In this latest episode of the More From Sam series, Sam and Jaron talk about current events. They discuss topics from Making Sense Community, including one-world government, the value of a degree as AI reshapes careers, and factory farming ethics, along with Mamdani's DSA-aligned candidates, Trump's humiliating capitulation in the Iran deal, the Tulsi Gabbard guru story, and other topics.
If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.
Chatbots might help you get work done faster — but at what cost? When we outsource our reasoning to artificial intelligence, we reduce ourselves to "middle managers for our own thoughts," says AI and design researcher Advait Sarkar. He examines the cognitive trade-offs of using AI at work and introduces a different kind of tool: one that encourages critical thinking, nudges reflection and actually helps you get smarter.
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Nicole Forsgren created the most widely used frameworks for measuring developer productivity—DORA and SPACE. She wrote the foundational book Accelerate and is about to release her newest book, Frictionless, a practical guide for helping teams move faster in the AI era. She’s currently Senior Director of Developer Intelligence at Google.
We discuss:
1. Why most productivity metrics are a lie
2. Signs that your engineering team could be moving much faster
3. Why AI accelerates coding but developers aren’t speeding up as much as you think
4. AI’s impact on engineers getting into “flow”
5. Her framework for building and scaling a developer experience team
6. The three components of developer experience: flow state, cognitive load, and feedback loops
—
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—
Where to find Nicole Forsgren:
• Twitter: https://twitter.com/nicolefv
• LinkedIn: https://www.linkedin.com/in/nicolefv/
• Website: https://nicolefv.com/
—
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• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Introduction to Nicole Forsgren
(05:09) The concept of developer experience (DevEx)
(08:33) Flow state and cognitive load in the age of AI
(12:02) Challenges in measuring productivity with AI
(21:19) The importance of developer experience for business value
(22:20) Common issues and solutions in developer experience
(26:49) Signs your eng team is moving too slow
(29:52) How AI is improving productivity
(33:32) Real examples of productivity improvements
(36:35) Introducing her new book, Frictionless
(43:40) How to get started building a DevEx team
(45:15) The impact of forming developer experience teams
(46:15) How to measure the impact of DevEx teams
(48:53) Measuring the impact of AI tools on productivity
(55:16) Survey design for developer experience
(57:59) Popular AI tools for developers
(59:08) Bringing a product mindset to DevEx improvements
(01:00:40) AI corner
(01:02:33) Lightning round and final thoughts
—
Referenced:
• How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google): https://www.lennysnewsletter.com/p/how-to-measure-and-improve-developer
• DORA: https://dora.dev/
• The SPACE framework: A comprehensive guide to developer productivity: https://getdx.com/blog/space-metrics/
• Measuring developer productivity with the DX Core 4: https://getdx.com/research/measuring-developer-productivity-with-the-dx-core-4/
• Gloria Mark’s website: https://gloriamark.com/
• Taking Flight with Copilot: https://dl.acm.org/doi/10.1145/3589996
• DevEx in Action: https://spawn-queue.acm.org/doi/10.1145/3639443
• CodeX: https://openai.com/codex/
• Devin: https://devin.ai/
• Abi Noda on LinkedIn: https://www.linkedin.com/in/abinoda/
• DX is joining Atlassian: https://getdx.com/blog/dx-is-joining-atlassian/
• GitHub Copilot: https://github.com/features/copilot
• Cursor: https://cursor.com/
• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell
• Gemini Code Assist: https://codeassist.google/
• Claude Code: https://www.claude.com/product/claude-code
• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper
• Love Is Blind on Netflix: https://www.netflix.com/title/80996601
• Shrinking on AppleTV+: https://tv.apple.com/us/show/shrinking/umc.cmc.apzybj6eqf6pzccd97kev7bs
• Ninja Creami: https://www.amazon.com/Ninja-NC301-CREAMi-Containers-Bundle/dp/B0BLGR5JPV/
• Jura coffee maker: https://www.amazon.com/Jura-Nordic-Automatic-Coffee-Machine/dp/B0CF65BFZ1/
—
Recommended books:
• Frictionless: https://developerexperiencebook.com/
• DevEx Workbook: https://developerexperiencebook.com/#workbook
• Outlive: The Science and Art of Longevity: https://www.amazon.com/Outlive-Longevity-Peter-Attia-MD/dp/0593236599
• Back Mechanic: https://www.amazon.com/Back-Mechanic-Stuart-McGill-2015-09-30/dp/B01FKSGJYC
• How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project, from Home Renovations to Space Exploration and Everything in Between: https://www.amazon.com/How-Big-Things-Get-Done/dp/0593239512/
• The Undoing Project: A Friendship That Changed Our Minds: https://www.amazon.com/dp/B01KBM82M4/
—
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Lenny may be an investor in the companies discussed.
