Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking, either everyone, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says, these foolish
Speaker 1: games are tearing me apart. I'm Jonathan Strickland, I'm Laura,
Speaker 1: and I'm Joe McCormick. Hey, Joe, do you do you
Speaker 1: ever play games? Joe? No, In fact, I don't play games.
Speaker 1: Joe is extremely serious. Lauren, do you play games? Lauren? Uh? Sometimes, yes, yes.
Speaker 1: You know, this is a question that I don't ask computers.
Speaker 1: I get scared to ask computers if they play games
Speaker 1: because I was brought up in the eighties when the
Speaker 1: the amazing film War Games came out. So I gotta
Speaker 1: be honest, I've never seen it, actually me neither. Wow. Okay, guys,
Speaker 1: prepare yourselves because this movie is amazing. So what now
Speaker 1: We're just gonna watch this movie and the podcast booth
Speaker 1: and we don't have time for that. I'm just gonna
Speaker 1: give you. I'm gonna sum up. So War Games is
Speaker 1: specifically the story of a a uh a hacker. A
Speaker 1: young hacker played by Matthew Broderick, who has Fred Savage
Speaker 1: not Fred said this is pre Fred Savage. Um, before
Speaker 1: Fred Savage, we had Matthew Broderick, right, Yeah, Matthew Broderick
Speaker 1: was the proto Fred Savage was. So Matthew Broderick is
Speaker 1: playing a part of a of a hacker and he
Speaker 1: he likes these particular computer games, so he hacks into
Speaker 1: a system in order to get a chance to play
Speaker 1: some of these games that have not been released, and
Speaker 1: one of them, one of them is Tic Tac Toe,
Speaker 1: but another one is Global Thermonuclear War. And it turns
Speaker 1: out that the AI developed by the computer scientists who
Speaker 1: created the games isn't really really sophisticated, and you can
Speaker 1: play these simulated global war scenarios. But spoiler alert, the
Speaker 1: program gets installed into an actual military facility. It essentially
Speaker 1: infects a military facility like a virus and then starts
Speaker 1: to take over actual defense systems and prepares to launch
Speaker 1: a full attack on the then Soviet Union. So it
Speaker 1: becomes the role of Matthew Broderick's character, along with the
Speaker 1: original programmer, to figure out a way to convince the computer, hey,
Speaker 1: you don't want to do that because it would be
Speaker 1: bad for everybody and you don't need to win this game.
Speaker 1: Computer you can let it go. They actually teach that
Speaker 1: that there is no way to win. In fact, the
Speaker 1: computer says, strange game. The only way to win is
Speaker 1: not to play. And the way that the computer figures
Speaker 1: this out is by using an analogy. It actually starts first.
Speaker 1: It starts with the various simulations of nuclear war uh,
Speaker 1: and it runs the simulations over and over and over again.
Speaker 1: Hasn't launched anything. It's just simulating. And so you're on
Speaker 1: the big like Norad screen you see these things playing
Speaker 1: out this which is the tic Tac Toe. The reason
Speaker 1: it switches to tic tac Toe is because if you
Speaker 1: play Tic tac Toe perfectly on both sides, you will
Speaker 1: always end in a draw. And so the lesson is
Speaker 1: there is no way to win this game. Playing this
Speaker 1: game makes no sense because you cannot win it if
Speaker 1: you are doing everything correctly. So I just saved you
Speaker 1: the the time of actually watching the movie. But it
Speaker 1: is a good film, guys, I actually really like it.
Speaker 1: All the joy in any Matthew Broderick film is not
Speaker 1: the plot. It's it's Matthew Brodick, right. So at that
Speaker 1: point the computer learns what we know that did you
Speaker 1: take that back kidding. Matthew Broderick is a Broadway superstar.
