Ali Behrouz, grad student at Cornell and Google researcher, discusses his potentially transformative work on new architectures for continual learning in AI. His paper "Nested Learning," praised by Jeff Dean as a possible paradigm shift, enables models to adapt to new context while preserving core knowledge by updating different layers at different frequencies, inspired by human memory systems. The conversation also covers his latest work on AI "sleep" for memory consolidation, why he sees all deep learning as associative memory, and the profound implications of continual learning for privacy, alignment, and the path to AGI.
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This year-end live show features nine rapid-fire conversations to make sense of AI’s 2025 and what might define 2026. PSA for AI builders: Interested in alignment, governance, or AI safety? Learn more about the MATS Summer 2026 Fellowship and submit your name to be notified when applications open: https://matsprogram.org/s26-tcr. Zvi Moshowitz maps the OpenAI–Anthropic–Google race, the denialism gap, and why his PDoom is still ~60–70%. Greg (ARC-AGI Prize), Eugenia Kuyda, Ali Behrouz, Logan Kirkpatrick, and Jungwon Hwang cover sample-efficient benchmarks and ARC-AGI 3, companions and human-flourishing metrics, continual-learning memory, Gemini 3 Flash for developers, and AI for scientific decisions.
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CHAPTERS:
(00:00) Sponsor: Gemini 3 in Google AI Studio
(00:31) Live show experiment
(02:26) Zvi: discourse and denial
(13:28) Continual learning and doom
(22:05) ArcAGI: what's missing (Part 1)
(22:09) Sponsors: MATS | Framer
(25:28) ArcAGI: what's missing (Part 2)
(31:58) Scaffolds and tiny models
(38:58) ArcAGI 3 game worlds
(45:13) AI companions landscape (Part 1)
(45:21) Sponsors: Shopify | Tasklet
(48:29) AI companions landscape (Part 2)
(58:14) Wabi apps and caution
(01:08:16) Nested learning, layered memory
(01:23:14) Gemini 3 Flash launch
(01:34:09) RAG, agents, dev advice
(01:42:20) Elicit speeds evidence synthesis
(01:57:57) Outro
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We know that the top-tier AI labs are spending unbelievable amounts of money on talent. But what are these researchers actually working on? And how do we know that they're making progress? And furthermore, how can we even measure that progress? On this episode, we speak with Jack Morris, an AI researcher and Ph.D. candidate at Cornell University, who is also a part-time researcher at Meta. We talk about what he does, and why breakthroughs seem to be lumpy and unpredictable. We also talk about the battle between open- and closed-source approaches, US vs. Chinese labs, and how an individual talent thinks about where they want to spend their time, balancing the desire for research and prestige with a big fat paycheck.
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Our last AI PhD grad student feature was Shunyu Yao, who happened to focus on Language Agents for his thesis and immediately went to work on them for OpenAI. Our pick this year is Jack Morris, who bucks the “hot” trends by -not- working on agents, benchmarks, or VS Code forks, but is rather known for his work on the information theoretic understanding of LLMs, starting from embedding models and latent space representations (always close to our heart).
Jack is an unusual combination of doing underrated research but somehow still being to explain them well to a mass audience, so we felt this was a good opportunity to do a different kind of episode going through the greatest hits of a high profile AI PhD, and relate them to questions from AI Engineering.
Papers and References made
* AI grad school:
* A new type of information theory:
* Embeddings
* Text Embeddings Reveal (Almost) As Much As Text: https://arxiv.org/abs/2310.06816
* Contextual document embeddings https://arxiv.org/abs/2410.02525
Harnessing the Universal Geometry of Embeddings: https://arxiv.org/abs/2505.12540
* Language models
* GPT-style language models memorize 3.6 bits per param:
* Approximating Language Model Training Data from Weights: https://arxiv.org/abs/2506.15553
* LLM Inversion
* “There Are No New Ideas In AI.... Only New Datasets”
* misc reference: https://junyanz.github.io/CycleGAN/
—
for others hiring AI PhDs, Jack also wanted to shout out his coauthor
Zach Nussbaum, his coauthor on Nomic Embed: Training a Reproducible Long Context Text Embedder.
