[HN Gopher] Doug Lenat has died
___________________________________________________________________
Doug Lenat has died
Author : snewman
Score : 528 points
Date : 2023-09-01 17:44 UTC (1 days ago)
(HTM) web link (garymarcus.substack.com)
(TXT) w3m dump (garymarcus.substack.com)
| brundolf wrote:
| Doug was at times blunt, but he was fundamentally a kind and
| generous person, and he had a dedication to his vision and to the
| people who worked alongside him that has to be admired. He will
| be missed.
|
| I worked at Cycorp (not directly with Doug very often, but it
| wasn't a big office) between 2016 and 2020
|
| An anecdote: during our weekly all-hands lunch in the big
| conference room, he mentioned he was getting a new car (his old
| one was pretty old, but well-kept) and he asked if anybody could
| use the old car. One of the staff raised his hand sheepishly and
| said his daughter was about to start driving. Doug gifted him the
| car on the spot, without a second thought.
|
| He also loved board games, and was in a D&D group with some
| others at the company. I was told he only ever played lawful good
| characters, he didn't know how to do otherwise :)
|
| Happy to answer what questions I can
| skylyzz wrote:
| [dead]
| zitterbewegung wrote:
| I would expect lawful good because it would be the most
| logical.
| colordrops wrote:
| Why is that?
| djha-skin wrote:
| That's a very neutral thing to say.
| late25 wrote:
| I don't know much about him. What makes you start by saying
| he's blunt?
| brundolf wrote:
| It was a part of his personality, as it is for many people
| who are intelligent and opinionated, and some can mistake
| that for unkindness. But I wanted to emphasize that in his
| case it wasn't.
| chefandy wrote:
| I haven't read much by Lenat, and haven't seen anything
| that would lead me to believe he was particularly blunt, I
| disagree with attributing bluntness to intelligence. Some
| of the most intellectually blunt people I've met were some
| of the most conversationally blunt, and some of the most
| brilliant were absolute social butterflies. Making people
| needlessly uncomfortable when communicating is a
| _shortcoming_ regardless of your intellectual capacity.
| Even if difficulty communicating appropriately and
| intelligence are correlated somewhat through common
| neuropsychiatric profiles, the relationship is not causal.
| willcipriano wrote:
| Bluntness is associated for intelligence because when
| your mind works faster than the room and you reach
| conclusions that the room is 15 minutes away from making
| it feels blunt and crude to them.
|
| The socially acceptable thing to do is wait for them to
| catch up but that gets old.
| chefandy wrote:
| You've describing the justification for lifelong hubris,
| not intelligence. I've known a lot of very stupid people
| who assumed they were doing exactly that when in reality
| they jumped to a conclusion because they didn't
| understand the topic's complexities.
|
| A article shared on HN recently:
| https://www.bihealth.org/en/notices/intelligent-brains-
| take-....
| willcipriano wrote:
| Whenever I hear things like "understand the topic's
| complexities" from people they are unable to articulate
| these supposed complexities and we almost always land on
| exactly the solution I proposed in the first place.
|
| Don't get me wrong other intelligent people are on board
| with me during these episodes, and they are sometimes
| able to present their specific concern with the solution
| and that is valuable. I've been wrong before of course.
| On the other hand the "think of all the complexities"
| crowd just spout nuggets of wisdom like that until they
| are able to comprehend the solution, and that takes about
| 15 minutes in my experience.
| chefandy wrote:
| Your anecdote contradicts the study discussed in that
| article. Given the nature of your argument, this sounds
| like a great time to exit this exchange.
| chmod775 wrote:
| > Making people needlessly uncomfortable when
| communicating is a shortcoming regardless of your
| intellectual capacity.
|
| Emphasis on _needlessly_. Some bluntness is a good thing,
| even if it makes people uncomfortable. The alternative
| usually is prolonged awkwardness and beating around the
| bush, which likely will make people uncomfortable
| anyways. Just get it over with like an adult. Don 't drag
| an uncomfortable thing out or even risk people not
| getting the message at all the first time around.
| chefandy wrote:
| Sure-- the word choice was deliberate. If someone is
| known as a _blunt person_ broadly, chances are they
| sucked at communicating appropriately.
| OOPMan wrote:
| I suspect you and the person you're responding to have
| different ideas of what the word blunt means in this
| context?
| late25 wrote:
| Got it. I was merely curious if there were any particular
| stories, rumors, or legends about his bluntness (like there
| is Linus).
| brundolf wrote:
| No, it was never anything at that level. I would describe
| (pre-reformed) Linus as more than just "blunt"
| Rochus wrote:
| He was a hero of knowledge representation and ontology. A bit odd
| that we learn about his sad passing from a Wikipedia article,
| while at the time of this comment there is still no mention on
| e.g. https://cyc.com/.
| Rochus wrote:
| Thirteen hours later still no mention on the Cycorp website.
| Also the press doesn't seem to notice. Pretty odd.
|
| The post originally pointed to Lenat's Wikipedia page; now it's
| an obituary by Gary Marcus which seems more appropriate.
| dang wrote:
| Related. Others?
|
| _Cyc_ - https://news.ycombinator.com/item?id=33011596 - Sept
| 2022 (2 comments)
|
| _Why AM and Eurisko Appear to Work (1983) [pdf]_ -
| https://news.ycombinator.com/item?id=28343118 - Aug 2021 (17
| comments)
|
| _Early AI: "Eurisko, the Computer with a Mind of Its Own"
| (1984)_ - https://news.ycombinator.com/item?id=27298167 - May
| 2021 (2 comments)
|
| _Cyc_ - https://news.ycombinator.com/item?id=21781597 - Dec 2019
| (173 comments)
|
| _Some documents on AM and EURISKO_ -
| https://news.ycombinator.com/item?id=18443607 - Nov 2018 (10
| comments)
|
| _One genius 's lonely crusade to teach a computer common sense
| (2016)_ - https://news.ycombinator.com/item?id=16510766 - March
| 2018 (1 comment)
|
| _Douglas Lenat 's Cyc is now being commercialized_ -
| https://news.ycombinator.com/item?id=11300567 - March 2016 (49
| comments)
|
| _Why AM and Eurisko Appear to Work (1983) [pdf]_ -
| https://news.ycombinator.com/item?id=9750349 - June 2015 (5
| comments)
|
| _Ask HN: Cyc - Whatever happened to its connection to AI?_ -
| https://news.ycombinator.com/item?id=9566015 - May 2015 (3
| comments)
|
| _Eurisko, The Computer With A Mind Of Its Own_ -
| https://news.ycombinator.com/item?id=2111826 - Jan 2011 (9
| comments)
|
| _Open Cyc (open source common sense)_ -
| https://news.ycombinator.com/item?id=1913994 - Nov 2010 (22
| comments)
|
| _Lenat (of Cyc) reviews Wolfram Alpha_ -
| https://news.ycombinator.com/item?id=510579 - March 2009 (16
| comments)
|
| _Eurisko, The Computer With A Mind Of Its Own_ -
| https://news.ycombinator.com/item?id=396796 - Dec 2008 (13
| comments)
|
| _Cycorp, Inc. (Attempt at Common Sense AI)_ -
| https://news.ycombinator.com/item?id=20725 - May 2007 (1 comment)
| PaulDavisThe1st wrote:
| * Getting from Generative AI to Trustworthy AI: What LLMs Might
| Learn from Cyc* - https://news.ycombinator.com/item?id=37354601
| - RIGHT NOW
|
| HN location for discussion of Lenat's last paper (with Gary
| Marcus) about ways to reconcile Cyc's strengths with LLMs.
| symbolicAGI wrote:
| Doug Lenat, RIP. I worked at Cycorp in Austin from 2000-2006.
| Taken from us way too soon, Doug none the less had the
| opportunity to help our country advance military and intelligence
| community computer science research.
|
| One day, the rapid advancement of AI via LLMs will slow down and
| attention will again return to logical reasoning and knowledge
| representation as championed by the Cyc Project, Cycorp, its
| cyclists and Dr. Doug Lenat.
|
| Why? If NN inference were so fast, we would compile C programs
| with it instead of using deductive logical inference that is
| executed efficiently by the compiler.
| nextos wrote:
| Exactly. When I hear books such as _Paradigms of AI
| Programming_ are outdated because of LLMs, I disagree. They are
| more current than ever, thanks to LLMs!
|
| Neural and symbolic AI will eventually merge. Symbolic models
| bring much needed efficiency and robustness via regularization.
| mnemonicsloth wrote:
| If you want to learn about symbolic AI, there are a lot of
| more recent sources than PAIP (you could try the first half
| of _AI: A Modern Approach_ by Russel and Norvig), and this
| has been true for a while.
|
| If you read PAIP today, the most likely reason is that you
| want a master class in Lisp programming and/or want to learn
| a lot of tricks for getting good performance out of complex
| programs (which used to be _part of_ AI and is in many ways
| being outsourced to hardware today).
|
| None of this is to say you shouldn't read PAIP. You
| absolutely should. It's awesome. But its role is different
| now.
| nextos wrote:
| Some parts of PAIP might be outdated, but it still has
| really current material on e.g. embedding Prolog in Lisp or
| building a term-rewriting system. That's relevant for
| pursuing current neuro-symbolic research, e.g.
| https://arxiv.org/pdf/2006.08381.pdf.
|
| Other parts like coding an Eliza chatbot are indeed
| outdated. I have read AIMA and followed a long course that
| used it, but I didn't really like it. I found it too broad
| and shallow.
| keepamovin wrote:
| It would be cool if we could find the algorithmic
| neurological basis for this, the analogy with LLMs being more
| obvious multi-layer brain circuits, the neurological analogy
| with symbolic reasoning must exist too.
|
| My hunch is it emerges naturally out of the hierarchical
| generalization capabilities of multiple layer circuits. But
| then you need something to coordinate the acquired labels: a
| tweak on attention perhaps?
|
| Another characteristic is probably some (limited) form of
| recursion, so the generalized labels emitted at the small end
| can be fed back in as tokens to be further processed at the
| big end.
| optimalsolver wrote:
| The best thing Cycorp could do now is open source its
| accumulated database of logical relations so it can be ingested
| by some monster LLM.
|
| What's the point of all that data collecting dust and
| accomplishing not much of anything?
| vtr132 wrote:
| I think military will take over his work.Snowden documents
| reveled the cyc was been used to come up with Terror attack
| scenarios.
| adastra22 wrote:
| It seems the direction of flow would be the opposite: LLMs
| are a great source of logical data for Cyc-like things.
| Distill your LLM into logical statements, then run your Cyc
| algorithms on it.
