Why Is the Human Brain So Efficient? (2018)

http://nautil.us/issue/86/energy/why-is-the-human-brain-so-efficient-rp

by rcshubhadeep • 6 years ago

208 166 6 years ago

jonnypotty4• 6 years 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• 6 years 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• 6 years ago

It doesn't specify who serves it at that speed either, could be some kind of "serving machine".

jonnypotty4• 6 years ago

I guess so. But I'd say humans would have no chance at this speed without another player to analyse visually.

Implicated• 6 years 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• 6 years ago

50% of the time it works 100% of the time

huffmsa• 6 years 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• 6 years 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• 6 years 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• 6 years 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).

jonnypotty4• 6 years 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• 6 years 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-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.

headalgorithm• 6 years ago

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

plutonorm2• 6 years ago

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

MaxBarraclough2• 6 years 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

What are the inaccuracies?

plutonorm2• 6 years ago

The same accusation could be levied at the original artical.

jonnypotty4• 6 years 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?

est312• 6 years 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• 6 years 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.

est312• 6 years 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_rationalist2• 6 years 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 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

ResidentSleeper• 6 years ago

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.

sa1• 6 years ago

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.

derefr3• 6 years ago

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.

kyuudou• 6 years ago

"...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

nicoburns2• 6 years ago

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.

bluedino• 6 years ago

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.

sddfd• 6 years 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.

dkersten2• 6 years 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-adams2• 6 years 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-...

martin-adams2• 6 years ago

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.

papito2• 6 years ago

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• 6 years ago

>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.

GuB-42• 6 years ago

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• 6 years ago

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).

Laakeri• 6 years 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 and the book Circuits of the Mind.

LordHeini• 6 years 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• 6 years 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• 6 years 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.

stared2• 6 years 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.

jcims2• 6 years 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.

rantwasp2• 6 years ago

but can it do math? can it paint? drink wine and muse on its own brain efficiency?

RivieraKid• 6 years ago

I don't know.

kristopolous2• 6 years 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.

EmilioMartinez3• 6 years 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.

kristopolous2• 6 years 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

derefr3• 6 years 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.)

satvikpendem• 6 years ago

You can even construct memory palaces which are very easy to learn. I still remember them from 10 years ago.

partyboat1586• 6 years ago

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.

jbay8084• 6 years ago

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.

Stratoscope• 6 years ago

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.

canjobear• 6 years ago

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.

joesb• 6 years ago

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.

jakeogh• 6 years ago

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

TeMPOraL2• 6 years ago

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.

EmilioMartinez3• 6 years ago

How do you even define accuracy for an analog process which solves for unknown formulations? Not saying you can't, but afaik there's no ground to call them accurate. It's certaintly chucking a lot of data in functional ways.

robotresearcher• 6 years ago

But our low-level motor actions and perceptions, coordinated by the brain, require a lot of precise computation.

Such as?

technovader• 6 years ago

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• 6 years ago

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.

mrwnmonm• 6 years ago

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

tehsauce• 6 years ago

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

dtnewman• 6 years 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• 6 years 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-Al2• 6 years 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 ;-)

jjoonathan3• 6 years 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).

ColanR• 6 years ago

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.

ars• 6 years ago

"a ruler can only measure less precisely than itself"

That's actually super interesting. How do you bootstrap (as it were) a ruler?

Like, assume you don't have any machine that is itself created by using a ruler (so no screws or gears, except hand cut ones).

Obviously it's possible, since we did it. But how do you do it?

hinkley• 6 years ago

They're Made Out of Meat, as a short film:

https://www.youtube.com/watch?v=7tScAyNaRdQ

Shorel• 6 years ago

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.

gwern• 6 years 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...

HarHarVeryFunny• 6 years 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.

Why Is the Human Brain So Efficient? (2018)

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