1 00:00:00,280 --> 00:00:02,840 Speaker 1: Brought to you by the reinvented two thousand twelve camera. 2 00:00:03,160 --> 00:00:08,920 Speaker 1: It's ready. Are you get in touch with technology? With 3 00:00:09,039 --> 00:00:17,760 Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone, 4 00:00:17,800 --> 00:00:20,079 Speaker 1: welcome to tech stuff. My name is Chris Pollette, and 5 00:00:20,079 --> 00:00:22,760 Speaker 1: I am an editor at how stuff works dot com. 6 00:00:23,160 --> 00:00:26,880 Speaker 1: Seeming across from me, as usual, is senior writer Jonathan Strickland. Yes, 7 00:00:26,960 --> 00:00:29,480 Speaker 1: I'm owed old enough to remember when the m c 8 00:00:29,640 --> 00:00:33,480 Speaker 1: P was just a chess program. There you go, that's 9 00:00:33,479 --> 00:00:35,920 Speaker 1: pretty good. It's a good quote. Huh yeah, yeah, we's 10 00:00:36,159 --> 00:00:40,120 Speaker 1: now I'm hungry for Pepperidge Farm. You can't get that 11 00:00:40,280 --> 00:00:44,400 Speaker 1: from hreh Um. We're going to talk today about some 12 00:00:44,479 --> 00:00:51,479 Speaker 1: pretty famous matchups between human beings and computers, uh, in 13 00:00:51,479 --> 00:00:54,960 Speaker 1: in various games. And this is one of those things 14 00:00:55,000 --> 00:00:59,040 Speaker 1: that people have been interested in. Well, really it goes 15 00:00:59,080 --> 00:01:03,520 Speaker 1: back even before computers, because it really kind of dips 16 00:01:03,520 --> 00:01:08,959 Speaker 1: into the whole man versus machine, um the idea, right, 17 00:01:09,000 --> 00:01:11,360 Speaker 1: I mean this goes all the way back to John 18 00:01:11,440 --> 00:01:17,600 Speaker 1: Henry versus the the steam steam engine. Yeah, and uh, 19 00:01:17,840 --> 00:01:20,160 Speaker 1: people have wondered like when we're when we reach a 20 00:01:20,160 --> 00:01:25,800 Speaker 1: point where computers would become better than humans at various 21 00:01:25,840 --> 00:01:29,759 Speaker 1: games and we've reached the point with several games where 22 00:01:29,959 --> 00:01:34,039 Speaker 1: we can pretty much definitively say computers are better at 23 00:01:34,040 --> 00:01:37,240 Speaker 1: it than we are, you know, And a lot of 24 00:01:37,280 --> 00:01:41,120 Speaker 1: the podcast where we've mentioned artificial intelligence or AI in 25 00:01:41,160 --> 00:01:47,680 Speaker 1: the past, UM, we have talked about gaming and in 26 00:01:47,720 --> 00:01:52,600 Speaker 1: those situations we were talking more about computer opponents when 27 00:01:52,600 --> 00:01:55,560 Speaker 1: you're playing against something in a computer game or a 28 00:01:55,640 --> 00:01:58,800 Speaker 1: console game, and that's a little different than what we're 29 00:01:58,840 --> 00:02:03,320 Speaker 1: talking about here. But because and UH, I think actually 30 00:02:03,360 --> 00:02:07,680 Speaker 1: I was I was reading an article UM by Jonathan Schaefer, 31 00:02:08,000 --> 00:02:12,120 Speaker 1: Vadim Blikko, and Michael Burrow in the I E E. 32 00:02:12,520 --> 00:02:17,960 Speaker 1: Spectrum and UH, it was talking about computer opponents in 33 00:02:18,200 --> 00:02:22,440 Speaker 1: UH in software, specifically the game Fear f E A R, 34 00:02:22,520 --> 00:02:25,400 Speaker 1: which is the first Encounter Assault Recon and how that 35 00:02:25,480 --> 00:02:29,360 Speaker 1: has that's known as for its excellent AI. But UM, 36 00:02:29,400 --> 00:02:34,480 Speaker 1: basically these games we're talking about today are are classic 37 00:02:34,560 --> 00:02:39,959 Speaker 1: board games, classic tests of UH, you know, one player's 38 00:02:40,120 --> 00:02:44,640 Speaker 1: skill and strategy and thinking against another human being. And 39 00:02:44,680 --> 00:02:47,320 Speaker 1: we wanted to know, you know, whether that's possible now 40 00:02:47,880 --> 00:02:49,440 Speaker 1: and sort of depends on the game and it sort 41 00:02:49,440 --> 00:02:52,480 Speaker 1: of depends on the opponent. But that's really according to 42 00:02:52,560 --> 00:02:56,200 Speaker 1: the authors in this in the Spectrum article. Um, it 43 00:02:56,280 --> 00:02:58,760 Speaker 1: really started with with board games, and that's one of 44 00:02:58,800 --> 00:03:01,840 Speaker 1: the things that has gone into a development of AI 45 00:03:01,919 --> 00:03:05,440 Speaker 1: in computer games of all kinds, was you know, these 46 00:03:05,480 --> 00:03:10,640 Speaker 1: attempts in the past to develop a formidable opponent, somebody 47 00:03:10,680 --> 00:03:12,519 Speaker 1: that or something that you can play against where you 48 00:03:12,520 --> 00:03:14,200 Speaker 1: don't have to go you don't really like to play 49 00:03:14,200 --> 00:03:16,919 Speaker 1: a game at chess, but you know, Steve's work in 50 00:03:16,960 --> 00:03:19,440 Speaker 1: a night and and I don't have anybody else that 51 00:03:19,520 --> 00:03:20,919 Speaker 1: I can think of, and I really want to play 52 00:03:20,960 --> 00:03:23,160 Speaker 1: a game of chess. We want something. You don't want to, uh, 53 00:03:23,280 --> 00:03:26,360 Speaker 1: something that's going to annihilate you instantly, nor do you 54 00:03:26,400 --> 00:03:31,760 Speaker 1: want something that's going to be a complete pushover. So yeah, exactly, 55 00:03:31,880 --> 00:03:35,400 Speaker 1: he's like playing me and chess. I only know how 56 00:03:35,400 --> 00:03:40,640 Speaker 1: to lose it chess. Um. So yeah, I mean it's 57 00:03:40,680 --> 00:03:44,800 Speaker 1: it's an interesting question and sort of a philosophical question. 58 00:03:44,840 --> 00:03:49,280 Speaker 1: It's not just programming. Yeah, I mean, there's a lot 59 00:03:49,320 --> 00:03:52,120 Speaker 1: of these games have have elements to them that go 60 00:03:52,240 --> 00:03:56,520 Speaker 1: beyond just a the physical number of moves that are 61 00:03:56,600 --> 00:04:00,800 Speaker 1: possible at any given point. Right, some games that's pretty 62 00:04:00,880 --> 00:04:05,520 Speaker 1: much all the game is. For example, Connect four. Oh yeah, 63 00:04:05,520 --> 00:04:09,800 Speaker 1: Connect four. We're gonna talk a little bit about solving games. Now, 64 00:04:09,960 --> 00:04:13,680 Speaker 1: to solve a game means that you have determined what 65 00:04:13,760 --> 00:04:16,400 Speaker 1: all the possible moves are, and you know what the 66 00:04:16,440 --> 00:04:20,960 Speaker 1: best possible move in any given situation is. Um. If 67 00:04:20,960 --> 00:04:23,760 Speaker 1: you have perfectly solved a game, then in theory, you 68 00:04:23,800 --> 00:04:26,920 Speaker 1: would be able to play a game, even pick up 69 00:04:26,920 --> 00:04:29,440 Speaker 1: a game that had already been started by someone else, 70 00:04:29,760 --> 00:04:32,960 Speaker 1: and play it from that point forward, playing the best 71 00:04:33,000 --> 00:04:36,040 Speaker 1: possible moves, even if that person who had previously been 72 00:04:36,040 --> 00:04:38,880 Speaker 1: playing made a lot of mistakes. Not many games have 73 00:04:38,960 --> 00:04:42,160 Speaker 1: been solved to that level, but that a lot of 74 00:04:42,240 --> 00:04:44,719 Speaker 1: games have been solved to the point where two players 75 00:04:44,839 --> 00:04:47,880 Speaker 1: starting off with a fresh board and going from there. 