[HN Gopher] Paradigms of A.I. Programming: Case Studies in Commo...
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Paradigms of A.I. Programming: Case Studies in Common Lisp (1991)
Author : abudabi123
Score : 220 points
Date : 2023-05-05 06:56 UTC (16 hours ago)
(HTM) web link (norvig.github.io)
(TXT) w3m dump (norvig.github.io)
| narag wrote:
| I bought this book in paper time ago, put it aside (I bought half
| a dozen at the same time) and when I finally got to read it, I
| found out that I had brought the wrong version. Too late to
| return it...
|
| A question: is any of the currently hot software written also in
| Lisp? I mean the LLMs, SD, etc.
| treeblah wrote:
| Grammarly posted an article a couple years ago about using
| Common Lisp:
| https://www.grammarly.com/blog/engineering/running-lisp-in-p...
| schemescape wrote:
| As someone learning Common Lisp for fun and planning to use
| it in the web, I'm a little disturbed by the "we manually
| force garbage collection periodically" part of that article.
| I haven't fully digested the commentary, so maybe I'm more
| concerned than necessary...
| vindarel wrote:
| > more concerned than necessary.
|
| I would say so, specially when you plan to use it for web
| dev (welcome!). Shinmera has released a game in CL heavily
| depending on CLOS (object system), and he says the GC is
| barely a matter.
|
| https://raw.githubusercontent.com/Shinmera/talks/master/els
| 2...
|
| https://kandria.com/ (https://github.com/Shirakumo/kandria)
|
| https://reader.tymoon.eu/article/413
| schemescape wrote:
| I recognize your user name. Great work on the Lisp
| Journey site! Several times I've had a question and then
| found the exact same thought expressed on your site
| (along with an answer).
| vindarel wrote:
| That's nice to hear, thanks for the feedback. I've been
| puzzled many times when starting out (or taking a not-so
| crowded path), so I'm glad the ones after me are having a
| better time.
|
| Now, your time to build cool things and share in the
| process ;)
|
| ---
|
| (https://lisp-journey.gitlab.io/)
| necroforest wrote:
| no
| vindarel wrote:
| The corollary question: examples of companies who use CL:
| https://github.com/azzamsa/awesome-lisp-companies/
| dimatura wrote:
| This book is fantastic, it was a great intro to both lisp and
| (GOF)AI in undergrad. My print copy was pricey - I'm glad to see
| it available for free!
| schemescape wrote:
| Does anyone have any other free Common Lisp book recommendations?
|
| I decided to give CL a try after reading about REPL-driven
| development, especially CL's interactive condition/debugging
| experience. I'm almost done going through Practical Common Lisp.
| It's been a fun experience so far!
|
| Edit: thanks, everyone!
| tmtvl wrote:
| CLiki (the Common Lisp wiki) has a list of books, some of which
| are freely available: https://www.cliki.net/Lisp%20books
| schemescape wrote:
| Thanks! I had run across this, but it's a big list, so if
| there are any you'd particularly recommend let me know!
| kaveh808 wrote:
| Not a book, but tutorial videos showing CL in use using 2D
| OpenGL graphics:
|
| https://www.youtube.com/playlist?list=PLTA6M4yZF0MzsMlNL0N67...
| guenthert wrote:
| Every once in a while I find even the language standard
| insufficiently clear (e.g. how to change the exponent character
| when printing double precision floats), then I dig again into
| Common Lisp the Language (2nd ed.) by Guy Steele available at
| https://www.cs.cmu.edu/Groups/AI/html/cltl/clm/clm.html .
| atemerev wrote:
| https://leanpub.com/lovinglisp -- this one is great, and the
| first thing I recommend
|
| https://lispcookbook.github.io/cl-cookbook/ -- also great and
| up to date
|
| https://awesome-cl.com/ -- for anything else.
| schemescape wrote:
| Wow, I had not run across awesome-cl.com yet, but it's just
| what I needed!
