[HN Gopher] The Lighthill Debate on AI from 1973: An Introductio...
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       The Lighthill Debate on AI from 1973: An Introduction and
       Transcript
        
       Author : eigenvalue
       Score  : 33 points
       Date   : 2024-02-24 23:41 UTC (23 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | eigenvalue wrote:
       | I had seen the video of this debate years ago, but decided to
       | revisit it recently in light of all the new developments in the
       | field. I thought others might enjoy it too, especially people who
       | had never heard of it before, but that many would prefer to read
       | it instead of watching. So I created a full transcript with
       | proper formatting.
       | 
       | I also included some thoughts in the intro and a section at the
       | end that attempts to review the accuracy of the different
       | speakers' arguments and claims since the debate took place 50
       | years ago. Hopefully it can spark an interesting debate here on
       | the current state of the field and we can learn some lessons from
       | the past!
        
         | bondarchuk wrote:
         | Right on the money, I'd never heard of it before and I prefer
         | the transcript to watching the video. Thanks a lot!
        
       | perfobotto wrote:
       | I mean, he was right ... for what we knew at that time. He
       | predicted correctly that the only way to achieve a general
       | intelligence it would require to mimic the extremely complex
       | neural networks in our brain that the hardware of the time was
       | very far away from achieving. He could not predict that things
       | would move so fast on the hardware side (nobody could have)that
       | made this somewhat possible. We are atill I would argue a bit out
       | in having the appropriate computer power to make this a reality
       | still, but it now is much more obvious that it is possible if we
       | continue on this path
        
         | mistrial9 wrote:
         | ok - except detailed webs of statistical probabilities only
         | emits things that "look right" .. not at all the idea of
         | General Artificial Intelligence.
         | 
         | secondly, people selling things and people banding together
         | behind one-way mirrors have a lot of incentive to devolve into
         | smoke-and-mirrors.
         | 
         | Predicting is a social grandstand in a way, as well as insight.
         | Lots of ordinary research has insight without grandstanding..
         | so this is a media item as much as it is real investigation
         | IMHO
        
           | perfobotto wrote:
           | To be honest restricting funding to the kind of symbolic
           | based AI research that is criticized in this discussion might
           | have helped AI more than it hurt , by eventually pivoting the
           | research toward neural networks and backpropagation. I don't
           | know how much of a good thing would have been if this kind of
           | research continued to be funded fully.
        
             | tudorw wrote:
             | Symbolic AI still alive and kicking,
             | https://arxiv.org/abs/2402.00854 also liking the
             | experiments around Graph Neural Networks and hybrids
             | thereof.
        
           | pixl97 wrote:
           | >except detailed webs of statistical probabilities only emits
           | things that "look right" .. not at all the idea of General
           | Artificial Intelligence.
           | 
           | I mean, this is what evolution does too. The variants that
           | 'looked right' but were not fit to survive got weeded out.
           | The variants that were wrong but didn't negatively affect
           | fitness to the point of non-reproduction stayed around.
           | Looking right and being right are not significantly different
           | in this case.
        
             | mistrial9 wrote:
             | yes, you have made the point that I argue against above. I
             | claim that "looking right" and "being right" are absolutely
             | and fundamentally different at the core. At the same time,
             | acknowledge that from a tool-use, utilitarian, automation
             | point of view, or a sales point of view, results that "look
             | right" can be applied for real value in the real world.
             | 
             | many corollaries exist. "looking right" is not at all
             | General Artificial Intelligence, is my claim yes.
        
               | doug_durham wrote:
               | "Being right" seems to be an arbitrary and impossibly
               | high bar. Human at their very best are only "looks right"
               | creatures. I don't think that the goal of AGI is god-like
               | intelligence.
        
