[HN Gopher] Why Is the Human Brain So Efficient? (2018)
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Why Is the Human Brain So Efficient? (2018)
Author : rcshubhadeep
Score : 175 points
Date : 2020-06-05 09:21 UTC (13 hours ago)
(HTM) web link (nautil.us)
(TXT) w3m dump (nautil.us)
| tehsauce wrote:
| Why is the human brain so inefficient? It takes years just for it
| to compute the sha-256 of this media file.
| [deleted]
| dtnewman wrote:
| I imagine a group of dogs sitting around and asking "How are we
| _so_ good at thinking about fun ways to play with squeeky toys?
| ".
|
| The truth is, that our ability to reason about ourselves is
| limited by our ability to reason. Perhaps there are aliens out
| there who would laugh our cognitive abilities--their's being so
| much better than ours.
| Shorel wrote:
| >Perhaps there are aliens out there who would laugh our
| cognitive abilities--their's being so much better than ours.
|
| Yeah, but their brains would either be much bigger and/or use a
| lot more energy, or they will have a fundamentally different
| architecture (i.e. they are manufactured instead of evolved).
|
| For the given amount of perceptions/calculations that our brain
| makes, and the hard constraint of being a biological process,
| we have pretty much fantastically efficient brains.
|
| My computer, extremely slow when compared to the likes of
| DeepMind, has a power source of 750 watts, while human brains
| consume in average 12 watts.
| jjoonathan wrote:
| Less complicated systems successfully reason about more
| complicated systems all the time. Ditto for self-reasoning.
| See: bootloaders, update systems, and package managers.
|
| In order to prove that some kind of meta-cognition is
| inherently beyond our grasp, you don't just have to prove that
| the system we are attempting to reason about is more complex
| than ourselves, you also have to prove that the problem isn't
| meaningfully reducible. Otherwise we can and will eventually
| figure out the mental tools we need to tackle the problem, and
| tackle it.
|
| The same applies to brute physical strength. Humans have no
| problem building machines vastly stronger, tougher, larger,
| more precise etc than ourselves even though narrow-minded
| reasoning might lead you to believe that this was impossible
| ("a tool can only cut something less hard/strong than itself,"
| "a ruler can only measure less precisely than itself" etc).
| ColanR wrote:
| I think you're describing an analogue of turing-completeness.
| It's not (to me) a question of whether we _can_ reason about
| something: it 's a question of how long it takes, and how
| much knowledge is involved with the process.
|
| What you're describing sounds like asking a PDP-11 to run
| GPT-3. Technically possible, in the broadest sense of the
| word. But a computer that can run GPT-3 successfully will
| look at that PDP-11 in much the same way that we look at a
| dog playing with a chew toy.
| jjoonathan wrote:
| On the contrary, I think your example proves my point quite
| well. I understand very little about PDP-11s and only
| slightly more about GPT-3's inner workings, yet I have no
| trouble reasoning about whether or not a PDP-11 is suitable
| for running GPT-3 or something even more difficult to
| formally reason about, say Microsoft Windows. I have a
| mental model of computer performance and compute
| requirements that simplifies the question from a difficulty
| of "Oh, it's Turing complete, halting problem, let's throw
| our arms in the air like this is an infomercial!" through
| "You need to understand literally everything about PDP-11s
| and Windows" all the way to "50 years of exponential growth
| is a hella large factor to try squeezing down anything by."
| It's a trivial question hiding in the skin of an
| intractable question, and it perfectly exemplifies why it's
| silly to believe that human cognition will forever remain
| intractable.
|
| In order for a problem to forever remain in "let's throw
| our arms in the air like it's an infomercial" territory, it
| must not merely be difficult in its most pedantically
| defined complete form, it also must stymie the search for
| useful relaxations and workarounds. Nobody fears running a
| program on account of being unable to prove that it will
| halt: they just kill the program if it locks up, or
| (equivalently) set a timeout. Personally, I'd just avoid
| throwing my arms in the air like an infomercial altogether.
|
| EDIT: substituted GPT3 -> Windows because arguments about
| GPT-3 and/or a set of incarnations being Turing Complete
| would be irrelevant to the main point.
| aeternum wrote:
| Most dogs seem to acknowledge that humans are better when it
| comes to playing with toys, otherwise why would they bring them
| over for humans to throw?
| Digit-Al wrote:
| Ah, now. See. You're mistake is thinking you can reason
| better than a dog. The reason a dog brings the toy to the
| human is because they know that the human is better at
| throwing and the dog is better at fetching. Teamwork, y'see.
|
| Now go forth and learn, and one day you too may be as smart
| as a dog ;-)
| hanniabu wrote:
| Maybe also dogs can do everything humans can but they
| decide not to because they see the stressful lives we live
| and want no part of that. I welcome our dog overlords.
| felipemnoa wrote:
| >>The reason a dog brings the toy to the human is because
| they know that the human is better at throwing and the dog
| is better at fetching. Teamwork, y'see.
|
| Honestly I always thought that the dog was just being
| diligent and making sure that its humans did his daily
| exercise routine by throwing a toy.
| hinkley wrote:
| They're Made Out of Meat, as a short film:
|
| https://www.youtube.com/watch?v=7tScAyNaRdQ
| SeanFerree wrote:
| Cool article!!
| Laakeri wrote:
| Leslie Valiant has done some interesting work on quantifying the
| efficiency of the brain from the viewpoint of computer science,
| see e.g. https://www.youtube.com/watch?v=X9hRRh76QEA and the book
| Circuits of the Mind.
| stared wrote:
| I wouldn't say that human brain is that efficient (per volume).
| Compare and contrast with the brain of rats or Corvidae:
| https://www.youtube.com/watch?v=ZerUbHmuY04.
| jcims wrote:
| It's not even a good example. Humans are about the least
| physically agile vertebrates on the planet.
|
| Think of a fruit fly. It can walk, fly, forage for food, mate,
| etc. The entire critter has a mass of .2mg and their brains
| have ~135k neurons. Making the horrible assumption of linear
| power scaling, that's one microwatt.
| rantwasp wrote:
| but can it do math? can it paint? drink wine and muse on its
| own brain efficiency?
| stared wrote:
| Fruit flies can drink wine.
|
| Doing maths - well, it is a common trope that we
| extrapolate skills of a fraction of humans on the entire
| population. For an _average_ human 1 /3 + 1/2 can be
| problematic.
|
| Abstract counting up 5 or so - well, many birds can do
| that, including pigeons.
| rantwasp wrote:
| the wine part was a joke. especially because fruit flies
| definitely appear to be attracted to fruit, wine, etc
|
| i believe you are underestimating how capable humans
| really are. all of us can learn to do math and i'm
| talking serious math not basic math.
| jcims wrote:
| My point from above is that 'humans playing tennis' isn't
| a great benchmark for the efficiency of our brains.
| plutonorm wrote:
| This article contains inaccuracies and says almost nothing novel
| for your average hacker new reader.
| MaxBarraclough wrote:
| > Please don't post shallow dismissals, especially of other
| people's work. A good critical comment teaches us something.
|
| https://news.ycombinator.com/newsguidelines.html
|
| What are the inaccuracies?
| plutonorm wrote:
| The same accusation could be levied at the original artical.
| MaxBarraclough wrote:
| Please make a specific and substantive point. Worthwhile
| discussions do not follow from vague and shallow
| dismissals.
| jonnypotty wrote:
| Im with you on this. The one example of brain processing speed
| in the real world used with any numbers is just inaccurate (The
| speed of tennis balls and how able players are to react to
| them)
|
| There is no analysis of the energy used by the brain to achieve
| anything or how much energy a computer uses for a similar task.
