Insurers have quietly become a major driver of the private credit boom, with numerous private equity shops striking deals with insurance companies or buying them outright. But the entanglement with private credit is also changing the insurance industry itself, raising a number of questions about risk and regulation. Today we speak to Andrew Granato and Pranjal Drall, authors of a new paper, “Private Credit's State Backstop: How Private Equity Socializes Risk Through Insurers," examining the relationship between private credit and insurance. Granato (an assistant professor at the UT Austin Law School) and Drall (JD-PhD student in Financial Economics at Yale) talk to us about how PE got so interested in insurance in the first place, how both sides benefit from the relationship, and why taxpayers might ultimately be on the hook.
Read more:
Blue Owl Surges as Leaders Stress It’s More Than a Direct Lender
Ares $29 Billion Private Credit Fund Sees Uptick in Non-Accruals
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Most AI systems follow a gradient, a mathematical slope that tells them exactly how to improve, step by step, toward a known goal. Neuroevolution doesn't follow any gradient. Instead, it runs hundreds or thousands of competing solutions simultaneously, spreads them across the space of possibilities as broadly as possible, and lets the best ones recombine, the same logic that drives biological evolution. The result, as Risto Miikkulainen explains to Craig Smith, is creativity: solutions that no human designer would have anticipated, that emerge routinely from the evolutionary process.
Miikkulainen is a professor at UT Austin and VP of AI Research at Cognizant AI Labs, and he has been working on this field since the 1980s, which makes him both a historian of it and one of its most active frontiersmen.
The conversation covers a remarkable range: a mystery model that outperformed every competitor in a recent stock trading competition with forensic footprints pointing to neuroevolutionary AI; Sakana AI's system that autonomously designed experiments, wrote a paper, and had it accepted at a major machine learning conference; and a pandemic decision system that trained overnight and made country-specific recommendations by morning, with Iceland actually following some of them, all the way to the prime minister.
Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
Quantum computing is advancing rapidly, raising significant questions for cryptography and blockchain. In this episode, Scott Aaronson, quantum computing expert, and Justin Drake, cryptography researcher at the Ethereum Foundation, join us to explore the impact of quantum advancements on Bitcoin, Ethereum, and the future of crypto security. Are your coins safe? How soon do we need post-quantum cryptography? Tune in as we navigate this complex, fascinating frontier.
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------ TIMESTAMPS
0:00 Intro 6:50 Google Willow Chip 11:58 How is Quantum Computing Accelerating? 19:27 Quantum vs Classical Computers 40:18 Why are Quantum Computers so weird? 46:18 Quantum Computing & Cryptography 52:53 What will Break Cryptography 54:45 Time Horizons 1:03:14 Accounts Getting Hacked 1:13:23 The Bitcoin Case 1:24:10 Quantum Money 1:29:44 The Ethereum Case 1:35:00 Closing Thoughts 1:36:24 Debrief with Justin Drake
------ RESOURCES
Scott Aaronson https://www.scottaaronson.com/
Justin Drake https://x.com/drakefjustin
------ Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures
Professor Swarat Chaudhuri from the University of Texas at Austin and visiting researcher at Google DeepMind discusses breakthroughs in AI reasoning, theorem proving, and mathematical discovery. Chaudhuri explains his groundbreaking work on COPRA (a GPT-based prover agent), shares insights on neurosymbolic approaches to AI.
