Podcasts

IntelVerse

August 2019

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode of the Data Show, I spoke with Parvez Ahammad, who leads the data science and machine learning efforts at Instart Logic. He has applied machine learning in a variety of domains, most recently to computational neuroscience and security. Along the way, he has assembled and managed teams of data scientists and has had to grapple with issues like explainability and interpretability, ethics, insufficient amount of labeled data, and adversaries who target machine learning models. As more companies deploy machine learning models into products, it’s important to remember there are many other factors that come into play aside from raw performance metrics.

Building Machine Learning Solutions That Can Withstand Adversarial Attacks

Today we are speaking with Ilan Gur, the Founder and Executive Director of Cyclotron Road, a fellowship program that supports hard science and technology innovators as they advance a project with the potential for global impact. Ilan founded Cyclotron Road in order to provide entrepreneurial scientists and engineers with access to support and infrastructure that he noticed was lacking during his time directing research and technology projects in academia, startups, and corporate labs. The program is based at Berkeley Lab and fellows collaborate with scientists from the lab and from UC Berkeley. Cyclotron Road’s initial focus has been on advanced energy and materials technologies, and they have supported 41 innovators coming from some the world's top research institutions since their launch in 2014. 

Prior to founding Cyclotron Road, Ilan served as program director at the Advanced Research Projects Agency-Energy (ARPA-E), where he managed a $50 million portfolio spanning energy storage, solar energy, and advanced materials. Prior to ARPA-E, he launched two energy startups based on materials and manufacturing innovations, including Seeo, an advanced lithium battery company that was acquired by Bosch in 2015.

Ilan Gur, Executive Director, Cyclotron Road

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI.

We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting new Strata Data tutorial that Burt will be co-teaching in March.

How Machine Learning Impacts Information Security

Three generals are voting on whether to attack or retreat from their siege of a castle. One of the generals is corrupt and two of them are not. What happens when the corrupted general sends different answers to the other two generals?

A Byzantine fault is "a condition of a computer system, particularly distributed computing systems, where components may fail and there is imperfect information on whether a component has failed. The term takes its name from an allegory, the "Byzantine Generals' Problem", developed to describe this condition, where actors must agree on a concerted strategy to avoid catastrophic system failure, but some of the actors are unreliable."

The Byzantine Generals' Problem and associated issues in maintaining reliable distributed computing networks is illuminating for both AI alignment and modern networks we interact with like Youtube, Facebook, or Google. By exploring this space, we are shown the limits of reliable distributed computing, the safety concerns and threats in this space, and the tradeoffs we will have to make for varying degrees of efficiency or safety.

The Byzantine Generals' Problem, Poisoning, and Distributed Machine Learning with El Mahdi El Mahmdi is the ninth podcast in the AI Alignment Podcast series, hosted by Lucas Perry. El Mahdi pioneered Byzantine resilient machine learning devising a series of provably safe algorithms he recently presented at NeurIPS and ICML. Interested in theoretical biology, his work also includes the analysis of error propagation and networks applied to both neural and biomolecular networks. This particular episode was recorded at the Beneficial AGI 2019 conference in Puerto Rico. We hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, iTunes, Google Play, Stitcher, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

If you're interested in exploring the interdisciplinary nature of AI alignment, we suggest you take a look here at a preliminary landscape which begins to map this space.

Topics discussed in this episode include:

The Byzantine Generals' Problem
What this has to do with artificial intelligence and machine learning
Everyday situations where this is important
How systems and models are to update in the context of asynchrony
Why it's hard to do Byzantine resilient distributed ML.
Why this is important for long-term AI alignment
An overview of Adversarial Machine Learning and where Byzantine-resilient Machine Learning stands on the map is available in this (9min) video . A specific focus on Byzantine Fault Tolerant Machine Learning is available here (~7min)

In particular, El Mahdi argues in the first interview (and in the podcast) that technical AI safety is not only relevant for long term concerns, but is crucial in current pressing issues such as social media poisoning of public debates and misinformation propagation, both of which fall into Poisoning-resilience. Another example he likes to use is social media addiction, that could be seen as a case of (non) Safely Interruptible learning. This value misalignment is already an issue with the primitive forms of AIs that optimize our world today as they maximize our watch-time all over the internet.

The latter (Safe Interruptibility) is another technical AI safety question El Mahdi works on, in the context of Reinforcement Learning. This line of research was initially dismissed as "science fiction", in this interview (5min), El Mahdi explains why it is a realistic question that arises naturally in reinforcement learning

El Mahdi's work on Byzantine-resilient Machine Learning and other relevant topics is available on
his Google scholar profile.