|
| One aspect of being new to the language is that I don't know
| which libraries are commonly used.
| kgwgk wrote:
| http://www.paulgraham.com/onlisp.html
|
| https://www.lurklurk.org/onlisp/onlisp.html
| schemescape wrote:
| Thanks! Embarrassingly, On Lisp slipped my mind, even though
| I already had the PDF on my computer.
| bighoki2885000 wrote:
| [dead]
| lukego wrote:
| This might be my favorite programming book of all time.
|
| It's the book that got me hooked on Common Lisp (from Scheme)
| too. Focusing less on the elegance of the language definition and
| more on writing elegant programs.
|
| (English isn't as elegantly defined as Lojban and but that didn't
| hold back Tolkien.)
| 7thaccount wrote:
| Lojban is elegant from I guess a logical/mathematical point of
| view, but I don't think from a human point of view. Iirc "in
| the land of invented languages", the author pointed out many
| issues with the language in that even it's biggest fans can't
| really speak it fluently...so what's the point? Esperanto is
| probably a better bar here, which I just started reading the
| Hobbit in lol. I will say that Lojbahn is super cool though.
| The attitudinals are particularly interesting.
| mr_luc wrote:
| Me too! I came looking for this sort of meta-comment. I've re-
| read the paper book a few times now.
|
| What makes it my favorite is how clear Norvig's writing is.
| It's easy to follow (both when reading it in English, and when
| following its execution if you're a programmer), and it
| introduces important ideas so effortlessly that, years later,
| it will give you a chuckle.
|
| Anyone interested in clearly communicating about technical
| topics, and with a knowledge of Lisp's nature and some idea of
| what programming in 1991 looked like, might be tickled to read
| Chapter 1; even its first few paragraphs are refreshing.
| lincpa wrote:
| [dead]
| hcks wrote:
| It's always quite ironic to see hipsters today praise GOFAI
| systems and belittle the deep learning 'hype', given that they
| were massively overhyped at the time and delivered next to
| nothing outside of some niches.
|
| Even funnier to see how someone is always quick to explain that
| 'NN are not real AI' when GOFAI was literally all about parsing,
| basic logic and search trees.
| discreteevent wrote:
| Also, Mark Watson has a number of free books on GOFAI in
| different programming languages (including Lisp) here:
|
| https://markwatson.com/#books
|
| Mark Watson on HN:
|
| https://news.ycombinator.com/user?id=mark_l_watson
|
| Edit: I should have said that you can also pay for his books:
|
| https://leanpub.com/u/markwatson
| rahimnathwani wrote:
| Mark's Langchain book is awesome - it covers not only how to
| use the main langchain features, but the motivation behind each
| of them.
| mark_l_watson wrote:
| Thanks for mentioning my books! I wrote my first Common Lisp
| book about the same time Peter wrote his book and I met him at
| the first Lisp Users meetup in San Diego. His Python short
| Python programs and notebooks are also fantastic. Off topic,
| but: I wonder if the new Python++ language Mojo that sits on
| top of MLIR will end up as the top AI language. Maybe?
| ssrc wrote:
| * This book, PAIP, by Peter Norvig
|
| * SICP, by Abelson and Sussman
|
| * Lisp (3rd ed), by Winston and Horn
|
| * The Art of Prolog, by Sterling and Shapiro
|
| It's like the same book written by different authors, and all of
| them are good.
| jimmaswell wrote:
| Everyone says SICP is this profound thing but I just couldn't
| get into it or LISP at all. Am I really missing out on anything
| after a good CS education and practical experience?
| jimbokun wrote:
| It's a good way to nail down a lot of the fundamentals, on
| top of which a good CS education can be built.
|
| So if you already have a solid CS education it's not really
| necessary, except maybe for the enjoyment of reading a really
| well thought through pedagogical work. Which can sometimes
| help coalesce concepts and the connections between them.
| ssrc wrote:
| I mean, I don't like shellfish and people tell me it's my
| loss, but as long as I have a balanced diet why should I
| care?
