1 00:00:00,000 --> 00:00:02,560 ever tried teaching a computer how to, well, 2 00:00:02,560 --> 00:00:03,760 betray its friends. 3 00:00:03,760 --> 00:00:05,720 Sounds like a recipe for disaster, right? 4 00:00:05,720 --> 00:00:09,440 Maybe, but it's also the focus of today's deep dive. 5 00:00:09,440 --> 00:00:11,320 We're looking at a research paper 6 00:00:11,320 --> 00:00:15,640 that throws AI into the world of a game called So Long Sucker. 7 00:00:15,640 --> 00:00:19,160 So Long Sucker, that's not one I'm familiar with. 8 00:00:19,160 --> 00:00:20,280 It's a bit of a classic, actually, 9 00:00:20,280 --> 00:00:22,080 a game rooted in game theory. 10 00:00:22,080 --> 00:00:25,120 You have to outsmart your opponents, form alliances, 11 00:00:25,120 --> 00:00:28,320 and then, yeah, sometimes break those alliances to win. 12 00:00:28,320 --> 00:00:29,840 So it's kind of like the game of diplomacy, 13 00:00:29,840 --> 00:00:30,800 but with higher stakes. 14 00:00:30,800 --> 00:00:31,480 Exactly. 15 00:00:31,480 --> 00:00:34,680 It's all about strategy, predicting your opponent's moves, 16 00:00:34,680 --> 00:00:37,520 and knowing when to make that pivotal backstab. 17 00:00:37,520 --> 00:00:40,000 Sounds like a nightmare to program into an AI. 18 00:00:40,000 --> 00:00:42,360 How did the researchers even approach that? 19 00:00:42,360 --> 00:00:44,720 Well, they used a few different methods, actually, 20 00:00:44,720 --> 00:00:46,760 classic AI learning techniques. 21 00:00:46,760 --> 00:00:49,920 Things called DQN, DDQN, and dueling DQN. 22 00:00:49,920 --> 00:00:51,600 They're basically different ways for an AI 23 00:00:51,600 --> 00:00:53,440 to learn through trial and error. 24 00:00:53,440 --> 00:00:55,520 So lots of practice rounds for the AI. 25 00:00:55,520 --> 00:00:56,200 Tons. 26 00:00:56,200 --> 00:00:58,480 I think thousands of games of So Long Sucker, 27 00:00:58,480 --> 00:01:01,880 the AI constantly playing, winning some, losing some, 28 00:01:01,880 --> 00:01:04,160 but always learning and refining its strategy. 29 00:01:04,160 --> 00:01:07,480 OK, so imagine a computer playing So Long Sucker nonstop, 30 00:01:07,480 --> 00:01:09,800 figuring things out as it goes. 31 00:01:09,800 --> 00:01:12,440 But wouldn't the original game's rules, 32 00:01:12,440 --> 00:01:16,520 with its complex scoring system, be a bit much for an AI 33 00:01:16,520 --> 00:01:17,560 to handle at first? 34 00:01:17,560 --> 00:01:18,960 That's a good point. 35 00:01:18,960 --> 00:01:20,960 And the researchers thought so, too. 36 00:01:20,960 --> 00:01:24,000 So to simplify things, they used a slightly tweaked version 37 00:01:24,000 --> 00:01:25,440 of So Long Sucker. 38 00:01:25,440 --> 00:01:28,120 Instead of all the complicated points and whatnot, 39 00:01:28,120 --> 00:01:31,120 they made it a simple winner takes all situation. 40 00:01:31,120 --> 00:01:34,160 Ah, so the AI could just focus on the ultimate goal being 41 00:01:34,160 --> 00:01:35,360 the last one standing. 42 00:01:35,360 --> 00:01:35,960 Makes sense. 43 00:01:35,960 --> 00:01:36,480 Right. 44 00:01:36,480 --> 00:01:39,680 But how do you even set up an AI to learn a game like this, 45 00:01:39,680 --> 00:01:41,760 where it has to outsmart other players? 46 00:01:41,760 --> 00:01:44,480 Well, they use a technique called cumulative learning, 47 00:01:44,480 --> 00:01:47,080 which is, well, imagine all four players in the game 48 00:01:47,080 --> 00:01:49,480 are actually controlled by the same AI brain. 49 00:01:49,480 --> 00:01:50,920 Just like they're all working together. 50 00:01:50,920 --> 00:01:51,960 In a way, yeah. 51 00:01:51,960 --> 00:01:54,160 They're all sharing their experiences, the wins, 52 00:01:54,160 --> 00:01:56,920 the losses, the clever moves, the dumb mistakes. 