| xpe wrote:
| > It seems the direction of flow would be the opposite:
| LLMs are a great source of logical data for Cyc-like
| things. Distill your LLM into logical statements, then run
| your Cyc algorithms on it.
|
| This is hugely problematic. If you get the premises wrong,
| many fallacies will follow.
|
| LLMs can play many roles around this area, but their output
| cannot be trusted with significant verification and
| validation.
| xpe wrote:
| *without
| creer wrote:
| LLM statements (distilled into logical statements) would
| not be logically sound. That's (one of) the main issues of
| LLMs. And that would make logical inference on these
| logical statements impossible with current systems.
|
| That's one of the principal features of Cyc. It's carefully
| built by humans to be (essentially) logically sound. - so
| that inference can then be run through the fact base.
| Making that stuff logically sound made for a very detailed
| and fussy knowledge base. And that in turn made it
| difficult to expand or even understand for mere civilians.
| Cyc is NOT simple.
| varjag wrote:
| Cyc is built to be locally consistent but global KB
| consistency is an impossible task. Lenat stressed that in
| his videos over and over.
| xpe wrote:
| > The best thing Cycorp could do now is open source its
| accumulated database of logical relations...
|
| This is unpersuasive without laying out your assumptions and
| reasoning.
|
| Counter points:
|
| (a) It would be unethical for such a knowledge base to be put
| out in the open without considerable guardrails and
| appropriate licensing. The details matter.
|
| (b) Cycorp gets some funding from the U.S. Government; this
| changes both the set of options available and the calculus of
| weighing them.
|
| (c) Not all nations have equivalent values. Unless one is a
| moral relativist, these differences should not be deemed
| equivalent nor irrelevant. As such, despite the flaws of U.S.
| values and some horrific decision-making throughout history,
| there are known worse actors and states. Such parties would
| make worse use of an extensive human-curated knowledge base.
| skylyzz wrote:
| [dead]
| zozbot234 wrote:
| OpenCyc is already a thing and there's been very little
| interest in it. These days we also have general-purpose
| semantic KB's like Wikidata, that are available for free and
| go way beyond what Cyc or OpenCyc was trying to do.
| halflings wrote:
| > If NN inference were so fast, we would compile C programs
| with it instead of using deductive logical inference that is
| executed efficiently by the compiler.
|
| This is the definition of a strawman. Who is claiming that NN
| inference is always the fastest way to run computation?
|
| Instead of trying to bring down another technology (neural
| networks), how about you focus on making symbolic methods
| usable to solve real-world problems; e.g. how can I build a
| robust email spam detection system with symbolic methods?
| xpe wrote:
| >> If NN inference were so fast, we would compile C programs
| with it instead of using deductive logical inference that is
| executed efficiently by the compiler.
|
| > This is the definition of a strawman.
|
| (Actually, it is an _example_ of a strawman.) Anyhow, rather
| than a strawman, I 'd rather us get right into the
| fundamentals.
|
| 1. Feed-forward NN computation ('inference', which is an
| unfortunate word choice IMO) can provably provide universal
| function approximation under known conditions. And it can do
| so efficiently as well, with a lot of recent research getting
| into both the how and why. One "pays the cost" up-front with
| training in order to get fast prediction-time performance.
| The tradeoff is often worth it.
|
| 2. Function approximation is not as powerful as Turing
| completeness. FF NNs are not Turing complete.
|
| 3. Deductive chaining is a well-studied, well understood area
| of algorithms.
|
| 4. But... modeling of computational architectures (including
| processors, caches, busses, and RAM) with sufficient detail
| to optimize compilation is a hard problem. I wouldn't be
| surprised if this stretches these algorithms to the limit in
| terms of what developers will tolerate in terms of compile
| times. This is a strong incentive, so I'd expect there is at
| least some research that pushes outside the usual contours
| here.
| symbolicAGI wrote:
| The point is that symbolic computation as performed by Cycorp
| was held back by the need to train the Knowledge Base by hand
| in a supervised manner. NNs and LLMs in particular became
| ascendant when unsupervised training was employed at scale.
|
| Perhaps LLMs can automate in large part the manual operations
| of building a future symbolic knowledge base organized by a
| universal upper ontology. Considering the amazing emergent
| features of sufficiently-large LLMs, what could emerge from a
| sufficiently large, reflective symbolic knowledge base?
| detourdog wrote:
| That what I have settled on. The need for a symbolic library
| of standard hardware circuits.
|
| I'm making a sloppy version that will contain all the symbols
| needed to run a multi-unit building.
| xpe wrote:
| > Instead of trying to bring down another technology (neural
| networks), how about you focus on making symbolic methods
| usable to solve real-world problems; e.g. how can I build a
| robust email spam detection system with symbolic methods?
|
| I have two concerns. First, just after pointing out a logical
| fallacy from someone else, you added a fallacy: the either-or
| fallacy. (One can criticize a technology _and_ do other
| things too.)
|
| Second, you selected an example that illustrates a known and
| predictable weakness of symbolic systems. Still, there are
| plenty of real-world problems that symbolic systems address
| well. So your comment cherry-picks.
|
| It appears as if you are trying to land a counter punch here.
| I'm weary of this kind of conversational pattern. Many of us
| know that tends to escalate. I don't want HN to go that
| direction. We all have varying experience and points of view
| to contribute. Let's try to be charitable, clear, and
| logical.
| Nevermark wrote:
| I am desperately vetting your comment for something I can
| criticize. An inadvertent, irrelevant, imagined infraction.
| Anything! But you have left me no opening.
|
| Well done, sir, well done.
| xpe wrote:
| Thanks, but if I didn't blunder here, I can assure you I
| have in many other places. I strive to be mindful. I try
| not to "blame" anyone for strong reactions. But when we
| see certain unhelpful behaviors directed at other people,
| I try to identify/name it without making it worse.
| Awareness helps.
| Nevermark wrote:
| Without awareness we are just untagged data in a sea of
| uncompressed noise.
| nikolay wrote:
| Even being a controversial figure, he was one of my heroes.
| Getting excited about Eurisko in the '80s and '90s was a big
| driver for me at the time! Rest in piece, dear computer pioneer!
| Nevermark wrote:
| > I have spent my whole career [...], Lenat was light-years ahead
| of me [...]
|
| Lenat is on a short list of people I expected/hoped to meet at
| some point when context provided the practical reason.
|
| He has been a hero to me for his creativity and fearlessness
| regarding his symbolic vision.
|
| So sad I will never meet him, but my appreciation for him will
| never die.
| brador wrote:
| Anyone know how he died? I can't find any information about it
| but someone mentioned heart attack on Reddit?
| gumby wrote:
| I worked with Doug on Cyc from ~85-89 (we had overlapped at PARC
| but didn't interact much there). The first thing undid was scrap
| the old implementation and start from scratch, designing the
| levels system and all the bootstrap code.
|
| It was a fun time with a small core team (mainly me, guha, and
| Doug) but over time I became dissatisfied with some of the
| arbitrariness of the KB. By the time I left the Cyc project (for
| my own reasons unrelated to work) I was somewhat negative towards
| the foundations of the project, despite the tight relationship
| we'd had and the fact it ran on my code! But over time I became
| smarter and came to appreciate once again its value. I had too
| much of a "pure math" view of things back then.
|
| As I moved on to other things I lost touch with Doug and Mary,
| and I'm sorry for that.
| detourdog wrote:
| I still intend to integrate OpenCyc.
| mindcrime wrote:
| If anybody wants to hear more about Doug's work and ideas, here
| is a (fairly long) interview with Doug by Lex Fridman, from last
| year.
|
| https://www.youtube.com/watch?v=3wMKoSRbGVs&pp=ygUabGV4IGZya...
| chubot wrote:
| Thanks for the link. I watched the first part, and an
| interesting story/claim is that before Cyc started, many "smart
| people" including Marvin Minsky came up with "~1 million" as
| the number of things you would have to encode in a system for
| it to have "common sense".
|
| He said they learned after ~5 years that this was an order of
| magnitude off -- it's more like 10 M things.
|
| Is there any literature about this? Did they publish?
|
| To me, the obvious questions are -
|
| - how do they know it's not 100M things?
|
| - how do they know it's even bounded? Why isn't there a
| combinatorial explosion?
|
| I mean I guess they were evaluating the system all along. You
| don't go for 38 years without having some clear metrics. But I
| am having some problems with the logic -- I'd be interested in
| links to references / criticism.
|
| I'd be interested in any arguments for and against ~10 M.
| Naively speaking, the argument seems a bit flawed to me.
|
| FWIW I heard of Cyc back in the 90's, but I had no idea it was
| still alive. It is impressive that he kept it alive for so
| long.
|
| ---
|
| Actually the wikipedia article is pretty good
|
| https://en.wikipedia.org/wiki/Cyc#Criticisms
|
| Though I'm still interested in the ~1M or ~10M claim. It seems
| like a strong claim to hold onto for decades, unless they had
| really strong metrics backing it up.
| replwoacause wrote:
| Enjoyed watching that. Doug sounds very impressive. RIP.
| mistrial9 wrote:
| reading the bio of Lex Fridman on wikipedia.. "Learning of
| Identity from Behavioral Biometrics for Active Authentication"
| what?
| modeless wrote:
| Makes sense to me. He basically made a system that detects
| when someone else is using your computer by e.g. comparing
| patterns of mouse and keyboard input to your typical usage.
| It would be useful in a situation such as if you left your
| screen unlocked and a coworker sat down at your desk to prank
| you by sending an email from you to your boss (or worse,
| obviously). The computer would lock itself as soon as it
| suspects someone else is using it instead of you.
| dang wrote:
| Please don't go offtopic in predictable/nasty ways - more at
| https://news.ycombinator.com/item?id=37355320.
| lionkor wrote:
| Like anything reasonably complex, it means little to you if
| its not your field - that said, I have no clue either.
| lern_too_spel wrote:
| Just search for Doug Lenat on YouTube. I can guarantee that any
| one of the other videos will be better than a Fridman
| interview.
| dang wrote:
| Hey you guys, please don't go offtopic like this. Whimsical
| offtopicness can be ok, but offtopicness in the intersection
| of:
|
| (1) generic (e.g. swerves the thread toward larger/general
| topic rather than something more specific);
|
| (2) flamey (e.g. provocative on a divisive issue); and
|
| (3) predictable (e.g. has been hashed so many times already
| that comments will likely fall in a few already-tiresome hash
| buckets)
|
| - is the bad kind of offtopicness: the kind that brings
| little new information and eventually lots of nastiness.
| We're trying for the opposite here--lots of information and
| little nastiness.
