1 00:00:00,000 --> 00:00:01,920 All right, so AI, right? 2 00:00:01,920 --> 00:00:05,880 It's gotten really good at some pretty wild stuff, 3 00:00:05,880 --> 00:00:07,400 like writing all kinds of creative things 4 00:00:07,400 --> 00:00:08,880 or translating languages. 5 00:00:08,880 --> 00:00:12,800 But when it comes to like really complex reasoning, 6 00:00:12,800 --> 00:00:16,080 you know, like figuring out problems with multiple steps, 7 00:00:16,080 --> 00:00:17,680 AI still kind of struggles. 8 00:00:17,680 --> 00:00:19,400 Yeah, that's definitely a challenge right now 9 00:00:19,400 --> 00:00:21,040 for a lot of the current AI models. 10 00:00:21,040 --> 00:00:22,160 So this is where this new paper 11 00:00:22,160 --> 00:00:23,200 we're looking at comes in, right? 12 00:00:23,200 --> 00:00:25,480 It's called Forest of Thought, 13 00:00:25,480 --> 00:00:29,320 Scaling Test Time Compute for Enhancing LLM Reasoning. 14 00:00:29,320 --> 00:00:31,720 And it basically introduces this really cool concept 15 00:00:31,720 --> 00:00:33,520 called Forest of Thought. 16 00:00:33,520 --> 00:00:35,920 It's supposed to help AI think better. 17 00:00:35,920 --> 00:00:36,920 At least that's what they're aiming for. 18 00:00:36,920 --> 00:00:37,760 Yeah, that's the gist of it. 19 00:00:37,760 --> 00:00:39,640 It's really tackling those limitations 20 00:00:39,640 --> 00:00:42,520 that we see with LLMs right now, large language models. 21 00:00:42,520 --> 00:00:44,040 Like when they're faced with a problem 22 00:00:44,040 --> 00:00:47,120 that requires step-by-step thinking to solve. 23 00:00:47,120 --> 00:00:48,600 Okay, so this is probably a dumb question, 24 00:00:48,600 --> 00:00:51,360 but for people like me who aren't AI experts, 25 00:00:51,360 --> 00:00:54,520 can you explain why like those existing methods, 26 00:00:54,520 --> 00:00:57,280 you know, things like chain of thought and tree of thought, 27 00:00:57,280 --> 00:00:58,160 how do they always cut it? 28 00:00:58,160 --> 00:01:00,440 Okay, so imagine you're solving a puzzle, right? 29 00:01:00,440 --> 00:01:03,800 Chain of Thought is kind of like trying to solve that puzzle 30 00:01:03,800 --> 00:01:07,680 by only looking at one possible solution at a time. 31 00:01:07,680 --> 00:01:09,640 So you get tunnel vision basically. 32 00:01:09,640 --> 00:01:11,600 Pretty much, like it might work 33 00:01:11,600 --> 00:01:13,600 if the puzzle is super simple, 34 00:01:13,600 --> 00:01:15,400 but if it's more complex, 35 00:01:15,400 --> 00:01:17,360 you're likely to go down the wrong path 36 00:01:17,360 --> 00:01:19,200 and completely miss the solution. 37 00:01:19,200 --> 00:01:21,200 Ah, so you end up going down a rabbit hole 38 00:01:21,200 --> 00:01:22,800 and never actually solving the problem. 39 00:01:22,800 --> 00:01:24,000 Exactly. 40 00:01:24,000 --> 00:01:26,920 Now tree of thought, that's a bit more advanced. 41 00:01:26,920 --> 00:01:28,560 It explores multiple paths, 42 00:01:28,560 --> 00:01:30,320 sort of like the branches of a tree, 43 00:01:30,320 --> 00:01:31,520 but even with this method, 44 00:01:31,520 --> 00:01:35,440 the AI usually only considers each path once. 45 00:01:35,440 --> 00:01:36,880 It doesn't go back and rethink 46 00:01:36,880 --> 00:01:39,080 if it might have messed up earlier on. 47 00:01:39,080 --> 00:01:41,320 Okay, I'm starting to see the problem here. 48 00:01:41,320 --> 00:01:44,040 So both methods have limitations, 49 00:01:44,040 --> 00:01:46,120 especially with those super complex problems 50 00:01:46,120 --> 00:01:48,720 that need more like careful consideration. 51 00:01:48,720 --> 00:01:51,960 So where does this forest of thought thing come in? 52 00:01:51,960 --> 00:01:53,640 I gotta say the name is pretty intriguing. 53 00:01:53,640 --> 00:01:55,640 Well, think of a forest with a bunch of trees, right? 