Speaker 1: Um No that that in mutually assured destruction. Once you
Speaker 1: enter a conflict, it's game over and nobody wins. It's
Speaker 1: kind of right there in the name. Yeah, So comparing
Speaker 1: nuclear war to tic tac toe is absurd but also
Speaker 1: kind of awesome. That's able to make this abstract connection
Speaker 1: between the two. Uh, and tic tac toe is what
Speaker 1: we would call a solved game. And we'll talk more
Speaker 1: about what solved versus unsolved means a little bit later. Uh,
Speaker 1: And we wanted to talk a little bit more about
Speaker 1: games and computers and whether or not we humans are
Speaker 1: doomed to always come in second place to computers in
Speaker 1: the long run. Will computers get to a point where
Speaker 1: they will always be better at whatever games we pick
Speaker 1: than we are? Well, maybe the stakes are high for
Speaker 1: the computers too. I mean, I'm sure that neither of
Speaker 1: you guys ever watched the nineties CG Cartoon Show reboot,
Speaker 1: because like five people in the world watched the nineties
Speaker 1: cartoon show Reboots. I had a friend who is into it,
Speaker 1: and I tried to watch it once. It's real, silly,
Speaker 1: it's real, real, silly, I never tried to watch it. Okay, okay,
Speaker 1: in reboot, you guys. The premise here is that when
Speaker 1: a user runs game software on a computer, all of
Speaker 1: the citizens of the computer are that are caught in
Speaker 1: the game's path must fight for their lives according to
Speaker 1: the rules of the game, and losing the game to
Speaker 1: the user means decimation or worse than one tenth destruction
Speaker 1: for all of the ones and the zeros and the
Speaker 1: sprites who make the computer run. So it's kind of
Speaker 1: like Tron. Actually, when you play a game, the user
Speaker 1: plays the game, but the people inside the computer live
Speaker 1: or die. Yeah, in this case, the people fight against
Speaker 1: the users, not for the users. Okay, so that's probably
Speaker 1: not really what's happening in in reality, but at any rate,
Speaker 1: so we wanted to talk about this, and in order
Speaker 1: to do so, we wanted to first mention the concept
Speaker 1: of game strategy or game theory. So there are volumes
Speaker 1: written on game theory, like textbooks of in formation on
Speaker 1: logic and game theory. But ultimately, if you really want
Speaker 1: to boil it down to its basics, game theory comes
Speaker 1: down to using strategy in order to make decisions to
Speaker 1: achieve a goal. That's really what game theory comes down to,
Speaker 1: once you get rid of everything else. And not all
Speaker 1: games actually rely on game theory. Some of them are
Speaker 1: more about random chance and less about strategic decisions made
Speaker 1: by the players. Yeah, I mean there are slot machines.
Speaker 1: I guess that counts as a game. There is no
Speaker 1: game theory for slot machines. Yeah. No. You you might
Speaker 1: think that there's a system to win at a slot machine.
Speaker 1: You are incorrect. Uh, it's all based on timing. And
Speaker 1: the timing we're talking about is down to like hundreds
Speaker 1: or thousands of a second, so there's no way impact
Speaker 1: whatsoever on the outcome of the machine. It's pure chance
Speaker 1: if you have pushed the button or pulled the lever
Speaker 1: at just that right moment, or something like like candy Land,
Speaker 1: where you're just rolling dice and moving pieces. Yeah. Yeah,
Speaker 1: candy Land is like a progressive slot machine. Yeah. I
Speaker 1: wish I could make a candy line joke, but it's
Speaker 1: been a long time since I've played that game. Alright. So,
Speaker 1: so games that do involve strategy, you should try it sometimes.
Speaker 1: It's pretty sweet, nice, nice, all right. So anyway, games
Speaker 1: that are have straightforward strategies include stuff like chess. That's
Speaker 1: pretty obvious, right, Checkers or drafts if you're English und
Speaker 1: draft Yeah, drafts, I've never heard this. Draft Checkers, that
Speaker 1: is the pro that's the proper name for Checkers. Actually, yeah,
Speaker 1: the the actual older name for Checkers as drafts. Do
Speaker 1: they have cracker barrels in the UK? I hope not.
Speaker 1: I doubt it. Yeah, I don't. I've never seen one.
Speaker 1: Uh if if you work at a cracker barrel in
Speaker 1: the UK, give us a shout. So other games that
Speaker 1: also involved strategy connect for Tic Tac toe go is
Speaker 1: another one. There are lots and lots of these, and
Speaker 1: some of the games have enough strict parameters or rules
Speaker 1: in play that make them solvable. Right, And so this
Speaker 1: is a concept in in game strategy that there are
Speaker 1: some games that are solved in other games that are
Speaker 1: not solved. And basically, a solved game is a game
Speaker 1: where if you know the starting conditions and players play
Speaker 1: at optimal performance, meaning they all make the perfect or
Speaker 1: best decision every time. Yeah, you can predict exactly what
Speaker 1: the game is going to be. Right, this would be
Speaker 1: This would be a ken to having two people sit
Speaker 1: down at a chessboard and before anyone's even touched a piece,
Speaker 1: one person says mate, and twelve moves and the other
Speaker 1: guys like, very well done, and they shake hands they leave.
Speaker 1: But of course chess is not solved, No, it is not.
Speaker 1: Chess is much too complicated to have solved. You can't
Speaker 1: know what all of the possible moves in chess are
Speaker 1: and not like our current computing powers, right, Uh, yeah,
Speaker 1: I mean there's so many possible series of moves in chess.
Speaker 1: I kind of doubt that could ever be calculated. You
Speaker 1: are not the only person to doubt that. As for
Speaker 1: solved games, there are three broad categories of solved games.
Speaker 1: There's ultra weekly solved games, weekly being w E a K.
Speaker 1: Not not like every week a game this really really
Speaker 1: comes out every week. This would be this is really
Speaker 1: really not strong. Uh. You can only predict the outcome
Speaker 1: accurately from the initial position, meaning before any player has
Speaker 1: made a move. And uh, at that point, it would
Speaker 1: just say like, all right, well, assuming that everyone plays optimally.
