The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666
77 snips
Jan 8, 2024 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.
AI Snips
Chapters
Transcript
Episode notes
LLMs and Statistical Biases
- LLMs exhibit impressive capabilities but operate statistically, prioritizing high-probability outputs.
- This often leads to errors when faced with less frequent or unusual inputs.
ROT10 and Hamlet
- GPT-4's response to unusual rotation ciphers highlights its statistical bias.
- Instead of correctly applying ROT10, it defaults to familiar text, like Hamlet's soliloquy.
Competence Models for LLMs
- LLMs lack robust competence models, hindering their ability to handle novel situations.
- Dietterich suggests incorporating competence models, similar to those in computer vision, to address this.