Full Video Episode
Timestamps
00:00 Introduction to Jack Morris01:18 Career in AI03:29 The Shift to AI Companies03:57 The Impact of ChatGPT04:26 The Role of Academia in AI05:49 The Emergence of Reasoning Models07:07 Challenges in Academia: GPUs and HPC Training11:04 The Value of GPU Knowledge14:24 Introduction to Jack's Research15:28 Information Theory17:10 Understanding Deep Learning Systems19:00 The "Bit" in Deep Learning20:25 Wikipedia and Information Storage23:50 Text Embeddings and Information Compression27:08 The Research Journey of Embedding Inversion31:22 Harnessing the Universal Geometry of Embeddings34:54 Implications of Embedding Inversion36:02 Limitations of Embedding Inversion38:08 The Capacity of Language Models40:23 The Cognitive Core and Model Efficiency50:40 The Future of AI and Model Scaling52:47 Approximating Language Model Training Data from Weights01:06:50 The "No New Ideas, Only New Datasets" Thesis
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
What does the future of blockchain look like, and where do multi-chain systems fit in? On this episode, we're joined by Emin Gün Sirer, CEO and Founder of Ava Labs and one of the most prominent figures in blockchain innovation. Emin unpacks how Avalanche is reshaping the decentralized world with its groundbreaking consensus protocol, multi-chain scaling, and commitment to maintaining blockchain’s core values.
We also discuss:
Avalanche vs. Bitcoin and Ethereum
Gaming and DeFi innovations on Avalanche
Fighting centralization and the challenges of scale
The role of stablecoins and central banks in Avalanche’s ecosystem
Chapters:
00:00 Defiant intro
00:07 If the future of blockchain is centralized, I don’t want to be here
00:54 intro to Emin Gün Sirer, Founder and CEO of Ava Labs
04:29 Building the fastest consensus protocol on the market
04:43 How is Avalanche different from Bitcoin?
08:10 Avalanche vs. other multi chain systems
12:16 L2 scaling vs. multi chain scaling
16:55 Evaluating Ethereum’s promises
19:45 Interoperability on Avalanche
20:25 What’s the narrative behind Avalanche?
23:47 Avalanche’s long term strategy 💫
26:25 Gaming example: OTG
29:16 How flexible are Avalanche chains?
31:13 How do you maintain the tenets of blockchain across chains?
33:05 What's the liquidity breakdown between these L1 chains?
34:54 Avalanche’s TVL vs users and adoption
38:00 Fighting centralization on Avalanche
43:15 AVAX value and uses
45:58 Increased activity on Avalanche
48:00 It’s been a Bitcoin story: where do alt projects fit in?
53:33 Stablecoins and exciting catalysts for DeFi
53:15 Assets want to be digital
55:35 How do stable coins fit into Avalanche?
56:19 Prediction: central banks getting into stablecoins
57:11 Challenges with central banks getting into stablecoins
59:13 The next major milestones for Avalanche
01:06:52 Closing Remarks
In this episode of The Cognitive Revolution, Ali Behrouz, a PhD student at Cornell University, delves into his research on enhancing memory mechanisms in large language models through his latest paper titled Titans. Behrouz discusses the limitations of current models in maintaining long-term coherence and introduces the concept of a neural network as a memory module. Highlighting architectures such as memory as context and memory as gate, he explains how these innovative approaches can significantly improve long-term memory retention in AI systems. The discussion also touches upon challenges such as catastrophic forgetting and the need for more effective models in reinforcement learning and decision-making tasks. This insightful conversation sheds light on the future directions and potential applications of advanced memory mechanisms in AI.
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Prof. Kevin Ellis and Dr. Zenna Tavares talk about making AI smarter, like humans. They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data.
They discuss two main ways AI can "think": one way is like following specific rules or steps (like a computer program), and the other is more intuitive, like guessing based on patterns (like modern AI often does). They found combining both methods works well for solving complex puzzles like ARC.
