What Computers Can't Do - with Kevin Buzzard

[@Thanatos2996]

You can tell it's a proper math talk when the slides were made in LaTeX.

[@bdjeosjfjdskskkdjdnfbdj]

not just latex but beamer too!

[@ornessarhithfaeron3576]

If it's in LaTeX, it must be true

[@latneyb]

I almost left because of the pants then I saw the slides and decided to stay.

[@ruffyistderhammer5860]

Nothing about this was proper math

[@tophersonX]

And because he couldn't be bothered figuring out how to make animated/interactive slides - what a nightmare

[@Sychonut]

Dude walked 10K laps around that damn table.

[@forbiddenera]

In his pajama pants, no less.

[@KelnelK]

That's quite a choice in trousers to wear for a lecture at the Royal Institution

[@mattsadventureswithart5764]

Proving that maths geeks are fabulous!

[@suntexi]

He didn't choose them; the RI randomly issues a 'uniform' to its lecturers.

[@unrealnews]

I couldn’t help but think of a certain investigator in Alan Moore’s From Hell.

[@stevejordan7275]

They're pyjamas. He also missed his morning coffee.

[@joeldixton5627]

@@suntexi wrong, he wears crazy trousers to all his imperial lectures

[@CyanBlackflower]

I love this channel. I try to watch and fully understand/comprehend at least 3 of the lectures posted here, per week, choosing a variety of topics to learn a little more about diverse subjects. Taking them in and taking time to digest and contemplate them, at my own rate, is making a big difference in the way I see and deal with the world at large. Expanding one's "horizons" in ANY way is never a bad idea IMO.

[@rahusphere]

This is great 👍

[@amarissimus29]

Four years later, we are pleased to inform you that the Royal Institution has been shuttered in an attempt to heal the wounds left by 200 years of colonizing the world with accurate and predictive scientific theories. With luck, we shall all soon return to our collective indigenous roots of poking each other with sharp sticks. We appreciate and fully validate your lived experience while we undergo this transformation.

[@CyanBlackflower]

@@amarissimus29 Abyssus Abyssum Vocat.

[@FarnhamTheDrunk1]

damn i had to check my youtube speed cause i was SURE it was running at 1.5 speed ^^

[@NipapornP]

haha, me too! ;) As a non native English, I couldn't follow him even on 0,75 speed, because he often cuts off half words. I just know, because many times his "talking" was inserted in written form as well.

[@Vector_Ze]

Mitzos SirReal: Thanks for the idea. I found it much easier to follow at 0.75X normal. I'm a southerner.

[@Cadaverine1990]

:P compared to my aunt he speaks rather slow. Heck when she speaks Spanish the native speakers tell her to slow down.

[@xXxserenityxXx]

Sounds normal on 0.75 haha

[@enmarsbar]

haha. I thought the same. even now I'm actually watching it in 1.5:p It becomes completely comical! :P

[@jamesh625]

Finally someone using beamer for a maths-based presentation. LaTeX for life!

[@velociraptor3207]

same here, doing them now with html5 never a powerpoint guy

[@paradigmnnf]

.. only present total garbage!

[@proloycodes]

@@paradigmnnf bruh what?

[@handle535]

The problem with saying that computers can't 'think' is that you are comparing something that is known very well (what computers do), with something about which we know almost nothing (how humans think). Neuroscientists have literally just scratched the surface on the subject of human thinking and we may find, as we dig deeper into human thinking, that at the bottom there lies a series of basic operations akin to computer instructions, that is every bit as predictable. A comparison such as that, between something known and something unknown is essentially meaningless. Anyone claiming to have an opinion on the subject really has nothing more than a guess - and not even an educated one. The reason why we feel intuitively that human thinking must be very different to what computers do is down to the old saying 'familiarity breeds contempt'. Our familiarity with computers leads us to downplay what they do, while our unfamiliarity with human thinking (as in how it works) leads us to treat it with a degree of awe and wonder that may not be due.

[@DaveLillethun]

Agreed. The argument here that computers cannot think is very weak. We believe from Church-Turing that a computer can perform any algorithm. So the question is simply whether or not human minds follow an algorithm, and the answer is.... we don't know nearly enough about the human mind yet to know whether or not it does (but we certainly haven't discovered anything yet that would preclude it following an algorithm). I find that even the best arguments against computers ever being able to do all the things a human mind can do ultimately rely on an assumption that there is something "special" about human minds (which has yet to be scientifically demonstrated) that computers lack the ability to do.

[@DaveLillethun]

@Epsilon Theta If the indeterministic functions can be modeled by deterministic functions with random inputs, then we could still make an algorithm that performs that behavior.

[@DaveLillethun]

Epsilon Theta I’m not asserting that computers are capable of human cognition, just that the argument against it is very weak. Although I would remind everyone that computers have been made to do a great many things that most people once thought they would never be able to do. That said, could you cite this Penrose paper? I’m curious to take a look. (Although, I will note that cognition would have to be doing something different from *Turing’s* (and Church’s) definition of “algorithm”... I’m not sure if this way AI defines it is any different...)

