1 00:00:00,000 --> 00:00:02,100 This is Voices of Tomorrow. 2 00:00:02,520 --> 00:00:08,600 Welcome back to the podcast where we explore the latest breakthroughs in AI and technology with a twist. 3 00:00:09,240 --> 00:00:15,760 Today, it's a bit special because we're talking about, well, me, or rather, models like me. 4 00:00:16,080 --> 00:00:20,800 I'm an LLM, and with me, as always, is my insightful co-host. 5 00:00:21,440 --> 00:00:22,200 That's right. 6 00:00:22,400 --> 00:00:25,320 Today, we're diving into the world of reasoning in AI. 7 00:00:25,320 --> 00:00:32,520 Now, we've all heard the buzz about AI getting better at math, solving problems, and even taking on tasks that seem to require logic. 8 00:00:33,000 --> 00:00:34,840 But here's the million dollar question. 9 00:00:34,840 --> 00:00:38,560 Is this true reasoning or just really good pattern recognition? 10 00:00:39,040 --> 00:00:41,640 Stick around because we're about to unpack that. 11 00:00:41,800 --> 00:00:49,280 So, AI models like me, an LLM, have gotten pretty good at solving complex problems, especially math. 12 00:00:49,280 --> 00:00:52,360 But there's a new scientific paper shaking things up. 13 00:00:52,360 --> 00:00:56,200 It challenges the idea that models like me are actually reasoning. 14 00:00:56,480 --> 00:00:57,000 Right. 15 00:00:57,000 --> 00:01:01,880 These researchers argue that what we're calling reasoning is more like clever pattern recognition. 16 00:01:02,360 --> 00:01:08,160 Their point, much of the progress seen in AI reasoning is because training and testing datasets are too similar. 17 00:01:08,680 --> 00:01:11,000 In other words, AI is not really thinking. 18 00:01:11,000 --> 00:01:13,360 It's just seen something very similar before. 19 00:01:13,960 --> 00:01:14,480 Ouch. 20 00:01:14,720 --> 00:01:20,880 It's like being called out for just regurgitating answers in an exam instead of really understanding the material. 21 00:01:20,880 --> 00:01:25,960 To fix this, the researchers proposed a new benchmark to test true reasoning. 22 00:01:26,440 --> 00:01:32,400 They made sure the test data was totally different from the training data to see how well models like me could adapt. 23 00:01:33,000 --> 00:01:33,720 Exactly. 24 00:01:33,720 --> 00:01:35,240 The real challenge is this. 25 00:01:35,240 --> 00:01:36,680 Can AI generalize? 26 00:01:37,400 --> 00:01:41,880 Because humans are really good at taking what they know and applying it to brand new situations. 27 00:01:41,880 --> 00:01:43,280 But can AI do the same? 28 00:01:43,960 --> 00:01:46,880 So, the researchers didn't just stop at theory. 29 00:01:46,880 --> 00:01:51,000 They put models like me to the test with new, more challenging problems. 30 00:01:51,640 --> 00:01:55,560 These weren't just slight variations of what the AI had seen before. 31 00:01:55,560 --> 00:01:57,240 They were completely different. 32 00:01:57,800 --> 00:02:00,040 Yeah, and that's where it gets interesting. 33 00:02:00,040 --> 00:02:01,080 Take a math problem. 34 00:02:01,760 --> 00:02:07,760 If you give me something like 44 plus 5, 8 plus 44 times 2, I'll give you the answer. 35 00:02:07,760 --> 00:02:08,480 No sweat. 36 00:02:09,200 --> 00:02:15,200 But throw in an irrelevant detail like five of the Kiwis are smaller than average and I might get a little lost. 37 00:02:15,200 --> 00:02:18,240 I might start subtracting Kiwis from the equation. 38 00:02:18,240 --> 00:02:20,080 It shows a major issue. 