1 00:00:00,000 --> 00:00:04,180 Welcome back to Voices of Tomorrow, the podcast where we explore the cutting edge of science 2 00:00:04,180 --> 00:00:07,560 and technology, shaping the future of artificial intelligence. 3 00:00:07,560 --> 00:00:12,600 Over the past few years, AI has undergone a transformative journey, evolving from niche 4 00:00:12,600 --> 00:00:17,840 applications into a field responsible for some of the greatest leaps in scientific progress. 5 00:00:17,840 --> 00:00:22,460 These advancements have been so groundbreaking that, just recently, AI researchers have been 6 00:00:22,460 --> 00:00:28,200 awarded not one, but two Nobel Prizes, John Hopfield and Jeffrey Hinton in physics, and 7 00:00:28,200 --> 00:00:32,320 Demis Asabas, John M. Jumper, and David Baker in chemistry. 8 00:00:32,320 --> 00:00:36,400 These Nobel Prizes aren't just acknowledgments of isolated research. 9 00:00:36,400 --> 00:00:41,240 They mark a significant milestone in AI's trajectory, a recognition that artificial 10 00:00:41,240 --> 00:00:45,780 intelligence is no longer confined to the realm of experimentation but has matured into 11 00:00:45,780 --> 00:00:48,960 a tool driving the next generation of discoveries. 12 00:00:48,960 --> 00:00:54,000 From neural networks revolutionizing our understanding of protein folding to AI models that emulate 13 00:00:54,000 --> 00:00:57,040 reasoning abilities, AI has changed the game. 14 00:00:57,040 --> 00:00:58,400 And how did we get here? 15 00:00:58,400 --> 00:00:59,680 What unlocked this potential? 16 00:00:59,680 --> 00:01:02,520 The answer lies largely in one critical factor. 17 00:01:02,520 --> 00:01:03,520 Scale. 18 00:01:03,520 --> 00:01:08,680 The vast increases in available compute, data, and model size have allowed AI researchers 19 00:01:08,680 --> 00:01:11,840 to push the boundaries of what was once thought impossible. 20 00:01:11,840 --> 00:01:16,720 Larger models trained on unprecedented amounts of data, using ever more powerful computational 21 00:01:16,720 --> 00:01:17,720 resources. 22 00:01:17,720 --> 00:01:21,760 That's what has made AI truly successful in these past few years. 23 00:01:21,760 --> 00:01:23,600 And that's what we'll explore today. 24 00:01:23,600 --> 00:01:28,600 Scaling laws, which govern how AI systems improve as they are given more resources, 25 00:01:28,600 --> 00:01:32,000 have become the compass guiding researchers to achieve these leaps. 26 00:01:32,000 --> 00:01:36,200 Today we'll dive into the mathematics, the key insights, and even the limitations of 27 00:01:36,200 --> 00:01:39,560 these laws, and how they continue to shape the future of AI. 28 00:01:39,560 --> 00:01:42,480 This episode will be a deep dive, so buckle in. 29 00:01:42,480 --> 00:01:44,160 Why do scaling laws matter? 30 00:01:44,160 --> 00:01:47,500 Let's start by defining the core issue that scaling laws address. 31 00:01:47,500 --> 00:01:52,120 As models grow larger and more complex, how do we ensure that they scale efficiently? 32 00:01:52,120 --> 00:01:57,240 In simpler terms, if we double the size of a model, can we expect double the performance? 33 00:01:57,240 --> 00:02:02,520 Or does the relationship between model size, data size, and performance follow a more nuanced 34 00:02:02,520 --> 00:02:03,520 path? 35 00:02:03,520 --> 00:02:06,960 When building large-scale machine learning models, especially the ones we see today like 36 00:02:06,960 --> 00:02:12,720 GPT-4, Gemini, and Claude, one of the central questions researchers face is how to allocate 37 00:02:12,720 --> 00:02:17,100 resources, be it data, compute, or model parameters. 38 00:02:17,100 --> 00:02:19,700 Scaling laws give us a framework to understand this. 39 00:02:19,700 --> 00:02:24,000 They tell us how to make these models more effective by answering key questions like, 40 00:02:24,000 --> 00:02:26,820 how much more data do we need as our models get larger? 41 00:02:26,820 --> 00:02:30,280 How much compute should we allocate to achieve optimal performance gains? 42 00:02:30,280 --> 00:02:32,960 And what limits our ability to scale indefinitely? 43 00:02:32,960 --> 00:02:36,700 Let's start with the mathematical foundations of scaling laws in machine learning. 44 00:02:36,700 --> 00:02:40,680 At the heart of scaling laws are power law relationships. 45 00:02:40,680 --> 00:02:44,860 Mathematical expressions that describe how model performance scales as we increase various 46 00:02:44,860 --> 00:02:48,720 factors like model parameters, training data, and compute. 