[@DarkestMirrored]

Yeah, as far as I'm concerned there's zero reason a computer *couldn't* think. Humans think. Humans are able to think because of our brains/nervous systems. A nervous system is a physical object whose behaviour is governed by physical laws. Computers can model and simulate physical laws and their effects on matter to an effectively arbitrary level of accuracy (the limits of which all have to do with scope and processing power- you can't make an "oracle" that can perfectly predict the universe without using at least as much matter and energy as the universe). Hence, if a computer was given enough accurate data/parameters, it could simulate a human brain with total or near-total accuracy. A simulated brain that produces behaviour identical to or comparable with that of a "real" brain is effectively indistinguishable from a "real" brain if you look at the output behaviours. It doesn't matter how the behaviours are generated, ultimately; if you were extremely patient and had an infinite lifespan I'm sure you could simulate a person and a little box environment for them to live in using a sufficiently large number of rocks assembled into logic gates. Furthermore, our brains are not terribly efficient. Its fairly rare for nature to produce something in "the most efficient way possible", and I feel confident saying that cognition is likely one of those things it has failed to produce efficiently. Thus, there are probably ways to get human-like behaviour and "thought" out of a system less complex/resource hungry/large than a human brain. We don't know how _yet,_ but there's nothing we know about physical laws that would imply its impossible. Even a "dumb" system can produce incredibly complicated behaviours and react to stimuli in "intelligent" manners, too. Just look at an ant nest.

[@myname9748]

@@DarkestMirrored Beautifully stated! Couldn't have said it better myself!

[@yuricahere]

16:48 problems reviewed in class: bisect an angle problems on the test: trisect an angle

[@recklessroges]

first 35:00 minutes some computer history working up to explain The Halting Problem. Then the rest is, "We have yet to prove is P=NP or (P not = NP)".

[@unvergebeneid]

Thank you for saving one hour of my life!

[@OttoIncandenza]

My issue with the halting problem is that you can't write a computer program that checks any computer program for bugs. But a human can check any computer program for pugs. So human thought is not the same as a computer?

[@realblender3D]

Yes, humans can check any computer program for bugs, but i don't think anybody has found a way (an algorithm) to eliminate all bugs, without error, meaning not leaving any out. People would pay a lot of money for that, if the method was somewhat fast. Only if this is the case, does this specific argument for human thought being different from that of a computer hold.

[@NetAndyCz]

" human can check any computer program for bugs"... well can they? Once those programs get thousands of lines of code (or tens of thousands, hundreds of thousands) humans need assistance of computer to see where the bug in the code is. There are so many different possible types of bugs. Humans have advantage of being able to look at things in more abstract way instead of testing every possible input, but I think it is just temporary till computers are "taught" to look for the same things humans look for.

[@Biomechanoid29ah]

Jesper Birch there are programming techniques that rely on brute force to weed out bugs, things like genetic algorithms and self programming neural networks can solve problems in peculiar and innovative ways (they aren't capable of finding said problems yet, but wait)

[@filthyfilter2798]

Lord Voldemort in pijamas pants and fine costume explaining awesome things :D

[@mattjones8010]

The idea that proving P=NP true would lead to all these shocking consequences (e.g. encryption breaking) assumes that any proof of P=NP would be 'constructive' i.e. that the proof itself would outline *how* (construct) we could quickly prove (move to P) something that's quickly verifiable (NP). This would be some general schema or framework applicable to any NP problem, like a computer program. Proofs, however, needn't be constructive: they needn't actually design some process to achieve the desired outcome but, rather, show its truth based on general principles. Mathematicians don't all agree that a P=NP proof would necessarily be constructive. So, even if P=NP is proven to be true, if the proof is non-constructive then we needn't immediately worry about chaos ensuing. Designing methods for 'cracking' individual NP problems might take an unreasonably long time (indeed, we've failed to do so for many basic ones so far e.g. factoring), so the impact would be limited.

[@arunavasarkar3600]

if p = np is someday proven the prove itself will give way how the complex np problem becomes p. so ya the proof will be enough to cause the breakdown. what you suggested is if someone finds an example that would not break things. but example and proof are two different things.

[@trudyandgeorge]

It's funny, the description of how the computer doesn't "think" was to point out that Kasparov wouldn't exhaustively go through each potential next move in his mind, he would employ some intuition and other "thinking stuff", whereas the computer basically exhaustively goes through each move until it finds the next best one to play. This is in fact not what the computer does. The whole point of computers playing chess is because of this fact. Chess has too large a search space to simply blast out a tree and collapse back on to the highest scoring leaf.

[@FranzKafkaRockOpera]

Yeah, I didn't think that was a very convincing argument either, and the distinction between proper thinking and running through all options isn't at all self-evident. Both Kasparov and the computer are obviously using shortcuts for efficiency, but the simplicity of chess's rules doesn't afford them a lot of leeway and they're basically evaluating the pertinence of potential moves in the same way.

[@kenh8265]

Thanks to the RI and Prof. Kevin Buzzard for a fast show intro into what's computable and what may not be. Brilliant!

[@antonnym214]

As an extra note: If you're looking for prime factors, then as you are testing whether a divisor is composite or not, the shortcut is that you have to test only up to the square root of the number you are factoring. e.g. SQR (100) = 10, which means to find all prime factors for 100, all you have to do is test the prime numbers between 2 and 10 (2, 3, 5, 7) And that's GAG (Good As Gold)

[@jerrygundecker743]

A killer robot forced him to wear those pants. No one would volunteer to do that.

