Selects: A List Of Games You Would Surely Lose to a Computer | STUFF YOU SHOULD KNOW

hi everyone it's Josh and for this. week's select I've chosen our 2018. episode some games you would surely lose. to a computer it's a philosophical. discussion about AI That's disguised as. an episode on computer games honestly we. didn't plan it to be like that it just. turned out that way we're pretty happy. that it did and in light of the recent. advances with machine learning like chat. GPT a few of the things we say seem. naively quaint now plus it has a dollop.

of our Tech stuff colleague Jonathan. Strickland at the end so that's a bonus. I hope you. enjoy welcome to stuff you should know a. production of iHeart. [Music]. Radio hey and welcome to the podcast I'm. Josh Clark there's Charles W Chuck. Bryant there's Jerry over there I'm just. going to come out and tell everybody. making fun of me for some weird reason.

vaguely weird ways but I'm all right so. check out have a story for you okay I'm. going to take us back to the. 1770s and The Swinging town of Vienna. not. Virginia not Viana Georgia which you. know that's how they pronounce it right. Viana Viana sausages right Vienna. Austria you ever been there Vienna. Austria no been to Brussels that was. pretty close.

yeah Vienna's lovely I'm sure I think. it's a lot like Brussels very clean. lovely town I just remember it being. very clean yeah very clean gorgeous. architecture weird little angled side. streets that're very narrow very pretty. town so we're in Vienna and there is a. dude skullking about going to the Royal. Palace in Vienna um his name is Wolf. Gang Von klin and he's an inventor he's.

an engineer he's a a pretty sharp dude. and he's got with him what would come to. be known as The Turk but he called it. the Mechanical Turk or the automaton. chess player and that's what it was it. was a it was a wooden figure that moved. mechanically seated at a cabinet and on. top of the cabinet was a chessboard and. when he brought it out to show to the. Royal Court he would. um it was cool kind of but nothing they.

they hadn't seen before because automata. was kind of a a hip Thing by then yeah. people loved uh building these uh. engineering these automat machines to do. various things and people are just. knocked out by the fact that you know. you hide these gears and levers behind. wood or a cloth and it looks as though. there's a real well not real but you. know what I mean that it's like a real. machine.

yeah but not they weren't fooled and. think like is that a real man it was but. it was for their time it was so Advanced. looking that it's like us seeing. exmachina in the movie theater sure does. that make sense yeah no it does make. sense but imagine seeing like X mckin. and being like I've seen this before. this isn't anything special okay yeah. and this thing to be clear look like a. uh is it Zar or zultan from big uh zar.

suan I don't know it's one of those two. for one of those two like this this. guy's wearing a turban and it's in a. glass case like a bust like you know. like a chest up thing yeah he's seated. at this this cabinet so there's no need. for legs or anything like that yeah but. the thing this is what was amazing about. the The Turk he could play chess and he. could play chess really well so yeah he. was like an automaton and he moved all. Herky jerky or whatever but he could.

play you in chess which was a huge huge. Advance at the time like this is. something that wouldn't come up again. until the 1990s more than 200 years. later this thing this automaton could. play a human being in chess and beat. them well yeah and it looked like when. the game started it would look down at. the chessboard and like his head. like H what should my first move be. right and if people I love this part if. people tried to cheat apparently.

Napoleon tried to cheat this thing cuz. this guy he he debuted at the vianes. court but then it you know it went on a. world tour yeah and and he was even it. was taken over by a successor to the guy. who toured with it even further people. went nuts for this stuff they did they. loved it because they were like this is. crazy I can't believe what I'm seeing. most people though were not taken in by. it they're like there's some trick here. sure but Von kemplin and the guy who. came after him I don't remember his name. um they would they would demonstrate you. could open this cabinet and you could.

see all the the workings of the. Mechanical Turk inside right so what I. was saying is if this thing sensed a. cheater like Napoleon supposedly did it. would you know Napoleon would move a. piece out of Turner IL legally or. something right this dude The Turk Turk. 182 would pick up the chest piece move. it back as if to say like no no Napoleon. that's see what you're doing and then if. the person attempted to move it again I. don't know how many times maybe two or. three times eventually it would just go.

ah and wipe his hand across the board. and knock off all the pieces game over. right which is pretty great yeah it's a. nice little feature yeah it is but it. even showed even more that this thing. was thinking for itself yeah that's the. key here right sure chess had been for a. very long time viewed as only something. that a human would be capable of because. it took a human intellect and there was. actually a guy in English um.

engineer I think he was a mechanical. engineer his name was um Robert Willis. he said that um chess was in quote the. province of intellect alone so the idea. that there is this automaton playing. chess blew people away oh yeah but again. people figured out like okay there's. something going on here we think that um. Von kemplin is controlling this thing. remotely somehow maybe using magnets or. whatever other people hit upon the idea. that there was a small person inside the.

cabinet who would hide when the C when. the workings were were shown when the. cabinet was open to show the workings. yeah and then when the cabinet was. closed again and the Mechanical Turk. started playing the person had crawled. back out and was actually controlling it. this seems to be the case that there was. a person controlling it but the idea. that it. was it was a a machine that could think. and beat humans in chess had like kind. of unsettling implications yeah this. author Philip uh.

