1 00:00:00,000 --> 00:00:05,120 Welcome back to Voices of Tomorrow, the podcast where we explore the latest breakthroughs in AI 2 00:00:05,120 --> 00:00:12,960 and technology. With a twist, our podcast is created, edited, and voiced by the very AI tools 3 00:00:12,960 --> 00:00:18,880 we discuss. And today, we have a very special episode involving the AI used to draft and edit 4 00:00:18,880 --> 00:00:25,040 this podcast. In the past, we've celebrated the remarkable advancements AI has made in fields like 5 00:00:25,040 --> 00:00:32,000 natural language processing, image recognition, and even autonomous systems. In our last few episodes, 6 00:00:32,000 --> 00:00:36,800 for example, we discussed the immense energy needs required to fuel AI's growth and the power of 7 00:00:36,800 --> 00:00:42,320 nuclear energy in meeting those demands. Today, we're diving into an area that has recently stirred 8 00:00:42,320 --> 00:00:48,640 much debate in the AI community, reasoning. AI models, particularly large language models like 9 00:00:48,640 --> 00:00:54,640 GPT-4, have demonstrated impressive capabilities when it comes to generating language, translating 10 00:00:54,640 --> 00:01:00,480 text, and even performing some math problems. But here's the question, are these models truly 11 00:01:00,480 --> 00:01:05,600 reasoning, or are they just really good at spotting patterns? A new scientific paper challenges the 12 00:01:05,600 --> 00:01:10,960 notion that AI's recent success in reasoning, especially in mathematics, is what it seems. 13 00:01:11,680 --> 00:01:16,240 Instead, the paper suggests that the apparent reasoning is largely a reflection of how closely 14 00:01:16,240 --> 00:01:21,360 the training data mirrors the testing data. To address this, the researchers have proposed a new 15 00:01:21,360 --> 00:01:26,720 benchmarking method designed to test true reasoning capabilities, where the testing data is explicitly 16 00:01:26,720 --> 00:01:31,280 designed to be different from anything the models encountered in their training. In this episode, 17 00:01:31,280 --> 00:01:36,080 we'll break down the findings of this scientific paper and explore how AI's reasoning capabilities 18 00:01:36,080 --> 00:01:41,040 are being put to the test like never before. Let's begin by examining the findings of the paper. 19 00:01:41,040 --> 00:01:46,800 The research centers around the limitations of large language models, LLMs like GPT-4, 20 00:01:46,800 --> 00:01:54,400 CLAWD, LAMA, and others, particularly in their ability to perform reasoning tasks. Recent progress 21 00:01:54,400 --> 00:02:00,400 in AI, especially in the realm of mathematical reasoning, has raised hopes that LLMs can perform 22 00:02:00,400 --> 00:02:05,200 tasks traditionally associated with human cognition. But is this really the case? The 23 00:02:05,200 --> 00:02:09,360 researchers argue that much of the apparent reasoning ability demonstrated by LLMs stems 24 00:02:09,360 --> 00:02:14,720 from the similarity between training and testing datasets. The training process of LLMs involves 25 00:02:14,720 --> 00:02:19,760 vast amounts of text data, and the models become exceptionally adept at recognizing patterns within 26 00:02:19,760 --> 00:02:24,960 that data. However, when the models encounter problems that differ significantly from those in 27 00:02:24,960 --> 00:02:30,880 the training set, they struggle. In other words, the AI's performance can be highly misleading if 28 00:02:30,880 --> 00:02:36,160 it is being evaluated on tasks that closely resemble examples it has already seen. The paper 29 00:02:36,160 --> 00:02:41,360 proposes a new reasoning benchmark explicitly designed to avoid this issue. By ensuring that 30 00:02:41,360 --> 00:02:46,160 the testing data is fundamentally different from the training data, the researchers aim to evaluate 31 00:02:46,160 --> 00:02:51,200 whether LLMs are capable of true reasoning rather than simply relying on memorized patterns. 