54 00:01:55,640 --> 00:01:56,520 I could picture that. 55 00:01:56,520 --> 00:01:59,880 Each tree represents a different way to solve a problem. 56 00:01:59,880 --> 00:02:03,760 And forest of thought, or FOTI, takes that idea 57 00:02:03,760 --> 00:02:05,120 and applies it to AI, 58 00:02:05,120 --> 00:02:08,360 basically integrates multiple reasoning trees together. 59 00:02:08,360 --> 00:02:11,640 So now the AI can explore tons of potential solutions 60 00:02:11,640 --> 00:02:13,160 all at the same time. 61 00:02:13,160 --> 00:02:16,280 So it's like having a whole army of AI's 62 00:02:16,280 --> 00:02:18,360 all working together to solve the puzzle, 63 00:02:18,360 --> 00:02:20,440 each one with its own unique approach. 64 00:02:20,440 --> 00:02:21,280 You got it. 65 00:02:21,280 --> 00:02:23,160 And that's where the real power comes from, 66 00:02:23,160 --> 00:02:25,320 leveraging all those different perspectives. 67 00:02:25,320 --> 00:02:26,960 Just like in the real world, you know? 68 00:02:26,960 --> 00:02:29,200 A team with people who have different skills and knowledge, 69 00:02:29,200 --> 00:02:31,080 they can usually solve problems way better 70 00:02:31,080 --> 00:02:32,680 than someone working alone. 71 00:02:32,680 --> 00:02:34,720 But FOTI tries to do the same thing with AI. 72 00:02:34,720 --> 00:02:37,640 So it's not just about making the AI brain bigger, 73 00:02:37,640 --> 00:02:41,320 it's about making it think in a smarter, more strategic way. 74 00:02:41,320 --> 00:02:42,240 Exactly. 75 00:02:42,240 --> 00:02:44,520 And how FOTI is built for efficiency too, 76 00:02:44,520 --> 00:02:47,160 it uses something called sparse activation. 77 00:02:47,160 --> 00:02:49,040 It's kind of like the AI picks and chooses 78 00:02:49,040 --> 00:02:52,880 which solution paths to really focus on within each tree. 79 00:02:52,880 --> 00:02:53,880 You know, it's like, if you're in a maze, 80 00:02:53,880 --> 00:02:55,200 you don't try every single path, 81 00:02:55,200 --> 00:02:56,720 you try to pick the ones that seem like 82 00:02:56,720 --> 00:02:57,960 they'll lead you to the exit. 83 00:02:57,960 --> 00:03:01,120 So the AI is getting smarter at prioritizing, 84 00:03:01,120 --> 00:03:02,760 avoiding those dead ends. 85 00:03:02,760 --> 00:03:03,600 That's pretty cool. 86 00:03:03,600 --> 00:03:05,480 Yep, and it gets even cooler. 87 00:03:05,480 --> 00:03:07,960 FOT can also learn from its mistakes. 88 00:03:07,960 --> 00:03:10,040 It has a system that's always evaluating 89 00:03:10,040 --> 00:03:12,800 its reasoning steps, and if it finds an error, 90 00:03:12,800 --> 00:03:14,880 it can actually correct itself as it goes. 91 00:03:14,880 --> 00:03:17,600 It's like having a built-in spell checker, but for logic. 92 00:03:17,600 --> 00:03:19,040 No, that's next level. 93 00:03:19,040 --> 00:03:21,600 So it's not just exploring all these different paths, 94 00:03:21,600 --> 00:03:23,960 it's double checking its work along the way too. 95 00:03:23,960 --> 00:03:25,960 I'm guessing that makes it way more accurate. 96 00:03:25,960 --> 00:03:28,960 You bet, especially when it comes to math problems, 97 00:03:28,960 --> 00:03:32,320 they've actually added in specific rule-based checks 98 00:03:32,320 --> 00:03:34,360 to catch those common calculation errors. 99 00:03:34,360 --> 00:03:37,520 So it's like the AI has a math tutor looking over its shoulder, 100 00:03:37,520 --> 00:03:40,680 making sure it doesn't make those silly arithmetic mistakes. 101 00:03:40,680 --> 00:03:42,440 Okay, so now we have this AI forest 102 00:03:42,440 --> 00:03:44,520 exploring all these different solution paths 103 00:03:44,520 --> 00:03:48,040 and constantly checking its work to avoid mistakes. 104 00:03:48,040 --> 00:03:50,720 But how does it actually decide on the best answer? 105 00:03:51,800 --> 00:03:53,600 You know, out of all those possibilities. 