To hear more, visit www.lennysnewsletter.com
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How do you architect a live streaming system to deal with more load than it’s ever been done before? Today, we hear from an architect of such a system: Ashutosh Agrawal, formerly Chief Architect of JioCinema (and currently Staff Software Engineer at Google DeepMind.)
We take a deep dive into video streaming architecture, tackling the complexities of live streaming at scale (at tens of millions of parallel streams) and the challenges engineers face in delivering seamless experiences. We talk about the following topics:
• How large-scale live streaming architectures are designed
• Tradeoffs in optimizing performance
• Early warning signs of streaming failures and how to detect them
• Why capacity planning for streaming is SO difficult
• The technical hurdles of streaming in APAC regions
• Why Ashutosh hates APMs (Application Performance Management systems)
• Ashutosh’s advice for those looking to improve their systems design expertise
• And much more!
—
Timestamps
(00:00) Intro
(01:28) The world record-breaking live stream and how support works with live events
(05:57) An overview of streaming architecture
(21:48) The differences between internet streaming and traditional television.l
(22:26) How adaptive bitrate streaming works
(25:30) How throttling works on the mobile tower side
(27:46) Leading indicators of streaming problems and the data visualization needed
(31:03) How metrics are set
(33:38) Best practices for capacity planning
(35:50) Which resources are planned for in capacity planning
(37:10) How streaming services plan for future live events with vendors
(41:01) APAC specific challenges
(44:48) Horizontal scaling vs. vertical scaling
(46:10) Why auto-scaling doesn’t work
(47:30) Concurrency: the golden metric to scale against
(48:17) User journeys that cause problems
(49:59) Recommendations for learning more about video streaming
(51:11) How Ashutosh learned on the job
(55:21) Advice for engineers who would like to get better at systems
(1:00:10) Rapid fire round
—
The Pragmatic Engineer deepdives relevant for this episode:
• Software architect archetypes https://newsletter.pragmaticengineer.com/p/software-architect-archetypes
• Engineering leadership skill set overlaps https://newsletter.pragmaticengineer.com/p/engineering-leadership-skillset-overlaps
• Software architecture with Grady Booch https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch
—
See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Today we’re joined by Victor Dibia, principal research software engineer at Microsoft Research, to explore the key trends and advancements in AI agents and multi-agent systems shaping 2025 and beyond. In this episode, we discuss the unique abilities that set AI agents apart from traditional software systems–reasoning, acting, communicating, and adapting. We also examine the rise of agentic foundation models, the emergence of interface agents like Claude with Computer Use and OpenAI Operator, the shift from simple task chains to complex workflows, and the growing range of enterprise use cases. Victor shares insights into emerging design patterns for autonomous multi-agent systems, including graph and message-driven architectures, the advantages of the “actor model” pattern as implemented in Microsoft’s AutoGen, and guidance on how users should approach the ”build vs. buy” decision when working with AI agent frameworks. We also address the challenges of evaluating end-to-end agent performance, the complexities of benchmarking agentic systems, and the implications of our reliance on LLMs as judges. Finally, we look ahead to the future of AI agents in 2025 and beyond, discuss emerging HCI challenges, their potential for impact on the workforce, and how they are poised to reshape fields like software engineering.
The complete show notes for this episode can be found at https://twimlai.com/go/718.
Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.
At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.
Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition.
Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.
Buy Chris' textbook here:
https://amzn.to/3vvLcCh
More about Prof. Chris Bishop:
https://en.wikipedia.org/wiki/Christopher_Bishop
https://www.microsoft.com/en-us/research/people/cmbishop/
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TOC:
00:00:00 - Intro to Chris
00:06:54 - Changing Landscape of AI
00:08:16 - Symbolism
00:09:32 - PRML
00:11:02 - Bayesian Approach
00:14:49 - Are NNs One Model or Many, Special vs General
00:20:04 - Can Language Models Be Creative
00:22:35 - Sparks of AGI
00:25:52 - Creativity Gap in LLMs
00:35:40 - New Deep Learning Book
00:39:01 - Favourite Chapters
00:44:11 - Probability Theory
00:45:42 - AI4Science
00:48:31 - Inductive Priors
00:58:52 - Drug Discovery
01:05:19 - Foundational Bias Models
01:07:46 - How Fundamental Is Our Physics Knowledge?