thickness great name British author or. sure Philip thickness yeah he uh he said. and you know people like you said all. those more complicated explanations in. this article he sent astutely points out. that he followed aam's Razer and. basically said he's got a little kid in. there he's got little a little Bobby. Fisher in there right that's really good. at chess and that's what's going on and. other people speculated that other you. know little people might be in there um.

just adults who would fit in there but. then you know there's the explanation. that he he would open it up and shine a. candle around and say you know nothing. to see here right. everyone um. so what's should we reveal the real deal. sure I think I did already well I don't. think you spelled it out as oh well. spell it out there was a little person. in there yeah not just one little person. but they would travel around and recruit. people I guess people would get tired. being in there or they'd forget about.

him and they'd starve and have to. replace him but it really was a trick. there was a little person in there they. did the same thing as like the magic. acts you know when they saw a person in. half it's the the lady just gets into a. tiny little ball in one section of that. box but my thing is this like this is. not a satisfying explanation to me Chuck. I think it's great how did the person. keep up with the board above well I mean. some I I don't know if they ever proved. exactly how was going that's what I'm.

saying oh okay whether or not I I think. they the the the zultar or I'm sorry the. Turk was just hollowed out and you you. would just put your arms through the arm. holes so you would craw up into the Turk. yeah you would become the Turk you and. the Turk would fuse that's what some. people thought I think that's what edar. alen Poe thought too he kidding me right. po other people thought that there were. the the the little person was underneath. the in the cabinet operating the trick.

with levers and stuff like that well. there could have been a mirror or. something you know I guess a little. telescopic mirror that's what's getting. me is how would they keep up with the. game right you could keep track of the. game but how could you see where the. other person moved you would know where. you moved but you wouldn't be able to. see where the other person that's what I. don't get just mirrors smoking mirrors. maybe so but the point is is it was a. fake it was a fraud but it ran some. really big questions about the idea of a. machine beating a person at something. like chess yeah and it uh really peaked.

the uh mind of one Charles Babbage he. was uh he was a kid or or young at least. at the time when he saw the Turk in. person and and a few years afterward he. began work on something called The. Difference Engine which was a machine. that he designed to to calculate. mathematics automatically so some point. to this is kind of maybe the beginnings. of humans trying to create AI.

well yeah with babbage's differential. machine or difference machine yeah. Difference Engine but at the very least. what this is is the first that I know of. example of man versus. machine even though it was really Man. versus man because it was a man in the. machine right it was a fraud yeah but it. was it it sparked that idea it. definitely did and that's something that. um like chess in particular has always. been like this idea of like if you can. teach a machine to play chess you really.

achieved a milestone and there's been. you know plenty of programs most most. notably deep blue yeah which we'll talk. about but there's there's been this idea. that like part of AI is chess teaching. it to play chess yeah but they they they. the people who develop AI never set out. to make a chess playing AI just to make. a machine that can play chess right. that's not the point chess has always. been this way to demonstrate the.

progress of artificial intelligence yeah. cuz it's a complex game that you can't. just program it like it almost has to. learn uh well it depends on how you come. at it at first right so initially they. did try to program it okay there's this. for for from basically 1950 to the about. the mid like about say 1950 to 2010 60. years right that is how they approached. Ai and chess is you figured out how to.

break chess down and explain it to a. computer now one if you could ideally. you would have this this computer or. this AI this artificial intelligence um. be able to think about the outcome of. every possible the every possible. outcome of a move before making it right. that's just not possible still today we. don't have computers that can do that. right so what you have to do is figure. out how to create shortcuts for the.

machine give it best practices that kind. of thing and that was actually laid out. in 1950 by a guy named Claude Shannon. who's a father of information Theory and. he wrote a a paper with a pretty on the-. noose title called programming a. computer for playing chess and you have. to say it like that when you say the. name yeah it's got a question mark in. the end right but he laid out two big. things um one is uh creating a function. of the different moves M and then.

another one is called a. minx and if those were the two things. that Shannon laid out and they. established about 50 or 60 years of. development in teaching an AI to play. chess yeah so this evaluation function. is just sort of the base the very basis. of it all kind of where it starts which. is you EV kind of give a number a create. a numerical. evaluation based on the state of the. board at that moment right right and.

assign a real number EV valuation to it. so um the highest number that you would. shoot for is obviously getting uh. Checkmate getting a a king in Checkmate. right right so what you've just done now. is by assigning a number to a state like. the pieces on a board um what you what. you've done is to say like shoot for. this number right the higher the number. like you're going to give this AI the. rule now the higher the number the more. desire that this move that could lead to.

that higher number function evaluation. function is what you want to do right. like capture the night or capture the. queen capture the queen would have a. higher evaluation number right exactly. so that's the function then there's. another one called the minia max yeah. this is pretty great where you want to. minimize the maximum and this is another. shortcut that they taught computers. maximum loss that is right yeah so what. they what they taught computers to do is. is so you no computer can look through. an entire game every possible outcome. right but what you there are computers.