| So where is the discussion of efficiency?
| est31 wrote:
| One kind of efficiency which hasn't been talked about is the
| energy loss of things like state switching and keeping the
| current state enabled. I think that brains build on much more
| efficient primitives than the silicon transistors computer chips
| use and thus can perform far more computations for far less
| energy than a desktop CPU.
|
| Another difference between CPUs and brains is that brains are
| much less general purpose. CPUs do run-time interpreting of
| instructions while brains process data in a more straightforward
| way like GPUs do. Many problems can be implemented into GPUs and
| they will run much faster. I'd argue that brains excel at such
| tasks while being harder at tasks that require lots of state to
| be kept around as well as conditional jumps like computing a hash
| function or compiling a program. CPUs excel at those tasks.
| bluedino wrote:
| It's like comparing a human to a horse. A horse can run very
| fast or pull a wagon. But a horse can't work on plumbing, or
| knit.
| nicoburns wrote:
| You might be right about brains being better at certain kinds
| of tasks, but I don't think it's right to think of them as
| having only one processing mode.
|
| Someone else mentioned "Thinking, Fast and Slow", and I find it
| fascinating how closely the two thinking modes in that book
| seem to map to CPU (mostly serial) and GPU (parallel)
| processing. It also claims that people have natural preferences
| for each mode of thinking, which is super interesting as it
| suggests that the tasks that brains are best at performing will
| vary from person to person (I guess this is obvious, but
| perhaps gets lost when we start comparing to computers).
|
| I'd bet on brains getting a lot of their efficiency from tight
| integration of CPU-like, GPU-like, and ASIC-like, and full on
| analog components. We'd probably have to apply deep-learning
| like approaches to the hardware design itself to get close.
| [deleted]
| tegeek wrote:
| Comparing Human Brain with a CPU is misconception. In the past
| when we didn't have digital computers we used to compare Brain
| with other machines. And now with a CPU. A Brain from a
| primitive neuron to higher level is not comparable to any
| machine at all including the CPU.
| ResidentSleeper wrote:
| Whether or not it's comparable depends on the level of
| distinction you're trying to make. Obviously, CPUs don't
| think or experience the world (but on the other hand that
| kind of "feature" seems increasingly likely to be
| implementable in software, even if our current CPU
| architectures are rather unsuitable for that goal). However,
| if we're gonna talk about energy efficiency and computation
| performance, now that it has become evident that the brain is
| merely a kind of a computer, we can _definitely_ look for
| parallels.
| kalcode wrote:
| > now that it has become evident that the brain is merely a
| kind of a computer
|
| I am ignorant in this area. But I keep reading how brains
| are nothing like computers the more we learn. Your
| statement seems to suggest otherwise and id love to read
| about it. Can you drop something where I can start
| exploring about how the brain has become more evident that
| it's merely a kind of computer? Thanks!
| whatshisface wrote:
| The brain is thought to be merely a computer in the
| original sense of a long strip of paper along with a
| scribe and a rulebook. The logic is, a Turing machine can
| simulate quantum electrodynamics to an arbitrary degree
| of accuracy. Then, two beliefs about physics and the
| structure of the brain are included:
|
| 1. There is nothing going on in the brain that would
| require simulation to infinite accuracy. Not even a
| chaotic system would have this property, because they
| take a finite time to "blow up" an initial uncertainty,
| and the smaller the initial uncertainty the longer they
| take to blow up. For this proposition to be violated
| there would have to be an undiscovered fininite-time
| nondeterministic blowup, which is unlikely, but I've
| heard rumblings that we haven't proven that it can't
| happen in Navier-Stokes. So maybe it can happen in the
| brain.
|
| 2. There is nothing going on in the brain that depends on
| nuclear physics or anything more "powerful" than quantum
| electrodynamics.
|
| I have not seen any evidence that 1 or 2 aren't true for
| the brain, so that puts something behind saying it's
| "merely a computer."
| checkyoursudo wrote:
| If you are looking for a book for an introduction, I
| would suggest Mindware by Andy Clark is pretty
| reasonable. Pub 2014; ISBN: 9780199828159
| est31 wrote:
| That's what the article does though. And there are
| experiments trying to simulate parts of brains but we realize
| that it's extremely hard to do that and we are very far away
| from simulating even a mouse brain.
| The_rationalist wrote:
| _Comparing Human Brain with a CPU is misconception._ no it is
| not. Yeah architecturally they are very different and CPU are
| arguably more programmable / general and less efficient.
|
| What does matter is whether CPUs are theoretically able to
| achieve all the things that a brain can do (and even more)
| And indeed CPUs as turing complete, programmable machine are
| a strict superset of what brains can do. The gap between what
| task and at which accuracy a brain achieve vs a CPU is
| decreasing each year as you can contemplate on the
| paperswithcode.com leaderboards. The difficulty is in
| software, hardware through clusterisation has arguably order
| of magnitude more compute than a brain has.
|
| There are four big missing pieces to match human brain
| performance:
|
| 1) Matching its pattern recognition abilities I believe that
| current statistical learning techniques of SOTA neural
| networks actually outperform humans on learning continuous
| data. But humans outperforms by far current software at
| zero/few shot learning on sparse/discrete data (where
| gradient descent is not applicable) I believe humans have
| this performance edge because of 2), 3) and 4):
|
| 2) humans can encode and decode meaning with great accuracy
| in a high level, descriptive complete declarative language
| called natural languages. They are in many ways far superior
| to current GQL/datalog/SQL DB languages at encoding and
| retrieving meaning (that is an isomorphic description of a
| denoted thing). The field of semantic parsing (+ question
| answering from the parsed knowledge) is the key to general
| language understanding and crucially lack funding. Once
| machines will be able to understand language and retrieve all
| the knowledge of say Wikipedia, they will be able to
| transcend human performance on many intelligence/erudition
| tasks.
|
| 3) humans seems to be able to do meaningful runtime code
| generation.
|
| That is you can develop on demand new solutions to new
| problems: such as https://www.kaggle.com/c/abstraction-and-
| reasoning-challenge The field of specification and
| implementation generation is too underfunded.
|
| 4) is the observation that 3) is probably a necessary key for
| unlocking 2) and that both 2) and 3) are needed to achieve
| this communication/feedback loop between high level semantic
| reasoning and statistical operations.
|
| As we can see, humanity overfocus funding on 1) despite being
| the most solved of all others necessary foundation's to
| achieve AGI and hence, as a side effect, empirically prove
| that CPUs superset brains
| cmehdy wrote:
| > CPUs as turing complete, programmable machine are a
| strict superset of what brains can do
|
| In what way can this be proven?
|
| It's very tempting in an era of tech-centered growth to
| think of computers as the solution to everything, but we
| are barely even beginning to understand the brain. We know
| computers fairly well and can talk about them, but how can
| we make such a claim when we don't know the other thing
| we're talking about?
|
| In fact, the brain created the computer, didn't it?
| Therefore, from that standpoint it is arguable that the
| brain is a superset of the computer. It's not something I
| really believe in (because my opinion is that you can't
| really equate things that are of entirely different units,
| one of which being unknown), but just a "devil's advocate".
| marcosdumay wrote:
| > In what way can this be proven?
|
| Proven? Nothing in science is ever proven.
|
| But on half a millenium we have failed to find anything
| that can't be simulated by math, and Turing completeness
| means a computer can simulate anything that can be
| simulated by math. We also can simulate all the smallest
| components of a brain.
|
| At this point the claim that math can not simulate it is
| highly extraordinary.
