44 ms·

Retrospective review of Gödel, Escher, Bach (1996) [pdf]

200 points by lawrenceyan 6y ago

mhneu 6y ago
At lot to agree with here: [Deep Blue] cannot answer elementary questions about chess such as “If a bishop is now on a black square, will it ever be on a white square?” ... All that it can do is to generate a next move of extraordinary quality. But in a deeper sense, the match between Deep Blue and Kasparov proves that Hofstadter was profoundly right. ... It is precisely this power of abstracting, of stepping outside the game to a higher-level view, that enabled Kasparov to find where Deep Blue was weak and to exploit this to win his match. Also, I strongly agree that the music/Bach connection in the book makes little sense. The only aspect of the book that really does not work for me is the attempt to integrate music. And there are some very interesting insights with benefit of 20 years of hindsight. Today, open-source chess programs are better than the best humans. (they have been "trained" via a form of adversarial learning.) And: Part of this, of course, is nostalgia for the halcyon years of AI when the funding agencies had more money to throw around than there were researchers to take it, and the universities had more tenure-track slots than there were Ph.D’s to fill them; Biden just announced a desire to DOUBLE American investment in R&D. Such an optimistic time we are at right now.
- karmakaze 6y ago
  > to a higher-level view, that enabled Kasparov to find where Deep Blue was weak I don't know if the same could be said of AlphaZero.
- Bud 6y ago
  As a professional singer of Bach, I feel that the integration of Bach made a lot of sense and worked very well. I am forced to wonder if the author of this review was a high-level musician.
  - hobs 6y ago
    As a person who is a musical idiot I thought it was a great way to establish the concept of patterns and self reference in something like music. The notion of the self referential cannons was seriously impressive to me and I thought served as a great jumping off point into more esoteric conversations in the book.
  - TheOtherHobbes 6y ago
    There still aren't Bach-equivalent counterpoint solvers. ML has been pretty disappointing for music. It can make some music-like objects, especially for short phrases, but it hasn't done a convincing job of producing the real thing. Music is hard. It's far harder than most people realise. Winning a game is a relatively easy problem, because you know when you have won. Music is much more nebulous. Grammatical correctness is preparatory to creative intent. Even basic metrics of correctness are rather fuzzy. ML doesn't have any concept of intent, so it tends to flail around in a statistical space - and to sound like it.
    - jancsika 6y ago
      Judgment of music quality also happens to be highly subjective. Suppose an AI created a Bach-like contrapuntal exercise with a lot of cross-relations (i.e., clashing dissonant sounds). Would scholars judge it to be at the level of Bach because of the handling of these numerous cross-relations? Or would they claim it isn't at sophisticated as Bach because having that many cross-relations isn't stylistically accurate? Based on the historiography of musicology I would guess the latter. Even though there's an unfinished extant Bach fugue in C minor where he's essentially role-playing a cross-relation obsessed AI composer. The history of theory is even worse. For example, Schenker created theories based on the mastery that Bach, Beethoven, and others displayed in their compositions. But when a piece by Bach didn't fit his theory, he claimed that Bach actually wrote it incorrectly! I'm afraid AI music research is going to have to put up with the same irrationality of humans as every other subjective endeavor. That is, musicians will kick and scream that AI "isn't doing it right," then when it inevitably becomes embedded as a core part of music making we'll hear all about how music produced with classical algorithms on old protools rigs sounds so much "warmer."
  - commandlinefan 6y ago
    On the other side of the spectrum, I myself am a relative musical neanderthal, having gone into the book not even knowing what the word "fugue" meant. I was fascinated by the way he related this sort of musical "recursion" back to general mathematical problem solving. When I read OP's dismissal of it, I figured I was just underthinking whether it was a good analogy or not, being the unsophisticate that I am - nice to see that somebody who does have some expertise on the subject weighing in.
  - _jal 6y ago
    Agree. I am not a musician, but have absorbed a lot by having lived with them. Weaving Bach in makes perfect sense.
  - pdonis 6y ago
    > As a professional singer of Bach, I feel that the integration of Bach made a lot of sense and worked very well. I am only an amateur player of Bach, but I felt the same way. However, it probably is worth noting that a major reason why Bach works in this context is the particular nature of his music--or more precisely the particular subsample of his music that GEB refers to. I don't think the music of most other composers, or even some of Bach's less contrapuntal music, would have made sense in this context.
- gameswithgo 6y ago
  Just a note on chess programs: Until very recently (months) the best chess programs consisted of both neural network based ones, and search based ones similar to deep blue. They would go back and forth for supremacy, and both are way better than any human. Recently, one of the top search based engines (stockfish) added neural networks on top of it, and it got significantly better still. This was a couple months back, maybe the pure neural nets have caught up again!
  - bolzano 6y ago
    Nice, do you have a link to any details on this ongoing battle?
- CPLX 6y ago
  I disagree. I’m a professional-level musician and a fan of GEB and I always felt the music and Bach analogies in the book were elegant and insightful.
reedf1 6y ago
I read this fresh out of university and it made me want for a culture that no longer exists. If it ever did, that is, for anyone beyond a few elites. The world may have been for two sigma individuals in the past but now it's for three or four sigma individuals. Leaving me in what feels like intellectual bronze or silver.
- jdonaldson 6y ago
  There is no single measure of intellect. We as a society just set some arbitrary goalposts and added some ways to measure contribution. The ability to take a step back and try to better understand reality will always be valuable, and there will always be a horizon there that we cannot measure.
  - ppezaris 6y ago
    Measuring intellect is one thing, valuing it is another. Within my lifetime I've sadly seen intellectual excellence valued less and less by society with each passing decade.
    - jdonaldson 6y ago
      I see that intellect is valued more and more intensely by a smaller and smaller group of people. I'm not sure what that means, but it gives me some small hope.
- gameswithgo 6y ago
  Best bet is probably something like cern or quality physics/math/philosophy faculty at a university somewhere
- trylfthsk 6y ago