76 00:04:48,400 --> 00:04:51,920 Speaker 1: Uh is there is there a perfect way to play 77 00:04:52,000 --> 00:04:56,360 Speaker 1: where you will either what will essentially where you'll never lose. Yeah, 78 00:04:56,880 --> 00:04:59,800 Speaker 1: you're speaking for personal experience. I think Reversey is one 79 00:04:59,800 --> 00:05:03,720 Speaker 1: of the games, UM, if you're not familiar, it's the 80 00:05:04,160 --> 00:05:07,640 Speaker 1: game with the black and white pieces that you try 81 00:05:07,680 --> 00:05:11,200 Speaker 1: to basically surround your opponent to the point where you 82 00:05:11,200 --> 00:05:14,640 Speaker 1: flip all the pieces to your color and or or 83 00:05:14,680 --> 00:05:17,640 Speaker 1: as many as you can and outnumber your opponents. Now 84 00:05:17,960 --> 00:05:21,080 Speaker 1: I've played, uh, computer opponents in which on the easy 85 00:05:21,120 --> 00:05:23,800 Speaker 1: mode it was a cakewalk, and I'd switch to the 86 00:05:23,839 --> 00:05:26,599 Speaker 1: next level, and it already it seemed as though the 87 00:05:26,640 --> 00:05:29,679 Speaker 1: machine had already been programmed with the best possible counter 88 00:05:29,760 --> 00:05:32,240 Speaker 1: move to whatever it was, to the point where if 89 00:05:32,279 --> 00:05:35,159 Speaker 1: I played on easy, I would almost always win unless 90 00:05:35,160 --> 00:05:39,719 Speaker 1: I did something really stupid, and on even medium, it 91 00:05:39,800 --> 00:05:43,280 Speaker 1: would know exactly how to counter every move I make, 92 00:05:43,360 --> 00:05:45,760 Speaker 1: to the point where I could not win no matter 93 00:05:45,839 --> 00:05:49,400 Speaker 1: how well I would play the game. And uh, I 94 00:05:49,760 --> 00:05:52,159 Speaker 1: think there are certain games for which you could say, okay, 95 00:05:52,160 --> 00:05:55,080 Speaker 1: well these are the moves. In this case, it's the 96 00:05:55,080 --> 00:05:58,200 Speaker 1: best possible move. And you know, when you're playing a person, 97 00:05:58,800 --> 00:06:01,479 Speaker 1: you go, well, I you know, oh, look he made 98 00:06:01,480 --> 00:06:04,240 Speaker 1: a mistake. And you know that. You know, Jonathan in 99 00:06:04,279 --> 00:06:07,200 Speaker 1: this case is sitting across from me. He made a mistake. Well, 100 00:06:07,240 --> 00:06:09,400 Speaker 1: he may not have been thinking about it. The computer 101 00:06:09,480 --> 00:06:11,320 Speaker 1: is not going to make a mistake. It's already been 102 00:06:11,360 --> 00:06:15,120 Speaker 1: programmed with that in mind. Yeah. The mistakes computers make 103 00:06:15,480 --> 00:06:20,560 Speaker 1: are based upon not expecting behavior from a human, right, right, 104 00:06:20,600 --> 00:06:22,760 Speaker 1: I mean that's that's the way computers make mistakes. They 105 00:06:22,760 --> 00:06:25,680 Speaker 1: don't make mistakes in the same way humans do. So 106 00:06:25,760 --> 00:06:29,560 Speaker 1: for example, getting back to the Connect for uh example, 107 00:06:30,240 --> 00:06:34,240 Speaker 1: that game has been solved to the point where, assuming 108 00:06:34,279 --> 00:06:37,640 Speaker 1: perfect play on both sides, yes, the first player will 109 00:06:37,640 --> 00:06:40,240 Speaker 1: always win. Yes, there is no way for the first 110 00:06:40,240 --> 00:06:44,520 Speaker 1: player to lose if both players are playing perfectly. Uh. 111 00:06:44,680 --> 00:06:47,920 Speaker 1: Perfect play means you go for that center spot, folks, 112 00:06:47,960 --> 00:06:50,719 Speaker 1: Just just an f y I if you need to, uh, 113 00:06:50,800 --> 00:06:52,880 Speaker 1: if you need to play Connect four and you're the 114 00:06:52,920 --> 00:06:56,080 Speaker 1: first player, get grabbed that center spot on the bottom 115 00:06:56,160 --> 00:06:58,480 Speaker 1: row and uh and then just play perfectly from there 116 00:06:58,480 --> 00:07:01,599 Speaker 1: and you'll always win. Easier so than done. Really, but 117 00:07:02,120 --> 00:07:04,400 Speaker 1: that that was one of the games that was solved 118 00:07:04,440 --> 00:07:06,200 Speaker 1: fairly early on. But there are other games have been 119 00:07:06,200 --> 00:07:09,680 Speaker 1: solved as well, and some games are not truly solvable. 120 00:07:10,520 --> 00:07:14,240 Speaker 1: In fact, I wrote an article about computer versus human 121 00:07:14,360 --> 00:07:17,680 Speaker 1: game matchups and the first one I mentioned was b 122 00:07:18,040 --> 00:07:22,800 Speaker 1: KG nine point eight. That would be the computer versus 123 00:07:23,080 --> 00:07:27,360 Speaker 1: Luigi Villa or Villa so uh so Luigi here he 124 00:07:27,480 --> 00:07:31,200 Speaker 1: was a backgammon player. Yes, as am I, I nowhere 125 00:07:31,240 --> 00:07:36,840 Speaker 1: near as good as Luigi. So so Luigi played a 126 00:07:36,960 --> 00:07:41,040 Speaker 1: match against b KG nine point eight in June nineteen 127 00:07:41,240 --> 00:07:47,160 Speaker 1: seventy nine, and the game was got a fair amount 128 00:07:47,160 --> 00:07:52,200 Speaker 1: of press because the computer program beat the the player. However, 129 00:07:52,360 --> 00:07:56,160 Speaker 1: backgammon has an element in it that is unlike games 130 00:07:56,200 --> 00:08:01,200 Speaker 1: like connect for or checkers or chess. Yes, that would 131 00:08:01,200 --> 00:08:04,880 Speaker 1: be dice. There's an element of chance in backgam and 132 00:08:04,960 --> 00:08:09,440 Speaker 1: so depending upon the dice rolls, you may be luckier 133 00:08:09,440 --> 00:08:12,040 Speaker 1: than your opponent, and that luck might be enough for 134 00:08:12,080 --> 00:08:15,800 Speaker 1: you to beat someone who is technically more skilled at 135 00:08:15,840 --> 00:08:17,440 Speaker 1: the game than you are, someone who is who is 136 00:08:17,480 --> 00:08:20,720 Speaker 1: better at formulating strategies and picking the right moves and 137 00:08:20,760 --> 00:08:24,720 Speaker 1: the best possible outcomes. Uh, you might win just because 138 00:08:24,800 --> 00:08:26,760 Speaker 1: you get on a lucky streak, and that seems to 139 