| mebassett wrote:
| I love SICP and am one of those fanboys who say everyone
| should read it.
|
| Though, really, a lot of what it covers is much more common
| knowledge now than it was when I first read it. High-order
| functions and functions-as-first-class-citizens (ch1) are
| ubiquitous and most programmers I know are comfortable with
| them (which wasn't the case in the early 2000s, at least in
| my circle). Lists, maps, pairs, and symbolic structures are
| covered in ch2, but most people are comfortable thinking in
| such terms now. Ch3 covers handling state, and I think there
| are good ideas in that chapter that still haven't been
| broadly disseminated.
|
| But where the book really shines is the last 2 chapters - I
| haven't seen much of those ideas (virtual register machine
| and writing a compiler for it) covered elsewhere. It's still
| a great way to get exposed to some fundamentals of computing
| from a pure software environment. But I think you could be
| quite a capable programmer without ever doing that.
| Barrin92 wrote:
| >Am I really missing out on anything after a good CS
| education
|
| I don't think you can have a good software education, in the
| sense of having a complete one, without studying LISP. It's a
| foundational paradigm of programming. It's kind of like
| studying physics and skipping Maxwell's equations,
| paraphrasing Alan Kay.
|
| https://www.gnu.org/software/mes/manual/html_node/LISP-as-
| Ma...
| zerr wrote:
| Try original SICP video lectures.
| vinodkd wrote:
| Just read it like a novel. Ignore the exercises. What struck
| me that it took 8 or 9 chapters until they even presented a
| loop, and it was none the worse for doing that. Actually
| helped my imperative lizard brain evolve.
|
| Yes, its a book that uses lisp, but its not a lisp book. Its
| about programming techniques, i felt.
|
| Of all the books that are usually recommended to be read and
| nobody actually does, this is the one that i actually read
| and liked.
| tmtvl wrote:
| You could give Concrete Abstractions* a try, I consider it to
| be the intermediate part between the Little Seasoned Schemer
| and SICP. As for whether you're really missing out on
| anything depends on how good your good CS education was.
|
| * https://gustavus.edu/mcs/max/concrete-abstractions.html
| pelasaco wrote:
| I think will be interesting to follow this book but with Clojure
| instead of Common Lisp. Great book from a totally different Era
| ducktective wrote:
| I've been keeping my eye on the so called "GOFAI" for a long time
| but with recent advances in ANN methods (DL, LLM), does it even
| make sense to further pursue the former?
|
| Personally, those "old" methods in the 80s make a lot more sense
| to me than recent statistical methods.
| kqr wrote:
| The company I work for makes a killing on applying a 1980s-era
| ML technique to a really tough modern business problem -- the
| resulting product is probably the best in the world at what
| it's doing.
|
| Old techniques have several things going for them, with one of
| the more important ones for us being explainability. A random
| person off the street could hypothetically, with an hour or two
| of training, diagnose problems just by looking at the structure
| of the model. That's very helpful for adapting to market needs.
|
| Generality is another big plus. Since the model encodes
| intuitive ideas there's a lot of room for using it in
| innovative ways.
|
| Older techniques also tend to produce better results with less
| data, because big data wasn't as much of a thing back then.
|
| Unless you have a crazy amount of resources, I think it's far
| better to be bleeding edge in as few things as possible.
| Solving a new business problem? Perhaps don't spend too much
| time on also solving all the childhood diseases of a new
| technology.
| i_don_t_know wrote:
| What 1980s-era ML technique are you using and why is it
| better suited for your application than something modern? Is
| it ,,just" the ability to explain solutions, or is there
| something else like guarantees for XYZ?
| kqr wrote:
| The core part of it is doing dimensionality reduction with
| shallow neural networks, but essentially all user-facing
| functionality we get by controlling the training methods
| and hooking into the neural network and querying it all
| sorts of ways.