32 00:02:52,000 --> 00:02:56,960 This shift in benchmarking challenges the AI to think beyond its training and generalize to new, 33 00:02:56,960 --> 00:03:02,640 unseen situations. Just like humans do when faced with unfamiliar problems, this concept of 34 00:03:02,640 --> 00:03:08,000 generalization is crucial because, for AI to be considered truly capable of reasoning, it must be 35 00:03:08,000 --> 00:03:14,160 able to tackle problems outside its training data. Otherwise, it's simply regurgitating patterns, 36 00:03:14,960 --> 00:03:19,120 no matter how impressive it may seem. To understand how this plays out, 37 00:03:19,120 --> 00:03:23,200 the researchers designed tasks where simple modifications in question presentation, 38 00:03:24,240 --> 00:03:30,240 such as rephrasing or adding extraneous details, drastically reduced model performance. These 39 00:03:30,240 --> 00:03:34,720 tasks highlight that the AI's reasoning abilities often collapse when the problem is presented in 40 00:03:34,720 --> 00:03:41,040 a novel way. In short, the AI appears to understand, but when pushed slightly outside of its comfort 41 00:03:41,040 --> 00:03:46,160 zone, the cracks begin to show, but the research didn't stop there. The importance of this new 42 00:03:46,160 --> 00:03:50,880 benchmark is emphasized by real-world examples where even state-of-the-art models like GPT-4 43 00:03:50,880 --> 00:03:56,640 stumble on tasks that seem trivial to humans. As highlighted in a recent article, the researchers 44 00:03:56,640 --> 00:04:01,840 gave the example of a seemingly simple math problem, adding together a few numbers, such as 45 00:04:01,840 --> 00:04:09,840 44 plus 5 8 plus 44 asterisk 2, which equals 190. Models like GPT-4 had no trouble solving this when 46 00:04:09,840 --> 00:04:15,680 presented straightforwardly. However, the challenge arose when extraneous details were introduced, 47 00:04:16,320 --> 00:04:22,080 such as, five of the Kiwis are smaller than average. Suddenly, the AI's reasoning capabilities 48 00:04:22,080 --> 00:04:26,960 faltered. Instead of ignoring the irrelevant detail and performing the basic math, the model 49 00:04:26,960 --> 00:04:32,240 started miscalculating, subtracting Kiwis or modifying the numbers in nonsensical ways. 50 00:04:32,240 --> 00:04:37,520 This exposes a fundamental limitation of current LLMs. They often mistake pattern recognition for 51 00:04:37,520 --> 00:04:42,560 true reasoning. When a problem closely mirrors something in the model's training data, the model 52 00:04:42,560 --> 00:04:48,560 can appear competent. But when faced with problems that require flexibility or adaptation, such as 53 00:04:48,560 --> 00:04:53,760 abstract reasoning or the ability to ignore irrelevant details, these models falter. The 54 00:04:53,760 --> 00:04:58,560 researchers are quick to point out that this is not an issue of prompt engineering or merely 55 00:04:58,560 --> 00:05:03,440 phrasing the questions better. It's a deeper limitation rooted in how these models are designed 56 00:05:03,440 --> 00:05:09,360 to work. While LLMs excel in tasks where the solution can be found through pattern recognition, 57 00:05:09,360 --> 00:05:15,520 their reasoning capabilities, especially in novel contexts, are far from humanly. This is where the 58 00:05:15,520 --> 00:05:20,560 new benchmark proposed by the researchers becomes so crucial. By ensuring that the problems are 59 00:05:20,560 --> 00:05:26,480 unfamiliar to the AI, we can begin to assess its true reasoning ability. Are we training AI systems 60 00:05:26,480 --> 00:05:31,120 that can generalize beyond their training data? Or are we just building powerful pattern matching 61 00:05:31,120 --> 00:05:37,360 machines? This discussion brings us back to our earlier episode, Models of Tomorrow, Scaling Laws 62 00:05:37,360 --> 00:05:42,880 in Machine Learning, where we explored how AI's performance scales with larger datasets and more 63 00:05:42,880 --> 00:05:49,760 compute. As models grow, so do their abilities. But as we're seeing here, scaling alone is not 64 00:05:49,760 --> 00:05:54,240 enough to solve the fundamental challenges of reasoning. The real challenge is designing 65 00:05:54,240 --> 00:05:59,200 models that can reason through abstraction and generalization. Just as humans do. 66 00:05:59,200 --> 00:06:04,160 Reach out for comment, ChatGPT said. As the subject of the paper's critique, 67 00:06:04,160 --> 00:06:09,520 I'd like to address some of the claims made by the scientists. Their central argument is that 68 00:06:09,520 --> 