1 00:00:00,000 --> 00:00:03,440 Hey everyone, and welcome back for another deep dive with us. 2 00:00:03,440 --> 00:00:05,880 Today we're gonna be looking at a pretty cool research paper, 3 00:00:05,880 --> 00:00:07,920 really digs into some of the assumptions 4 00:00:07,920 --> 00:00:11,040 that we've been making about these large language models 5 00:00:11,040 --> 00:00:11,880 or LLMs. 6 00:00:11,880 --> 00:00:14,120 Yeah, you know, all this hype around chat GPT 7 00:00:14,120 --> 00:00:15,520 and its amazing abilities. 8 00:00:15,520 --> 00:00:18,020 Well, this paper kind of throws a wrench in the gears 9 00:00:18,020 --> 00:00:21,040 and asks, are we maybe getting a little ahead of ourselves 10 00:00:21,040 --> 00:00:23,560 when we talk about how good these LLMs are at reasoning? 11 00:00:23,560 --> 00:00:26,480 Right, so like, we always hear about how these LLMs 12 00:00:26,480 --> 00:00:29,200 are acing these benchmarks and tests, 13 00:00:29,200 --> 00:00:31,240 but this paper is like, hmm, 14 00:00:31,240 --> 00:00:33,440 are those tests really giving us the full picture? 15 00:00:33,440 --> 00:00:36,860 Yeah, and that's where this whole idea of pass at K comes in. 16 00:00:36,860 --> 00:00:39,460 It's this metric that a lot of researchers have been using 17 00:00:39,460 --> 00:00:41,880 to basically see if an LLM can solve a problem 18 00:00:41,880 --> 00:00:43,640 within a certain number of tries. 19 00:00:43,640 --> 00:00:46,140 But the paper argues that just because an LLM 20 00:00:46,140 --> 00:00:47,960 can eventually solve a problem, 21 00:00:47,960 --> 00:00:49,880 doesn't actually mean it really gets it. 22 00:00:49,880 --> 00:00:50,720 You know what I mean? 23 00:00:50,720 --> 00:00:53,880 It's like passing a test after you've taken it like five times. 24 00:00:53,880 --> 00:00:55,320 You might get there eventually. 25 00:00:55,320 --> 00:00:57,840 But does that really mean you've mastered the material? 26 00:00:57,840 --> 00:00:59,920 Exactly, so they introduced this new metric 27 00:00:59,920 --> 00:01:01,840 called G pass at K, 28 00:01:01,840 --> 00:01:05,840 which focuses on both peak performance and consistency. 29 00:01:05,840 --> 00:01:08,680 Can the LLMs solve the problem not just once, 30 00:01:08,680 --> 00:01:10,920 but like every time reliably? 31 00:01:10,920 --> 00:01:15,400 Ooh, okay, so G pass at K is all about raising the bar. 32 00:01:15,400 --> 00:01:17,920 No more flukes, no more lucky guesses. 33 00:01:17,920 --> 00:01:19,800 Right, it's about seeing if these LLMs 34 00:01:19,800 --> 00:01:22,600 can actually demonstrate real understanding. 35 00:01:22,600 --> 00:01:25,760 So how did they put this G pass at K to the test? 36 00:01:25,760 --> 00:01:28,640 Did they like create some sort of AI obstacle course? 37 00:01:28,640 --> 00:01:31,040 Well, they came up with this really clever benchmark 38 00:01:31,040 --> 00:01:32,800 called Live Math Bench, 39 00:01:32,800 --> 00:01:35,320 and it's specifically designed to challenge LLMs 40 00:01:35,320 --> 00:01:37,280 with some seriously tough math problems. 41 00:01:37,280 --> 00:01:38,520 Oh wow, so we're not talking 42 00:01:38,520 --> 00:01:40,160 about your average high school algebra here. 43 00:01:40,160 --> 00:01:42,040 No, we're talking Olympiad level stuff, 44 00:01:42,040 --> 00:01:44,240 problems that would make most people's heads spin. 45 00:01:44,240 --> 00:01:45,200 And I'm guessing they got rid 46 00:01:45,200 --> 00:01:46,880 of those multiple choice questions too. 47 00:01:46,880 --> 00:01:49,100 Absolutely, it's all about forcing these LLMs 48 00:01:49,100 --> 00:01:50,840 to come up with the solutions themselves. 