  My feelings on this are very similar; Looking at my competition in trying to enter grad school from industry this past year has me awed and feeling totally outclassed (partially due to the increased field due to covid) - undergrad publications, completely flawless academic records, awards, recommendations from famous professors, etc. By compare, I'm just some mook from a small state school with average grades & good test scores. Perhaps I need to be more willing to make certain sacrifices, since it's increasingly clear so many do. In that respect, I'm deeply humbled.
misiti3780 6y ago
I tried reading GEB years ago and then gave up, but then I read "I Am A Strange Loop" and thought it was fascinating. I think the latter is just smaller version of the former, but more concise as he/they was able to think about it for a few more years.
- leephillips 6y ago
  he/they?
  - jdonaldson 6y ago
    Hofstadter is a polymath, and he's arguably changed the understanding of the philosophy of existence as much as the understanding of these philosophies have changed him. I actually like using "they" to describe this process.
    - leephillips 6y ago
      Because he’s too smart to be just one person?
      - jdonaldson 6y ago
        Maybe we recognize the contributions and influences of others in his work? Hofstadter is a big time guy, but he's not a self-centered egoist like Kurzweil. He'll regularly talk about the ideas he's bounced around with buddies like Daniel Dennett, etc. I see him as more of a poet than a philosopher or scientist. He is piecing together little bits of truth from many, many, different domains and showing how they reach the same conclusions. There's been nobody like him for over 50 years, so it's very difficult to draw comparisons to others, or even measure the impact of his contributions.
        mprovost 6y ago
        I talked myself into one of his classes as an undergrad and definitely came away with the belief that he was the smartest person I would ever meet. It's been 23 years and that's still true and I'd take a bet today that it will continue to be true for the rest of my life.
zabzonk 6y ago
A bit easier is Metamagical Themas https://www.wikiwand.com/en/Metamagical_Themas https://www.wikiwand.com/en/Metamagical_Themas which is a collection of articles from Scientific American when he took over the Mathematical Games column.
- azhenley 6y ago
  How does it compare to I Am A Strange Loop? All 3 are on my list but I found GEB very difficult to get started.
  - zabzonk 6y ago
    > How does it compare to I Am A Strange Loop? Don't know, haven't read it. But Metamagical Themas is a considerably easier ride than GEB, as it is made up of (mostly) independent articles.
  - 8fGTBjZxBcHq 6y ago
    It's a collection of self-contained columns gathered together. They're all over the place in subject so if you went looking much more likely to find something specifically to draw you in. Also you can just skip ones you're not into without losing the overall picture like in GEB. Different kind of book but similar explorations. I'd also say overall more enjoyable unless you're specifically into the thesis of GEB.
  - Kranar 6y ago
    I Am a Strange Loop is for sure the least technical of the three but it's best if you read it last since it's a very emotional and touching denouement to the other two books. You can read GEB or MT in any order, perhaps MT might be more approachable to start with since while it is quite technical, it's a series of articles that can be read independently of one another and each article has a bit of an aha moment, as opposed to GEB which is one loooong marathon where the aha moment is absolutely enormous, but requires a great deal of preparation.
SamBam 6y ago
Thinking on the "the halcyon years of AI," in the 70s and (maybe?) 80s, was there anything really "missed" then, or have most of the modern advances in AI only really been possible due to the increase in computing power? To put it another way, if you were transported by to 1979 knowing what you know now (or maybe able to bring a good book or two on the subject) would you be able to revolutionize the field of AI ahead of its time?
- gameswithgo 6y ago
  You might be able to help a bit sure. There are some algorithmic improvements that have been made, so you could bring those back in time. Then you could just assure people that if the spent the time to develop huge datasets, and do enough parallel computation that you can get good results. But it would have been very slow back then.
- PartiallyTyped 6y ago
  I'd say yes. You could bring Support Vector Machines [0], bring the Vapnik-Chervonenkis theory of statistical learning [1]. You could fast forward the whole field of Reinforcement Learning, you would be able to show that it's possible to solve problems like passing gradients through a sampling process using the reparameterization trick [2], you would be able to show that you can pass gradients through a sorting process [3]. You would also have experience of working with autodiff software and build such. Imho the advent of autograd, tf, torch and so on helped tremendously in accelerating progress and research because the focus of development is not on correctness anymore. [0] https://en.wikipedia.org/wiki/Support-vector_machine https://en.wikipedia.org/wiki/Support-vector_machine [1] https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_th... [2] https://openreview.net/forum?id=33X9fd2-9FyZd https://openreview.net/forum?id=33X9fd2-9FyZd [3] https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_torchsort_fast_differentiable_sorting_and https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_t...
- peter303 6y ago
  I took the MIT AI course 6.034 in 1972 from Patrick Winston. He taught that course periodically until his passing a couple years ago. The 2016? Lectures on MIT opencourseware. I would estimate there was 2/3 overlap between the 1972 and 2016 versions. That course is heavy on heuristics and not big data. Around 1979 an MIT group lead by Gerald Sussman (still working) designed a workstation specifically to accelerate LISP. It was hypothesized a computer that ran LISP a thousand times faster would revolution AI. It did not. However the two LISP workstations that saw commercial sales did jump start the interactive graphics workstation market (UNIX, C and C++). Dedicated language machines could not keep up with the speed improvements of general CPUs. On the other hand custom neural chips from Google, Apple, Nvidia (and soon MicroSoft) have really helped AI techniques based upon deep convolutional neural networks. Neural chips run orders of magnitude faster than general CPUs by using simpler arithmetic and parallelism.
lacker 6y ago
This review is a bit of a window into the past itself... after all, more time has passed since this review was written than elapsed between GEB’s publication and this review. In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. It underestimated what behaviors could be emergent from running simple algorithms on large datasets. It is one of the most beautiful expressions of the ideas of the “classic” age of 1970’s AI, awesome to read but in the end somewhat incorrect about the future. Perhaps one day the pendulum will swing back, and we will discover that large datasets are in some ways overrated, and clever aesthetic senses of pattern are necessary for progress. On that day it will be quite interesting to reread GEB.