that can look pretty far down the line. at every possible outcome and what you. can say is okay um you want to find the. evaluation function that is the worst. case scenario the maximum loss and then. find the move that will minimize the. possibility for that outcome Yeah by and. this is you're only limited by your. programming power but by looking not. only at the state of the board right now.

but if I make this move and I move the. the pawn to this spot what are the next. like three moves possibly that could. happen as a result of this move right. and you're only limited like I said by. programming power so obviously the more. juice you have the more moves ahead that. you can look exactly and then they just. shy away from ones with the higher. function number exactly or a lower. function number depending on how you've. programmed it but they they they're. making these decision ision based on. these rules um and then there's other.

things you can do like little shortcuts. to say if if a if a um a decision tree. leads to a uh the other players King. being in Checkmate don't even think. about that move any further don't. evaluate any longer just abandon it. because we would never want to make that. move right so there's all these. shortcuts you can do and that's what. they did to teach computers that's what. um deep blue did when it beat Gary. Casper of in 1997 it was this huge. massive computer that knew a lot of CH a.

lot about Chess it had a lot of rules a. lot of incredibly intricate programming. that was extremely sharp and it it. actually won it became the first. computer to beat an actual human chess. Grandmaster in like regulation match. play yeah I mean and I don't think. Casper off gets enough credit for like. willing being willing to do this MH. because it was a big deal for him to. lose it was in this community and the AI.

Community it would sent shock waves and. everyone that was alive remembers even. if you didn't know anything about either. one remembers deep blue being all over. the news it was a really big deal and. Kasparov put his name on the line and. lost yeah and I was wondering Chuck how. how like you would get somebody to do. that I'm sure a mountain of cash I guess. that would probably be part of it but I. also paid I mean. I don't know I bet I bet that's out.

there we just I just didn't look it up. so um that's possible it's also possible. that they said look man like this is. chess we're talking about or whatever. but really what you're doing is helping. Advance artificial intelligence right. because we're not at we're not really. trying ultimately to win chess games. we're trying to cure cancer I mean yeah. we're going to take your title because. we're gonna beat you or our machines. going to beat you but even still you're. going to be helping with cancer think of. the cancer Casper off that's proba what. they said should we take a break yeah.

let's uh wait well should we tease our. special guest first is he okay I can. smell him I don't think we even said. we're gonna have a special guest later. in the episode Mr Jonathan Strickland of. tech stuff nice she been a long time. since like years since we had strick on. the last time we had strick on was like. 2009 with the Necronomicon episode what. is why where's he been besides sitting. in between us every day it's been a. Strickland drought is what it's been. yeah so Strickland's coming later but. we're going to come back after this and.

talk a little bit more about uh Man. versus. machine welome to stuff you should. [Applause]. [Music]. know okay dude so what we just described. was how AI was taught to play things.

like chess or to think like you take. something you figure out how to break it. down in into little rules and and and. things that a computer can think of. right and then follow these kind of. rules to to make the best decision. that's how it used to be the the way. that it's done now that everybody's. doing now is where you are creating a. machine that teaches itself yeah that's. the jam that was the Breakthrough you. may have noticed back back in about.

2013. 2014 all of a sudden things like Siri. and Alexa um got way better at what they. are doing they got way less confused oh. really your um navigation app got a lot. better yeah the reason why is because. these these this type this new type of. AI this new type of machine learning. that can teach itself and learn on its. own uh just hit the scene and they just. started exploding and one of the things. that they were first trained on was.

games yeah and it makes sense um and if. you thought chess was complicated and. difficult uh when it comes to these new. AIS that they're teaching to teach. themselves game strategy they said we. might as well dive in to the Chinese. strategic game go because uh it has been. called the most complex game ever. devised by humans yeah and this was. actually that was actually a quote from. uh Demi Hass. the neuroscientist and the founder of.

Deep Mind which uh was deep mind they. were purchased by Google or were they. are always part of Google I don't know. if they were a spun-off branch or where. they they were purchased but it's one of. Google's AI outfits well they're they're. one of the teams yeah that are designing. these new programs and um to give you an. idea of how complex go is uh it deals. with a board with with different stones. and there. are uh 10. how do you even say that 10 to the 170th.

power so that means 170. zeros uh and take that number and that's. the number of possible configurations of. a go board right so like you say chess. is very complex and complicated and it's. very difficult to to master go and I've. never played go of you no so it's. supposedly it's easy to learn right but. very complicated in its Simplicity right. right exactly it's extremely difficult. to master there was a guy in the late. 90s and I'm guessing that uh that he was.

saying this after deep blue beat Casper. off um it was an astrophysicist from. Princeton he said that it would probably. be a hundred years before a computer. beats a human at go to give you an idea. of just how complex go is that deep BL. would just be casprov and this guy's. saying it'll still be 100 years before. any anyone gets beat at go by a computer. and he was a someone who knew about this. stuff who's an astrophysicist he wasn't.

just some Sho at home and drunk in his. recliner right just making asinine. predictions um so and again we've we've. said this before but I want to reiterate. the people that uh I think alphao is the. name of this program the people that. created this at Deep Mind they they. wanted to stress that this is uh a. problemsolving program we're just. teaching it this game at first just to. to make it learn and to see if it can. get good at what it does but uh they.

