- monatlichErscheinungsrhythmus
- 2 h 14typische Länge
- 3. Aug.letzte Folge
Folgenzu den neuesten
29 - Science of Deep Learning with Vikrant Varma
In 2022, it was announced that a fairly simple method can be used to extract the true beliefs of a language model on any given topic, without having to actually understand the topic at hand. Earlier, in 2021, it was announced that neural ne
28 - Suing Labs for AI Risk with Gabriel Weil
How should the law govern AI? Those concerned about existential risks often push either for bans or for regulations meant to ensure that AI is developed safely - but another approach is possible. In this episode, Gabriel Weil talks about hi
27 - AI Control with Buck Shlegeris and Ryan Greenblatt
A lot of work to prevent AI existential risk takes the form of ensuring that AIs don't want to cause harm or take over the world---or in other words, ensuring that they're aligned. In this episode, I talk with Buck Shlegeris and Ryan Greenb
26 - AI Governance with Elizabeth Seger
The events of this year have highlighted important questions about the governance of artificial intelligence. For instance, what does it mean to democratize AI? And how should we balance benefits and dangers of open-sourcing powerful AI sys
25 - Cooperative AI with Caspar Oesterheld
Imagine a world where there are many powerful AI systems, working at cross purposes. You could suppose that different governments use AIs to manage their militaries, or simply that many powerful AIs have their own wills. At any rate, it see
24 - Superalignment with Jan Leike
Recently, OpenAI made a splash by announcing a new "Superalignment" team. Lead by Jan Leike and Ilya Sutskever, the team would consist of top researchers, attempting to solve alignment for superintelligent AIs in four years by figuring out
23 - Mechanistic Anomaly Detection with Mark Xu
Is there some way we can detect bad behaviour in our AI system without having to know exactly what it looks like? In this episode, I speak with Mark Xu about mechanistic anomaly detection: a research direction based on the idea of detecting
Survey, store closing, Patreon
Very brief survey: bit.ly/axrpsurvey2023 Store is closing in a week! Link: store.axrp.net/ Patreon: patreon.com/axrpodcast Ko-fi: ko-fi.com/axrpodcast
22 - Shard Theory with Quintin Pope
What can we learn about advanced deep learning systems by understanding how humans learn and form values over their lifetimes? Will superhuman AI look like ruthless coherent utility optimization, or more like a mishmash of contextually acti
21 - Interpretability for Engineers with Stephen Casper
Lots of people in the field of machine learning study 'interpretability', developing tools that they say give us useful information about neural networks. But how do we know if meaningful progress is actually being made? What should we want
20 - 'Reform' AI Alignment with Scott Aaronson
How should we scientifically think about the impact of AI on human civilization, and whether or not it will doom us all? In this episode, I speak with Scott Aaronson about his views on how to make progress in AI alignment, as well as his wo
Store, Patreon, Video
Store: https://store.axrp.net/ Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Video: https://www.youtube.com/watch?v=kmPFjpEibu0
19 - Mechanistic Interpretability with Neel Nanda
How good are we at understanding the internal computation of advanced machine learning models, and do we have a hope at getting better? In this episode, Neel Nanda talks about the sub-field of mechanistic interpretability research, as well
New podcast - The Filan Cabinet
I have a new podcast, where I interview whoever I want about whatever I want. It's called "The Filan Cabinet", and you can find it wherever you listen to podcasts. The first three episodes are about pandemic preparedness, God, and cryptocur
18 - Concept Extrapolation with Stuart Armstrong
Concept extrapolation is the idea of taking concepts an AI has about the world - say, "mass" or "does this picture contain a hot dog" - and extending them sensibly to situations where things are different - like learning that the world work
17 - Training for Very High Reliability with Daniel Ziegler
Sometimes, people talk about making AI systems safe by taking examples where they fail and training them to do well on those. But how can we actually do this well, especially when we can't use a computer program to say what a 'failure' is?
16 - Preparing for Debate AI with Geoffrey Irving
Many people in the AI alignment space have heard of AI safety via debate - check out AXRP episode 6 (axrp.net/episode/2021/04/08/episode-6-debate-beth-barnes.html) if you need a primer. But how do we get language models to the stage where t
15 - Natural Abstractions with John Wentworth
Why does anybody care about natural abstractions? Do they somehow relate to math, or value learning? How do E. coli bacteria find sources of sugar? All these questions and more will be answered in this interview with John Wentworth, where w
14 - Infra-Bayesian Physicalism with Vanessa Kosoy
Late last year, Vanessa Kosoy and Alexander Appel published some research under the heading of "Infra-Bayesian physicalism". But wait - what was infra-Bayesianism again? Why should we care? And what does any of this have to do with physical
13 - First Principles of AGI Safety with Richard Ngo
How should we think about artificial general intelligence (AGI), and the risks it might pose? What constraints exist on technical solutions to the problem of aligning superhuman AI systems with human intentions? In this episode, I talk to R
12 - AI Existential Risk with Paul Christiano
Why would advanced AI systems pose an existential risk, and what would it look like to develop safer systems? In this episode, I interview Paul Christiano about his views of how AI could be so dangerous, what bad AI scenarios could look lik
11 - Attainable Utility and Power with Alex Turner
Many scary stories about AI involve an AI system deceiving and subjugating humans in order to gain the ability to achieve its goals without us stopping it. This episode's guest, Alex Turner, will tell us about his research analyzing the not
10 - AI's Future and Impacts with Katja Grace
When going about trying to ensure that AI does not cause an existential catastrophe, it's likely important to understand how AI will develop in the future, and why exactly it might or might not cause such a catastrophe. In this episode, I i
9 - Finite Factored Sets with Scott Garrabrant
Being an agent can get loopy quickly. For instance, imagine that we're playing chess and I'm trying to decide what move to make. Your next move influences the outcome of the game, and my guess of that influences my move, which influences yo
8 - Assistance Games with Dylan Hadfield-Menell
How should we think about the technical problem of building smarter-than-human AI that does what we want? When and how should AI systems defer to us? Should they have their own goals, and how should those goals be managed? In this episode,
7.5 - Forecasting Transformative AI from Biological Anchors with Ajeya Cotra
If you want to shape the development and forecast the consequences of powerful AI technology, it's important to know when it might appear. In this episode, I talk to Ajeya Cotra about her draft report "Forecasting Transformative AI from Bio
7 - Side Effects with Victoria Krakovna
One way of thinking about how AI might pose an existential threat is by taking drastic actions to maximize its achievement of some objective function, such as taking control of the power supply or the world's computers. This might suggest a
6 - Debate and Imitative Generalization with Beth Barnes
One proposal to train AIs that can be useful is to have ML models debate each other about the answer to a human-provided question, where the human judges which side has won. In this episode, I talk with Beth Barnes about her thoughts on the
5 - Infra-Bayesianism with Vanessa Kosoy
The theory of sequential decision-making has a problem: how can we deal with situations where we have some hypotheses about the environment we're acting in, but its exact form might be outside the range of possibilities we can possibly cons
4 - Risks from Learned Optimization with Evan Hubinger
In machine learning, typically optimization is done to produce a model that performs well according to some metric. Today's episode features Evan Hubinger talking about what happens when the learned model itself is doing optimization in ord