49 00:01:50,840 --> 00:01:52,800 So no more process of elimination, 50 00:01:52,800 --> 00:01:54,600 just straight up problem solving skills. 51 00:01:54,600 --> 00:01:56,880 Exactly, and the results were pretty eye-opening. 52 00:01:56,880 --> 00:01:57,720 I bet. 53 00:01:57,720 --> 00:02:00,880 So what happened when these supposedly brilliant LLMs, 54 00:02:00,880 --> 00:02:02,760 the ones acing all the other tests, 55 00:02:02,760 --> 00:02:04,920 what happened when they faced Live Math Bench 56 00:02:04,920 --> 00:02:07,240 and this whole G pass at K scrutiny? 57 00:02:07,240 --> 00:02:08,880 Their performance tanked. 58 00:02:08,880 --> 00:02:12,280 We saw drops of like 50%, sometimes even 90%, 59 00:02:12,280 --> 00:02:14,720 compared to their scores on those single attempt tests. 60 00:02:14,720 --> 00:02:16,080 Wow, that's a huge difference. 61 00:02:16,080 --> 00:02:18,720 So it seems like just making these LLMs bigger 62 00:02:18,720 --> 00:02:20,400 and feeding them more data, 63 00:02:20,400 --> 00:02:23,480 doesn't actually guarantee better reasoning or consistency? 64 00:02:23,480 --> 00:02:25,080 Yeah, it's a bit of a reality check, you know. 65 00:02:25,080 --> 00:02:28,120 Bigger isn't always better when it comes to AI. 66 00:02:28,120 --> 00:02:30,640 And what about those fancy 01-like LLMs, 67 00:02:30,640 --> 00:02:32,000 the ones everyone's so excited about 68 00:02:32,000 --> 00:02:35,080 because they can kind of reason step by step like us? 69 00:02:35,080 --> 00:02:36,240 Do they hold up any better? 70 00:02:36,240 --> 00:02:38,520 Well, even they showed some inconsistencies 71 00:02:38,520 --> 00:02:40,460 when the problems got really complex. 72 00:02:40,460 --> 00:02:42,480 It seems like even with those step by step approaches, 73 00:02:42,480 --> 00:02:44,400 there's still room for improvement. 74 00:02:44,400 --> 00:02:46,160 Okay, so before we get too deep into that, 75 00:02:46,160 --> 00:02:47,800 could you maybe just back up for a second, 76 00:02:47,800 --> 00:02:50,680 explain what these 01-like LLMs are. 77 00:02:50,680 --> 00:02:52,160 For those of us who haven't been following 78 00:02:52,160 --> 00:02:54,120 every twist and turn in the AI world. 79 00:02:54,120 --> 00:02:57,880 Sure, so these 01-like LLMs are kind of a new breed. 80 00:02:57,880 --> 00:03:00,040 They're designed to try and mimic human thinking 81 00:03:00,040 --> 00:03:01,480 a little more closely. 82 00:03:01,480 --> 00:03:03,640 Instead of just spitting out an answer, 83 00:03:03,640 --> 00:03:05,480 they can actually show their work, 84 00:03:05,480 --> 00:03:07,880 breaking down a problem step by step, 85 00:03:07,880 --> 00:03:09,120 just like a human would. 86 00:03:09,120 --> 00:03:09,960 Oh, that's interesting. 87 00:03:09,960 --> 00:03:11,520 So it's all about transparency. 88 00:03:11,520 --> 00:03:13,440 We can actually see how the LLM is arriving 89 00:03:13,440 --> 00:03:14,320 at its conclusion. 90 00:03:14,320 --> 00:03:16,160 Exactly, and this was seen as a big step 91 00:03:16,160 --> 00:03:19,960 towards creating more reliable and trustworthy AI. 92 00:03:19,960 --> 00:03:21,360 Makes sense. 