106 00:03:53,600 --> 00:03:56,800 That's where consensus-guided decision-making comes in. 107 00:03:56,800 --> 00:03:59,800 It's basically like all the different trees in the forest 108 00:03:59,800 --> 00:04:02,160 vote on what they think is the best solution, 109 00:04:02,160 --> 00:04:04,840 but it's not a simple majority rules kind of thing. 110 00:04:04,840 --> 00:04:06,120 If there isn't a clear winner, 111 00:04:06,120 --> 00:04:09,000 then they have a special LLM, like an expert judge. 112 00:04:09,000 --> 00:04:11,560 And that judge makes the final decision 113 00:04:11,560 --> 00:04:14,320 based on the reasoning provided by all the trees. 114 00:04:14,320 --> 00:04:16,880 So it's combining the knowledge of the whole forest 115 00:04:16,880 --> 00:04:19,360 with the expertise of a specialist. 116 00:04:19,360 --> 00:04:21,920 That sounds like a pretty solid way to make a decision. 117 00:04:21,920 --> 00:04:25,720 Now the big question, how well does this FOTI actually work? 118 00:04:25,720 --> 00:04:27,000 I mean, did they test it out? 119 00:04:27,000 --> 00:04:28,680 Oh yeah, they put it through its paces. 120 00:04:28,680 --> 00:04:30,480 They tested it on a few benchmarks, 121 00:04:30,480 --> 00:04:33,520 including this game called Game of 24. 122 00:04:33,520 --> 00:04:34,760 You have four numbers 123 00:04:34,760 --> 00:04:37,320 and you got to use basic math to get to 24. 124 00:04:37,320 --> 00:04:38,440 It's harder than it sounds. 125 00:04:38,440 --> 00:04:39,400 Yeah, I remember that game. 126 00:04:39,400 --> 00:04:41,160 It could be a real brain teaser. 127 00:04:41,160 --> 00:04:43,040 Well, with enough trees in the forest, 128 00:04:43,040 --> 00:04:45,840 FOTI actually achieved almost perfect accuracy. 129 00:04:45,840 --> 00:04:48,440 It totally blew the standard tree of thought method 130 00:04:48,440 --> 00:04:49,280 out of the water. 131 00:04:49,280 --> 00:04:50,400 That's impressive. 132 00:04:50,400 --> 00:04:53,240 Beating the standard method by that much is a big deal. 133 00:04:53,240 --> 00:04:57,400 They also tested it on a dataset of math word problems 134 00:04:57,400 --> 00:05:00,560 called GSM 8K and a more challenging math dataset 135 00:05:00,560 --> 00:05:01,680 called Math. 136 00:05:01,680 --> 00:05:03,040 Heh heh heh heh heh. 137 00:05:03,040 --> 00:05:04,000 What were the results? 138 00:05:04,000 --> 00:05:07,000 A FOTI consistently did better than the other methods 139 00:05:07,000 --> 00:05:08,760 across all the different difficulty levels. 140 00:05:08,760 --> 00:05:12,080 And what's even more exciting is that even a small increase 141 00:05:12,080 --> 00:05:16,680 in the number of trees led to big improvements in accuracy. 142 00:05:16,680 --> 00:05:18,760 So it seems like FT really benefits 143 00:05:18,760 --> 00:05:21,360 from having more computational power, 144 00:05:21,360 --> 00:05:23,120 you know, more trees in the forest. 145 00:05:23,120 --> 00:05:25,440 So the more trees you have, the better it performs. 146 00:05:25,440 --> 00:05:26,280 Oh. 147 00:05:26,280 --> 00:05:27,120 That's a good sign. 148 00:05:27,120 --> 00:05:29,160 It suggests that this approach could really scale up 149 00:05:29,160 --> 00:05:31,800 and get even better as we get more powerful computers. 150 00:05:31,800 --> 00:05:33,280 But I'm sure there are limitations, right? 151 00:05:33,280 --> 00:05:34,400 I mean, nothing's perfect. 152 00:05:34,400 --> 00:05:35,240 That's right. 153 00:05:35,240 --> 00:05:36,240 FOTI is super promising, 154 00:05:36,240 --> 00:05:38,800 but it still depends heavily on the LLMs themselves, 155 00:05:38,800 --> 00:05:40,200 the underlying language model. 156 00:05:40,200 --> 00:05:43,520 So if those LLMs have biases or gaps in their knowledge, 157 00:05:43,520 --> 00:05:45,600 well, FOT is going to inherit those problems. 158 00:05:45,600 --> 00:05:46,440 Makes sense. 159 00:05:46,440 --> 00:05:47,600 It's not like a magic solution. 