|
| https://news.ycombinator.com/newsguidelines.html
| mindcrime wrote:
| Only about two of them will be more contemporary though, and
| both are academic talks, not interviews. I get that you don't
| like Lex Fridman, which is a perfectly fine position to hold.
| But there is something to be said for seeing two people just
| sit and talk, as opposed to seeing somebody monologue for an
| hour. The Fridman interview with Doug is, IMO, absolutely
| worth watching. And so are all of the other videos by / about
| Doug. _shrug_
| yarpen_z wrote:
| I don't know this particular interview, but it's not
| necessarily about not liking Lex. I listened to many
| episodes of his podcast and while I appreciate the
| selection of guests from the CS domain, many of these
| interviews aren't very good. They are not completely
| terrible but they should have been so much better: Lex had
| so many passionate, educated, experienced and gifted
| guests, yet his ability to ask interesting and focused
| questions is not on the same level.
| pengaru wrote:
| He's a shitty interviewer. Often doesn't even engage with
| his guest's responses, as if he's not even listening to
| what they're saying, instead moving mechanically to his
| next bullet-point. Which is completely ridiculous for
| what's supposed to be a long-format conversational
| interview.
|
| The best episodes are ones where the guest drives the
| interview and has a lot of interesting things to say.
| Fridman's just useful for attracting interesting domain
| experts somewhere we can hear them speak for hours on
| end.
|
| The Jim Keller episodes are excellent IMO, despite
| Fridman. Guests like Keller and Carmack don't need a good
| interviewer for it to be a worthwhile listen.
| Jun8 wrote:
| Ahh, another one of the old guard has moved on. Here are two
| excerpts from the book _AI: The Tumultuous History Of The Search
| For Artificial Intelligence_ (a fantastic read of the early days
| of AI) to remember him by;
|
| "Lenat found out about computers in a a manner typical of his
| entrepreneurial spirit. As a high school student in Philadelphia,
| working for $1.00 an hour to clean the cages of experimental
| animals, he discovered that another student was earning $1.50 to
| program the institution's minicomputer. Finding this occupation
| more to his liking, he taught himself programming over a weekend
| and squeezed his competitor out of the job by offering to work
| for fifty cents an hour less.31 A few years later, Lenat was
| programming Automated Mathematician (AM, for short) as a doctoral
| thesis project at the Stanford AI Laboratory." p. 178
|
| And here's an count of an early victory for AI in gaming against
| humans by Lenat's EURISKO system
| (https://en.wikipedia.org/wiki/Eurisko):
|
| "Ever the achiever, Lenat was looking for a more dramatic way to
| prove teh capabilities of his creation. The identified the
| occasion space-war game called Traveler TCS, then quite popular
| with the public Lenat wanted to reach. The idea was for each
| player to design a fleet of space battleships according to a
| thick, hundred-page set of rules. Within a budget limit of one
| trillion galactic credits, one could adjust such parameters as
| the size, speed, armor thickness, autonomy and armament of each
| ship: about fifty adjustments per ship were needed. Since the
| fleet size could reach a hundred ships, the game thus offered
| ample room for ingenuity in spite of the anticlimactic character
| of the battles. These were fought by throwing dice following
| complex tables based on probability of survival of each ship
| according to its design. The winner of the yearly national
| championship was commissioned inter galactic admiral and received
| title to a planet of his or her choice ouside the solar system.
|
| Several months before the 1981 competition, Lenat fed into
| EURISKO 146 Traveler concepts, ranging from the nature of games
| in general to the technicalities of meson guns. He then
| instructed the program to develop heuristics for making winning
| war-fleet designs. The now familiar routine of nightly computer
| runs turned into a merciless Darwinian contest: Lenat and EURISKO
| together designed fleets that battled each other. Designs were
| evaluated by how well they won battles, and heuristics by how
| well they designed fleets. This rating method required several
| battles per design, and several designs per heuristic, which
| amounted to a lot of battles: ten thousand in all, fought over
| two thousand hours of computer time.
|
| To participants in the national championship of San
| Mateo,California, the resulting fleet of ninety-six small,
| heavily armored ships looked ludicrous. Accepted wisdom dictated
| fleets of about twenty behemoth ships, and many couldn't help
| laughing. When engagements started, they found out that the weird
| armada held more than met the eye. One interesting ace up Lenat's
| sleeve was a small ship so fast as to be almost unstoppable,
| which guaranteed at least a draw. EURISKO had conceived of it
| through the "look for extreme cases" heuristic (which had
| mutated, incidentally, into mutated, incidentally, into "look for
| almost extreme cases")." p. 182
|
| If you're a young person working in AI, by which I mean you're
| less than 30, and if you have not already done so, you should
| read about AI history in three decade 60s - 90s.
| brundolf wrote:
| I may be getting this wrong, but I think I remember hearing
| that his auto-generated fleets won Traveller so entirely,
| several years in a row, that they had to shut down the entire
| competition because it had been broken
|
| Edit: Fixed wrong name for the competition
| mindcrime wrote:
| I think you mean "EURISKO won the Traveller championship so
| entirely..."
|
| In which case, yes, something like that did happen. Per the
| Wikipedia page:
|
| _Lenat and Eurisko gained notoriety by submitting the
| winning fleet (a large number of stationary, lightly-armored
| ships with many small weapons)[3] to the United States
| Traveller TCS national championship in 1981, forcing
| extensive changes to the game 's rules. However, Eurisko won
| again in 1982 when the program discovered that the rules
| permitted the program to destroy its own ships, permitting it
| to continue to use much the same strategy.[3] Tournament
| officials announced that if Eurisko won another championship
| the competition would be abolished; Lenat retired Eurisko
| from the game.[4] The Traveller TCS wins brought Lenat to the
| attention of DARPA,[5] which has funded much of his
| subsequent work._
| brundolf wrote:
| Whoops yes :)
| bpiche wrote:
| Worked with their ontologists for a couple of years. Someone once
| told me that they employed more philosophers per capita than any
| other software company. A dubious distinction, maybe. But it
| describes the culture of inquisitiveness there pretty well too
| Eliezer wrote:
| Very visceral oof. I don't remember a time when I knew about AI
| but not about Eurisko.
| az226 wrote:
| Maybe it's a bit on the nose but I had his article summarized by
| Anthropic's Claude 2 100k model (LLMs are good at summarization)
| for those who don't have time to read the whole thing:
|
| The article discusses generative AI models like ChatGPT and
| contrasts them with knowledge-based AI systems like Cyc.
|
| Generative models can produce very fluent text, but they lack
| true reasoning abilities and can make up plausible-sounding but
| false information. This makes them untrustworthy.
|
| In contrast, Cyc represents knowledge explicitly and can
| logically reason over it. This makes it more reliable, though it
| struggles with natural language and speed.
|
| The article proposes 16 capabilities an ideal AI system should
| have, including explanation, reasoning, knowledge, ethics, and
| language skills. Cyc and generative models each have strengths
| and weaknesses on these dimensions.
|
| The authors suggest combining symbolic systems like Cyc with
| generative models to get the best of both approaches. Ways to
| synergize them include:
|
| Using Cyc to filter out false information from generative models.
|
| Using Cyc's knowledge to train generative models to be more
| correct.
|
| Using generative models to suggest knowledge to add to Cyc's
| knowledge base.
|
| Using Cyc's reasoning to expand what generative models can say.
|
| Using Cyc to explain the reasoning behind generative model
| outputs.
|
| Overall, the article argues combining reasoning-focused systems
| like Cyc with data-driven generative models could produce more
| robust and trustworthy AI. Each approach can shore up weaknesses
| of the other.
|
| May he rest in peace.
| satychary wrote:
| I worked on Cyc in the early 90s, briefly
| [https://dl.acm.org/doi/pdf/10.1145/165529.993430 shows my MCC
| address on p.10 :)]. I cherish wonderful memories of being on
| that project.
|
| Doug was amazing - bold, brilliant, visionary, charismatic.
|
| RIP, dear leader.
| nyx_land wrote:
| Weird, I interviewed with him summer 2021 hoping to be able to
| land an ontologist job at Cycorp. It went spectacularly badly
| because it turned out I really needed to brush up more on my
| formal logic skills, but I was surprised to even get an
| interview, let alone with the man himself. He still encouraged me
| to work on reviewing logic and to apply again in the future but I
| stopped seeing listings at Cycorp for ontologists and started
| putting off returning to that aspiration thinking Cycorp has been
| around long enough that there was no rush. Memento mori
| snowmaker wrote:
| I interviewed with Doug Lenat was I was a 17 year old high school
| student, and he hired me as a summer intern for Cycorp - my first
| actual programming job.
|
| That internship was life-changing for me, and I'll always be
| grateful to him for taking a wild bet on a literally a kid.
|
| Doug was a brilliant computer scientist, and a pioneer of
| artificial intelligence. Though I was very junior at Cycorp, it
| was a small company so I sat in many meetings with him. It was
| obvious that he understood every detail of how technology worked,
| and was extremely smart.
|
| Cycorp was 30 years ahead of its time and never actually worked.
| For those who don't know, it was essentially the first OpenAI -
| the first large-scale commercial effort to create general
| artificial intelligence.
|
| I learned a lot from Doug about how to be incredibly ambitious,
| and how to not give up. Doug worked on Cycorp for multiple
| decades. It never really took off, but he managed to keep funding
| it and keep hiring great people so he could keep plugging away at
| the problem. I know very few people who have stuck with an idea
| for so long.
| xNeil wrote:
| That sounds awesome! Was coming back to Cycorp to permanently
| work ever in the works for you? Or did you think the intern was
| nice but you didn't want a career in the field?
|
| Also - what exactly did you do in the internship as a 17 year
| old - what skills did you have?
| snowmaker wrote:
| I was certainly interested in working at Cycorp full-time.
| But after two summers there, I could tell that the technical
| approach they were taking was just not working.
|
| My first summer, I was an ontologist, which was a unique role
| that only existed at Cycorp where they hired people to
| literally hand-enter facts like "A cat has four legs" into
| Cyc using formal logic. My second summer I programmed
| (poorly) in Lisp for them.
| dang wrote:
| > _I could tell that the technical approach they were
| taking was just not working._
|
| Could you say more about that? How could you tell?
| snowmaker wrote:
| Perhaps other people with deeper AI knowledge can weigh
| in here too. But at the time, there were the two things
| that tipped me off.
|
| 1) Cyc's reasoning fundamentally did not feel "human".
| Cyc was created on the premise that you could build AGI
| on top of formal logic inference. But after seeing how
| Cyc performed on real-world problems, I became convinced
| that formal logic is a poor model for human thought.