00:08:26,800 --> 00:08:29,960 Speaker 1: be what happened with this computer program. Yes, should we 140 00:08:30,000 --> 00:08:35,440 Speaker 1: mention our earlier podcast on random number generators, because if 141 00:08:35,440 --> 00:08:38,000 Speaker 1: you were playing a computer and you were not using 142 00:08:38,280 --> 00:08:42,480 Speaker 1: an actual die or in this case dice, then uh, 143 00:08:42,920 --> 00:08:45,840 Speaker 1: and you're relying on the computer to generate random numbers, Well, 144 00:08:45,920 --> 00:08:50,400 Speaker 1: in general you can't because they're they're using an algorithm 145 00:08:50,760 --> 00:08:56,360 Speaker 1: and the computer number generators will generally go in a sequence. Now, 146 00:08:56,480 --> 00:08:58,360 Speaker 1: this kind of goes back to what Chris was saying 147 00:08:58,360 --> 00:09:00,920 Speaker 1: earlier about playing a video game in a video game 148 00:09:00,920 --> 00:09:04,360 Speaker 1: world where you're playing against video game opponents. Technically in 149 00:09:04,440 --> 00:09:07,920 Speaker 1: that kind of environment. Uh, the AI is totally different 150 00:09:08,000 --> 00:09:11,319 Speaker 1: from a board game because it controls the entire environment. 151 00:09:11,800 --> 00:09:15,040 Speaker 1: So you could create a video game that compensates for 152 00:09:15,120 --> 00:09:19,680 Speaker 1: really strong players by pitting the environment against the players. Yes, 153 00:09:19,840 --> 00:09:21,920 Speaker 1: you know. I mean it's it's completely within the realm 154 00:09:21,920 --> 00:09:25,440 Speaker 1: of possibility. In the situations we're talking about. It's supposed 155 00:09:25,480 --> 00:09:29,480 Speaker 1: to be a fair game between our as fair game 156 00:09:29,520 --> 00:09:31,640 Speaker 1: as you can get between a computer and a human. 157 00:09:32,200 --> 00:09:33,920 Speaker 1: There's not supposed to be any element that's going to 158 00:09:34,000 --> 00:09:36,440 Speaker 1: be within the computer's control that could that could weigh 159 00:09:36,480 --> 00:09:39,560 Speaker 1: it in favor of the computer, right, because it's supposed 160 00:09:39,559 --> 00:09:43,040 Speaker 1: to be a test of intelligence relative intelligence, like as 161 00:09:43,080 --> 00:09:47,640 Speaker 1: we mentioned with Alan Touring, Yes, yes, the touring test um. Now, 162 00:09:47,640 --> 00:09:50,240 Speaker 1: in that case, the touring test was a tested determined 163 00:09:50,280 --> 00:09:53,320 Speaker 1: whether or not you could tell the difference between a 164 00:09:53,520 --> 00:09:57,800 Speaker 1: human respondent and a computer responded to a series of questions. 165 00:09:58,160 --> 00:10:00,760 Speaker 1: In this case, we're talking about what or a computer 166 00:10:00,840 --> 00:10:03,480 Speaker 1: is able to formulate a winning strategy against the human. 167 00:10:04,320 --> 00:10:08,360 Speaker 1: The next one on my list was Chinook versus Marion 168 00:10:08,480 --> 00:10:13,840 Speaker 1: Tinsley with Checkers. Yes, this was Checkers. Now Tinsley, Uh, 169 00:10:14,600 --> 00:10:20,800 Speaker 1: was an accomplished checkers player, amazing champion. He had won 170 00:10:20,880 --> 00:10:26,520 Speaker 1: the championship from nineteen fifty five to nineteen two, and 171 00:10:26,559 --> 00:10:30,959 Speaker 1: he had only lost five games between nineteen fifty and 172 00:10:33,160 --> 00:10:36,160 Speaker 1: that seems reasonably decent. I think I've only played five 173 00:10:36,200 --> 00:10:41,200 Speaker 1: games since n probably lost all five of them. Well, 174 00:10:41,600 --> 00:10:45,320 Speaker 1: this is another game, though, if unless I misunderstand the 175 00:10:45,480 --> 00:10:49,720 Speaker 1: article that you so aptly wrote, um uh, in which 176 00:10:49,760 --> 00:10:53,200 Speaker 1: there is a way to play perfectly. Yeah, this was 177 00:10:53,360 --> 00:10:56,120 Speaker 1: that was not discovered till two thousand seven, or not 178 00:10:56,120 --> 00:10:59,840 Speaker 1: not truly proven until two thousand seven. So when Tinsley 179 00:11:00,040 --> 00:11:04,480 Speaker 1: played against chinook Um, the game had yet to be solved, 180 00:11:04,520 --> 00:11:10,120 Speaker 1: and sadly, Tensley actually passed away before he could. Before 181 00:11:10,160 --> 00:11:12,480 Speaker 1: they could definitively say whether or not the computer was 182 00:11:12,520 --> 00:11:16,320 Speaker 1: a superior player, they would play thirties something games in 183 00:11:16,320 --> 00:11:20,400 Speaker 1: a row and end and draw a draw every time. Um. 184 00:11:20,480 --> 00:11:23,080 Speaker 1: But that suggests that I'm sorry, go ahead, I'm just 185 00:11:23,080 --> 00:11:27,680 Speaker 1: gonna say that suggests that that Tense was able to 186 00:11:27,679 --> 00:11:31,120 Speaker 1: play a perfect game. Yes, because in two thousand and seven, 187 00:11:31,640 --> 00:11:36,080 Speaker 1: the team that that created chinook did prove or demonstrate 188 00:11:36,200 --> 00:11:38,240 Speaker 1: that they had solved the game, and that if you 189 00:11:38,280 --> 00:11:41,200 Speaker 1: play perfectly on either side. Let's say both sides are 190 00:11:41,200 --> 00:11:43,520 Speaker 1: playing perfectly, it will the game will always end in 191 00:11:43,520 --> 00:11:46,840 Speaker 1: a draw. So checkers is different from Connect four and 192 00:11:46,880 --> 00:11:49,360 Speaker 1: that if you if both sides are playing perfectly, there 193 00:11:49,520 --> 00:11:51,280 Speaker 1: is no winner, It's going to be a draw, whereas 194 00:11:51,280 --> 00:11:54,280 Speaker 1: with Connect four, whoever goes first wins. There's actually I 195 00:11:54,320 --> 00:11:56,599 Speaker 1: remember reading about a game I didn't jot down it 196 00:11:56,720 --> 00:11:59,720 Speaker 1: shot down my notes unfortunately, where it was the second 197 00:11:59,720 --> 00:12:02,720 Speaker 1: play if the second player plays perfectly, If both players 198 00:12:02,760 --> 00:12:05,600 Speaker 1: are playing perfectly, the second player will always win, which 199 00:12:05,600 --> 00:12:08,240 Speaker 1: is which is interesting because you know, it just shows 200 00:12:08,280 --> 00:12:12,440 Speaker 1: that it all depends upon the actual style of the game. Now, 201 00:12:12,440 --> 00:12:14,160 Speaker 1: the next one that's on my list is probably the 202 00:12:14,160 --> 00:12:17,760 Speaker 1: biggest one. Yes, yeah, the one that everyone knows or 203 00:12:17,840 --> 00:12:22,280 Speaker 1: has heard about, right, IBM's famous deep blue Yes against 204 00:12:22,360 --> 00:12:26,839 Speaker 1: Gary Kasparov, who was the world champion chess player at 205 00:12:26,840 --> 00:12:30,360 Speaker 1: the time. And uh they actually had two matches to 206 00:12:30,559 --> 00:12:34,000 Speaker 1: two series of games. The first was in n and 207 00:12:34,040 --> 00:12:39,160 Speaker 1: in that series Kasparov actually came out the victor. He 208 00:12:39,320 --> 00:12:47,160 Speaker 1: managed to thank you, came out the winner, you know, wiener. 