160 00:05:47,600 --> 00:05:48,440 Yeah. 161 00:05:48,440 --> 00:05:49,880 That's definitely a big step in the right direction. 162 00:05:49,880 --> 00:05:51,480 I'm kind of curious about... 163 00:05:51,480 --> 00:05:53,760 Actually, before you ask, I have a question for you. 164 00:05:53,760 --> 00:05:56,200 Thinking about everything we've talked about so far, 165 00:05:56,200 --> 00:05:58,480 what's the most interesting thing about FOTI to you? 166 00:05:58,480 --> 00:06:00,560 Hmm, that's a good question. 167 00:06:00,560 --> 00:06:03,240 I think what really strikes me is how FOTI 168 00:06:03,240 --> 00:06:06,480 is kind of mimicking the way humans solve problems. 169 00:06:06,480 --> 00:06:09,160 We don't usually just follow one single line of thought. 170 00:06:09,160 --> 00:06:11,640 We brainstorm different ideas, we try things out, 171 00:06:11,640 --> 00:06:13,600 we make mistakes, we learn from them. 172 00:06:13,600 --> 00:06:16,920 And the way FOT incorporates multiple perspectives 173 00:06:16,920 --> 00:06:18,840 and allows for that self-correction. 174 00:06:18,840 --> 00:06:20,000 It feels very human. 175 00:06:20,000 --> 00:06:21,440 That's a great observation. 176 00:06:21,440 --> 00:06:23,120 One of the most exciting things about FOTI 177 00:06:23,120 --> 00:06:25,240 is that it's moving us closer to AI 178 00:06:25,240 --> 00:06:26,920 that can think more like us. 179 00:06:26,920 --> 00:06:29,160 AI that can reason and solve problems 180 00:06:29,160 --> 00:06:30,760 in a way that feels natural. 181 00:06:30,760 --> 00:06:32,720 Which opens up a whole new can of worms, doesn't it? 182 00:06:32,720 --> 00:06:35,280 The more sophisticated these AI systems get, 183 00:06:35,280 --> 00:06:37,880 the more we have to think about the potential consequences, 184 00:06:37,880 --> 00:06:38,800 both good and bad. 185 00:06:38,800 --> 00:06:39,640 Absolutely. 186 00:06:39,640 --> 00:06:42,200 We need to be having those conversations right now 187 00:06:42,200 --> 00:06:44,240 about how to make sure these systems are aligned 188 00:06:44,240 --> 00:06:46,240 with our values, about how to prevent them 189 00:06:46,240 --> 00:06:48,840 from amplifying biases or causing new problems. 190 00:06:48,840 --> 00:06:50,440 Yeah, it's a huge responsibility. 191 00:06:50,440 --> 00:06:52,560 And it's not just on the researchers and developers, 192 00:06:52,560 --> 00:06:53,640 it's on all of us. 193 00:06:53,640 --> 00:06:56,520 Policymakers, industry leaders, the public, 194 00:06:56,520 --> 00:06:59,440 we all need to be involved in shaping the future of AI. 195 00:06:59,440 --> 00:07:03,360 But I have to say, the potential benefits are incredible. 196 00:07:03,360 --> 00:07:06,840 Imagine using OT to speed up scientific discoveries 197 00:07:06,840 --> 00:07:08,800 or develop new medical treatments 198 00:07:08,800 --> 00:07:11,560 or even tackle things like climate change. 199 00:07:11,560 --> 00:07:13,080 It's really exciting to think about. 200 00:07:13,080 --> 00:07:14,040 I completely agree. 201 00:07:14,040 --> 00:07:17,160 This is a very exciting time to be working in AI. 202 00:07:17,160 --> 00:07:18,200 For sure. 203 00:07:18,200 --> 00:07:19,680 Well, that brings us to the end of part one 204 00:07:19,680 --> 00:07:22,240 of our deep dive into this fascinating world 205 00:07:22,240 --> 00:07:24,080 of forest of thought. 206 00:07:24,080 --> 00:07:26,240 So welcome back to our deep dive in forest of thought 207 00:07:26,240 --> 00:07:28,320 before we went to the break, you're about to ask something. 208 00:07:28,320 --> 00:07:30,440 Oh yeah, I was just thinking about all the different ways 209 00:07:30,440 --> 00:07:32,160 this technology could be used. 210 00:07:32,160 --> 00:07:35,720 The research mainly focused on math and logic problems. 211 00:07:35,720 --> 00:07:36,560 Yeah. 212 00:07:36,560 --> 00:07:37,520 But what about other stuff? 