BIG successes of Lisp (was ...)

Discussion in 'Python' started by mike420, Oct 14, 2003.

  1. mike420

    mike420 Guest

    In the context of LATEX, some Pythonista asked what the big
    successes of Lisp were. I think there were at least three *big*

    a. web site uses Lisp for algorithms, etc.
    b. Yahoo store was originally written in Lisp.
    c. Emacs

    The issues with these will probably come up, so I might as well
    mention them myself (which will also make this a more balanced

    a. AFAIK Orbitz frequently has to be shut down for maintenance
    (read "full garbage collection" - I'm just guessing: with
    generational garbage collection, you still have to do full
    garbage collection once in a while, and on a system like that
    it can take a while)

    b. AFAIK, Yahoo Store was eventually rewritten in a non-Lisp.
    Why? I'd tell you, but then I'd have to kill you :)

    c. Emacs has a reputation for being slow and bloated. But then
    it's not written in Common Lisp.

    Are ViaWeb and Orbitz bigger successes than LATEX? Do they
    have more users? It depends. Does viewing a PDF file made
    with LATEX make you a user of LATEX? Does visiting Yahoo
    store make you a user of ViaWeb?

    For the sake of being balanced: there were also some *big*
    failures, such as Lisp Machines. They failed because
    they could not compete with UNIX (SUN, SGI) in a time when
    performance, multi-userism and uptime were of prime importance.
    (Older LispM's just leaked memory until they were shut down,
    newer versions overcame that problem but others remained)

    Another big failure that is often _attributed_ to Lisp is AI,
    of course. But I don't think one should blame a language
    for AI not happening. Marvin Mins ky, for example,
    blames Robotics and Neural Networks for that.
    mike420, Oct 14, 2003
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  2. mike420

    Paul Rubin Guest

    I'm skeptical that's the reason for the shutdowns, if they're using a
    reasonable Lisp implementation.
    The Yahoo Store software was written by some small company that sold
    the business to some other company that didn't want to develop in
    Lisp, I thought. I'd be interested to know more.
    Actually, Hemlock is much more bloated. However, Emacs's reputation
    for bloat came from the 1 mips VAX days, when it was bigger than less
    capable editors such as vi. However, compared with the editors people
    run all the time on PC's nowadays (viz. Microsoft Word), Emacs is tiny
    and fast. In fact if I want to look in a big mail archive for (say)
    mentions of Python, it's faster for me to read the file into Emacs and
    search for "python" than it is for me to pipe the file through "more"
    and use "more"'s search command.
    I missed the earlier messages in this thread but Latex wasn't written
    in Lisp. There were some half-baked attempts to lispify TeX, but
    afaik none went anywhere.
    Well, they were too bloody expensive too.
    Actually, there are many AI success stories, but the AI field doesn't
    get credit for them, because as soon as some method developed by AI
    researchers becomes useful or practical, it stops being AI. Examples
    include neural networks, alpha-beta search, natural language
    processing to the extent that it's practical so far, optical character
    recognition, and so forth.
    Paul Rubin, Oct 14, 2003
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  3. Daniel P. M. Silva, Oct 14, 2003
  4. mike420

    Erann Gat Guest

    Yahoo (obviously).
    That's right. Yahoo ultimately reimplemented Yahoo Store in C++.

    Erann Gat, Oct 14, 2003
  5. In a context like that, my first guess would be "the other company
    didn't have any Lisp programmers". The second would be that the
    programmers they did have didn't like (their idea of) Lisp.

    Assuming there is some truth in that, it would probably have been
    rationalised in other terms of course.
    Absolutely true - and many more.

    I have come to believe that cognitive psychologists (of whatever field
    - there seems to have been a 'cognitive revolution' in most variants
    of psychology) should have some experience of programming - or at
    least of some field where their conception of what is possible in
    terms of information processing would get some hard testing.