209 00:12:47,880 --> 00:12:51,280 Speaker 1: Uh So Kasparov one in the first series of games 210 00:12:51,280 --> 00:12:56,320 Speaker 1: in and then IBM redoubled their efforts, and as Kasparov 211 00:12:56,440 --> 00:13:00,480 Speaker 1: says in one of his articles, um they redoubled it's uh, 212 00:13:00,520 --> 00:13:03,600 Speaker 1: they doubled its its processing power. Uh. The article I 213 00:13:03,640 --> 00:13:05,720 Speaker 1: was referring to it is called the Chess Master and 214 00:13:05,920 --> 00:13:11,280 Speaker 1: the Computer, which was a uh, actually pretty interesting article 215 00:13:11,920 --> 00:13:15,720 Speaker 1: at any rate. In that game, Kasparov won the first 216 00:13:16,160 --> 00:13:19,640 Speaker 1: game of the match in the series, lost the second one. 217 00:13:20,360 --> 00:13:22,960 Speaker 1: The next three were draws, and the final game deep 218 00:13:23,000 --> 00:13:26,920 Speaker 1: blue one. So in that case, deep Blue one the series. 219 00:13:27,679 --> 00:13:31,760 Speaker 1: And this was this made news worldwide because chess was 220 00:13:31,800 --> 00:13:35,360 Speaker 1: one of those games that that people thought this game 221 00:13:35,400 --> 00:13:39,239 Speaker 1: is so complex and it relies so much upon intuition 222 00:13:39,480 --> 00:13:43,720 Speaker 1: and and strategy that goes beyond just numbers, that it 223 00:13:43,760 --> 00:13:46,880 Speaker 1: was going to take ages before a computer could beat 224 00:13:46,960 --> 00:13:51,880 Speaker 1: a true chess champion. And go ahead, I was gonna say, 225 00:13:51,880 --> 00:13:54,400 Speaker 1: And this isn't just any chess champion. I mean he 226 00:13:54,440 --> 00:13:59,280 Speaker 1: had the highest rating, yes, offered by the the world 227 00:13:59,280 --> 00:14:03,280 Speaker 1: body that covered chess. Yeah, there are various ways to 228 00:14:03,400 --> 00:14:07,080 Speaker 1: rate chess players, but yes, Kasparov held the highest rating 229 00:14:07,240 --> 00:14:10,040 Speaker 1: of all human players at this point. There are computer 230 00:14:10,120 --> 00:14:13,160 Speaker 1: players that have higher ratings, in fact, ratings higher than 231 00:14:13,200 --> 00:14:19,600 Speaker 1: what people thought were possible. UM. But UH. The interesting 232 00:14:19,640 --> 00:14:23,080 Speaker 1: thing here about this, this UH matchup was that Kasparov 233 00:14:23,160 --> 00:14:26,400 Speaker 1: said he thought that he had not prepared properly for 234 00:14:26,440 --> 00:14:28,160 Speaker 1: the games, and that he wished he could have had 235 00:14:28,200 --> 00:14:35,720 Speaker 1: another rematch. But ibm H shut down that that project 236 00:14:35,760 --> 00:14:37,800 Speaker 1: because they had pretty much proven their point, you know 237 00:14:37,840 --> 00:14:39,720 Speaker 1: that they were They set out to create a computer 238 00:14:39,760 --> 00:14:42,160 Speaker 1: program that could beat the world champion in chess, and 239 00:14:42,160 --> 00:14:44,680 Speaker 1: it and it worked, So why continue that? I mean, 240 00:14:44,720 --> 00:14:48,480 Speaker 1: there's no other application really for that. So they shut 241 00:14:48,520 --> 00:14:51,000 Speaker 1: down that program and and Kesperov never had a chance 242 00:14:51,040 --> 00:14:54,720 Speaker 1: to to try and and UH and and prove that 243 00:14:54,760 --> 00:14:59,240 Speaker 1: he could beat this this computer. Now, since then, Kasparov 244 00:14:59,280 --> 00:15:04,120 Speaker 1: has played other computer programs that are on UM extremely 245 00:15:04,160 --> 00:15:07,400 Speaker 1: powerful machines, and in some cases he has played to 246 00:15:07,400 --> 00:15:10,360 Speaker 1: a draw or one, and in other cases he has lost. 247 00:15:10,600 --> 00:15:13,000 Speaker 1: And he has said that we have essentially reached the 248 00:15:13,040 --> 00:15:18,320 Speaker 1: era where a a powerful computer running the right software, 249 00:15:18,680 --> 00:15:22,080 Speaker 1: or even desktop computers running the right software, can beat 250 00:15:22,400 --> 00:15:26,480 Speaker 1: a a grand master chess player. UM. And part of 251 00:15:26,520 --> 00:15:31,400 Speaker 1: that is because these these computer programs often have an 252 00:15:31,560 --> 00:15:37,560 Speaker 1: entire database of opening moves available to to UH to 253 00:15:37,680 --> 00:15:40,680 Speaker 1: look at before making any kind of a move against 254 00:15:40,720 --> 00:15:43,800 Speaker 1: an opponent. And the other is that chess is a 255 00:15:43,880 --> 00:15:46,280 Speaker 1: really complicated game. You can't really map out all the 256 00:15:46,320 --> 00:15:49,800 Speaker 1: possible moves easily because there's so many different pieces and 257 00:15:49,840 --> 00:15:51,560 Speaker 1: they all behave differently, and there are a lot of 258 00:15:51,560 --> 00:15:56,160 Speaker 1: different options at any given time. But what chess computers 259 00:15:56,240 --> 00:16:00,480 Speaker 1: do very well is they can plot out all the 260 00:16:00,480 --> 00:16:03,280 Speaker 1: possible moves once you get down to a certain number 261 00:16:03,280 --> 00:16:07,320 Speaker 1: of chess pieces towards the end game. And so a 262 00:16:07,320 --> 00:16:10,200 Speaker 1: lot of these chess programs are very very good at 263 00:16:10,320 --> 00:16:13,520 Speaker 1: plotting out the best moves when they're only say seven 264 00:16:13,800 --> 00:16:17,040 Speaker 1: or eight pieces on the board. Once you get more 265 00:16:17,080 --> 00:16:22,160 Speaker 1: than that, the the different variables are so vast that 266 00:16:22,160 --> 00:16:25,280 Speaker 1: it's a lot harder to account for all of them. 267 00:16:25,560 --> 00:16:31,680 Speaker 1: That being said, it's gonna be pretty much it'll it'll 268 00:16:31,720 --> 00:16:34,600 Speaker 1: take a lot of luck, really to beat a powerful 269 00:16:34,680 --> 00:16:38,360 Speaker 1: chess program at this point, it can happen. Um. Casparov 270 00:16:38,440 --> 00:16:42,479 Speaker 1: has shown that by playing in a kind of unconventional 271 00:16:42,480 --> 00:16:45,120 Speaker 1: way you can fool a machine. He he actually did 272 00:16:45,160 --> 00:16:49,320 Speaker 1: fool Deep Blue into sacrificing a piece that it shouldn't have, 273 00:16:49,520 --> 00:16:51,760 Speaker 1: and that was how he managed to win one of 