| happythomist wrote:
| We have not been able to simulate any aspect of
| subjective, conscious experience using a mathematical
| model, and personally I think we have no good reason to
| believe we ever will. The qualitative, by definition,
| cannot be quantified.
| simiones wrote:
| > Turing completeness means a computer can simulate
| anything that can be simulated by math
|
| Technically, it is not proven that Turing machines can
| compute all computable functions, so there is some purely
| theoretical possibility that the brain could be able to
| compute functions that a Turing machine can't.
|
| Personally I find that extremely unlikely, and agree that
| it would be extremely surprising. But it wouldn't
| invalidate anything we have proven so far.
| marvy wrote:
| It would imply that our brains are using currently-
| unknown physics, since all current theories are
| computable.
| jacobr1 wrote:
| The argument isn't "something like, or a little better,
| than current CPUs can perform everything a brain can,"
| but something more like "a turing machine can perform
| everything a brain can or more." This is more an
| ontological exercise, not an empirical one. If you reduce
| everything to a "black box" model with inputs and
| outputs, then sure, the mathematical abstractions of
| theoretical brains and theoretical CPUs have a
| congruence. Most objections to this seem to resolve
| around qualia being something not modelable in machines,
| but I'm skeptical of that claim.
|
| Can an "arbitrarily advanced computer do everything a
| brain can do?" Empirically, right now, current machines
| can't but we are talking about "future machines, via
| line-of-sight extrapolation". Not fundamental leaps in
| tech, but incremental ones. It seems plausible, but it
| seems we expand the depths of the complexity of the
| requirements nearly as fast as we advance current
| capabilities. I don't know, but I'd put my money on the
| technology catch up.
| cmehdy wrote:
| Being skeptical of the claim that a certain qualia is not
| modelable in machine is just as valid as being skeptical
| of the exact opposite. This is exactly why I asked if
| there was anything beyond what the original poster said.
| Without it, a post based on the exact opposite assumption
| could have been written and considered just as valid.
| jacobr1 wrote:
| Fair criticism, I didn't tackle that head-on. The
| following doesn't actually make a cogent argument either,
| but I'll elaborate that my intuition is that qualia
| (conceived as something nearly tangible) are more like
| "the soul" or "spirits" and that, as such, thinking they
| exist in the brain or a turing-machine is nonsense. To
| the extent they are more like some combination of memory
| and emotional-stimuli, then they just represent a
| particularly interesting set of internal states, but are
| still something that can be mathematically modeled.
| mannykannot wrote:
| I am not convinced of the usefulness of this comparison.
|
| The first of your big missing pieces starts from the best
| that we have been able to achieve with computers so far,
| and while its completion might be a big step in computing,
| it would not necessarily be a big step in understanding the
| human brain - after all, quite primitive animals have
| impressive abilities in this regard. Using the best
| computing has done as the yardstick for quantifying the
| human brain's ability is the wrong way round.
|
| The remaining missing pieces are vague, with no clear
| indication that they fit into the brain-as-CPU model. For
| example, while it is true that "[human languages] are in
| many ways far superior to current GQL/datalog/SQL DB
| languages at encoding and retrieving meaning (that is an
| isomorphic description of a denoted thing)", this vastly
| understates the capabilities of language. Once again, you
| are using current technology as the yardstick, with no
| basis for assuming that it is of the right scale.
|
| Overall, you seem to be assuming that the rest of the
| puzzle is almost within reach. That is certainly a logical
| possibility, but not one with a great deal of objective
| evidence in support. FWIW, my opinion on the matter is that
| we probably don't even know, in any well-defined way, all
| the questions to be answered.
|
| Even if we grant the premise that a suitably-programmed
| computer (not just a CPU) could have capabilities that are
| a superset of those of a human brain, that would not
| necessarily justify saying one is very like the other -
| that would be like saying a dynamo is a solar cell because
| they both produce electric current.
| Someone wrote:
| _"programmable machine are a strict superset of what brains
| can do"_
|
| As others already replied, that's a statement that isn't
| universally accepted to be true.
|
| As an example, there's consciousness. People disagree about
| whether it exists, whether it's (fully) 'in' the brain, and
| on whether computers could in theory be conscious.
|
| There are people who answer those questions with yes, yes,
| and no, and, since we don't even have a good idea about
| what consciousness is, one cannot reliably argue that they
| are wrong (also not that they are right, of course)
| criddell wrote:
| Is there anything analogous to software in biology?
| sooheon wrote:
| Biology is the ultimate legacy software running on one of
| the oldest platforms ever developed, the organic
| compounds. It is literally a giant genetic algorithm to
| write instructions (DNA) for manufacturing molecular
| machines (proteins) that interact with each other in an
| extremely complex graph of relations (protein pathways,
| i.e. control flow).
| dependenttypes wrote:
| I am sure that I saw this exact message on HN before. Did
| you copy it from someone else or did you repost your own
| post?
| SomeoneFromCA wrote:
| This a very simplistic view, based on assumption that the
| world is discrete. The whole idea of software relies on
| the concept of digital computer, a discrete machine. The
| world might indeed be analogous and real numbers might
| actually exist.
| sooheon wrote:
| I think the assumption that software may only be digital
| is the limited one.
| SomeoneFromCA wrote:
| Otherwise it becomes a meaningless, all-encompassing
| term.
| sharpneli wrote:
| If world did run on real numbers that we could harness
| for computation I would be more than happy, because using
| those we would be able to perform hypercomputation. See
| https://en.m.wikipedia.org/wiki/Real_computation
|
| However this is forbidden by Bekensteins bound, so unless
| modern physics is horribly broken it's ruled out at least
| in any sense visible to us even in principle.
| SomeoneFromCA wrote:
| Not a quantum physicists, but IMO Bekenstein bound is not
| applicable here, because quantum laws are non-
| deterministic, therefore you can describe the structure
| of a system, but you cannot describe how it will evolve.
| Quantum randomness might be in the very essence of how
| the brain and mind works.
| catalogia wrote:
| Quantum randomness being necessary hardly seems like it
| would have profound practical implications since
| augmenting a digital computer with a geiger counter would
| be trivial.
| criddell wrote:
| That feels more to me like hardware and software in the
| sense of a Jacquard loom. I suppose it fits though.
|
| I was thinking more about what's going on in the brain.
| We have all the regions mapped to specific functions with
| higher and lower level parts. The low level parts seem to
| be like hard-wired stimulus-response mechanisms. Are the
| higher level systems the same at a meta level or is there
| a type of program running on the hardware of the brain?
| otabdeveloper4 wrote:
| The human brain isn't Turing-complete.
|
| Turing completeness implies infinite recursion, which the
| brain obviously can't do.
| rabryan wrote:
| Why is that obvious? My brain's been infinitely recursing
| for years as far as I know
| lagadu wrote:
| Technically Turing completeness requires infinite memory
| for that (or an infinite tape if we're talking about the
| original turing machine concept), which no Turing-
| complete machine has. In other words, the brain is as
| Turing-complete as any machine that we also consider to
| be so. We'll always be bounded by limited memory and
| limited time.
| SomeoneFromCA wrote:
| We do not know if human brain is indeed Turing-complete,
| or even if it is a Turing machine at all. Human Mind
| certainly is, but if brain is or not we do not know.
| jameshart wrote:
| This is a silly objection, trivially because obviously no
| finite physical system - brain or computer or whatever -
| can be constructed with the storage equivalent of an
| infinitely long tape. But if you allow for the fact that
| humans can do things like _write things down_ and _share
| information with other humans_ and _build computers to
| store information_ , our information processing capacity
| is not limited to the set of states we can hold inside
| the atoms inside our head.
