36 episodes

Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

Generally Intelligent Kanjun Qiu

    • Technology
    • 4.8 • 16 Ratings

Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

    Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training

    Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training

    Ari Morcos is the CEO of DatologyAI, which makes training deep learning models more performant and efficient by intervening on training data. He was at FAIR and DeepMind before that, where he worked on a variety of topics, including how training data leads to useful representations, lottery ticket hypothesis, and self-supervised learning. His work has been honored with Outstanding Paper awards at both NeurIPS and ICLR.

    Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks.

    About Imbue

    Imbue is an independent research company developing AI agents that mirror the fundamentals of human-like intelligence and that can learn to safely solve problems in the real world. We started Imbue because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

    We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

    Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.

    Website: ⁠https://imbue.com/⁠

    LinkedIn: ⁠https://www.linkedin.com/company/imbue-ai/⁠

    Twitter: @imbue_ai

    • 1 hr 34 min
    Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models

    Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models

    Percy Liang is an associate professor of computer science and statistics at Stanford. These days, he’s interested in understanding how foundation models work, how to make them more efficient, modular, and robust, and how they shift the way people interact with AI—although he’s been working on language models for long before foundation models appeared. Percy is also a big proponent of reproducible research, and toward that end he’s shipped most of his recent papers as executable papers using the CodaLab Worksheets platform his lab developed, and published a wide variety of benchmarks.

    Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks.

    About Imbue

    Imbue is an independent research company developing AI agents that mirror the fundamentals of human-like intelligence and that can learn to safely solve problems in the real world. We started Imbue because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.

    Website: ⁠https://imbue.com/⁠

    LinkedIn: ⁠https://www.linkedin.com/company/imbue-ai/⁠

    Twitter: ⁠@imbue_ai

    • 1 hr 1 min
    Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

    Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

    Seth Lazar is a professor of philosophy at the Australian National University, where he leads the Machine Intelligence and Normative Theory (MINT) Lab. His unique perspective bridges moral and political philosophy with AI, introducing much-needed rigor to the question of what will make for a good and just AI future.

    Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks.

    About ImbueImbue is an independent research company developing AI agents that mirror the fundamentals of human-like intelligence and that can learn to safely solve problems in the real world. We started Imbue because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.

    Website: https://imbue.com/LinkedIn: https://www.linkedin.com/company/imbue-ai/Twitter: @imbue_ai

    • 1 hr 55 min
    Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

    Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

    Tri Dao is a PhD student at Stanford, co-advised by Stefano Ermon and Chris Re. He’ll be joining Princeton as an assistant professor next year. He works at the intersection of machine learning and systems, currently focused on efficient training and long-range context.



    About Generally Intelligent 

    We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.  

    We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.  

    Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.  



    Learn more about us

    Website: https://generallyintelligent.com/

    LinkedIn: linkedin.com/company/generallyintelligent/ 

    Twitter: @genintelligent

    • 1 hr 20 min
    Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

    Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

    Jamie Simon is a 4th year Ph.D. student at UC Berkeley advised by Mike DeWeese, and also a Research Fellow with us at Generally Intelligent. He uses tools from theoretical physics to build fundamental understanding of deep neural networks so they can be designed from first-principles. In this episode, we discuss reverse engineering kernels, the conservation of learnability during training, infinite-width neural networks, and much more.


    About Generally Intelligent 

    We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.  

    We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.  

    Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.  



    Learn more about us

    Website: https://generallyintelligent.com/

    LinkedIn: linkedin.com/company/generallyintelligent/ 

    Twitter: @genintelligent

    • 1 hr 1 min
    Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition

    Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition

    Bill Thompson is a cognitive scientist and an assistant professor at UC Berkeley. He runs an experimental cognition laboratory where he and his students conduct research on human language and cognition using large-scale behavioral experiments, computational modeling, and machine learning. In this episode, we explore the impact of cultural evolution on human knowledge acquisition, how pure biological evolution can lead to slow adaptation and overfitting, and much more.



    About Generally Intelligent 

    We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.  

    We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.  

    Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.  



    Learn more about us

    Website: https://generallyintelligent.com/

    LinkedIn: linkedin.com/company/generallyintelligent/ 

    Twitter: @genintelligent

    • 1 hr 15 min

Customer Reviews

4.8 out of 5
16 Ratings

16 Ratings

Hatch55461 ,

Smart people discussing things intelligently

Phenomenal podcast. I love hearing the intuition behind research as it has helped me in my own endeavors. Thank you, thank you, thank you.

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