274 00:16:51,800 --> 00:16:56,479 Speaker 1: the games in the round. So computers do make mistakes, 275 00:16:57,160 --> 00:17:01,600 Speaker 1: but um, it's it, you know, it's it's hard to 276 00:17:02,120 --> 00:17:04,720 Speaker 1: it's hard to predict when that's gonna happen. They don't 277 00:17:04,760 --> 00:17:08,240 Speaker 1: behave the same way humans do. So um and Kasprov 278 00:17:08,280 --> 00:17:12,879 Speaker 1: actually has said that now champion chess players are starting 279 00:17:12,920 --> 00:17:17,200 Speaker 1: to adopt more computer like approaches to playing and has 280 00:17:17,240 --> 00:17:22,840 Speaker 1: even uh talked about he's he's kind of a champion 281 00:17:22,880 --> 00:17:27,280 Speaker 1: to a concept called advanced chess, which involves a player 282 00:17:28,000 --> 00:17:32,919 Speaker 1: consulting a computer during play. So it's so it's a 283 00:17:32,920 --> 00:17:36,960 Speaker 1: combination of human intuition and the ability of a computer 284 00:17:37,119 --> 00:17:40,800 Speaker 1: to have that entire database of every game that's ever 285 00:17:40,840 --> 00:17:45,080 Speaker 1: been played, essentially, and to rely on that and see like, oh, well, 286 00:17:45,080 --> 00:17:47,920 Speaker 1: what what is this series of opening moves? Is that? 287 00:17:48,200 --> 00:17:50,840 Speaker 1: Is that something that's been played before? And if so, 288 00:17:50,960 --> 00:17:53,560 Speaker 1: what's the best thing for me to do? And you know, 289 00:17:53,760 --> 00:17:55,919 Speaker 1: there are points where the human takes over and starts 290 00:17:55,960 --> 00:17:58,159 Speaker 1: to make moves, or perhaps the human has a move 291 00:17:58,240 --> 00:18:00,240 Speaker 1: in mind and programs it into the computer, or to 292 00:18:00,280 --> 00:18:04,320 Speaker 1: see what possible counter moves could happen. Um, And it's 293 00:18:04,400 --> 00:18:06,200 Speaker 1: it's kind of it in a way. You might think, well, 294 00:18:06,240 --> 00:18:08,560 Speaker 1: that's sort of cheating, but in another way, you might think, Hey, 295 00:18:08,600 --> 00:18:11,920 Speaker 1: this is an example of computers and human intelligence merging 296 00:18:11,960 --> 00:18:15,680 Speaker 1: in a way. Yeah, yeah, I see what you're saying. Yeah, 297 00:18:15,720 --> 00:18:17,399 Speaker 1: that's one of the things that I have a problem 298 00:18:17,400 --> 00:18:22,480 Speaker 1: with with certain games is the lack of encyclopedic knowledge 299 00:18:22,840 --> 00:18:27,439 Speaker 1: of everything you could possibly do. And in reading your article, 300 00:18:28,080 --> 00:18:31,240 Speaker 1: I actually was thinking about this from a personal standpoint 301 00:18:31,240 --> 00:18:36,080 Speaker 1: before I got to the next example. But yeah, I um, 302 00:18:36,119 --> 00:18:38,840 Speaker 1: I'm a fan of the Scrabble brand cross word game. 303 00:18:39,600 --> 00:18:43,840 Speaker 1: I love their trademark. We have to use that trademark, um. 304 00:18:43,920 --> 00:18:45,439 Speaker 1: Although I love the game, so it doesn't bother me 305 00:18:45,480 --> 00:18:49,479 Speaker 1: so much as quirky. UM. So the thing is, if 306 00:18:49,480 --> 00:18:53,440 Speaker 1: you go to a lot of the uh, the clubs 307 00:18:53,600 --> 00:18:58,280 Speaker 1: that really get into the game, UM, you will find 308 00:18:58,400 --> 00:19:01,399 Speaker 1: that a lot of them offer lit and lists and 309 00:19:01,520 --> 00:19:05,080 Speaker 1: lists of words, all kinds of words, lists of words 310 00:19:05,080 --> 00:19:08,159 Speaker 1: that you can make with different particular letter combinations that 311 00:19:08,240 --> 00:19:11,720 Speaker 1: you might have on your tile rack. And I keep thinking, Man, 312 00:19:11,800 --> 00:19:14,520 Speaker 1: if I had access to all these words without having 313 00:19:14,560 --> 00:19:17,639 Speaker 1: to study these lists, I might, you know, really be 314 00:19:17,720 --> 00:19:20,480 Speaker 1: able to wamp up on some people playing scrabble. Now, 315 00:19:20,560 --> 00:19:23,160 Speaker 1: there are people who spend lots and lots and lots 316 00:19:23,200 --> 00:19:25,399 Speaker 1: of time studying the word lists, so they will be 317 00:19:25,480 --> 00:19:31,800 Speaker 1: armed with all these different lexicographical weapons. Um. Yeah, I 318 00:19:31,800 --> 00:19:33,760 Speaker 1: don't know if lexicography is a word. I'll have to 319 00:19:33,800 --> 00:19:40,440 Speaker 1: look it up. I'm not sure that would work in scrabble. Um. Okay, However, Um, 320 00:19:40,480 --> 00:19:44,640 Speaker 1: it seems that, uh that in your next example, that 321 00:19:45,280 --> 00:19:49,800 Speaker 1: there is a computer able to play at that level, 322 00:19:49,840 --> 00:19:52,160 Speaker 1: and that seems like it would have a severe pose, 323 00:19:52,200 --> 00:19:56,239 Speaker 1: a severe disadvantage for the human opponent. Yeah. Quackle is 324 00:19:56,320 --> 00:20:01,040 Speaker 1: the the program um that you're referring to calls a 325 00:20:01,040 --> 00:20:04,320 Speaker 1: computer program that um that was able to beat a 326 00:20:04,440 --> 00:20:08,760 Speaker 1: champion named David Boys in a series of games of 327 00:20:08,840 --> 00:20:12,560 Speaker 1: scrabble and Quackle. Again. Scrabble is one of those games 328 00:20:12,600 --> 00:20:15,960 Speaker 1: that also comes down in part to luck, because it 329 00:20:16,000 --> 00:20:19,040 Speaker 1: all depends on what tiles you have in your you know, 330 00:20:19,280 --> 00:20:23,119 Speaker 1: you have at your disposal. Uh. And Quackle was able 331 00:20:23,160 --> 00:20:26,600 Speaker 1: to beat David Boys fair and square. Right, It was 332 00:20:26,640 --> 00:20:29,040 Speaker 1: able to fair and square in the sense that it 333 00:20:29,080 --> 00:20:32,800 Speaker 1: wasn't it wasn't making guesswork of the game. It was 334 00:20:33,320 --> 00:20:36,080 Speaker 1: building words based upon what it had available and what 335 00:20:36,119 --> 00:20:39,000 Speaker 1: was on the board already. Um. And yes, it did 336 00:20:39,040 --> 00:20:41,919 Speaker 1: have an enormous database of words. So that gave it 337 00:20:42,080 --> 00:20:45,440 Speaker 1: an advantage in that, you know, human beings, some of 338 00:20:45,520 --> 00:20:48,760 Speaker 1: us are really good at remembering you know, thousands of 339 00:20:48,800 --> 00:20:53,000 Speaker 1: different word letter combinations that make legitimate words. Others we 340 00:20:53,080 --> 00:20:55,200 Speaker 1: rack our brains like I've got a V, A, B, 341 00:20:55,520 --> 00:20:57,960 Speaker 1: and F and in a queue, and oh what can 342 00:20:58,000 --> 00:21:03,160 Speaker 1: I make? You know? So