Speaker 1: For example, with Connect four, the first player is always
Speaker 1: gonna wine if you always you know, if no matter
Speaker 1: if both players are playing perfectly, player one always wins
Speaker 1: Connect four. Uh. Weekly solved games include not just the
Speaker 1: prediction of the outcome, but also a strategy for its
Speaker 1: eiaving it from the initial positions. So essentially saying, here
Speaker 1: is the the process through which you will always hit
Speaker 1: this particular outcome. Uh. Strongly solved games include strategy to
Speaker 1: achieve the best possible outcome from any point of the game,
Speaker 1: even parts of the game where someone may have made
Speaker 1: a mistake earlier on. UH. So this would be where
Speaker 1: if you would look at a game that's already in
Speaker 1: progress and say, all right, from this point forward, we're
Speaker 1: going to have perfect play on both sides and you
Speaker 1: could still predict who was going to win. So you
Speaker 1: gave the example of tic tac toe in the movie
Speaker 1: War Games. But this is a solved game if players,
Speaker 1: If both players are playing perfectly, and there are perfect
Speaker 1: ways to play, there the optimal moves you can make
Speaker 1: in any given game, then it's always going to end
Speaker 1: in a draw. That's exactly right, which is why I
Speaker 1: say that's why the computer it comes to the conclusion
Speaker 1: that there is no point in playing because there's no
Speaker 1: way to win. So winning a tic tac toe do
Speaker 1: pens on your opponent making a mistake right exactly. You
Speaker 1: also mentioned Connect four. Yes, that one was solved in twice.
Speaker 1: It was by two different independent researchers, and it was
Speaker 1: weekly solved to show that the first player can always
Speaker 1: force a win given perfect play, and in it was
Speaker 1: actually strongly solved to the point where at any given
Speaker 1: moment within a game, if you took over from that
Speaker 1: point forward, you could you could predict who was going
Speaker 1: to win based upon that previous positioning. It wouldn't always
Speaker 1: be player one in that case, because they may have
Speaker 1: made mistakes. So next we have nine Men's Morris. I
Speaker 1: don't know what that is. If you played Assassin's Creed
Speaker 1: Black Flag, you would because it's a game that exists
Speaker 1: within that game and you get achievements for playing it
Speaker 1: and winning. So I played a lot of nine Men's Morris.
Speaker 1: Nine Men's Morris involves it is a not no, it's
Speaker 1: not specifically a pirate game. There's a board game in
Speaker 1: which you have spaces where you can place a piece,
Speaker 1: and so the first part of the game involves placing
Speaker 1: your pieces on the board strategically. The second part of
Speaker 1: the game involves moving those pieces along specific pathways that
Speaker 1: are available to you and you're trying to line up
Speaker 1: three pieces in a line, and if you can line
Speaker 1: up three pieces in a line, then you are allowed
Speaker 1: to take one of your opponent's pieces off the board. Well,
Speaker 1: whereas your opponent is trying to do the same thing
Speaker 1: while also preventing you from lining up three three pieces
Speaker 1: in a line. So one of the things I've noticed
Speaker 1: so far about all of these examples is that they
Speaker 1: don't include any element of luck. Yeah, these are all
Speaker 1: again very much strategic games. These are games where it
Speaker 1: requires the player to make a decision, and then the
Speaker 1: player's decision is uh, you know, whatever happens from that.
Speaker 1: The consequences of that are all based upon the strategy
Speaker 1: of the other player as well. So I'm sure we'll
Speaker 1: talk more in later in the episode about games that
Speaker 1: are a combination of skill in law. Yes, but but
Speaker 1: checkers or or droughts, drafts, Well, we're we're American. We
Speaker 1: can say drafts, some of the British douce drafts. But
Speaker 1: so you're saying checkers like they play at the cracker barrel?
Speaker 1: Was that's a solved game? Solved in two thousand seven.
Speaker 1: It had been close to being solved for much longer
Speaker 1: than that, but officially solved in two thousand seven, which
Speaker 1: was proving that from the initial standpoint standpoint, both players
Speaker 1: can play to a draw with optimal play. So so
Speaker 1: even if you go second, yeah, you can at least
Speaker 1: play to a draw. You may not win, but you
Speaker 1: can at least play to a draw. So if you
Speaker 1: are player too and you lose, it's your fault. Uh.
Speaker 1: It took. It took eighteen years to solve checkers. At
Speaker 1: one point they had two hundred computers working on this. Yeah,
Speaker 1: that's because there are five hundred billion billion possible arrangements
Speaker 1: that could appear on an eight pie eight checkerboard. So
Speaker 1: it's real impractical to analyze every single one of those
Speaker 1: that this The solving of it was the odyssey of
Speaker 1: one Jonathan Schaefer, who's a comp scientist who began with
Speaker 1: just sixteen megs of memory on a computer in and
Speaker 1: and built out this this solvability issue. Uh it took.
Speaker 1: It took really the scaling up of computer power to
Speaker 1: make it possible. This is you know this, this is
Speaker 1: not simple stuff. Obviously, it's interesting because a lot of
Speaker 1: these games are very easy for us to grasp as players.