53 00:01:56,920 --> 00:01:59,640 It all goes into this collective pool of knowledge 54 00:01:59,640 --> 00:02:01,560 that the AI uses to get better. 55 00:02:01,560 --> 00:02:03,080 So it's a hive mind situation. 56 00:02:03,080 --> 00:02:03,960 Very sci-fi. 57 00:02:03,960 --> 00:02:05,120 Exactly. 58 00:02:05,120 --> 00:02:08,080 And during all this training, the AI agents 59 00:02:08,080 --> 00:02:10,520 had to grapple with two main challenges. 60 00:02:10,520 --> 00:02:12,120 OK, lay it on me. 61 00:02:12,120 --> 00:02:13,120 Challenge number one. 62 00:02:13,120 --> 00:02:16,400 Figuring out the rules, of course, which moves are allowed. 63 00:02:16,400 --> 00:02:18,040 How do you capture piles of chips? 64 00:02:18,040 --> 00:02:19,120 Who gets to go next? 65 00:02:19,120 --> 00:02:20,400 All that jazz. 66 00:02:20,400 --> 00:02:22,600 Basically, the nitty gritty mechanics of the game. 67 00:02:22,600 --> 00:02:25,920 So like learning the rule book before you could actually play, 68 00:02:25,920 --> 00:02:27,360 what was the second challenge? 69 00:02:27,360 --> 00:02:29,920 Ah, that's where things get interesting. 70 00:02:29,920 --> 00:02:33,360 The AI also had to learn how to play strategically, 71 00:02:33,360 --> 00:02:35,800 when to cooperate, when to betray, 72 00:02:35,800 --> 00:02:38,040 how to predict what their opponents might do. 73 00:02:38,040 --> 00:02:41,400 You know, basically mastering the art of so long sucker. 74 00:02:41,400 --> 00:02:43,000 So did they succeed? 75 00:02:43,000 --> 00:02:46,240 Did the AI become some kind of backstabbing mastermind? 76 00:02:46,240 --> 00:02:48,160 Well, they did surprisingly well. 77 00:02:48,160 --> 00:02:50,440 After around 2,000 games, give or take, 78 00:02:50,440 --> 00:02:52,080 they were consistently scoring about half 79 00:02:52,080 --> 00:02:54,000 the maximum possible points, which 80 00:02:54,000 --> 00:02:55,800 means they were definitely getting the hang of the rules 81 00:02:55,800 --> 00:02:57,560 and coming up with some decent strategies. 82 00:02:57,560 --> 00:02:58,440 Half the points. 83 00:02:58,440 --> 00:02:59,240 Not bad. 84 00:02:59,240 --> 00:02:59,800 Not bad at all. 85 00:02:59,800 --> 00:03:02,280 Anything else that stood out on their performance? 86 00:03:02,280 --> 00:03:04,240 Any interesting kid bits? 87 00:03:04,240 --> 00:03:06,640 There is actually, if you look at Figure 1 in the paper, 88 00:03:06,640 --> 00:03:10,240 it shows a graph of how the different AI algorithms performed 89 00:03:10,240 --> 00:03:11,400 over time. 90 00:03:11,400 --> 00:03:13,240 And what's really interesting is that they all 91 00:03:13,240 --> 00:03:16,560 improved with more practice, but some were definitely 92 00:03:16,560 --> 00:03:17,880 more effective than others. 93 00:03:17,880 --> 00:03:21,680 So even in the world of AI, some were just better students 94 00:03:21,680 --> 00:03:22,320 than others. 95 00:03:22,320 --> 00:03:23,280 Seems that way. 96 00:03:23,280 --> 00:03:25,560 But here's something that I found really interesting, 97 00:03:25,560 --> 00:03:27,320 and maybe even a little bit reassuring. 98 00:03:27,320 --> 00:03:28,680 OK, I'm intrigued. 99 00:03:28,680 --> 00:03:29,560 Tell me more. 100 00:03:29,560 --> 00:03:31,920 Even after all those thousands of games, 101 00:03:31,920 --> 00:03:35,720 the AI still occasionally messed up and made illegal moves. 102 00:03:35,720 --> 00:03:36,480 Really? 103 00:03:36,480 --> 00:03:37,400 They slipped up. 104 00:03:37,400 --> 00:03:39,640 So even with all that training, they couldn't always 105 00:03:39,640 --> 00:03:41,080 play by the rules perfectly. 106 00:03:41,080 --> 00:03:41,720 Exactly. 107 00:03:41,720 --> 00:03:43,560 And you know what this tells us. 108 00:03:43,560 --> 00:03:46,680 It highlights a key difference between us humans and AI. 109 00:03:46,680 --> 00:03:49,960 Humans, we learn so long sucker, way faster. 