said it is built with uh the idea that. any task that has a lot of data that is. unstructured and you want to find. patterns in the data and then decide. what to do right and that's kind of like. what we're talking about it's it. crunches down all these possible options. AKA data to decide what move should I. make right and you could apply that. ideally they're going to apply this to. Alzheimer's and cancer and all sorts of. things right that's General general. purpose thinking right yeah and thinking. on the Fly too when faced with novel.

stuff so one of the reasons why it's. good to use games like chest or go or. whatever there those are called perfect. information games where both players or. anybody watching has all the information. that's available on it there are. definite rules there's structure it's a. good Proving Ground but as we'll see AI. makers are getting further and further. away from those structure games as their. AI becomes more and more sophisticated. because the the structure and the.

limitations aren't necessarily needed. anymore because these things are. starting to be able to think on their. own in a very generalized and even. creative way yeah it's really really. interesting yeah uh the way that they. like you said um earlier before the. break that uh we we don't have computers. that can run all the possibilities so. what they teach in the case of alphago. this program teaches Itself by playing. itself in the in these games and go. specifically M and the more it plays.

itself the more it learns and the more. uh ability it has during a game to. choose a move by narrowing down. possibilities so instead of like well. there are 20 million different. variations here uh by playing itself. it's able to say well in this scenario. there really only 50 different moves. that I could or should make right or. that's kind of a simplified way to say. it but right no but it's true but that's. that's exactly right and what they're. doing is basically the same thing that a. human does it's it's going back to its.

memory banks yeah exactly it's. experience and saying well I've I've. been faced with something like this. before and this is what I used and it. was successful 40 out of 50 times I'll. do this one this is a pretty reasonable. move yeah that is what humans do yeah. not only I mean boy we screwed up the. chess episode but I get the idea that. when you're a chess master you don't. just think what do the numbers say and. what does the book say right but oh man. I did this move that one time and it.

didn't go as the book said right so. that's now factored into my thinking. right except imagine being able to learn. from scratch and get to that point in. eight days or eight hours yeah so that. go team the alpha go the first ep the. first iteration of alpha go I think they. started working on it in. 2014 and in 2016 at the end of 2016 they. Unleashed it um secretly on to an alpha. Go website and it started just wiping.

the floor with everybody yeah. everybody's like this thing's pretty. good oh it's Alpha go what year is this. that was the end of 2016 okay so chess. had already come and gone like oh yeah. by this point you you can download a. program that's like deep blue right that. was that's a great Point yeah like today. the stuff you play chess with on your. laptop is even more advanced than deep. blue was in the 9s and it's just on your. laptop um but this is so this is go this.

is the end of 2016 the end of. 2017 um alphao was replaced with alphao. zero it learned what Alpha go had taken. two years or three years to learn in 40. days by teaching itself and it beat the. master yeah and finally in the May of. 2017 alphago took on uh key G mhm the. highest ranked go player in the world.

don't know if he or she still is no le Z. doll is the current or was until alpha. alpha go beat him oh man yeah do they. get knocked off and Alpha go is the. champion yeah like that's that's not. fair I I I if it's match play and the. player the human players accepted a. challenge from the computer I don't see. why it wouldn't be the world champion or. do they just now say on websites like. human champion in italics with like a.

sneer right maybe yeah interesting what. do they call that wetwear like your. brain your neurons and all that what. instead of Hardware it's wet wear oh I. don't know about that I think that's the. term for it what does that mean though. it means like you you have a a substrate. right your intelligence your intellect. is based on your neurons and they're. firing all that stuff and it's wet and. Squishy and meat then there's Hardware. that you can do the same thing on you.

can build Intelligence on but it's. Hardware it's not wetware oh interesting. so it's probably it it's the wetwear. champion versus the hardware Champion. but wetwear is italicized with the snare. uh so where things really got. interesting because you were talking. earlier about um what what's it with the. chess and go what are they called what. kind of games uh perfect information. games right then you think in my my. first thought when you said that was. well yeah but then there's there's games.

like poker like Texas Holdem right where. there are a set of rules but poker is. not about the set of rules it is about. sitting down in front of whatever five. or six people and lying bluffing and. getting away with. it being Bluff like there's so many. human emotions and contextual Clues and. and uh micro expressions and all these. things like sure. you could never ever teach a machine to.

win at Texas Holden poker yeah it'll be. a hundred years at least before that. happens I predict no they did it man and. more than one team has done it yeah I. read um there was one from Carnegie. melon called the liberatus AI go Melon. Heads yeah go th the Thorton. melons uh yeah I. mean was University of Alberta has one. called Deep stack that was the one I. wrote about okay and it actually here's.

the thing like if you read the the. release on it you're like you don't know. how this thing works do you oh really. yeah and I'm pretty sure they they don't. fully get it because it's one of the. problems actually talk about this in the. existential risks series scare that is. to be released right that there is a. there is a type of machine learning. where the machine teaches itself but we. don't really understand how it's. teaching proba the scariest right or. what it's learning but that's the most. prevalent one that's what a lot of this.

