[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 : 55 points
Date : 2024-02-24 23:41 UTC (1 days 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.
| rep_lodsb wrote:
| Humans "at their very best" are at least _trying_ to be
| right. Language models don 't - they are not concerned
| with any notion of objective truth, or even with "looking
| right" in order to gain social status like some human
| bullshitter - they are simply babbling.
|
| That this strategy is apparently enough to convince a
| large number of (supposedly) intelligent people otherwise
| is very troubling!
|
| Not saying that General AI is impossible, or that LLMs
| couldn't be a useful _component_ in their architecture.
| But what we have right now is just a speech center, what
| 's missing is the rest of the brain.
|
| Also, simply replicating / approximating something
| produced by natural evolution seems to me like the wrong
| approach, for both practical and ethical reasons: if we
| get something with >= human-like intelligence, it would
| be a black box we could never understand how any part of
| it actually works, and it might be a sentient being
| capable of suffering.
| 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
| frozenseven wrote:
| Most of the relevant similarities are there. Every plausible
| model in computational neuroscience is based on neural nets
| or a close approximation thereof, everything else is either
| magic or a complete non-starter.
| YeGoblynQueenne wrote:
| What makes it "much more obvious that it is possible" to
| simulate the human brain? If you're thinking of artificial
| neural nets, those clearly have nothing to do with human
| intelligence, which was very obviously not _learned_ by
| training on millions of examples of human intelligence; that
| would have been a complete non-starter. But that 's all that
| artificial neural nets can do, learn from examples of the
| outputs of human intelligence.
|
| It is just as clear that there is one more ability that human
| brains have, than the ability to learn from observations, and
| that's the ability to _reason_ from what is already known,
| without training on any more observations. That is how we can
| deal with novel situations that we have never experienced
| before. Without this ability, a system is forever doomed to be
| trapped in the proximal consequences of what it has observed.
|
| And it is just as clear that neural nets are completely
| incapable of doing anything remotely like reasoning, much as
| the people in the neural nets community keep trying, and
| trying. The branch of AI that Lighthill almost dealt a lethal
| blow to (his idiotic report brought about the first AI winter),
| the branch of AI inaugurated and championed by McCarthy,
| Michie, Simon and Newell, Shannon, and others, is thankfully
| still going and still studying the subject of reasoning- and
| making plenty of progress, while flying under the hype.
| 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....
| KRAKRISMOTT wrote:
| This isn't special, the same thing happened to the inventor of
| dynamic programming (and large swathes of control theory and
| reinforcement learning) Richard Bellman.
| 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.
| v3ss0n wrote:
| You are totally forgetting about most important part, GPUs.
| They didn't exist back in 1973
| BlueTemplar wrote:
| I have an "encyclopedia of cybernetics" for kids books from the
| 1960s-1970s. Among other things it has two chapters on
| artificial intelligence (mentioning neural networks), and
| another on Moore's Law, trying to extrapolate computing power
| up to IIRC our time. IIRC their predictions/speculations turned
| to be pretty accurate, except for the photonic and DNA-based
| computers...
| eigenvalue wrote:
| Sounds like an awesome book! I wish more kids books were
| sophisticated like that.
| wrp wrote:
| James Lighthill is sometimes criticized for commenting on a field
| in which he was not expert, so it adds context to look at why he
| was solicited for comment. At that time, fluid dynamics was one
| of the most prestigious technical fields in British academia,
| being very difficult, mathematical, and successful. Lighthill was
| the superstar of the field, so he was an obvious choice when the
| Science Research Council went looking for someone with status and
| technical chops to give a disinterested analysis of AI research.
| It is rather like how Richard Feynman was drafted to investigate
| the Challenger disaster.
|
| TFA says _...Lighthill was no fool. And yet, he was very
| confidently and persuasively wrong about the potential for AI..._
| I disagree, his report (which is short and readable) raises
| issues that are still pertinent. We currently have debate over
| whether GPT represents true intelligence and Lighthill 's
| comments foreshadow those of current skeptics.
|
| The author of TFA is clearly not a skeptic and accepts the views
| of John McCarthy. I have studied the debate between McCarthy and
| Hubert Dreyfus over the potential of AI, and I personally think
| that Dreyfus was right, current hype over ChatGPT
| notwuthstanding.
| dang wrote:
| Related:
|
| _Review of "Artificial Intelligence: A General Survey" (1993)_ -
| https://news.ycombinator.com/item?id=21700906 - Dec 2019 (9
| comments)
|
| _John McCarthy (and others) vs. Lighthill on AI in 1973_ -
| https://news.ycombinator.com/item?id=856843 - Oct 2009 (1
| comment)
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