110 00:03:49,960 --> 00:03:51,920 Give us a couple rounds, and we kind of get the gist. 111 00:03:51,920 --> 00:03:54,600 But the AI needed thousands of games 112 00:03:54,600 --> 00:03:56,680 to get to a similar level. 113 00:03:56,680 --> 00:03:59,280 So what you're saying is we humans are still 114 00:03:59,280 --> 00:04:02,520 the reigning champions of deception, at least for now. 115 00:04:02,520 --> 00:04:03,400 For now, yeah. 116 00:04:03,400 --> 00:04:05,000 And that difference in learning speed, 117 00:04:05,000 --> 00:04:08,560 it really points to a limitation of these classic AI 118 00:04:08,560 --> 00:04:09,600 methods. 119 00:04:09,600 --> 00:04:11,120 They're great at learning the rules, 120 00:04:11,120 --> 00:04:12,720 but they struggle with the nuance, 121 00:04:12,720 --> 00:04:15,360 with the adaptability that humans bring to a game 122 00:04:15,360 --> 00:04:16,960 like so long sucker. 123 00:04:16,960 --> 00:04:19,200 So while the AI was busy crunching numbers 124 00:04:19,200 --> 00:04:21,080 and trying to memorize the rule book, 125 00:04:21,080 --> 00:04:25,120 we humans were using our intuition, our social skills, 126 00:04:25,120 --> 00:04:28,040 and maybe just a little bit of that natural human tendency 127 00:04:28,040 --> 00:04:30,760 towards, well, you know, being sneaky. 128 00:04:30,760 --> 00:04:31,800 Huh. 129 00:04:31,800 --> 00:04:33,280 I think you hit the nail in the head there. 130 00:04:33,280 --> 00:04:36,760 The AI was playing by the book while humans were, 131 00:04:36,760 --> 00:04:39,200 while reading between the lines, so to speak. 132 00:04:39,200 --> 00:04:42,200 OK, so AI might not be ready to take over the world of board 133 00:04:42,200 --> 00:04:46,040 games just yet, but it's definitely made some progress. 134 00:04:46,040 --> 00:04:48,960 What does this research tell us about where AI is headed? 135 00:04:48,960 --> 00:04:51,480 Is it all about becoming masters of deception? 136 00:04:51,480 --> 00:04:52,720 Or is there something more to it? 137 00:04:52,720 --> 00:04:54,600 I think it's definitely more than just, you know, 138 00:04:54,600 --> 00:04:56,200 teaching AI to be sneaky. 139 00:04:56,200 --> 00:04:58,680 This research is really about pushing the boundaries of what 140 00:04:58,680 --> 00:05:01,720 AI can do, exploring the possibilities of how these 141 00:05:01,720 --> 00:05:04,520 learning methods can be applied to complex situations. 142 00:05:04,520 --> 00:05:07,040 So it's like, we're not just trying to create an AI that 143 00:05:07,040 --> 00:05:08,560 can beat us at every game. 144 00:05:08,560 --> 00:05:12,080 We're trying to understand how AI can learn and adapt 145 00:05:12,080 --> 00:05:15,200 to challenges that, you know, even we humans find tricky. 146 00:05:15,200 --> 00:05:16,040 Exactly. 147 00:05:16,040 --> 00:05:18,360 And it's about, well, think of it this way. 148 00:05:18,360 --> 00:05:22,040 They've proven that AI can grasp the basics of a game like 149 00:05:22,040 --> 00:05:25,720 So Longsucker, which is pretty impressive in itself. 150 00:05:25,720 --> 00:05:28,600 But the real question now is, how do we take it to the next level? 151 00:05:28,600 --> 00:05:33,840 How do we make the AI a more cunning, more strategic player? 152 00:05:33,840 --> 00:05:34,800 Now you're speaking my language. 153 00:05:34,800 --> 00:05:36,320 What's the secret sauce? 154 00:05:36,320 --> 00:05:38,400 How do we make the AI even more formidable? 155 00:05:38,400 --> 00:05:41,280 Well, there's no secret sauce yet, but the paper does suggest 156 00:05:41,280 --> 00:05:44,000 some pretty exciting avenues for future research. 