01:12:05 - Transformers
01:12:59 - Why Does Deep Learning Work?
01:16:59 - Inscrutability of NNs
01:18:01 - Example of Simulator
01:21:09 - Control
Leslie Lamport is a computer scientist & mathematician who won ACM’s Turing Award in 2013 for his fundamental contributions to the theory and practice of distributed and concurrent systems. He also created LaTeX and TLA+, a high-level language for “writing down the ideas that go into the program before you do any coding.”
Join the discussion
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Featuring:
Leslie Lamport – Website
Jerod Santo – Website, GitHub, LinkedIn, Mastodon, X
Show Notes:
Leslie Lamport - A.M. Turing Award Laureate
The Man Who Revolutionized Computer Science With Math - YouTube
TLA+ Helps Programmers Squash Bugs Before Coding - IEEE Spectrum
The TLA+ Home Page
Something missing or broken? PRs welcome!
This episode is brought to you by DX—a platform for measuring and improving developer productivity.
—
Dr. Nicole Forsgren is a developer productivity and DevOps expert who works with engineering organizations to make work better. Best known as co-author of the Shingo Publication Award-winning book Accelerate and the DevOps Handbook, 2nd edition and author of the State of DevOps Reports, she has helped some of the biggest companies in the world transform their culture, processes, tech, and architecture. Nicole is currently a Partner at Microsoft Research, leading developer productivity research and strategy, and a technical founder/CEO with a successful exit to Google. In a previous life, she was a software engineer, sysadmin, hardware performance engineer, and professor. She has published several peer-reviewed journal papers, has been awarded public and private research grants (funders include NASA and the NSF), and has been featured in the Wall Street Journal, Forbes, Computerworld, and InformationWeek. In today’s podcast, we discuss:
• Two frameworks for measuring developer productivity: DORA and SPACE
• Benchmarks for what good and great look like
• Common mistakes to avoid when measuring developer productivity
• Resources and tools for improving your metrics
• Signs your developer experience needs attention
• How to improve your developer experience
• Nicole’s Four-Box framework for thinking about data and relationships
—
Find the full transcript at: https://www.lennysnewsletter.com/p/how-to-measure-and-improve-developer
—
Where to find Nicole Forsgren:
• Twitter: https://twitter.com/nicolefv
• LinkedIn: https://www.linkedin.com/in/nicolefv/
• Website: https://nicolefv.com/
—
Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• Twitter: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Nicole’s background
(07:55) Unpacking the terms “developer productivity,” “developer experience,” and “DevOps”
(10:06) How to move faster and improve practices across the board
(13:43) The DORA framework
(18:54) Benchmarks for success
(22:33) Why company size doesn’t matter
(24:54) How to improve DevOps capabilities by working backward
(29:23) The SPACE framework and choosing metrics
(32:51) How SPACE and DORA work together
(35:39) Measuring satisfaction
(37:52) Resources and tools for optimizing metrics
(41:29) Nicole’s current book project
(45:43) Common pitfalls companies run into when rolling out developer productivity/optimizations
(47:42) How the DevOps space has progressed
(50:07) The impact of AI on the developer experience and productivity
(54:04) First steps to take if you’re trying to improve the developer experience
(55:15) Why Google is an example of a company implementing DevOps solutions well
(56:11) The importance of clear communication
(57:32) Nicole’s Four-Box framework
(1:05:15) Advice on making decisions
(1:08:56) Lightning round
—
Referenced:
• Chef: https://www.chef.io/
• DORA: https://dora.dev/
• GitHub: https://github.com/
• Microsoft Research: https://www.microsoft.com/en-us/research/
• What is DORA?: https://devops.com/what-is-dora-and-why-you-should-care/
• Dustin Smith on LinkedIn: https://www.linkedin.com/in/dustin-smith-b0525458/
• Nathen Harvey on LinkedIn: https://www.linkedin.com/in/nathen/