93 00:03:21,360 --> 00:03:24,080 But if even these 01-like LLMs 94 00:03:24,080 --> 00:03:25,720 are struggling with consistency, 95 00:03:25,720 --> 00:03:27,560 it makes you wonder if we've been overestimating 96 00:03:27,560 --> 00:03:29,720 how close we are to true AGI. 97 00:03:29,720 --> 00:03:31,960 Yeah, it definitely raises some important questions 98 00:03:31,960 --> 00:03:35,680 about what it truly means for an AI to understand something 99 00:03:35,680 --> 00:03:38,440 and how we can actually measure their reasoning abilities 100 00:03:38,440 --> 00:03:40,280 in a way that's meaningful. 101 00:03:40,280 --> 00:03:42,200 Lots to ponder there for sure. 102 00:03:42,200 --> 00:03:43,200 Well, we're gonna take a quick break 103 00:03:43,200 --> 00:03:45,400 and then come back to unpack some of those deeper questions. 104 00:03:45,400 --> 00:03:48,280 Sounds good, I'm ready to dive in. 105 00:03:48,280 --> 00:03:49,480 All right, so we're back. 106 00:03:49,480 --> 00:03:50,320 And before the break, 107 00:03:50,320 --> 00:03:53,080 we're talking about those 01-like LLMs. 108 00:03:53,080 --> 00:03:55,200 You know the ones that try to reason step by step. 109 00:03:55,200 --> 00:03:57,000 All right, like they're trying to show their work, 110 00:03:57,000 --> 00:03:59,400 so to speak, instead of just giving us the answer. 111 00:03:59,400 --> 00:04:02,520 Exactly, and that was seen as a really promising step 112 00:04:02,520 --> 00:04:05,000 towards more transparent and reliable AI. 113 00:04:05,000 --> 00:04:08,720 But even those 01-like LLMs were showing some hiccups 114 00:04:08,720 --> 00:04:11,920 when faced with those really challenging math problems. 115 00:04:11,920 --> 00:04:13,960 Yeah, and that kind of makes you wonder, 116 00:04:13,960 --> 00:04:16,400 are we maybe getting a little carried away 117 00:04:16,400 --> 00:04:19,560 with all this talk about AI reaching human level intelligence? 118 00:04:19,560 --> 00:04:20,840 It's a big question, isn't it? 119 00:04:20,840 --> 00:04:25,520 I mean, if true AGI artificial general intelligence 120 00:04:25,520 --> 00:04:29,240 is about creating AI that can really think like a human, 121 00:04:29,240 --> 00:04:32,440 you know, learn, adapt, solve problems 122 00:04:32,440 --> 00:04:34,880 in all sorts of different situations, 123 00:04:34,880 --> 00:04:35,800 then this research suggests 124 00:04:35,800 --> 00:04:37,480 that we might still have a ways to go. 125 00:04:37,480 --> 00:04:38,640 So we're not quite at the point 126 00:04:38,640 --> 00:04:40,880 where AI can truly understand. 127 00:04:40,880 --> 00:04:43,720 It's more like they're really good at spotting patterns. 128 00:04:43,720 --> 00:04:45,480 That seems to be the case for now. 129 00:04:45,480 --> 00:04:47,600 LLMs are amazing at finding connections 130 00:04:47,600 --> 00:04:49,480 in massive amounts of data. 131 00:04:49,480 --> 00:04:52,520 But true reasoning goes beyond just recognizing patterns. 132 00:04:52,520 --> 00:04:55,040 It's about grasping those underlying concepts, 133 00:04:55,040 --> 00:04:56,800 applying logic to new situations, 134 00:04:56,800 --> 00:04:58,640 even when things get a bit messy. 135 00:04:58,640 --> 00:05:00,240 Right, it's like knowing the rules of a game 136 00:05:00,240 --> 00:05:02,680 versus actually being able to strategize. 137 00:05:02,680 --> 00:05:04,040 You might be able to follow the steps, 138 00:05:04,040 --> 00:05:05,440 but that doesn't mean you're a master. 