157 00:05:44,000 --> 00:05:47,360 Like, what if we combine these classic AI methods 158 00:05:47,360 --> 00:05:51,120 with algorithms that understand things like human psychology 159 00:05:51,120 --> 00:05:53,200 or, you know, even negotiation tactics? 160 00:05:53,200 --> 00:05:55,400 Ooh, getting into the mind games now. 161 00:05:55,400 --> 00:05:58,280 So we're talking about an AI that can not only play by the rules, 162 00:05:58,280 --> 00:06:01,640 but also understand how its opponents are feeling, right? 163 00:06:01,640 --> 00:06:05,040 Predict their moves based on, like, emotional cues. 164 00:06:05,040 --> 00:06:06,080 Exactly. 165 00:06:06,080 --> 00:06:09,680 Imagine an AI that can sense when a player is about to betray 166 00:06:09,680 --> 00:06:13,440 an alliance based on subtle shifts in their gameplay. 167 00:06:13,440 --> 00:06:15,200 That would be a total game changer, wouldn't it? 168 00:06:15,200 --> 00:06:17,160 Yeah, that's some next level AI right there. 169 00:06:17,160 --> 00:06:18,720 But isn't that kind of a huge leap? 170 00:06:18,720 --> 00:06:21,760 I mean, we're still trying to get robots to walk in a straight line 171 00:06:21,760 --> 00:06:23,280 without bumping into things, right? 172 00:06:23,280 --> 00:06:26,400 True, but the field of AI is moving so fast. 173 00:06:26,400 --> 00:06:28,320 I mean, look at the advancements we're seeing in areas 174 00:06:28,320 --> 00:06:32,160 like natural language processing or even sentiment analysis. 175 00:06:32,160 --> 00:06:34,800 It's not totally crazy to think that those advancements 176 00:06:34,800 --> 00:06:38,240 could be applied to create AI that can navigate the social 177 00:06:38,240 --> 00:06:43,000 and psychological aspects of games like, you know, so long sucker. 178 00:06:43,000 --> 00:06:46,320 So the big takeaway here is that this isn't just about AI 179 00:06:46,320 --> 00:06:47,600 playing games. 180 00:06:47,600 --> 00:06:51,480 It's about AI learning how to interact with us humans 181 00:06:51,480 --> 00:06:52,600 on a much deeper level. 182 00:06:52,600 --> 00:06:53,600 Absolutely. 183 00:06:53,600 --> 00:06:56,800 And the implications of that, they go way beyond board games. 184 00:06:56,800 --> 00:06:59,720 Think about things like diplomacy or business negotiations, 185 00:06:59,720 --> 00:07:02,280 even just our everyday social interactions online. 186 00:07:02,280 --> 00:07:02,600 Hold on. 187 00:07:02,600 --> 00:07:05,360 Are you saying we could have AI negotiators hammering out 188 00:07:05,360 --> 00:07:09,120 peace treaties or, like, closing multi-million dollar deals? 189 00:07:09,120 --> 00:07:11,040 It might not be as far fetched as it sounds. 190 00:07:11,040 --> 00:07:14,880 I mean, if AI can learn to master deception, cooperation, 191 00:07:14,880 --> 00:07:17,920 all those things in a game like so long sucker, 192 00:07:17,920 --> 00:07:20,960 who knows what other complex human interactions it 193 00:07:20,960 --> 00:07:21,760 could handle, right? 194 00:07:21,760 --> 00:07:22,260 OK. 195 00:07:22,260 --> 00:07:27,000 I'm both excited and slightly terrified by that thought. 196 00:07:27,000 --> 00:07:29,200 But let's bring it back to the research for a second. 197 00:07:29,200 --> 00:07:32,640 The paper was pretty clear that while the AI made progress, 198 00:07:32,640 --> 00:07:35,080 it still got a long way to go before it can truly 199 00:07:35,080 --> 00:07:36,720 match human performance. 200 00:07:36,720 --> 00:07:37,520 Right. 201 00:07:37,520 --> 00:07:40,600 One of the key takeaways is that difference in learning speed 202 00:07:40,600 --> 00:07:41,640 we talked about earlier. 203 00:07:41,640 --> 00:07:45,560 Remember, humans, we pick up on the nuances of so long sucker 204 00:07:45,560 --> 00:07:46,880 pretty quickly. 