that one was playing pretty 343 00:21:03,240 --> 00:21:05,359 Speaker 1: much by the rules. What was interesting to me was 344 00:21:06,160 --> 00:21:10,760 Speaker 1: I ran across a story about a graduate student named 345 00:21:10,760 --> 00:21:14,800 Speaker 1: Mark Richards who came up with a program, a scrabble 346 00:21:14,840 --> 00:21:17,080 Speaker 1: program that goes a step further. It doesn't just have 347 00:21:17,280 --> 00:21:20,800 Speaker 1: a massive database of words that can play uh. And 348 00:21:21,359 --> 00:21:25,800 Speaker 1: another advantage that scrabble UH computer programs have is that 349 00:21:26,080 --> 00:21:29,080 Speaker 1: let's say that you have two lines of like two 350 00:21:29,119 --> 00:21:32,000 Speaker 1: words that are fairly close to one another on the 351 00:21:32,040 --> 00:21:35,280 Speaker 1: scrabble board, um, and but you know they're separated. There's 352 00:21:35,320 --> 00:21:39,800 Speaker 1: maybe like five blank tiles between the two words. These 353 00:21:39,800 --> 00:21:43,119 Speaker 1: computer programs are much better at looking at those those 354 00:21:43,200 --> 00:21:47,520 Speaker 1: configurations and determining words that can span those gaps that 355 00:21:47,560 --> 00:21:51,760 Speaker 1: are than humans are. And so you'll see words played 356 00:21:51,800 --> 00:21:54,439 Speaker 1: by computers that most humans never would have thought of. 357 00:21:54,520 --> 00:21:57,760 Speaker 1: Because to to have taken that into account, that big 358 00:21:57,800 --> 00:22:00,880 Speaker 1: gap into account is just kind of beyond what most 359 00:22:00,920 --> 00:22:03,560 Speaker 1: of us do when we play. There are good players 360 00:22:03,560 --> 00:22:05,720 Speaker 1: out there who are really good at this, but most 361 00:22:05,760 --> 00:22:07,199 Speaker 1: of us, you know, well, we sit there and we 362 00:22:07,240 --> 00:22:09,600 Speaker 1: concentrate on one letter at a time, or if words 363 00:22:09,600 --> 00:22:11,080 Speaker 1: are close enough, we might be able to get two 364 00:22:11,160 --> 00:22:14,080 Speaker 1: letters that are already on the board incorporated into whatever 365 00:22:14,119 --> 00:22:16,720 Speaker 1: we're about to lay down. Well, what Mark Richards did 366 00:22:16,760 --> 00:22:19,480 Speaker 1: was he on a step further than that. He created 367 00:22:19,520 --> 00:22:25,080 Speaker 1: a program that could guess what letters the opponent had 368 00:22:25,119 --> 00:22:29,159 Speaker 1: at his or her disposal bye bye. You know, there 369 00:22:29,200 --> 00:22:32,200 Speaker 1: are only so many letters that are in a scrabble game, 370 00:22:32,520 --> 00:22:36,119 Speaker 1: so it's counting tiles, counting cards. By counting cards in Vegas, 371 00:22:36,400 --> 00:22:39,320 Speaker 1: it would count tiles. So you would start playing the game, 372 00:22:39,359 --> 00:22:41,359 Speaker 1: and at the beginning of the game, the computer really 373 00:22:41,400 --> 00:22:44,680 Speaker 1: can't tell what you have because it's only the only 374 00:22:44,760 --> 00:22:46,840 Speaker 1: information it has at the very beginning of the game 375 00:22:46,960 --> 00:22:50,520 Speaker 1: is which tiles are in it's uh, it's vault right. 376 00:22:50,640 --> 00:22:52,960 Speaker 1: Anything beyond that it doesn't know. So I mean it 377 00:22:53,000 --> 00:22:56,119 Speaker 1: would know like, okay, well one of the q you 378 00:22:56,840 --> 00:23:00,760 Speaker 1: tiles is in uh in my hand, which means that 379 00:23:00,800 --> 00:23:06,560 Speaker 1: there is one less one fewer out in the actual game, right, Yeah, 380 00:23:07,080 --> 00:23:09,119 Speaker 1: you can't really say, well, what are the odds that 381 00:23:09,160 --> 00:23:12,159 Speaker 1: he's holding that? You know, it's it's astronomical. You know, 382 00:23:12,200 --> 00:23:14,760 Speaker 1: there's no qu and scrabble, right is not qu I 383 00:23:14,800 --> 00:23:17,159 Speaker 1: thought of what it's just Q some of the some 384 00:23:17,200 --> 00:23:21,400 Speaker 1: of the others that are similar, because almost every instance 385 00:23:21,480 --> 00:23:23,600 Speaker 1: that Q appears in the English language is followed by 386 00:23:23,640 --> 00:23:26,640 Speaker 1: the letter you. Um, that's right. So at any rate, 387 00:23:26,720 --> 00:23:30,360 Speaker 1: the so Q, I'm just gonna say, somebody's gonna write 388 00:23:30,359 --> 00:23:32,359 Speaker 1: in so well, I don't play a lot of scrabble, clearly, 389 00:23:32,720 --> 00:23:36,360 Speaker 1: but at any rate, so the computer what it can 390 00:23:36,400 --> 00:23:38,000 Speaker 1: do is as the game goes on, it can start 391 00:23:38,040 --> 00:23:42,760 Speaker 1: predicting with better and better accuracy, which tiles you probably 392 00:23:42,760 --> 00:23:45,400 Speaker 1: hold in your in your hand. So what it what 393 00:23:45,440 --> 00:23:48,040 Speaker 1: it does, It will start playing words and playing parts 394 00:23:48,040 --> 00:23:51,240 Speaker 1: of the board that will block off the best options 395 00:23:51,400 --> 00:23:55,359 Speaker 1: you would have and when it becomes your turn, so 396 00:23:55,400 --> 00:23:58,639 Speaker 1: it's blocking you from the combinations that would get you 397 00:23:58,640 --> 00:24:02,359 Speaker 1: the most points. So so you're you're handicapped even further 398 00:24:02,480 --> 00:24:05,080 Speaker 1: than you were just from playing the game fair and 399 00:24:05,119 --> 00:24:12,359 Speaker 1: square even. You know, another interesting thing, and another interesting 400 00:24:12,400 --> 00:24:16,119 Speaker 1: point you made that was nice um in the Uh. 401 00:24:16,840 --> 00:24:20,120 Speaker 1: Discussion of Quackle is that it in order to actually 402 00:24:20,160 --> 00:24:25,160 Speaker 1: play uh David Boys, it actually had to beat another 403 00:24:25,200 --> 00:24:28,840 Speaker 1: computer in tournament play before it was allowed to play him, 404 00:24:29,160 --> 00:24:32,760 Speaker 1: which I think is funny. So I had computer versus computer. Yeah, 405 00:24:32,760 --> 00:24:34,320 Speaker 1: there are a few. There are a few tournaments that 406 00:24:34,359 --> 00:24:36,879 Speaker 1: I've seen like that where it's been computer pitted against 407 00:24:36,880 --> 00:24:40,560 Speaker 1: computer and then the winner goes up against a human champion. Um. 408 00:24:40,600 --> 00:24:43,480 Speaker 1: And then there have been other exhibit games like I 409 00:24:43,520 --> 00:24:46,119 Speaker 1: remember there's one with Kasparov where he was playing against 410 00:24:46,119 --> 00:24:49,600 Speaker 1: thirty two computers and he won all of all of 411 00:24:49,600 --> 00:24:53,520 