[@prathameshjoshi9199]

It was a very smooth journey from Killer Robots to P vs NP an millennium Price Problem 😁

[@iammichaeldavis]

After years of watching these, I just now tonight saw the final end card that declares these videos are released under a Creative Commons license. That is so, so cool. I ❤️ the RI

[@jmarsh5485]

What that?

[@geoffpot]

I think your example about the K program is wrong or incomplete. Considering that a program could be good or bad with different inputs(as evidenced by the program K itself), a program that determines if a program is good or bad would ALSO have to take in that programs inputs. So when you feed K into K, you'd also have to pass what you were passing to K, which if it had inputs(as K does) would also have to take the inputs into that function. So somewhere in the call stack there would either be missing parameters, or a scalar value. I think the reason we can't write a program that perfectly checks other programs for bugs is because the input space is infinite, which means the method checking for infinite loops would always be an infinite loop itself. If you limit it to programs that have no inputs(and would be valid single inputs for K) then I'm pretty sure you can build something that checks any code for bugs. Thoughts are welcome if I've missed something obvious here...

[@Grrblt]

What you've missed is that K doesn't take any input. Program Y takes input (another program X) and says whether X is good or bad.. K does the following: ask Y if K is good or bad, and depending on the answer, do the opposite - thus showing that the answer given by Y is wrong.

[@Pascal6274]

@@Grrblt I think you might have misunderstood something. K takes a program as input. As the quote in 33:09 states, it receives a program as input and behaves differently whether you input a good or a bad one.

[@Pascal6274]

The definition of "good" and "bad" seems to me like it's not specific enough in the video. You're right, if a program is good or bad is highly dependent on the input. If you define "bad" as crashing for any input, then K could just be a bad program, as K not crashing for the input K doesn't make it a good program. I think you're right, the proof might be incomplete.

[@Pascal6274]

The solution is to look at programs input into themselves. So you would have to make another program P that checks for any input X, If X input into itself would make it crash. Then P input into P cannot give a valid answer. I can recommend this video: kzbin.info/www/bejne/b2O6eYFjpaZ5edU

[@Grrblt]

@@Pascal6274 K itself doesn't take an arbitrary program. It only ever needs to know about program Y, and itself. Y is the one that takes an arbitrary other program. If Y works as claimed, it can answer the question "is program K (with no inputs) good or bad?" That is the way this proof is *supposed* to work. If his slides claim differently then he has added unnecessary complexity and, I think, in this case actually broken the proof.

[@rilian226]

Algorithm at ~29:25 gets stuck in a loop if x=10.

[@hybmnzz2658]

Also 5

[@shanefoster5305]

Those robots aren't killer robots... they are mostly designed to assist troops by bringing them supplies.

[@samwise210]

First half of the talk: "If I define thinking as something only humans can do, I can then state authoritatively that computers can't do it. I will fail to mention that modern agents approximate more and more the methods (that we think) a human uses to think." The second half of the talk is actually a pretty good description of complexity problems, but slightly lacking in that it doesn't mention the existence of EXP or greater problems.

[@Enonymouse_]

Great speaker, very energetic which is what you need when dealing with complex and dry subjects.

[@gegwen7440]

IMO quite the opposite. Speaking way to fast while running around means that after no more that 10min I stopped his ramblings and started to read the comments. Going by the amount of dislikes I fancy others may also hold that view.

[@Croqueta-s1f]

"dry subjects" ???

[@obnoxious.]

He's so excited, and he just can't hide it.

[@ThinkTank255]

Regarding the beginning of the talk, and how computers cannot think, apparently he has never seen AlphaGo or AlphaGo Zero or anything else going on in modern machine learning.

[@satadhi]

what is not thinking ! man !

[@ThinkTank255]

Yes, especially Kevin Buzzard.

[@taragnor]

AlphaGo doesn't really think. Artificial Neural networks are basically just a form of directed brute force at their core.

[@Reddles37]

So is your own brain though.

[@TheNemocharlie]

@@Reddles37 I'm not convinced that is true, although you make a good point. In some ways it's like an infinitely fast computer that encompasses all human experience. But would a neural network be capable of concieving something outside human experience? Could it, for example, replace Einstein and Mozart and van Gogh? Write all those papers and all that music? Could it really lay claim to all that creativity? It's not as if they have an infinite amount of time. Let's say there are only 237 years before human extinction (an estimate based on unpublished data that is by definition inarguable). Could they do it by then?

[@stephenfowler4115]

You cannot trisect an arbitrary angle however trisection of a sixty degree angle with a compass and a ruler may be possible. Take an angle of 180° and construct three adjacent sixty degree angles.

[@davidhasen7983]

Turing's conclusion that you cannot construct a computer program that will say whether any program will not get into an infinite loop is a lot like Russell's example of the set of all sets that are not elements of themselves. It seems to have the same basic structure. That set cannot exist, because if that set is in the set it is out, and if it's out of the set, it's in.

[@antonnym214]

Boston Dynamics "Big Dog" is designed to be a robotic pack animal, not to kill things. It's no more a killer than a burro.

[@anglachel7407]

It's just a question of time until someone puts a grenade launcher on it.

