Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas
Sep 18, 2024
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In this discussion, Rylan Schaeffer, a PhD student at Stanford specializing in the engineering and mathematics of intelligence, shares intriguing insights about evaluating AI capabilities. He explores the evolving interplay between neuroscience and machine learning, arguing that breakthroughs in AI often do not require insights from human brains. Rylan also reflects on his struggles during his academic journey, emphasizing resilience and adaptability in research. Finally, he highlights the challenges of model evaluation and the phenomenon of model collapse in generative models.
The fundamental difference between biological intelligence and AI enables diverse problem-solving methods, crucial for developing human-like intelligence in technology.
Rylan Schaeffer's research on emergent abilities challenges traditional evaluation metrics, advocating for nuanced assessment methods to better understand AI capabilities.
Engaging in critical assessments of prevailing research ideas fosters innovation and progress in AI, essential for evolving technology and methodologies.
Deep dives
Distinction Between Biological and Artificial Intelligence Solutions
The biological brain processes information and develops intelligent behavior through a genetic bottleneck, necessitating that knowledge be condensed into DNA before being passed on to offspring. In contrast, artificial intelligence does not need to navigate such a bottleneck, which allows for diverse problem-solving approaches and algorithms. This fundamental difference means that solutions produced by AI may be more varied and innovative than those derived from biological models. Understanding this distinction is crucial for researchers developing AI that mirrors human-like intelligence.
Evolving Research Interests in AI
The speaker began their journey into AI and machine learning through hands-on competitions in college, which sparked a deep interest in applying deep reinforcement learning. This interest shifted towards biotech, utilizing deep learning for DNA sequencing, followed by advanced studies at University College London, focusing on computational cognitive neuroscience. Ultimately, the academic path led to a PhD in neuroscience-inspired AI at Stanford, with a pivotal moment arising from a scientific disagreement with professors that led to a flourishing paper on emergent abilities. This evolution highlights how interactions with the academic and research community can drive researchers towards innovative and critical thinking.
Critique of Neuroscience-Inspired AI
Initially, the hypothesis was that in order to develop better machine learning models, insights from neuroscience should be reverse-engineered for a deeper understanding. However, emerging views posited that many breakthroughs in AI have occurred without grounding in biological principles, suggesting models can achieve success with independent advancements. The argument was further strengthened by historical analysis showing that most machine learning innovations have fed back into neuroscience rather than the reverse. The key takeaway from this critique is that the tasks performed by artificial networks differ significantly from those of biological brains, leading to disparate solutions.
Emergent Abilities and Model Evaluation
Research has focused on the phenomenon of emergent abilities in language models, where performance can shift dramatically at certain scales, raising questions about evaluation metrics used in model assessments. A significant finding was that stringent metrics could skew perceptions of model capabilities, indicating the need for more nuanced evaluation techniques that allow for partial credit. The research led to the conclusion that understanding how different factors influence model evaluation—including metrics used and the amount of training data—is crucial for making accurate predictions about AI capabilities. The paper's insights challenge existing evaluation methods, pushing for improved strategies that account for these variances.
The Importance of Critical Work in AI Research
A call to action was made for researchers to engage in critical work, emphasizing its value in examining the limitations of prevailing ideas within AI. The discussion referenced pivotal scientific experiments, such as the Michelson-Morley experiment, which shifted the paradigms of thought by disproving dominant theories. The speaker expressed that engaging in critical assessments not only drives progress but also aids the community by revealing inefficacies in existing frameworks. Ultimately, fostering an environment where researchers feel empowered to challenge established norms is essential for the advancement of technology and methodologies in AI.
Rylan Schaeffer is a PhD student at Stanford studying the engineering, science, and mathematics of intelligence. He authored the paper “Are Emergent Abilities of Large Language Models a Mirage?”, as well as other interesting refutations in the field that we’ll talk about today. He previously interned at Meta on the Llama team, and at Google DeepMind.
Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks.
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