The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666

Jan 8, 2024
01:05:18
Snipd AI
Thomas Dietterich, distinguished professor emeritus at Oregon State University, discusses large language models (LLMs) and their limitations, including lack of modularity, hallucinations, and struggles with novel situations. The conversation also explores uncertainty quantification in machine learning models, the concept of hallucination in LLMs, emergent properties in machine learning, promising opportunities in AI research, and challenges in mixing instructions and parameters in language models.
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Podcast summary created with Snipd AI

Quick takeaways

  • 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.

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.

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