25 ms·

Why Is the Human Brain So Efficient? (2018)

208 points by rcshubhadeep 6y ago

jonnypotty 6y ago
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 6y ago
  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 6y ago
  It doesn't specify who serves it at that speed either, could be some kind of "serving machine".
  - jonnypotty 6y ago
    I guess so. But I'd say humans would have no chance at this speed without another player to analyse visually.
- Implicated 6y ago
  '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.
  - hacker_9 6y ago
    50% of the time it works 100% of the time
  - huffmsa 6y ago
    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.
  - emsy 6y ago
    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.
  - ajuc 6y ago
    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.
- ekelsen 6y ago
  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 6y ago
    Thanks for this. Much better than what I did although I don't think 58 is a third of 126, more like a half.
- cepth 6y ago
  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-locations-november-2019 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 https://youtu.be/5gcvLbtaNxM, Murray against Raonic (147 mph): https://youtu.be/8GYX4ZIPJsg 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 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.
headalgorithm 6y ago
See discussion from 2018: https://news.ycombinator.com/item?id=16895124 https://news.ycombinator.com/item?id=16895124
plutonorm 6y ago
This article contains inaccuracies and says almost nothing novel for your average hacker new reader.
- MaxBarraclough 6y ago
  > 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 https://news.ycombinator.com/newsguidelines.html What are the inaccuracies?
  - plutonorm 6y ago
    The same accusation could be levied at the original artical.
    - MaxBarraclough 6y ago
      Please make a specific and substantive point. Worthwhile discussions do not follow from vague and shallow dismissals.
- jonnypotty 6y ago
  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 6y ago
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.
- tegeek 6y ago
  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.
  - est31 6y ago
    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 6y ago
    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 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
sddfd 6y ago
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 6y ago
  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 6y ago
    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-stronger http://nautil.us/blog/just-imagining-a-workout-can-make-you-...
    - dkersten 6y ago
      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).
Laakeri 6y ago
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 https://www.youtube.com/watch?v=X9hRRh76QEA and the book Circuits of the Mind.
LordHeini 6y ago
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.
- JoeAltmaier 6y ago
  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.
- rimliu 6y ago
  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.
stared 6y ago
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 https://www.youtube.com/watch?v=ZerUbHmuY04.
- jcims 6y ago
  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 6y ago
    but can it do math? can it paint? drink wine and muse on its own brain efficiency?
    - stared 6y ago
      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 6y ago
        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 6y ago
        My point from above is that 'humans playing tennis' isn't a great benchmark for the efficiency of our brains.
RivieraKid 6y ago
I don't know.
kristopolous 6y ago
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.
- EmilioMartinez 6y ago
  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.
  - kristopolous 6y ago
    It's like measuring a gun in units of swords. You can achieve similar ends but they aren't the same. Or the companionship of a spouse in units of pets. The fundamental unit is at the affect level, not the application. That's a very important distinction. Any model comparing the application then is fundamentally misinformation. It's even worse because it suggests an equivocation is possible. As if you can get X units of dogs as a substitute for Y units of spouse. You just have to find the magic ratio! Wrong framings can stick people in insolvable problems well, sometimes for generations. It's really bad. They're fundamentally different and pretending otherwise is just going in the one of the most useless directions possible
- derefr 6y ago
  > 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.)
mrwnmonm 6y ago
The brain is weird. You can figure out how to split an atom, then forget your keys inside your car.
tehsauce 6y ago
Why is the human brain so inefficient? It takes years just for it to compute the sha-256 of this media file.
- deleted 6y ago
  [deleted]
dtnewman 6y ago
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.
- aeternum 6y ago
  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 6y ago
    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 6y ago
      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 6y ago
      >>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.
- jjoonathan 6y ago
  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).
gwern 6y ago
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...
SeanFerree 6y ago
Cool article!!
HarHarVeryFunny 6y ago
"At a global level, the architectures of the brain and the computer resemble each other, consisting of largely separate circuits for input, output, central processing, and memory." This is fundamentally wrong, even at the 10 mile high summary level. In our brain processing and memory are not in the least bit separate, and memory distinct from processing doesn't really exist. If you really had to make a computer analogy of how the brain works, it's more like self-modifying code where the only memory of the data flowing through it is the changes to the code that were made as the result of that prior flow.