|
| We switched from more modern techniques primarily because
| they needed too much data to work well, but the other
| things I mentioned are the benefits we noted along the way.
| I don't know if that answers your question.
| npsomaratna wrote:
| Cost is also important. LLMs are expensive to run.
|
| I think that going forward, we'll see a mix of "normal"
| programming, LLMs, and simpler machine learning techniques
| all combined together, because of economic reasons.
| kqr wrote:
| Hm. I'm not sure about cost. I haven't done any sort of
| analysis but I would guess with 75 % certainty that the
| techniques we use need more watts per byte of data than
| modern techniques. Modern techniques are very efficient in
| terms of their operation -- it's just that they achieve
| performance by needing to do so much operation!
| bmitc wrote:
| I was assuming they were referring to the data
| collection, storage, and processing and model training.
| blihp wrote:
| It depends on what you were expecting from GOFAI. The future
| state is likely to be a combination of approaches where each
| makes sense rather than a single one. We're still in the
| honeymoon phase with deep learning and disillusionment with its
| ultimate limitations is likely still to come.
| kazinator wrote:
| You can run it in an air-gapped embedded system with (by
| today's standards) low RAM, and a slow CPU.
|
| Being able to trace how an answer is derived is also worth
| something.
| dimatura wrote:
| This book is a good intro to Lisp itself, which is worth
| knowing in any case- and symbolic AI is probably one of the
| best domains for it!
|
| As for GOFAI in the age of DL/LLM, yes, you should know it, for
| a couple of reasons. A lot of these techniques aren't really
| considered "AI" anymore, they're just regular CS algorithms
| everybody should know: graph search, backtracking,
| optimization, parsing, etc. The other is that a lot of newer
| DL/LLM is actually going back to these old problems, but
| bringing all the new techniques to deal with limitations of the
| classical algorithms.
| bionhoward wrote:
| it all loops around, pretty soon we'll all be back to good old
| fashioned philosophy
| Muller20 wrote:
| Depends on your application. AFAIK they are still used in games
| because they are easier to control and people want to play
| against simple AI agents.
| danwee wrote:
| > Personally, those "old" methods in the 80s make a lot more
| sense to me than recent statistical methods.
|
| Same for me.
|
| > I've been keeping my eye on the so called "GOFAI" for a long
| time but with recent advances in ANN methods (DL, LLM), does it
| even make sense to further pursue the former?
|
| I think it still matters. Plenty of examples in the tech
| industry where "old" tech/paradigms became the new "hype". They
| say it's all a cycle.
| jstx1 wrote:
| If it's for work or for building something useful, the book is
| a waste of time. If it's for your personal interest, you can
| read and learn anything you want.
|
| Personally I avoid books like this one (similar to how I avoid
| very esoteric languages) because I want to spend my time on
| things are interesting and useful instead of only interesting.
| [deleted]
| abecedarius wrote:
| Think of this as a "learn to program better" book, even if you
| don't care about GOFAI. It has a _lot_ of very nice code, and
| commentary without blather. I 've said this about as long as I
| knew of it, so this isn't just revisionism.
| hiepph wrote:
| With modern and trendy music, should we listen to old and
| classical music. Should we forget the history? I think not. My
| favourite genres are Jazz and classical.
|
| Sorry if you don't get the metaphor, but it's like so.
| cyberbanjo wrote:
| Jazz is a contemporary pop music, so this doesn't even make
| sense
| jstx1 wrote:
| The metaphor only works if you assume that software has only
| aesthetic utility.
| oumua_don17 wrote:
| But what is GOFAI?
|
| https://en.wikipedia.org/wiki/GOFAI
| jsmorph wrote:
| The section [0] on pattern matching [1] was an important
| inspiration for some pattern matching that's running in large-
| scale production today [2].
|
| [0] https://norvig.github.io/paip-
| lisp/#/chapter5?id=_52-pattern...
|
| [1] https://github.com/norvig/paip-
| lisp/blob/main/lisp/patmatch....