The Byzantine Generals' Problem, Poisoning, and Distributed Machine Learning with El Mahdi El Mhamdi

Jack Goldsmith interviews John Judis about his book on the virtues and vices nationalism, why nationalism is a necessary prerequisite to democracy and social welfare, why we are seeing a global movement toward right-wing as opposed to left-wing nationalism and populism, and what Donald Trump got right in putting nationalist concerns at the center of the American political agenda.

The Nationalist Revival

See the full article here: https://futureoflife.org/2019/02/21/human-cognition-and-the-nature-of-intelligence-with-joshua-greene/

"How do we combine concepts to form thoughts? How can the same thought be represented in terms of words versus things that you can see or hear in your mind's eyes and ears? How does your brain distinguish what it's thinking about from what it actually believes? If I tell you a made up story, yesterday I played basketball with LeBron James, maybe you'd believe me, and then I say, oh I was just kidding, didn't really happen. You still have the idea in your head, but in one case you're representing it as something true, in another case you're representing it as something false, or maybe you're representing it as something that might be true and you're not sure. For most animals, the ideas that get into its head come in through perception, and the default is just that they are beliefs. But humans have the ability to entertain all kinds of ideas without believing them. You can believe that they're false or you could just be agnostic, and that's essential not just for idle speculation, but it's essential for planning. You have to be able to imagine possibilities that aren't yet actual. So these are all things we're trying to understand. And then I think the project of understanding how humans do it is really quite parallel to the project of trying to build artificial general intelligence." -Joshua Greene

Josh Greene is a Professor of Psychology at Harvard, who focuses on moral judgment and decision making. His recent work focuses on cognition, and his broader interests include philosophy, psychology and neuroscience. He is the author of Moral Tribes: Emotion, Reason, and the Gap Bewtween Us and Them.  Joshua Greene's research focuses on further understanding key aspects of both individual and collective intelligence. Deepening our knowledge of these subjects allows us to understand the key features which constitute human general intelligence, and how human cognition aggregates and plays out through group choice and social decision making. By better understanding the one general intelligence we know of, namely humans, we can gain insights into the kinds of features that are essential to general intelligence and thereby better understand what it means to create beneficial AGI. This particular episode was recorded at the Beneficial AGI 2019 conference in Puerto Rico. We hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, iTunes, Google Play, Stitcher, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here.

Topics discussed in this episode include:

-The multi-modal and combinatorial nature of human intelligence
-The symbol grounding problem
-Grounded cognition
-Modern brain imaging
-Josh's psychology research using John Rawls’ veil of ignorance
-Utilitarianism reframed as 'deep pragmatism'

AIAP: Human Cognition and the Nature of Intelligence with Joshua Greene

Lanhee Chen breaks down the debate on single-payer health care, its different types, and why none of them will work.

Lanhee Chen: Why the Health Care Wars Just Won’t End

In this month’s podcast, Ariel spoke with Ashley Llorens, the Founding Chief of the Intelligent Systems Center at the John Hopkins Applied Physics Laboratory, and Francesca Rossi, the IBM AI Ethics Global Leader at the IBM TJ Watson Research Lab and an FLI board member, about developing AI that will make us safer, more productive, and more creative. Too often, Rossi points out, we build our visions of the future around our current technology. Here, Llorens and Rossi take the opposite approach: let's build our technology around our visions for the future.

FLI Podcast: Applying AI Safety & Ethics Today with Ashley Llorens & Francesca Rossi

In this episode of the Sufi Heart Podcast, Omid concludes his exploration of the poem “The Conference of the Birds”.
Omid looks at the themes of Sufi poet Farid ud-Din Attar’s poem which represent our own spiritual journey and relationship with God.

Be sure to listen to the first part of this story: https://soundcloud.com/beherenownetwork/omid-safi-sufi-heart-ep-12-the-conference-of-the-birds

Omid Safi – Sufi Heart – Ep. 13 – The Conference of the Birds part 2

In this podcast, Lucas spoke with Jade Leung from the Center for the Governance of AI (GovAI). GovAI strives to help humanity capture the benefits and mitigate the risks of artificial intelligence. The center focuses on the political challenges arising from transformative AI, and they seek to guide the development of such technology for the common good by researching issues in AI governance and advising decision makers. Jade is Head of Research and Partnerships at GovAI, where her research focuses on modeling the politics of strategic general purpose technologies, with the intention of understanding which dynamics seed cooperation and conflict.

Topics discussed in this episode include:

-The landscape of AI governance
-The Center for the Governance of AI’s research agenda and priorities
-Aligning government and companies with ideal governance and the common good
-Norms and efforts in the AI alignment community in this space
-Technical AI alignment vs. AI Governance vs. malicious use cases
-Lethal autonomous weapons
-Where we are in terms of our efforts and what further work is needed in this space

You can take a short (3 minute) survey to share your feedback about the podcast here: www.surveymonkey.com/r/YWHDFV7

AIAP: On the Governance of AI with Jade Leung

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.