[@boggers]

The angle trisection proof had me intrigued so I looked into it a bit more. Turns out you CAN trisect an angle using nothing but a straight edge and a compass. Archimedes did it, but his method uses a mark on the ruler. You could put the compass next to the unmarked ruler to get the same result as a marked ruler. The 1837 proof relies on a imaginary nerf compass that collapses when lifted from the page and as such cannot measure distances. An imaginary collapsing compass couldn't bisect an angle either, since you need to draw two circles the same size.

[@goesuptoeleven]

"Because it is defined in simple terms, but complex to prove unsolvable, the problem of angle trisection is a frequent subject of pseudomathematical attempts at solution by naive enthusiasts. These "solutions" often involve mistaken interpretations of the rules, or are simply incorrect." Wiki

[@ronald3836]

Trisecting an angle using compass and ruler and without somehow cheating is impossible. The proof involves showing that the numbers/length you can construct with compass and ruler are combinations of +,-,*,/ and ✓. These numbers will be the root of a polynomial with integral coefficients of degree a power of two. If you could trisect an angle, you could construct the third root of two with ruler and compass, which is a root of x^3-2, which is a polynomial of degree 3. So this is not possible.

[@handle535]

If P=NP then it doesn't mean that we suddenly obtain a P algorithm for every NP problem. It only says that an algorithm must exist, not what it is, or how to find it, or that we will find it, or how long it will take to find it if we ultimately do. All it does is guarantee that we are not wasting our time by working on the problem. If P!=NP, it does not mean that all problems currently thought to be NP have no P, only that *some* NP problems have no P. This would also not mean that all encryption algorithms are unbreakable or even that any currently used encryption algorithm is unbreakable. This is because a given encryption algorithm may rely on a problem that turns out to have a P algorithm even if there remain other problems that are NP and not P. Furthermore, even if the encryption algorithm relies on a problem that is not P, there could still be flaws in the algorithm that allow the asymmetry to be sidestepped. This is why encryption algorithms can be considered 'broken' even though they make use of NP problems for which there exists no known P. All it would say is that there *can* be unbreakable encryption algorithms, not that any given algorithm is unbreakable, or that any known algorithm is unbreakable, or how to find an unbreakable algorithm, or if we ever will find such an algorithm.

[@Grrblt]

If P=NP then we actually *already have* a P algorithm for every NP problem. What it does is to iteratively try every other algorithm for not-too-long. If the other algorithm runs for too long, kill it and try another one. If the other algorithm gives an answer, check it for errors. If correct then we're done, otherwise start over with a different algorithm. If we've tried all algorithms, start over from the beginning with a little bit more time allowed. Since the problem is in NP and P=NP, then some P algorithm exists, and our program will eventually try that algorithm out with enough time allowance, and it will give a correct answer. So as you can see, our "super-algorithm" isn't very clever and even though it runs in polynomial time, it's going to be a very big polynomial so it will still be extremely slow in practical terms. It's called Levin Search if you want to google more about it.

[@OwlTiny]

Trisecting can be achieved at the third order of the method shown. First half, second level one quart, third level on twelfth, every fourth intersection is one third of the angle.

[@heyandy889]

Other than the opening few minutes of FUD, quite a wonderful, general-audience accessible to the idea of P=NP.

[@makeshiftaltruist7530]

I have seen people get stuck in thought loops... Friend of mine took too much LCD and just kept repeating the same thought process for hours. It was terrifying... to realize we are just biological computer programs

[@tophan5146]

That's why I recommend OLED, it provides way better psychedelic experience

[@KatKevaKelise]

Makeshift Altruist LCD?????

[@srikarbabusriram7675]

@@KatKevaKelise MN....@0

[@ornessarhithfaeron3576]

Your friend should try an e-ink panel next time

[@kylethompson1379]

Maybe you were the one stuck in a loop. How can we tell.

[@erichodge567]

This was the best introduction of this problem to a lay audience that I have ever seen.

[@zugzwangelist]

Amazing talk. Kevin Buzzard is the boss!

[@virtualatheist]

The analytical engine was not built by Babbage because the accuracy required for the engineering of the components was beyond the limits of the time. It was built by a university team in the 20th century.

[@gregg4]

There are a couple of mistakes in this lectures. An example is at about 1:00:10, "guaranteed cannot be solved quickly". This is not true. If P=NP than there definitely exists a way that the problem can be solved quickly. If P not equal to NP than that doesn't rule out the existence of such a solution, we just cannot be sure. His slides include the word "probably" but that is not how he said it.

[@invictus327]

Proving that P = NP represents the computational (and socioeconomic) equivalent of vacuum decay in physics: catastrophic and irreversible disassembly. So, we are able to know that there exists a possible state of meta-mathematical knowledge in which a proof indicates the hard end of secure online transactions, encryption and unproblematic personal privacy assurance, or it does not. There would be good things, certainly, but only at a cost of extinguishing the computationally-facilitated financial, security and privacy systems (such as they are) that currently exist. There is a one million dollar prize up for grabs in this space. Personally, and given these high stakes, I think it is probably worth much more than a million dollars for someone NOT to find a solution but, just as with the intial testing of hydrogen bombs and the fact that it was unknown if they might generate an accelerating chain reaction that incinerated the entire atmosphere of our planet, caution is generally not the first consideration in matters of curious invention.