Speaker 1: We understand the rules and the basics and the and
Speaker 1: general strategies pretty intuitively. But when it comes to proving
Speaker 1: the you know that you have solved the game, that
Speaker 1: you know definitively how the outcome will will be based
Speaker 1: upon perfect play, that's a lot trickier. Some games are
Speaker 1: only partially solved, So chess is partially solved, but only
Speaker 1: from an end game standpoint. Yeah. Yeah, So if you say, um,
Speaker 1: you know, the only pieces left on the board, or
Speaker 1: these five or something, and they're in these positions, there
Speaker 1: is a perfect way to play. And as you get
Speaker 1: more pieces on the board, it gets way more complicated
Speaker 1: because you have more options, more variables, and so I
Speaker 1: think when you get up to about seven pieces, it's
Speaker 1: really that's really like the limit of how far you
Speaker 1: can solve. And not all of those uh permutations are solved,
Speaker 1: yet some of them are, because again it depends on
Speaker 1: the combination of pieces. Pieces move in different ways depending
Speaker 1: upon there that what piece you're talking about, whether it's
Speaker 1: upon or a bishop or a rook or whatever. Uh.
Speaker 1: And then there's some other ones that have been solved
Speaker 1: for smaller versions of the game, like Go Go as
Speaker 1: a game, which is really interesting, and that you can
Speaker 1: play it on different size boards that determine how many
Speaker 1: pieces are in place. So a five by five board
Speaker 1: of go has been solved. There's a perfect way to
Speaker 1: play it, Yes, but most people play it on something
Speaker 1: like a nineteen by nineteen board, which is nowhere close
Speaker 1: to being solved. So again, is partially because of just
Speaker 1: the amazing complexity of the game at that scale. Sure, Now,
Speaker 1: whether or not a game has been solved doesn't necessarily
Speaker 1: mean that humans can beat a computer at playing it consistently. Yeah,
Speaker 1: as it turns out, computers don't have to have a
Speaker 1: solved that they don't have to know quote unquote the
Speaker 1: solution in order to still beat the pants off a
Speaker 1: human opponent, even a really good human opponent. Yeah. So
Speaker 1: chess is not solved, but we've gotten to the point
Speaker 1: where computers will always be the best human chess player,
Speaker 1: even way back. Yeah, this was the famous case. It's
Speaker 1: sort of like the people people likened this to the
Speaker 1: story of John Henry and the and the uh steam
Speaker 1: engine that was laying down tracks, like the man versus
Speaker 1: machine story. But this was the man versus machine story
Speaker 1: for the twentieth century. So in u there was actually
Speaker 1: a chess rematch between Gary Kasparov and IBM's Deep Blue computer.
Speaker 1: And Kasparov and Deep Blue had met in ninety six
Speaker 1: for a series of six games, which Kasparov won four
Speaker 1: to two. But in the nineties seven match, Deep Blue
Speaker 1: one it beat Kasparov. I think it's like three and
Speaker 1: a half games to two and a half games, the
Speaker 1: haves being I believe draws. So they you know, now
Speaker 1: we saw a machine beat a chess master for the
Speaker 1: first time. By the way, Kasparov actually demanded a rematch,
Speaker 1: he had match. Yeah, he had claimed that there was
Speaker 1: some hanky panky going on, and IBM declined his request
Speaker 1: because they said they essentially had proven, they had proven
Speaker 1: what they set out to prove. And since then these
Speaker 1: chess programs have become more powerful and sophisticated. But what
Speaker 1: they're actually doing isn't evaluating strategy so much as running
Speaker 1: through every single possible option at that time. Give then
Speaker 1: the board and the peace position. Yeah, like in encryption
Speaker 1: or decryption, I should say, this is called brute force. Yeah,
Speaker 1: you're essentially throwing everything you can at the system to
Speaker 1: find out what works right. So that's that's really what
Speaker 1: brute force is all about. So it might look at
Speaker 1: the chess board and say, okay, if I move my
Speaker 1: queen here, what would be my opponent's best next move
Speaker 1: or what would be all of his or her possible
Speaker 1: next ye yeah, yeah. So let's say let's say that
Speaker 1: you say, all right, let's move my queen. Uh up,
Speaker 1: you know, one square we're moving it, moving the queen
Speaker 1: forward one square. What are all the possible responses to
Speaker 1: that move? And then what are my responses to those?
Speaker 1: And then after figuring all that out, all right, well,
Speaker 1: what if we move the queen two squares? What are
Speaker 1: all the possible responses and what are my And that's
Speaker 1: for every single piece that's in a position that can
Speaker 1: be moved. And remember chess has certain rules that also
Speaker 1: complicate things like there's the rule about castling. Castling adds
Speaker 1: another variable to that sort of stuff. There's also timing.
Speaker 1: I mean, you can't have the computer takes seventeen hours
Speaker 1: to decide what moved moved to make yea. So now
Speaker 1: those roote force with computers that are sufficiently they're sufficiently fast,
Speaker 1: they can make these decisions relatively quickly. If we humans
Speaker 1: played like this, a chess game would last year entire life.
