Podcasts about mlir

News includes the latest release of ElixirLS v0.24.0 and the unveiling of Expert, the upcoming Elixir Language Server. We dive into the details of NervesHub v2.0's improvements for IoT device management and introduce the exciting Elixir Stream Week with José Valim among other top experts. We also look at the exciting updates in Elixir's core Machine Learning libraries and more! Show Notes online - http://podcast.thinkingelixir.com/224 (http://podcast.thinkingelixir.com/224) Elixir Community News https://x.com/lukaszsamson/status/1843038898701864991 (https://x.com/lukaszsamson/status/1843038898701864991?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirLS v0.24.0 was released, marking a major update to the Elixir Language Server. https://elixirforum.com/t/elixirls-the-elixir-language-server/5857/226?u=lukaszsamson (https://elixirforum.com/t/elixirls-the-elixir-language-server/5857/226?u=lukaszsamson?utm_source=thinkingelixir&utm_medium=shownotes) – Discussion on ElixirForum about the latest ElixirLS release. https://github.com/elixir-lsp/elixir-ls/blob/v0.24.0/CHANGELOG.md (https://github.com/elixir-lsp/elixir-ls/blob/v0.24.0/CHANGELOG.md?utm_source=thinkingelixir&utm_medium=shownotes) – Changelog for ElixirLS v0.24.0 detailing improvements and new features. https://github.com/elixir-lang/expert (https://github.com/elixir-lang/expert?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for the new Elixir Language Server named Expert, currently private. https://x.com/lukaszsamson/status/1843039621657272690 (https://x.com/lukaszsamson/status/1843039621657272690?utm_source=thinkingelixir&utm_medium=shownotes) – Łukasz Samson hinted on Twitter about the Expert server's progress towards feature parity with ElixirLS. https://elixir-webrtc.org/elixir-stream-week (https://elixir-webrtc.org/elixir-stream-week?utm_source=thinkingelixir&utm_medium=shownotes) – The Membrane project is organizing 'Elixir Stream Week', an online event from October 21-25, 2024 with top Elixir experts. https://x.com/josevalim/status/1841793652932157908 (https://x.com/josevalim/status/1841793652932157908?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim announced his speaking engagement during Elixir Stream Week, discussing 'Projects that never were: Elixir, LISPs, and MLIR'. https://x.com/TheErlef/status/1843362970442539041 (https://x.com/TheErlef/status/1843362970442539041?utm_source=thinkingelixir&utm_medium=shownotes) – The full schedule for Elixir Stream Week, featuring prominent figures like José Valim, Mateusz Front, and Chris McCord. https://x.com/josevalim/status/1843376791169478955 (https://x.com/josevalim/status/1843376791169478955?utm_source=thinkingelixir&utm_medium=shownotes) – Announcement of new versions of Nx, Axon, and Bumblebee with enhanced features for machine learning in Elixir. https://github.com/elixir-nx/nx (https://github.com/elixir-nx/nx?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for Nx, now at version 0.9.0. https://hex.pm/packages/bumblebee (https://hex.pm/packages/bumblebee?utm_source=thinkingelixir&utm_medium=shownotes) – Hex package for Bumblebee, now at version 0.6.0. https://hex.pm/packages/axon (https://hex.pm/packages/axon?utm_source=thinkingelixir&utm_medium=shownotes) – Hex package for Axon, now at version 0.7.0. https://elixirforum.com/t/nerveshub-v2-0-0-officially-released/66305 (https://elixirforum.com/t/nerveshub-v2-0-0-officially-released/66305?utm_source=thinkingelixir&utm_medium=shownotes) – Discussion on the release of NervesHub v2.0, introducing improvements in IoT device management. https://github.com/nerves-hub/nerveshubweb/releases/tag/v2.0.0 (https://github.com/nerves-hub/nerves_hub_web/releases/tag/v2.0.0?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub release notes for NervesHub v2.0.0, detailing new features and enhancements. https://www.nerves-hub.org/ (https://www.nerves-hub.org/?utm_source=thinkingelixir&utm_medium=shownotes) – Overview of NervesHub, a tool for IoT device management, and its significance. https://x.com/germsvel/status/1843593999946317975 (https://x.com/germsvel/status/1843593999946317975?utm_source=thinkingelixir&utm_medium=shownotes) – German Velasco shares an ElixirStream video tip about HEEx component naming and styling. https://www.elixirstreams.com/tips/anchor-component (https://www.elixirstreams.com/tips/anchor-component?utm_source=thinkingelixir&utm_medium=shownotes) – Detailed tip by German Velasco on creating a named HEEx component for consistent styling. https://x.com/jskalc/status/1843622806241575360 (https://x.com/jskalc/status/1843622806241575360?utm_source=thinkingelixir&utm_medium=shownotes) – LiveVue v0.5.0 release announcement, integrating Phoenix LiveView with the Vue framework. https://github.com/Valian/live_vue/tree/main (https://github.com/Valian/live_vue/tree/main?