Professor Swarat Chaudhuri:
https://www.cs.utexas.edu/~swarat/
SPONSOR MESSAGES:
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/
TOC:
[00:00:00] 0. Introduction / CentML ad, Tufa ad
1. AI Reasoning: From Language Models to Neurosymbolic Approaches
[00:02:27] 1.1 Defining Reasoning in AI
[00:09:51] 1.2 Limitations of Current Language Models
[00:17:22] 1.3 Neuro-symbolic Approaches and Program Synthesis
[00:24:59] 1.4 COPRA and In-Context Learning for Theorem Proving
[00:34:39] 1.5 Symbolic Regression and LLM-Guided Abstraction
2. AI in Mathematics: Theorem Proving and Concept Discovery
[00:43:37] 2.1 AI-Assisted Theorem Proving and Proof Verification
[01:01:37] 2.2 Symbolic Regression and Concept Discovery in Mathematics
[01:11:57] 2.3 Scaling and Modularizing Mathematical Proofs
[01:21:53] 2.4 COPRA: In-Context Learning for Formal Theorem-Proving
[01:28:22] 2.5 AI-driven theorem proving and mathematical discovery
3. Formal Methods and Challenges in AI Mathematics
[01:30:42] 3.1 Formal proofs, empirical predicates, and uncertainty in AI mathematics
[01:34:01] 3.2 Characteristics of good theoretical computer science research
[01:39:16] 3.3 LLMs in theorem generation and proving
[01:42:21] 3.4 Addressing contamination and concept learning in AI systems
REFS:
00:04:58 The Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/
00:11:42 Software 2.0, https://medium.com/@karpathy/software-2-0-a64152b37c35
00:11:57 Solving Olympiad Geometry Without Human Demonstrations, https://www.nature.com/articles/s41586-023-06747-5
00:13:26 Lean, https://lean-lang.org/
00:15:43 A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play, https://www.science.org/doi/10.1126/science.aar6404
00:19:24 DreamCoder (Ellis et al., PLDI 2021), https://arxiv.org/abs/2006.08381
00:24:37 The Lambda Calculus, https://plato.stanford.edu/entries/lambda-calculus/
00:26:43 Neural Sketch Learning for Conditional Program Generation, https://arxiv.org/pdf/1703.05698
00:28:08 Learning Differentiable Programs With Admissible Neural Heuristics, https://arxiv.org/abs/2007.12101
00:31:03 Symbolic Regression With a Learned Concept Library (Grayeli et al., NeurIPS 2024), https://arxiv.org/abs/2409.09359
00:41:30 Formal Verification of Parallel Programs, https://dl.acm.org/doi/10.1145/360248.360251
01:00:37 Training Compute-Optimal Large Language Models, https://arxiv.org/abs/2203.15556
01:18:19 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, https://arxiv.org/abs/2201.11903
01:18:42 Draft, Sketch, and Prove: Guiding Formal Theorem Provers With Informal Proofs, https://arxiv.org/abs/2210.12283
01:19:49 Learning Formal Mathematics From Intrinsic Motivation, https://arxiv.org/pdf/2407.00695
01:20:19 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353
01:23:58 Learning to Prove Theorems via Interacting With Proof Assistants, https://arxiv.org/abs/1905.09381
01:39:58 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353
01:42:24 Programmatically Interpretable Reinforcement Learning (Verma et al., ICML 2018), https://arxiv.org/abs/1804.02477
This episode is sponsored by Crusoe. Crusoe Cloud is a scalable, clean, high-performance cloud, optimized for AI and HPC workloads, and powered by wasted, stranded or clean energy. Crusoe offers virtualized compute and storage solutions for a range of applications - including generative AI, computational biology, and rendering.
Visit crusoecloud.com to see what climate-aligned computing can do for your business.
On episode #143 of Eye on AI, Craig Smith sits down with Scott Aaronson, Schlumberger Centennial Chair of Computer Science at The University of Texas and director of its Quantum Information Center.
In this episode, we cut through the quantum computing hype and explore its profound implications for AI. We reveal the practicality of quantum computing, examining how companies are leveraging it to solve intricate problems, like vehicle routing, using D-Wave systems. Scott and I delve into the distinctions between quantum annealing and Grover-type speedups, shedding light on the potential of hybrid solutions that blend classical and quantum elements.
Shifting gears, we delve into the synergy between quantum computing and AI safety. Scott shares insights from his work at OpenAI, particularly a project aimed at fine-tuning language models like GPT for detecting AI-generated text, highlighting the implications of such advanced AI technology's potential misuse.
If you enjoyed this podcast, please consider leaving a 5-star rating on Spotify and a review on Apple Podcasts.
Craig Smith's Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
(00:00) Preview and Introduction
(04:13) Demystifying Quantum Computing
(16:04) Leveraging Quantum Computers for Optimization
(31:01) What is Quantum Computing?
(42:40) Advancements and Challenges in Quantum Computing
(54:57) Machine Learning and AI Safety
Many people consider space to be the next frontier and equally an infinite horizon to explore. But the reality is that not all “space” is the same and there are strategic zones that don’t only matter up there – but down here on Earth. Lower Earth Orbit (LEO) is one of those regions – a zone filled with satellites that support life on Earth, from agriculture to climate to navigation to defense. Unfortunately, these live satellites are not alone in our space highways. LEO is getting increasingly clogged with space debris; we’re polluting our skies just like we’re polluting our land.