- colordrops 6y ago
  Why is there a pendulum? Aren't both necessary and two sides of the same coin? I know little of modern AI, but I've seen work where both low level raw power for NNs is combined with a symbolic organization of many NNs.
  - ghaff 6y ago
    To some degree. But an enormous part of the (useful but narrow) success of AI is a specific set of ML tech, especially supervised learning. And the amount of computational horsepower and data required gives one at least some pause given what the human brain can do with apparently so much less. Which implies there are probably significant advances required in cognitive science and neurophysiology and perhaps other fields of studies we haven't even considered. We may need other ways to solve problems that have proven more elusive to brute force than it looked like 5 years or so ago (e.g. general purpose full self-driving in busy cities).
  - nextos 6y ago
commandlinefan 6y ago
If you pick up a more recent edition of the book, it includes a new preface where Hofstadter actually addresses a lot of this himself - he admits to being "embarrassed" by his claim that computers that could beat people at chess would get bored of chess and talk about poetry, although I think he's being too hard on himself.
nicholast 6y ago
GEB as literature stands up much better than GEB as musings on AI. The book practically invented a new form of expression that hasn't been matched to this day, with recursion between modalities in every chapter.
jplr8922 6y ago
I dont think that AI will get close to the vision in GEB. As many said in this thread, what we have is more power and more data to solve narrow problems. One of the point of EGB is that perceived details may aggregate to something larger and form another pattern unrelated to what the algo what initially trained for. True AI will have to find a way around this. When I was working as an quantitative strategist in a trading firm, I alway made sure that my algos had a ''killswitch'' which required human intervention in case the market did not exibit usual patterns found in the training sets. Skin in the game is the best cure against techno-evengelical optimism. Also, I had the opportunity to practice zen meditation with a monk who had actual koan training. The idea that computer will replicate that process soon is ridiculous. Buddhist and asian philosophy are, at their cores, anti-aristotle. Who can make this into a AI now? https://aeon.co/essays/the-logic-of-buddhist-philosophy-goes-beyond-simple-truth https://aeon.co/essays/the-logic-of-buddhist-philosophy-goes...
- tbalsam 6y ago
  This feels a lot like the kind of "enlightened anti-'AI'" opinion that's more in vogue in some circles. I think if you're following the literature, there's a significant amount that's pointing to the above generalization capabilities that you're contending against because it may not seem likely from your personal experience. If you were to look at your arguments, I think you're extending the idea of expert systems from the 90's and so on and using that to paint a picture of the entire research field of deep learning process today. What we have has the capabilities to not be bound by those things, just the long bootstrapping process takes time. But we're getting closer year by year, and it's more and more exciting along that front as time goes along. As for experience, I am and have been a practicioner in this field for several years.
  - jplr8922 6y ago
    Hm interesting, do you have an recent article to share?
    - tbalsam 6y ago
      I'm not sure in particular anything that would be useful beyond certain trends. Things like AlphaFoldv2 and some of the cooperative-competitive emergent RL strategies are interesting/important. You might find some of the concurrent work on open set/zero shot very interesting. I think it's an asymptotic approach on some degrees. Like you noted with meditation -- this is something very hard for a computer to achieve, for a computer has to have a demonstrable concept/actualized sense of self to be able to let go and connect to some of the more spiritual elements there. Conversely, if arguing from a non-materialistic manner, then you could also argue there's some sort of bridge there that could be crossed for that particular kind of connection. Materially, some kind of artistic manifold. But without some kind of spiritual connection, there may always be something missing there. However, we as humans on the interpreting end of things may make the spiritual connections ourselves, so some kind of manifold discovery may not need that kind of connection to properly function. And it may! Who knows? In the end, all in all -- I think a lot of it looks at the open-ended discovery-and-low-label-self-dermination-and-assignment, if you're looking at (what I'd personally consider to be) the most interesting research all rattling around in there. :))))
mindcrime 6y ago
One thing I initially found dissapointing when reading this was this bit: I am working from memory here. Charniak’s thesis was never published, and I have not seen a copy since 1981 Luckily, it turns out that Charniak's phd thesis is available online these days. https://dspace.mit.edu/bitstream/handle/1721.1/6892/AITR-266.pdf?sequence=2&isAllowed=y https://dspace.mit.edu/bitstream/handle/1721.1/6892/AITR-266...
maxwells-daemon 6y ago
My research (automated theorem proving with RL) sits partway between "good old-fashioned AI" and modern deep learning, and GEB struck me as amazingly prescient, with lots of lessons for modern AI research. There's a growing sense among many ML researchers that there's something fundamentally missing in the "NNs + lots of data + lots of compute" picture. GPT-3 knows that 2+2=4 and that 3+4=7, but it doesn't know that 2+2+3=7. These heart of these kinds of problems indeed seems to be the sense of abstraction / problem reimagining / "stepping outside the game" that Hofstadter spent so much time talking about. Chess (accidentally) turned out to be easy enough for a narrow algorithm to work well. But I'd be surprised if other problems don't require an intelligence general enough to say "I'm bored, let's do something else," and I don't believe current algorithms can get there at any scale.
- yaseer 6y ago
  In my former research I was interested in automated theorem proving (constructing a variant of the lambda calculus from which we can run genetic algorithms). Also, my gamer tag is maxwells_demon, similar to your HN name. Unfortunately, the 14 year-olds in online games don't appreciate jokes about thermodynamics.
eternalban 6y ago