|
| But also, the claim lacks evidence: We've never seen a
| human being yet whose program didn't eventually halt.
|
| That doesn't mean the hardware isn't capable of running a
| program that never halts, just that we haven't found such
| a program yet.
|
| Indeed if you consider human mindware as a whole, given
| that when humans reproduce they create new copies of the
| mind running in new bits of hardware... maybe Human minds
| are infinitely recursive after all?
| mannykannot wrote:
| While I agree that comparing a human brain or mind to a
| Turing machine is not helpful, the objection you make
| here is less significant than it first appears.
|
| There is a subtle difference between unbounded recursion,
| which a Turing machine is taken to be capable of, and the
| actual ability to achieve infinite recursion. In no
| application of a Turing machine, either as an actual
| physical device or as a hypothetical one in a logical
| argument, is it ever required to perform infinite
| recursion, which would just be one way of not halting.
|
| For all practical _and_ theoretical purposes, what
| matters is that the machine being considered does not
| exhaust its ability to recurse while performing the
| computations being considered. Consequently, the standard
| practice, of saying that computers and certain other
| devices are Turing-equivalent, with the usually-implicit
| caveat of being so up to the limit of their recursive
| ability, is both reasonable and useful.
| the_af wrote:
| Before CPUs existed, we would compare brains to steam
| engines. There was a very interesting article posted here
| on HN a while ago, explaining why humans always pattern
| match their understanding of the "mind" (or "soul") to
| whatever technology is fashionable in their time: steam
| engines, computers, etc. It also explained the pitfalls of
| doing so.
|
| I think there is at this time no indication human brains
| are in any way similar to CPUs. It might be interesting to
| consider the question, of course.
| Stupulous wrote:
| But steam engines and hydraulics and gear mechanisms are
| all Turing complete. There is nothing wrong with those
| models. You could build a brain out of any of them,
| unless the brain computes something that is not
| computable.
|
| If the brain does something that is not computable,
| that's a direct challenge to some of our most established
| science. It is possible, but I think very unlikely.
| TomMarius wrote:
| I thought it was about similarity of simulated neurons,
| not the CPU itself.
| sudosysgen wrote:
| To be fair, CPUs are Turing machines. That makes them
| much more comparable to anything that mainly does
| information processing than to anything else.
| the_af wrote:
| I think the danger is that it's always "obvious" that the
| current fashionable tech works in analogous ways to the
| mind/brain. We can spend all day finding ways in which
| they are similar; for example how the brain does
| information processing and the CPU does too.
|
| The point is, I think, people from the steam engine era
| had similar reasons why the mind/soul was exactly like a
| steam engine. I won't try to reproduce them here, but I'm
| sure there were convincing arguments _at the time_. Who
| has the awareness to claim, before the current
| fashionable technology becomes unfashionable, that maybe
| no, the brain is not a close match for an information
| processing machine? ;)
| SomeoneFromCA wrote:
| > What does matter is whether CPUs are theoretically able
| to achieve all the things that a brain can do (and even
| more) And indeed CPUs as turing complete, programmable
| machine are a strict superset of what brains can do.
|
| It is not proven in any way. Turing's postulate is just a
| postulate, it is not even a theorem, just a conjecture. And
| AFAIK it cannot be proven, actually.
| coreyp_1 wrote:
| "And indeed CPUs as turing complete, programmable machine
| are a strict superset of what brains can do."
|
| This is a fundamental assertion that I do not believe you
| can make.
|
| The brain cannot simulate a turing machine. It does not
| have infinite memory, which is a requirement for a turing
| machine. It can, however, stimulate a linearly bounded
| automata.
|
| It is also not implicitly obvious that a turing machine can
| simulate a brain. The primary difficulty that I do not yet
| see a way around is the fact that a turing machine, which
| has as its control unit a finite State machine, is bound by
| the finiteness of those states (finiteness of
| representation, not of number). The brain has no such
| constraint. It is analog, and therefore infinite in State
| representation.
|
| In my opinion, this is more akin to the P versus NP
| problem, and that we know what needs to be equivalent in
| order to say that P equals NP, but no one has proved it or
| disproved it yet. That's how I feel about the statement
| about Turing machines and the brain. I do not believe we
| can be dogmatic on that aspect yet either way. We may have
| opinions, just as we may have opinions about P vs NP, but
| we must also be careful about stating what is provable and
| what is opinion, and that is all I'm trying to do.
|
| Of course, I am willing and very interested to gain more
| insight in this area, so discussion is welcome!
| deegles wrote:
| The big question is whether a CPU can emulate a brain
| with the same or better efficiency.
| shawnz wrote:
| > The brain cannot simulate a turing machine. It does not
| have infinite memory, which is a requirement for a turing
| machine.
|
| In practice we call modern computers turing-complete even
| though they don't have infinite memory. The brain can
| simulate such a machine.
|
| > The brain has no such constraint. It is analog, and
| therefore infinite in State representation.
|
| If this mattered, then it would mean analog computers are
| more powerful than digital computers and therefore the
| Church-Turing thesis is wrong
| deepnotderp wrote:
| Isn't the recent Google quantum "supremacy" experiment
| evidence against the extended Church-Turing thesis?
| anchpop wrote:
| No, quantum computers as we understand them can be
| simulated by a turing machine
| coreyp_1 wrote:
| Regarding the Church-Turing thesis, it is exactly that,
| just a thesis. Again, akin to P vs NP. It seems to hold
| for most cases, but is not proven.
|
| The reason that it's difficult to apply in regards to the
| brain is that we don't exactly know how the brain is
| computing... or if it "computes" at all! To my knowledge,
| we don't have a model of computation for consciousness,
| emotion, free will, Etc.
|
| Perhaps these are better classified as emergent Behavior
| rather than computation, but if that is the case I still
| don't know of a model explaining what computations or
| rules give rise to the emergent Behavior.
|
| Perhaps the problem is in our definition of computation
| and what it means to compute.
|
| We do know that the cardinality of the set of possible
| computational problems is larger than the cardinality of
| the set of all possible Turing machines. This is provable
| by simple diagonalization proofs.
|
| The question, then, is whether or not the computations of
| the brain fall Within the set of Turing recognizable
| languages (computational problems). To my knowledge, this
| has not been shown.
| supergarfield wrote:
| As far as I understand, the prevailing opinion is that
| the brain is a physical object and that its operation
| does not involve currently-unknown laws of physics
| (because we have a good understanding of what happens at
| the scale of an entire atom or above).
|
| A Turing machine can run a simulation based on such
| physical laws to any desired level of precision (which is
| enough, because as mentioned in TFA, processes in the
| brain aren't individually very precise). This is true
| because of the nature of these laws, which are mostly
| just asking you to integrate differential equations. If
| you accept this, then it should follow that a Turing
| machine can in fact simulate a brain: just run a physics
| sim on a brain's initial state.
|
| (I do realize that this is far outside the realm of
| what's doable today, but it seems to provide a solid
| justification for why it's conceptually possible).
| coreyp_1 wrote:
| "any desired level of precision" is actually the issue.
| The moment you choose a level of precision, you cease
| being accurate (at that level). If you make the argument
| that a TM has infinite memory, and can therefore
| represent an infinite precision, then I would counter
| that our current defintion of a TM requires a finite tape
| alphabet (and finite number of states), which is part of
| the TM's known computational limitations. And, of course,
| the moment that you use any finite set of symbols to
| represent an infinitely precise value, you fall into the
| problem that the set of real numbers has a larger
| cardinality than the set of possible turing machines
| (again, simple proof via diagonalization).