In this episode, we have the pleasure of speaking with all three cofounders of Privateer – Steve Wozniak, Alex Fielding, and Dr. Moriba Jah, as they explore just how much junk is up there, how this challenge is expected to progress with time due to lower launch costs, and ultimately, what infrastructure is missing in this fragile ecosystem – from tracking to global treaties to a sharing economy of satellites.
By the end of the episode, listeners should be more equipped to understand how our infrastructure in space vastly impacts life on Earth, how the preservation of this ecosystem is crucial, and how Privateer is providing the map to better understand and fix the issue.
Timestamps:
With Steve Wozniak
00:00 - Intro
3:24 - Why space and why now?
8:55 - The changing perception around space
13:29 - Exponential technologies and thinking different
16:32 - Inventors vs engineers vs visionaries
18:46 - Early days at Apple and moving towards the future
20:53 - Steve’s personal fascinations
23:58 - How vocabulary drives awareness
1:21:55 - Woz returns!
With Alex Fielding and Dr. Moriba Jah
24:43 - Is space really an infinite void?
25:55 - The growing pollution in space
29:14 - The impact of space down on Earth
30:34 - The challenge of space policy and governance
38:27 - Orbital highways and carrying capacities
41:05 - Dependence on space infrastructure and its fragility
45:14 - Privateer’s role in the evolving ecosystem
46:52 - Democratizing space through data sharing
49:45 - Can we undo the damage that’s been done?
52:01 - Determining intent in space
58:17 - Talent needed in the space industry
1:01:04 - Privateer’s biggest challenges
1:09:22 - Space stewardship and Hawaii’s kuleana
1:15:19 - Who inspires Alex?
Resources:
Privateer’s website: https://mission.privateer.com/
Privateer’s Wayfinder tool: https://mission.privateer.com/
Privateer on Twitter: https://twitter.com/PrivateerSpace
Steve Wozniak on Twitter: https://twitter.com/stevewoz
Alex Fielding on Twitter: https://twitter.com/Alex__Fielding
Dr. Moriba Jah on Twitter: https://twitter.com/moribajah
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Today we’re joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and oceanography. In our conversation with Patrick, we explore some of the challenges of computational oceanography, the potential use cases for machine learning in this field, as well as how it can be used to support scientists in solving simulation problems, and the role of differential programming and how it is expressed in his work.
The complete show notes for this episode can be found at twimlai.com/go/557
Jeremi Suri is a historian at UT Austin. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(14:33) – Power of charisma
(20:24) – US presidency
(31:10) – Aliens
(36:17) – Bill Clinton
(39:07) – Students of history
(43:58) – George Washington
(46:44) – Putin
(53:27) – FDR
(1:08:39) – Henry Kissinger
(1:18:32) – Realpolitik
(1:30:22) – What is a just war?
(1:36:27) – Cold war
(1:40:44) – Communism in the United States
(1:50:42) – Vaccines and the future of the human species
(1:55:57) – Book recommendations
(1:57:31) – Learning another language
(2:01:58) – Advice for young people
(2:08:10) – Grandmother
(2:11:03) – Meaning of life
Risto Miikkulainen is a computer scientist at UT Austin. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(06:51) – If we re-ran Earth over 1 million times
(10:08) – Would aliens detect humans?
(12:46) – Evolution of intelligent life
(16:31) – Fear of death
(22:47) – Hyenas
(26:12) – Language
(29:43) – The magic of programming
(35:43) – Neuralink
(43:15) – Surprising discoveries by AI
(46:49) – How evolutionary computation works
(58:12) – Learning to walk
(1:01:25) – Robots and a theory of mind
(1:10:29) – Neuroevolution
(1:20:47) – Tesla Autopilot
(1:24:11) – Language and vision
(1:29:53) – Aliens communicating with humans
(1:35:29) – Would AI learn to lie to humans?
(1:42:03) – Artificial life
(1:46:56) – Cellular automata
(1:52:32) – Advice for young people
(1:57:09) – Meaning of life
Scott Aaronson is a Professor of Computer Science at The University of Texas at Austin, and director of its Quantum Information Center.
He's the author of one of the most interesting blogs on the internet: https://www.scottaaronson.com/blog/ and the book “Quantum Computing since Democritus”.
He was also my professor for a class on quantum computing.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.
Episode website here.
Follow me on Twitter to get updates on future episodes and guests.