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for LiveVue, focusing on end-to-end reactivity with LiveView and Vue. https://github.com/woutdp/live_svelte (https://github.com/woutdp/live_svelte?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for LiveSvelte, an integration for reactivity with Svelte and LiveView. https://wout.space/notes/live-svelte (https://wout.space/notes/live-svelte?utm_source=thinkingelixir&utm_medium=shownotes) – Notes on LiveSvelte, detailing its usage and benefits for reactive interfaces. https://www.jonashietala.se/blog/2024/10/08/writinghomeassistantautomationsusinggenserversin_elixir/ (https://www.jonashietala.se/blog/2024/10/08/writing_home_assistant_automations_using_genservers_in_elixir/?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post by Jonas Hietala on implementing HomeAssistant automations using Elixir GenServers. https://x.com/bernheisel/status/1843276992437010566 (https://x.com/bernheisel/status/1843276992437010566?utm_source=thinkingelixir&utm_medium=shownotes) – David Bernheisel shared his appearance on Elixir Friends podcast with German Velasco. https://share.transistor.fm/s/1e964a13 (https://share.transistor.fm/s/1e964a13?utm_source=thinkingelixir&utm_medium=shownotes) – Podcast episode featuring David Bernheisel discussing various programming topics and life experiences. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - Dave Lucia - @davydog187 (https://twitter.com/davydog187)

In this second installment of our series, we're joined by Philip Brown, who returns to share his insights on running lean startups in the Elixir ecosystem. We dive into the art of supporting Elixir systems on a shoestring budget. Philip will walk us through his must-haves for MVPs, offering practical advice on how to leverage cost-effective solutions without sacrificing functionality. We'll discuss the balancing act between delivering a product and managing expenses, and highlight the essential tools that keep a scrappy startup's Elixir system robust yet affordable. Tune in for a candid look at making the most of limited resources, insights into Prise.com, ElixirMerge.com strategies, and more for the aspiring bootstrapper! Show Notes online - http://podcast.thinkingelixir.com/192 (http://podcast.thinkingelixir.com/192) Elixir Community News - https://github.com/erlang/eep/pull/59 (https://github.com/erlang/eep/pull/59?utm_source=thinkingelixir&utm_medium=shownotes) – Erlang EEP 68, a new JSON module, has been accepted and merged into OTP's standard library. - https://www.erlang.org/eeps/eep-0068 (https://www.erlang.org/eeps/eep-0068?utm_source=thinkingelixir&utm_medium=shownotes) – Details on Erlang EEP 68, which claims to significantly outperform other JSON libraries like Jason and jiffy. - Speculation on whether EEP 68 will be included in OTP 27 and its performance compared to existing JSON libraries. - https://github.com/electric-sql/pglite (https://github.com/electric-sql/pglite?utm_source=thinkingelixir&utm_medium=shownotes) – PostgreSQL has been redeveloped as a WASM library called PGlite, allowing it to run in browsers and other environments without additional dependencies. - https://twitter.com/sasajuric/status/1762394843341353390 (https://twitter.com/sasajuric/status/1762394843341353390?utm_source=thinkingelixir&utm_medium=shownotes) – Announcement of the printing of 'Elixir in Action 3rd Edition' and its rank on Manning's bestseller list. - Remember to use the "devtalk.com" or "mljuric3" coupon codes until March 9th for 45% off the ebook. - Release updates on Language Server Protocols (LSPs) for Elixir and Erlang, detailing new features and improvements like autocomplete and diagnostics. - https://twitter.com/zeddotdev/status/1757882887972528152 (https://twitter.com/zeddotdev/status/1757882887972528152?utm_source=thinkingelixir&utm_medium=shownotes) – Zed, a high-performance, multiplayer code editor now offers support for Erlang. - https://zed.dev/ (https://zed.dev/?utm_source=thinkingelixir&utm_medium=shownotes) – Zed editor's official website where you can find more information about its capabilities and open source nature. - https://twitter.com/thmsmlr/status/1762210503810507140 (https://twitter.com/thmsmlr/status/1762210503810507140?utm_source=thinkingelixir&utm_medium=shownotes) – Thomas Millar introduces kinolivereload, a library for LiveBook to auto-reload cells when source code changes. - https://github.com/thmsmlr/kino_livereload (https://github.com/thmsmlr/kino_livereload?