    Brains are not computers, of course - and even more important, they
    were evolved rather than designed. But then a lot of cognitive
    psychologists don't seem to take any notice of the underlying
    'hardware' at all. In the field I'm most interested in - autism - the
    main cognitive 'theories' are so sloppy that it is very difficult to
    pick out the 5% of meaningfullness from the 95% of crud.

    Take 'theory of mind', for instance - the theory that autistic people
    have no conception that other people have minds of their own. This is
    nice and simple at 'level 1' - very young autistics seem not to keep
    track of other peoples states of mind when other children of the same
    age do, just as if they don't realise that other people have minds of
    their own. But the vast majority of autistics pass level 1 theory of
    mind tests, at least after the age of about 5.

    The cognitive psychologists answer to this is to apply level 2 and
    higher theory of mind tests. They acknowledge that these tests are
    more complex, but they don't acknowledge that they are testing
    anything different. But take a close look and it rapidly becomes
    apparent that the difference between these tests and the level 1 tests
    is simply the amount of working memory that is demanded.

    Actually, you don't have to study the tests to realise that - just
    read some of the autistic peoples reactions to the tests. They
    *understand* the test, but once the level is ramped up high enough
    they can't keep track of everything they are expected to keep track
    of. It's rather like claiming that if you can't simultaneously
    remember 5000 distinct numbers you must lack a 'theory of number'!

    But when autistic people have described things that suggest they have
    'theory of mind' (e.g. Temple Grandin), the experts response has
    typically been to suggest that either the auther or her editor is a
    liar (e.g. Francesca Happé, 'the autobiographical writings of three
    Asperger syndrome adults: problems of interpretation and implications
    for theory', section in 'Autism and Asperger Syndrome' edited by Uta

    It's not even as if the 'theory of mind' idea has any particular
    predictive power. The symptoms of autism vary dramatically from one
    person to another, and the distinct symptoms vary mostly independently
    of one another. Many symptoms of autism (e.g. sensory problems) have
    nothing to do with awareness of other people.

    The basic problem, of course, is that psychologists often lean more
    towards the subjective than the objective, and towards intuition
    rather than logic. It is perhaps slightly ironic that part of my own
    theory of autism (integrating evolutionary, neurological and cognitive
    issues) has as a key component an IMO credible answer to 'what is
    intuition, how does it work, and why is (particularly social)
    intuition disrupted in autism?'

    But despite all this, it is interesting to note that cognitive
    psychology and AI have very much the same roots (even down to mainly
    the same researchers in the early days) and that if you search hard
    enough for the 'good stuff' in psychology, it doesn't take long before
    you start finding the same terms and ideas that used to be strongly
    associated with AI.

    Creating a machine that thinks like a person was never a realistic
    goal for AI (and probably won't be for a very long time yet), but that
    was always the dreaming and science fiction rather than the fact. Even
    so, it is hard to think of an AI technique that hasn't been adopted by
    real applications.

    I remember the context where I first encountered Bayes theorem. It was
    in AI - expert systems, to be precise - along with my first encounter
    with information theory. The value of Bayes theorem is that it allows
    an assessment of the probability of a hypothesis based only on the
    kinds of information that can be reasonably easily assessed in
    studies, approximated by a human expert, or even learned by the expert
    system in some contexts as it runs. The probability of a hypothesis
    given a single fact can be hard to assess, but a good approximation of
    the probability of that fact given the hypothesis is usually easy to
    assess given a simple set of examples.