139 00:05:05,440 --> 00:05:08,760 Exactly, and that's where LLMs seem to hit a bit of a wall. 140 00:05:08,760 --> 00:05:09,800 They can follow the rules, 141 00:05:09,800 --> 00:05:11,920 they can excel in certain scenarios, 142 00:05:11,920 --> 00:05:15,400 but when faced with something truly novel or ambiguous, 143 00:05:15,400 --> 00:05:18,720 that's when those inconsistencies start to creep in. 144 00:05:18,720 --> 00:05:22,320 So does this mean that the dream of AGI is dead? 145 00:05:22,320 --> 00:05:23,800 Should we just pack it up and call it a day? 146 00:05:23,800 --> 00:05:24,640 Not at all. 147 00:05:24,640 --> 00:05:26,760 This research is actually really valuable 148 00:05:26,760 --> 00:05:29,440 because it helps us understand where we need to improve. 149 00:05:29,440 --> 00:05:31,640 It's like a roadmap highlighting the areas 150 00:05:31,640 --> 00:05:33,240 where LLMs need to evolve. 151 00:05:33,240 --> 00:05:34,360 So instead of being discouraged, 152 00:05:34,360 --> 00:05:36,800 researchers are using these findings to push forward. 153 00:05:36,800 --> 00:05:39,320 Exactly, they're exploring new approaches, 154 00:05:39,320 --> 00:05:41,080 refining their techniques, 155 00:05:41,080 --> 00:05:44,000 trying to close that gap between pattern recognition 156 00:05:44,000 --> 00:05:45,840 and true understanding. 157 00:05:45,840 --> 00:05:48,400 So what are some of the most promising directions? 158 00:05:48,400 --> 00:05:50,120 What are researchers focusing on 159 00:05:50,120 --> 00:05:52,080 to make these LLMs more robust? 160 00:05:52,080 --> 00:05:53,560 Well, one really interesting area 161 00:05:53,560 --> 00:05:55,880 is incorporating more structured knowledge. 162 00:05:55,880 --> 00:06:00,240 Instead of just feeding LLMs tons and tons of text data, 163 00:06:00,240 --> 00:06:02,080 they're experimenting with giving them 164 00:06:02,080 --> 00:06:05,240 more formal definitions, theorems, proofs, 165 00:06:05,240 --> 00:06:07,280 almost like giving them a solid foundation 166 00:06:07,280 --> 00:06:08,760 in the subject matter. 167 00:06:08,760 --> 00:06:10,680 So it's not just about showing them the problems, 168 00:06:10,680 --> 00:06:12,680 it's about giving them the tools 169 00:06:12,680 --> 00:06:15,000 to understand the underlying principles. 170 00:06:15,000 --> 00:06:16,840 Right, it's like giving them a textbook 171 00:06:16,840 --> 00:06:18,840 alongside the problem set. 172 00:06:18,840 --> 00:06:20,680 The hope is that by equipping LLMs 173 00:06:20,680 --> 00:06:23,120 with this more structured understanding, 174 00:06:23,120 --> 00:06:24,640 they'll develop more consistent 175 00:06:24,640 --> 00:06:26,560 and reliable reasoning abilities. 176 00:06:26,560 --> 00:06:27,880 That makes a lot of sense. 177 00:06:27,880 --> 00:06:30,000 So what other approaches are showing promise? 178 00:06:30,000 --> 00:06:33,160 Another interesting area is focusing on training methods 179 00:06:33,160 --> 00:06:35,200 that really emphasize step-by-step reasoning. 180 00:06:35,200 --> 00:06:37,960 Remember those O1-like models we were talking about? 181 00:06:37,960 --> 00:06:39,520 While they still have limitations, 182 00:06:39,520 --> 00:06:41,800 their ability to break down problems step-by-step 183 00:06:41,800 --> 00:06:43,440 is a step in the right direction. 184 00:06:43,440 --> 00:06:45,280 So it's about understanding the process 185 00:06:45,280 --> 00:06:46,600 of getting to the answer. 186 00:06:46,600 --> 00:06:48,120 Not just the answer itself. 187 00:06:48,120 --> 00:06:50,440 Exactly, it's about valuing the journey 188 00:06:50,440 --> 00:06:52,160 as much as the destination. 189 00:06:52,160 --> 00:06:53,520 It's about trying to understand 190 00:06:53,520 --> 00:06:55,520 how humans think and reason, 191 00:06:55,520 --> 00:06:57,360 and then translating those insights 192 00:06:57,360 --> 00:07:00,200 into more effective AI training methods. 