Timestamps
(0:00) - Intro
(0:33) - Journey through high school and college
(12:37) - Early work
(19:15) - Why quantum computing took so long
(33:30) - Contributions from outside academia
(38:18) - Busy beaver function
(53:50) - New quantum algorithms
(1:03:30) - Clusters
(1:06:23) - Complexity and economics
(1:13:26) - Creativity
(1:24:07) - Advice to young people
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Scott Aaronson is a quantum computer scientist. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
Scott’s Blog: https://www.scottaaronson.com/blog/
Our previous episode: https://www.youtube.com/watch?v=uX5t8EivCaM
PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
00:00 – Introduction
07:46 – Simulation
12:38 – Theories of everything
18:18 – Consciousness
40:32 – Roger Penrose on consciousness
50:44 – Turing test
54:31 – GPT-3
1:03:02 – Universality of computation
1:09:33 – Complexity
1:15:38 – P vs NP
1:27:57 – Complexity of quantum computation
1:40:03 – Pandemic
1:53:49 – Love
Today we’re joined by Diana Marculescu, Professor of Electrical and Computer Engineering at UT Austin.
We caught up with Diana to discuss her work on hardware-aware machine learning. In particular, we explore her keynote, “Putting the “Machine” Back in Machine Learning: The Case for Hardware-ML Model Co-design” from CVPR 2020. We explore how her research group is focusing on making models more efficient so that they run better on current hardware systems, and how they plan on achieving true co
Today we’re joined by Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant AI. Risto joined us back on episode #47 to discuss evolutionary algorithms, and today we get an update on the latest on the topic. In our conversation, we discuss use cases for evolutionary AI and the latest approaches to deploying evolutionary models. We also explore his paper “Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential,” which digs into the historical evolution of AI.
Scott Aaronson is a professor at UT Austin, director of its Quantum Information Center, and previously a professor at MIT. His research interests center around the capabilities and limits of quantum computers and computational complexity theory more generally.
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.
This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.
This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching “Ride Home” in your podcast app.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
00:00 – Introduction
05:07 – Role of philosophy in science
29:27 – What is a quantum computer?
41:12 – Quantum decoherence (noise in quantum information)
49:22 – Quantum computer engineering challenges
51:00 – Moore’s Law
56:33 – Quantum supremacy
1:12:18 – Using quantum computers to break cryptography
1:17:11 – Practical application of quantum computers
1:22:18 – Quantum machine learning, questionable claims, and cautious optimism
1:30:53 – Meaning of life
"Most of what we send into outer space never comes back," says astrodynamicist and TED Fellow Moriba Jah. In this forward-thinking talk, Jah describes the space highways orbiting earth and how they're mostly populated by space junk. Learn more about his quest to develop and scale the world's first crowdsourced space traffic monitoring system -- and how it could help solve the debris problem in near-earth space.
Hosted on Acast. See acast.com/privacy for more information.
Kara Swisher's executive producer, Erica Anderson, talks with four TED Fellows at the 2019 TED Conference in Vancouver. In this collection of mini-interviews, you'll hear from biologist Danielle Lee, space environmentalist Moriba Jah, astrophysicist Erika Hamden and Good Food Institute founder Bruce Friedrich.
Learn more about your ad choices. Visit podcastchoices.com/adchoices
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. This time around we’re joined by Kristen Grauman, a professor in the department of computer science at UT Austin. Kristen specializes in Computer Vision and joined me leading up to her talk at the Deep Learning Summit “Learning where to look in video”. Kristen & I cover the details from her talk, like exploring how a vision system can learn how to move and where to look. Kristen considers how an embodied vision system can internalize the link between “how I move” and “what I see”, explore policies for learning to look around actively, and learn to mimic human videographer tendencies, automatically deciding where to look in unedited 360 degree video. The notes for this show can be found at twimlai.com/talk/85. For series details, visit twimlai.com/reinvent.
My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we've received a ton of requests for -- evolutionary algorithms. During our talk we discuss some of the things Sentient is working on in the financial services and retail fields, and we dig into the technology behind it, evolutionary algorithms, which is also the focus of Risto’s research at UT. I really enjoyed this interview and learned a ton, and I’m sure you will too! Notes for this show can be found at twimlai.com/talk/47.
In this episode of the SuperDataScience Podcast, I chat with Vice President of Research at Sentient AI, Risto Miikkulainen. You will hear about the applications of AI across multiple fields, learn about the 2 types of AI - the Evolutionary Algorithms and Reinforcement Learning Algorithms, and also get valuable insights on how AI is changing the employment landscape.
If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/67