[@1e1001]

29:19 : 1. Set x to user input 2. If x == 10 then crash, else go to step 3 3. If x < 100 then go to step 4, else go to step 6 4. Double x 5. Go to step 2 6. Print "hello" 7. Exit This means that if you input 10, 5, 2.5, 1.25, ... Then it'll crash, otherwise it'll reach 100 and exit

[@jeremyphillips7827]

Don't forget about zero and negative numbers as inputs. These will cause an infinite loop as x remains at zero or gets progressively smaller in value. (i.e., 0*2=0, -n*2=-2n)

[@dougsteel7414]

Addiction is evidence that human thought can proceed rationally and get caught in a loop, awareness of the loop isn't the problem. A computer isn't told not to get caught in a loop. Compilers and runtimes can preempt complex recursive traps, in the same way people do, and in fact are better at it. Time constraints employ a heuristic, if people write one into a program it's seen as a patch or hack, when ironically this is an example of human thought management

[@HorusHeretic]

Drinking game, each time he walks around the table..... Take a shot.

[@QqJcrsStbt]

Boston Dynamics were designing and building amechanised Sgt Reckless. Its purposuse is to carry heavy, ammunition, food, water, mortars, mortar rounds and other materiel over terrain. Not really a killer. You showed an RC version which can hardly be called a robot.

[@philsheppard532]

Bisect the triangle. measure a distance up each side of the angle , put a line across the two points , bisect this line and connect this point with the starting point . No need for the compasses at all ?

[@PifflePrattle]

Is this the first time a lecturer at the RIi has thrown a jacket over his jim jams to give the lecture?

[@vin-cc9nk]

this guy propably was behind that one black mirror ep with the boston dynamics killer robots

[@jukkajylanki4769]

On the video it is suggested that Deep Blue was brute forcingly going through even "silly" configurations in its search, but that would have been a gross understatement and a harsh insult against the Deep Blue engineering team. Reality was very far on the contrary - Deep Blue software was running the most sophisticated levels of chess search algorithms at the time, which certainly did not spend time looking at silly configurations. Of course even the fact that Deep Blue used an efficient and well researched chess search algorithm does not dispute or alter the point that the lecturer was making about the question whether the computer was "thinking" or not.

[@vitakyo982]

Run in a loop : abs(ln(n)) Start with n=2 & reinject the result in the formula & so on . Can you tell the value after a million step without running it ? Does it ever stop ? Does it repeat itself ?

[@lukalot_]

I think that is should be able to bisect the angle with just a ruler. make the angle, measure x amount of space up each edge of the angle, say 4 inches. Mark the ends of the 4 inches and draw a line between them. Measure the line between the marks, and you will come up with some length. divide the length in half and measure that much and add a dot at that point. Now draw a line from the base of the angle and the dot. Done... right?

[@amadexi]

It's quite confusing for regular people to claim that's it's "what computers can't do". It's more general than that, it's about the limits of computing and logical processes, it also applies to humans which in technical terms are also computers (as in, we compute data).

[@phizzhead53]

Also every cell in your body is a turing machine as well

[@gJonii]

@@phizzhead53 no? What do you imagine inputs and outputs to cells are?

[@zdcyclops1lickley190]

If you can't tell if you are interacting with a computer. Then whatever the computer IS doing. Produces the same results. Much ado about nothing.

[@deplant5998]

Does multiplication increase the entropy of the universe and factorisation reduce it?

[@theosmid8321]

do not understand the mathemtics but am aware of the implications. got ample words for it.

[@jeremyphillips7827]

For the program at 29:19, the answer I got was that if x

[@TheOnlyAndreySotnikov]

It's not that it's always difficult to solve an NP problem. There are methods that solve them quickly. The problem is that these methods don't work quickly on all inputs. If on some input it takes long to find a solution, you have no way to know if a solution doesn't exist, or it just will take a hundred years of calculations. However, many practical NP problems can be solved quickly, or sometimes a slightly suboptimal solution is fine.

[@master_yoda.]

how they even find such a great speakers...

[@jthadcast]

the distance is always "the length from the pencil tip to the spiky bit." words to live by

[@DavidFMayerPhD]

1. Atanasoff & Berry constructed and operated the FIRST electronic digital computer at Iowa State University from 1939-1942. Nobody was interested in the least. 2. Turing's proof that the "Halting Problem" was impossible to solve has this (not too obvious corollary): It is impossible to predict the result of an algorithm (computer program) without actually running the program. If this is not clear to you, think about it for a few minutes and it will suddenly clarify itself. 3. Programs that can run in Polynomial Time may sound like they are computable IN PRACTICE, but this is not really true. Example: Suppose that a program with N (at least 10) as an input runs in N^(9999) time [N to the power 9999] then it will prove to be insoluble in practice since 10^(9999) Planck time units is far longer than the lifetime of the Universe so far. In the real world, depending upon the program, a program that runs in Linear time (power 1) is nearly always (with obvious exceptions) able to finish, while a program that runs in Polynomial Time where the degree of the polynomial is larger than 10 or so, will never get done. From a PRACTICAL point of view, a program that runs in Polynomial Time may very well be intractable, even when the degree of the polynomial is fairly small.