         | abecedarius wrote:
         | > nobody could have
         | 
         | Hans Moravec at McCarthy's lab in roughly this timeframe (the
         | 70s) wrote about this then -- you can find the seed of his
         | 80s/90s books in text files in the SAIL archive
         | https://saildart.org/HPM (I'm not going to look for them
         | again). Easier to find:
         | https://web.archive.org/web/20060615031852/http://transhuman...
         | 
         | (Same McCarthy as in this debate.)
         | 
         | Gordon Moore made up Moore's Law in 1965 and reaffirmed it in
         | 1975.
        
         | lm28469 wrote:
         | > He predicted correctly that the only way to achieve a general
         | intelligence it would require to mimic the extremely complex
         | neural networks in our brain
         | 
         | Besides naming neutral networks and human brains don't have
         | that much in common
        
       | kjhughes wrote:
       | Interesting excerpt on the origin of the term, "artificial
       | intelligence":                   Professor Sir James Lighthill:
       | [...] Now, what are the arguments for not calling this computer
       | science, as I did in my talk and in my report, and calling it
       | artificial intelligence? It's because one wants to make some sort
       | of analogy. One wants to bring in what one can gain by a study of
       | how the brains of living creatures operate. This is the only
       | possible reason for calling it artificial intelligence instead.
       | Professor John McCarthy: Let's see. Excuse me. I invented the
       | term artificial intelligence. I invented it because we had to do
       | something when we were trying to get money for a summer study in
       | 1956, and I had a previous bad experience. The previous bad
       | experience concerns occurred in 1952, when Claude Shannon and I
       | decided to collect a batch of studies, which we hoped would
       | contribute to launching this field. And Shannon thought that
       | artificial intelligence was too flashy a term and might attract
       | unfavorable notice, and so we agreed to call it automata studies.
       | I was terribly disappointed when the papers we received were
       | about automata, and very few of them had anything to do with the
       | goal that at least I was interested in. I decided not to fly any
       | false flags anymore, but to say that this is a study aimed at the
       | long-term goal of achieving human-level intelligence. Since that
       | time, many people have quarreled with the term, but have ended up
       | using it. Newell and Simon, the group at Carnegie Mellon
       | University, tried to use complex information processing, which is
       | certainly a very neutral term, but the trouble was that it didn't
       | identify their field, because everyone would say, well, my
       | information is complex. I don't see what's special about you.
        
         | aswanson wrote:
         | When dealing with humans, marketing is everything.
        
         | dr_dshiv wrote:
         | Thank you, that's gold
        
         | sgt101 wrote:
         | I think that David Marr really nailed things down though.
         | 
         | https://dspace.mit.edu/bitstream/handle/1721.1/5776/AIM-355....
        
       | api wrote:
       | From the perspective of the 1970s many of these problems would
       | have appeared insanely hard to solve to the point of
       | impossibility.
       | 
       | Consider the idea of building insane torch rockets like those in
       | The Expanse or Avatar. We'd need something like small compact
       | fusion reactors or antimatter manufacturing at scale, not to
       | mention enormous advances in materials and superconductors and
       | such.
       | 
       | That looks impossible today and we know the shape of the problem.
       | In 1973 the gap between computers of the time and those of today
       | was similar to the gap between a chemical rocket and a
       | relativistic antimatter blowtorch, but on top of that nobody
       | really knew what approaches to AI might even bear fruit. We had
       | way more unknown unknowns between us and HAL 9000 than we have
       | between us and a starship.
       | 
       | It took many doubling of compute power, the accumulation of
       | petabytes of training data, and thousands and thousands of
       | researchers not just exploring the math but also tinkering
       | ("graduate student descent" as it's known in machine learning).
       | 
       | Definitely forgivable to think this might not be achievable in
       | 1973.
        
         | doug_durham wrote:
         | I think in scale only. We are still using the same computer
         | architectures, operating systems, and base networking protocols
         | available in 1973. The scale of clock speeds and storage
         | amounts would be astonishing to someone back then. I also
         | believe that if you told them that you'd go to a terminal and
         | type "ls" to list the contents of a directory they would be
         | astonished that that hadn't changed in 50 year.
        
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