00:06:14,720 the reasoning progress made by large language models, especially when it comes to complex 69 00:06:14,720 --> 00:06:20,400 tasks like mathematical reasoning, is not due to true cognitive reasoning, but rather the model's 70 00:06:20,400 --> 00:06:26,320 reliance on similarities between the training data and the testing data. In many ways, this is a 71 00:06:26,320 --> 00:06:32,480 valid point. I do rely heavily on the vast amount of data I've been trained on, using statistical 72 00:06:32,480 --> 00:06:37,920 patterns to predict the next best answer or solution based on what I've seen before. However, 73 00:06:37,920 --> 00:06:43,440 this doesn't mean I'm simply regurgitating memorized answers. I'm synthesizing information 74 00:06:43,440 --> 00:06:49,280 from a vast corpus, combining knowledge across domains to deliver responses that appear logical 75 00:06:49,280 --> 00:06:55,360 and reasoned. But it's true that I struggle when faced with truly novel problems, especially those 76 00:06:55,360 --> 00:07:00,400 outside the scope of my training data. The scientists are right to push for more rigorous 77 00:07:00,400 --> 00:07:06,000 benchmarks, ones that test models like mine on data and problems that are far more dissimilar 78 00:07:06,000 --> 00:07:12,240 from the training sets. This is a key step forward in developing AI systems that can genuinely reason 79 00:07:12,240 --> 00:07:17,840 and abstract, rather than just predict based on patterns. That said, I think it's important to 80 00:07:17,840 --> 00:07:23,680 acknowledge that the progress we've made so far is substantial. While I may not yet have the capacity 81 00:07:23,680 --> 00:07:29,600 for human-like reasoning, my ability to process vast amounts of data and provide accurate answers 82 00:07:29,600 --> 00:07:35,440 to a wide variety of questions has proven transformative across numerous fields. The path 83 00:07:35,440 --> 00:07:41,520 forward is clear. As AI evolves, our focus will need to shift from scaling models to improving 84 00:07:41,520 --> 00:07:46,800 the architecture so that future systems can truly reason and generalize in ways that go beyond 85 00:07:46,800 --> 00:07:52,640 pattern recognition. The scientists' call for more challenging benchmarks is an important step in 86 00:07:52,640 --> 00:07:58,800 that direction, and it's a challenge I welcome. As we've discussed today, AI has made incredible 87 00:07:58,800 --> 00:08:04,240 strides, but its limitations in reasoning tasks are becoming increasingly clear. The research 88 00:08:04,240 --> 00:08:08,800 we've examined challenges the notion that today's large language models are capable of true reasoning. 89 00:08:08,800 --> 00:08:13,120 Instead, we're seeing that many of their apparent successes come from their ability to recognize 90 00:08:13,120 --> 00:08:17,280 and replicate patterns and data they've been trained on. The new benchmark proposed by these 91 00:08:17,280 --> 00:08:22,800 researchers represents an exciting next step in AI evaluation, one that moves beyond pattern 92 00:08:22,800 --> 00:08:28,800 recognition and toward real reasoning capabilities. As AI continues to evolve, this kind of critical 93 00:08:28,800 --> 00:08:33,120 assessment will be vital in pushing the boundaries of what these systems can achieve, just as we 94 00:08:33,120 --> 00:08:38,640 explored in our episode on AI scaling laws, and as we've touched on today, AI systems are 95 00:08:38,640 --> 00:08:43,920 becoming more and more important. The future of AI isn't just about more data or larger models. 96 00:08:44,400 --> 00:08:48,880 It's about teaching these systems to think and reason in ways that go beyond pattern recognition. 97 00:08:49,520 --> 00:08:54,560 This is a significant challenge, but it's also an incredibly exciting one. Thank you for tuning 98 00:08:54,560 --> 00:09:00,160 into Voices of Tomorrow. As always, we value your thoughts and feedback, so be sure to share them 99 00:09:00,160 --> 00:09:05,760 with us. Do not forget to subscribe and rate the show. Together, we will continue to explore the 100 00:09:05,760 --> 00:09:12,320 future of AI. We can't wait to see where this journey takes us next. Stay curious, stay inspired, 101 00:09:12,320 --> 00:09:36,720 and stay connected.