• What is CI/CD?: https://about.gitlab.com/topics/ci-cd/
• Trunk-based development: https://cloud.google.com/architecture/devops/devops-tech-trunk-based-development
• DORA DevOps Quick Check: https://dora.dev/quickcheck/
• Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations: https://www.amazon.com/Accelerate-Software-Performing-Technology-Organizations/dp/1942788339
• The SPACE of Developer Productivity: https://queue.acm.org/detail.cfm?id=3454124
• DevOps Metrics: Nicole Forsgren and Mik Kersten: https://queue.acm.org/detail.cfm?id=3182626
• How to Measure Anything: Finding the Value of Intangibles in Business: https://www.amazon.com/How-Measure-Anything-Intangibles-Business/dp/1118539273/
• GitHub Copilot: https://github.com/features/copilot
• Tabnine: https://www.tabnine.com/the-leading-ai-assistant-for-software-development
• Nicole’s Decision-Making Spreadsheet: https://docs.google.com/spreadsheets/d/1wItAODkhZ-zKnnFbyDERCd8Hq2NQ03WPvCfigBQ5vpc/edit?usp=sharing
• How to do linear regression and correlation analysis: https://www.lennysnewsletter.com/p/linear-regression-and-correlation-analysis
• Good Strategy/Bad Strategy: The difference and why it matters: https://www.amazon.com/Good-Strategy-Bad-difference-matters/dp/1781256179/
• Designing Your Life: How to Build a Well-Lived, Joyful Life: https://www.amazon.com/Designing-Your-Life-Well-Lived-Joyful/dp/1101875321
• Ender’s Game: https://www.amazon.com/Enders-Game-Ender-Quintet-1/dp/1250773024/ref=tmm_pap_swatch_0
• Suits on Netflix: https://www.netflix.com/title/70195800
• Ted Lasso on AppleTV+: https://tv.apple.com/us/show/ted-lasso
• Never Have I Ever on Netflix: https://www.netflix.com/title/80179190
• Eight Sleep: https://www.eightsleep.com/
• COSRX face masks: https://www.amazon.com/COSRX-Advanced-Secretion-Hydrating-Moisturizing/dp/B08JSL9W6K/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe
Today we’re joined by Robert Osazuwa Ness, a senior researcher at Microsoft Research, Professor at Northeastern University, and Founder of Altdeep.ai. In our conversation with Robert, we explore whether large language models, specifically GPT-3, 3.5, and 4, are good at causal reasoning. We discuss the benchmarks used to evaluate these models and the limitations they have in answering specific causal reasoning questions, while Robert highlights the need for access to weights, training data, and architecture to correctly answer these questions. The episode discusses the challenge of generalization in causal relationships and the importance of incorporating inductive biases, explores the model's ability to generalize beyond the provided benchmarks, and the importance of considering causal factors in decision-making processes.
The complete show notes for this episode can be found at twimlai.com/go/638.
Thanks to the over 1m people that have checked out the Rise of the AI Engineer. It’s a long July 4 weekend in the US, and we’re celebrating with a podcast feed swap!
We’ve been big fans of Nathan Labenz and Erik Torenberg’s work at the Cognitive Revolution podcast for a while, which started around the same time as we did and has done an incredible job of hosting discussions with top researchers and thinkers in the field, with a wide range of topics across computer vision (a special focus thanks to Nathan’s work at Waymark), GPT-4 (with exceptional insight due to Nathan’s time on the GPT-4 “red team”), healthcare/medicine/biotech (Harvard Medical School, Med-PaLM, Tanishq Abraham, Neal Khosla), investing and tech strategy (Sarah Guo, Elad Gil, Emad Mostaque, Sam Lessin), safety and policy, curators and influencers and exceptional AI founders (Josh Browder, Eugenia Kuyda, Flo Crivello, Suhail Doshi, Jungwon Byun, Raza Habib, Mahmoud Felfel, Andrew Feldman, Matt Welsh, Anton Troynikov, Aravind Srinivas).
If Latent Space is for AI Engineers, then Cognitive Revolution covers the much broader field of AI in tech, business and society at large, with a longer runtime to go deep on research papers like TinyStories. We hope you love this episode as much as we do, and check out CogRev wherever fine podcasts are sold!