GEB, and its rebuttal, Emperor's New Mind [1], are both fantastic books. Both are must reads regardless of one's views on AI (or approaches to AI). I never bought Hofstadter's thesis -- am solidly in Penrose camp -- and find it interesting that Penrose went on to develop his thesis even further [2][3] while Hofstadter has not. That said, Hofstadter is clearly a far more engaging and talented writer, and in context of Symbolic AI, his book outshines (imho) Minsky's Society of Mind [4] (which I found rather underwhelming). There is a mild sense of tragedy about Hofstadter. Something about massive early success.. [1]: https://www.goodreads.com/book/show/179744.The_Emperor_s_New_Mind https://www.goodreads.com/book/show/179744.The_Emperor_s_New... [2]: https://en.wikipedia.org/wiki/Roger_Penrose#Physics_and_consciousness https://en.wikipedia.org/wiki/Roger_Penrose#Physics_and_cons... [3]: https://en.wikipedia.org/wiki/Orchestrated_objective_reduction https://en.wikipedia.org/wiki/Orchestrated_objective_reducti... [4]: https://www.goodreads.com/book/show/326790.The_Society_of_Mind https://www.goodreads.com/book/show/326790.The_Society_of_Mi...
peter303 6y ago
I had the fortune of being in a Stanford Chinese language class with Doug in 1976 when was writing GEB. He passed around lineprinter drafts of chapters for feedback. I dont know if anyone anticipated the book would be a bestseller and win the Pulitzer. (Though mosts authors secretly hope for that. Doug hypothesized that knowing a language so different from English as Chinese might shed insight on how the mind works. I think he went onto other AI topics such as analogies and patterns. More recently sounds like he is looking at Chinese again from his blogs.
- galaxyLogic 6y ago
  I think language is the key to AI and thus Symbolic reasoning is. I wouldn't call someone intelligent who can not explain how they arrive at certain conclusions. They might be correct and useful conclusions but if you or the neural network can not communicate to others how they come to their conclusions their knowledge can not be shared with others. If it can not be shared by means of some kind of language which explains its reasoning based on logical primitives, we don't really call it intelligent, do we. We don't call a person or machine intelligent if they can't explain their reasoning. We don't really trust answers without learning the rationale behind them. And isn't this what is happening with some neural networks, they usually give more or less correct answer but sometimes, can give a totally wrong answer too. Not really trustworthy because the logic behind the answers is simply not there.
- savanaly 6y ago
  Where does he blog?
jimhefferon 6y ago
I didn't understand most of the essay. > The dialogues interspersed between the chapters are, indeed, often contrived, arch, heavy-handed, and excessively cute, but they have virtues that, pedagogically and presentationally if not aesthetically, far outweigh these drawbacks; they are uniformly clear, vivid, memorable, and thought-provoking. Another sentence that I didn't understand. Sigh. I often think that I am just not smart enough to be in this field. But, in any event, I found GEB inspiring. Thinking about these world-changing ideas by approaching them from different directions turned me on. I wrote a book on the Theory of Computation that was inspired by it -- OK it is not itself inspiring, but it tries to give readers all kinds of ways to explore the material, while perhaps not saying anything that can't be proved. I hope some people here might be interested: https://hefferon.net/computation/index.html https://hefferon.net/computation/index.html
- moonchild 6y ago
  > The dialogues interspersed between the chapters... There are dialogues, and they are interspersed between chapters of traditional prose > ...are, indeed, often XXX, but they have virtues that, YYY, far outweigh these drawbacks; they are ZZZ Said dialogues have negative attributes XXX. However, they also have positive attributes ZZZ. These positive attributes are characterized as YYY. > XXX contrived made to suit a purpose > arch underhanded > heavy-handed obvious > YYY pedagogically good for teaching ...and so on.
ralphc 6y ago
It's ostensibly a book about AI, but when I first read it, summer after high school (or after freshman college year?), it blew my mind as a book that showed me that computer science was science and mathematics and not just typing in programs and fiddling in BASIC on my TRS-80.
kazinator 6y ago
> The truth is that, since music does not denote, the issues of levels of representation and self-reference really do not arise I absolutely disagree. Firstly, music may not denote in the same way as math notation or a sentence of natural language, but it does refer to something external: it triggers sensations in the listener, and it does so in some ways that are objective. For instance, some notes drive satisfyingly ("resolve toward") some other notes, whereas in other situations there is a sense of ambiguity. Using harmonic trickery, the composer can predictably lead the listener around in a labyrinth; the book goes into that. Secondly, if we accept the premise that music doesn't denote anything external, then we have to confront the remaining fact that music references, and therefore denotes, other music, which is potentially itself. Lack of external reference not only does not preclude self-reference, but emphasizes it as the only interesting option. Music intersects with computer science, obviously; there are people who do computational music. There are hard problems in music. Bach belongs in that book, because we have not produced a convincing Bach robot. Why is Escher acceptable but not Bach? An Escher drawing like The Waterfall denotes something: it denotes a nicely drawn piece of architecture denoting an artificial waterfall driving a waterwheel. But the denotation isn't the point; it's the impossibility of the object itself, and of the perpetual motion of the waterwheel. The art has numerous details which don't contribute to that at all. For instance, the waterfall structure has numerous bricks, all carefully drawn. Music is the same. It has sensual aspects, like the timbre of instruments and the expressiveness of the performance, or the power of many instruments in unison and so on. We can't be distracted and fooled by that.
shakezula 6y ago
I read this book in high school and it seriously changed my views on life in a lot of different ways, as well as opened my mind to a lot of new ideas and ways of thinking I had never considered before. This review brought some good memories of that book back. I should go dig that out of storage and give it another read.
Ice_cream_suit 6y ago
The abstract approach to AI is still around and fruitful although it may not be fad of the day among the Phronix crowd. For example look at a recent paper by Ernest Davis, the author of the review linked to above: https://arxiv.org/pdf/2004.13831.pdf https://arxiv.org/pdf/2004.13831.pdf A Review of Winograd Schema Challenge Datasets and Approaches Vid Kocijan1∗ , Thomas Lukasiewicz1,2 , Ernest Davis3 , Gary Marcus4 and Leora Morgenstern5 1University of Oxford 2Alan Turing Institute, London 3New York University 4Robust AI 5Systems & Technology Research / PARC firstname.lastname@cs.ox.ac.uk, davise@cs.nyu.edu, gary.marcus@nyu.edu, leora.morgenstern@gmail.com Abstract: The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.
lioeters 6y ago
"I'm So Meta, Even This Acronym"