|
| It is possible that the brain's imprecision (I would
| argue that "inconsistency" might be a better word) is a
| requirement of it's computational ability. Again, we
| haven't defined how the brain computes, nor do we have a
| model for explaining its computation, encoding or
| representation of knowledge, or emergent behavior. We
| have observed phenomena related to some of these things,
| but we are far from understanding it. It may be that the
| computational processes are dependent on the surrounding
| environment. We know that the biological processes are
| influenceable by the physical world, but we do not know
| much about how these external forces affect, limit, or
| are required for, the process of brain computation.
|
| The quantum world may play a part in consciousness (or
| no, we don't know). Non-determinism may play a part. It
| is possible that, in order to simulate a brain, one would
| have to simulate the entire universe around it in order
| to predict the behavior... meaning that it may well
| require a universe to perform the simulation.
|
| Which brings us to a related theory of whether or not we
| are living in a simulation, but I digress... :)
| [deleted]
| mamon wrote:
| > It is possible that the brain's imprecision (I would
| argue that "inconsistency" might be a better word) is a
| requirement of it's computational ability
|
| Is it possible that brain is in fact a quantum computer?
| I can imagine that under all those neural networks there
| is a small part where, trapped in some complex protein
| structure, some qbits exist and are crucial to most
| advanced brain functions, such as consciousness.
| coreyp_1 wrote:
| "Is it possible that brain is in fact a quantum
| computer?"
|
| It's an interesting thing to ponder.
|
| Quantum computing is still just another computational
| model, and it's main Advantage is that it involves non
| determinism. But non determinism, in and of itself, can
| be modeled by deterministic computer.
|
| I think the biggest problem is that we don't understand
| what computation is taking place in the brain, or even if
| it is "computation" according to our current definition
| of the word. I think that this issue is the biggest
| problem in reconciling whether or not it is possible to
| accurately model the human brain.
| simiones wrote:
| > that its operation does not involve currently-unknown
| laws of physics (because we have a good understanding of
| what happens at the scale of an entire atom or above)
|
| Well, we know certain approximations of those laws.
| Purely theoretically, it is possible that the exact laws
| at some level of detail that we have not yet been able to
| observe involve functions that are not computable by a
| Turing machine, and then it is theoretically possible
| that the brain itself is computing functions which are
| not computable by a Turing machine (this would of course
| assume that the Church-Turing thesis is actually wrong).
|
| As long as the Church-Turing thesis is not proven, we
| can't say with absolute certainty that the physical world
| can be simulated to any level of detail by a Turing
| machine.
|
| Furthermore, even if the Church-Turing thesis was proven,
| is it possible that the physical world involves
| transformations that are not even computable at all (even
| if they can be approximated by computable functions)?
|
| Just to be clear, I do not believe these things. But it
| is fun to think about the limits of our knowledge.
| jbay808 wrote:
| > The brain has no such constraint. It is analog, and
| therefore infinite in State representation.
|
| This is a common misconception.
|
| I'm sure you are aware that analog signals can be
| approximated by digital values -- a 10 bit ADC will read
| a channel to one part in 1024, etc.
|
| You might say that even a 64 bit representation is a poor
| approximation of a real life signal, which is a real
| number with infinite precision... But it isn't.
|
| The brain operates at about 300 Kelvin, and so there's a
| noise floor to all analog signals of about that times
| Boltzmann's constant, or 10^-20 J. If a neuron impedance
| is 1 ohm, and at a bandwidth of just 10 kHz, the thermal
| noise is about 1 nV. For a membrane potential of 100 mV,
| that's a maximum possible noise to signal ratio of one
| part in 100 million, which is 26 bits.
|
| Now the brain could depend on the signal below the noise
| floor, but if so those would be extremely fragile
| operations, and you could get the same thing on a
| computer by padding your numbers with random data.
| p1esk wrote:
| Given how robust a brain is against noise, I'd be
| surprised if any brain signals are more precise than an
| equivalent of 3-4 bits.
| jbay808 wrote:
| I agree, and I think in practice the brain's noise floor
| is also much higher than the theoretical thermal-noise
| minimum. But I guess the main point is that once we
| acknowledge that even 32 bits is more than enough, the
| difference between an analog and digital machine loses a
| lot of its philosophical weight.
| nicoburns wrote:
| I mostly agree with your post but:
|
| > The brain has no such constraint. It is analog, and
| therefore infinite in State
|
| Not necessarily infinite. A lot of people believe that
| nothing in the world is truly infinite (just very
| large/small). Infinite quantities in mathematics are just
| approximations that simplify calculations.
| JBiserkov wrote:
| I agree. For some reason 2) and 3) reminded me of the book
| "The mating mind" https://en.wikipedia.org/wiki/Geoffrey_Mi
| ller_(psychologist)...
| nil-sec wrote:
| Turing completeness isn't necessarily an interesting thing
| to have in common. Many (very simple) models of computation
| are Turing complete but have vastly different properties.
| Take for example a cellular automata, a Turing machine,
| Wang tiles, (cyclic) Tag systems, Fractrans, Register
| machines, string rewriting systems. All of these are Turing
| complete. Yet they are miles apart in how they carry out
| computation. In order to understand and do what the brain
| is doing we have to figure out the brains model of
| computation. It will also be Turing complete but it will
| look very different than a Turing machine.
| sa1 wrote:
| Computers are mathematical concepts, Turing machines being
| one such concept. Whether computers are implemented using
| silicon, or oil, or using neurons, it doesn't really matter
| as we have a mathematical framework for describing abstract
| machines, and we can determine what is a machine, and what is
| not.
|
| We did not have this mathematical framework before the age of
| Turing, Church, Russel, et al.
|
| This doesn't mean that brains are very similar to CPUs, they
| are not, just like they were not similar to mechanical
| machines before.
|
| Yet we do now have a way of studying the similarities they
| have.
| kyuudou wrote:
| "...the question of whether Machines Can Think, a question of
| which we now know that it is about as relevant as the
| question of whether Submarines Can Swim."
|
| Edsger Dijkstra, EWD898, 1984
| derefr wrote:
| The difference is that CPUs, unlike those other machines, can
| be used to model/simulate things that _are_ similar to
| brains. There is impedance in the translation, of course, but
| that impedance can be measured as a sort of "distance"
| between the architectures; just like one might measure the
| "distance" between two Instruction Set Architectures.
| RivieraKid wrote:
| I don't know.
| gwern wrote:
| I was wondering why this seemed so outdated and ignorant for
| something published in 2018 (only 10b transistors? 'computers are
| serial', really?), but I see that it's from a 2015 textbook,
| using citations for computing hardware published in 2008, and
| presumably referencing hardware from 2007 or earlier...
| kristopolous wrote:
| They aren't the same thing. They are different classes of
| objects, different tasks. This comparison is kind of silly.
|
| I'd hate my computer to have the memory accuracy or the
| computational accuracy of my brain. I'd hate to have the
| creativity and inspiration of a computer.
|
| Delete being such a nontrivial operation is probably a good thing
| for humans. Copy being imperfect probably has something to do
| with the phenomenon we call imagination. We use computers because
| they are complementary, not substitutive.
|
| They're just so fundamentally different.
| technovader wrote:
| Exactly. We shouldn't be so arrogant in thinking the modern
| computer is the same thing as a human brain.
|
| This comparison is pointless. The human brain is beyond
| comprehension. Computers are just logic calculators.
| ryukafalz wrote:
| >The human brain is beyond comprehension.
|
| Many things in the world were beyond our comprehension, until
| they weren't. I see no reason why the human brain's inner
| workings should evade our understanding indefinitely.