A key idea is "compositionality" - building big ideas from small ones, like LEGOs. This is powerful but can also be overwhelming. Another important idea is "abstraction" - understanding things simply, without getting lost in details, and knowing there are different levels of understanding.
Ultimately, they believe the best AI will need to explore, experiment, and build models of the world, much like humans do when learning something new.
SPONSOR MESSAGES:
***
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
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***
TRANSCRIPT:
https://www.dropbox.com/scl/fi/3ngggvhb3tnemw879er5y/BASIS.pdf?rlkey=lr2zbj3317mex1q5l0c2rsk0h&dl=0
Zenna Tavares:
http://www.zenna.org/
Kevin Ellis:
https://www.cs.cornell.edu/~ellisk/
TOC:
1. Compositionality and Learning Foundations
[00:00:00] 1.1 Compositional Search and Learning Challenges
[00:03:55] 1.2 Bayesian Learning and World Models
[00:12:05] 1.3 Programming Languages and Compositionality Trade-offs
[00:15:35] 1.4 Inductive vs Transductive Approaches in AI Systems
2. Neural-Symbolic Program Synthesis
[00:27:20] 2.1 Integration of LLMs with Traditional Programming and Meta-Programming
[00:30:43] 2.2 Wake-Sleep Learning and DreamCoder Architecture
[00:38:26] 2.3 Program Synthesis from Interactions and Hidden State Inference
[00:41:36] 2.4 Abstraction Mechanisms and Resource Rationality
[00:48:38] 2.5 Inductive Biases and Causal Abstraction in AI Systems
3. Abstract Reasoning Systems
[00:52:10] 3.1 Abstract Concepts and Grid-Based Transformations in ARC
[00:56:08] 3.2 Induction vs Transduction Approaches in Abstract Reasoning
[00:59:12] 3.3 ARC Limitations and Interactive Learning Extensions
[01:06:30] 3.4 Wake-Sleep Program Learning and Hybrid Approaches
[01:11:37] 3.5 Project MARA and Future Research Directions
REFS:
[00:00:25] DreamCoder, Kevin Ellis et al.
https://arxiv.org/abs/2006.08381
[00:01:10] Mind Your Step, Ryan Liu et al.
https://arxiv.org/abs/2410.21333
[00:06:05] Bayesian inference, Griffiths, T. L., Kemp, C., & Tenenbaum, J. B.
https://psycnet.apa.org/record/2008-06911-003
[00:13:00] Induction and Transduction, Wen-Ding Li, Zenna Tavares, Yewen Pu, Kevin Ellis
https://arxiv.org/abs/2411.02272
[00:23:15] Neurosymbolic AI, Garcez, Artur d'Avila et al.
https://arxiv.org/abs/2012.05876
[00:33:50] Induction and Transduction (II), Wen-Ding Li, Kevin Ellis et al.
https://arxiv.org/abs/2411.02272
[00:38:35] ARC, François Chollet
https://arxiv.org/abs/1911.01547
[00:39:20] Causal Reactive Programs, Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares
http://www.zenna.org/publications/autumn2022.pdf
[00:42:50] MuZero, Julian Schrittwieser et al.
http://arxiv.org/pdf/1911.08265
[00:43:20] VisualPredicator, Yichao Liang
https://arxiv.org/abs/2410.23156
[00:48:55] Bayesian models of cognition, Joshua B. Tenenbaum
https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/
[00:49:30] The Bitter Lesson, Rich Sutton
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
[01:06:35] Program induction, Kevin Ellis, Wen-Ding Li
https://arxiv.org/pdf/2411.02272
[01:06:50] DreamCoder (II), Kevin Ellis et al.
https://arxiv.org/abs/2006.08381
[01:11:55] Project MARA, Zenna Tavares, Kevin Ellis
https://www.basis.ai/blog/mara/
In this episode of Empire, Emin Gün Sirer and John Wu explain why Avalanche is the most scalable, decentralized L1. We start with why developers choose Avalanche, then quickly transition into the L2 debate. Emin explains why Subnets are superior and how the L2 vision and L2 reality are miles apart. We also talk about Avalanche's consensus engine, business development, the multichain future and more!