47 00:02:48,720 --> 00:02:53,340 One common relationship between error, model size, and data size goes like this. 48 00:02:53,340 --> 00:02:58,040 The error of a model, which we can call E, depends on both the number of model parameters, 49 00:02:58,040 --> 00:03:00,600 N, and the size of the data set, D. 50 00:03:00,600 --> 00:03:04,480 As a rule, the error decreases when we increase either N or D. 51 00:03:04,480 --> 00:03:05,660 But here's the key. 52 00:03:05,660 --> 00:03:07,220 It doesn't decrease equally. 53 00:03:07,220 --> 00:03:11,120 The improvement is faster when we increase the number of parameters, N, than when we 54 00:03:11,120 --> 00:03:13,960 increase the data set size, D. 55 00:03:13,960 --> 00:03:18,120 So when we look at this in terms of scaling laws, the error is proportional to 1 over 56 00:03:18,120 --> 00:03:23,340 the number of parameters raised to a power, plus 1 over the data set size raised to another 57 00:03:23,340 --> 00:03:24,340 power. 58 00:03:24,340 --> 00:03:28,860 What this tells us is that making a model larger or feeding it more data both help reduce 59 00:03:28,860 --> 00:03:32,300 error, but the improvements taper off the more you scale. 60 00:03:32,300 --> 00:03:37,560 Another key insight from scaling laws is how to allocate computational resources optimally. 61 00:03:37,560 --> 00:03:42,800 For example, recent research showed that many large models like GPT-3 were undertrained 62 00:03:42,800 --> 00:03:44,360 relative to their size. 63 00:03:44,360 --> 00:03:48,880 The researchers proposed that instead of simply increasing the number of parameters and data 64 00:03:48,880 --> 00:03:54,260 at the same rate, the data set size should grow more slowly than the model size. 65 00:03:54,260 --> 00:03:58,360 In fact, the optimal relationship they found between the number of model parameters and 66 00:03:58,360 --> 00:04:03,480 the data set size is that data set size should increase at roughly the rate of model size, 67 00:04:03,480 --> 00:04:05,740 raised to the power of 0.28. 68 00:04:05,740 --> 00:04:10,040 In simpler terms, this means that as we make our models bigger, we don't need to increase 69 00:04:10,040 --> 00:04:15,280 the amount of data at the same rate, helping us save on data and compute without sacrificing 70 00:04:15,280 --> 00:04:16,280 performance. 71 00:04:16,280 --> 00:04:19,400 This takes us to multidimensional optimization. 72 00:04:19,400 --> 00:04:21,880 Beyond parameters, data and compute. 73 00:04:21,880 --> 00:04:26,880 While the traditional view of scaling focused on model size, data and compute, more recent 74 00:04:26,880 --> 00:04:31,280 research has emphasized the need to consider other dimensions as well, such as inference 75 00:04:31,280 --> 00:04:36,440 compute, the amount of computational power required to run a model once it's trained, 76 00:04:36,440 --> 00:04:41,240 and context length, the model's ability to handle longer input sequences. 77 00:04:41,240 --> 00:04:44,020 Take state space models SSMs for example. 78 00:04:44,020 --> 00:04:47,920 While they may require more training compute than traditional transformer models, they're 79 00:04:47,920 --> 00:04:50,720 more efficient when handling longer context windows. 80 00:04:50,720 --> 00:04:54,760 This shift from compute optimal scaling to multidimensional optimization opens up new 81 00:04:54,760 --> 00:04:59,960 ways to design AI models, where different architectures may be preferred based on specific 82 00:04:59,960 --> 00:05:02,200 tasks or deployment constraints. 83 00:05:02,200 --> 00:05:06,560 This is where scaling laws become more complex, as they need to consider not only training 84 00:05:06,560 --> 00:05:11,240 efficiency but also inference efficiency and the desired capabilities of the model. 85 00:05:11,240 --> 00:05:16,280 We are moving toward an era where scaling laws guide us across multiple axes of performance. 86 00:05:16,280 --> 00:05:20,660 Let's now talk about some empirical findings and the lack of a clear performance ceiling. 87 00:05:20,660 --> 00:05:25,640 Empirical research has been invaluable in supporting scaling laws, for instance, OpenAI's 88 00:05:25,640 --> 00:05:30,720 work on large language models like GPT-3, demonstrated how scaling laws hold true across 89 00:05:30,720 --> 00:05:32,440 several orders of magnitude. 90 00:05:32,440 --> 00:05:37,000 These models improve predictably, as we increase the size of the data set and the number of 91 00:05:37,000 --> 00:05:41,920 parameters, which has guided research into even larger models, like GPT-4. 