Videos

See AI2's full collection of videos on our YouTube channel.
Viewing 1-10 of 249 videos
  • Figuring out how the world works: causality in a world full of real people Thumbnail

    Figuring out how the world works: causality in a world full of real people

    February 28, 2024  |  Konrad Kording
    Abstract: Causality is key to many branches of science, engineering, and the alignment of AI systems. I will start by highlighting the difficulties of causal inference in the real world, and build some intuition about why in the real world causality is difficult while it seems easy in our mind. I will continue by…
  • Machine-Checked Proofs, and the Rise of Formal Methods in Mathematics Thumbnail

    Machine-Checked Proofs, and the Rise of Formal Methods in Mathematics

    February 16, 2024  |  Leonardo de Moura
    Abstract: The domains of mathematics and software engineering witness a rapid increase in complexity. As generative artificial intelligence emerges as a potential force in mathematical exploration, a pressing imperative arises: ensuring the correctness of machine-generated proofs and software constructs. The Lean…
  • Beyond Test Accuracies for Studying Deep Neural Networks Thumbnail

    Beyond Test Accuracies for Studying Deep Neural Networks

    February 9, 2024  |  Kyunghyun Cho
    Abstract: Already in 2015, Leon Bottou discussed the prevalence and end of the training/test experimental paradigm in machine learning. The machine learning community has however continued to stick to this paradigm until now (2023), relying almost entirely and exclusively on the test-set accuracy, which is a…
  • Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking Thumbnail

    Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

    February 2, 2024  |  Jonathan Berant
    Abstract: Reward models are commonly used in the process of large language model alignment but are prone to reward hacking, where the true reward diverges from the estimated reward as the language model drifts out-of-distribution. In this talk, I will discuss a recent study on the use of reward ensembles to…
  • Integrated Systems for Computational Scientific Discovery Thumbnail

    Integrated Systems for Computational Scientific Discovery

    January 23, 2024  |  Pat Langley
    Abstract: In this talk, I challenge the AI research community to develop and evaluate integrated discovery systems. There has been a steady stream of AI work on scientific discovery since the 1970s, much of it leading to published results in fields like astronomy, biology, chemistry, and physics. However, most…
  • Language AI for RNA Virus and RNA Vaccine Thumbnail

    Language AI for RNA Virus and RNA Vaccine

    November 29, 2023  |  Liang Huang
    Abstract: Linguistics and biology are two sides of the same coin. This talk features several highly unexpected connections between them which yield efficient algorithms with substantial biological impacts. One such connection (Nature, 2023) is between messenger RNA (mRNA) vaccines and formal language theory…
  • OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text Thumbnail

    OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text

    November 28, 2023  |  Keiran Paster
    Abstract: There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models. For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from…
  • The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI Thumbnail

    The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI

    November 13, 2023  |  Dr. Fei-Fei Li
    Dr. Fei-Fei Li joins us for a fireside chat with Ali. She discusses her latest book, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI. Bio: Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human…
  • Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing Thumbnail

    Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing

    November 2, 2023  |  Tom Sherborne
    Abstract: Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has primarily considered silver-standard data augmentation or zero-shot methods, however, exploiting few-shot gold data is…
  • On Parameter Efficiency of Neural Language Models Thumbnail

    On Parameter Efficiency of Neural Language Models

    November 2, 2023  |  Chen Liang
    Abstract: Pre-trained neural language models have demonstrated remarkable generalizability in various downstream tasks, such as natural language understanding and question answering. However, these models have grown to contain hundreds of billions of parameters, making them difficult to be deployed in…