200 points by lawrenceyan 6y ago

At lot to agree with here: [Deep Blue] cannot answer elementary questions about chess such as “If a bishop is now on a black square, will it ever be on a white square?” ... All that it can do is to generate a next move of extraordinary quality. But in a deeper sense, the match between Deep Blue and Kasparov proves that Hofstadter was profoundly right. ... It is precisely this power of abstracting, of stepping outside the game to a higher-level view, that enabled Kasparov to find where Deep Blue was weak and to exploit this to win his match. Also, I strongly agree that the music/Bach connection in the book makes little sense. The only aspect of the book that really does not work for me is the attempt to integrate music. And there are some very interesting insights with benefit of 20 years of hindsight. Today, open-source chess programs are better than the best humans. (they have been "trained" via a form of adversarial learning.) And: Part of this, of course, is nostalgia for the halcyon years of AI when the funding agencies had more money to throw around than there were researchers to take it, and the universities had more tenure-track slots than there were Ph.D’s to fill them; Biden just announced a desire to DOUBLE American investment in R&D. Such an optimistic time we are at right now.

karmakaze 6y ago
> to a higher-level view, that enabled Kasparov to find where Deep Blue was weak I don't know if the same could be said of AlphaZero.
Bud 6y ago
As a professional singer of Bach, I feel that the integration of Bach made a lot of sense and worked very well. I am forced to wonder if the author of this review was a high-level musician.
- hobs 6y ago
  As a person who is a musical idiot I thought it was a great way to establish the concept of patterns and self reference in something like music. The notion of the self referential cannons was seriously impressive to me and I thought served as a great jumping off point into more esoteric conversations in the book.
- TheOtherHobbes 6y ago
  There still aren't Bach-equivalent counterpoint solvers. ML has been pretty disappointing for music. It can make some music-like objects, especially for short phrases, but it hasn't done a convincing job of producing the real thing. Music is hard. It's far harder than most people realise. Winning a game is a relatively easy problem, because you know when you have won. Music is much more nebulous. Grammatical correctness is preparatory to creative intent. Even basic metrics of correctness are rather fuzzy. ML doesn't have any concept of intent, so it tends to flail around in a statistical space - and to sound like it.
  - jancsika 6y ago
    Judgment of music quality also happens to be highly subjective. Suppose an AI created a Bach-like contrapuntal exercise with a lot of cross-relations (i.e., clashing dissonant sounds). Would scholars judge it to be at the level of Bach because of the handling of these numerous cross-relations? Or would they claim it isn't at sophisticated as Bach because having that many cross-relations isn't stylistically accurate? Based on the historiography of musicology I would guess the latter. Even though there's an unfinished extant Bach fugue in C minor where he's essentially role-playing a cross-relation obsessed AI composer. The history of theory is even worse. For example, Schenker created theories based on the mastery that Bach, Beethoven, and others displayed in their compositions. But when a piece by Bach didn't fit his theory, he claimed that Bach actually wrote it incorrectly! I'm afraid AI music research is going to have to put up with the same irrationality of humans as every other subjective endeavor. That is, musicians will kick and scream that AI "isn't doing it right," then when it inevitably becomes embedded as a core part of music making we'll hear all about how music produced with classical algorithms on old protools rigs sounds so much "warmer."
- commandlinefan 6y ago
  On the other side of the spectrum, I myself am a relative musical neanderthal, having gone into the book not even knowing what the word "fugue" meant. I was fascinated by the way he related this sort of musical "recursion" back to general mathematical problem solving. When I read OP's dismissal of it, I figured I was just underthinking whether it was a good analogy or not, being the unsophisticate that I am - nice to see that somebody who does have some expertise on the subject weighing in.
- _jal 6y ago
  Agree. I am not a musician, but have absorbed a lot by having lived with them. Weaving Bach in makes perfect sense.
- pdonis 6y ago
  > As a professional singer of Bach, I feel that the integration of Bach made a lot of sense and worked very well. I am only an amateur player of Bach, but I felt the same way. However, it probably is worth noting that a major reason why Bach works in this context is the particular nature of his music--or more precisely the particular subsample of his music that GEB refers to. I don't think the music of most other composers, or even some of Bach's less contrapuntal music, would have made sense in this context.
gameswithgo 6y ago
Just a note on chess programs: Until very recently (months) the best chess programs consisted of both neural network based ones, and search based ones similar to deep blue. They would go back and forth for supremacy, and both are way better than any human. Recently, one of the top search based engines (stockfish) added neural networks on top of it, and it got significantly better still. This was a couple months back, maybe the pure neural nets have caught up again!
- bolzano 6y ago
  Nice, do you have a link to any details on this ongoing battle?
CPLX 6y ago
I disagree. I’m a professional-level musician and a fan of GEB and I always felt the music and Bach analogies in the book were elegant and insightful.

reedf1 6y ago

I read this fresh out of university and it made me want for a culture that no longer exists. If it ever did, that is, for anyone beyond a few elites. The world may have been for two sigma individuals in the past but now it's for three or four sigma individuals. Leaving me in what feels like intellectual bronze or silver.

jdonaldson 6y ago
There is no single measure of intellect. We as a society just set some arbitrary goalposts and added some ways to measure contribution. The ability to take a step back and try to better understand reality will always be valuable, and there will always be a horizon there that we cannot measure.
- ppezaris 6y ago
  Measuring intellect is one thing, valuing it is another. Within my lifetime I've sadly seen intellectual excellence valued less and less by society with each passing decade.
  - jdonaldson 6y ago
    I see that intellect is valued more and more intensely by a smaller and smaller group of people. I'm not sure what that means, but it gives me some small hope.
gameswithgo 6y ago
Best bet is probably something like cern or quality physics/math/philosophy faculty at a university somewhere
trylfthsk 6y ago
My feelings on this are very similar; Looking at my competition in trying to enter grad school from industry this past year has me awed and feeling totally outclassed (partially due to the increased field due to covid) - undergrad publications, completely flawless academic records, awards, recommendations from famous professors, etc. By compare, I'm just some mook from a small state school with average grades & good test scores. Perhaps I need to be more willing to make certain sacrifices, since it's increasingly clear so many do. In that respect, I'm deeply humbled.