213 00:07:37,520 --> 00:07:41,440 Could FOTB be used for writing or translation 214 00:07:41,440 --> 00:07:42,640 or even something creative? 215 00:07:42,640 --> 00:07:44,080 That's a really great question. 216 00:07:44,080 --> 00:07:47,840 And it gets to the core of what makes FOTI so interesting. 217 00:07:47,840 --> 00:07:51,040 The initial research focused on those specific areas. 218 00:07:51,040 --> 00:07:53,360 But the main idea is behind FOTI, 219 00:07:53,360 --> 00:07:55,560 like using multiple reasoning paths 220 00:07:55,560 --> 00:07:58,000 and having that constant self-correction, 221 00:07:58,000 --> 00:08:01,080 that could totally be applied to a ton of different tasks. 222 00:08:01,080 --> 00:08:04,560 Imagine using OT to generate different styles of writing 223 00:08:04,560 --> 00:08:07,440 or to explore different ways to translate something, 224 00:08:07,440 --> 00:08:10,920 each one with its own subtle differences and interpretations. 225 00:08:10,920 --> 00:08:12,040 Oh, wow, that would be wild. 226 00:08:12,040 --> 00:08:14,560 It'd be like having a whole team of AI writers 227 00:08:14,560 --> 00:08:16,600 and translators all working together, 228 00:08:16,600 --> 00:08:19,480 each one bringing its own unique flair to the project. 229 00:08:19,480 --> 00:08:20,000 Exactly. 230 00:08:20,000 --> 00:08:22,280 And it could even help us get past some of the limitations 231 00:08:22,280 --> 00:08:24,200 we see with current AI models. 232 00:08:24,200 --> 00:08:27,200 You know, like the tendency to generate repetitive text 233 00:08:27,200 --> 00:08:29,440 or get stuck in certain writing patterns. 234 00:08:29,440 --> 00:08:31,800 It sounds like the possibilities are pretty much endless. 235 00:08:31,800 --> 00:08:33,840 But FOTI is still a new thing, right? 236 00:08:33,840 --> 00:08:35,520 So what are the next steps? 237 00:08:35,520 --> 00:08:38,120 What needs to happen for it to become more widely used 238 00:08:38,120 --> 00:08:40,000 and really make an impact? 239 00:08:40,000 --> 00:08:43,000 Well, there are a few key areas that researchers are working on. 240 00:08:43,000 --> 00:08:45,240 One is figuring out how to apply FOT 241 00:08:45,240 --> 00:08:50,240 to a wider range of tasks, you know, beyond just math and logic. 242 00:08:50,240 --> 00:08:52,680 This might involve creating new types of reasoning trees, 243 00:08:52,680 --> 00:08:55,920 specifically for those tasks, or finding ways 244 00:08:55,920 --> 00:08:58,120 to adapt the existing FOTI framework 245 00:08:58,120 --> 00:09:00,080 to deal with different kinds of data. 246 00:09:00,080 --> 00:09:03,560 So it's like we need to expand the forest, so to speak, 247 00:09:03,560 --> 00:09:05,560 add different kinds of trees that are specialized 248 00:09:05,560 --> 00:09:06,440 for different jobs. 249 00:09:06,440 --> 00:09:07,640 That's a great way to put it. 250 00:09:07,640 --> 00:09:10,200 Another big focus is improving the LLMs themselves. 251 00:09:10,200 --> 00:09:13,240 Remember, FOTI is built on top of these language models. 252 00:09:13,240 --> 00:09:16,480 So the better the LLMs get, the better FOTI will perform. 253 00:09:16,480 --> 00:09:18,760 It's all about having a solid foundation right, 254 00:09:18,760 --> 00:09:21,440 making sure those trees have healthy soil to grow in. 255 00:09:21,440 --> 00:09:22,360 You got it. 256 00:09:22,360 --> 00:09:24,720 And of course, there's the whole computational side of things. 257 00:09:24,720 --> 00:09:28,360 FOTI uses that sparse activation thing to be more efficient. 258 00:09:28,360 --> 00:09:31,600 But as we start using it for more and more complex stuff, 259 00:09:31,600 --> 00:09:33,600 finding ways to optimize its performance 260 00:09:33,600 --> 00:09:35,280 and reduce the computing power it needs 261 00:09:35,280 --> 00:09:36,480 is gonna be essential. 262 00:09:36,480 --> 00:09:39,640 So it's like we need to make the forest more sustainable, 263 00:09:39,640 --> 00:09:42,800 help it thrive without using up too many resources. 