Speaker 1: those games. Yeah, thirty two to nothing. Uh. And then 412 00:24:53,520 --> 00:24:57,760 Speaker 1: the last one in my list was a computer program 413 00:24:57,840 --> 00:25:01,479 Speaker 1: that was playing the game of Go. And Go is 414 00:25:01,920 --> 00:25:05,840 Speaker 1: particularly interesting in that it has if you're playing on 415 00:25:05,880 --> 00:25:10,080 Speaker 1: a full board, it's a grid of nineteen by nineteen lines, 416 00:25:10,160 --> 00:25:13,120 Speaker 1: and you play your pieces on the inner where those 417 00:25:13,119 --> 00:25:16,639 Speaker 1: lines intersect in the grid, and that's a huge number 418 00:25:17,480 --> 00:25:21,840 Speaker 1: and and so the the potential moves and go is big. 419 00:25:21,920 --> 00:25:24,560 Speaker 1: Is there there are more potential moves in that game 420 00:25:24,600 --> 00:25:26,960 Speaker 1: than than pretty much any of the other games we've 421 00:25:26,960 --> 00:25:31,520 Speaker 1: talked about. And there are also situations that can pop 422 00:25:31,640 --> 00:25:34,359 Speaker 1: up where just because of the nature of the game, 423 00:25:35,040 --> 00:25:38,399 Speaker 1: a move may or may not be technically legal, or 424 00:25:38,520 --> 00:25:41,679 Speaker 1: there may actually be room for you to discuss the 425 00:25:41,760 --> 00:25:44,400 Speaker 1: legality of a particular move, which makes it even more 426 00:25:44,400 --> 00:25:46,560 Speaker 1: difficult for a computer to win because the computer just 427 00:25:46,600 --> 00:25:50,960 Speaker 1: can't make that consideration. So um Go is one of 428 00:25:50,960 --> 00:25:54,480 Speaker 1: those games where we have seen computers beat champions at Go. 429 00:25:54,840 --> 00:25:58,040 Speaker 1: But we've also seen cases where these these really powerful 430 00:25:58,080 --> 00:26:01,600 Speaker 1: computer programs have been beaten by Go players, and sometimes 431 00:26:01,640 --> 00:26:03,960 Speaker 1: they are Go players who are, you know, eight or 432 00:26:04,040 --> 00:26:08,760 Speaker 1: nine years old. So it's one of those where if 433 00:26:08,760 --> 00:26:11,400 Speaker 1: you're talking about solving a game, it's probably gonna take 434 00:26:11,400 --> 00:26:14,040 Speaker 1: a while to solve that nineteen by nineteen grid game 435 00:26:14,080 --> 00:26:16,640 Speaker 1: because it's just like I said that, the potential number 436 00:26:16,680 --> 00:26:21,640 Speaker 1: of moves are I mean, it's it's it's enormous. Yeah yeah, 437 00:26:23,320 --> 00:26:26,160 Speaker 1: but that's uh, I mean, it's such a difficult game too. 438 00:26:26,200 --> 00:26:29,399 Speaker 1: And I mean the the player that this particular Go 439 00:26:29,560 --> 00:26:32,760 Speaker 1: program took on certainly one of the best in the world, 440 00:26:32,760 --> 00:26:36,359 Speaker 1: and it took a supercomputer, a crazy supercomputer, yeah, with 441 00:26:36,400 --> 00:26:40,920 Speaker 1: five and twelve corps, Yeah, five and twelve corps to 442 00:26:40,920 --> 00:26:44,760 Speaker 1: to calculate the best possible move. If nothing else, this 443 00:26:44,880 --> 00:26:47,560 Speaker 1: kind of says. It kind of points to that the 444 00:26:47,800 --> 00:26:53,280 Speaker 1: huge hurdle of creating an artificially intelligent machine capable of 445 00:26:54,119 --> 00:26:57,520 Speaker 1: thinking in a way that humans think. You know, we 446 00:26:57,600 --> 00:27:02,320 Speaker 1: take it for granted how complicated thinking actually is until 447 00:27:02,359 --> 00:27:07,040 Speaker 1: we try to mimic it using machinery. Because just even 448 00:27:07,119 --> 00:27:11,360 Speaker 1: using something where you've got a a closed environment with 449 00:27:11,840 --> 00:27:15,720 Speaker 1: known rules that you have to follow, it's still incredibly 450 00:27:15,800 --> 00:27:22,560 Speaker 1: difficult to match human performance in that. Yeah, yeah, fascinating 451 00:27:22,600 --> 00:27:26,000 Speaker 1: stuff though, Yeah yeah, And all the players we talked 452 00:27:26,040 --> 00:27:27,720 Speaker 1: about could beat the pants off of me and just 453 00:27:27,760 --> 00:27:31,000 Speaker 1: about any game. There are other computer program problems that 454 00:27:31,040 --> 00:27:34,680 Speaker 1: are interesting to to look at, like um, the people 455 00:27:34,720 --> 00:27:37,520 Speaker 1: who solved checkers, or some of the people who worked 456 00:27:37,560 --> 00:27:40,560 Speaker 1: on the whole checkers problem have moved on to things 457 00:27:40,600 --> 00:27:44,520 Speaker 1: like poker and poker like Texas hold Them and that's 458 00:27:44,520 --> 00:27:48,520 Speaker 1: a game that is also difficult to uh to beat 459 00:27:49,320 --> 00:27:51,359 Speaker 1: or two, it's it's hard to figure out a way 460 00:27:51,400 --> 00:27:53,800 Speaker 1: to program a computer to play that at the same 461 00:27:53,880 --> 00:27:56,560 Speaker 1: level as a human champion. Because you think about poker, 462 00:27:57,200 --> 00:28:00,960 Speaker 1: you're dealing with hidden information because you don't know what 463 00:28:01,160 --> 00:28:04,560 Speaker 1: cards someone else may or may not hold. UM, and 464 00:28:04,640 --> 00:28:08,679 Speaker 1: you're dealing with strategies like bluffing and um. Another thing 465 00:28:08,680 --> 00:28:11,960 Speaker 1: about computers is you can upset them. So you're not 466 00:28:12,000 --> 00:28:14,879 Speaker 1: going to get a computer to play on tilt, but 467 00:28:15,160 --> 00:28:18,960 Speaker 1: you can make a computer think that your cards are 468 00:28:19,000 --> 00:28:22,280 Speaker 1: better than what you hold or or worse, because there 469 00:28:22,320 --> 00:28:26,160 Speaker 1: are a lot of strategies that involve tricking someone into 470 00:28:26,240 --> 00:28:28,840 Speaker 1: thinking that you're holding a weak hand so that they 471 00:28:28,920 --> 00:28:31,719 Speaker 1: over commit themselves in a bet and then you sweep 472 00:28:31,720 --> 00:28:34,240 Speaker 1: in and you just take all that money and then 473 00:28:34,280 --> 00:28:38,200 Speaker 1: you run away laughing and throwing chips at people. That's 474 00:28:38,200 --> 00:28:42,000 Speaker 1: how I Playone likes to play with me, well except 475 00:28:42,000 --> 00:28:43,520 Speaker 1: for the people who pick up the chips because they're 476 00:28:43,520 --> 00:28:47,320 Speaker 1: like awesome free money. So you're saying then that if 477 00:28:47,360 --> 00:28:50,320 Speaker 1: you were playing a computer opponent at poker, you wouldn't 478 00:28:50,320 --> 00:28:53,360 Speaker 1: necessarily need to worry about walking away or running, and 479 00:28:53,360 --> 00:28:55,200 Speaker 1: you might even be able to count your money while 480 00:28:55,200 --> 00:28:58,080 Speaker 1: you're sitting at the table. Yes, that's exactly what I'm saying. 