    Funny how a current popular application of this approach (spam
    filtering) is not considered to be an expert system, or even to be AI
    at all. But AI was never meant to be in your face. Software acts more
    intelligently these days, but the methods used to achieve that are
    mostly hidden.
    Stephen Horne, Oct 14, 2003
  6. mike420

    Peter Seibel Guest

    Of course to do so, they had to--according to Paul Graham--implement a
    Lisp interpreter in C++! And they had to leave out some features that
    depended on closures. So folks who are running the old Lisp version
    may never "upgrade" to the new version since it would mean a
    functional regression. Graham's messages on the topic to the ll1 list
    are at:


    Peter Seibel, Oct 14, 2003
  7. mike420

    Kenny Tilton Guest

    I hope he made another $40m off them in consulting fees helping them
    with the port. :)
    Kenny Tilton, Oct 14, 2003
  8. You are misinformed. Orbitz runs a `server farm' of hundreds of
    computers each running the ITA faring engine. Should any of these
    machines need to GC, there are hundreds of others waiting to service
    prunesquallor, Oct 14, 2003
  9. Stephen Horne wrote:
    OK, but Reverend Bayes developed it well before "AI" was even conceived,
    around the middle 18th century; considering Bayes' theorem to be part
    of AI makes just about as much sense as considering addition in the
    same light, if "expert systems" had been the first context in which
    you had ever seen numbers being summed. In the '80s, when at IBM
    Research we developed the first large-vocabulary real-time dictation
    taking systems, I remember continuous attacks coming from the Artificial
    Intelligentsia due to the fact that we were using NO "AI" techniques --
    rather, stuff named after Bayes, Markov and Viterbi, all dead white
    mathematicians (it sure didn't help that our languages were PL/I, Rexx,
    Fortran, and the like -- no, particularly, that our system _worked_,
    the most unforgivable of sins:). I recall T-shirts boldly emblazoned
    with "P(A|B) = P(B|A) P(A) / P(B)" worn at computational linguistics
    conferences as a deliberately inflammatory gesture, too:).

    Personally, I first met Rev. Bayes in high school, together with the
    rest of the foundations of elementary probability theory, but then I
    did admittedly go to a particularly good high school; neither of my
    kids got decent probability theory in high school, though both of
    them met it in their first college year (in totally different fields,
    neither of them connected with "AI" -- financial economics for my
    son, telecom engineering for my daughter).

    I don't see how using Bayes' Theorem, or any other fundamental tool
    of probability and statistics, connects a program to "AI", any more
    than using fundamental techniques of arithmetic or geometry would.

    Alex Martelli, Oct 14, 2003
  10. mike420> c. Emacs has a reputation for being slow and bloated.

    People making that claim most often does not understand what Emacs
    really is or how to use it effectively. Try to check out what other
    popular software use up on such peoples machines, stuff like KDE or
    gnome or mozilla or any Java based application.

    This just isn't a very relevant issue on modern equipment.

    mike420> For the sake of being balanced: there were also some *big*
    mike420> failures, such as Lisp Machines. They failed because
    mike420> they could not compete with UNIX (SUN, SGI) in a time when
    mike420> performance, multi-userism and uptime were of prime importance.

    It is still a question of heated debate what actually killed the lisp
    machine industry.

    I have so far not seen anybody dipsuting that they were a marvel of
    technical excellence, sporting stuff like colour displays, graphical
    user interfaces and laser printers way ahead of anybody else.

    In fact the combined bundle of a Symbolics machine is so good that
    there still is a viable market for those 20-30 years old machines
    (been there, done that, still needs to get it to run :) I challenge
    you to get a good price for a Sun 2 with UNIX SYSIII or whatever they
    were equipped with at the time.

    As far as I know Symbolics was trying to address the price issues but
    the new generation of the CPU was delayed which greatly contributed to
    the original demise and subsequent success of what we now know as
    stock hardware. Do not forget that when the Sun was introduced it was
    by no means obvious who was going to win the war of the graphical
    desktop server.