Speaker 1: You would never finish the game because you would be
Speaker 1: constantly evaluating all these potential moves before finally settling settling
Speaker 1: on the one that's waited to be the strongest. But
Speaker 1: that's essentially what chess games and other computer games are doing.
Speaker 1: They are looking at all the different possibilities and picking
Speaker 1: the one that's the most advantageous or at least advantageous
Speaker 1: to a certain degree. Because if you set your difficulty,
Speaker 1: because a lot of chess programs allow you to set
Speaker 1: the difficulty of the thing, what they'll do is they'll say,
Speaker 1: all right, well, we'll just look for x amount of
Speaker 1: time and use the best one out of all of that,
Speaker 1: rather than evaluate the entire board and all the pieces. Yeah,
Speaker 1: and so in this case, the strength of the program
Speaker 1: would probably be based on something like how many moves
Speaker 1: in advance can it look right? And and how quickly
Speaker 1: can it execute that, because if it can't execute it
Speaker 1: within enough time, it may have to, you know, cut
Speaker 1: back on that. Um So, one thing I thought was
Speaker 1: really interesting is that there's a new approach to creating
Speaker 1: a chess playing computer that does not rely on brute
Speaker 1: force and It comes from Matthew Lay of the Imperial
Speaker 1: College of London, and what he did was he developed
Speaker 1: a learning algorithm and a neural network, which we've talked
Speaker 1: about so much on this show, right, neural networks and
Speaker 1: learning algorithms to teach a computer rather than to program
Speaker 1: a computer. So he taught a computer chess. He uh.
Speaker 1: He created a computer program called it Giraffe and started
Speaker 1: to teach it how to play chess. Before he even
Speaker 1: got started teaching at chess, he tested it against a
Speaker 1: a standardized system that is used to evaluate how well
Speaker 1: a computer program does in chess, as a top score
Speaker 1: fifteen thousand, so before we had even schooled it, where
Speaker 1: it knew the it knew the less. It knew the
Speaker 1: rules of chess, but didn't hadn't really learned the value
Speaker 1: of various gambits. It scored six thousand, which is not bad.
Speaker 1: After he schooled it, and by that I mean he
Speaker 1: fed on five million different chess positions into the database,
Speaker 1: because you need huge data sets for learning algorithms to work.
Speaker 1: Then he set Giraffe against itself. It played itself in
Speaker 1: a series of games to start learning which positions were
Speaker 1: the most valuable, which one's made you vulnerable, which one's
Speaker 1: led to victory or defeat. He then tested it again
Speaker 1: and this time it scored uh, like nine thousand, seven hundred,
Speaker 1: so much higher. Yeah, yeah, And that still means that
Speaker 1: it's not perfect, right, but it's it's at a level
Speaker 1: that is consider stint with an international master of chess.
Speaker 1: So this is a computer program that learned chess, and
Speaker 1: it looks at the entire board and the piece positions
Speaker 1: on the board and then evaluates that based upon their
Speaker 1: attacks and defense capabilities. So it's kind of the way
Speaker 1: we play chess. Yeah, exactly. I think statistically speaking, it
Speaker 1: only picks the quote unquote best move about half of
Speaker 1: the time. Yeah, a little less than half, like forty
Speaker 1: six percent of the time it picks the the quote
Speaker 1: unquote best move, but the best move is it tends
Speaker 1: to be in the top three choices around seventy percent
Speaker 1: of the time. And that's pretty good when you consider
Speaker 1: it's not doing brute force, it's not planning all of
Speaker 1: these out. It's it's essentially taking a look and then intuitively,
Speaker 1: which is a weird thing to say, but that's as
Speaker 1: close as I can get feeling out which one is
Speaker 1: the best approach, So about half the time it gets
Speaker 1: the absolute best choice, and seventy percent of the time
Speaker 1: the best choice is within its top three options. But
Speaker 1: this doesn't mean that you cannot beat the pants right
Speaker 1: off of a computer in some games. Yeah, you can
Speaker 1: totally do that in some games. There are some games
Speaker 1: that computers still cannot beat the best human players, And
Speaker 1: then there are other games where computers are just horrible,
Speaker 1: like they can't even beat average players. Yeah, if you're
Speaker 1: really stronge, well, yeah, when you when you play a
Speaker 1: game where the rules are made up on the fly,
Speaker 1: it's very hard for computers. X k c D is
Speaker 1: responsible for that particular example. Uh So go A is
Speaker 1: a good example, like I was saying before, where you
Speaker 1: have a board of like nineteen by nineteen positions that
Speaker 1: you can start to play in. A really strong human
Speaker 1: player can beat a computer in that case, because computers
Speaker 1: just can't deal with all the variables. And uh, it's
Speaker 1: still a challenge. I mean, it's still really hard, but
Speaker 1: it's possible. Other games don't rely heavily on strategy, so
Speaker 1: then the computers are at a loss because the one
Speaker 1: thing they do really well may not play as an
Speaker 1: important role in those games, and especially if you have
Speaker 1: incorporated randomness into your gameplay in some respect, like whether
Speaker 1: it's dice or shuffling up cards, that kind of stuff
Speaker 1: where your position, however you wanted to fine, that is
Speaker 1: not fully determined just by your strategy, but also by luck. Yeah. Yeah,
Speaker 1: And lots of games involve a little bit of luck,
Speaker 1: like a poker or scrabble. Yeah, poker is a is
Speaker 1: a very interesting example to me because you could argue
Speaker 1: about that there are different levels of skill involved in poker.
