edm2.com/index.php/Procyon_Common_Lisp
7thaccount 6y ago
I don't think Python has the same issues. It was very popular as a scripting language long before it became good at numerical and data science work. It has always been free/open source and never required special hardware to run. Even if ML tanks, the impact to Python would be minimal.

bumbada 6y ago

I would say AI roots and research are lisp, as 99% of all early research work was done on lisp. Lisp was the language people like Richard Stallman or John McCarthy(the inventor of Lisp) used at MIT AI laboratory: https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artificial_Intelligence_Laboratory https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artif... Everybody used Lisp there and young people that learned from the masters learned lisp too. But that was AI 1.0. Then came the AI winter and 2.0 spring with GPUs that were programmed in C dialects and gave incredible levels of raw power. So, as a high level access to low level C code, python was picked by most people.

blackrock 6y ago
It would be great if Python had a PEP to automatically compile down to a binary executable file, and resolve all dependencies for it.