    Christian Lynbech | christian #\@ defun #\. dk
    Hit the philistines three times over the head with the Elisp reference manual.
    - (Michael A. Petonic)
    Christian Lynbech, Oct 14, 2003
  11. OK, but you can't say that a system isn't artificial intelligence just
    because it uses Bayes theorem or any other method either - it isn't
    about who first described the formula or algorithm or whatever.
    Well, I don't see how a neural net can be considered intelligent
    whether trained using back propogation, forward propogation or a
    genetic algorithm. Or a search algorithm, whether breadth first, depth
    first, prioritised, using heuristics, or applying backtracking I don't
    care. Or any of the usual parsing algorithms that get applied in
    natural language and linguistics (Early etc). I know how all of these
    work so therefore they cannot be considered intelligent ;-)

    Certainly the trivial rule-based expert systems consisting of a huge
    list of if statements are, IMO, about as unintelligent as you can get.

    It's a matter of the problem it is trying to solve rather than simply
    saying 'algorithm x is intelligent, algorithm y is not'. An
    intelligent, knowledge-based judgement of whether an e-mail is or is
    not spam is to me the work of an expert system. The problem being that
    once people know how it is done, they stop thinking of it as

    Perhaps AI should be defined as 'any means of solving a problem which
    the observer does not understand' ;-)

    Actually, I remember an article once with the tongue-in-cheek claim
    that 'artificial stupidity' and IIRC 'artificial bloody mindedness'
    would be the natural successors to AI. And that paperclip thing in
    Word did not exist until about 10 years later!
    Stephen Horne, Oct 14, 2003
  12. Sorry - missed this bit on the first read.

    I never limited my education to what the school was willing to tell
    me, partly because having Asperger syndrome myself meant that the
    library was the best refuge from bullies during break times.

    I figure I first encountered Bayes in the context of expert systems
    when I was about 14 or 15. I imagine that fits roughly into the high
    school junior category, but I'm not American so I don't know for sure.
    Stephen Horne, Oct 14, 2003
  13. See: Stories

    They don't use garbage collection, they do explicit memory allocation
    from pools. More details were given in the ILC 2002 talk "ITA Software
    and Orbitz: Lisp in the Online Travel World" by Rodney Daughtrey:

    The talk's slides are included in the ILC 2002 proceedings available
    from Franz, Inc. As for shutdown for maintenance, the slides seem to
    suggest that they use online patching.

    Paolo Amoroso, Oct 14, 2003
  14. mike420

    Edi Weitz Guest

    Where do you "know" that from? Have you any quotes or numbers to back
    up your claims or are you just trying to spread FUD?
    Others have debunked this already.
    Please leave the guessing to people who are better at it.

    Edi Weitz, Oct 14, 2003
  15. Heh, me neither, of course.
    Not for me, as it was non-smoking and I started smoking very young;-).

    But my house was always cluttered with books, anyway. However,
    interestingly enough, I had not met Bayes' theorem _by that name_,
    only in the somewhat confusing presentation known as "restricted
    choice" in bridge theory -- problem is, Borel et Cheron's "Theorie
    Mathematique du Bridge" was out of print for years, until (I think)
    Mona Lisa Press finally printed it again (in English translation --
    the French original came out again a while later as a part of the
    reprint of all of Borel's works, but always was much costlier), so
    my high school got there first (when I was 16). My kids' exposure
    to probability theory was much earlier of course (since I taught
    them bridge when they were toddlers, and Bayes' Theorem pretty
    obviously goes with it).

    I'm not American either -- I say "high school" to mean what in Italy
    is known as a "Liceo" (roughly the same age range, 14-18).

    Alex Martelli, Oct 14, 2003
  16. Stephen Horne wrote:
    Clarke's law...?-)

    The AAAI defines AI as:

    "the scientific understanding of the mechanisms underlying thought and
    intelligent behavior and their embodiment in machines."