Speaker 1: I mean, there are a lot of it is just luck,
Speaker 1: it's the cards you're drawing, but skill, the the effect
Speaker 1: of skill on poker emerges over the course of playing
Speaker 1: many hands. There there are three things I would argue
Speaker 1: that go into determining whether or not you're going to
Speaker 1: be successful at any given poker game where you're not
Speaker 1: not a hand but a game ease of hands. One
Speaker 1: is luck the cards that you draw. That's going to
Speaker 1: be part of it too. And keep in mind that
Speaker 1: you know, bluffing is still a strategy in poker. If
Speaker 1: you're effective at bluffing, you may be able to win
Speaker 1: even with lousy cards. Two is an understanding of the
Speaker 1: statistical probability of what other people are holding in their hands.
Speaker 1: So knowing what you should bet and how you should
Speaker 1: play based on the cards you have and the probability
Speaker 1: of receiving other cards. So if it's texas hold them
Speaker 1: and you're looking at the flop, which are the first
Speaker 1: three cards that are laid down face up, and you have,
Speaker 1: you're looking at the cards in your hand. You can
Speaker 1: then start to think, well, those cards are now eliminated
Speaker 1: from my opponent's hands. There's no way they can hold
Speaker 1: those cards because I know what they are, I see them.
Speaker 1: What are the combinations that could beat the cards I
Speaker 1: have combined with the flop. That's the sort of stuff
Speaker 1: you have to start thinking about, what are the odds
Speaker 1: that someone at this table can beat the hand that
Speaker 1: I have. Then there's the third part that computers really
Speaker 1: can't handle, just getting a read on the playing style
Speaker 1: of your opponents, knowing which ones are aggressive, which ones
Speaker 1: are timid, which ones might be playing on tilt. While
Speaker 1: it's especially difficult because humans are good at playing against expectations,
Speaker 1: or good humans are, I mean, so a computer can
Speaker 1: look at the what you've done in previous hands and
Speaker 1: say well, okay, this player tends to be raising the
Speaker 1: bed every time, and so I think that this player
Speaker 1: is probably bluffing sometimes. Like you could design a program
Speaker 1: that wouldn't make that analysis, but it's hard to design
Speaker 1: a program that understands that that other player is trying
Speaker 1: to get you to think that that's how he or
Speaker 1: she plays, so he or she can then surprise you
Speaker 1: when you're vulnerable the good old check raise or something
Speaker 1: along those things lines. I remember seeing poker games where
Speaker 1: really really strong poker players, could you know what they
Speaker 1: would start talking during the tournament, and like as they're
Speaker 1: looking at their opponent with and they're deciding what to do,
Speaker 1: and they even figure out what cards the person's holding,
Speaker 1: like at least one of them, uh, one of them
Speaker 1: in particular, I remember him saying, like, you gotta I
Speaker 1: guess you've got to have a queen. And I look
Speaker 1: at the cards that have been shown, including the ones
Speaker 1: that you know held by the other players, that I
Speaker 1: know what those cards are, but he doesn't. He hasn't
Speaker 1: seen them because he's not watching the camera where it
Speaker 1: reveals where all the cards are. And it was amazing
Speaker 1: to me because the guy held a queen and it
Speaker 1: was phenomenal that he was able to use deductive reasoning
Speaker 1: based upon the guy's behavior and the cards that were
Speaker 1: already out and the ones that he held in his hand,
Speaker 1: and was able to be that accurate. Now, computers could cheat,
Speaker 1: they could know that if it was built into the program.
Speaker 1: But I gotta tell you, I've played a lot of
Speaker 1: games that are that put you against a computer opponent
Speaker 1: in poker. They're lousy, they're not consistent, they don't they
Speaker 1: don't behave with any logic. I mean, if you're playing
Speaker 1: Texas hold Them and you're playing one of those poker games,
Speaker 1: half the time they're going for they're going to see
Speaker 1: the entire hand, like the flop and the turn the river.
Speaker 1: They want to see the whole thing. And they might
Speaker 1: have like a two seven, and you're thinking, no human
Speaker 1: apart from a couple of crazy people, would ever stay
Speaker 1: in this hand that long. They would have folded immediately.