|
| [2] https://github.com/Comcast/sheens#pattern-matching
| codr7 wrote:
| I think everyone would be better off surveying the AI classics
| before diving head first into ML. It's the nuclear option for
| problem solving; and sometimes you can get away with simpler
| approaches that are easier to implement, reason about and debug.
| Sirenos wrote:
| I would caution that it's a matter of priorities.
|
| Currently, statistical/data-driven approaches work best, and
| that's what you will be expected to use whether you are
| building your own products, or working for an employer. Most
| people don't care about the GOFAI approaches anymore, seeing
| them as outmoded in all respects.
|
| However, if you are curious and want to understand more of the
| history of approaches we have tried, and learn some really
| interesting algorithms along the way, I think studying the old
| school problems and their solutions can be both intellectually
| stimulating, and potentially increase your depth of
| understanding. After all, it's only once you've tried to solve
| a problem and failed miserably that you start to appreciate the
| depth of its complexity.
|
| That depth of appreciation is sorely lacking in today's new
| cohorts, who are basically blinded by the incredibly convincing
| outputs of our cream-of-the-crop LLMs.
| time_to_smile wrote:
| I'm a huge fan of classical AI, and adore PAIP, but this isn't
| really true if your goal is anything other than a deep
| understanding of AI in the most general sense.
|
| While it would be great if everyone interested in the topic was
| well versed in the fundamentals, the truth is if you want to do
| anything from building something cool over the weekend to
| getting an actual job doing AI work, you're much better off
| starting not only with ML, but specifically with current SotA
| neural networks.
|
| If you really want to get started in AI I highly recommend
| building even a trivial implementation of Stable Diffusion on
| your own. Not just because it's cool, but because at its heart
| it is an excellent demonstration of how current differentiable
| programming works. Diffusion models involve chaining together 3
| separate models into an entire system that learns to solve a
| complex task. Once you understand this deeply, you can now
| solve a very broad range of tricky problems and are really
| approaching what we think of when we think of AI.
|
| Differentiable programming is really the current pathway to any
| sort of AI solution to a problem.
|
| I say this as the token "have you tried logistic regression?"
| guy in my org.
| anlunx wrote:
| Any recommendation for learning how to build a diffusion
| model?
| freeone3000 wrote:
| The downside of differential programming is the absolutely
| massive amounts of training data and time required. Several
| orders of magnitude over boosted decision trees or even SVMs.
| If your function's domain is fairly well understood, save
| yourself a few weeks and a few thousand dollars.
| jstx1 wrote:
| There's nothing intrinsically nuclear about ML and it includes
| a bunch of simple approaches too.
|
| The advice to spend your limited time and attention on outdated
| approaches seems counterproductive. The things in this book
| aren't just old - they ended up being a dead end in research.
| So if it's 2023 and you have 20 hours to learn something new,
| you can do much better than this book.
| dreamcompiler wrote:
| This attitude is not just ignorant; it's dangerous.
|
| I'm seeing rampant use of ML now for problems we already know
| how to solve in much simpler ways: linear control theory,
| bayesian statistics, Kalman filters, etc. "Oh hey, no need to
| study those old, dry topics. Just throw a bunch of training
| data at this GPU-bound black box and it will probably work."
|
| That's right, it will probably work. Until it doesn't. And
| then you won't be able to debug it. More important: You won't
| be able to predict when the system will fail, because it's a
| black box. And if it's controlling a high-consequence system,
| when it fails people could die.
|
| The moral is that if your problem falls into one of the
| already known easy-to-solve domains, you should use the old
| techniques. It will probably need at least 1/10^6 the CPU
| resources as an ML approach and you'll be able to
| characterize its failure regimes in advance.
| jstx1 wrote:
| You're arguing with a point that I haven't made.
|
| > I'm seeing rampant use of ML now for problems we already
| know how to solve in much simpler ways: linear control
| theory, bayesian statistics, Kalman filters, etc.