Subscribe to the Cognitive Revolution on:
* Website
* Apple Podcasts
* Spotify
* Youtube
Good Data is All You Need
The work of Ronen and Yuanzhi echoes a broader theme emerging in the midgame of 2023:
* Falcon-40B (trained on 1T tokens) outperformed LLaMA-65B (trained on 1.4T tokens), primarily due to the RefinedWeb Dataset that runs CommonCrawl through extensive preprocessing and cleaning in their MacroData Refinement pipeline.
* UC Berkeley LMSYS’s Vicuna-13B is near GPT-3.5/Bard quality at a tenth of their size, thanks to fine-tuning from 70k user-highlighted ChatGPT conversations (indicating some amount of quality).
* Replit’s finetuned 2.7B model outperforms the 12B OpenAI Codex model based on HumanEval, thanks to high quality data from Replit users
The path to smaller models leans on better data (and tokenization!), whether from cleaning, from user feedback, or from synthetic data generation, i.e. finetuning high quality on outputs from larger models. TinyStories and Phi-1 are the strongest new entries in that line of work, and we hope you’ll pick through the show notes to read up further.
Show Notes
* TinyStories (Apr 2023)
* Paper: TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
* Internal presentation with Sebastien Bubeck at MSR
* Twitter thread from Ronen Eldan
* Will future LLMs be based almost entirely on synthetic training data? In a new paper, we introduce TinyStories, a dataset of short stories generated by GPT-3.5&4. We use it to train tiny LMs (* Phi-1 (Jun 2023)
* Paper: Textbooks are all you need (HN discussion)
* Twitter announcement from Sebastien Bubeck:
* phi-1 achieves 51% on HumanEval w. only 1.3B parameters & 7B tokens training dataset and 8 A100s x 4 days = 800 A100-hours. Any other >50% HumanEval model is >1000x bigger (e.g., WizardCoder from last week is 10x in model size and 100x in dataset size).
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
Nathan Labenz sits down with Ronen Eldan and Yuanzhi Li of Microsoft Research to discuss the small natural language dataset they created called TinyStories. Tiny Stories is designed to reflect the full richness of natural language while still being small to support research with modest compute budgets. Using this dataset, they began to explore aspects of language model performance, behavior, and mechanism by training a series of models that range in size from just 1 million to a maximum of 33 million parameters – which is still just 2% the scale of GPT-2. In this conversation, Nathan, Ronen, and Yuanzhi touch on LM reasoning, emergence, interpretability, and what understanding can be extended to LLMs.
RECOMMENDED PODCAST:
The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix’s culture deck Patty McCord.
https://link.chtbl.com/hrheretics
LINKS:
Tiny Stories paper: https://huggingface.co/papers/2305.07759
TIMESTAMPS:
(00:00) Episode Preview
(07:12) The inspiration for the Tiny Stories project
(15:07) Sponsor: Omneky
(15:44) Creating the Tiny Stories dataset
(21:27) GPT-4 vs GPT-3.5
(24:13) Did the TinyStories team try any other versions of GPT-4
(29:23) Curriculum models and weirder curriculums
(35:34) What does reasoning mean?
(46:27) What does emergence mean?
(01:01:44) The curriculum development space
(01:11:40) The similarities between models and human development
(01:20:12) Fewer layers vs. more layers
(01:29:22) Attention heads
(01:33:40) Semantic attention head
(01:36:54) Neuron technique used in developing the TinyStories model
(01:52:20) Interpretability work that inspires Ronen and Yuanzhi
TWITTER:
@CogRev_Podcast
@EldanRonen (Ronen)
@labenz (Nathan)
@eriktorenberg (Erik)
SPONSORS:
Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries.From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive
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This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.
Music Credit: MusicLM
More show notes and reading material released in our Substack: https://cognitiverevolution.substack.com
Scott’s back on Twitter, and Elon says the platform could be cash-flow positive this quarter - coincidence? Kara and Scott discuss growing calls for Senator Feinstein to resign, a delay in the Dominion v. Fox News trial, and impressive JPMorgan Chase earnings. Also, a tech consultant has been arrested for the murder of Cash App founder Bob Lee. And U.S. National Security is in disarray over Discord after an Air National Guardsman allegedly leaked classified documents on the platform. Then, we’re joined by Principal Researcher at Microsoft Research Lab and Professor at USC Annenberg, Kate Crawford to talk everything AI.
