07:20
Transformer Interpretability: 6: How to reverse-engineer model mechanisms by finding circuits
07:56
Transformer Interpretability 5: What is a circuit, and how does it explain LLM behavior?
06:38
Transformer Interpretability 4: How to best measure context-mixing in Transformers
05:02
Transformer Interpretability 3: Why it is crucial to track how Transformers mix information
07:27
Transformer Interpretability 2: How can we explain the behaviour of neural models?
05:41
Transformer Interpretability 1: Why Interpretability is important in the Age of LLMs
02:37
5. Three rules of thumb for responsible use of Generative AI
03:29
4. Ethical issues with Generative AI
06:44
3. How do Large Language Models work?
02:43
2. How do Generative AI systems work?
02:45
1. What is Generative AI?
10:12
Dieuwke Hupkes: PhD defence on neural networks and natural language processing
05:24
Explaining voting outcomes with artificial intelligence
07:01
Why no voting system is fair - and how AI can help
X
About
This is the channel of the Institute for Logic, Language and Computation at the University of Amsterdam.
We believe that science is exciting. Research should not only be done behind closed lab doors, but be made accessible to a broader public. Therefore, our videos explain research at our institute and related topics.
For more information on ongoing research projects and open problems, check out our blog: resources.illc.uva.nl/illc-blog/
Tags