[@fromvoid3764]

"It is impossible to predict the result of an algorithm (computer program) without actually running the program." I would argue that this statement only holds for a subset of algorithms. Take an algorithm that takes an input as counter for a loop and adds one to a variable (starting at 0) on every loop. At the end it prints the variable. You don't need to run through all the loops to predict the result. Steven Wolframs idea of irreducible complexity comes to mind here. I would guess your statement holds only true for algorithms with that property.

[@DavidFMayerPhD]

@@fromvoid3764 I should have written: "It is impossible to predict the result of an ARBITRARY algorithm (computer program) without actually running the program." Sure, trivial programs such as STOP can be predicted. What is impossible is to predict the results of an ARBITRARY program without actually performing the calculations. Only an infinitesimal fraction of algorithms can be predicted. The rest, 99.9999999999999999999...% cannot be predicted.

[@aufdermitte7143]

yeah, there are many algorithms that according to complexity theory are the best but nonetheless are never used in practice because they are only better than other algorithms for extremely large n. Also the distinction between polynomial and non-polynomial is too crude for being useful in practice, in real life something that runs in O(n^3) or O(n^4) is already close to being useless.

[@DavidFMayerPhD]

@@aufdermitte7143 A fact conveniently ignored by theorists.

[@Treeninja01]

I think we missed the Joke slides he mentioned earlier :'(

[@arulpraveen]

hahaha...So people definitely lie :P

[@djrise0]

49:08 After that long video of umms and ahhhs, volume shifts and pitch oscillations...This single moment was surreal.

[@Falcrist]

It's nice that he personally doesn't think computers "think", but the terminology is horribly vague, and the way humans think is poorly understood.

[@TheNoodlyAppendage]

around 50:00 Yes, computers cannot solve EVERY problem, but for the class of problems that can be solved by computers, they work well enough for use as a tool to solve those problems and save true consciousness to focus on other aspects of the higher dimensional problem.

[@ashwinkafle1771]

But you could trisect by making half angles 3 times. That'll be messy and hard but can be done by compass and ruler just use a large angle and different colors and it'll make sense. For small angles, you'd have to be very careful. Even the half angle can be very tricky for small angles!

[@Xentagia]

No you can't. You half the original angle, then if you half the halves you get quarters. If you half those you get eighths. 2 * 1/8 = 1/4 and 2 * 1/4 = 1/2. You never get 1/3, only 1/(2^n). You could combine these to get 3/8, 5/16, 11/32 and so on, getting close and closer to 1/3, but you never actually get there

[@crabsynth3480]

Thanks for this Nice Lecture. Excellent Quality & Content

[@dannygjk]

His description of how chess engines work was totally without any background knowledge.

[@odw32]

I do not understand people who claim "computers can never think". What are humans but trained, weighted and backpropagated neural nets? If computers can not think, than what are our own neurons doing so differently? ML is currently disjointed in specializations, and at simpler mammal levels of intelligence. It's only thinking in the same way the visual cortex or the memory of a dolphin "thinks". But artificial brains are being pieced together, neural nets are now often joining together for more and more complex tasks.

Wait, I found a way to trisect an angle with a ruler and a compass. I haven’t been able to probe it, but it works somehow. Angle ABC is given. Draw Circle DB with D being on one of the angle’s sides. Mark E where the Circle DB intersect the other side of the angle. Construct equilateral triangle DEF with F being farther away from B than D or E. Find the midpoints of DF and EF, and label these G and H, respectively. Draw GB and HB and, voila, trisected angle. If you do all this properly, you get three equal angles, all which can be physically measured to be exactly 1/3 of the original angle, but you should leave appropriate room for error unless you did every step perfectly. If anyone can somehow disprove it, I’d be very happy to hear, and if anyone can prove it I’d be happier. This only works with angles 180 degrees or less, though. If you want up to 360, you can bisect that angle and complete the proof on the two new angles, and take the trisecting lines which would be most applicable because I can find no better way of describing them

[@romzi8157]

If P=NP - does it mean L=NL? If L=NL - does it mean P=NP? Does solving one of these 2 problems leading to solve the other one? Do they have same kind of decision algorithm?

[@mattbox87]

Yay, RI! Computer Science!

[@Lovuschka]

Google might not have killer robots, but it has AlphaZero. So most of that talk about Kasparov vs. Deep Blue was just obsolete a year later (i.e. in 2018), when that evolution of computers in chess reached a level beyond human comprehension.

[@romzi8157]

Quantum computers in the present-future are able to solve every problem in EXP like a problem in P - including all the NP and NPC problems. So quantum computers can easily brake the encryption code of the Internet. It’s already starting to happen - this ability. So the internet should find fast solutions to this - make much harder (security) codes to break - by making decipher it a problem harder than EXP - if they (the cyber security system persons) can.

[@romzi8157]

Maybe I’m wrong - Scientists now believe quantum computers can’t solve in a polynomial set of instructions problems in NPC - though they can reduce its complexity to the root of the complexity which is when using regular computers.