205 00:07:46,880 --> 00:07:49,480 But the AI, it needed thousands of games 206 00:07:49,480 --> 00:07:51,360 to reach a decent level of play. 207 00:07:51,360 --> 00:07:52,360 So what's the bottleneck? 208 00:07:52,360 --> 00:07:54,480 Why is it so much slower on the uptake? 209 00:07:54,480 --> 00:07:56,000 I think it comes down to the difference 210 00:07:56,000 --> 00:07:58,720 between explicit and implicit knowledge. 211 00:07:58,720 --> 00:08:01,440 You see, humans, we bring a lot of implicit knowledge 212 00:08:01,440 --> 00:08:04,760 to the game, things like social cues, intuition, even just 213 00:08:04,760 --> 00:08:06,600 an understanding of human nature. 214 00:08:06,600 --> 00:08:08,960 And we do it often without even realizing it. 215 00:08:08,960 --> 00:08:12,200 This is like we've got this secret playbook in our heads 216 00:08:12,200 --> 00:08:13,880 that we're not even consciously aware of. 217 00:08:13,880 --> 00:08:14,760 Exactly. 218 00:08:14,760 --> 00:08:17,240 And that's what's missing from the AI's toolkit right now, 219 00:08:17,240 --> 00:08:18,440 these classic algorithms. 220 00:08:18,440 --> 00:08:21,120 They're great at processing information, learning the rules. 221 00:08:21,120 --> 00:08:25,440 But they struggle to grasp those subtle, unspoken aspects 222 00:08:25,440 --> 00:08:27,280 of human interaction. 223 00:08:27,280 --> 00:08:29,480 So it's not just about making AI smarter. 224 00:08:29,480 --> 00:08:32,880 It's about making it more, well, more human, 225 00:08:32,880 --> 00:08:35,000 more able to understand and adapt 226 00:08:35,000 --> 00:08:39,280 to all the complexities of social situations. 227 00:08:39,280 --> 00:08:40,320 That's the real challenge. 228 00:08:40,320 --> 00:08:42,800 And it's what makes this research so fascinating. 229 00:08:42,800 --> 00:08:44,640 It's not just about winning or losing a game. 230 00:08:44,640 --> 00:08:48,960 It's about using AI to hold up a mirror to ourselves 231 00:08:48,960 --> 00:08:51,240 to better understand how we think and behave. 232 00:08:51,240 --> 00:08:52,840 We've covered a lot of ground here. 233 00:08:52,840 --> 00:08:55,400 The AI's performance, the challenges it faced, 234 00:08:55,400 --> 00:08:57,480 the potential implications for the future, 235 00:08:57,480 --> 00:08:58,680 anything else we should highlight, 236 00:08:58,680 --> 00:09:00,360 any surprises or unanswered questions 237 00:09:00,360 --> 00:09:01,560 that popped up for you? 238 00:09:01,560 --> 00:09:03,120 One thing that really struck me was, 239 00:09:03,120 --> 00:09:05,760 remember how we talked about the AI making those occasional 240 00:09:05,760 --> 00:09:06,680 illegal moves? 241 00:09:06,680 --> 00:09:09,200 Yeah, it was kind of funny, but also a little bit comforting. 242 00:09:09,200 --> 00:09:09,880 Right. 243 00:09:09,880 --> 00:09:11,840 But what's interesting is that those illegal moves 244 00:09:11,840 --> 00:09:13,360 weren't just random. 245 00:09:13,360 --> 00:09:15,360 They often happened when the AI was facing 246 00:09:15,360 --> 00:09:18,160 a particularly complex situation, something 247 00:09:18,160 --> 00:09:20,120 it hadn't really encountered before. 248 00:09:20,120 --> 00:09:23,000 So it's like they were trying to think outside the box, 249 00:09:23,000 --> 00:09:25,080 but maybe tripped over the box in the process. 250 00:09:25,080 --> 00:09:26,560 Huh, exactly. 251 00:09:26,560 --> 00:09:29,600 It suggests that even though the AI was learning the rules, 252 00:09:29,600 --> 00:09:32,040 it wasn't always able to apply them correctly 253 00:09:32,040 --> 00:09:33,680 in every situation. 254 00:09:33,680 --> 00:09:36,480 There is still a disconnect between understanding 255 00:09:36,480 --> 00:09:38,320 the concept of a rule and knowing 256 00:09:38,320 --> 00:09:41,320 how to use it effectively in a dynamic environment. 