- -
Timestamps:
(00:00) Introduction
(01:01) Avalanche’s Core Thesis
(04:17) Why Build on Avalanche?
(14:19) The Problem with L2s
(19:08) Avalanche's Tradeoffs
(23:41) The Consensus Engine
(30:00) Learnings From Other Networks
(33:29) Solana vs Avalanche
(40:59) Avalanche’s Security Guarantees
(45:00) Business Development Strategy
(55:09) Warp: Communication Between Subnets
(1:00:01) On-Chain Governance
(1:01:41) Avalanche’s Community
(1:08:29) A Multichain Future?
- -
Follow Emin: https://twitter.com/el33th4xor
Follow John: https://twitter.com/John1wu
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- -
Resources
Avalanche
https://www.avax.network/
Ava Labs
https://www.avalabs.org/
- -
Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, and our guests may hold positions in the companies, funds, or projects discussed.
Thanks to work from home, and other trends, workers are being electronically monitored by their bosses like never before. But some industries have had experience with this for awhile. Truck drivers, in particular, have been under legally-required electronic monitoring for several years now. Not only are their hours and miles electronically logged, increasingly they're subject to facial cameras and other types of body monitoring. On this episode, we speak with Karen Levy, a professor at Cornell and the author of "Data Driven: Truckers, Technology, and the New Workplace Surveillance" to discuss how surveillance works within the trucking industry, and what it means for everyone else.
See omnystudio.com/listener for privacy information.
Welcome to The Chopping Block! Crypto insiders Haseeb Qureshi, Robert Leshner and Tarun Chitra chop it up about the latest news in the digital asset industry. In this episode, Emin Gün Sirer, the emperor of Avalanche, also joined the conversation
Show topics: Gün’s take on the Ava Labs conspiracy story and whether there was any truth in the videos
The impact of the Merge on ETH issuance and energy usage
What’s going on with the ETHPoW fork, and how the team has been messing up
The TL;DR of the technical side of Ethereum scaling, explained by Tarun
How, in the beginning, proof of stake was designed to resemble proof of work
What Ethereum miners are going to do after the Merge
Whether increasing hash rates spike prices of PoW coins
How the Basic Attention Token is like the Stanford prison experiment
Whether the fights between Gün and other founders were positive or negative
How an exchange delisted every privacy coin
Whether Coinbase's support of the lawsuit against the US Treasury over Tornado Cash was a PR move
How this moment in history resembles the 90s and the rise of the internet
Whether there are blockchain-haters and what the crypto industry can do better
How there can’t be a single chain to meet every need
How FTX and Coinbase have different approaches to regulation
How would everyone celebrate a successful Merge and whether there’s a chance of it failing
Hosts
Haseeb Qureshi, managing partner at Dragonfly Capital
Tarun Chitra, managing partner at Robot Ventures
Robert Leshner, founder of Compound
Guest
Emin Gün Sirer, Founder and CEO of Ava Labs
Episode Links
Ava Labs Accusations: Emin Gün Sirer’s statement
CoinDesk article
Crypto Leaks article
Tornado Cash
Treasury Press release
Coinbase supports lawsuit against US Treasury
Huobi delisting privacy coins
Previous coverage of the Tornado Cash Sanctions on Unchained:
Is TRM Labs Blocking Addresses From DeFi Protocols? Ari Redbord Says No
Tornado Cash Sanctioned. Did the Government Overstep Its Bounds?
The Chopping Block: Did OFAC Overstep by Sanctioning Tornado Cash?
Given the Sanctions on Tornado Cash, Is Ethereum Censorship Resistant?
Preston Van Loon on Ethereum's Merge and His Lawsuit Against Treasury
ETH Proof of Work and Miners: ChainID missing; the request from Coinbase
CoinDesk article
The ETHPoW team said the fork will be deployed within 24 hours of the Merge
ETHPoW team’s promise to abolish EIP-1559
ETHPoW first blocks
The Merge: Ethereum Foundation Merge announcement.
Ethereum carbon emission reductions.