481 00:28:58,120 --> 00:28:59,800 Speaker 1: I mean, even if the machine didn't get upset with you. 482 00:29:00,200 --> 00:29:06,440 Speaker 1: Climb stairs. What a gambler? Yeah, well it's yeah, I'm no, 483 00:29:06,520 --> 00:29:08,840 Speaker 1: I'm not gonna quote anymore from that song. That's done. 484 00:29:09,400 --> 00:29:15,200 Speaker 1: But anyway, Kenny. Nice. Nice, So this will wrap up 485 00:29:15,240 --> 00:29:19,120 Speaker 1: this discussion before I have an aneurysm Um, speaking of running, 486 00:29:19,160 --> 00:29:22,600 Speaker 1: I should probably get to that. Yeah, So it'll be 487 00:29:22,640 --> 00:29:26,560 Speaker 1: interesting to see what what the future of of computers 488 00:29:26,640 --> 00:29:29,480 Speaker 1: and gaming holds, because we've already gotten to a point, 489 00:29:29,520 --> 00:29:33,280 Speaker 1: like I said, where we've reached a state where computers 490 00:29:33,280 --> 00:29:36,520 Speaker 1: can beat the best players in many, not all, but 491 00:29:36,640 --> 00:29:41,760 Speaker 1: many games. Um, will we eventually see UH chess championships 492 00:29:41,800 --> 00:29:46,360 Speaker 1: played between computers? Well, well, will we ever actually say, 493 00:29:46,400 --> 00:29:47,960 Speaker 1: you know what, this comes to a point where we 494 00:29:48,000 --> 00:29:51,960 Speaker 1: have to legitimately award a computer program the title of 495 00:29:52,040 --> 00:29:55,880 Speaker 1: world chess Champion? I doubt it. Well, I mean they 496 00:29:55,960 --> 00:30:00,640 Speaker 1: do use the UH console games on no Play to 497 00:30:00,800 --> 00:30:03,160 Speaker 1: predict the outcome of the Super Bowl. So yeah, but 498 00:30:03,280 --> 00:30:06,720 Speaker 1: I mean, if we allow a chess game to become 499 00:30:06,760 --> 00:30:10,720 Speaker 1: world champions, shouldn't we also allow things like I don't know, 500 00:30:10,920 --> 00:30:14,280 Speaker 1: like the Tesla Roadster to compete in the hundred yard dash. Me. 501 00:30:16,200 --> 00:30:19,160 Speaker 1: I'm just saying, like, you know, you're already stacking the deck. 502 00:30:19,240 --> 00:30:22,000 Speaker 1: I mean, yeah, I don't. Well, I mean it takes 503 00:30:22,040 --> 00:30:23,680 Speaker 1: all the fun out of it for people to Yeah, 504 00:30:23,760 --> 00:30:25,520 Speaker 1: I would hate to get run over by a roadster 505 00:30:25,560 --> 00:30:28,240 Speaker 1: while trying to run the dash. Take the fun out 506 00:30:28,240 --> 00:30:30,880 Speaker 1: of it. But that's a good point. Yeah, So don't 507 00:30:30,880 --> 00:30:33,160 Speaker 1: get run over by a computer, is what what we're 508 00:30:33,160 --> 00:30:35,520 Speaker 1: getting at. It was a long way around to that 509 00:30:35,560 --> 00:30:39,280 Speaker 1: moral of the story apparently. At any rate, if you 510 00:30:39,320 --> 00:30:41,120 Speaker 1: want to learn more about it, you can read the article. 511 00:30:41,160 --> 00:30:44,400 Speaker 1: It's it's about the hang on. I've got the title. 512 00:30:44,440 --> 00:30:46,560 Speaker 1: Actually I supposed to say. I'm about to work my 513 00:30:46,560 --> 00:30:50,600 Speaker 1: way around it. It's top five computer versus human game matchups. Um, 514 00:30:50,600 --> 00:30:52,840 Speaker 1: it's a It was fun to write, and I was 515 00:30:52,920 --> 00:30:55,880 Speaker 1: so pleased that I could find other games besides chess, 516 00:30:55,880 --> 00:30:58,720 Speaker 1: because that's the one everyone thinks of. But there are 517 00:30:58,800 --> 00:31:02,680 Speaker 1: other games out there that people have created programs that 518 00:31:02,720 --> 00:31:04,920 Speaker 1: are you know, these computers are really good at playing 519 00:31:04,960 --> 00:31:07,160 Speaker 1: those games. Haven't found one that can really take me 520 00:31:07,200 --> 00:31:12,480 Speaker 1: on shoots and ladders yet. I'm sure it's coming and uh, 521 00:31:12,480 --> 00:31:16,680 Speaker 1: of course. Sorry. We're gonna wrap this up, you guys. 522 00:31:16,680 --> 00:31:19,600 Speaker 1: If you have any comments or questions, you can contact 523 00:31:19,720 --> 00:31:22,720 Speaker 1: us on Facebook and Twitter are handled. There is tech 524 00:31:22,840 --> 00:31:25,960 Speaker 1: Stuff h s W or you can email us. The 525 00:31:26,000 --> 00:31:30,240 Speaker 1: address is tech stuff at how stuff works dot com. 526 00:31:30,280 --> 00:31:37,959 Speaker 1: And Chris and I will talk to you again really soon, Jonathan. Um, 527 00:31:38,360 --> 00:31:41,080 Speaker 1: actually this was just handed to me. It looks like 528 00:31:41,120 --> 00:31:43,520 Speaker 1: how stuff Works dot Com now has an iPhone app. 529 00:31:43,880 --> 00:31:47,640 Speaker 1: Sweet that awesome. Yeah, actually, um, I got to to 530 00:31:47,640 --> 00:31:49,600 Speaker 1: take a look at this earlier, and guys, this is 531 00:31:49,600 --> 00:31:52,520 Speaker 1: pretty cool. The iPhone app is a sort of a 532 00:31:52,520 --> 00:31:54,560 Speaker 1: way to integrate all the cool stuff we do at 533 00:31:54,600 --> 00:31:56,800 Speaker 1: how stuff Works dot com. So you, guys may have 534 00:31:56,840 --> 00:31:58,880 Speaker 1: listened one of our podcasts and we talked about there's 535 00:31:58,880 --> 00:32:00,920 Speaker 1: this great article on the like, but you're not at 536 00:32:00,920 --> 00:32:03,240 Speaker 1: your computer, so you can't really check it. Well. The 537 00:32:03,280 --> 00:32:07,200 Speaker 1: iPhone app actually lets you browse articles and blog posts, 538 00:32:07,320 --> 00:32:09,400 Speaker 1: even lets you interact on Facebook and Twitter, and you 539 00:32:09,440 --> 00:32:11,880 Speaker 1: can listen to podcasts at the same time. And it 540 00:32:11,920 --> 00:32:14,320 Speaker 1: has all the house stuff works dot Com podcasts on it, 541 00:32:14,360 --> 00:32:16,680 Speaker 1: not just ours, but you know good ones too, so 542 00:32:16,760 --> 00:32:18,800 Speaker 1: you can listen to those and look at the articles 543 00:32:18,840 --> 00:32:21,680 Speaker 1: and and go on Facebook and Twitter, and it should 544 00:32:21,760 --> 00:32:25,880 Speaker 1: work perfectly with your iPhones and iPod touch it awesome. 545 00:32:26,000 --> 00:32:28,640 Speaker 1: What's it looks like. It's now available on the iTunes store, 546 00:32:28,840 --> 00:32:30,600 Speaker 1: so that's good to know. How much does it cost? 547 00:32:31,440 --> 00:32:47,720 Speaker 1: It's freeze Ah brought to you by the reinvented two 548 00:32:47,760 --> 00:32:50,320 Speaker 1: thousand twelve camera. It's ready, Are you