Speaker 1: But computer, if you yeah, if you haven't programmed in
Speaker 1: the concept of of personal stakes in a game, then
Speaker 1: a computer is going to behave radically. It also doesn't
Speaker 1: help that in most computer games, you're obviously playing with
Speaker 1: fake money, so there's no there's no consequence to losing,
Speaker 1: and probably it's not like in Reboot where the computer
Speaker 1: losing is going to destroy part of its little interior
Speaker 1: computer town. Probably not. Another game that includes both random
Speaker 1: chance and strategy would be something like Yachtzi. Pretty simple game, right,
Speaker 1: But when you make a role, then you have to
Speaker 1: decide what category does that role go into on your sheet?
Speaker 1: And every time you put down a category that one is,
Speaker 1: it's then inaccessible to you for the rest of that
Speaker 1: round of the game. Right, So if I roll three fives,
Speaker 1: I could say, all right, well I want this to
Speaker 1: go on my fives category, or I might say, no,
Speaker 1: I want this to go in my three of a
Speaker 1: kind category. Um, so there's a bit of strategy as
Speaker 1: well as the random chance. You don't know what your
Speaker 1: next role is going to be. Another one I would
Speaker 1: think of is Risk, Like there's some amount of strategy,
Speaker 1: but there's also a large degree of luck based on
Speaker 1: dice rolls. Yeah, here's the way I know how how
Speaker 1: the game is gonna go. For Risk, if you ask
Speaker 1: me to play, I've already lost. Uh yeah, I would say.
Speaker 1: A way of predicting the end of Risk is did
Speaker 1: you start playing it will end with people quitting and
Speaker 1: rage slipping the table and hating you forever. I just go,
Speaker 1: I just go straight to the hate because I I've
Speaker 1: only ever played it once and it was one of
Speaker 1: those things where because of the way the game went,
Speaker 1: I was eliminated before I even had a chance to
Speaker 1: do anything. Oh yeah, yeah, things like like Monopoly and
Speaker 1: Settle of Catan are other examples of games that are
Speaker 1: partial strategy and partial random chance uh largely table flipping.
Speaker 1: There are, of course, other games that involve absolutely no strategy,
Speaker 1: like Shoots and Ladders or Snakes and Ladders or the
Speaker 1: aforementioned candy Land. Yeah. These are games that are more
Speaker 1: like distractions than anything else. It's it's you know, you
Speaker 1: can kind of get the feeling of hey, I won
Speaker 1: or darn I lost, but then eventually you get to
Speaker 1: the point where you're like, no, I roll dice and
Speaker 1: that's what determined everything wicked. We move one space at
Speaker 1: a time, a citizens quote, isn't it all right? And
Speaker 1: then there are games that rely heavily on intuition and interpretation.
Speaker 1: These are games that are not designed with like cards
Speaker 1: or dice. Necessarily, they can incorporate those, but I'm thinking
Speaker 1: of stuff like pictionary. Yeah, see, that's great. You can
Speaker 1: have definite skill at this game. You can get good
Speaker 1: at it. But I'm sure computers are just awful. Well yeah,
Speaker 1: because I mean you can you can create abstract representations
Speaker 1: of things in these games which humans can understand because
Speaker 1: we can have these weird associative things in our brains that, oh,
Speaker 1: because you're acting that way, I happen to know that
Speaker 1: the thing you're acting out is pulp fiction, you know,
Speaker 1: which could could be whether it's, you know, mimicking something
Speaker 1: that's going on in the movie and that's how I
Speaker 1: know it, or it's because you've acted out the individual words.
Speaker 1: However that might be Wait a second, I take that back,
Speaker 1: because actually I think a computer could probably destroy people
Speaker 1: at pictionary by just Google image searching the term and
Speaker 1: then drawing pictures of the results that come up. A
Speaker 1: computer is probably better at drawing than I am. I'm
Speaker 1: pretty crap at drawing. It would be good at drawing,
Speaker 1: It would not necessarily be good at guessing, because it
Speaker 1: would then have to look at the abstract nature of
Speaker 1: things that we draw and then figure out what concrete
Speaker 1: concept is linked to that. Although I'm wondering right now
Speaker 1: if something like like shrade, well not shrades, probably because
Speaker 1: that would involve full my robot, really lame party. But
Speaker 1: it's something like like Google Deep Dream could be successful
Speaker 1: at pictionary by kind of reverse engineering, I don't know.
Speaker 1: And well, we're seeing some more sophisticated image recognition software
Speaker 1: out there where it looks like we could get to
Speaker 1: a point where computers could at least make a guess
Speaker 1: based upon uh, certain drawings. And there's certain certain shapes
Speaker 1: that seem to be more or less universal when you
Speaker 1: want to draw something, so like a cat, you know,
Speaker 1: you see, besides those who are actually really good at drawing,
Speaker 1: for those of us like me who struggle with keeping
Speaker 1: within the lines in a coloring book, it's going to
Speaker 1: be a circle and a couple of triangles for years.
Speaker 1: You know, you know that, oh that's a cat. Um.
Speaker 1: There's certain things I think that are almost universal in
Speaker 1: that respect, and computers could easily start to recognize those
Speaker 1: as well. So the big difference obviously is the role
Speaker 1: that rules play and that strategy works within those rules.