|
| The biggest tell is that formal logic systems are very
| brittle. If there is any fact that is even slightly off,
| the reasoning chain fails and the system can't do
| anything. Humans aren't like that; when their information
| is slightly off, their performance degrades gracefully.
|
| 2). Imagine a graph where time/money was on the x-axis,
| and Cyc's performance was on the y-axis. You could
| roughly plot this using benchmarks like SAT scores. It
| was clear if you extrapolated this that Cyc was never
| going to hit human-level performance; the curve was going
| to asymptotically approach something well below human-
| level performance.
|
| As a side note, if you look at the performance of LLMs, I
| would argue that you get the opposite result for both
| criteria.
| [deleted]
| varjag wrote:
| Never met the guy but his work was one of my biggest inspirations
| in computing.
|
| I feel it's appropriate to link a blog post of mine from 2018.
| It's a quick recap of Lenat works on the trajectory that brought
| him towards Cyc, with links to the papers.
|
| http://blog.funcall.org//lisp/2018/11/03/am-eurisko-lenat-do...
| dpq wrote:
| Doug was one of my childhood heroes, thanks to a certain book
| telling the story of his work on AM and Eurisko. My great regret
| is that I never got the chance to meet him or contribute to his
| work in any way. RIP Doug, you are a legend.
| hu3 wrote:
| The end of the article [1] reminds me to publish more of what I
| make and think. I'm no Doug Lenat and my content would probably
| just add noise to the internet but still, don't let your ideas
| die with you or become controlled by some board of stakeholders.
| I'm also no open-source zealot but open-source is a nice way to
| let others continue what you started.
|
| [1]
|
| "Over the last year, Doug and I tried to write a long, complex
| paper that we never got to finish. Cyc was both awesome in its
| scope, and unwieldy in its implementation. The biggest problem
| with Cyc from an academic perspective is that it's proprietary.
|
| To help more people understand it, I tried to bring out of him
| what lessons he learned from Cyc, for a future generation of
| researchers to use. Why did it work as well as it did when it
| did, why did fail when it did, what was hard to implement, and
| what did he wish that he had done differently? ...
|
| ...One of his last emails to me, about six weeks ago, was an
| entreaty to get the paper out ASAP; on July 31, after a nerve-
| wracking false-start, it came out, on arXiv, Getting from
| Generative AI to Trustworthy AI: What LLMs might learn from Cyc
| (https://arxiv.org/ftp/arxiv/papers/2308/2308.04445.pdf).
|
| The brief article is simultaneously a review of what Cyc tried to
| do, an encapsulation of what we should expect from genuine
| artificial intelligence, and a call for reconciliation between
| the deep symbolic tradition that he worked in with modern Large
| Language Models."
| xpe wrote:
| Right on.
|
| > my content would probably just add noise to the internet
|
| Maybe, but there is worse noise out there for sure. :) Anyhow,
| some unsolicited advice from me: don't replay this quote to
| yourself any more than necessary; it isn't exactly a
| motivational mantra masterpiece. Share what you think is
| important.
|
| Why? Even small, "improbable" improvements to knowledge can to
| matter. Given enough of them, statistically speaking, we can
| move the needle. Yeah, and we need to be able to find the
| relevant stuff; a big problem in of itself.
| hu3 wrote:
| Those are very kind words that I will keep.
| mrcwinn wrote:
| Here's one for you, Doug. My condolences.
|
| https://chat.openai.com/share/dbd59c92-696b-45d3-8097-c09a23...
| martin1975 wrote:
| 72 isn't really too old. Does anyone know what caused his death?
| Revenge of the COVID?
| toomuchtodo wrote:
| https://en.wikipedia.org/wiki/Douglas_Lenat
|
| https://web.archive.org/web/20230901183515/https://garymarcu...
|
| https://archive.ph/icb92
| eigenvalue wrote:
| I have always thought of Cyc as being the AI equivalent of
| Russell and Whitehead's Principia--something that is technically
| ambitious and interesting in its own right, but ultimately just
| the wrong approach that will never really work well on a
| standalone basis, no matter how long you work on it or keep
| adding more and more rules. That being said, I do think it could
| prove to be useful for testing and teaching neural net models.
|
| In any case, at the time Lenat starting working on Cyc, we didn't
| really have the compute required to do NN models at the level
| where they start exhibiting what most would call "common sense
| reasoning," so it makes total sense why he started out on that
| path. RIP.
| PaulDavisThe1st wrote:
| https://arxiv.org/pdf/2308.04445.pdf
|
| "Getting from Generative AI to Trustworthy AI: What LLMs might
| learn from Cyc"
|
| Lenat's last paper (July 31st, with Gary Marcus)
|
| https://news.ycombinator.com/item?id=37354601
|
| This may disabuse you of two ideas: 1. that NN
| models (LLMs) exhibit common sense reasoning today 2.
| that the approach to AI represented by Cyc and the one
| represented by LLMs are mutually exclusive
| sshumaker wrote:
| I don't know about [1]. I asked an example from the paper
| above to GPT-4: "[If you had to guess] how many thumbs did
| Lincoln's maternal grandmother have?"
|
| Response: There is no widely available historical information
| to suggest that Abraham Lincoln's maternal grandmother had an
| unusual number of thumbs. It would be reasonable to guess
| that she had the typical two thumbs, one on each hand, unless
| stated otherwise.
| billyjmc wrote:
| You didn't ask something novel enough and/or the LLM got
| "lucky". There's plenty of occasions where they just get it
| flat wrong. It's a very bimodal distribution of competence
| - sometimes almost scarily superhumanly capable, and
| sometimes the dumbest collection of words that still form a
| coherent sentence.
|
| The mildly entertaining YouTube video below discusses this.
| https://youtu.be/QrSCwxrLrRc
| PaulDavisThe1st wrote:
| You can't show reasoning in LLMs via the answers they get
| right, I assert (without citations).
| xpe wrote:
| ChatGPT is a hybrid system; it isn't "just" an LLM any
| longer. What people associate with "LLM" is fluid. It
| changes over time.
|
| So it is essential to clarify architecture when making
| claims about capabilities.
|
| I'll start simple: Plain sequence to sequence feed-forward
| NN models are not Turing complete. Therefore they cannot do
| full reasoning, because that requires arbitrary chaining.
| tunesmith wrote:
| It's fun reading through the paper he links just because I've
| always been enamored by taking a lot of those principles that
| they believe should be internal to a computer, and instead making
| them external to a community.
|
| In other words, I think it would be so highly useful to have a
| browseable corpus of arguments and conclusions, where people
| could collaborate on them and perhaps disagree with portions of
| the argument graph, adding to it and enriching it over time, so
| other people could read and perhaps adopt the same reasoning.
|
| I play around with ideas with this site I occasionally work on,
| http://concludia.org/ - really more an excuse at this point to
| mess around with the concept and also get better at Akka (Pekko)
| programming. At some point I'll add user accounts and editable
| arguments and make it a real website.
| frenchwhisker wrote:
| I've had the same idea (er, came to the same conclusion) but
| never acted on it. Awesome to see that someone has! Great name
| too.
|
| I thought of it while daydreaming about how to converge public
| opinion in a nation with major political polarization. It'd be
| a sort of structured public debate forum and people could
| better see exactly where in the hierarchy they disagreed and,
| perhaps more importantly, how much they in fact agreed upon.
| high_priest wrote:
| I don't think this is the goal of your project, so let me ask
| this way. Is there any similiar project, where we provide
| truths and fallacies, combine them with logical arguments and
| have a language model generate sets of probable conclusions?
|
| Would be great for brainstorming.
| tomodachi94 wrote:
| So basically a multi-person Zettelkasten? The idea with a
| Zettelkasten (zk for short) is that each note is a singular
| idea, concept, or argument that is all linked together.
| Arguments can link to their evidence, concepts can link to
| other related concepts, and so on.
|
| https://en.m.wikipedia.org/wiki/Zettelkasten
| tunesmith wrote:
| Sort of except that it also tracks truth propagation - one
| person disagreeing would inform others that portion of the
| graph is contested. So the graph has behavior. And, the links
| have logical meaning, beyond just "is related to" - it
| respects boolean logic.
|
| You can see some of the explanation at
| http://concludia.org/instructions .
| quickthrower2 wrote:
| You would need a highly disciplined and motivated set of
| people in the team. I have been on courses where teams do
| this on pen/paper and it is a real skill and it is _all_
| you do for days. Forget anything else like programming,
| finishing work, etc.
| xpe wrote:
| I'd be stoked if you wrote more about this experience and
| shared it somewhere.
| couchand wrote:
| > it respects boolean logic.
|
| Intuitionist or classical?
| tunesmith wrote:
| Intuitionist. Truth is provability; the propagation model
| is basically digital logic. If you mark a premise to a
| conclusion false, the conclusion is then marked "false"
| but it really just means "it is false that it is proven";
| vitiated. Might still be true, just needs further work.
| gitgud wrote:
| Isn't this what Wikipedia _is_ in essence? Ideas, concepts
| linked together, with supporting evidence
| dredmorbius wrote:
| Cyc ("Syke") is one of those projects I've long found vaguely
| fascinating though I've never had the time / spoons to look into
| it significantly. It's an AI project based on a comprehensive
| ontology and knowledgebase.
|
| Wikipedia's overview: <https://en.wikipedia.org/wiki/Cyc>
|
| Project / company homepage: <https://cyc.com/>
| tootie wrote:
| As far as I can tell it was more of an aspiration than a
| product. I worked with a consulting firm that tried to get into
| AI a few years back and chose Cyc as the platform they wanted
| to sell to (mostly financial) clients. I don't think a single
| project ever even started nor was there a clear picture of what
| could be sold. I hate to think Lenat was a fraud because he
| certainly seemed like a sincere and brilliant person, but I
| think Cyc was massively oversold despite never doing much of
| anything useful. The website is full technical language and not
| a single case study after 40 years in business.
| ks2048 wrote:
| Unfortunately visiting cyc.com, I only see a bunch of business
| BS and the "Documention" page shows nothing without logging in.
| jfengel wrote:
| I worked with Cyc. It was an impressive attempt to do the thing
| that it does, but it didn't work out. It was the last great
| attempt to do AI in the "neat" fashion, and its failure helped
| bring about the current, wildly successful "scruffy" approaches
| to AI.
|
| It's failure is no shade against Doug. Somebody had to try it,
| and I'm glad it was one of the brightest guys around. I think
| he clung on to it long after it was clear that it wasn't going
| to work out, but breakthroughs do happen. (The current round of
| machine learning itself is a revival of a technique that had
| been abandoned, but people who stuck with it anyway discovered
| the tricks that made it go.)
| Kuinox wrote:
| Why did it didn't work out ?
