AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666
Jan 8, 2024
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Thomas Dietterich, a distinguished professor emeritus at Oregon State University, dives into the latest trends in AI and machine learning. He discusses the strengths and weaknesses of large language models like GPT-4, while exploring their potential limitations in reasoning. The conversation covers topics like uncertainty quantification and the fascinating world of 'hallucinations' in language models. Dietterich also offers predictions for 2024 and motivates newcomers to tap into the field's endless possibilities.
Large Language Models like Chat GPT and GPT-4 created excitement and discussion in the AI field.
Papers by Sebastian Bubeck and Thomas McCoy highlight the limitations and weaknesses of GPT-4.
Uncertainty quantification is significant in improving the reliability and trustworthiness of language models.
Deep dives
The Impact of Chat GPT and GPT-4 on the AI Field
The podcast episode highlights the impact and excitement generated by Chat GPT and GPT-4 in the machine learning and deep learning community. These models, released by OpenAI, created a lot of interest and discussion in the AI field. Chat GPT particularly gained popularity for its impressive capabilities in natural language processing and its ability to solve a wide range of tasks. The release of GPT-4, trained on both language and vision, further added to the excitement. The episode also mentions key papers associated with these models, such as OpenAI's technical report on GPT-4 and Microsoft Research's experiments with GPT-4. These papers sparked debates and discussions within and outside the community.
Understanding Limitations and Weaknesses of GPT-4
The podcast episode delves into the limitations and weaknesses of GPT-4, despite its remarkable capabilities. It highlights Microsoft Research's paper led by Sebastian Bubeck, which pointed out the limitations of GPT-4 in terms of cognitive abilities and reasoning skills. The paper shows that GPT-4 falls short when it comes to fundamental abilities like learning from its own experience or reasoning. Another paper led by Thomas McCoy from Princeton focuses on the reliance of GPT-4 on next-token prediction, which can affect its performance and lead to limited generalization. These papers shed light on the need for further research and development to address these weaknesses in language models.
Uncertainty Quantification and Its Importance
The podcast episode highlights the significance of uncertainty quantification in language models. It discusses the role of uncertainty estimation in improving the reliability and trustworthiness of model predictions. Various techniques for uncertainty estimation are explored, including conformal prediction, ensemble-based approaches, and analyzing internal states of language models. The episode mentions papers by Angela Popolis and Bates, Kendall and Gahl, Fadeva et al., and others that delve into uncertainty estimation methods. The ability to assess and quantify uncertainty can enhance decision-making, enable active learning, and improve the robustness of language models.
Uncertainty quantification in large language models
One promising area of research is uncertainty quantification in large language models (LLMs). The podcast discusses how LLMs can have both epistemic (model uncertainty) and alliatoric (labeling uncertainty) uncertainties. The speaker highlights the importance of understanding and distinguishing between these uncertainties. They mention a recent 2023 paper, 'Direct Epistemic Uncertainty Prediction,' that explores how to improve uncertainty estimation in LLMs. The podcast also mentions challenges in uncertainty quantification as models become deeper, more complex, and less interpretable. Overall, uncertainty quantification in LLMs is an evolving area of research with potential applications in various domains.
Hallucination and the need for improved detection
The podcast touches upon the challenge of hallucination in large language models (LLMs). Initially used in tasks like image captioning and abstractive summarization, the term 'hallucination' has become less well-defined when applied to LLMs. The speaker emphasizes the need for a formal taxonomy or classification of different types of errors made by LLMs. They mention two recent survey papers on hallucination in LLMs but highlight the importance of understanding the underlying causes of such errors. The episode also discusses the emergence of capabilities in LLMs and the challenges of predicting their behavior at scale. Additionally, the speaker acknowledges the need for tools and research on detecting out-of-distribution queries and better understanding the limits of LLMs' knowledge.
Today we continue our AI Trends 2024 series with a conversation with Thomas Dietterich, distinguished professor emeritus at Oregon State University. As you might expect, Large Language Models figured prominently in our conversation, and we covered a vast array of papers and use cases exploring current research into topics such as monolithic vs. modular architectures, hallucinations, the application of uncertainty quantification (UQ), and using RAG as a sort of memory module for LLMs. Lastly, don’t miss Tom’s predictions on what he foresees happening this year as well as his words of encouragement for those new to the field.
The complete show notes for this episode can be found at twimlai.com/go/666.
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