Knowledge Distillation with Helen Byrne

Helen Byrne

Knowledge Distillation is the podcast that brings together a mixture of experts from across the Artificial Intelligence community. We talk to the world’s leading researchers about their experiences developing cutting-edge models as well as the technologists taking AI tools out of the lab and turning them into commercial products and services.  Knowledge Distillation also takes a critical look at the impact of artificial intelligence on society – opting for expert analysis instead of hysterical headlines.We are committed to featuring at least 50% female voices on the podcast – elevating the many brilliant women working in AI. Host Helen Byrne is a VP at the British AI compute systems maker Graphcore where she leads the Solution Architects team, helping innovators build their AI solutions using Graphcore’s technology.   Helen previously led AI Field Engineering and worked in AI Research, tackling problems in distributed machine learning.  Before landing in Artificial Intelligence, Helen worked in FinTech, and as a secondary school teacher. Her background is in mathematics and she has a MSc in Artificial Intelligence.  Knowledge Distillation is produced by Iain Mackenzie. 

Episodes

  1. Neuroscience and AI with Basis co-founder Emily Mackevicius

    04/15/2024

    Neuroscience and AI with Basis co-founder Emily Mackevicius

    Emily Mackevicius is a co-founder and director of Basis, a nonprofit applied research organization focused on understanding and building intelligence while advancing society’s ability to solve intractable problems. Emily is a member of the Simons Society of Fellows, and a postdoc in the Aronov lab and the Center for Theoretical Neuroscience at Columbia’s Zuckerman Institute. Her research uncovers how complex cognitive behaviors are generated by networks of neurons through local interactions and learning mechanisms. Links to work mentioned in this episode:  Basis, the research institute co-founded by Emily: basis.aiEmily's work with Fang et. al. relating brain computations to AI/ML algorithms: https://elifesciences.org/articles/80680Basis blog post about this work (Fang et. al.): https://www.basis.ai/blog/sr-fang2023/Stachenfeld et al. paper: https://www.nature.com/articles/nn.4650 Emily's work with Michale Fee relating Reinforcement Learning algorithms to brain areas that birds use when they learn to sing: https://www.sciencedirect.com/science/article/abs/pii/S0959438817302349Emily's work with Aronov lab colleagues on how the hippocampus forms one-shot/episodic memory 'barcodes' in food-caching birds: https://www.cell.com/cell/fulltext/S0092-8674(24)00235-6NPR story about this work: https://www.npr.org/2024/04/05/1198909635/chickadee-bird-brain-memory-brain-pattern-foodGithub collab-creatures repo for the Basis collaborative intelligent systems project: https://github.com/BasisResearch/collab-creaturesBasis's core open-source code repository for causal reasoning, ChiRho: https://basisresearch.github.io/chirho/getting_started.htmlBasis's city policy dashboard, polis: http://polis.basis.ai/

    35 min
  2. Papers of the Month with Charlie Blake, Research Engineer at Graphcore

    02/02/2024

    Papers of the Month with Charlie Blake, Research Engineer at Graphcore

    Charlie Blake from Graphcore’s research team discusses their AI Papers of the Month for January 2024.  Graphcore research has been collating and sharing a review of the most consequential AI papers internally, every month, for a number of years.  Now – for the first time – the research team is making this valuable resource public, to help the wider AI community keep up-to-date with the most exciting breakthroughs.  Papers of the Month for January 2024 (with some work from December 2023) includes:  Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding  https://arxiv.org/abs/2312.05328 Authors: Talfan Evans, Shreya Pathak, Hamza Merzic, et al. (Google DeepMind, UCL)  Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws https://arxiv.org/abs/2401.00448 Authors: Nikhil Sardana and Jonathan Frankle (MosaicML)  Analyzing and Improving the Training Dynamics of Diffusion Models https://arxiv.org/abs/2312.02696 Authors: Tero Karras et al. (Nvidia, Aalto University)  Solving olympiad geometry without human demonstrations https://www.nature.com/articles/s41586-023-06747-5 Authors: Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He and Thang Luong (Google DeepMind, New York University)  To read about January’s Papers of the Month, visit the Graphcore blog. https://www.graphcore.ai/posts/great-teachers-and-beyond-chinchilla-papers-of-the-month-jan-2024

    44 min
  3. NeurIPS Special

    12/22/2023

    NeurIPS Special

    NeurIPS is the world’s largest AI conference, where leading AI practitioners come together to share the latest research and debate the way forward for artificial intelligence.  In this special episode, Helen examines some of the big themes of NeurIPS 2023 and talks to a range of attendees about their work, the big issues of the day, and what they’ve seen at NeurIPS that caught their attention.  It’s fair to say that LLMs loomed large over this year’s conference, but there’s plenty more to discuss – from AI’s potential to combat climate change to new techniques for computational efficiency.  Helen’s guests are:  Sofia Liguori – Research Engineer at Google Deepmind, specialising in the application of AI to sustainability and climate change.  Priya Donti – Assistant Professor in Electrical Engineering and Computer Science at MIT and Co-founder of Climate Change AI. Priya discusses the challenges associated with introducing leading-edge AI systems into highly complex real-world power generation and delivery systems.  Irene Chen – Assistant Professor at UC Berkeley and UCSF’s Computational Precision Health program. Irene talks about her goal of delivering more equitable healthcare at a time when AI is set to disrupt the field. She also discusses the potential to make use of commercial LLMs in a way that protects sensitive user data.  James Briggs – AI engineer at Graphcore. James and colleagues were presenting their paper ‘Training and inference of large language models using 8-bit floating point’ at this year’s NeurIPS. James explains their work and the importance of using smaller numerical representations to unlock computational efficiency in AI.  Abhinav (Abhi) Venigalla – is a member of the technical staff at Databricks. The company provides a range of products to help organisations unlock the potential of enterprise-grade AI. Abhi talks about the increasing emphasis on inference tools and computational efficiency as AI moves out of the research lab and into commercial deployment.

    44 min

About

Knowledge Distillation is the podcast that brings together a mixture of experts from across the Artificial Intelligence community. We talk to the world’s leading researchers about their experiences developing cutting-edge models as well as the technologists taking AI tools out of the lab and turning them into commercial products and services.  Knowledge Distillation also takes a critical look at the impact of artificial intelligence on society – opting for expert analysis instead of hysterical headlines.We are committed to featuring at least 50% female voices on the podcast – elevating the many brilliant women working in AI. Host Helen Byrne is a VP at the British AI compute systems maker Graphcore where she leads the Solution Architects team, helping innovators build their AI solutions using Graphcore’s technology.   Helen previously led AI Field Engineering and worked in AI Research, tackling problems in distributed machine learning.  Before landing in Artificial Intelligence, Helen worked in FinTech, and as a secondary school teacher. Her background is in mathematics and she has a MSc in Artificial Intelligence.  Knowledge Distillation is produced by Iain Mackenzie.