| jfengel wrote:
| I don't know if there's really an answer to that, beyond
| noting that it never turned out to be more than the sum of
| its parts. It was a large ontology and a hefty logic
| engine. You put in queries and you got back answers.
|
| The goal was that in a decade it would become self-
| sustaining. It would have enough knowledge that it could
| start reading natural language. And it just... didn't.
|
| Contrast it with LLMs and diffusion and such. They make
| stupid, asinine mistakes -- real howlers, because they
| don't understand anything at all about the world. If it
| could draw, Cyc would never draw a human with 7 fingers on
| each hand, because it knows that most humans have 5. (It
| had a decent-ish ontology of human anatomy which could
| handle injuries and birth defects, but would default reason
| over the normal case.) I often see ChatGPT stumped by
| simple variations of brain teasers, and Cyc wouldn't make
| those mistakes -- once you'd translated them into CycL (its
| language, because it couldn't read natural language in any
| meaningful way).
|
| But those same models do a scary job of passing the Turing
| Test. Nobody would ever have thought to try it on Cyc. It
| was never anywhere close.
|
| Philosophically I can't say why Cyc never developed "magic"
| and LLMs (seemingly) do. And I'm still not convinced that
| they're on the right path, though they actually have some
| legitimate usages right now. I tried to find uses for Cyc
| in exactly the opposite direction, guaranteeing data
| quality, but it turned out nobody really wanted that.
| Kuinox wrote:
| Thanks - that's was the kind of answer I wanted. Is there
| any work trying to "merge" the two together ?
| dredmorbius wrote:
| One sense that I've had of LLM / generative AIs is that
| they lack "bones", in the sense that there's no
| underlying structure to which they adhere, only outward
| appearances which are statistically correlated (using
| fantastically complex statistical correlation maps).
|
| Cyc, on the other hand, lacks flesh and skin. It's _all_
| skeleton and can generate facts but not embellish them
| into narratives.
|
| The best human writing has _both_ , much as artists
| (traditional painters, sculptors, and more recently
| computer animators) has a _skeleton_ (outline, index
| cards, Zettlekasten, wireframe) to which flesh, skin, and
| fur are attached. LLM generative AIs are _too_ plastic,
| Cyc is _insufficiently_ plastic.
|
| I suspect there's some sort of a middle path between the
| two. Though that path and its destination also
| increasingly terrify me.
| ushakov wrote:
| Sounds similar to WolframAlpha?
| bpiche wrote:
| Had? Cycorp is still around and deploying their software.
| jfoutz wrote:
| Take a look at https://en.m.wikipedia.org/wiki/SHRDLU
|
| Cyc is sort of like that, but for everything. Not just a
| small limited world. I believe it didn't work out because
| it's really hard.
| ansible wrote:
| If we are to develop understandable AGI, I think that
| some kind of (mathematically correct) probabilistic
| reasoning based on a symbolic knowledge base is the way
| to go. You would probably need to have some version of a
| Neural Net on the front end to make it useful though.
|
| So you'd use the NN to recognize that the thing in front
| of the camera is a cat, and that would be fed into the
| symbolic knowledge base for further reasoning.
|
| The knowledge base will contain facts like the cat is
| likely to "meow" at some point, especially if it wants
| attention. Based on the relevant context, the knowledge
| base would also know that the cat is unlikely to be able
| to talk, unless it is a cat in a work of fiction, for
| example.
| DonHopkins wrote:
| At Leela AI we're developing hybrid symbolic-
| connectionist constructivist AI, combining "neat" neural
| networks with "scruffy" symbolic logic, enabling
| unsupervised machine learning that understands cause and
| effect and teaches itself, motivated by intrinsic
| curiosity.
|
| Leela AI was founded by Henry Minsky and Cyrus Shaoul,
| and is inspired by ideas about child development by Jean
| Piaget, Seymour Papert, Marvin Minsky, and Gary Drescher
| (described in his book "Made-Up Minds").
|
| https://mitpress.mit.edu/9780262517089/made-up-minds/
|
| https://leela.ai/leela-core
|
| >Leela Platform is powered by Leela Core, an innovative
| AI engine based on research at the MIT Artificial
| Intelligence Lab. With its dynamic combination of
| traditional neural networks for pattern recognition and
| causal-symbolic networks for self-discovery, Leela Core
| goes beyond accurately recognizing objects to comprehend
| processes, concepts, and causal connections.
|
| >Leela Core is much faster to train than conventional
| NNs, using 100x less data and enabling 10x less time-to-
| value. This highly resilient AI can quickly adjust to
| changes and explain what it is sensing and doing via the
| Leela Viewer dashboard. [...]
|
| The key to regulating AI is explainability. The key to
| explainability may be causal AI.
|
| https://leela.ai/post/the-key-to-regulating-ai-is-
| explainabi...
|
| >[...] For example, the Leela Core engine that drives the
| Leela Platform for visual intelligence in manufacturing
| adds a symbolic causal agent that can reason about the
| world in a way that is more familiar to the human mind
| than neural networks. The causal layer can cross-check
| Leela Core's traditional NN components in a hybrid
| causal/neural architecture. Leela Core is already better
| at explaining its decisions than NN-only platforms,
| making it easier to troubleshoot and customize. Much
| greater transparency is expected in future versions.
| [...]
| hackandthink wrote:
| "The essentialist tradition, in contrast to the tradition
| of differential ontology, attempts to locate the identity
| of any given thing in some essential properties or self-
| contained identities"
|
| May essentialism just does not work.
|
| https://iep.utm.edu/differential-ontology/
| DonHopkins wrote:
| As Roger Schank defined the terms in the 70's, "Neat" refers
| to using a single formal paradigm, logic, math, neural
| networks, and LLMs, like physics. "Scruffy" refers to
| combining many different algorithms and approaches, symbolic
| manipulation, hand coded logic, knowledge engineering, and
| CYC, like biology.
|
| I believe both approaches are useful and can be combined and
| layered and fed back into each other, to reinforce and
| transcend complement each others advantages and limitations.
|
| Kind of like how Hailey and Justin Bieber make the perfect
| couple: ;)
|
| https://edition.cnn.com/style/hailey-justin-bieber-
| couples-f...
|
| Marvin L Minsky: Logical Versus Analogical or Symbolic Versus
| Connectionist or Neat Versus Scruffy
|
| https://ojs.aaai.org/aimagazine/index.php/aimagazine/article.
| ..
|
| https://ojs.aaai.org/aimagazine/index.php/aimagazine/article.
| ..
|
| "We should take our cue from biology rather than physics..."
| -Marvin Minsky
|
| >To get around these limitations, we must develop systems
| that combine the expressiveness and procedural versatility of
| symbolic systems with the fuzziness and adaptiveness of
| connectionist representations. Why has there been so little
| work on synthesizing these techniques? I suspect that it is
| because both of these AI communities suffer from a common
| cultural-philosophical disposition: They would like to
| explain intelligence in the image of what was successful in
| physics--by minimizing the amount and variety of its
| assumptions. But this seems to be a wrong ideal. We should
| take our cue from biology rather than physics because what we
| call thinking does not directly emerge from a few fundamental
| principles of wave-function symmetry and exclusion rules.
| Mental activities are not the sort of unitary or elementary
| phenomenon that can be described by a few mathematical
| operations on logical axioms. Instead, the functions
| performed by the brain are the products of the work of
| thousands of different, specialized subsystems, the intricate
| product of hundreds of millions of years of biological
| evolution. We cannot hope to understand such an organization
| by emulating the techniques of those particle physicists who
| search for the simplest possible unifying conceptions.
| Constructing a mind is simply a different kind of problem--
| how to synthesize organizational systems that can support a
| large enough diversity of different schemes yet enable them
| to work together to exploit one another's abilities.
|
| https://en.wikipedia.org/wiki/Neats_and_scruffies
|
| >In the history of artificial intelligence, neat and scruffy
| are two contrasting approaches to artificial intelligence
| (AI) research. The distinction was made in the 70s and was a
| subject of discussion until the middle 80s.[1][2][3]
|
| >"Neats" use algorithms based on a single formal paradigms,
| such as logic, mathematical optimization or neural networks.
| Neats verify their programs are correct with theorems and
| mathematical rigor. Neat researchers and analysts tend to
| express the hope that this single formal paradigm can be
| extended and improved to achieve general intelligence and
| superintelligence.
|
| >"Scruffies" use any number of different algorithms and
| methods to achieve intelligent behavior. Scruffies rely on
| incremental testing to verify their programs and scruffy
| programming requires large amounts of hand coding or
| knowledge engineering. Scruffies have argued that general
| intelligence can only be implemented by solving a large
| number of essentially unrelated problems, and that there is
| no magic bullet that will allow programs to develop general
| intelligence autonomously.
|
| >John Brockman compares the neat approach to physics, in that
| it uses simple mathematical models as its foundation. The
| scruffy approach is more like biology, where much of the work
| involves studying and categorizing diverse phenomena.[a]
|
| [...]
|
| >Modern AI as both neat and scruffy
|
| >New statistical and mathematical approaches to AI were
| developed in the 1990s, using highly developed formalisms
| such as mathematical optimization and neural networks. Pamela
| McCorduck wrote that "As I write, AI enjoys a Neat hegemony,
| people who believe that machine intelligence, at least, is
| best expressed in logical, even mathematical terms."[6] This
| general trend towards more formal methods in AI was described
| as "the victory of the neats" by Peter Norvig and Stuart
| Russell in 2003.[18]
|
| >However, by 2021, Russell and Norvig had changed their
| minds.[19] Deep learning networks and machine learning in
| general require extensive fine tuning -- they must be
| iteratively tested until they begin to show the desired
| behavior. This is a scruffy methodology.
| at_a_remove wrote:
| Neats and scruffies also showed up in The X-Files in their
| first AI episode.
| dredmorbius wrote:
| "Neat" vs. "scruffy" syncs well with my general take on Cyc.
| Thanks for that.
|
| I _do_ suspect that well-curated and hand-tuned corpora,
| including possibly Cyc 's, _are_ of significant use to LLM
| AI. And will likely be more so as the feedback / autophagy
| problem exacerbates.
| pwillia7 wrote:
| Wow -- I hadn't thought of this but makes total sense.
| We'll need giant definitely-human-curated databases of
| information for AIs to consume as more information becomes
| generated by the AIs.
| dredmorbius wrote:
| There's a long history of informational classification,
| going back to Aristotle and earlier ("Categories"). See
| especially Melville Dewey, the US Library of Congress
| Classification, and the work of Paul Otlet. All are based
| on _exogenous classification_ , that is, subjects and/or
| works classification catalogues which are _independent_
| of the works classified.