You can find Kate on Twitter at @katecrawford, and can buy “Atlas of AI” here.
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Today we’re joined by Robert Osazuwa Ness, a senior researcher at Microsoft Research, to break down the latest trends in the world of causal modeling. In our conversation with Robert, we explore advances in areas like causal discovery, causal representation learning, and causal judgements. We also discuss the impact causality could have on large language models, especially in some of the recent use cases we’ve seen like Bing Search and ChatGPT. Finally, we discuss the benchmarks for causal modeling, the top causality use cases, and the most exciting opportunities in the field.
The complete show notes for this episode can be found at twimlai.com/go/616.
Dr. Emre Kiciman, Senior Principal Researcher at Microsoft Research joins the podcast to share his world-leading knowledge on causal machine learning.
This episode is brought to you by Datalore (datalore.online/SDS), the collaborative data science platform, and by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What is causal machine learning? [5:52]
• Causal machine learning vs correlational machine learning [10:10]
• Emre’s DoWhy open-source library [16:17]
• The four key steps of causal inference [21:24]
• How and why Emre’s key steps of causal inference will impact ML [26:36]
• Emre's thoughts on the future of causal inference and AGI [34:09]
• How Emre leverages social media data to solve social problems [38:36]
• What's next for Emre's research [46:02]
• The software tools Emre highly recommends [55:16]
• What he looks for in the data science researchers he hires [58:45]
Additional materials: www.superdatascience.com/613
Glen Weyl, RadicalxChange Foundation founder and political economist & social technologist at Microsoft Special Projects, and Puja Ohlhaver, strategist at Flashbots, discuss “soulbound tokens” and their implications for collaboration and social organization in a variety of spaces. Topics covered include:
how Glen got involved in crypto, wrote a book, and came to co-write a paper with Vitalik Buterin
how Puja studied economics and got in touch with Glen in pursuit of a middle ground between left and right politics
what soulbound NFTs are and how they work
how Vitalik’s paper articulated how the concept of the soulbound token could advance decentralized collaboration in web3
how decentralized reputation can enable larger networks of coordination
how identity can be understood through the lens of community participation
how the name ‘soulbound token’ came about
how these tokens could provide a technology for those who value scarcity but disdain speculation
how identity-dedicated tokens could work on a technical level (including recovery)
what is DeSoc and why is it important?
how non-transferable tokens can improve DAO organization, including resolving issues with quadratic funding
how identity-locked tokens can be protected from bots and AI abuse
how these tokens could support community privacy and responsible information disclosure
Thank you to our sponsors!
Crypto.com: https://crypto.onelink.me/J9Lg/unconfirmedcardearnfeb2021
Beefy Finance: https://beefy.finance/
Related Reading
Decentralized Society: Finding Web3’s Soul (Weyl, Ohlhaver, and Buterin; 2022)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4105763
Georg Simmel, German neo-Kantian who laid groundwork for antipositivism
https://en.wikipedia.org/wiki/Georg_Simmel
How Blockchains Can Help Create Little Democracies Everywhere
https://unchainedpodcast.com/how-blockchains-can-help-create-little-democracies-everywhere/
Soulbound Tokens
Vitalik on Soulbound tokens
https://vitalik.ca/general/2022/01/26/soulbound.html
TL;DR written by Glen
https://www.coindesk.com/layer2/2022/05/11/after-defi-desoc-finding-web-3s-soul/
Other write-ups
https://nftnow.com/guides/soulbound-tokens-sbts-meet-the-tokens-that-may-change-your-life/
https://www.radicalxchange.org/concepts/soulbound-tokens/
https://fortune.com/2022/05/26/what-are-soulbound-tokens-web3-buterin/
https://thedefiant.io/vitalik-soulbound-tokens/
Projects Mentioned
Ceramic
https://ceramic.network/
Verifiable Credentials Data Model (w3)
https://www.w3.org/TR/vc-data-model/#abstract
Optimism
https://optimism.io/
Gitcoin
https://gitcoin.co/
Glen Weyl
Personal Website
https://glenweyl.com/
Twitter (@glenweyl)
https://twitter.com/glenweyl/
Puja Ohlhaver
LinkedIn
https://www.linkedin.com/in/puja-ohlhaver-b44b878
Twitter
https://mobile.twitter.com/pujaohlhaver
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Today we’re joined by friend of the show Timnit Gebru, the founder and executive director of DAIR, the Distributed Artificial Intelligence Research Institute. In our conversation with Timnit, we discuss her journey to create DAIR, their goals and some of the challenges shes faced along the way. We start is the obvious place, Timnit being “resignated” from Google after writing and publishing a paper detailing the dangers of large language models, the fallout from that paper and her firing, and the eventual founding of DAIR. We discuss the importance of the “distributed” nature of the institute, how they’re going about figuring out what is in scope and out of scope for the institute’s research charter, and what building an institution means to her. We also explore the importance of independent alternatives to traditional research structures, if we should be pessimistic about the impact of internal ethics and responsible AI teams in industry due to the overwhelming power they wield, examples she looks to of what not to do when building out the institute, and much much more!