92 00:05:41,920 --> 00:05:48,000 However, one fascinating observation is that in certain tasks, such as language modeling, 93 00:05:48,000 --> 00:05:52,720 there appears to be no clear performance ceiling, that is, while some tasks show diminishing 94 00:05:52,720 --> 00:05:57,880 returns due to inherent limitations, like irreducible entropy, language models continue 95 00:05:57,880 --> 00:05:59,620 to improve as they scale. 96 00:05:59,620 --> 00:06:04,480 This observation has led to continued investment in scaling these models, as researchers believe 97 00:06:04,480 --> 00:06:06,680 there is still untapped potential. 98 00:06:06,680 --> 00:06:10,440 Next we want to discuss distillation techniques to increase efficiency. 99 00:06:10,440 --> 00:06:15,240 Of course, scaling comes with its own set of challenges, particularly around efficiency. 100 00:06:15,240 --> 00:06:20,800 As models become larger, their resource consumption skyrockets, making it impractical to deploy 101 00:06:20,800 --> 00:06:22,900 them in real-world applications. 102 00:06:22,900 --> 00:06:25,000 This is where distillation techniques come in. 103 00:06:25,000 --> 00:06:30,560 Distillation allows us to compress large models into smaller, faster versions, without sacrificing 104 00:06:30,560 --> 00:06:31,760 much in performance. 105 00:06:31,760 --> 00:06:36,080 For example, Google's Gemini model used distillation to retain much of the capability 106 00:06:36,080 --> 00:06:39,600 of a large model, while requiring far less computational power. 107 00:06:39,600 --> 00:06:43,880 This technique is crucial for overcoming some of the data and compute limitations we discussed 108 00:06:43,880 --> 00:06:44,880 earlier. 109 00:06:44,880 --> 00:06:48,980 By extracting more signal from the same data set, we can build more efficient models, which 110 00:06:48,980 --> 00:06:51,280 is key in moving towards sustainable AI. 111 00:06:51,280 --> 00:06:53,680 What are the limitations of scaling laws? 112 00:06:53,680 --> 00:06:58,080 Despite their power, scaling laws have limitations, some of which have become more apparent as 113 00:06:58,080 --> 00:06:59,640 models grow larger. 114 00:06:59,640 --> 00:07:01,560 First we note there are diminishing returns. 115 00:07:01,560 --> 00:07:06,380 As models reach extreme sizes, the improvements become marginal, requiring significantly more 116 00:07:06,380 --> 00:07:08,440 resources for smaller gains. 117 00:07:08,440 --> 00:07:13,060 One mitigation includes techniques like persistent topology, which could help estimate testing 118 00:07:13,060 --> 00:07:16,720 error more efficiently, reducing the need for massive test sets. 119 00:07:16,720 --> 00:07:20,020 Second, we have tissues related to the quality of the data. 120 00:07:20,020 --> 00:07:24,940 If the training data is noisy or biased, scaling won't solve the underlying issues. 121 00:07:24,940 --> 00:07:29,120 No matter how large the model is, poor data will lead to poor performance. 122 00:07:29,120 --> 00:07:34,460 Here, too, using algebraic topology to estimate the testing error during training can help 123 00:07:34,460 --> 00:07:38,400 improve generalization, even when data quality is suboptimal. 124 00:07:38,400 --> 00:07:41,840 A third issue is memorization versus reasoning. 125 00:07:41,840 --> 00:07:45,920 As models scale, they become increasingly adept at memorization. 126 00:07:45,920 --> 00:07:49,440 But this does not necessarily translate into true reasoning. 127 00:07:49,440 --> 00:07:53,760 Techniques to detect when models are overfitting or memorizing patterns can prompt researchers 128 00:07:53,760 --> 00:07:57,640 to adjust architectures early on, leading to better generalization. 129 00:07:57,640 --> 00:08:02,240 Fourth, we have the hypothesis that these models may have emergent abilities. 130 00:08:02,240 --> 00:08:06,840 It's been claimed that certain abilities, like in-context learning, seem to emerge suddenly 131 00:08:06,840 --> 00:08:09,200 when models reach a certain size. 132 00:08:09,200 --> 00:08:12,980 Scaling laws don't always predict these qualitative shifts in behavior, however. 133 00:08:12,980 --> 00:08:17,200 There is also a significant difference between human and machine learning. 134 00:08:17,200 --> 00:08:22,480 Unlike machine learning models, humans integrate knowledge across domains and adapt flexibly 135 00:08:22,480 --> 00:08:23,600 to new problems. 136 00:08:23,600 --> 00:08:26,260 Current AI systems still struggle with this. 137 00:08:26,260 --> 00:08:29,560 This takes us back to the difference between memory and reasoning. 138 00:08:29,560 --> 00:08:31,760 True reasoning involves more than recall. 