257 00:09:41,320 --> 00:09:43,360 Kind of like learning a new language. 258 00:09:43,360 --> 00:09:44,880 You might know the grammar rules, 259 00:09:44,880 --> 00:09:46,920 but using them in a fast-paced conversation 260 00:09:46,920 --> 00:09:48,440 is a whole different ball game. 261 00:09:48,440 --> 00:09:49,280 Exactly. 262 00:09:49,280 --> 00:09:51,280 And that's where the limitations of these classic AI 263 00:09:51,280 --> 00:09:52,640 methods become clear. 264 00:09:52,640 --> 00:09:55,280 They're great at pattern recognition, rule learning, 265 00:09:55,280 --> 00:09:59,320 but they struggle with that flexible context-sensitive 266 00:09:59,320 --> 00:10:01,920 reasoning that we humans are so good at. 267 00:10:01,920 --> 00:10:04,040 So the next step in AI evolution isn't just 268 00:10:04,040 --> 00:10:06,320 about teaching AI what to think. 269 00:10:06,320 --> 00:10:08,040 It's about teaching it how to think. 270 00:10:08,040 --> 00:10:08,840 Exactly. 271 00:10:08,840 --> 00:10:12,480 And that's where the next wave of AI research needs to focus. 272 00:10:12,480 --> 00:10:15,400 We need algorithms that can learn not just from data, 273 00:10:15,400 --> 00:10:17,960 but from experience, from interaction, 274 00:10:17,960 --> 00:10:21,320 from the messy, unpredictable, real world. 275 00:10:21,320 --> 00:10:24,920 What are your thoughts on the future of AI in games like this? 276 00:10:24,920 --> 00:10:27,640 Do you think we'll ever see an AI that can truly master 277 00:10:27,640 --> 00:10:33,160 the art of deception and outsmart even the most cunning 278 00:10:33,160 --> 00:10:34,320 human player? 279 00:10:34,320 --> 00:10:35,680 That's the big question, isn't it? 280 00:10:35,680 --> 00:10:37,320 And honestly, it's tough to say. 281 00:10:37,320 --> 00:10:39,640 The field is evolving so rapidly. 282 00:10:39,640 --> 00:10:43,000 What seems impossible today might be commonplace tomorrow. 283 00:10:43,000 --> 00:10:44,600 So there's hope for us humans yet. 284 00:10:44,600 --> 00:10:48,000 We might not end up as pawns in some grand AI scheme. 285 00:10:48,000 --> 00:10:49,760 Well, let's not get ahead of ourselves. 286 00:10:49,760 --> 00:10:52,640 But I do think it's important to remember that AI is a tool. 287 00:10:52,640 --> 00:10:55,640 And like any tool, it can be used for good or for, well, 288 00:10:55,640 --> 00:10:56,520 not so good. 289 00:10:56,520 --> 00:10:58,960 The real question is, how do we ensure that its development 290 00:10:58,960 --> 00:11:00,440 benefits humanity? 291 00:11:00,440 --> 00:11:01,440 That's a good point. 292 00:11:01,440 --> 00:11:03,760 But before we get too deep into the philosophical side 293 00:11:03,760 --> 00:11:05,840 of things, let's circle back to this specific research. 294 00:11:05,840 --> 00:11:09,320 What were some of its key strengths and limitations? 295 00:11:09,320 --> 00:11:11,120 One of the strengths, definitely, 296 00:11:11,120 --> 00:11:13,760 is the innovative way they used cumulative learning 297 00:11:13,760 --> 00:11:15,880 to train the AI agents. 298 00:11:15,880 --> 00:11:18,160 That shared experience thing, it really speeds up 299 00:11:18,160 --> 00:11:19,960 the learning process, allows the AI 300 00:11:19,960 --> 00:11:22,760 to grasp the game's fundamentals much quicker. 301 00:11:22,760 --> 00:11:24,960 And simplifying the scoring system, 302 00:11:24,960 --> 00:11:28,000 making it winner takes all, that was a smart move too. 303 00:11:28,000 --> 00:11:31,000 Gave the AI a clear objective to focus on, 304 00:11:31,000 --> 00:11:34,000 made it easier to really hone in on those strategic elements 305 00:11:34,000 --> 00:11:34,600 of the game. 306 00:11:34,600 --> 00:11:35,880 Exactly. 307 00:11:35,880 --> 00:11:37,680 But of course, there are limitations. 