The complete show notes for this episode can be found at twimlai.com/go/568
Jaron Lanier is a computer scientist, composer, artist, author, and founder of the field of virtual reality. 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
(08:04) – What is reality?
(12:17) – Turing machines
(13:35) – Simulating our universe
(19:49) – Video games and other immersive experiences
(23:37) – Death and consciousness
(32:08) – Designing human-centric AI
(33:42) – Empathy with robots
(37:33) – Social media incentives
(49:53) – Data dignity
(57:26) – Jack Dorsey and Twitter
(1:09:10) – Bitcoin and cryptocurrencies
(1:13:51) – Government overreach and freedom
(1:24:06) – GitHub and TikTok
(1:26:16) – The Autodidactic Universe
(1:31:07) – Humans and the mystery of music
(1:37:17) – Defining moments
(1:48:03) – Mortality
(1:49:56) – The meaning of life
Today we continue our AI in Innovation series joined by Dan Bohus, senior principal researcher at Microsoft Research, and Siddhartha Sen, a principal researcher at Microsoft Research.
In this conversation, we use a pair of research projects, Maia Chess and Situated Interaction, to springboard us into a conversation about the evolution of human-AI interaction. We discuss both of these projects individually, as well as the commonalities they have, how themes like understanding the human experience appear in their work, the types of models being used, the various types of data, and the complexity of each of their setups.
We explore some of the challenges associated with getting computers to better understand human behavior and interact in ways that are more fluid. Finally, we touch on what excites both Dan and Sid about their respective projects, and what they’re excited about for the future.
The complete show notes for this episode can be found at https://twimlai.com/go/499.
Today we’re joined by Jabran Zahid, a Senior Researcher at Microsoft Research.
In our conversation with Jabran, we explore their recent endeavor into the complete mapping of which T-cells bind to which antigens through the Antigen Map Project. We discuss how Jabran’s background in astrophysics and cosmology has translated to his current work in immunology and biology, the origins of the antigen map, the biological and how the focus was changed by the emergence of the coronavirus pandemic.
We talk through the biological advancements, and the challenges of using machine learning in this setting, some of the more advanced ML techniques that they’ve tried that have not panned out (as of yet), the path forward for the antigen map to make a broader impact, and much more.
The complete show notes for this episode can be found at twimlai.com/go/485.
Thousands of languages thrive across the globe, yet modern speech technology -- and all of its benefits -- supports just over a hundred. Computational linguist Kalika Bali dreams of a day when technology acts as a bridge instead of a barrier, working passionately to build new and inclusive systems for the millions who speak low-resource languages. In this perspective-shifting talk, she outlines what happens when a language is omitted from the digital landscape -- and what is gained when communities can keep pace with the future.
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One of the recurring themes we talk about a lot on the a16z Podcast is how software changes organizations, and vice versa... More broadly: it’s really about how companies of all kinds innovate with the org structures and tools that they have.
But we've come a long way from the question of "does IT matter" to answering the question of what org structures, processes, architectures, and roles DO matter when it comes to companies -- of all sizes -- innovating through software and more.
So in this episode (a re-run of a popular episode from a couple years ago), two of the authors of the book Accelerate: The Science of Lean Software and DevOps, by Nicole Forsgren, Jez Humble, and Jean Kim join Sonal Chokshi to share best practices and large-scale findings about high performing companies (including those who may not even think they’re tech companies). Nicole was co-founder and CEO of Dora, which was acquired by Google in December 2018; she will soon be joining GitHub as VP of Research & Strategy. Jez was CTO at DORA; is currently in Developer Relations at Google Cloud; and is the co-author of the books The DevOps Handbook, Lean Enterprise, and Continuous Delivery.