is is like these machines it's like. here's here's. chess go figure it out and they they go. okay got it how' you do that wouldn't. you like to know so that's the scariest. presentation you will see on AI is when. someone says well how does all this work. and they go right but we just know it. can beat a human a poker but the thing. about deep stack at the University of. Alberta is that it learned somehow. some sort of intuition because that's.

what's required it's not just the. perfect information where you have all. the information on the board it's with. poker you don't know what the other. person's cards are and you don't know if. they're lying or bluffing or what. they're doing um so that's an imperfect. information game so that would require. intuition and apparently not one but two. different research groups taught AI to. into it yeah Carnegie melon came out in. uh January of 2017 with its liberatus Ai.

and they said they spent 20 days playing. 120,000 hands of Texas holdam with four. professional poker players and one and. smoked him basically got up to they. weren't playing with real money. obviously but they um they that would. have been great they were playing with. Skittles like me as a kid funded their. next project uh liberatus was up by 1.7. million and one of the quotes from one. of the poker players. that he made to Wired Magazine said I. felt like I was playing against someone.

who was cheating like it could see my. cards I'm not accusing it of cheating it. was just that good right so that's a. really interesting thing man that they. could teach self teach a program or a. program could teach itself intuition. right that's creepy yeah I thought this. part was interesting the Atari stuff. yeah um this gets pretty fun Google deep. mind uh let uh it's. AI um wreak havoc on Atari 49 different.

Atari 2600 games see if it could figure. out how to win and apparently the most. uh difficult one was Miss Pac-Man which. uh is a tough game still man Miss. Pac-Man they nailed it that's still one. of the great games but the but their um. their game or their Q deep Q Network. algorithm beat it I think it got the. highest score 99. 99,900 points and no human or machine.

has ever achieved that high score from. what I understand amazing and the way. this one does it the hybrid reward. architecture that it uses is really. interesting it's it says here it. generates a top agent that's like a. senior manager and then all these other. 150 individual agents so it's almost. like they've devised this artificial. structural hierarchy of these little. worker agents that go out and collect. I guess data and then move it up the.

chain to this uh top agent right and. then this thing. says um okay you know I think that. you're probably right you what these. agents are probably doing and I don't. know this is exactly true but there's. there are models out there like this. where the agent says this is um you have. a 90% chance of success at getting this. pellet um if we take this action. somebody else says you got a 82% chance. of evading this ghost if we go this way.

and then the the the top agent the. senior manager can put all this stuff. together and say well if I listen to. this guy and this guy not only will I. evade this ghost I'll go get this pellet. um and it's based on what what. confidence level that the the lower. agents have in success in in. recommending these moves and then the. top agent weighs these things wow they. should give him a little a little cap. but all this is happening like that oh. yeah you know what I'm saying this isn't.

like hold on hold on everybody what is. Harvey Harvey what do you have to say. well let's get some Chinese in here and. and hash it out and everybody sits there. and orderers some Chinese food then you. wait for it to come and then you pick up. the meeting from that point on and then. finally Harvey gives his idea but he. forgot what he was talking about so he. just sits down and eats his egg roll. well here's a pretty frightening uh. survey uh there was a survey of more. than 350 AI researchers and they had the. following things to say and these are.

the pros that are doing this for a. living they predicted that within 10. years AI will drive better than we do by. 2049 they will be able to write a. best-selling novel AI will generate this. and by 2053 uh be better at performing. uh surgery than humans are you know so. again one of the things that about the. field of artificial intelligence which. you know a lot about now famous yeah do. it is famous for making huge predictions.

that did not pan out sure but you've. also seen it's it's also famous for. beating predictions that that you know. have been levied against it um but there. is something in there Chuck that stands. out to me and that's the idea of an AI. writing a novel like for a very long. time I thought well yeah okay you can. teach a a robot arm to like put a car. part or something somewhere if you. wanted to just follow these these. mechanics things or it can use inition. or it can use logic and reason but to.

create that's different right that was. like the New Frontier it used to be. chess and then it used then was go the. next Frontier is creativity and they're. starting to bang on that door big time. there's a a game designing AI called. Angelina out of the University of. Falmouth which I always want to say. Falmouth yeah but we'll just call it. Falmouth like it's supposed to and. Angelina actually comes up with ideas. for new games not um like a different.

level or something like like you should. put a purple loin cloth on that player. you know that'll look kind of cool like. new games but whacked out games that. humans would never think of uh one. example I saw is in a dungeon Battle. Royale game a player controls like 10. players at once and some you have to. sacrifice to be killed to save the. others like just stuff that human. wouldn't necessarily think of this AI is. coming up with well I mean when you. think of creatively especially something.

like writing a novel or a film if there. are only seven stories I mean is that. sort of the thinking that they're. basically every every dramatic story is. a variation of one of seven things yeah. so I mean you can look at like um AI as. scary and in some ways it it very much. is and can be but there's also like. definitely a level of excitement to the. whole thing and the idea that there are. artificial Minds that are coming online. or that have come online now that are.

out there that are they'll they'll just. Naturally by definition see things. differently than we do yeah and the idea. that they can come up with stuff that. we've never even thought of that is just. going to knock our socks off hopefully. in good ways um that's that's a really. cool thing and so maybe there's just. seven as far as humans know but there's. an unlimited amount is if you put. computer Minds to thinking about these. kind of things that's the premise of it. right so the robot would be like you. never thought of Boy Meets Girl Meets.

well. troby but see even that's a variation of. a just imagine something that just we've. never even thought of well Gina how they. should do this if they do do that is uh. is not is just release a book and not. tell anyone that it was written by an AI. program because if they do that then. it's going to be so under scrutiny oh. yeah they should secretly release this. book and then after it's a New York. Times bestseller MH say meet the.