264 00:09:42,800 --> 00:09:43,840 That's a perfect analogy. 265 00:09:43,840 --> 00:09:46,200 You really hit the nail on the head there. 266 00:09:46,200 --> 00:09:47,560 But even with all those challenges, 267 00:09:47,560 --> 00:09:49,360 I'm still super excited about FOTI. 268 00:09:49,360 --> 00:09:52,840 The idea of creating AI that can really reason 269 00:09:52,840 --> 00:09:54,760 and solve complex problems, 270 00:09:54,760 --> 00:09:57,440 AI that can understand the nuances of a situation 271 00:09:57,440 --> 00:09:59,920 and weigh different options and make informed decisions, 272 00:09:59,920 --> 00:10:01,360 that's what gets me fired up. 273 00:10:01,360 --> 00:10:03,800 Yeah, it's like we're moving away from AI as a tool 274 00:10:03,800 --> 00:10:05,240 and towards AI as a partner. 275 00:10:05,240 --> 00:10:06,400 It's a pretty big shift. 276 00:10:06,400 --> 00:10:07,240 Absolutely. 277 00:10:07,240 --> 00:10:08,400 And as we move in that direction, 278 00:10:08,400 --> 00:10:10,680 it's super important to remember that AI development 279 00:10:10,680 --> 00:10:12,200 doesn't happen in isolation. 280 00:10:12,200 --> 00:10:13,680 It's influenced by society 281 00:10:13,680 --> 00:10:15,520 and has the power to change society too. 282 00:10:15,520 --> 00:10:16,680 For sure. 283 00:10:16,680 --> 00:10:19,480 The more powerful these AI systems become, 284 00:10:19,480 --> 00:10:21,080 the more careful we need to be. 285 00:10:21,080 --> 00:10:23,000 We need to make sure they reflect our values 286 00:10:23,000 --> 00:10:25,360 and that they don't make existing problems worse 287 00:10:25,360 --> 00:10:26,760 or create new ones. 288 00:10:26,760 --> 00:10:27,880 I couldn't agree more. 289 00:10:27,880 --> 00:10:30,240 It's a responsibility we all share. 290 00:10:30,240 --> 00:10:32,400 Researchers, developers, policymakers, 291 00:10:32,400 --> 00:10:34,520 industry leaders, the public, 292 00:10:34,520 --> 00:10:37,400 we all need to be part of the conversation. 293 00:10:37,400 --> 00:10:39,080 But I'm still optimistic. 294 00:10:39,080 --> 00:10:42,120 The potential benefits are just too big to ignore. 295 00:10:42,120 --> 00:10:44,640 Think about the breakthroughs we could have in medicine 296 00:10:44,640 --> 00:10:47,520 and science and tackling things like climate change. 297 00:10:47,520 --> 00:10:48,520 If we can do this right, 298 00:10:48,520 --> 00:10:52,640 if we can harness the power of AI responsibly and ethically, 299 00:10:52,640 --> 00:10:54,120 well, the possibilities are pretty amazing. 300 00:10:54,120 --> 00:10:54,960 That's a good point. 301 00:10:54,960 --> 00:10:57,520 It's a reminder that this is about more than just technology. 302 00:10:57,520 --> 00:11:00,080 It's about what kind of future we wanna create. 303 00:11:00,080 --> 00:11:01,120 Well said. 304 00:11:01,120 --> 00:11:03,800 I wanna go back to the question I asked before the break. 305 00:11:03,800 --> 00:11:05,520 Thinking about everything we've talked about, 306 00:11:05,520 --> 00:11:08,160 what part of FOTI stands out to you the most? 307 00:11:08,160 --> 00:11:10,160 What are the areas where you see the most potential 308 00:11:10,160 --> 00:11:13,360 or maybe the areas where you have questions or concerns? 309 00:11:13,360 --> 00:11:15,800 Honestly, the thing that excites me the most about FOTI 310 00:11:15,800 --> 00:11:19,520 is that it could help AI go beyond those simple tasks. 311 00:11:19,520 --> 00:11:22,160 Towards a deeper, more nuanced understanding in the world, 312 00:11:22,160 --> 00:11:24,000 it's a step closer to creating AI 313 00:11:24,000 --> 00:11:26,240 that thinks more like humans do, 314 00:11:26,240 --> 00:11:28,880 which is both fascinating and a little bit scary 315 00:11:28,880 --> 00:11:29,920 if you think about it. 316 00:11:29,920 --> 00:11:30,960 I totally get that. 317 00:11:30,960 --> 00:11:35,000 It's an exciting but challenging time to be working on this stuff. 