Yejin Choi, a preeminent computer scientist at the University of Washington and expert in AI, dives deep into the capabilities of large language models like ChatGPT. She discusses how these models learn and reason differently from humans, raising questions about their understanding of reality. The conversation explores the challenges of AI's predictive abilities, the complexities of aligning AI with human values, and the implications of misinformation. Choi highlights the stark differences between human creativity and AI's limitations, emphasizing the need for improved AI literacy.
Large language models (LLMs) like ChatGPT lack common-sense understanding and analogical reasoning.
Incorporating symbolic reasoning into LLMs can enhance their capabilities and common-sense understanding.
Reinforcement Learning with Human Feedback (RLHF) is crucial for training LLMs to reduce bias and toxicity, but ongoing research is necessary to address limitations.
Deep dives
The Theory of Punctuated Equilibrium in Evolutionary Biology
The episode discusses the theory of punctuated equilibrium, which challenges the prevailing notion of gradualism in evolutionary biology. The theory suggests that evolution can occur through sudden rapid changes interspersed with long periods of stability. The speaker draws parallels between this idea and phase transitions in physics where gradual changes at microscopic levels can lead to sudden macroscopic changes. The relevance of punctuated equilibrium to the topic of artificial intelligence is highlighted, as the field is experiencing a sudden rapid change akin to a phase transition.
The Capabilities and Limitations of Large Language Models
The conversation centers around large language models (LLMs) and their ability to understand and exhibit common sense. LLMs, such as Chat GPT, are trained to predict the most likely next word given a sequence of words. While they can generate fluent and impressive answers, they may not fully comprehend the meaning behind the words they produce. The limitations of LLMs in context comprehension and analogical reasoning are discussed. Evaluating the factuality and reliability of LLMs is highlighted as a major research challenge.
The Intersection of Neural Networks and Symbolic Reasoning
The episode explores the potential for incorporating symbolic reasoning into modern AI, specifically in the context of large language models. Symbolic reasoning allows for deep understanding, theory of mind, and complex logical operations, which are currently lacking in LLMs. The fusion of deep learning techniques with symbolic reasoning is seen as a promising avenue for improving the capabilities and common sense understanding of AI systems. The challenge lies in developing innovative algorithms that combine the strengths of both approaches.
Improving AI through Reinforcement Learning with Human Feedback
The podcast episode discusses how large language models like ChatGPT are trained and the limitations in terms of language quality and bias. It highlights the importance of Reinforcement Learning with Human Feedback (RLHF) to train these models after initial pre-training using internet data. RLHF focuses on getting good scores based on human evaluation, enabling the models to avoid toxic or biased responses. While RLHF results in considerable reduction of toxicity and enhancement of factuality, it does not completely eliminate these issues. The episode emphasizes the need for ongoing research and improvements to strike a balance between alignment with diverse human values and managing the limitations of AI.
The Limits and Challenges of AI in Common Sense and Creativity
The podcast explores the limitations of AI in understanding common sense and being truly creative. AI models like ChatGPT lack innate desire for learning and struggle to grasp basic common sense concepts. They can mimic various writing styles and generate creative content by remixing existing patterns, but they heavily rely on what humans have taught them before. The challenge lies in enabling AI to extrapolate and create genuinely new ideas. The episode discusses the need for more modular and dynamically flexible AI systems, as well as the importance of understanding the fundamental differences between human intelligence and artificial intelligence. It also highlights concerns about AI's role in misinformation and emphasizes the need for AI literacy, platform solutions, and regulations to address these challenges.
Over the last year, AI large-language models (LLMs) like ChatGPT have demonstrated a remarkable ability to carry on human-like conversations in a variety of different concepts. But the way these LLMs "learn" is very different from how human beings learn, and the same can be said for how they "reason." It's reasonable to ask, do these AI programs really understand the world they are talking about? Do they possess a common-sense picture of reality, or can they just string together words in convincing ways without any underlying understanding? Computer scientist Yejin Choi is a leader in trying to understand the sense in which AIs are actually intelligent, and why in some ways they're still shockingly stupid.
Yejin Choi received a Ph.D. in computer science from Cornell University. She is currently the Wissner-Slivka Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research director at AI2 overseeing the project Mosaic. Among her awards are a MacArthur fellowship and a fellow of the Association for Computational Linguistics.