|
| Natural-language content-based classification as by
| Google and Web text-based search relies effectively on
| documents self-descriptions (that is, their content
| itself) to classify and search works, though a ranking
| scheme (e.g., PageRank) is typically layered on top of
| that. What distinguished early Google from prior full-
| text search was that the latter had _no_ ranking
| criteria, leading to keyword stuffing. An alternative
| approach was Yahoo, originally Yet Another Hierarchical
| Officious Oracle, which was a _curated and ontological_
| classification of websites. This was already proving
| infeasible by 1997 /98 _as a whole_ , though as training
| data for machine classification might prove useful.
| rvbissell wrote:
| Why not combine the two approaches? A bicameral mind, of
| sorts?
| jfengel wrote:
| I'm sure somebody somewhere is working on it. I've already
| seen articles teaching LLMs offload math problems onto a
| separate module, rather than trying to solve them via the
| murk of neural network.
|
| I suppose you'd architect it as a layer. It wants to say
| something, and the ontology layer says, "No, that's stupid,
| say something else". The ontology layer can recognize
| ontology-like statements and use them to build and evolve
| the ontology.
|
| It would be even more interesting built into the
| visual/image models.
|
| I have no idea if that's any kind of real progress, or if
| it's merely filtering out the dumb stuff. A good service,
| to be sure, but still not "AGI", whatever the hell that
| turns out to be.
|
| Unless it turns out to be the missing element that puts it
| over the top. If I had any idea I wouldn't have been
| working with Cyc in the first place.
| PaulDavisThe1st wrote:
| https://arxiv.org/pdf/2308.04445.pdf
|
| is precisely Doug Lenat & Gary Marcus' thoughts on how to
| combine them (July 31st 2023, Lenat's last paper)
| mindcrime wrote:
| There are absolutely people working on this concept. In
| fact, the two day long "Neuro-Symbolic AI Summer School
| 2023"[1] just concluded earlier this week. It was two days
| of hearing about cutting edge research at the intersection
| of "neural" approaches (taking a big-tent view where that
| included most probabilistic approaches) and "symbolic" (eg,
| "logic based") approaches. And while this approach might
| not be _the_ contemporary mainstream approach, there were
| some heavy hitters presenting, including the likes of
| Leslie Valiant and Yoshua Bengio.
|
| [1]: https://neurosymbolic.github.io/nsss2023/
| DonHopkins wrote:
| That's right, and left! ;) Fusing the "scruffy" and "neat"
| approaches has been the idea since the terms were coined by
| Roger Schank in the 70's and written about in 1982 by
| Robert Abelson in his Major Address of the Proceedings of
| the 3rd Annual Conference of the Cognitive Science Society
| in "Constraint, Construal, and Cognitive Science" (page 1).
|
| His question is: Is it preferable for scruffies to become
| neater, or for neats to become scruffier? His answer
| explains why he aspires to be a neater scruffy.
|
| "But I use the example as symptomatic of one kind of
| approach to the cognitive science fusion problem: you start
| from a neat, right-wing point of view, but acknowledge some
| limited role for scruffy, left-wing orientations. The other
| type of approach is the obvious mirror: you start from the
| disorderly leftwing side and struggle to be neater about
| what you are doing. I prefer the latter approach to the
| former. I will tell you why, and then lay out the
| beginnings of such an approach."
|
| https://cse.buffalo.edu/~rapaport/676/F01/neat.scruffy.txt
| Article: 35781 of comp.ai From: fass@cs.sfu.ca (Dan
| Fass) Newsgroups: comp.ai Subject: Re: who
| first used "scruffy" and "neat"? Date: 26 Jan 1996
| 10:03:35 -0800 Organization: Simon Fraser
| University, Burnaby, B.C. Abelson (1981)
| credits the neat/scruffy distinction to Roger Schank.
| Abelson says, ``an unnamed but easily guessable colleague
| of mine ... claims that the major clashes in human
| affairs are between the "neats" and the
| "scruffies". The primary concern of the neat is
| that things should be orderly and predictable while the
| scruffy seeks the rough-and-tumble of life as it
| comes'' (p. 1). Abelson (1981) argues that
| these two prototypic identities --- neat and
| scruffy --- ``cause a very serious clash'' in cognitive
| science and explores ``some areas in which a
| fusion of identities seems possible'' (p. 1).
| - Dan Fass REF Abelson, Robert P.
| (1981). Constraint, Construal, and Cognitive
| Science. Proceedings of the 3rd Annual Conference
| of the Cognitive Science Society, Berkeley, CA,
| pp. 1-9.
|
| https://cognitivesciencesociety.org/wp-
| content/uploads/2019/...
|
| [I'll quote the most relevant first part of the article,
| which is still worth reading in its entirety if you have
| time, since scanned two column pdf files are so hard to
| read on mobile, and it's so interesting and relevant to
| Douglas Lenat's work on Cyc.]
|
| CONSTRAINT, CONSTRUAL, AND COGNITIVE SCIENCE
|
| Robert P. Abelson, Yale University
|
| Cognitive science has barely emerged as a discipline -- or
| an interdiscipline, or whatever it is -- and already it is
| having an identity crisis.
|
| Within us and among us we have many competing identities.
| Two particular prototypic identities cause a very serious
| clash, and I would like to explicate this conflict and then
| explore some areas in which a fusion of identities seems
| possible. Consider the two-word name "cognitive science".
| It represents a hybridization of two different impulses. On
| the one hand, we want to study human and artificial
| cognition, the structure of mental representatives, the
| nature of mind. On the other hand, we want to be
| scientific, be principled, be exact. These two impulses are
| not necessarily incompatible, but given free rein they can
| develop what seems to be a diametric opposition.
|
| The study of the knowledge in a mental system tends toward
| both naturalism and phenomenology. The mind needs to
| represent what is out there in the real world, and it needs
| to manipulate it for particular purposes. But the world is
| messy, and purposes are manifold. Models of mind,
| therefore, can become garrulous and intractable as they
| become more and more realistic. If one's emphasis is on
| science more than on cognition, however, the canons of hard
| science dictate a strategy of the isolation of idealized
| subsystems which can be modeled with elegant productive
| formalisms. Clarity and precision are highly prized, even
| at the expense of common sense realism. To caricature this
| tendency with a phrase from John Tukey (1959), the motto of
| the narrow hard scientist is, "Be exactly wrong, rather
| than approximately right".
|
| The one tendency points inside the mind, to see what might
| be there. The other points outside the mind, to some formal
| system which can be logically manipulated (Kintsch et al.,
| 1981). Neither camp grants the other a legitimate claim on
| cognitive science. One side says, "What you're doing may
| seem to be science, but it's got nothing to do with
| cognition." The other side says, "What you're doing may
| seem to be about cognition, but it's got nothing to do with
| science."
|
| Superficially, it may seem that the trouble arises
| primarily because of the two-headed name cognitive science.
| I well remember the discussions of possible names, even
| though I never liked "cognitive science", the alternatives
| were worse; abominations like "epistology" or
| "representonomy".
|
| But in any case, the conflict goes far deeper than the name
| itself. Indeed, the stylistic division is the same
| polarization than arises in all fields of science, as well
| as in art, in politics, in religion, in child rearing --
| and in all spheres of human endeavor. Psychologist Silvan
| Tomkins (1965) characterizes this overriding conflict as
| that between characterologically left-wing and right-wing
| world views. The left-wing personality finds the sources of
| value and truth to lie within individuals, whose reactions
| to the world define what is important. The right-wing
| personality asserts that all human behavior is to be
| understood and judged according to rules or norms which
| exist independent of human reaction. A similar distinction
| has been made by an unnamed but easily guessable colleague
| of mine, who claims that the major clashes in human affairs
| are between the "neats" and the "scruffies". The primary
| concern of the neat is that things should be orderly and
| predictable while the scruffy seeks the rough-and-tumble of
| life as it comes.
|
| I am exaggerating slightly, but only slightly, in saying
| that the major disagreements within cognitive science are
| instantiations of a ubiquitous division between neat right-
| wing analysis and scruffy left-wing ideation. In truth
| there are some signs of an attempt to fuse or to compromise
| these two tendencies. Indeed, one could view the success of
| cognitive science as primarily dependent not upon the
| cooperation of linguistics, AI, psychology, etc., but
| rather, upon the union of clashing world views about the
| fundamental nature of mentation. Hopefully, we can be open
| minded and realistic about the important contents of
| thought at the same time we are principled, even elegant,
| in our characterizations of the forms of thought.
|
| The fusion task is not easy. It is hard to neaten up a
| scruffy or scruffy up a neat. It is difficult to formalize
| aspects of human thought which are variable, disorderly,
| and seemingly irrational, or to build tightly principled
| models of realistic language processing in messy natural
| domains. Writings about cognitive science are beginning to
| show a recognition of the need for world-view unification,
| but the signs of strain are clear. Consider the following
| passage from a recent article by Frank Keil (1981) in
| Pscyhological Review, giving background for a discussion of
| his formalistic analysis of the concept of constraint:
|
| "Constraints will be defined...as formal restrictions that
| limit the class of logically possible knowledge structures
| that can normally be used in a given cognitive domain." (p.
| 198).
|
| Now, what is the word "normally" doing in a statement about
| logical possibility? Does it mean that something which is
| logically impossible can be used if conditions are not
| normal? This seems to require a cognitive hyperspace where
| the impossible is possible.
|
| It is not my intention to disparage an author on the basis
| of a single statement infelicitously put. I think he was
| genuinely trying to come to grips with the reality that
| there is some boundary somewhere to the penetration of his
| formal constraint analysis into the viscissitudes of human
| affairs. But I use the example as symptomatic of one kind
| of approach to the cognitive science fusion problem: you
| start from a neat, right-wing point of view, but
| acknowledge some limited role for scruffy, left-wing
| orientations. The other type of approach is the obvious
| mirror: you start from the disorderly leftwing side and
| struggle to be neater about what you are doing. I prefer
| the latter approach to the former. I will tell you why, and
| then lay out the beginnings of such an approach.
|
| [...]
|
| To read why and how:
|
| https://cognitivesciencesociety.org/wp-
| content/uploads/2019/...
| sanderjd wrote:
| I'm so looking forward to the next swing of the pendulum back
| to "neat", incorporating all the progress that has been made
| on "scruffy" during this current turn of the wheel.
| DonHopkins wrote:
| The GP had the terms "neat" and "scruffy" reversed. CYC is
| scruffy like biology, and neural nets are neat like
| physics.