misiti3780 6y ago

I tried reading GEB years ago and then gave up, but then I read "I Am A Strange Loop" and thought it was fascinating. I think the latter is just smaller version of the former, but more concise as he/they was able to think about it for a few more years.

leephillips 6y ago
he/they?
- jdonaldson 6y ago
  Hofstadter is a polymath, and he's arguably changed the understanding of the philosophy of existence as much as the understanding of these philosophies have changed him. I actually like using "they" to describe this process.
  - leephillips 6y ago
    Because he’s too smart to be just one person?
    - jdonaldson 6y ago
      Maybe we recognize the contributions and influences of others in his work? Hofstadter is a big time guy, but he's not a self-centered egoist like Kurzweil. He'll regularly talk about the ideas he's bounced around with buddies like Daniel Dennett, etc. I see him as more of a poet than a philosopher or scientist. He is piecing together little bits of truth from many, many, different domains and showing how they reach the same conclusions. There's been nobody like him for over 50 years, so it's very difficult to draw comparisons to others, or even measure the impact of his contributions.
      - mprovost 6y ago
        I talked myself into one of his classes as an undergrad and definitely came away with the belief that he was the smartest person I would ever meet. It's been 23 years and that's still true and I'd take a bet today that it will continue to be true for the rest of my life.

zabzonk 6y ago

A bit easier is Metamagical Themas https://www.wikiwand.com/en/Metamagical_Themas https://www.wikiwand.com/en/Metamagical_Themas which is a collection of articles from Scientific American when he took over the Mathematical Games column.

azhenley 6y ago
How does it compare to I Am A Strange Loop? All 3 are on my list but I found GEB very difficult to get started.
- zabzonk 6y ago
  > How does it compare to I Am A Strange Loop? Don't know, haven't read it. But Metamagical Themas is a considerably easier ride than GEB, as it is made up of (mostly) independent articles.
- 8fGTBjZxBcHq 6y ago
  It's a collection of self-contained columns gathered together. They're all over the place in subject so if you went looking much more likely to find something specifically to draw you in. Also you can just skip ones you're not into without losing the overall picture like in GEB. Different kind of book but similar explorations. I'd also say overall more enjoyable unless you're specifically into the thesis of GEB.
- Kranar 6y ago
  I Am a Strange Loop is for sure the least technical of the three but it's best if you read it last since it's a very emotional and touching denouement to the other two books. You can read GEB or MT in any order, perhaps MT might be more approachable to start with since while it is quite technical, it's a series of articles that can be read independently of one another and each article has a bit of an aha moment, as opposed to GEB which is one loooong marathon where the aha moment is absolutely enormous, but requires a great deal of preparation.

SamBam 6y ago

Thinking on the "the halcyon years of AI," in the 70s and (maybe?) 80s, was there anything really "missed" then, or have most of the modern advances in AI only really been possible due to the increase in computing power? To put it another way, if you were transported by to 1979 knowing what you know now (or maybe able to bring a good book or two on the subject) would you be able to revolutionize the field of AI ahead of its time?

gameswithgo 6y ago
You might be able to help a bit sure. There are some algorithmic improvements that have been made, so you could bring those back in time. Then you could just assure people that if the spent the time to develop huge datasets, and do enough parallel computation that you can get good results. But it would have been very slow back then.
PartiallyTyped 6y ago
I'd say yes. You could bring Support Vector Machines [0], bring the Vapnik-Chervonenkis theory of statistical learning [1]. You could fast forward the whole field of Reinforcement Learning, you would be able to show that it's possible to solve problems like passing gradients through a sampling process using the reparameterization trick [2], you would be able to show that you can pass gradients through a sorting process [3]. You would also have experience of working with autodiff software and build such. Imho the advent of autograd, tf, torch and so on helped tremendously in accelerating progress and research because the focus of development is not on correctness anymore. [0] https://en.wikipedia.org/wiki/Support-vector_machine https://en.wikipedia.org/wiki/Support-vector_machine [1] https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_th... [2] https://openreview.net/forum?id=33X9fd2-9FyZd https://openreview.net/forum?id=33X9fd2-9FyZd [3] https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_torchsort_fast_differentiable_sorting_and https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_t...
peter303 6y ago
I took the MIT AI course 6.034 in 1972 from Patrick Winston. He taught that course periodically until his passing a couple years ago. The 2016? Lectures on MIT opencourseware. I would estimate there was 2/3 overlap between the 1972 and 2016 versions. That course is heavy on heuristics and not big data. Around 1979 an MIT group lead by Gerald Sussman (still working) designed a workstation specifically to accelerate LISP. It was hypothesized a computer that ran LISP a thousand times faster would revolution AI. It did not. However the two LISP workstations that saw commercial sales did jump start the interactive graphics workstation market (UNIX, C and C++). Dedicated language machines could not keep up with the speed improvements of general CPUs. On the other hand custom neural chips from Google, Apple, Nvidia (and soon MicroSoft) have really helped AI techniques based upon deep convolutional neural networks. Neural chips run orders of magnitude faster than general CPUs by using simpler arithmetic and parallelism.

lacker 6y ago

This review is a bit of a window into the past itself... after all, more time has passed since this review was written than elapsed between GEB’s publication and this review. In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. It underestimated what behaviors could be emergent from running simple algorithms on large datasets. It is one of the most beautiful expressions of the ideas of the “classic” age of 1970’s AI, awesome to read but in the end somewhat incorrect about the future. Perhaps one day the pendulum will swing back, and we will discover that large datasets are in some ways overrated, and clever aesthetic senses of pattern are necessary for progress. On that day it will be quite interesting to reread GEB.