Speaker 1: If you have very well defined rules and you have
Speaker 1: a lot of restrictions on what can actually happen within
Speaker 1: the game, and there's not a lot of variability there.
Speaker 1: Those are games that are easier to solve and also
Speaker 1: easier for computers to totally dominate. Um, the more you
Speaker 1: get into variable rules or different styles of play, different
Speaker 1: options that you could have during the course of the game. Intelligence,
Speaker 1: that's a big one. Humans will do better at those two. Yeah,
Speaker 1: And this is partially described the former part more so
Speaker 1: by the game theory concept of convergence versus divergence, which
Speaker 1: says that games that are more solvable tend to converge
Speaker 1: their pieces and possible moves towards the end of the game.
Speaker 1: In other words, there are fewer ways to win the
Speaker 1: further along in the game that you get. Less solvable
Speaker 1: games have more pieces in play and and more possible
Speaker 1: moves towards the end, thus making it harder, even with
Speaker 1: a very powerful computer, to to list all of the
Speaker 1: potential piece positions and moves that can lead to a
Speaker 1: winning board layout. So yeah, it's it's what do you
Speaker 1: guys think? Do you guys think we're going to eventually
Speaker 1: be in a future where no matter what the game is,
Speaker 1: apart from you know, kill the computer, the computer is
Speaker 1: always going to win. Uh yeah, I think for for
Speaker 1: essentially all math based or strictly logic based games that
Speaker 1: that involved what would you call them, you know, numerical
Speaker 1: quantities or something as the ways of scoring and measuring advantage,
Speaker 1: computers are going to dominate. Even even in the ones
Speaker 1: where they're not winning now, they will soon be able
Speaker 1: to defeat all human experts because it's really just just
Speaker 1: a matter of of programming and processing power. Sure well,
Speaker 1: I mean I if it comes down to ultimately there
Speaker 1: is a best way to do this, computers have the
Speaker 1: advantage and that they can evaluate all the other ways. Now,
Speaker 1: the ones I don't know about are the ones like,
Speaker 1: uh like the games that have social intelligence aspects which
Speaker 1: I mentioned, like bluffing in poker, and obviously the randomness
Speaker 1: of it also plays a factor in that there can
Speaker 1: be quote unquote luck where the computer could end up
Speaker 1: with the worst rolls, the worst cards, whatever it may be,
Speaker 1: and that is enough to offset its strategic dominance of
Speaker 1: the game. Although I do wonder that that even with
Speaker 1: with poor luck, because because you can you can have
Speaker 1: a poor hand and still when it poker. I wonder
Speaker 1: if especially with with digital imaging and recognition. A computer
Speaker 1: could be taught to to identify and exploit a person's
Speaker 1: tells well, especially better than a human if you could
Speaker 1: use a an algorithm similar to the one we talked
Speaker 1: about with the sound, where it can actually detect your
Speaker 1: heartbeat through your neck. It's looking it's looking at your
Speaker 1: pulse through your neck and can tell what it increases,
Speaker 1: and thus can map that to how excited you are
Speaker 1: about the hand or how much dread you're feeling about
Speaker 1: the hand you have. Yeah, you're talking about some some
Speaker 1: I mean that's a that's a computer that can really
Speaker 1: read in a pope that I'm only kind of joking. Yeah, yeah, no,
Speaker 1: I mean that. And like my nude eye dilation temperature changes,
Speaker 1: look at it in the in the infrared spectrum laser infrared,
Speaker 1: uh voice changes. Sure, yeah, wow, we were I don't
Speaker 1: think computers will ever be better than humans at the
Speaker 1: most dangerous game hunting humans. I stand to disagree, but
Speaker 1: that's a whole documentary series about that. Uh yeah, it's um.
Speaker 1: You know, I think there are certain games that are
Speaker 1: are inherently human that that I think computers will The
Speaker 1: only time computers will really start winning is when humans
Speaker 1: and computers. There's no longer a distinction between the two,
Speaker 1: where the two have merged at some point and become
Speaker 1: one unified thing. And who knows, we might be playing
Speaker 1: totally different games at that point. But yeah, this was
Speaker 1: a fun thing to think about, just like, what are
Speaker 1: the games of the future going to be like, not
Speaker 1: just you know, in the video game sphere, but just
Speaker 1: games in general. What is play in the future? And um,
Speaker 1: I'm sure we're going to see a lot more examples
Speaker 1: of people still wanting to test their skills, both against
Speaker 1: human and computer opponents. Um, it could be fun, it
Speaker 1: could be really frustrating if you're like me and have
Speaker 1: an incredible competitive streak paired with an almost comedic inability
Speaker 1: to win a game. Um, and as long as I
Speaker 1: can beat a computer at Betrayal at the House in
Speaker 1: the Hill, I'm happy. So let's just keep that street going, folks.
Speaker 1: That's all I'm saying. All right, guys, if you have
Speaker 1: any suggestions for future episodes of forward Thinking, I recommend
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