Whopper right the author of this right. you know his interests are roller. skating playing Tic-Tac-Toe and global. thermonuclear war all right should we. take a break and get Strickland in here. yeah we're going to end the Strickland. drought because it is about to rain. Strickland in this piece you. gross to don't you should.

[Music]. know okay we're back and get this the. scent of strick has permeated our place. it's a beautiful scent it's smells like. a uh soldering gun and a circuit board. and a feel of lavender in a protein. bar that's fair I was going to say Draco.

Noir but that would have been a lie is. that how you say it I always called it. drar drar that's that's fair jakar H I. always pronounced it beniton. colors that was what I wore oh is that. what you wore yeah during my what I call. the year of cologne M I had a couple. 8ish uh this is scintilating why am why. am I here oh yeah so we know that you. already know because we talked via email. about this but we'll tell everybody else.

we have brought you in here because you. are the master of tech and we are. talking Tech today which we've talked. about without you before but frankly. Chuck and I and Jerry huddled and we. said this is not quite as good without. strick so let's try something different. gotcha and and we're talking about games. and and Machine versus man and that. whole that whole Evolution and how. that's gone super crazy over the last. few years Games Without Frontiers. Gabriel would say War without fear and. we've talked I mean we've talked a lot.

about um the evolution of uh machine. learning and how now it's starting to. take off like a rocket because they can. teach themselves right but one thing we. haven't really talked about uh are. solved games I mean we talked about. Chess yeah we talked about go right. would those constitute solved games not. really uh so so a solved game is the. concept where if you were to assume. perfect play on either sides of the game. you would always know how it was going. to end which we always assume perfect. play right yeah that's kind of our bag.

that's stuff you should know motto so. perfect Play Just meaning that no one. ever makes a mistake so very much the. way I do my work right this stuff you. should know exactly so if you were to. take a game like Tic-tac-toe and you. assume perfect play on both sides it is. always going to end in a draw which is. what in war games yes right the only way. to win is not to play right yes so a. game like quitter talk like Connect 4. whoever goes first is always going to. win assuming perfect play on both sides.

yes what I don't think I've played. Connect 4 that's where you drop or in a. long time that's the one where you drop. the little uh tokens kind of like. Checkers we did an interstitial with. playing Connect 4 remember I was faking. it though and you had perfect play so I. knew it was useless no I was going to. say that I'm so humiliated by all the. Connect 4 games that I've lost starting. even yeah terrible but I mean perfect. play that's something that that. obviously only the the best players.

typically achieve with with. significantly complex games obviously. the simpler the game the easier it is to. play perfectly right tic-tac-toe if you. know once you've mastered the basics of. Tic-tac-toe and the other person has. you're never really going to win unless. someone has just made a a silly mistake. because they weren't paying attention. like they put a star instead of an X. which doesn't count automatically. disqualifies you one thing I found. that's very enjoyable is playing with. little kids who haven't figured out that.

tic taac toe is very easy to play Smash. their face in the board rub it in yeah I. mean it's same reason why I like to to. join in on Little League games because I. can really whale that ball out of the. park yeah I really Mak me feel like a. man that's the most tech stuffy thing. you've ever said you really wail that. ball out of the park well to be fair I. did just do a tech stuff episode about. the technology behind baseball bats so. it's still fresh on the Mind nice have. to listen to that one actually it's a. lot of fun so there have been a lot of. games that have been solved Checkers was.

one that was recently solved back in. well recently by the early 90s uh when. it was played against a computer called. Chinook and uh c i n o k yeah like the. helicopter or the winds that blow. through Alberta exactly and so there are. certain games that are more easily. solved than others you do it through an. algorithm but other games like chess are. more complicated because you can in. chess you have multiple moves that you. can do where you can you can move a.

piece back the way you win right it's. not you're not committed to going a. specific Direction with certain pieces. never thought about that like with a. knight you know you can you could go. right back to where you started on your. next move if you wanted to uh and that. creates more complexity right so the. more complex the game the more difficult. it is to solve and some games are not. solvable simply because you'll never. know what the full state of the game is. from any uh given moment did you have a. chance to talk about the difference.

between perfect knowledge and imperfect. knowledge in a game yeah yeah we talked. about that some yeah so so computers. obviously they do really well if they. understand the exact state of the game. all the way through if they if they have. perfect knowledge all of the. information's there on the board right. right and and all players can see all. information at all times but games like. poker which you guys talked about. obviously you have imperfect information. you only know part of the state of the. game that's why those games have been.