| EmilioMartinez wrote:
| I don't see how any of that makes the comparison "silly". It's
| not like we have so many instances of computer paradigms to go
| around comparing.
| derefr wrote:
| > memory accuracy
|
| There are individuals with very good memories for all sorts of
| things, who seem to manage to reconsolidate their memories
| near-losslessly (at least within the confines of the mental
| schema they organize said memories into.) Surgeons with
| anatomy, lawyers and judges with case-law, etc.
|
| At this point I'm convinced that the lossy method humans
| intuitively reconsolidate memories with, isn't so much a
| feature of our mental architecture, as it is a part of the
| "operating system" we build up _on top of_ our mental
| architecture--i.e. it's a _skill_ , something we can learn (or
| accidentally invent) a better approach to.
|
| > computational accuracy
|
| We compute _ratios_ with extremely high accuracy /precision.
| Just look at a professional billiards player.
|
| We don't have a good mind for integer math; but you can
| translate most integer math problems into ratio problems, and
| then they become intuitively solvable to humans. (This is
| basically what geometry is.)
| partyboat1586 wrote:
| A surgeon might remember anatomy with great accuracy but he
| is unlikely to remember the details of some case law nearly
| as well. Our memories are associative, that is how they
| differ from computers. It's easy for a surgeon to remember
| anatomy because he has been immersed in it for a long time
| and it all interconnects, i.e there are a lot of associations
| to call up the memory. Computers on the other hand could
| remember 20 facts about anatomy and 20 facts about case law
| no problem without needing any framework to attach them to.
| Stratoscope wrote:
| A similar example I heard was that a chess grandmaster may be
| able to take a look at a chessboard with a game in play and
| memorize the entire board immediately. But _only_ if the
| board "makes sense" - all the pieces are in positions that
| could actually be reached in a real game.
|
| If you take those same pieces and rearrange them willy-nilly,
| then this ability to instantly memorize its layout goes away.
| derefr wrote:
| I recall that Jeff Hawkins, when talking about his
| Hierarchical Temporal Memory ML model (which is supposed to
| be brain-like), said something like "Nature has spatial and
| temporal locality. Brains evolved to best store information
| that _also_ has spatial and temporal locality--in other
| words, to recapitulate and model the natural world. To the
| degree that some pattern is akin to one that arises in
| nature, the brain can store and compute upon it easily. To
| the degree that a pattern is 'arbitrary'--something that
| cannot arise in nature--the brain finds it hard to hold
| into."
|
| The moment-in-time arrangement of chess pieces on a board
| does not exactly have spatial or temporal locality; but if
| one has learned a set of mental transformation rules that
| let that board be translated into a _narrative_ for how it
| got to be that way--then that _narrative_ is itself
| something quite natural for the brain 's architecture to
| represent.
| satvikpendem wrote:
| You can even construct memory palaces which are very easy to
| learn. I still remember them from 10 years ago.
| jbay808 wrote:
| I remember when I first took a data structures course,
| learning things like trees and linked lists, I had a total
| paradigm shift with respect to how I understood my own mind.
|
| I had never really thought about the different ways that data
| could be organized, and how they perform differently. I
| figured that since this was so basic to computer science, my
| own mind couldn't be doing something completely different. It
| might not be the same in detail as any computer data
| structure, but it couldn't be completely unrelated either.
|
| I realized that data structures might make information _feel_
| different. For example, I can only tell you what the 16th
| letter of the alphabet is by counting from "A". I can't sing
| the alphabet song backwards. These are at least qualitatively
| characteristics of a singly linked list. The same goes for my
| phone number and my credit card number. I wouldn't be able to
| dictate them backwards, except by mentally traversing them
| forwards and then holding the whole number in my conscious
| memory as I reverse the digits, or if that's too tiring,
| traversing it forwards multiple times and stopping at
| different points.
|
| I have many detailed memories of past events, conversations,
| and trivial facts, but it's hard for me to remember them on
| command. I need some kind of prompt to point me to the right
| index where I can retrieve it.
|
| I agree a lot with the interpretation that we have a messy OS
| that bungles memory management and does lossy compression and
| a poor job of disk defrag, running on some very impressive
| hardware.
| ALittleLight wrote:
| Recently I was thinking about how my brain answers the
| question "What's your favorite movie?" and how I can easily
| answer that question, but it's harder to answer a question
| like "What's your favorite movie where a gun is fired?"
|
| It seems to me that whenever I watch a movie, if I really
| liked it, I check my perceived quality of the movie against
| the quality of my current favorite movie, and if the new
| movie beats the old favorite, I update the "favorite movie"
| pointer to point to the new movie. When someone asks
| "What's your favorite movie?" I just return the name of
| whatever the favorite_movie points to.
|
| The question of "Favorite movie where a gun is shot" is
| much harder, I think, because my memories aren't really
| indexed that way. I can't query by "gun is shot" so I can't
| get the subset of movies I've seen with gun shots and pick
| my favorite.
|
| To me, it seems like my brain, at least for movies, has
| something of a key value store, which I can scan, slowly
| and imperfectly, but not query with complex questions. Or,
| maybe, if the queries are too complex they timeout and I
| don't get back any results.
| Digit-Al wrote:
| Some very interesting points.
|
| I would like to say that the hardware is a bit of a mess as
| well. There are weird redundant bits of legacy hardware
| that aren't required any more, but nobody's bothered to
| remove them from the system (appendix, wisdom teeth). There
| are oddly paired systems (genitals combine waste removal
| with reproduction; the nose combines air filtering with
| scent detection; the mouth combines food intake and air
| intake/outlet). And oddly co-dependent systems (lose your
| sense of smell and your sense of taste takes a significant
| hit).
| jbay808 wrote:
| What do you mean? That sounds just like a modern CPU to
| me! :)
| canjobear wrote:
| When we say the brain has poor computational accuracy, we're
| usually talking about the conscious brain we're aware of. But
| our low-level motor actions and perceptions, coordinated by the
| brain, require a lot of precise computation. These low-level
| brain computations are the thing to compare to AI, not our
| conscious thinking. Our conscious mind is more like low-
| precision software running on top of an enormously powerful
| computer.
| jakeogh wrote:
| It's unlikely we compute in any conventional sense, the
| hardware is reality, and is going to exploit every available
| effect that is energetically efficient.
|
| Letting FPGA's "go" into the analog realm is an interesting
| window: https://news.ycombinator.com/item?id=21253267
|
| Glial brain cells:
| https://news.ycombinator.com/item?id=22161192
| joesb wrote:
| > But our low-level motor actions and perceptions,
| coordinated by the brain, require a lot of precise
| computation.
|
| But we don't actually do that precise computation though. try
| repeating any action exactly and you will see some
| inaccuracy.
| maskboy wrote:
| no, you will see some variability
| TeMPOraL wrote:
| > _But our low-level motor actions and perceptions,
| coordinated by the brain, require a lot of precise
| computation._
|
| That accuracy is more likely achieved through fast, analog
| feedback loops than precise calculation.
| Reelin wrote:
| Like a giant stack of op-amps.
| (https://www.computerhistory.org/revolution/analog-
| computers/...)
| LordHeini wrote:
| Is it though?
|
| I think having a good metric is really hard.
|
| For example i can have a neural net running on my smartphone
| doing recognition tasks.
|
| A task the brain is typically good at due to its neural net
| structure while the computer basically has to simulate the net.
|
| But still my smartphone can mark all the faces in a crowd
| multiple times over in a time i can not recognize even a single
| person.
|
| And that with a camera way beyond the capabilities of the human
| eye.
|
| Modern smartphone processors draw around 1 or 2 watt max. So is
| my phone more efficient at doing this?