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We're revisiting a favorite interview featuring Eswar Prasad, the Tolani Senior Professor of Trade Policy at Cornell University and author of “The Future of Money: How the Digital Revolution is Transforming Currencies and Finance,” j
Eswar discusses the state of play regarding the global financial ecosystem, the role of the US Dollar, and the opportunities and risks of cryptocurrencies and decentralized finance.
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In this episode from July 2019, Kurt House, CEO and co-founder of Kobold Metals, John Thompson, professor of earth and geosciences at Cornell; and Connie Chan, a16z general partner for consumer, talk with editorial partner Hanne Winarsky about the way technology is transforming how we find cobalt, and the mining industry as a whole, as well as the science behind why cobalt is so critical for batteries, the data and knowledge behind mining today vs the past, and more.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Chris Mason is a professor of genomics, physiology, and biophysics at Cornell, doing research on the long-term effects of space on the human body. He is the author of The Next 500 Years: Engineering Life to Reach New Worlds. 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.
(0:00) – Introduction
(7:43) – Human extinction awareness
(16:29) – Heat death of the universe
(22:05) – Alone in the universe
(25:41) – Aliens
(33:50) – Entropy goggles
(48:04) – Genetics
(56:14) – Scott Kelly
(1:02:12) – Adapting to space
(1:12:13) – Sex in space
(1:14:46) – Colonizing planets
(1:21:25) – Culture on Mars
(1:25:51) – Commercial space flights
(1:33:09) – Podcast in space
(1:40:43) – Axiom Space
(1:42:59) – Designing space experiments
(1:49:49) – Robots in space
(1:52:30) – Space exploration
(1:56:28) – War in space
(2:00:05) – Launch toward the Second Sun
(2:06:14) – Chlorohumans
(2:11:50) – Extreme microbiome project
(2:18:17) – Space travel breakthroughs
(2:30:15) – Clones
(2:36:08) – AI age prediction
(2:41:38) – Advice for young people
(2:47:56) – Dark times
(2:52:19) – Mortality
(2:56:37) – Visiting ISS and deep space
(2:57:46) – Meaning of life
Eswar Prasad, the Tolani Senior Professor of Trade Policy at Cornell University and author of “The Future of Money: How the Digital Revolution is Transforming Currencies and Finance,” joins Scott to discuss the state of play regarding the global financial ecosystem, the role of the US Dollar, and the opportunities and risks of cryptocurrencies and decentralized finance. Follow Eswar on Twitter, @EswarSPrasad.
Scott opens with his thoughts on Discovery's decision to shut down CNN+.
Algebra of Happiness: functions of success.
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When Emin Gun Sirer first came on The Defiant in May 2020, Avalanche was not even on mainnet yet. Since then, it has risen to become one of the leading smart contract networks, while the AVAX token has risen over 7x in the last 12 months. We talk about how the project got here and how it wants to create a path for faster and more scalable decentralized network, amid heavy competition from other Layer 1 blockchains.
Emin makes the case for Avalache’s novel consensus mechanism, paired with subnets with their own virtual machines, and he also has scathing criticism for Ethereum and other Layer 2 scaling solutions. To him, once a platform starts building Layer 2s, it means they’ve run out of ideas on how to make a good Layer 1. We also discuss the big picture goal for Avalanche; Bitcoin centers around digital gold, Ethereum strives to create the world computer, and Avalanche wants to digitize all assets. If it's on a balance sheet, Emin says, it can be on a blockchain, and we discuss what it will take for it to get there.
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Today we’re joined by Kavita Bala, the Dean of Computing and Information Science at Cornell University.
Kavita, whose research explores the overlap of computer vision and computer graphics, joined us to discuss a few of her projects, including GrokStyle, a startup that was recently acquired by Facebook and is currently being deployed across their Marketplace features. We also talk about StreetStyle/GeoStyle, projects focused on using social media data to find style clusters across the globe.
Kavita shares her thoughts on the privacy and security implications, progress with integrating privacy-preserving techniques into vision projects like the ones she works on, and what’s next for Kavita’s research.
The complete show notes for this episode can be found at twimlai.com/go/410.