colordrops 6y ago
Why is there a pendulum? Aren't both necessary and two sides of the same coin? I know little of modern AI, but I've seen work where both low level raw power for NNs is combined with a symbolic organization of many NNs.
- ghaff 6y ago
  To some degree. But an enormous part of the (useful but narrow) success of AI is a specific set of ML tech, especially supervised learning. And the amount of computational horsepower and data required gives one at least some pause given what the human brain can do with apparently so much less. Which implies there are probably significant advances required in cognitive science and neurophysiology and perhaps other fields of studies we haven't even considered. We may need other ways to solve problems that have proven more elusive to brute force than it looked like 5 years or so ago (e.g. general purpose full self-driving in busy cities).
- nextos 6y ago

commandlinefan 6y ago

If you pick up a more recent edition of the book, it includes a new preface where Hofstadter actually addresses a lot of this himself - he admits to being "embarrassed" by his claim that computers that could beat people at chess would get bored of chess and talk about poetry, although I think he's being too hard on himself.

nicholast 6y ago

GEB as literature stands up much better than GEB as musings on AI. The book practically invented a new form of expression that hasn't been matched to this day, with recursion between modalities in every chapter.

jplr8922 6y ago

I dont think that AI will get close to the vision in GEB. As many said in this thread, what we have is more power and more data to solve narrow problems. One of the point of EGB is that perceived details may aggregate to something larger and form another pattern unrelated to what the algo what initially trained for. True AI will have to find a way around this. When I was working as an quantitative strategist in a trading firm, I alway made sure that my algos had a ''killswitch'' which required human intervention in case the market did not exibit usual patterns found in the training sets. Skin in the game is the best cure against techno-evengelical optimism. Also, I had the opportunity to practice zen meditation with a monk who had actual koan training. The idea that computer will replicate that process soon is ridiculous. Buddhist and asian philosophy are, at their cores, anti-aristotle. Who can make this into a AI now? https://aeon.co/essays/the-logic-of-buddhist-philosophy-goes-beyond-simple-truth https://aeon.co/essays/the-logic-of-buddhist-philosophy-goes...

tbalsam 6y ago
This feels a lot like the kind of "enlightened anti-'AI'" opinion that's more in vogue in some circles. I think if you're following the literature, there's a significant amount that's pointing to the above generalization capabilities that you're contending against because it may not seem likely from your personal experience. If you were to look at your arguments, I think you're extending the idea of expert systems from the 90's and so on and using that to paint a picture of the entire research field of deep learning process today. What we have has the capabilities to not be bound by those things, just the long bootstrapping process takes time. But we're getting closer year by year, and it's more and more exciting along that front as time goes along. As for experience, I am and have been a practicioner in this field for several years.
- jplr8922 6y ago
  Hm interesting, do you have an recent article to share?
  - tbalsam 6y ago
    I'm not sure in particular anything that would be useful beyond certain trends. Things like AlphaFoldv2 and some of the cooperative-competitive emergent RL strategies are interesting/important. You might find some of the concurrent work on open set/zero shot very interesting. I think it's an asymptotic approach on some degrees. Like you noted with meditation -- this is something very hard for a computer to achieve, for a computer has to have a demonstrable concept/actualized sense of self to be able to let go and connect to some of the more spiritual elements there. Conversely, if arguing from a non-materialistic manner, then you could also argue there's some sort of bridge there that could be crossed for that particular kind of connection. Materially, some kind of artistic manifold. But without some kind of spiritual connection, there may always be something missing there. However, we as humans on the interpreting end of things may make the spiritual connections ourselves, so some kind of manifold discovery may not need that kind of connection to properly function. And it may! Who knows? In the end, all in all -- I think a lot of it looks at the open-ended discovery-and-low-label-self-dermination-and-assignment, if you're looking at (what I'd personally consider to be) the most interesting research all rattling around in there. :))))

mindcrime 6y ago

One thing I initially found dissapointing when reading this was this bit: I am working from memory here. Charniak’s thesis was never published, and I have not seen a copy since 1981 Luckily, it turns out that Charniak's phd thesis is available online these days. https://dspace.mit.edu/bitstream/handle/1721.1/6892/AITR-266.pdf?sequence=2&isAllowed=y https://dspace.mit.edu/bitstream/handle/1721.1/6892/AITR-266...

maxwells-daemon 6y ago

My research (automated theorem proving with RL) sits partway between "good old-fashioned AI" and modern deep learning, and GEB struck me as amazingly prescient, with lots of lessons for modern AI research. There's a growing sense among many ML researchers that there's something fundamentally missing in the "NNs + lots of data + lots of compute" picture. GPT-3 knows that 2+2=4 and that 3+4=7, but it doesn't know that 2+2+3=7. These heart of these kinds of problems indeed seems to be the sense of abstraction / problem reimagining / "stepping outside the game" that Hofstadter spent so much time talking about. Chess (accidentally) turned out to be easy enough for a narrow algorithm to work well. But I'd be surprised if other problems don't require an intelligence general enough to say "I'm bored, let's do something else," and I don't believe current algorithms can get there at any scale.

yaseer 6y ago
In my former research I was interested in automated theorem proving (constructing a variant of the lambda calculus from which we can run genetic algorithms). Also, my gamer tag is maxwells_demon, similar to your HN name. Unfortunately, the 14 year-olds in online games don't appreciate jokes about thermodynamics.