[@romzi8157]

But it has proven quantum computers can solve RSA and Factorization in general - in a polynomial running time. Factorization and RSA which is based on Factorization problem - are problems in NPI - problems harder then P but easier than NPC. NPI = NP Intermediate - problems that not as easy as problems in P but not as hard as problems in NPC. PRIME was in NPI till it was proven in 2002 - that it’s a problem in P - by a sophisticated proof in theory numbers field by a team of 3 mathematicians in India.

[@ILikeSongs5]

To find a definitive answer to the question does P=NP. One must first understand the limitations of the system in question. In the case of computers, current technology has its limitations but future technology may have different capabilities and limitations, perhaps less, which then changes the equation but not necessarily changing the answer. Regardless, we must ask the question and find the answer to know for sure.

[@patrickwienhoft7987]

P=NP is a purely theoretical problem defined by Turing Machines. It has nothing to do with our current state of technology.

[@Ykpaina988]

HERBERT DRReyFuss >Martin Heidegger>WittgensteinBERNARD STEIGLER. Consciousness needs to be embodied. I am the Machine.

[@tonyennis3008]

This was about 10 minutes of material stretched into an hour.

[@botboy0]

what? I think its the other way around . He compressed a multi hour lecture into a single hour, didnt you see how fast he talked?

[@mander40101]

I'm going to re-watch this and count how many times he says "killer robot".

[@rex8255]

If one looks at the history of self driving cars, at least the early iterations, it could be argued that they qualify as "killer robots". It's just that the killing part and the target part are pretty much random.

[@RFC3514]

29:13 - It doesn't crash, it hangs. Different things.

[@andreimotinga1826]

lectures like this are why it's hard for me to sit through one-hour lectures... I kept hoping that it's gonna get better. it didn't :(

[@davef21370]

Why should it get better? It's an hour of information you may or may not need. If you need it sit it out, if not go home. It's that simple.

[@TheGrapist18]

it's surface level all the way through

[@jojolafrite90]

Get better? What are you talking about?!

[@Centigonos]

@The Grapist: Of course it is. It's intended to be of interest to laypeople, not to experts in the field. I think, he does a brilliant job of verbalizing the whole thing for its target audience without dumbing it down.

[@skilz8098]

Consider that the question itself does N=NP? Is an NP problem itself. Then I'd like to think that there is a bit of ambiguity and recursion here that makes this question to complex to resolve. Therefore, I think it might be impossible to either prove or disprove the original assessment. This tends to lead to Set Theory. If the set P is fully contained in the set NP where P is a subset of NP how can one determine if a given NP problem is either an intersection or a union of P? I think that this question is a paradox...

[@johnmcnair3875]

Ok so the point that you cant use a straight edge and compass to trisect an angle is fundamentally flawed. Every angle represents an arch and calculus states that you can divide an area under an arch into an infinite number of sections with an equal width. If the argument is the tools available, its quite easy to trisect 360 180 and 90 degree angles using a compass and straight edge. Other angles take alittle more ingenuity but can be done as well.

[@DJayDiamond]

4 billion cycles per second, not 4 billion instuctions...you can have different sizes of instructions so you can have multiple instructions per cycle

[@brianfretwell3886]

I don't think mainframes can tap dance. That's one thing computers can't do.

[@keplergelotte7207]

Did they leave treats hidden around the desk?

[@logically1028]

To trisect an angle, after its been bisect, cant we put ruler on the two cuts made by compass and join them by a line, then divide the length of the line by 3 and put dots on that line as per the result and then draw lines from 'o' to these dots, and i think we will have trisected it...!!

[@Belgrove]

But you didn't use the compasses to divide the line by three. You used arithmetic, and it does not trisect the angle.

[@pankajdeepsahota6159]

this is by far one of the best speeches i have ever heard !!

[@bevvox]

What happens when all assumptions, all prerequisites are wrong..?

[@OzzMazz]

1. Computers don't think! 2. If you can't solve a problem with pencil and paper, you can't write a computer program. End of story.

[@CascadeHush]

So the reason there is no money in Star Trek is the same reason there are no locks on the doors - in that universe encryption can be trivially broken, therefore electronic commerce is impossible, money in meaningless, banks serve no function and wealth cannot be abstractly represented by any kind of currency.

[@gbiota1]

A problem with this logic is that Kasparov doesn't have intuition, he has memory of past failure. New players don't have intuition, because they haven't stored memories of previous similar situations and bad responses to them.

[@connorskudlarek8598]

Intuition can come from experiences other than playing chess though. You can bring experience from poker to chess, and that is called intuition. You have no experience with this situation (chess), but you have other experiences. Someone might be bluffing with a move, and your intuition (not your experience with chess) might be telling you that the move they just made isn't an aggressive move but a defensive one trying to trick you. Intuition really is an instinctive ability to recognize something, rather than relying on rationality to recognize something. I am sure, even the most advanced chess player, still plays with their gut every so often.