308 00:11:37,680 --> 00:11:40,120 As we discussed, the AI's learning speed 309 00:11:40,120 --> 00:11:42,280 is still lagging behind humans. 310 00:11:42,280 --> 00:11:45,280 And it really struggles with those subtle, unspoken aspects 311 00:11:45,280 --> 00:11:47,760 of the game, like reading social cues, 312 00:11:47,760 --> 00:11:50,080 anticipating when someone's about to backstab them. 313 00:11:50,080 --> 00:11:52,480 And those illegal moves, while amusing, 314 00:11:52,480 --> 00:11:55,240 they also highlight the AI's limitations in terms 315 00:11:55,240 --> 00:11:57,000 of applying the rules consistently, 316 00:11:57,000 --> 00:11:58,720 especially in tricky situations. 317 00:11:58,720 --> 00:12:01,760 Yeah, it's a good reminder that while AI has made amazing 318 00:12:01,760 --> 00:12:04,360 progress, there's still a gap between its ability 319 00:12:04,360 --> 00:12:07,160 to understand a concept and its ability 320 00:12:07,160 --> 00:12:09,520 to use it flawlessly in a complex, unpredictable 321 00:12:09,520 --> 00:12:10,240 environment. 322 00:12:10,240 --> 00:12:14,880 So it seems like the future of AI in games like So Longsucker 323 00:12:14,880 --> 00:12:17,600 really hinges on bridging that gap 324 00:12:17,600 --> 00:12:21,040 between the explicit knowledge and the implicit understanding. 325 00:12:21,040 --> 00:12:21,640 Absolutely. 326 00:12:21,640 --> 00:12:24,480 And that's what makes this area of research so exciting. 327 00:12:24,480 --> 00:12:27,760 Imagine AI algorithms that can not only process data, 328 00:12:27,760 --> 00:12:31,480 but also learn from experience, adapt to changing situations, 329 00:12:31,480 --> 00:12:35,000 even anticipate human behavior based on those subtle cues 330 00:12:35,000 --> 00:12:35,760 we talked about. 331 00:12:35,760 --> 00:12:37,400 It's almost like we're talking about giving AI 332 00:12:37,400 --> 00:12:41,160 a sense of intuition, like a little touch of that human spark. 333 00:12:41,160 --> 00:12:43,120 And if we can achieve that, the possibilities 334 00:12:43,120 --> 00:12:44,720 are truly mind-blowing. 335 00:12:44,720 --> 00:12:47,520 So to sum up this part of our deep dive, 336 00:12:47,520 --> 00:12:49,200 it seems this research has opened up 337 00:12:49,200 --> 00:12:52,320 a whole bunch of exciting avenues for future exploration. 338 00:12:52,320 --> 00:12:53,080 For sure. 339 00:12:53,080 --> 00:12:57,080 The potential applications of AI in areas like game theory, 340 00:12:57,080 --> 00:13:01,240 strategic decision making, even social interaction are huge. 341 00:13:01,240 --> 00:13:03,280 And we've really just scratched the surface. 342 00:13:03,280 --> 00:13:06,640 And even though AI might not be ready to completely outmaneuver 343 00:13:06,640 --> 00:13:09,360 us in game of So Longsucker just yet, 344 00:13:09,360 --> 00:13:11,600 this research has given us a pretty good glimpse of what 345 00:13:11,600 --> 00:13:13,080 might be possible down the road. 346 00:13:15,600 --> 00:13:18,760 And we're back for the final round of our deep dive 347 00:13:18,760 --> 00:13:22,160 into the world of AI taking on So Longsucker. 348 00:13:22,160 --> 00:13:25,120 We've explored the game itself, how well the AI did, 349 00:13:25,120 --> 00:13:26,880 and even touched on the future of AI, 350 00:13:26,880 --> 00:13:30,520 like how it could potentially understand maybe even mimic 351 00:13:30,520 --> 00:13:31,600 human behavior. 352 00:13:31,600 --> 00:13:33,200 It's been a wild ride, hasn't it? 353 00:13:33,200 --> 00:13:35,480 We've seen these AI agents, powered 354 00:13:35,480 --> 00:13:38,800 by all those fancy algorithms, learn this really complex game 355 00:13:38,800 --> 00:13:41,720 and come up with some surprisingly effective strategies. 356 00:13:41,720 --> 00:13:44,440 But like we've been saying, they're not perfect, not yet, 357 00:13:44,440 --> 00:13:45,080 at least. 