318 00:11:35,000 --> 00:11:36,920 But I think that if we work together, 319 00:11:36,920 --> 00:11:40,520 we can use FOTI to do some really incredible things, 320 00:11:40,520 --> 00:11:42,120 things that could benefit everyone. 321 00:11:42,120 --> 00:11:44,680 Yeah, it's a future worth working for. 322 00:11:44,680 --> 00:11:47,800 So before we wrap up part two of our deep dive, 323 00:11:47,800 --> 00:11:50,720 is there anything else you think people should know about FOTI? 324 00:11:50,720 --> 00:11:52,720 Any key takeaways? 325 00:11:52,720 --> 00:11:55,040 The main thing to remember is that FOTI 326 00:11:55,040 --> 00:11:57,760 is a different way of thinking about AI reasoning. 327 00:11:57,760 --> 00:12:01,000 It's not just about building bigger, more powerful LLMs. 328 00:12:01,000 --> 00:12:03,240 It's about using them in a smarter way, 329 00:12:03,240 --> 00:12:06,520 about embracing those ideas of diverse perspectives 330 00:12:06,520 --> 00:12:09,920 and continuous learning and collective intelligence. 331 00:12:09,920 --> 00:12:11,760 So it's not just about the size of the forest, 332 00:12:11,760 --> 00:12:14,480 but about how well the trees work together 333 00:12:14,480 --> 00:12:15,440 and learn from each other. 334 00:12:15,440 --> 00:12:16,160 Exactly. 335 00:12:16,160 --> 00:12:18,600 And it's important to remember that this is all still very new. 336 00:12:18,600 --> 00:12:20,320 There's so much more research to do. 337 00:12:20,320 --> 00:12:22,760 We need to fully understand what a FOTI can do, 338 00:12:22,760 --> 00:12:24,040 what its limitations are, 339 00:12:24,040 --> 00:12:26,360 and how we can use it in all sorts of different fields. 340 00:12:26,360 --> 00:12:27,880 It's like we've just planted the seeds 341 00:12:27,880 --> 00:12:30,800 for a whole new forest of possibilities. 342 00:12:30,800 --> 00:12:32,880 And it's going to be fascinating to watch it grow 343 00:12:32,880 --> 00:12:34,240 and evolve over time. 344 00:12:34,240 --> 00:12:35,080 I like that. 345 00:12:35,080 --> 00:12:37,080 That's a great way to think about it. 346 00:12:37,080 --> 00:12:39,560 So that wraps up part two of our exploration 347 00:12:39,560 --> 00:12:41,000 of forest of thought. 348 00:12:41,000 --> 00:12:42,440 And we're back. 349 00:12:42,440 --> 00:12:46,240 It's the final part of our deep dive into forest of thought. 350 00:12:46,240 --> 00:12:50,160 You know, after learning about all the cool things FOTI could do, 351 00:12:50,160 --> 00:12:52,400 I'm starting to see forests everywhere now. 352 00:12:52,400 --> 00:12:54,600 Well, it is a really good way to think about it. 353 00:12:54,600 --> 00:12:57,840 How we can make AI better at reasoning. 354 00:12:57,840 --> 00:13:00,560 And the key thing to remember is that FOTI is all about 355 00:13:00,560 --> 00:13:02,840 changing how we approach AI reasoning. 356 00:13:02,840 --> 00:13:06,240 It's not just about building bigger and more powerful LLMs. 357 00:13:06,240 --> 00:13:09,240 It's about using those LLMs in a smarter way, 358 00:13:09,240 --> 00:13:12,360 about finding those creative ways to combine different approaches 359 00:13:12,360 --> 00:13:14,800 and really harness that collective intelligence. 360 00:13:14,800 --> 00:13:15,160 Yeah. 361 00:13:15,160 --> 00:13:17,360 And it shows that sometimes the best way to innovate 362 00:13:17,360 --> 00:13:19,520 isn't to create something totally new. 363 00:13:19,520 --> 00:13:22,360 It's to find new ways to use what we already have. 364 00:13:22,360 --> 00:13:25,000 Like with FOTI, it's all about combining those multiple reasoning 365 00:13:25,000 --> 00:13:29,120 approaches and letting the AI learn and correct itself as it goes. 366 00:13:29,120 --> 00:13:31,400 And that leads to some pretty impressive results. 