eternalban 6y ago

GEB, and its rebuttal, Emperor's New Mind [1], are both fantastic books. Both are must reads regardless of one's views on AI (or approaches to AI). I never bought Hofstadter's thesis -- am solidly in Penrose camp -- and find it interesting that Penrose went on to develop his thesis even further [2][3] while Hofstadter has not. That said, Hofstadter is clearly a far more engaging and talented writer, and in context of Symbolic AI, his book outshines (imho) Minsky's Society of Mind [4] (which I found rather underwhelming). There is a mild sense of tragedy about Hofstadter. Something about massive early success.. [1]: https://www.goodreads.com/book/show/179744.The_Emperor_s_New_Mind https://www.goodreads.com/book/show/179744.The_Emperor_s_New... [2]: https://en.wikipedia.org/wiki/Roger_Penrose#Physics_and_consciousness https://en.wikipedia.org/wiki/Roger_Penrose#Physics_and_cons... [3]: https://en.wikipedia.org/wiki/Orchestrated_objective_reduction https://en.wikipedia.org/wiki/Orchestrated_objective_reducti... [4]: https://www.goodreads.com/book/show/326790.The_Society_of_Mind https://www.goodreads.com/book/show/326790.The_Society_of_Mi...

peter303 6y ago

I had the fortune of being in a Stanford Chinese language class with Doug in 1976 when was writing GEB. He passed around lineprinter drafts of chapters for feedback. I dont know if anyone anticipated the book would be a bestseller and win the Pulitzer. (Though mosts authors secretly hope for that. Doug hypothesized that knowing a language so different from English as Chinese might shed insight on how the mind works. I think he went onto other AI topics such as analogies and patterns. More recently sounds like he is looking at Chinese again from his blogs.

galaxyLogic 6y ago
I think language is the key to AI and thus Symbolic reasoning is. I wouldn't call someone intelligent who can not explain how they arrive at certain conclusions. They might be correct and useful conclusions but if you or the neural network can not communicate to others how they come to their conclusions their knowledge can not be shared with others. If it can not be shared by means of some kind of language which explains its reasoning based on logical primitives, we don't really call it intelligent, do we. We don't call a person or machine intelligent if they can't explain their reasoning. We don't really trust answers without learning the rationale behind them. And isn't this what is happening with some neural networks, they usually give more or less correct answer but sometimes, can give a totally wrong answer too. Not really trustworthy because the logic behind the answers is simply not there.
savanaly 6y ago
Where does he blog?

jimhefferon 6y ago

I didn't understand most of the essay. > The dialogues interspersed between the chapters are, indeed, often contrived, arch, heavy-handed, and excessively cute, but they have virtues that, pedagogically and presentationally if not aesthetically, far outweigh these drawbacks; they are uniformly clear, vivid, memorable, and thought-provoking. Another sentence that I didn't understand. Sigh. I often think that I am just not smart enough to be in this field. But, in any event, I found GEB inspiring. Thinking about these world-changing ideas by approaching them from different directions turned me on. I wrote a book on the Theory of Computation that was inspired by it -- OK it is not itself inspiring, but it tries to give readers all kinds of ways to explore the material, while perhaps not saying anything that can't be proved. I hope some people here might be interested: https://hefferon.net/computation/index.html https://hefferon.net/computation/index.html

moonchild 6y ago
> The dialogues interspersed between the chapters... There are dialogues, and they are interspersed between chapters of traditional prose > ...are, indeed, often XXX, but they have virtues that, YYY, far outweigh these drawbacks; they are ZZZ Said dialogues have negative attributes XXX. However, they also have positive attributes ZZZ. These positive attributes are characterized as YYY. > XXX contrived made to suit a purpose > arch underhanded > heavy-handed obvious > YYY pedagogically good for teaching ...and so on.

ralphc 6y ago

It's ostensibly a book about AI, but when I first read it, summer after high school (or after freshman college year?), it blew my mind as a book that showed me that computer science was science and mathematics and not just typing in programs and fiddling in BASIC on my TRS-80.

kazinator 6y ago

> The truth is that, since music does not denote, the issues of levels of representation and self-reference really do not arise I absolutely disagree. Firstly, music may not denote in the same way as math notation or a sentence of natural language, but it does refer to something external: it triggers sensations in the listener, and it does so in some ways that are objective. For instance, some notes drive satisfyingly ("resolve toward") some other notes, whereas in other situations there is a sense of ambiguity. Using harmonic trickery, the composer can predictably lead the listener around in a labyrinth; the book goes into that. Secondly, if we accept the premise that music doesn't denote anything external, then we have to confront the remaining fact that music references, and therefore denotes, other music, which is potentially itself. Lack of external reference not only does not preclude self-reference, but emphasizes it as the only interesting option. Music intersects with computer science, obviously; there are people who do computational music. There are hard problems in music. Bach belongs in that book, because we have not produced a convincing Bach robot. Why is Escher acceptable but not Bach? An Escher drawing like The Waterfall denotes something: it denotes a nicely drawn piece of architecture denoting an artificial waterfall driving a waterwheel. But the denotation isn't the point; it's the impossibility of the object itself, and of the perpetual motion of the waterwheel. The art has numerous details which don't contribute to that at all. For instance, the waterfall structure has numerous bricks, all carefully drawn. Music is the same. It has sensual aspects, like the timbre of instruments and the expressiveness of the performance, or the power of many instruments in unison and so on. We can't be distracted and fooled by that.

shakezula 6y ago

I read this book in high school and it seriously changed my views on life in a lot of different ways, as well as opened my mind to a lot of new ideas and ways of thinking I had never considered before. This review brought some good memories of that book back. I should go dig that out of storage and give it another read.

Ice_cream_suit 6y ago

The abstract approach to AI is still around and fruitful although it may not be fad of the day among the Phronix crowd. For example look at a recent paper by Ernest Davis, the author of the review linked to above: https://arxiv.org/pdf/2004.13831.pdf https://arxiv.org/pdf/2004.13831.pdf A Review of Winograd Schema Challenge Datasets and Approaches Vid Kocijan1∗ , Thomas Lukasiewicz1,2 , Ernest Davis3 , Gary Marcus4 and Leora Morgenstern5 1University of Oxford 2Alan Turing Institute, London 3New York University 4Robust AI 5Systems & Technology Research / PARC firstname.lastname@cs.ox.ac.uk, davise@cs.nyu.edu, gary.marcus@nyu.edu, leora.morgenstern@gmail.com Abstract: The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.

lioeters 6y ago

"I'm So Meta, Even This Acronym"