358 00:13:45,080 --> 00:13:47,000 Yeah, for me, one of the most fascinating things 359 00:13:47,000 --> 00:13:49,640 about this research is that it's not just about AI 360 00:13:49,640 --> 00:13:51,280 playing games. 361 00:13:51,280 --> 00:13:54,200 It's about using AI to understand ourselves a bit better. 362 00:13:54,200 --> 00:13:54,960 Absolutely. 363 00:13:54,960 --> 00:13:57,440 By studying how these algorithms learn and adapt, 364 00:13:57,440 --> 00:13:59,320 we can gain some really interesting insights 365 00:13:59,320 --> 00:14:02,080 into our own thought processes, how we make decisions, 366 00:14:02,080 --> 00:14:04,280 and even the nature of intelligence itself. 367 00:14:04,280 --> 00:14:05,560 Pretty deep stuff, right? 368 00:14:05,560 --> 00:14:08,360 It's like holding up a mirror to our own minds, 369 00:14:08,360 --> 00:14:12,040 but the mirror is powered by complex math and mountains 370 00:14:12,040 --> 00:14:12,560 of data. 371 00:14:12,560 --> 00:14:14,000 That's a great way to put it. 372 00:14:14,000 --> 00:14:16,240 And maybe we can even learn a thing or two 373 00:14:16,240 --> 00:14:18,520 about strategic thinking from these AI agents. 374 00:14:18,520 --> 00:14:21,240 So little edge in the game of life never hurts, right? 375 00:14:21,240 --> 00:14:22,480 Never hurts. 376 00:14:22,480 --> 00:14:26,600 But let's not forget to give credit where credit is due. 377 00:14:26,600 --> 00:14:28,560 The researchers who did this study, 378 00:14:28,560 --> 00:14:30,520 they've made some incredible strides 379 00:14:30,520 --> 00:14:32,880 in pushing the boundaries of what AI can do. 380 00:14:32,880 --> 00:14:35,080 And as they said in the paper, this is just the beginning, 381 00:14:35,080 --> 00:14:36,760 just a taste of what's to come. 382 00:14:36,760 --> 00:14:39,120 And that's what I find so exciting about this field. 383 00:14:39,120 --> 00:14:42,000 It's a constant journey of discovery, constantly pushing 384 00:14:42,000 --> 00:14:44,280 the limits of what we thought was possible. 385 00:14:44,280 --> 00:14:46,600 Who knows what amazing breakthroughs are just 386 00:14:46,600 --> 00:14:48,120 around the corner, right? 387 00:14:48,120 --> 00:14:51,440 So as we wrap up our so long sucker AI deep dive, 388 00:14:51,440 --> 00:14:53,680 what's the one key takeaway you want our listeners 389 00:14:53,680 --> 00:14:54,800 to remember? 390 00:14:54,800 --> 00:14:55,920 For me, it's this. 391 00:14:55,920 --> 00:14:58,280 AI is more than just code and algorithms. 392 00:14:58,280 --> 00:15:00,920 It reflects our own creativity, our desire 393 00:15:00,920 --> 00:15:02,800 to understand the world around us, 394 00:15:02,800 --> 00:15:04,160 and maybe even gives us a glimpse 395 00:15:04,160 --> 00:15:06,440 into the future of intelligence itself. 396 00:15:06,440 --> 00:15:07,600 Well said. 397 00:15:07,600 --> 00:15:09,080 And on that note, we'll leave you 398 00:15:09,080 --> 00:15:11,160 with a final thought to ponder. 399 00:15:11,160 --> 00:15:13,400 If AI can learn to master deception 400 00:15:13,400 --> 00:15:17,000 and strategic thinking in a game like so long sucker, 401 00:15:17,000 --> 00:15:19,080 what other parts of human behavior 402 00:15:19,080 --> 00:15:23,040 might it eventually be able to copy or even maybe surpass? 403 00:15:23,040 --> 00:15:25,680 It's a big question and one that will definitely 404 00:15:25,680 --> 00:15:28,640 keep driving research and innovation in AI for years 405 00:15:28,640 --> 00:15:29,480 to come. 406 00:15:29,480 --> 00:15:31,240 Thanks for joining us on this deep dive. 407 00:15:31,240 --> 00:15:34,080 Until next time, keep exploring, keep asking questions, 408 00:15:34,080 --> 00:15:44,640 and keep pushing those boundaries of knowledge.