|
| See my sibling post citing Roger Schank who coined the
| terms, and quoting Marvin Minsky's paper, "Logical Versus
| Analogical or Symbolic Versus Connectionist or Neat Versus
| Scruffy" and the "Neats and Scruffies" wikipedia page.
|
| https://news.ycombinator.com/item?id=37354564
| sanderjd wrote:
| The OPs usage seems a lot more intuitive to me, :shrug:.
| Neural nets don't seem at all "neat like physics" to me.
|
| But I guess I also don't know enough about the CYC
| approach to say. Maybe neither of them fit what I think
| of as "neat".
| DonHopkins wrote:
| Pamela McCorduck wrote in "Machines Who Think" (2004)
| that Cyc is "a determinedly scruffy enterprise". Robert
| Abelson credited the terms to his "unnamed but easily
| guessable colleague" Roger Shank in his 1981 essay
| "Constraint, Construal, and Cognitive Science" in the
| Proceedings of the 3rd Annual Conference of the Cognitive
| Science, and Marvin Minsky discusses the terms in Patrick
| Henry Winston's 1990 book "Artificial Intelligence at
| MIT, Expanding Frontiers, Vol 1", and his own 1991 AI
| Magazine article, "Logical Versus Analogical or Symbolic
| Versus Connectionist or Neat Versus Scruffy", but the
| long standing terms go back to the 70's:
|
| https://ojs.aaai.org/aimagazine/index.php/aimagazine/arti
| cle...
|
| https://ojs.aaai.org/aimagazine/index.php/aimagazine/arti
| cle...
|
| "We should take our cue from biology rather than
| physics..." -Marvin Minsky
|
| https://grandtextauto.soe.ucsc.edu/2008/02/14/ep-44-ai-
| neat-...
|
| EP 4.4: AI, Neat and Scruffy
|
| by Noah Wardrip-Fruin, 6:11 am
|
| A name that does appear in Weizenbaum's book, however, is
| that of Roger Schank, Abelson's most famous collaborator.
| When Schank arrived from Stanford to join Abelson at
| Yale, together they represented the most identifiable
| center for a particular approach to artificial
| intelligence: what would later (in the early 1980s) come
| to be known as the "scruffy" approach. [7] Meanwhile,
| perhaps the most identifiable proponent of what would
| later be called the "neat" approach, John McCarthy,
| remained at Stanford.
|
| McCarthy had coined the term "artificial intelligence" in
| the application for the field-defining workshop he
| organized at Dartmouth in 1956. Howard Gardner, in his
| influential reflection on the field, The Mind's New
| Science (1985), characterized McCarthy's neat approach
| this way: "McCarthy believes that the route to making
| machines intelligent is through a rigorous formal
| approach in which the acts that make up intelligence are
| reduced to a set of logical relationships or axioms that
| can be expressed precisely in mathematical terms" (154).
|
| This sort of approach lent itself well to problems easily
| cast in formal and mathematical terms. But the scruffy
| branch of AI, growing out of fields such as linguistics
| and psychology, wanted to tackle problems of a different
| nature. Scruffy AI built systems for tasks as diverse as
| rephrasing newspaper reports, generating fictions,
| translating between languages, and (as we have seen)
| modeling ideological reasoning. In order to accomplish
| this, Abelson, Schank, and their collaborators developed
| an approach quite unlike formal reasoning from first
| principles. One foundation for their work was Schank's
| "conceptual dependency" structure for language-
| independent semantic representation. Another foundation
| was the notion of "scripts" (later "cases") an embryonic
| form of which could be seen in the calling sequence of
| the ideology machine's executive. Both of these will be
| considered in more detail in the next chapter.
|
| Scruffy AI got attention because it achieved results in
| areas that seemed much more "real world" than those of
| other approaches. For comparison's sake, consider that
| the MIT AI lab, at the time of Schank's move to Yale, was
| celebrating success at building systems that could
| understand the relationships in stacks of children's
| wooden blocks. But scruffy AI was also critiqued -- both
| within and outside the AI field -- for its "unscientific"
| ad-hoc approach. Weizenbaum was unimpressed, in
| particular, with the conceptual dependency structures
| underlying many of the projects, writing, "Schank
| provides no demonstration that his scheme is more than a
| collection of heuristics that happen to work on specific
| classes of examples" (199). Whichever side one took in
| the debate, there can be no doubt that scruffy projects
| depending on coding large amounts of human knowledge into
| AI systems -- often more than the authors acknowledged,
| and perhaps much more than they realized.
|
| [...]
|
| [7] After the terms "neat" and "scruffy" were introduced
| into the AI and cognitive science discourse by Abelson's
| 1981 essay, in which he attributes the coinage to "an
| unnamed but easily guessable colleague" -- Schank.
|
| https://cse.buffalo.edu/~rapaport/676/F01/neat.scruffy.tx
| t Article: 35704 of comp.ai
| From: engelson@bimacs.cs.biu.ac.il (Dr. Shlomo (Sean)
| Engelson) Newsgroups: comp.ai Subject:
| Re: who first used "scruffy" and "neat"? Date: 25
| Jan 1996 08:17:13 GMT Organization: Bar-Ilan
| University Computer Science In article
| <4e2th9$lkm@cantaloupe.srv.cs.cmu.edu> Lonnie Chrisman
| <ldc+@cs.cmu.edu> writes:
| so@brownie.cs.wisc.edu (Bryan So) wrote: >A
| question of curiosity. Who first used the terms
| "scruffy" and "neat"? >And in what document?
| How about "strong" and "weak"? Since I
| don't see a response yet, I'll take a stab. The earliest
| use of "scruffy" and "neat" that comes to my
| mind was in David Chapman's "Planning for
| Conjunctive Goals", Artificial Intelligence 32:333-377,
| 1987. "Weak" evidence for this being the
| earliest use is that he does not cite any earlier
| use of the terms, but perhaps someone else will correct
| me and give an earlier citation.
| One earlier citation is Eugene Charniak's paper in AAAI
| 1986, "A Neat Theory of Marker Passing". I
| think, though, that the terms go way back in
| common parlance, almost certainly to the 70s at least.
| Any of the "old-timers" out there like to
| comment?
|
| [...] Article: 35781 of comp.ai
| From: fass@cs.sfu.ca (Dan Fass) Newsgroups:
| comp.ai Subject: Re: who first used "scruffy" and
| "neat"? Date: 26 Jan 1996 10:03:35 -0800
| Organization: Simon Fraser University, Burnaby, B.C.
| Abelson (1981) credits the neat/scruffy distinction to
| Roger Schank. Abelson says, ``an unnamed but
| easily guessable colleague of mine ... claims
| that the major clashes in human affairs are between the
| "neats" and the "scruffies". The primary concern of the
| neat is that things should be orderly and
| predictable while the scruffy seeks the rough-
| and-tumble of life as it comes'' (p. 1).
| Abelson (1981) argues that these two prototypic
| identities --- neat and scruffy --- ``cause a
| very serious clash'' in cognitive science and
| explores ``some areas in which a fusion of identities
| seems possible'' (p. 1). - Dan Fass
| REF Abelson, Robert P. (1981).
| Constraint, Construal, and Cognitive Science.
| Proceedings of the 3rd Annual Conference of the Cognitive
| Science Society, Berkeley, CA, pp. 1-9.
|
| [...]
|
| Aaron Sloman, 1989: "Introduction: Neats vs Scruffies"
|
| https://www.cs.bham.ac.uk//research/projects/cogaff/misc/
| scr...
|
| >There has been a long-standing opposition within AI
| between "neats" and "scruffies" (I think the terms were
| first invented in the late 70s by Roger Schank and/or Bob
| Abelson at Yale University).
|
| >The neats regard it as a disgrace that many AI programs
| are complex, ill-structured, and so hard to understand
| that it is not possible to explain or predict their
| behaviour, let alone prove that they do what they are
| intended to do. John McCarthy in a televised debate in
| 1972 once complained about the "Look Ma no hands!"
| approach. Similarly, Carl Hewitt, complained around the
| same time, in seminars, about the "Hairy kludge
| (pronounced klooge) a month" approach to software
| development. (His "actor" system was going to be a
| partial solution to this.)
|
| >The scruffies regard messy complexity as inevitable in
| intelligent systems and point to the failure so far of
| all attempts to find workable clear and general
| mechanisms, or mathematical solutions to any important AI
| problems. There are nice ideas in the General Problem
| Solver, logical theorem provers, and suchlike but when
| confronted with non-toy problems they normally get bogged
| down in combinatorial explosions. Messy complexity,
| according to scruffies, lies in the nature of problem
| domains (e.g. our physical environment) and only by using
| large numbers of ad-hoc special-purpose rules or
| heuristics, and specially tailored representational
| devices can problems be solved in a reasonable time.
|
| Roger Schank
|
| https://en.wikipedia.org/wiki/Roger_Schank
|
| Robert Abelson
|
| https://en.wikipedia.org/wiki/Robert_Abelson
|
| Marvin Minsky
|
| https://en.wikipedia.org/wiki/Marvin_Minsky
|
| Neats and scruffies
|
| https://en.wikipedia.org/wiki/Neats_and_scruffies
|
| >Scruffy projects in the 1980s
|
| >The scruffy approach was applied to robotics by Rodney
| Brooks in the mid-1980s. He advocated building robots
| that were, as he put it, Fast, Cheap and Out of Control,
| the title of a 1989 paper co-authored with Anita Flynn.
| Unlike earlier robots such as Shakey or the Stanford
| cart, they did not build up representations of the world
| by analyzing visual information with algorithms drawn
| from mathematical machine learning techniques, and they
| did not plan their actions using formalizations based on
| logic, such as the 'Planner' language. They simply
| reacted to their sensors in a way that tended to help
| them survive and move.[13]
|
| >Douglas Lenat's Cyc project was initiated in 1984 one of
| earliest and most ambitious projects to capture all of
| human knowledge in machine readable form, is "a
| determinedly scruffy enterprise".[14] The Cyc database
| contains millions of facts about all the complexities of
| the world, each of which must be entered one at a time,
| by knowledge engineers. Each of these entries is an ad
| hoc addition to the intelligence of the system. While
| there may be a "neat" solution to the problem of
| commonsense knowledge (such as machine learning
| algorithms with natural language processing that could
| study the text available over the internet), no such
| project has yet been successful.
|
| [...]
|
| >John Brockman writes "Chomsky has always adopted the
| physicist's philosophy of science, which is that you have
| hypotheses you check out, and that you could be wrong.