[@gbiota1]

When I got to the end of this I saw that it was a bit long, I hope you find it worth your time and consideration. In so far as there is symmetry between the rules of different games, situations, or systems, strategies can be applied laterally with a degree of success depending on the degree of symmetry. You gave one example, a more general one is the attitude of Winston Churchill. In his early days he flew planes that could only maintain their height by going against the wind. He applied this experience to his life's philosophy, in 'always moving against resistance'. The challenges of the world are sufficiently diverse that the ability to do this has probably proved to have significant survival value. We seek the lesson, the abstracted principle that can be applied elsewhere, and sometimes we find them. Kasparov's memories of past failure may not be restricted solely to chess, but until he has experience in chess, he can not identify symmetries between those experiences and chess. He can't yet apply the abstracted principles he has picked up elsewhere. Once he can begin finding symmetries, which takes a kernel of experience, he can access and benefit from broader experiences of failure. The feeling you get when you 'go with your gut' is a calculation your brain makes, you just can't inspect how it is making it, but you couldn't inspect how your brain adds 2+2 either. The explanation you give for "why" is just a matter of articulation that happens after your brain has already done the work you weren't able to watch. You can't watch how you articulate that very precisely either, even though its something your consciousness plays a bigger role in moving through the steps of. I think this distinction about intuition (and its often vague meaning) is important, because to do otherwise sets our expectations up for failure. I think many people want some reason to believe that we contain something innately valuable, not just to each other, but to the world objectively (and I don't deny the existence of objectivity, either). If that belief is dependent on something we are calling intuition, we may be disappointed when systems are developed which can identify symmetries between different systems and situations. The Go computer, the Chess computer, may then be able to draw from those simplified experiences in its search for battle field tactics, medical diagnosis, or engineering. Then we'll just have to ask ourselves what we are good for all over again.

[@twelvetenth8580]

My answer. Whitout viewing the video. The only things humans can but machines can not are ironically those abilities of us which we still do not understand well enough to create them artificially.

[@PLF...]

Whenever someone say "well a computer can't do this!" it usually doesn't take long before someone makes a computer do it. This won't be an exception.

[@DeviHisgen]

somebody tell this dude not to make his coffee with energy drink and amphetamine. He really tested my computing speed lmfao

[@moldyboi354]

Them pants tho😁

[@malporveresto]

Juras Kumar The contrast between his pants and the historical Ri lecture hall.

[@moldyboi354]

malporveresto True that. Nice talk tho. Kinda goes against the dangerous AI narrative

[@TechyBen]

Anti Killer Robot Pants. ;)

[@MatthewMarshall96]

He ignored a lot of modern advances in AI; never really qualified his opinion that computers can't think in a meaningful way, either. Yes the first ever algorithms used to win games used brute force, but even by Deep Blue that was changing (chess having too large a branching factor to brute force) and nowadays we have Alpha Go which performs phenomenally well in Go which has an obscene branching factor so it is definitely doing some kind of thinking akin to humans. Plus he did admit near the start that dangerous autonomous robots are possible with current technology, ignoring the whole thinking part. Then also pointed out we can never prove a program has no bugs, so even without an AI choosing to be harmful for a reason we'd consider "human" it could choose to do something harmful!

[@TechyBen]

AI is dangerous in the same way a car is, or a dam, or a hammer or a fist. The difference being, AI can be let go, a loooooong way away, then the end result happens. Where as most cars only roll down a hill when the breaks are left off.

[@Illu07]

0010001010100100101 has only two google hits

[@arekkrolak6320]

the guy was so dedicated to arrive on time he even appeared in his pyjama :)

[@michselholiday6542]

Surely there is a quicker way to unmultiply.

[@milanstevic8424]

surely.. a long time ago, in a galaxy far, far away....

[@greg2spook]

by eliminating the known 0-2-4-6-8- and 5.... same program for finding prime numbers... in reverse

[@joeroganjosh9333]

This was 2017 ? His cosmology is a few years out of date. Current understanding is the geometry of the universe is flat and expanding exponentially (WMAP etc.) suggesting it will continue to expand and not slow, stop and contract.

[@robotaholic]

Couldn't you just program a computer to make mistakes and this guy can calm down lol

[@RealRobotZer0]

Draw an angle , triple it, then claim you did it backwards.

[@connorskudlarek8598]

That wouldn't be trisecting an angle. That'd be making 3 congruent angles. It would create a single angle that is trisected, but it didn't trisect a given angle. Same end construct, completely different though.

[@martonlerant5672]

Proving that you can write a program that can get stuck isnt the same as proving that you can't write a program that doesn't get stuck. Btw. humans (and other biological organisms) can also get stuck - motivation is the only thing preventing people from that. Pigeons can get stuck, put multiple pigeons in a skinner box, there is good chance that some of said pigeon will do random stuff (like flapping its wings) while an othrr pigeon ttiggers the rewards. And then said pigeon gets stuck brlieving that it needs toflap its wings to get rewarded. If an other pigeon triggers the reward while its still desperately flapping, it will be even more sure. Same thing can easily happen with humans. The only thing preventing a true infinite loop is death - but then computers break too.

There's a LOT of offtopic stuff in this. You could skip probably over 20 minutes at the beginning and still understand everything. And in total, it's just Halting problem and P vs. NP. So if you know these two already (there are many much better explanations of both on KZbin), then you can skip 64 minutes.

[@thekaiser4333]

Mr. Buzzard - Who did the Boston Dynamics killer robot kill in the video?

[@larryfinley9221]

It seems to me that it would be easy to invent a computer controlled armored machine that kills everything. The difficult thing would be trying to get it to make decisions on who to kill and who to leave alone. Too many variables. (i.e. Self driving cars that can handle every scenario is a similar problem)