367 00:13:31,400 --> 00:13:32,200 Right. 368 00:13:32,200 --> 00:13:34,840 And what I find really fascinating is that FOTI 369 00:13:34,840 --> 00:13:37,800 seems to be doing something similar to what humans do 370 00:13:37,800 --> 00:13:40,040 when they're trying to solve a tough problem. 371 00:13:40,040 --> 00:13:43,120 We don't usually stick to one single line of thought. 372 00:13:43,120 --> 00:13:44,320 We brainstorm. 373 00:13:44,320 --> 00:13:45,880 We explore different options. 374 00:13:45,880 --> 00:13:46,920 We mess up. 375 00:13:46,920 --> 00:13:48,880 And we learn from those mistakes. 376 00:13:48,880 --> 00:13:51,720 And FOTI basically lets AI do the same thing. 377 00:13:51,720 --> 00:13:56,040 So it's like AI is becoming less of a tool and more of a partner. 378 00:13:56,040 --> 00:13:57,840 It's not just following instructions anymore. 379 00:13:57,840 --> 00:14:00,720 It's bringing its own ideas and perspectives to the table. 380 00:14:00,720 --> 00:14:01,520 Exactly. 381 00:14:01,520 --> 00:14:03,760 But as we move towards that kind of partnership, 382 00:14:03,760 --> 00:14:07,000 we got to keep in mind that AI development isn't happening in a bubble. 383 00:14:07,000 --> 00:14:08,880 It's connected to society in a big way. 384 00:14:08,880 --> 00:14:11,040 And it has the power to really change things. 385 00:14:11,040 --> 00:14:12,320 Yeah, for sure. 386 00:14:12,320 --> 00:14:15,640 As AI systems get more powerful and sophisticated, 387 00:14:15,640 --> 00:14:19,200 we have a responsibility to make sure they're being used for good. 388 00:14:19,200 --> 00:14:21,400 We need to be talking about responsible development, 389 00:14:21,400 --> 00:14:23,600 about making sure they align with our values, 390 00:14:23,600 --> 00:14:26,720 and don't end up making existing problems worse or creating new ones. 391 00:14:26,720 --> 00:14:27,960 100%. 392 00:14:27,960 --> 00:14:30,440 This isn't just a job for researchers and developers. 393 00:14:30,440 --> 00:14:33,240 It's something that everyone needs to be involved in. 394 00:14:33,240 --> 00:14:35,760 Policymakers, industry leaders, the public, 395 00:14:35,760 --> 00:14:39,520 we all need to be part of shaping the future of AI. 396 00:14:39,520 --> 00:14:41,760 But I'm optimistic. 397 00:14:41,760 --> 00:14:42,920 I really am. 398 00:14:42,920 --> 00:14:45,440 Because the potential benefits are huge. 399 00:14:45,440 --> 00:14:49,240 Think about what we could achieve if we use AI the right way. 400 00:14:49,240 --> 00:14:52,960 In medicine, in scientific discovery, in fighting climate change. 401 00:14:52,960 --> 00:14:55,960 If we can do this responsibly and ethically, 402 00:14:55,960 --> 00:14:58,200 the possibilities are incredible. 403 00:14:58,200 --> 00:15:00,440 It's exciting and a little daunting at the same time, right? 404 00:15:00,440 --> 00:15:01,840 Yeah, I know what you mean. 405 00:15:01,840 --> 00:15:03,600 But I think it's a challenge worth taking on. 406 00:15:03,600 --> 00:15:04,640 Absolutely. 407 00:15:04,640 --> 00:15:07,040 Well, I think that's a great place to wrap up our deep dive 408 00:15:07,040 --> 00:15:08,720 into the forest of thought. 409 00:15:08,720 --> 00:15:10,400 It's been a really fascinating exploration, 410 00:15:10,400 --> 00:15:12,680 and I hope everyone listening has learned something new 411 00:15:12,680 --> 00:15:14,880 and maybe even feels a little inspired to think about 412 00:15:14,880 --> 00:15:16,720 how AI is going to shape our world. 413 00:15:16,720 --> 00:15:18,520 Thanks for having me. It's been a pleasure. 414 00:15:18,520 --> 00:15:20,480 And thanks to all of you for listening to this episode 415 00:15:20,480 --> 00:15:21,640 of The Deep Dive. 416 00:15:21,640 --> 00:15:23,240 Be sure to subscribe and join us again 417 00:15:23,240 --> 00:15:25,880 for more deep dives into the world of AI. 418 00:15:25,880 --> 00:15:48,880 Until next time, stay curious.