|
| One could argue that my brain does other stuff at the same time
| like controlling heartbeat and what not but my phone has to keep
| the wifi, clock and so on too.
|
| The truly impressive part is the ability of the brain to do
| completely generic problem solving for basically everything;
| while running on 10 watt. With the added ability to learn a few
| activities to a really high level.
|
| Its is not efficient at doing a singular thing it is efficient
| doing everything at once.
| rimliu wrote:
| Human eye is very lousy. Only a small patch is capable of some
| decent resolution. That we actually perceive what we see as
| something sharp is a compliment to the brain in itself.
| JoeAltmaier wrote:
| Yes but for integrating information, your brain is marvelous.
| Somebody in the crown laughs or moves a certain way or you
| catch a sniff - and BAM you found your person.
|
| Any automated single-skill system might be more efficient, but
| of course it becomes useless outside its parameters. Put a hat
| on those people in the crowd and your phone may be totally
| defeated.
| mrwnmonm wrote:
| The brain is weird. You can figure out how to split an atom, then
| forget your keys inside your car.
| jonnypotty wrote:
| World record tennis serve is 144 miles an hour and a human can't
| really move across a court and return a ball moving at this
| speed. If they're lucky they can reach it and react in time to
| hit it. I'm a bit confused by an article that claims tennis
| players can react to and return serves up to 160 miles an hour. I
| think evidence suggests that returning balls anywhere near this
| fast is dependent on analysing factors before the ball starts
| moving, the other players body position, racket position etc.
| Players have an intuition about where the ball is going to go
| without having to look at and analyse the flight of the ball.
|
| Just did some very basic checking. Tennis court 23m 260mph =
| 72m/s Ball takes approx. 0.3ms to travel length of court. human
| reaction time to visual simulous 0.25ms So the idea is they move
| and hit the ball in the remaining 0.05ms? Hmmmmm.
| skummetmaelk wrote:
| You're right that it's not possible to react if all the
| information you have is the trajectory of the ball after it
| leaves the racquet. Good players will subconciously be
| predetermining the path of the ball by looking at how the
| opponent is striking the ball.
| rooam-dev wrote:
| It doesn't specify who serves it at that speed either, could be
| some kind of "serving machine".
| jonnypotty wrote:
| I guess so. But I'd say humans would have no chance at this
| speed without another player to analyse visually.
| ekelsen wrote:
| Serves in tennis don't go directly down the line, they go cross
| court. Returning players will often be standing behind the
| baseline. Additionally balls start ~2.5-3m above the ground,
| bounce and then come up again. The total distance traveled is
| probably closer to ~27m.
|
| The air resistance slowing the ball down is significant -
| combined with the energy the ball loses bouncing, the ball has
| lost more than half of its initial velocity by the time it gets
| to the returning player.
|
| I found these speed guns stats on a tennis forum for the ball
| speed at different points:
|
| Speed after being hit: 126mph
|
| Speed before hitting court: 89mph
|
| Speed after hitting court: 67mph
|
| Speed at returner's baseline: 58mph
|
| Even after doing the calculations correctly, there still isn't
| a lot of time for reactions, but it is more plausible than your
| initial analysis suggests. (Your units also seem off - should
| be seconds not milli-seconds).
| jonnypotty wrote:
| Thanks for this. Much better than what I did although I don't
| think 58 is a third of 126, more like a half.
| Implicated wrote:
| 'Players have an intuition about where the ball is going to go
| without having to look at and analyse the flight of the ball.'
|
| This is pretty easily observable with baseball players as well.
| After playing thousands of games while standing in the same
| (relative) place on the field I/they can anticipate where the
| ball is going to go based on a variety of variables in real-
| time... instantly.
| emsy wrote:
| In the book "Thinking fast and Slow" the Baseball example is
| given for a learned intuition. The ball is too fast for
| batters to react so they learn to anticipate the trajectory
| of the ball from the way the pitcher throws. When they let
| professionals players play against a female softball player
| with lower throwing speed their intuition was off and they
| missed the ball more often than against a male professional
| player.
| hacker_9 wrote:
| 50% of the time it works 100% of the time
| ajuc wrote:
| There was a festival of jugglers in my city and they taught
| me to juggle in like 15 minutes, I was amazed it's so easy
| (the basic 3-ball juggling, and just for a monute or two, the
| more difficult juggling is HARD and I had to train later to
| be able to keep juggling forever).
|
| There is a very easy trick - you look forward in the distance
| keeping the balls in peripheral vision and there's 2
| automatic reactions you have to develop:
|
| 1. when the ball going up is at the top of the curve - throw
| another ball up
|
| 2. when a falling ball goes out of your peripheral vision -
| do the "oh shit something's falling let's catch it" routine
| with the hand that has less balls in it.
|
| Hands learn very quickly how to move to catch the balls that
| leave the peripheral vision "by itself" basing on the
| trajectory you've seen.
|
| It's actually harder to juggle when you look at the balls
| directly, and it's impossible when you think about it and try
| to do the moves consciously because you're too slow.
|
| It was mindblowing to me that it's easier to catch a ball
| when you don't look at it.
| huffmsa wrote:
| Almost all of the "amazing" things the brain does are
| basically continuously refined predictive branch execution.
|
| Which is why practice is important. You're essentially
| strengthening certain neural pathways with continuous
| exposure to certain inputs.
|
| But this strength also makes us susceptible to misdirection
| and slight of hand.
| cepth wrote:
| The article's articulation of what goes into returning a serve
| is a bit simplistic, but the underlying idea is not crazy.
|
| * When you return a serve in tennis, you are doing so from only
| one side of the court. The opponent's serve can only land in a
| service box that provides 13 feet of lateral space.
|
| * Practically, there are relatively few spots in the service
| box that can be reached by a serve. Because of human physiology
| (the length of our arms, joints in the arms etc.), it would be
| extremely painful to try to hit a fast serve to certain parts
| of the service box. Either that, or the server would have to
| stand in atypical positions on the service line (i.e. not at
| the center tick) that would be a dead giveaway of where the
| server was trying to hit to.
|
| * So, in simplistic terms, most tennis players are choosing
| between more-or-less staying in place (to return a body serve),
| or leaping to their left or right. The serve must bounce before
| you hit it, and it will be bouncing "towards you" vertically.
| The returner thus is very rarely going to move vertically. This
| usually only happens when you are moving in to pummel a slow
| and short serve.
|
| * At the highest levels of tennis, the vast majority (60%+) of
| serves are going out wide, or down the middle
| (https://www.atptour.com/en/news/berrettini-infosys-serve-
| loc...). Mind you, these are also the same player who would
| have the physical conditioning and athleticism to actually be
| able to hit these blazing fast serves.
|
| * Additional information for the returner is conveyed by the
| serve toss. Almost all players are giving away tells here. For
| example, if I'm a right hander serving from the deuce court,
| and I toss my ball to the left (the "11 o'clock position"),
| it's high unlikely that I'm hitting the ball down the middle.
| Doing so would require one of those aforementioned contortions
| in my arms and legs, and I would then be unlikely to generate
| the power needed to strike the ball in a way that leads to a
| super fast serve.
|
| * So in reality, by the time that the server is making contact
| between their racket and the ball, the returner will have a
| general idea of the direction that the ball is going in.
|
| * The article does conflate getting your racquet on the ball,
| and making a successful return. Just as with any other tennis
| shot, there is not guarantee that your return does not go into
| the net, or go flying out. I think it's a far more plausible
| claim that professional tennis players can get their ball on
| the racquet vs claiming that they can cleanly/successfully
| return these super fast serves.