more difficult more challenging for. computers to get better than humans. until relatively recently and there have. been two major ways of doing that you. either throw more processing power at it. right like you get a supercomputer or. you create neural networks artificial. neural networks and you start teaching. computers to quote unquote learn the way. people do so we talked about that and. one of the things that we talked about. was how there's this idea. that they the programmers especially say.

the people who are making programs that. are playing poker and are getting good. at poker aren't exactly sure how the. machines are learning to play poker or. what they're learning they're just. getting better at poker yeah do they. know how they're learning poker or they. just know that they're learning poker. and that they're good at it now like. where's the intuition how is that being. learned an excellent question the way it. typically is learned especially with. artificial neural networks is that you. you set up the computer to play millions. of hands of poker that are are randomly.

assigned so it's it's truly as random as. computers can get that's a whole. philosophical discussion that I don't. think we're ready to go into right now. but you have games come up where the. computer is playing itself Millions upon. millions of times and learning every. single time how the statistics play out. how different betting strategies play. out. it it's sort of partitioning its own. mind to play against itself and through.

that process it's as if you as a human. player were playing thousands of games. with your friends and you start to. figure out oh when I have these. particular cards and they're in my hand. and let's say we're playing Texas hold. them and the community cards are are. these then I know that generally. speaking maybe three times out of 10 I. end up winning maybe I shouldn't bet in. well the computer's doing that but on a. scale that far dwarfs what any human can. do and in a in a fraction of the amount.

of time right and so it it's sort of. it's intuition in the sense of it's just. done it so much right but is that does. that mean it's completely ignoring um. micro expressions and facial cues so. that didn't even come into play I should. say Strickland just nodded yet yeah I. was I was waiting for how many years. have you been doing this well I still. nod when I do a solo show and I do a lot. of expressive dance what do you think. Jonathan I don't know Jonathan it it. gets lonely in here guys no but yes what.

you're saying the all the Tells right. the Tells that you would use as a human. player the computer does not pick up on. this typically speaking data yes. typically what it would do is it would. study the outcomes of the games from a. purely statistical expression so well. that makes more sense most of these. poker games tend to be computer-based. poker games so it's not that it's. playing like it's it's not like there's. a computer that says push 10 more chips. into the table you know it's eye tick. right exactly little it's a little winky.

face emoticon like I don't have good. cards no it's it's all usually over uh. like sort of like internet poker which a. lot of the people who play professional. poker cut their teeth on especially you. know in the the more recent generations. of of professional poker players those. kids today yeah those they don't know. what it's like to be in a smoky Saloon. Like Money Maker when Money Maker Rose. to the top a few years ago well more. like a decade ago now uh he had come. from the world of Internet poker and so.

he was using those same sort of skills. in a real world setting but obviously. there are subtle things that we humans. do in our Expressions that computers do. not pick up on and in fact that leads us. sort of into the realm of games where. computers don't do as well as humans. yeah is that list you sent a joke or is. it real no that's real I it does seem. like it's weird like one of the games on. there is dictionary for example right. tag or tag yeah but these are some of.

these are are they sound silly but when. you start to think about them in terms. of computation and Robotics you start to. realize how incredibly complex it is. from a technical perspective but. incredibly easy it is for your average. human being okay so with humans a game. of tag once you know the basics it's. it's all Instinct you you know what to. do you run after the person you try to. catch up with them and you tag them but. you also know push them in the back as. hard as you can well if you're Josh you. push him as hard as you can but most of. us we tag and we're not trying to cause.

harm robots however robots not so good. on the second te stuffiest thing you. said I'm just saying Isaac aov Isaac. aov's rules of L robotics aside robots. are not very good at judging how hard. they have to hit something in order to. make contact right they're not as good. at uh even your bipedal robots that walk. around like people even the ones that. can run and do fli and stuff have you. seen that one the other day that the. footage of that thing running and. jumping it's really impressive and and.

super creepy yeah but even so that's. that's a clip of the best of if you ever. if you ever see the clips where they. show all the times the robots falling. over yeah or pouring hot coffee in. someone's head yes uh but they always. play those clip shows to Yaky sacks yes. do this is true so so DARPA had its big. robotics challenge a few years ago where. they had bipedal robot OTS tried to go. through a scenario that was simulating. the Fukushima nuclear disaster so the.

interesting thing was the robot had to. complete a series of tasks that would. have been mundane to humans things like. open up a door and walk through it and. pick up a power to power tool and use it. against the wall yeah and you can watch. the footage of some of these robots. doing things like being unable to to. open the door because they they can't. tell if they need to pull or push or. they open the door but then immediately. fall over the threshold of the door.

right and when you see that you realize. as advanced as robotics is as advanced. as machine learning has become and as. incredible as our technology has. progressed there are still things that. are fundamentally simple to your average. human right that are incredibly. complicated from a technical standpoint. like a six-year-old can play Jinga. better than a robot right right right. okay but the thing is is we're talking. robots here and we go more and more and. more online and our world becomes more. and more like web- based rather than.

reality. based doesn't the the fact that a robot. can't walk through a door matter less. and less and the idea that that machines. are learning intellect and creativity. reasoning you just blew my mind that. that's becoming more and more vital and. important and something we should be. paying attention to it absolutely is. something we should pay attention to I. mean we have robotic stock Traders. they're they're trading on thousands of.