208 points by rcshubhadeep 6y ago

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 6y ago
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 6y ago
It doesn't specify who serves it at that speed either, could be some kind of "serving machine".
- jonnypotty 6y ago
  I guess so. But I'd say humans would have no chance at this speed without another player to analyse visually.
Implicated 6y ago
'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.
- hacker_9 6y ago
  50% of the time it works 100% of the time
- huffmsa 6y ago
  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.
- emsy 6y ago
  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.
- ajuc 6y ago
  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.
ekelsen 6y ago
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 6y ago
  Thanks for this. Much better than what I did although I don't think 58 is a third of 126, more like a half.
cepth 6y ago
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-locations-november-2019 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 https://youtu.be/5gcvLbtaNxM, Murray against Raonic (147 mph): https://youtu.be/8GYX4ZIPJsg 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 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.

headalgorithm 6y ago

See discussion from 2018: https://news.ycombinator.com/item?id=16895124 https://news.ycombinator.com/item?id=16895124

plutonorm 6y ago

This article contains inaccuracies and says almost nothing novel for your average hacker new reader.

MaxBarraclough 6y ago
> 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 https://news.ycombinator.com/newsguidelines.html What are the inaccuracies?
- plutonorm 6y ago
  The same accusation could be levied at the original artical.
  - MaxBarraclough 6y ago
    Please make a specific and substantive point. Worthwhile discussions do not follow from vague and shallow dismissals.
jonnypotty 6y ago
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 6y ago

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.

tegeek 6y ago
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.
- est31 6y ago
  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 6y ago
  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 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

sddfd 6y ago

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 6y ago
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 6y ago
  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-stronger http://nautil.us/blog/just-imagining-a-workout-can-make-you-...
  - dkersten 6y ago
    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).

Laakeri 6y ago

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 https://www.youtube.com/watch?v=X9hRRh76QEA and the book Circuits of the Mind.

LordHeini 6y ago

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.

JoeAltmaier 6y ago
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.
rimliu 6y ago
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.

stared 6y ago

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 https://www.youtube.com/watch?v=ZerUbHmuY04.

jcims 6y ago
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 6y ago
  but can it do math? can it paint? drink wine and muse on its own brain efficiency?
  - stared 6y ago
    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 6y ago
      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 6y ago
        My point from above is that 'humans playing tennis' isn't a great benchmark for the efficiency of our brains.

RivieraKid 6y ago

I don't know.

kristopolous 6y ago

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.

EmilioMartinez 6y ago
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.
- kristopolous 6y ago
  It's like measuring a gun in units of swords. You can achieve similar ends but they aren't the same. Or the companionship of a spouse in units of pets. The fundamental unit is at the affect level, not the application. That's a very important distinction. Any model comparing the application then is fundamentally misinformation. It's even worse because it suggests an equivocation is possible. As if you can get X units of dogs as a substitute for Y units of spouse. You just have to find the magic ratio! Wrong framings can stick people in insolvable problems well, sometimes for generations. It's really bad. They're fundamentally different and pretending otherwise is just going in the one of the most useless directions possible
derefr 6y ago
> 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.)

mrwnmonm 6y ago

The brain is weird. You can figure out how to split an atom, then forget your keys inside your car.

tehsauce 6y ago

Why is the human brain so inefficient? It takes years just for it to compute the sha-256 of this media file.

deleted 6y ago
[deleted]

dtnewman 6y ago

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.

aeternum 6y ago
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 6y ago
  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 6y ago
    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 6y ago
    >>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.
jjoonathan 6y ago
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).

gwern 6y ago

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...

SeanFerree 6y ago

Cool article!!

HarHarVeryFunny 6y ago

"At a global level, the architectures of the brain and the computer resemble each other, consisting of largely separate circuits for input, output, central processing, and memory." This is fundamentally wrong, even at the 10 mile high summary level. In our brain processing and memory are not in the least bit separate, and memory distinct from processing doesn't really exist. If you really had to make a computer analogy of how the brain works, it's more like self-modifying code where the only memory of the data flowing through it is the changes to the code that were made as the result of that prior flow.