    But just about any mathematical theory is an abstraction of "mechanisms
    underlying thought": unless we want to yell "AI" about any program doing
    computation (or, for that matter, memorizing information and fetching it
    back again, also simplified versions of "mechanisms underlying thought"),
    this had better be a TAD more nuanced. I think a key quote on the AAAI
    pages is from Grosz and Davis: "computer systems must have more than
    processing power -- they must have intelligence". This should rule out
    from their definition of "intelligence" any "brute-force" mechanism
    that IS just processing power. Chess playing machines such as Deep
    Blue, bridge playing programs such as GIB and Jack (between the two
    of them, winners of the world computer bridge championship's last 5 or
    6 editions, regularly grinding into the dust programs described by their
    creators as "based on artificial intelligence techniques" such as
    expert systems), dictation-taking programs such as those made by IBM
    and Dragon Systems in the '80s (I don't know if the technology has
    changed drastically since I left the field then, though I doubt it),
    are based on brute-force techniques, and their excellent performance
    comes strictly from processing power. For example, IBM's speech
    recognition technology descended directly from the field of signal
    processing -- hidden Markov models, Viterbi algoriths, Bayes all over
    the battlefield, and so on. No "AI heritage" anywhere in sight...

    Alex Martelli, Oct 14, 2003
  17. mike420

    Ivan Toshkov Guest

    Besides, when I read the description of orbiz, I was with the
    impression, that they prealocated the memory for just that reason: to
    remove the need for garbage collection.
    Ivan Toshkov, Oct 14, 2003
  18. mike420

    Joe Marshall Guest

    It's clear to me that LMI killed itself by an attempt to rush the
    LMI-Lambda to market before it was reasonably debugged. A lot of LMI
    machines were DOA. It's amazing how fast you can lose customers that

    As far as Symbolics goes... I *think* they just saturated the market.
    Joe Marshall, Oct 14, 2003
  19. mike420

    robert Guest

    i boldly disagree. back when i first heard about AI (the '70s, i'd say),
    the term had a very specific meaning: probablistic decision making with
    feedback. a medical diagnosis system would be the archetypal example.
    my recollection of why few ever got made was: feedback collection was not
    always easy (did the patient die because the diagnosis was wrong? and
    what is the correct diagnosis? and did we get all the symtoms right?, etc),
    and humans were unwilling to accept the notion of machine determined
    decision making. the machine, like humans before it, would learn from its
    mistakes. this was socially unacceptable.

    everything else is just rule processing. whether done with declarative
    typeless languages like Lisp or Prolog, or the more familiar imperative
    typed languages like Java/C++ is a matter of preference. i'm currently
    working with a Prolog derivative, and don't find it a better way. fact
    is, i find typeless languages (declarative or imperative) a bad thing for
    large system building.

    robert, Oct 14, 2003
  20. mike420

    Isaac To Guest

    Alex> Chess playing machines such as Deep Blue, bridge playing programs
    Alex> such as GIB and Jack ..., dictation-taking programs such as those
    Alex> made by IBM and Dragon Systems in the '80s (I don't know if the
    Alex> technology has changed drastically since I left the field then,
    Alex> though I doubt it), are based on brute-force techniques, and their
    Alex> excellent performance comes strictly from processing power.

    Nearly no program would rely only on non-brute-force techniques. On the
    other hand, all the machines that you have named uses some non-brute-force
    techniques to improve performance. How you can say that they are using
    "only" brute-force techniques is something I don't quite understand. But
    even then, I can't see why this has anything to do with whether the machines
    are intelligent or not. We cannot judge whether a machine is intelligent or
    not by just looking at the method used to solve it. A computer is best at
    number crunching, and it is simply natural for any program to put a lot more
    weights than most human beings on number crunching. You can't say a machine
    is unintelligent just because much of it power comes from there. Of course,
    you might say that the problem does not require a lot of intelligence.

    Whether a system is intelligent must be determined by the result. When you
    feed a chess configuration to the big blue computer, which any average
    player of chess would make a move that will guarantee checkmate, but the
    Deep Blue computer gives you a move that will lead to stalemate, you know
    that it is not very intelligent (it did happen).

    Isaac To, Oct 14, 2003
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