Come hang with the bad boys of natural language processing (NLP)! Jack Morris joins Daniel and Chris to talk about TextAttack, a Python framework for adversarial attacks, data augmentation, and model training in NLP. TextAttack will improve your understanding of your NLP models, so come prepared to rumble with your own adversarial attacks!
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Featuring:
Jack Morris – Website, GitHub, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
TextAttack
Attacking Machine Learning with Adversarial Examples
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Emin Gun Sirer is a Cornell University computer science professor who has been deeply involved in the Bitcoin and Ethereum communities from the very early days. We talk about how he got started in the field, building a cryptocurrency long before Bitcoin. Gun, as friends call him, then turned his attention to Ethereum, notably catching the bug in The DAO, but failing to alert the community about it.
After years scrutinizing existing blockchains, he’s back at making one himself with AVA, whose testnet launched two weeks ago. He explains the inner workings of this network, the first built over the Avalanche protocol, and how it can process several thousands of transactions per second at latencies of under one second.
He also talks about his plans to release a subnetwork called Athereum shortly after the AVA mainnet launches in July. Athereum will be almost identical to Ethereum; it will replicate its smart contracts and assets, and ETH holders will hold the equivalent amount of ATH. Sirer says the intention is for Athereum to serve as a safety net in case something goes wrong with ETH 2.0. If this sounds like Ethereum’s old friend wants to bring on some serious competition to the second-biggest chain, that’s because he is. Still, he’s quick to highlight he wants Ethereum to succeed and that he’s not after Ethereum dollars, but rather after money flows that are outside of Ethereum.
with Kurt House (@kurtzhouse), John Thompson, Connie Chan (@conniechan) and Hanne Tidnam (@omnivorousread)
The exploration for and mining of certain metals has driven huge epochs of human civilization, from copper and iron to gold and diamonds. In this conversation, Kurt House, CEO and co-founder of KoBold Metals; John Thompson, professor of earth and geosciences at Cornell and longtime advisor to the mining industry; and Connie Chan, general partner for consume, talk with Hanne Tidnam about why it is that cobalt is suddenly one of the most important metals on the planet.
Because this metal makes today's best batteries for phones, electric cars, and more, we have gone from little to enormous demand -- with that demand expected to only increase. This conversation covers the way technology is transforming how we find cobalt, and the mining industry as a whole. Along the way we touch on the science behind why exactly it is that cobalt is so damn good in batteries; what we know about what makes cobalt as a metal 'tick', where it's currently mined, and where it's most likely to be found; what data and knowledge used to drive mining; and what the new data sources, technologies, and techniques are today, from geophysical/ geochemical data, to agricultural information, to old boxes collected over centuries in the basements and attics of mining cos…. all of this to satisfy the incredible spike of demand for this material, as we enter a new age of battery metals.
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Today we’re joined by Karen Levy, assistant professor in the department of information science at Cornell University. Karen’s research focuses on how rules and technologies interact to regulate behavior, especially the legal, organizational, and social aspects of surveillance and monitoring. In our conversation, we discuss how data tracking and surveillance can be used in ways that can be abusive to various marginalized groups, including detailing her extensive research into truck driver surveillance.
Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.
Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”
In the final episode of our re:Invent series, we're joined by Thorsten Joachims, Professor in the Department of Computer Science at Cornell University. We discuss his presentation “Unbiased Learning from Biased User Feedback,” looking at some of the inherent and introduced biases in recommender systems, and the ways to avoid them. We also discuss how inference techniques can be used to make learning algorithms more robust to bias, and how these can be enabled with the correct type of logging policies.
Cornell University computer science professor Emin Gun Sirer, an influential figure in the cryptocurrency and blockchain space, describes his ideas for improving security in the space, his skepticism about how to scale these networks, and how the last time financial institutions invested in their systems appears to be for Y2K. He also tells us how growing up in an environment where he saw a lot of scams helps him find problems in code, explains why Bitcoin is the “universal bug bounty,” and reveals how two high school students saved burgeoning cryptocurrency network Ethereum “like in the movies — just before the clock was going to expire.”
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