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.ai
• Emily's work with Fang et. al. relating brain computations to AI/ML algorithms: https://elifesciences.org/articles/80680
• Basis 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/S0959438817302349
• Emily'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)00235-6
• NPR story about this work: https://www.npr.org/2024/04/05/1198909635/chickadee-bird-brain-memory-brain-pattern-food
• Github collab-creatures repo for the Basis collaborative intelligent systems project: https://github.com/BasisResearch/collab-creatures
• Basis's core open-source code repository for causal reasoning, ChiRho: https://basisresearch.github.io/chirho/getting_started.html
• Basis's city policy dashboard, polis: http://polis.basis.ai/

Stability AI’s Stable Diffusion model is one of the best known and most widely used text-to-image systems.
The decision to open-source both the model weights and code has ensured its mass adoption, with the company claiming more than 330 million downloads.
Details of the latest version - Stable Diffusion 3 - were revealed in a paper, published by the company in March 2024.
In this episode, Stability AI’s Kate Hodesdon joins Helen to discuss some of SD3’s new features, including improved capabilities for generating text within images and overall image quality.
Kate also talks about developments to the underlying model structure of Stable Diffusion, as well as the challenges associated with creating models that deliver more efficient inference.
The Stable Diffusion 3 paper can be found here: https://arxiv.org/pdf/2403.03206.pdf

No organisation in the AI world is under more intense scrutiny than OpenAI. The maker of Dall-E, GPT4, ChatGPT and Sora is constantly pushing the boundaries of artificial intelligence and has supercharged the enthusiasm of the general public for AI technologies.
With that elevated position come questions about how OpenAI can ensure its models are not used for malign purposes.
In this interview we talk to Rosie Campbell from OpenAI’s policy research team about the many processes and safeguards in place to prevent abuse. Rosie also talks about the forward-looking work of the policy research team, anticipating longer-term risks that might emerge with more advanced AI systems.
Helen and Rosie discuss the challenges associated with agentic systems (AI that can interface with the wider world via APIs and other technologies), red-teaming new models, and whether advanced AIs should have ‘rights’ in the same way that humans or animals do.

You can read the paper referenced in this episode ‘Practices for Governing Agentic AI Systems’ co-written by Rosie and her colleagues: https://cdn.openai.com/papers/practices-for-governing-agentic-ai-systems.pdf

Watch the video of the interview here: https://www.youtube.com/watch?v=81LNrlEqgcM