|
| How many of these techniques are in the book in the
| original post?
|
| I'm not saying the we should throw ML at everything, I'm
| saying the Norvig's book isn't useful in 2023.
| medo-bear wrote:
| Norvig's book is extremely useful for a whole different
| reason. It remains one of the best advanced books on
| general software design. Don't get distracted by the
| title
| abecedarius wrote:
| Those are all in his other textbook -- presumably he'd
| agree that AIMA is much more up to date than PAIP. I do
| think PAIP is still great for programmers wanting to
| improve their craft.
| bmitc wrote:
| Outdated in what sense?
| phtrivier wrote:
| > So if it's 2023 and you have 20 hours to learn something
| new, you can do much better than this book.
|
| Practically, which other book / ressource should someone with
| little time check out first ?
| BeetleB wrote:
| Introduction to Statistical Learning
| bcrosby95 wrote:
| People seem to say Norvig's "other" book is still relevant.
| I assume that's "Artificial Intelligence: a Modern
| Approach".
|
| My version is from when I went to school 20 years ago. I
| assume it's been greatly updated over the past couple of
| decades. I wonder if it's worth taking a spin through the
| new edition.
| Jtsummers wrote:
| It's up to a 4th edition now (I had the 2nd when I was in
| school). Here are the full contents for the current
| edition:
|
| http://aima.cs.berkeley.edu/contents.html
|
| Chapters 19 on are going to be the biggest additions from
| the earlier editions.
| phtrivier wrote:
| The 4rth edition is from 2019. My understanding is that
| the world of AI has changed about a quadrillion times
| since those days, right ;) ?
|
| (Interesting that AI is finally catching up with
| javascript frameworks.)
|
| My personnal goal is to find some time to dig into
| https://course.fast.ai/ , assuming it's not terribly
| outdated, either.
| Jtsummers wrote:
| > The 4rth edition is from 2019. My understanding is that
| the world of AI has changed about a quadrillion times
| since those days, right ;) ?
|
| The world isn't moving that fast. Transformers and LLMs
| are built on neural networks and lots of data and fast
| computers. You could jump straight to that point, but
| even the course you've pointed to starts off with more
| foundational ANN topics before getting to transformers.
| Much of which is at least in the TOC for the current
| edition of AIMA. Ought to be complementary texts.
|
| Also, only fools ignore history, "classical" AI and
| topics also covered in the book are still applicable.
| ANNs aren't going to solve all the world's problems.
| Other techniques that fall under the category of "AI" are
| still applicable and very effective for a large number of
| real-world problems (and much more efficient than LLMs).
| codr7 wrote:
| Compared to any other approach to AI we've come up with,
| neural networks are way more complicated.
|
| Besides, they're not the final solution to anything.
|
| Learning your history is the only way to avoid repeating the
| same mistakes.
| jstx1 wrote:
| You should really know that there's more to ML than neural
| networks. Those are the simpler approaches I was referring
| to (like linear models, ensemble models etc). They are
|
| 1. Machine learning.
|
| 2. Not neural networks.
|
| 3. Not in Norvig's book.
|
| 4. Still useful and relevant.
| lukego wrote:
| In fairness they are almost certainly in Norvig's _other_
| book...
| codr7 wrote:
| TIL; can't remember ever hearing about them before, where
| are they used?
|
| But it seems like we finally agree on something, simpler
| approaches to AI that predate neural networks are still
| potentially useful.
|
| Because you're not going to claim that ML is the only
| useful kind of AI, are you?
| [deleted]
| djtango wrote:
| I don't deny that AI from the lisp days is out of favour
| today but I recall Neural Networks sat in a niche corner of
| maths mostly dormant for quite a while too...
|
| I'm not an expert in either but am confident that progress is
| non linear. Are there any ideas that you think are definitely
| bad (or even possibly good) from the lisp days?