Trades per second right fast so fast. that we have had stock market booms and. crashes that last less than a second. long due to that so the the robot army. that will ultimately defeat us is not. something from the Terminator it's. invisible right it's online or it will. be online it's it's it's what's. determining our retirement right or yeah. the global economy or um our Municipal. Water apply or whatever yeah no there's. the fascinating thing to me about this.

is not just that we're. training machine intelligence to learn. and to perform at a level better than. humans but that we're putting a lot of. trust in those devices in things that. have real incredible impact on our lives. the significant enough impact where if. things were to go south it would be. really bad for us uh and not in that. Terminator respect like Terminator is a. terrifying uh dystopian science fiction.

story but then when you realize what. could really happen behind the scenes. you you think oh the robots don't have. to do any physical harm to us to really. mess things up right so there are. certainly some uh cases for us to be. very Vigilant in the way we uh deploy. this artificial intell it right from the. outset exactly that's is it too late uh. depends no not necessarily I think I. think uh it's I don't think it's too. late but I think it's getting to that.

point of no return very very quickly by. December of this year yeah well if. you're if you're someone like if you're. someone like Elon Musk you'd say if we. don't do something now we're we're. totally going to plummet off the edge of. the cliff but now is a window that is. rapidly closing yes yeah yeah the now is. the now is a time where we've got a. deadline we don't know exactly when that. deadline is going to be up but we know. that it's not getting further out it. we're just getting closer to that.

deadline so and a lot of this is is. covered in deep conversations in the. artificial intelligence and machine. learning fields that uh has been going. on for ages to the point where you even. have bodies like the European Union that. have debated on Concepts like granting. personhood to artificial intelligence so. th this is a really fascinating and deep. subject that and and the the games thing. is a great entry point into having that.

conversation uh you know I I'm lucky if. I can win a game of chess against. another human being oh yeah right so we. can't even describe. chess I I do my big thing is I do that. night thing I call it the night Shuffle. I just move them back and forth right I. just castle if I can Castle then I'm I'm. I'm so. happy and that's the third Tech. stuffiest thing they come in threes well. strick thank you for and by I you should. stick around for listener mail I think. you should too I'd love to and and throw.

out any funny comments that you have I. I'll throw out comments and then Jerry. can decide which ones are funny okay all. right fair enough all right so uh if you. want to know more about ai go listen to. Tech stuff strick does this every week. how what days Monday Tuesday Wednesday. Thursday and Friday wow that's amazing. buddy and wherever you find your podcast. okay and you've been doing it for years. so if you love this there's a whole big. backlog 900 plus episode you're. celebrating uh you're celebrating your.

tenure as well right yep I sure am I'll. be uh we'll be turning 10 and Tech stuff. on June 11th ni congratulations. anniversary well since I said happy. anniversary it means it's time for. listener. mail uh guys I'm going to call this uh. Matt graining and cultural relativism. about that nice hey guys love your. podcast so much the massive archive. makes for Endless learning and. entertainment my favorite part is you. are such rad guys including Strickland. and I could totally imagine how how did.

they know I could totally imagine myself. getting a beer with you two uh but. without strickling your Simpsons. episodes were absolutely perfect I used. to live in Portland and drove on. Flanders and love joy streets a lot wait. is this Matt graining okay uh Matt. graining Drew Bart and the sidewalk. cement behind Lincoln High School in. downtown Portland uh you can Google that. I would like to offer one interesting. observation though I've noticed that on. several episodes you guys have said that. you are cultural. relativists is that pronounced right.

yeah yeah uh but then in nearly every. episode I hear you pass moral judgments. on all the messed up stuff that people. do whether it's racism freak shows or. crematoriums bearing bodies on the Sigh. you guys are never shy to condemn. something that deserves to be condemned. reminds me of something I read from Yale. sociologist Philip Gorski who uh points. out that our own relativism is rarely as. radical as our Theory requires we can't. be complete relativists in our daily. lives uh he then gives the example of.

how academic social scientists or dieh. hard relativists get furious at uh and. moralistic at the data fudging of other. researchers uh anyway love the show guys. love Tech stuff especially and we'll. forever be indebted to you for your. hilarity and knowledgeability cheers. Jesse lusco PS go Tech stuff that's. that's sweet how about that yeah thanks. a lot Jesse um there was an actual. episode and I don't remember which one. it was where we abandoned our cultural.

relativism do you remember cuz we used. to just be like no judgment no judgment. we just can't judge you know and then. finally we're like you know what no. that's not true we changed our or. philosophy to include the idea that. there are moral absolutes that are. Universal although sometimes we are just. judgy even beyond that look at us yeah. uh well if you want to get in touch with. us you can send send us an email to. stuff podcast hous stuffwork.

docomo house storks.com nice and then. hang out with us at our home on the web. stuff you should know.com. and just go to TCH stuff just search it. in Google I come up all the time fair. [Music]. enough stuff you should know is a. production of iHeart radio for more. podcasts my heart radio visit the iHeart. Radio app Apple podcasts or wherever you. listen to your favorite show. [Music].

is.