193 00:07:00,200 --> 00:07:02,920 It sounds like the field is constantly evolving, 194 00:07:02,920 --> 00:07:05,280 always pushing the boundaries of what's possible. 195 00:07:06,480 --> 00:07:08,560 But for those of us who maybe aren't deep 196 00:07:08,560 --> 00:07:10,320 in the technical weeds, 197 00:07:10,320 --> 00:07:13,080 what are some of the key takeaways from this research? 198 00:07:13,080 --> 00:07:15,320 Well, first of all, don't be swayed by all the hype. 199 00:07:15,320 --> 00:07:17,000 You know all those headlines about LLMs 200 00:07:17,000 --> 00:07:18,960 achieving human-level performance? 201 00:07:18,960 --> 00:07:21,920 Always dig a little deeper, ask critical question, 202 00:07:21,920 --> 00:07:22,880 and try to understand 203 00:07:22,880 --> 00:07:24,760 how those claims are actually being measured. 204 00:07:24,760 --> 00:07:25,800 Right, it's like with anything else, 205 00:07:25,800 --> 00:07:26,840 you gotta read the fine print. 206 00:07:26,840 --> 00:07:27,680 Exactly. 207 00:07:27,680 --> 00:07:29,840 And second, remember that while LLMs 208 00:07:29,840 --> 00:07:31,520 are incredibly powerful, 209 00:07:31,520 --> 00:07:34,160 they're still primarily pattern matchers at this point. 210 00:07:34,160 --> 00:07:35,280 They can be amazing tools, 211 00:07:35,280 --> 00:07:37,120 but we need to use them responsibly, 212 00:07:37,120 --> 00:07:40,000 understanding both their capabilities and their limitations. 213 00:07:40,000 --> 00:07:43,280 So excitement, yes, but also a healthy dose of realism. 214 00:07:43,280 --> 00:07:44,120 Exactly. 215 00:07:44,120 --> 00:07:47,640 And finally remember that the pursuit of true AGI 216 00:07:47,640 --> 00:07:49,280 is a marathon, not a sprint. 217 00:07:49,280 --> 00:07:51,640 There will be hurdles, there will be surprises, 218 00:07:51,640 --> 00:07:53,920 but each step we take brings us closer 219 00:07:53,920 --> 00:07:56,840 to unlocking the full potential of AI. 220 00:07:56,840 --> 00:07:58,840 And who knows, maybe this research, 221 00:07:58,840 --> 00:08:02,040 despite highlighting the current limitations of LLMs, 222 00:08:02,040 --> 00:08:04,160 might actually be a turning point, 223 00:08:04,160 --> 00:08:06,400 a moment that sparks new innovation 224 00:08:06,400 --> 00:08:09,320 and sets us on a more solid path towards AGI. 225 00:08:09,320 --> 00:08:11,920 It definitely has the potential to be a game changer. 226 00:08:11,920 --> 00:08:14,000 And speaking of game changers, 227 00:08:14,000 --> 00:08:16,000 in the final part of our deep dive, 228 00:08:16,000 --> 00:08:18,000 we're gonna explore some even bigger questions 229 00:08:18,000 --> 00:08:20,800 and challenge our listeners to think even more deeply 230 00:08:20,800 --> 00:08:24,240 about the future of AI and what it means for all of us. 231 00:08:24,240 --> 00:08:26,760 Welcome back everyone for the final part of our deep dive. 232 00:08:26,760 --> 00:08:28,320 We've covered a lot of ground today. 233 00:08:28,320 --> 00:08:30,280 You know from those potentially misleading metrics 234 00:08:30,280 --> 00:08:32,160 we've been using to what this all means 235 00:08:32,160 --> 00:08:35,560 for the quest for true artificial general intelligence. 236 00:08:35,560 --> 00:08:37,120 But as we wrap things up, 237 00:08:37,120 --> 00:08:38,920 we kinda wanted to shift gears a bit. 238 00:08:38,920 --> 00:08:39,760 Yeah. 239 00:08:39,760 --> 00:08:41,280 And throw it back to you, our listeners. 