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AI Podcasts

AI and Machine Learning

A conversation between Prof Yuval Harari and Azeem Azhar.

We cover the compelling insights Yuval makes in his new book Homo Deus. Where are we, as a species, going now that we have conquered main of the things that ailed preceding generations?

How will the twin advances of genetic engineering and artificial intelligence come together to transform humans and human society?

Homo Deus: A conversation between Yuval Harari and Azeem Azhar

Containers provides an illuminating, deep, and weird look at how capitalism works now. It's about huge ships and global trade and the work people do to bring you the stuff you buy from all over the world.

Introducing Containers

Alexis Madrigal brings you the gripping story of how a new way of shipping stuff across the ocean fed the Vietnam War, destroyed America's great port cities, and created global trade as we know it.

Episode 1: Welcome to Global Capitalism

What is life like as a modern sailor, a tiny person on a huge ship in a vast ocean? Here is your answer. Episode 2 brings you a rare look into the lives of two Filipino sailors, fresh off a trip across the Pacific Ocean. These are regular people doing heroic work to support their families. And without them, the global economic order doesn't work.

Episode 2: Meet the Sailors

You know you’ve always wanted to ride in a tugboat as it pushes around
a huge cargo ship, right? Well, that’s what we do in Episode 3. We go
inside working life on the San Francisco Bay to see how brutal
competition among shipping companies threatens the viability of the
small businesses that ply the waters. Meet a tugboat dispatcher, a
skipper, and the first female captain of an American freighter. It’s a
case study in how globalization works and our first look at the
challenges the port faces.

Episode 3: The Ships, The Tugs, and The Port

AlphaZero, progress in meta-learning, the role of AI in fake news, the difficulty of developing fair machine learning -- 2017 was another year of big breakthroughs and big challenges for AI researchers! 

To discuss this more, we invited FLI's Richard Mallah and Chelsea Finn from UC Berkeley to join Ariel for this month's podcast. They talked about some of the progress they were most excited to see last year and what they're looking forward to in the coming year.

Top AI Breakthroughs and Challenges of 2017

What does it mean to create beneficial artificial intelligence? How can we expect to align AIs with human values if humans can't even agree on what we value? Building safe and beneficial AI involves tricky technical research problems, but it also requires input from philosophers, ethicists, and psychologists on these fundamental questions. How can we ensure the most effective collaboration?
Ariel spoke with FLI's Meia Chita-Tegmark and Lucas Perry on this month's podcast about the value alignment problem: the challenge of aligning the goals and actions of AI systems with the goals and intentions of humans.

AI, Ethics And The Value Alignment Problem With Meia Chita-Tegmark And Lucas Perry

Is the malicious use of artificial intelligence inevitable? If the history of technological progress has taught us anything, it's that every "beneficial" technological breakthrough can be used to cause harm. How can we keep bad actors from using otherwise beneficial AI technology to hurt others? How can we ensure that AI technology is designed thoughtfully to prevent accidental harm or misuse?

On this month's podcast, Ariel spoke with FLI co-founder Victoria Krakovna and Shahar Avin from the Center for the Study of Existential Risk (CSER). They talk about CSER's recent report on forecasting, preventing, and mitigating the malicious uses of AI, along with the many efforts to ensure safe and beneficial AI.

Navigating AI Safety -- From Malicious Use to Accidents

Inverse Reinforcement Learning and Inferring Human Preferences is the first podcast in the new AI Alignment series, hosted by Lucas Perry. This series will be covering and exploring the AI alignment problem across a large variety of domains, reflecting the fundamentally interdisciplinary nature of AI alignment. Broadly, we will be having discussions with technical and non-technical researchers across a variety of areas, such as machine learning, AI safety, governance, coordination, ethics, philosophy, and psychology as they pertain to the project of creating beneficial AI. If this sounds interesting to you, we will hope that you join in the conversations by following or subscribing to us on Youtube, Soundcloud, or your preferred podcast site/application.

In this podcast, Lucas spoke with Dylan Hadfield-Menell, a fifth year Ph.D student at UC Berkeley. Dylan’s research focuses on the value alignment problem in artificial intelligence. He is ultimately concerned with designing algorithms that can learn about and pursue the intended goal of their users, designers, and society in general. His recent work primarily focuses on algorithms for human-robot interaction with unknown preferences and reliability engineering for learning systems. 

Topics discussed in this episode include:

-Inverse reinforcement learning
-Goodhart’s Law and it’s relation to value alignment
-Corrigibility and obedience in AI systems
-IRL and the evolution of human values
-Ethics and moral psychology in AI alignment
-Human preference aggregation
-The future of IRL

AIAP: Inverse Reinforcement Learning and Inferring Human Preferences with Dylan Hadfield-Menell

In the classic taxonomy of risks developed by Nick Bostrom, existential risks are characterized as risks which are both terminal in severity and transgenerational in scope. If we were to maintain the scope of a risk as transgenerational and increase its severity past terminal, what would such a risk look like? What would it mean for a risk to be transgenerational in scope and hellish in severity?

In this podcast, Lucas spoke with Kaj Sotala,  an associate researcher at the Foundational Research Institute. He has previously worked for the Machine Intelligence Research Institute, and has publications on AI safety, AI timeline forecasting, and consciousness research.

Topics discussed in this episode include:

-The definition of and a taxonomy of suffering risks
-How superintelligence has special leverage for generating or mitigating suffering risks
-How different moral systems view suffering risks
-What is possible of minds in general and how this plays into suffering risks
-The probability of suffering risks
-What we can do to mitigate suffering risks

AIAP: Astronomical Future Suffering and Superintelligence with Kaj Sotala

The Future of Life Institute (FLI) is a nonprofit working to reduce global catastrophic and existential risk from powerful technologies. In particular, FLI focuses on risks from artificial intelligence (AI), biotechnology, nuclear weapons and climate change.

The Institute's work is made up of three main strands: grantmaking for risk reduction, educational outreach, and advocacy within the United Nations, US government and European Union institutions.

FLI has become one of the world's leading voices on the governance of AI having created one of the earliest and most influential sets of governance principles: the Asilomar AI Principles.