| kmavm wrote:
| It's "out of favor" because it completely failed as a
| research program. Let's not equivocate about this; it's
| nice to understand heuristic search, and there was a time
| when things like compilation were poorly understood enough
| to seem like AI. But as a path towards machines that
| succeed at cognitive tasks, these approaches are like
| climbing taller and taller trees in the hopes of getting to
| the moon.
| brabel wrote:
| Try using neural networks on a computer from 30 years ago,
| or even 10 years ago, and you'll quickly realize why they
| were not feasible for almost any problem when this book was
| written.
| eggy wrote:
| I was studying evolutionary computation back in the 80s.
| Yes, you did not have the large ANNs you have today, but
| you also broke down your problem into manageable,
| computable bits (no pun intended). Working through Koza's
| book on Genetic Programming in the 90s, or Mark Watson's
| book on ANNS, chaos theory, and a bunch of other stuff,
| was very illuminating. I particularly liked the
| artificial life stuff out of the Santa Fe Institute. I
| still have a VHS from the 2nd proceedings. I could have
| rode the ML/DL wave to a high salary job, but I took
| another path building real-world objects and machines,
| and I am very happy I did. I remember when WordPerfect
| was popular and people spoke of careers in
| wordprocessing. I always saw it as evolution of the cube
| farm, and of course, it is. Now I have friends who work
| in industry with ML/DL, and they joke that it's the same
| but with a lot more things to track and keep running. I
| like GOFAI for the simple matter that sometimes 90% is
| good enough, and 125% is a waste of time, water in the
| blender. When robotics was taking off I saw the two
| schools of thought: Rodney Brooke's subsumption
| architecture making it simple to create emergent
| behavior, and Mark Tilden's analog circuits and discrete
| components. I found Tilden's work more interesting. It
| spoke to me about continuous or biological-like
| processes. I am now into researching neurocomputing and
| following the developments in designing neurocomputing
| hardware. I was so happy to have received free tickets to
| watch Terminator 2 back in the day, but now I feel like I
| have a front-row seat to it!
| myth_drannon wrote:
| Do you remember the names of topics from Sant Fe's
| "artifical life stuff"? Was it agent-based modeling? They
| have a bunch of courses on
| https://www.complexityexplorer.org, but it's mostly Game
| Theory, Chaos, Complexity...
| mistrial9 wrote:
| plenty of game programming has ingested "agents with
| actions" kind of programming directions; I believe that
| procedural code with some understanding of state-
| machines, is all that "artificial life" content was..
| minus some details.
| chaxor wrote:
| Neural networks were known as being "always second best" for
| many decades not that long ago...
|
| Its very possible ( _highly likely_ even) that elements of
| gofai end up being implemented into some of the upcoming RL
| /GNN combo based architectures. I _highly_ doubt that the
| transformer will be the end-all-be-all for generating
| representations. At the very least, many of those 'in-the-
| know' around these GNNs realize that sheaf-NNs are much more
| expressive and can yield far better general results if
| improved properly for long range dependences - perhaps with a
| performer or longformer -like addition.
|
| Ultimately, some of the best researchers in the field
| (Velockovic is one of the best, and heavily focused on
| dynamic programming for example) are not just focused on the
| transformer or any of the hype around it right now. In order
| to improve, you have to look elsewhere. Understanding old
| methods is typically a great resource to draw that
| inspiration / algorithm from.
| abecedarius wrote:
| Do you have a favorite overview of sheaf NNs? First I've
| heard of them, though I've read a bit about GNNs.
| jefffoster wrote:
| This is an absolutely fantastic book!
|
| Read it to learn how to program, learn a bit about AI as an added
| benefit.
| perihelions wrote:
| MACSYMA has been open-sourced since this book was published as
| Maxima,
|
| https://maxima.sourceforge.io/documentation.html
|
| https://en.wikipedia.org/wiki/Maxima_(software)
| pfdietz wrote:
| Macsyma/Maxima's programming style is very archaic though,
| reflecting that it is from an era before Common Lisp and
| lexical binding.
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