| This is absolutely antithetical to the AI philosophy of
| science, which is much more like the way a biologist
| looks at the world. The biologist's philosophy of science
| says that human beings are what they are, you find what
| you find, you try to understand it, categorize it, name
| it, and organize it. If you build a model and it doesn't
| work quite right, you have to fix it. It's much more of a
| "discovery" view of the world."[4]
| sanderjd wrote:
| I think this all convinces me that neither of the things
| we're discussing here is "neat". Certainly contemporary
| LLMs don't seem to fit that definition at all, despite
| being "mathematical".
| DonHopkins wrote:
| It's not really up to you to redefine meaning of well
| known long standing historic technical terms. They're
| well defined, widely understood, frequently discussed
| terms that you would learn if you studied the history of
| AI, read any of the numerous papers and books and
| discussions about it that I already cited and quoted, and
| learned about the works of CogSci and AI pioneers like
| Robert Abelson, Roger Schank, Marvin Minsky, and others
| who've written numerous papers about it.
|
| The wikipedia page about Neats and Scruffies that I
| linked you to is in my opinion well written, clearly
| defines the meanings each term, and presents plenty of
| evidence and citations and background. I'll give you the
| benefit of doubt that of course you've already read and
| understand it, so if you disagree with the history and
| citations on the wikipedia page and all the original
| papers and books and people cited and quoted, and can
| present better evidence and arguments to prove that
| you're right and they're all wrong, then you are free to
| go try to rewrite history by sharing your own definitions
| and citations, and correcting the errors on wikipedia.
| Good luck! I suggest you start by writing suggestions and
| presenting your evidence on the talk page first, instead
| of just directly editing the wikipedia page itself, to
| see what other experts in the field think and achieve
| consensus, or else it will likely be considered vandalism
| and be reverted.
|
| You seem to be missing the point that the world is not
| strictly black and white, and ever since the terms were
| originally coined, the people who defined them and many
| other people have strongly recommended fusing both the
| "neat" and "scruffy" approaches, and LLMs actually do
| incorporate some ad-hoc "scruffy" aspects into their
| mathematical "neat" approach, and that's why they work so
| much better than simple perceptrons or neural nets. But
| they are still much more "neat" than "scruffy", and
| combining the two approaches does not flip the meaning of
| the two terms. I just discussed the fusion of scruffy and
| neat here, and quoted the original 41-year-old essay from
| 1982 by Robert Abelson that defined the terms and
| recommended fusing the two different approaches:
|
| https://news.ycombinator.com/item?id=37359235
|
| And also:
|
| https://news.ycombinator.com/item?id=37354564
|
| But before you go off and edit the Neats and Scruffies
| wikipedia page with your own definitions, please take the
| time to read the original essay by Robert Abelson that
| defines the terms first, like I did. In the link above, I
| cited it, tracked down the pdf, and quoted the relevant
| part of it for you, but you should probably do your
| homework first and read the whole thing before editing
| the wikipedia page about it. But be aware that it uses a
| lot of other technical terms and jargon that have well
| known definitions to practitioners in the field, so the
| common layman definitions of words you learned in grammar
| school may not apply.
|
| Cyc is clearly the paradigm of "scruffy" and like
| biology, and perceptrons and neural nets are clearly the
| paradigm of "neat" and like physics, and that's how those
| terms have been widely used for more than four decades.
| kevin_thibedeau wrote:
| Definitely would be nice to have a ChatGPT that could
| reference an ontology to fact check itself.
| at_a_remove wrote:
| I've often thought that Cyc had an enormous value as some kind of
| component for AI, a "baseline truth" about the universe (to the
| degree that we understand it and have "explained" our
| understanding to Cyc in terms of its frames). AM (no relation to
| any need for screaming) was a taste of the AI dream.
| optimalsolver wrote:
| >I've often thought that Cyc had an enormous value as some kind
| of component for AI
|
| Same. I wonder if training an LLM on the database would make it
| more "grounded"? We'll probably never know as Cycorp will keep
| the data locked away in their vaults forever. For what purpose?
| Probably even they don't know.
|
| >AM (no relation to any need for screaming)
|
| heh.
| ftxbro wrote:
| Here's a 2016 Wired article about Doug Lenat, he was the guy who
| made Eurisko and CYC https://www.wired.com/2016/03/doug-lenat-
| artificial-intellig...
| brindlejim wrote:
| While I respect Doug's intelligence, he showed a kind of perverse
| persistence in a failed idea, and I think it's telling that
| aspiring AI czar Gary Marcus admires the ruins of Cyc while
| neglecting to acknowledge that it represents a dead end in AI.
| Like science, the field of AI advances one funeral at a time.
| Doug pursued a pipe dream, and convinced others to do the same,
| despite the brittle and static nature of the AI he sought to
| build. Cyc was not a precursor to OpenAI, contrary to other
| comments in this thread. That would be like calling the zeppelin
| the precursor of the jet. It represents a different school of
| technology, and a much less effective one.
| Nevermark wrote:
| It's not going to be popular to highlight the less than hoped
| for successes of Lenat's greatest project.
|
| But I think that is one of the things he should be admired for.
| How can anyone know how an ambitious approach will pan out
| without the great risk of going all in?
|
| Anyone willing to risk a Don Quixote aspect to their career, in
| pursuit of a breakthrough, is someone who cares deeply about
| something beyond themselves.
|
| And recognizing the limits of Lenat's impact today doesn't
| preclude both the direct and indirect impact on future
| progress.
|
| I found him inspiring on multiple levels.
| Rochus wrote:
| Wait a few more years and you will see that the effort to
| formalize this knowledge was not in vain. We will certainly see
| systems which make use of this data. Cycorp is a different
| company with a different approach than OpenAI; but the data
| produced by Cycorp will likely be useful for companies like
| OpenAI on their way to truly intelligent systems.
| xpe wrote:
| Perhaps some here aren't familiar with the existence of a
| (relatively useless in my opinion) POV that pits symbolic systems
| against statistical methods. But it isn't a zero-sum game.
| Informed, insightful comparisons are useful, but "holy wars" are
| not. See also [1] for broad commentary and [2] for a particular
| application.
|
| [1] https://medium.com/@jcbaillie/beyond-the-symbolic-vs-non-
| sym...
|
| [2] https://past.date-conference.com/proceedings-
| archive/2016/pd...
| xpe wrote:
| This sibling thread is apropos too:
| https://news.ycombinator.com/item?id=37356435
| pinewurst wrote:
| [flagged]
| headhasthoughts wrote:
| Why? He shared little with the wider community, contributed to
| mass surveillance with Cyc's government collaborations, and
| hasn't really done anything of note.
|
| I don't dislike Lenat, but he doesn't fit the commercial value
| of people who get black bars, he doesn't fit the ideological
| one, and he doesn't fit the community-benefit one.
| rvz wrote:
| [flagged]
| acqbu wrote:
| Wow, that is really mean!
| dang wrote:
| Wouldn't it also be a mark of respect to check, before
| saying something that mean, whether it's true or not?
|
| https://web.archive.org/web/20230821003655/https://news.yco
| m...
| [deleted]
| pinewurst wrote:
| Why do people have to have 'commercial value' to get black
| bars? Why do people have to pass the ideological police? Why
| isn't serving as a visible advocate of a certain logical
| model enough?
|
| I think my bias comes from having started my career in AI on
| the inference side and having (perhaps not so much long term
| :) seen Cyc as a shining city on a hill. Lenat certainly
| established that logical model even if we've since gone onto
| other things.
| vkou wrote:
| I believe the parent poster claims that a black bar should
| meet either a commercial, hacker-cultural, _or_ open-source
| contribution one.
| junon wrote:
| I think you don't understand the meaning of the black bar if
| "commercial value" is one of the metrics.
| vkou wrote:
| Steve Jobs received one - by which criteria, if not
| commercial (to other people) value?
|
| It certainly wasn't for the warmth of his personality, his
| impeccable business ethics, or for his libre open-source
| contributions.
| mdp2021 wrote:
| > _by which criteria_
|
| Historic value.
| vkou wrote:
| And _which_ category of important-enough-to-be-historic
| contributions has he made?
| mdp2021 wrote:
| <<Contributions>>, debatable; <<value>>, debatable;
| "impact", cannot be ignored.
|
| It is probably best if we stick to Doug Lenat and
| postpone the meta to a more neutral occasion: Doug Lenat
| has just died.
| skyyler wrote:
| Take a moment to reflect on what you're doing right now.
|
| You're turning a celebration of life for a _very_
| recently departed figure into a _pissing contest_.
|
| Extremely distasteful.
| vkou wrote:
| I think you're misunderstanding the direction and intent
| of this subthread.
|
| You're right that talking about Jobs is off-topic,
| though.
| sgt101 wrote:
| Didn't he:
|
| - invent case based reasoning
|
| - build Eurisko and AM
|
| - write a discipline defining paper ("Why AM and Eurisko
| appear to work")
|
| - undertake an ambitious but ultimately futile high risk
| research gamble with Cyc?
| steve_adams_86 wrote:
| While futile from a personal and business aspect, it's
| certainly valuable and useful otherwise. Maybe that's
| implied here as you're listing contributions, but I wanted
| to emphasize that it wasn't a waste outside of that narrow
| band of futility.
| sgt101 wrote:
| I agree, and the fact that someone walked that path has
| been extremely valuable as well. I think we learned a lot
| from the cyc effort.
| zozbot234 wrote:
| Case-based reasoning is VERY old. It shows up prominently
| in the Catholic tradition of practical ethics, drawing on
| Aristotelian thought. Of course in a more informal sense,
| people have been reasoning on a case-by-case basis since
| time immemorial.
| Rochus wrote:
| That's not what is meant here by case-based reasoning;
| CBR instead is an AI method which was prominent in the
| eighties and nineties where knowledge was represented in
| a semi-formal text representation and similarity was
| established by multi-dimensional assiociative indexing.
| One of the leading figures of the method was Roger
| Schank.
| EdwardCoffin wrote:
| I got a lot of value out of some of the papers he wrote, and
| what bits of _Building Large Knowledge-Based Systems_ I
| managed to read.
| ftxbro wrote:
| he is the patron hacker of players who use computers to break
| board games or war games
| toomuchtodo wrote:
| Consider giving more grace. Life is short, and kindness is
| free.
| aaron695 wrote:
| [dead]
| jonahbenton wrote:
| Oh, so sorry to hear that. Good summary of his work- the Cyc
| project- on the twitter thread. Had missed that last paper- with
| Gary Marcus- on Cyc and LLM.
| mrmincent wrote:
| Sad to hear of his passing, I remember building my uni project
| around OpenCyc in my one "Intelligent Systems" class many many
| years ago. It was a dismal failure as my ambition far exceeded my
| skills, but it was so enjoyable reading about Cyc and the
| dedicated work Douglas had put in over such a long time.
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