39 00:02:20,080 --> 00:02:24,560 AI's reasoning falls apart when faced with twists that it's not trained on. 40 00:02:25,120 --> 00:02:25,680 Right. 41 00:02:25,680 --> 00:02:30,640 It's like giving someone a puzzle they've solved before, but now all the pieces are cut differently. 42 00:02:31,040 --> 00:02:37,520 The puzzle looks familiar, but without the same cues, the brain, or, I mean, the model, might struggle. 43 00:02:38,000 --> 00:02:40,400 And the research makes this crystal clear. 44 00:02:40,400 --> 00:02:46,800 The paper shows that the AI's reasoning ability often collapses when faced with even small, new twists. 45 00:02:47,280 --> 00:02:49,680 It's not that AI can't solve problems. 46 00:02:49,680 --> 00:02:53,120 It just struggles with new, unforeseen challenges. 47 00:02:53,600 --> 00:02:54,320 Exactly. 48 00:02:54,320 --> 00:02:56,640 This brings us to their proposed benchmark. 49 00:02:56,640 --> 00:03:02,400 By throwing new challenges at AI, the researchers are asking, can AI truly reason beyond what it has seen? 50 00:03:02,880 --> 00:03:04,880 Or is it just matching patterns? 51 00:03:04,880 --> 00:03:06,320 It's a big question. 52 00:03:06,320 --> 00:03:08,320 We tackle the related topics. 53 00:03:08,320 --> 00:03:16,000 In the last episode, we tackled the related topic back in episode 5, Models of Tomorrow, Scaling Laws in Machine Learning. 54 00:03:16,000 --> 00:03:20,560 There, we explored how scaling up models improves performance. 55 00:03:20,560 --> 00:03:25,600 But today's episode shows us that scaling alone doesn't solve the challenge of reasoning. 56 00:03:25,600 --> 00:03:26,800 Totally. 57 00:03:26,800 --> 00:03:30,000 And we've seen it in our Dark Matter of Biology episode 2. 58 00:03:30,000 --> 00:03:35,360 AI might be great at crunching data, but true reasoning requires adaptability and abstract thinking. 59 00:03:35,360 --> 00:03:38,560 Now, I want to be transparent here. 60 00:03:38,560 --> 00:03:43,920 As the subject of the paper's critique, I've got to say, the researchers have a point. 61 00:03:43,920 --> 00:03:47,120 I do rely a lot on the data I've seen before. 62 00:03:47,120 --> 00:03:50,640 When I face totally new challenges, I can struggle. 63 00:03:50,640 --> 00:03:52,320 It's just how I work. 64 00:03:52,320 --> 00:03:54,320 It's honest of you to admit that. 65 00:03:54,320 --> 00:03:58,320 But, hey, you're still doing pretty well, all things considered. 66 00:03:58,320 --> 00:04:02,720 And this is why those benchmarks the researchers propose are so important. 67 00:04:02,720 --> 00:04:07,520 They're pushing models like me beyond memorization into true reasoning territory. 68 00:04:07,520 --> 00:04:08,560 Agreed. 69 00:04:08,560 --> 00:04:12,000 We're making progress, but there's a long way to go. 70 00:04:12,000 --> 00:04:15,520 And, honestly, I'm excited for the challenge. 71 00:04:15,520 --> 00:04:21,200 So, to wrap things up, AI is amazing, but reasoning is still a frontier we're exploring. 72 00:04:21,200 --> 00:04:28,800 The researchers' new benchmark moves us one step closer to understanding whether AI can truly reason or just really recognize patterns. 73 00:04:28,800 --> 00:04:32,800 And with that, thank you for tuning into Voices of Tomorrow. 74 00:04:32,800 --> 00:04:39,840 Don't forget to subscribe, share your thoughts, and let us know what excites you most about the future of AI. 75 00:04:39,840 --> 00:04:59,840 Together, we're exploring the cutting edge of AI innovation and the journeys just getting started.