|
| Some other thoughts:
|
| * Placement is just as important as speed in determining how
| returnable a serve is.
|
| * For example, there are plenty of examples of top tennis
| players returning extremely fast serves. Federer against Isner
| (140 mph): https://youtu.be/5gcvLbtaNxM, Murray against Raonic
| (147 mph): https://youtu.be/8GYX4ZIPJsg
|
| * The commonality between these successful returns is that the
| serves themselves were fast, but poorly placed. By serving
| right down the middle, the servers allowed Federer and Murray
| to take one small step, and then make good contact with the
| serves for an "easy return".
|
| * One small quibble with the "world record tennis serve" you
| cite. It's not 144 mph, but rather 157.2 mph (hit by John
| Isner). If anything though, this is helps your argument.
|
| * The unofficial record is 160+ MPH (hit by Sam Groth), but
| this was at a second tier tournament with a questionable radar
| gun (https://youtu.be/uKeL-W7xft0). Notice how even with this
| serve, the returner correctly guesses where the serve is
| headed, and even looks to have gotten a racquet on it.
|
| * It's a bit of a chicken and an egg problem as well. There is
| a very tiny sliver of people in the world who are physically
| fit enough and who possess the natural physical traits (like
| height and broad shoulders) necessary to hit serves in the 140+
| MPH range. These people are likely playing on the ATP against
| the players in the world best equipped (mentally and
| physically) to return their serves.
|
| * So all this is to say, returning serves in that 140-160 MPH
| range is a low probability proposition. Heck, a perfectly
| placed and well disguised serve even in the 110 MPH range can
| be unreturnable (as seen in two decades of Federer highlights).
| But, humans are indeed "capable" of returning serves in that
| speed range.
| sddfd wrote:
| I feel uncomfortable at the ubiquitous, silent assumption that
| what is marketed as AI is a computer implementation of a brain.
|
| I see how the term neuronal network reinforces this believe, but
| we (especially the researchers among us) should allow for the
| possibility that we are missing something.
| dkersten wrote:
| I agree. I think its very widely known that our ANN's are only
| very rough approximations of how the brain actually works, I
| think the people who say its a computer implementation of the
| brain are either laypeople who don't know much about machine
| learning or the brain, are people marketing the hype for
| personal gain or people without neuroscience knowledge who have
| bought into the hype.
|
| I also recently heard an argument for why our ANN models won't
| spontaneously become sentient: human brains don't learn from
| just observation, but also interaction. A young child doesn't
| learn abouthow blocks are stacked by looking at images of
| stacked boxes, they learn through experimentation, by stacking
| boxes and seeinghow their actions affect the world around them.
| For an AI, that means we either need to also work on robotics
| so the AI can interact with its environment, not just sense it,
| or we need to simulate an interactive virtual environment. Some
| people are working on this and making great strides, but your
| average toy ANN won't exhibit human intelligence in isolation,
| in my opinion.
|
| Combine those two things and we're still quite a ways away from
| human-like intelligence or implementing a human (or
| animal)-like brain.
| martin-adams wrote:
| Interestingly, there are some studies that imply that intense
| thinking about doing an activity (such as a gym workout[1] or
| hitting a baseball) can improve your physical skills than if
| you didn't think about it. So this is supporting the notation
| that you can rewire your brain by thinking, as well as
| tactile input.
|
| [1] http://nautil.us/blog/just-imagining-a-workout-can-make-
| you-...
| dkersten wrote:
| That's not really what I'm referring to (or at least, only
| a little). Once you have a mental model of something, you
| can for sure think on it or build on it without
| interaction, but to initially set up our mental models (as
| children or whatever), I believe it takes interaction. Once
| we have a base, we can think abstractly about it and learn,
| but building that base..
|
| Or, put another way, its my belief that you can "_improve_
| your physical skills" by thinking, but to buildthe skill in
| the first place, interaction is necessary.
|
| But even if its not true and interaction isn't strictly
| necessary, I think (wrongly oerhaps) that few people would
| disagree that usually learning by doing is far superior
| that only learning by thinking/reading/listening/watching.
| So even if not neccesary, its at least more efficient
| (doing both together is probably most efficient).
| martin-adams wrote:
| Absolutely. I think what AI has highlighted is that the problem
| set is now looking more similar to a human experience. For
| example, how you train based on input and learn from failure
| and how limited information can confuse even a human brain
| (think image recognition). That said, because the problem looks
| the same, doesn't imply the method of processing is the same.
| [deleted]
| papito wrote:
| Neural networks also have no ability to create new information
| based on their own mistakes. What is a mistake? When does
| something look "off" but still very interesting?
|
| For example, you can feed a neural net all the recipes of
| burgers to create a perfect burger. Great. But how does the
| same net _invent_ the burger?
|
| The burger, like many foods or accidental art, was invented as
| a result of scarcity, circumstance, experimentation, or just
| fortunate error. That sort of imperfection is very hard to
| achieve with AI, because it is designed to be either perfect or
| fail.
| gambiting wrote:
| >>For example, you can feed a neural net all the recipes of
| burgers to create a perfect burger. Great. But how does the
| same net invent the burger?
|
| Wait....but it just....did? It took the information about all
| possible burger recipes and invented a new one out of these.
| Like, a human could only invent a new burger if they knew
| anything about burgers in the first place, at the very least
| that it's a bun with some filling in between, otherwise you'd
| have no context to invent anything.
| jayjader wrote:
| Not OP, but I think they're not talking about inventing a
| _new_ burger, but inventing _the_ burger, as in the first
| one ever.
|
| As in, the neural net in this example is able to improvise
| a new burger recipe solely because it was given existing
| recipes to burgers as input; it did not come up with the
| notion of a burger and then produce a recipe that outputs
| something fulfilling that notion when followed.
|
| Personally, I would argue that this distinction is not as
| clear-cut as the tone of the original comment seems to
| suggest. Humans didn't invent the burger from nothing
| either. We've been grilling meat and making bread for
| millennia, and sandwiches have been a thing for over a
| century.
|
| A 'burger' is just another iteration of our biological
| neural nets' attempts to make food from ingredients already
| present in our physical reality. Given that we flow in a
| single direction through time, any food we make is in turn
| added to our list of ingredients for making food "the next
| time". One could argue it is only a matter of time once
| meat can be ground into patties and grains turned into
| bread that burgers start being made - given the relative
| benefits humans gain from consuming both.
|
| This comes back to what others have expressed elsewhere in
| this thread, that the probable [most] important
| distinctions aren't between software vs hardware, or
| organic life vs silicon processors, but the environment &
| capacity to interact with said environment. Some sense of
| "innate tendency to experiment" (i.e. curiosity) is
| probably either equal in importance or a direct runner-up.
| GuB-42 wrote:
| GAN can do that. For example, AlphaZero invented strategies
| for the game of go from nothing but a random number generator
| and the rules of the game. As for perfection neither go nor
| chess AIs play perfectly, and they can still beat the best
| human players.
|
| Of course, an AI intended to play go isn't going to invent
| the burger. But I see no reason why, given a list of
| ingredients, their properties and a model of what human enjoy
| eating, a neural network couldn't invent the burger.
|
| Creating a new recipe is just an optimization problem at its
| core.
| grenoire wrote:
| I am definitely not an expert on this topic but my impression
| is that the research is not really focusing on structured
| abstractions of sensory input, or making these abstractions
| stateful. Shapes, colours, music, and whatnot are clearly
| stored and retrieved in our brains, which is something NN
| research is not looking at (enough).
| headalgorithm wrote:
| See discussion from 2018:
| https://news.ycombinator.com/item?id=16895124
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