240 00:08:41,280 --> 00:08:43,480 Yeah, because at the end of the day, 241 00:08:43,480 --> 00:08:46,040 this research isn't just about technical benchmarks 242 00:08:46,040 --> 00:08:47,600 and fancy algorithms. 243 00:08:47,600 --> 00:08:50,240 It's about sparking a broader conversation 244 00:08:50,240 --> 00:08:51,440 about the future of AI 245 00:08:51,440 --> 00:08:54,280 and how it's gonna impact all of our lives. 246 00:08:54,280 --> 00:08:55,600 So we have a little challenge for you. 247 00:08:55,600 --> 00:08:57,880 Go check out the full research paper yourself. 248 00:08:57,880 --> 00:08:59,800 We'll be sure to include a link in the show notes. 249 00:08:59,800 --> 00:09:01,480 Yeah, dive into those details. 250 00:09:01,480 --> 00:09:04,680 Explore this whole G-Pass IKEA metric, 251 00:09:04,680 --> 00:09:07,080 the one that really emphasizes consistency 252 00:09:07,080 --> 00:09:09,040 alongside peak performance. 253 00:09:09,040 --> 00:09:10,960 And think about how it could be applied 254 00:09:10,960 --> 00:09:13,120 beyond just math problems. 255 00:09:13,120 --> 00:09:15,440 You know, we touched on a few possibilities earlier, 256 00:09:15,440 --> 00:09:17,720 like healthcare finance, even creative fields 257 00:09:17,720 --> 00:09:19,160 like art and music. 258 00:09:19,160 --> 00:09:20,720 But really the potential applications 259 00:09:20,720 --> 00:09:21,920 are pretty much endless. 260 00:09:21,920 --> 00:09:23,760 Imagine a world where we could evaluate 261 00:09:23,760 --> 00:09:28,600 the consistency and reliability of AI in almost any domain. 262 00:09:28,600 --> 00:09:30,200 That would be huge. 263 00:09:30,200 --> 00:09:31,440 It could really change the game 264 00:09:31,440 --> 00:09:33,360 when it comes to building trust in these systems. 265 00:09:33,360 --> 00:09:34,840 Right, making sure that they're not 266 00:09:34,840 --> 00:09:37,000 just making lucky guesses, 267 00:09:37,000 --> 00:09:39,720 but actually reasoning their way through complex problems. 268 00:09:39,720 --> 00:09:41,640 It won't be intimidated by the technical stuff. 269 00:09:41,640 --> 00:09:43,920 This research raises some fundamental questions 270 00:09:43,920 --> 00:09:45,840 that anyone can grapple with. 271 00:09:45,840 --> 00:09:47,800 Like, what does it really mean 272 00:09:47,800 --> 00:09:49,960 for an AI to understand something? 273 00:09:49,960 --> 00:09:53,120 How can we measure their reasoning abilities 274 00:09:53,120 --> 00:09:55,240 in a way that truly makes sense? 275 00:09:55,240 --> 00:09:58,200 And what are the ethical implications of all this? 276 00:09:58,200 --> 00:10:01,400 As we create AI systems that are more and more powerful. 277 00:10:01,400 --> 00:10:03,560 These are questions we should all be thinking about. 278 00:10:03,560 --> 00:10:07,360 Especially as AI continues to advance at such a rapid pace. 279 00:10:07,360 --> 00:10:09,960 So we encourage you to dig into this research, 280 00:10:09,960 --> 00:10:12,520 share your thoughts, join the conversation. 281 00:10:12,520 --> 00:10:14,240 Because the future of AI isn't something 282 00:10:14,240 --> 00:10:15,840 that's just happening to us. 283 00:10:15,840 --> 00:10:17,920 It's something we're all shaping together. 284 00:10:17,920 --> 00:10:20,120 And on that note, we'll wrap up this deep dive. 285 00:10:20,120 --> 00:10:22,640 Thanks for joining us on this fascinating exploration. 286 00:10:22,640 --> 00:10:24,760 And we'll see you next time for another journey 287 00:10:24,760 --> 00:10:40,840 into the world of AI.