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I think you could probably go back and track the stages of grief, probably that is what I went through. But I think if you do it right, you end up at acceptance. And that's where I ended up. And that's not to say that I've fully accepted the idea that the golden toad is extinct. Personally, I do still hold out hope that it could still be out there in those forests." - Trevor Ritland This conversation is with Trevor Ritland, who—along with his twin brother Kyle—authored The Golden Toad . The book chronicles their remarkable journey into Costa Rica’s cloud forest, once home to hundreds of brilliant golden toads that would emerge for just a few weeks each year—until, one day, they vanished without a trace. What began as a search for a lost species soon became something much more profound: a confrontation with ecological grief, a meditation on hope, and a powerful call to protect the natural world while we still can. Links: SpeciesUnite.com Kyle and Trevor: https://kyleandtrevor.com/ Instagram: https://www.instagram.com/adventureterm/ Goodreads - https://www.goodreads.com/book/show/222249677-the-golden-toad Amazon - https://www.amazon.com/Golden-Toad-Ecological-Mystery-Species/dp/163576996…
Generally Intelligent
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Inhalt bereitgestellt von Kanjun Qiu. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Kanjun Qiu oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.
…
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37 Episoden
Alle als (un)gespielt markieren ...
Manage series 2906499
Inhalt bereitgestellt von Kanjun Qiu. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Kanjun Qiu oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.
…
continue reading
37 Episoden
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Generally Intelligent

1 Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas 1:02:51
1:02:51
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Rylan Schaeffer is a PhD student at Stanford studying the engineering, science, and mathematics of intelligence. He authored the paper “Are Emergent Abilities of Large Language Models a Mirage?”, as well as other interesting refutations in the field that we’ll talk about today. He previously interned at Meta on the Llama team, and at Google DeepMind. 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/X: @imbue_ai…
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Generally Intelligent

1 Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training 1:34:19
1:34:19
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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…
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Generally Intelligent

1 Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models 1:01:55
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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…
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Generally Intelligent

1 Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI 1:55:45
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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 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…
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Generally Intelligent

1 Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference 1:20:29
1:20:29
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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…
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1 Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize 1:01:54
1:01:54
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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…
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Generally Intelligent

1 Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition 1:15:24
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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…
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Generally Intelligent

1 Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms 1:45:56
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Ben Eysenbach is a PhD student from CMU and a student researcher at Google Brain. He is co-advised by Sergey Levine and Ruslan Salakhutdinov and his research focuses on developing RL algorithms that get state-of-the-art performance while being more simple, scalable, and robust. Recent problems he’s tackled include long horizon reasoning, exploration, and representation learning. In this episode, we discuss designing simpler and more principled RL algorithms, 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…
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Generally Intelligent

1 Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant 1:26:45
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Jim Fan is a research scientist at NVIDIA and got his PhD at Stanford under Fei-Fei Li. Jim is interested in building generally capable autonomous agents, and he recently published MineDojo, a massively multiscale benchmarking suite built on Minecraft, which was an Outstanding Paper at NeurIPS. In this episode, we discuss the foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant. 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…
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Generally Intelligent

1 Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems 1:34:49
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Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talk about the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems.…
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1 Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time 1:44:54
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Noam Brown is a research scientist at FAIR. During his Ph.D. at CMU, he made the first AI to defeat top humans in No Limit Texas Hold 'Em poker. More recently, he was part of the team that built CICERO which achieved human-level performance in Diplomacy. In this episode, we extensively discuss ideas underlying both projects, the power of spending compute at inference time, and much more.…
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1 Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory 1:44:00
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Sugandha Sharma is a Ph.D. candidate at MIT advised by Prof. Ila Fiete and Prof. Josh Tenenbaum. She explores the computational and theoretical principles underlying higher cognition in the brain by constructing neuro-inspired models and mathematical tools to discover how the brain navigates the world, or how to construct memory mechanisms that don’t exhibit catastrophic forgetting. In this episode, we chat about biologically inspired neural architectures, how memory could be implemented, why control theory is underrated and much more.…
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1 Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress 1:49:18
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Nicklas Hansen is a Ph.D. student at UC San Diego advised by Prof Xiaolong Wang and Prof Hao Su. He is also a student researcher at Meta AI. Nicklas' research interests involve developing machine learning systems, specifically neural agents, that have the ability to learn, generalize, and adapt over their lifetime. In this episode, we talk about long-horizon planning, adapting reinforcement learning policies during deployment, why algorithms don't drive research progress, and much more!…
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1 Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning 1:56:42
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Jack Parker-Holder recently joined DeepMind after his Ph.D. with Stephen Roberts at Oxford. Jack is interested in using reinforcement learning to train generally capable agents, especially via an open-ended learning process where environments can adapt to constantly challenge the agent's capabilities. Before doing his Ph.D., Jack worked for 7 years in finance at JP Morgan. In this episode, we chat about open-endedness, evolving agents and environments, online adaptation, offline learning with world models, and much more.…
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1 Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from 1:52:35
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Celeste Kidd is a professor of psychology at UC Berkeley. Her lab studies the processes involved in knowledge acquisition; essentially, how we form our beliefs over time and what allows us to select a subset of all the information we encounter in the world to form those beliefs. In this episode, we chat about attention and curiosity, beliefs and expectations, where certainty comes from, and much more.…
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