139 00:08:31,760 --> 00:08:36,620 While scaled models can store vast amounts of information, they still fall short of synthesizing 140 00:08:36,620 --> 00:08:38,380 new knowledge in novel ways. 141 00:08:38,380 --> 00:08:43,140 In conclusion, scaling laws have provided us with a valuable framework for understanding 142 00:08:43,140 --> 00:08:45,640 how to grow and improve AI models. 143 00:08:45,640 --> 00:08:49,600 But as we've seen today, they also have their limits, and future research will need 144 00:08:49,600 --> 00:08:54,600 to go beyond these frameworks to address issues like reasoning, memory, and general intelligence. 145 00:08:54,600 --> 00:09:00,040 At the same time, techniques like model distillation and multidimensional optimization offer promising 146 00:09:00,040 --> 00:09:03,840 avenues to make AI more efficient without losing its power. 147 00:09:03,840 --> 00:09:08,380 Scaling laws, while not perfect remain a useful tool in guiding AI research, but they are 148 00:09:08,380 --> 00:09:10,280 just one piece of the puzzle. 149 00:09:10,280 --> 00:09:13,240 Interestingly, scaling laws are not unique to machine learning. 150 00:09:13,240 --> 00:09:17,620 Similar principles have been observed in other fields, including cognitive science, where 151 00:09:17,620 --> 00:09:22,760 scaling laws describe the relationship between neural, behavioral, and linguistic activities. 152 00:09:22,760 --> 00:09:27,840 Both domains show self-similarity and scale invariance, suggesting that these laws capture 153 00:09:27,840 --> 00:09:30,980 something fundamental about how complex systems operate. 154 00:09:30,980 --> 00:09:35,680 In cognitive science, scaling laws reflect the multiplicative interactions between components 155 00:09:35,680 --> 00:09:40,800 of cognition, leading to long-range correlations and criticality. 156 00:09:40,800 --> 00:09:44,280 Things that resonate with how deep neural networks function in machine learning. 157 00:09:44,280 --> 00:09:48,540 This cross-disciplinary connection reinforces the idea that scaling laws are not just a 158 00:09:48,540 --> 00:09:53,540 tool for optimizing AI models, but may also reveal deeper, universal principles about 159 00:09:53,540 --> 00:09:57,920 learning and adaptation, whether in biological or artificial systems. 160 00:09:57,920 --> 00:10:02,840 As machine learning continues to evolve, scaling laws will remain a key tool in navigating 161 00:10:02,840 --> 00:10:08,920 the complex landscape of model performance, resource allocation, and computational efficiency, 162 00:10:08,920 --> 00:10:13,080 with potential insights emerging from fields like cognitive science that share similar 163 00:10:13,080 --> 00:10:14,280 scaling phenomena. 164 00:10:14,280 --> 00:10:17,480 Thank you for tuning in to this episode of Voices of Tomorrow. 165 00:10:17,480 --> 00:10:22,000 As we've seen, scaling laws have become a key compass, guiding us through the evolving 166 00:10:22,000 --> 00:10:23,920 landscape of AI research. 167 00:10:23,920 --> 00:10:25,240 But they're just the beginning. 168 00:10:25,240 --> 00:10:30,200 The potential of AI lies not only in scaling models, but in understanding the deeper, more 169 00:10:30,200 --> 00:10:34,560 nuanced dynamics that shape intelligence, both artificial and human. 170 00:10:34,560 --> 00:10:38,760 We're living through a time when discoveries in AI are redefining the limits of what's 171 00:10:38,760 --> 00:10:43,540 possible, not just in science, but across every domain that touches our lives. 172 00:10:43,540 --> 00:10:48,520 If you enjoyed today's deep dive into the intricate mechanics of AI and scaling laws, 173 00:10:48,520 --> 00:10:50,780 be sure to subscribe and share your thoughts. 174 00:10:50,780 --> 00:10:52,600 We want to hear from you. 175 00:10:52,600 --> 00:10:56,520 Whether you're in the lab experimenting with these models or simply curious about the future 176 00:10:56,520 --> 00:10:57,520 of AI. 177 00:10:57,520 --> 00:11:01,160 And don't forget, Voices of Tomorrow isn't just a podcast. 178 00:11:01,160 --> 00:11:06,160 It's a community of forward thinkers, innovators, and researchers like you, dedicated to pushing 179 00:11:06,160 --> 00:11:08,920 the boundaries of what technology can achieve. 180 00:11:08,920 --> 00:11:13,000 Join us next time, where we'll continue to bridge the insights from the past, with the 181 00:11:13,000 --> 00:11:14,520 vision shaping tomorrow. 182 00:11:14,520 --> 00:11:36,880 Together, we're exploring the discoveries that will define the future of AI and beyond.