Stay Updated:
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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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Today Robert Osazuwa Ness, ML Research Engineer at Gamalon and Instructor at Northeastern University joins us to discuss Causality, what it means, and how that meaning changes across domains and users, and our upcoming study group based around his new course sequence, “Causal Modeling in Machine Learning," for which you can find details at twimlai.com/community.
Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.
In this glimpse into our technological future, cryptographer Craig Costello discusses the world-altering potential of quantum computers, which could shatter the limits set by today’s machines -- and give code breakers a master key to the digital world. See how Costello and his fellow cryptographers are racing to reinvent encryption and secure the internet.
Hosted on Acast. See acast.com/privacy for more information.
The cells in your body are like computer software: they’re “programmed” to carry out specific functions at specific times. If we can better understand this process, we could unlock the ability to reprogram cells ourselves, says computational biologist Sara-Jane Dunn. In a talk from the cutting-edge of science, she explains how her team is studying embryonic stem cells to gain a new understanding of the biological programs that power life -- and develop “living software” that could transform medicine, agriculture and energy.
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We’ve mentioned ML/AI in the browser and in JS a bunch on this show, but we haven’t done a deep dive on the subject… until now! Victor Dibia helps us understand why people are interested in porting models to the browser and how people are using the functionality. We discuss TensorFlow.js and some applications built using TensorFlow.js
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Featuring:
Victor Dibia – Website, GitHub, LinkedIn, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
data2vis
TJbot
TensorFlow.js
Handtrack.js
ConvNet Playground
JS Party
Upcoming Events:
Register for upcoming webinars here!
About Nicole Forsgren, PhD
Dr. Nicole Forsgren does research and strategy at Google Cloud following the acquisition of her startup DevOps Research and Assessment (DORA) by Google. She is co-author of the Shingo Publication Award winning book Accelerate: The Science of Lean Software and DevOps, and is best known for her work measuring the technology process and as the lead investigator on the largest DevOps studies to date. She has been an entrepreneur, professor, sysadmin, and performance engineer. Nicole’s work has been published in several peer-reviewed journals. Nicole earned her PhD in Management Information Systems from the University of Arizona, and is a Research Affiliate at Clemson University and Florida International University.
Links Referenced
Twitter Username: @nicolefv
LinkedIn URL: https://www.linkedin.com/in/nicolefv/
Personal site: nicolefv.com
Company site: cloud.google.com/devops
Manifold: https://www.manifold.co/
About Nicole Forsgren, PhD
Dr. Nicole Forsgren does research and strategy at Google Cloud following the acquisition of her startup DevOps Research and Assessment (DORA) by Google. She is co-author of the Shingo Publication Award winning book Accelerate: The Science of Lean Software and DevOps, and is best known for her work measuring the technology process and as the lead investigator on the largest DevOps studies to date. She has been an entrepreneur, professor, sysadmin, and performance engineer. Nicole’s work has been published in several peer-reviewed journals. Nicole earned her PhD in Management Information Systems from the University of Arizona, and is a Research Affiliate at Clemson University and Florida International University.
Links Referenced:
Twitter Username: @nicolefv
LinkedIn URL: https://www.linkedin.com/in/nicolefv/
Personal site: nicolefv.com
Company site: cloud.google.com/devops
X-Team: x-team.com/cloud
In this episode of the SuperDataScience Podcast, I chat with the Machine Learning Research Scientist, John Langford. You will hear about unsupervised, supervised learning and reinforcement learning, and the differences between the three. You will learn about applications of contextual bandits and reinforcement learning in general, YOLO style algorithms versus simulator algorithms, technics for avoiding local optimums. You will also learn about the balance between exploration and exploitation, learning to search and active learning.
If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/275
Computer philosophy writer and "founding father of virtual reality," Jaron Lanier, chats with Verge editor-in-chief Nilay Patel about why he's optimistic about the future. Lanier shares his thoughts on how the "manipulation economy" has reshaped the world we live in and why we should be controlling and profiting from our own data.
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as well as The Verge's Why'd You Push That Button?
and our wonderful YouTube channel Verge Science
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