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for kinolivereload, showcasing its live reload feature for LiveBook cells. - https://fly.io/phoenix-files/what-if-s3-could-be-a-fast-globally-synced-key-value-database-that-s-tigris/ (https://fly.io/phoenix-files/what-if-s3-could-be-a-fast-globally-synced-key-value-database-that-s-tigris/?utm_source=thinkingelixir&utm_medium=shownotes) – An article describing Tigris, a globally-synced, S3-compatible file storage service designed for Fly.io. - Explanation of how Elixir applications can take advantage of Tigris as a key-value store using :erlang.termtobinary for storing any Elixir data structure. - https://twitter.com/wojtekmach/status/1759511154131427516 (https://twitter.com/wojtekmach/status/1759511154131427516?utm_source=thinkingelixir&utm_medium=shownotes) – Release announcement for Req v0.4.10, an HTTP client for Elixir with added functionality for stubbing responses in concurrent tests. - https://elixirforum.com/t/req-a-batteries-included-http-client-for-elixir/48494/34 (https://elixirforum.com/t/req-a-batteries-included-http-client-for-elixir/48494/34?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir Forum discussion thread about the latest updates and features in Req. - https://hexdocs.pm/req/Req.Test.html (https://hexdocs.pm/req/Req.Test.html?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation for the Req.Test module, detailing new functions for stubbing HTTP responses in tests. - https://github.com/elixir-nx/bumblebee/blob/main/CHANGELOG.md (https://github.com/elixir-nx/bumblebee/blob/main/CHANGELOG.md?utm_source=thinkingelixir&utm_medium=shownotes) – Changelog for Bumblebee's recent releases, including updates and new feature highlights. - Mention of contributions from Jonatan Kłosko and added Bumblebee features such as Mistral LLM support and the :seed option for generating inputs. - https://twitter.com/polvalente/status/1762234885777817666 (https://twitter.com/polvalente/status/1762234885777817666?utm_source=thinkingelixir&utm_medium=shownotes) – Nx v0.7.0 is released with EXLA's MLIR implementation, enabling new possibilities like quantization and Apple Metal support. - https://github.com/brainlid/langchain (https://github.com/brainlid/langchain?utm_source=thinkingelixir&utm_medium=shownotes) – Update to the Elixir LangChain library to align with the latest features of Bumblebee, allowing conversations with various LLMs. - Additional details on how the updated LangChain library supports LLMs like Llama 2, Mistral, and Zephyr, and its flexibility in changing conversation models. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Discussion Resources - https://podcast.thinkingelixir.com/133 (https://podcast.thinkingelixir.com/133?utm_source=thinkingelixir&utm_medium=shownotes) – Previous interview with Philip about his service Prise.com - https://elixirmerge.com (https://elixirmerge.com?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir Merge newsletter - https://github.com/elixir-haystack/haystack (https://github.com/elixir-haystack/haystack?utm_source=thinkingelixir&utm_medium=shownotes) – Haystack - Simple extendible search engine written in Elixir - https://fly.io (https://fly.io?utm_source=thinkingelixir&utm_medium=shownotes) - https://sentry.io/ (https://sentry.io/?utm_source=thinkingelixir&utm_medium=shownotes) - https://www.appsignal.com/ (https://www.appsignal.com/?utm_source=thinkingelixir&utm_medium=shownotes) - https://github.com/wyeworks/boom (https://github.com/wyeworks/boom?utm_source=thinkingelixir&utm_medium=shownotes) - https://postmarkapp.com/ (https://postmarkapp.com/?utm_source=thinkingelixir&utm_medium=shownotes) - ElixirMerge sends email's at 8:00am in the user's timezone. - https://sendgrid.com/en-us (https://sendgrid.com/en-us?utm_source=thinkingelixir&utm_medium=shownotes) - https://resend.com/ (https://resend.com/?utm_source=thinkingelixir&utm_medium=shownotes) - https://aws.amazon.com/ses/ (https://aws.amazon.com/ses/?utm_source=thinkingelixir&utm_medium=shownotes) - https://plausible.io/ (https://plausible.io/?utm_source=thinkingelixir&utm_medium=shownotes) - https://clarity.microsoft.com/ (https://clarity.microsoft.com/?utm_source=thinkingelixir&utm_medium=shownotes) - https://github.com/spreedly/kaffe (https://github.com/spreedly/kaffe?utm_source=thinkingelixir&utm_medium=shownotes) - https://www.metabase.com/ (https://www.metabase.com/?utm_source=thinkingelixir&utm_medium=shownotes) - https://clarity.microsoft.com/ (https://clarity.microsoft.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Microsoft Clarity Guest Information - https://twitter.com/philipbrown (https://twitter.com/philipbrown?utm_source=thinkingelixir&utm_medium=shownotes) – on Twitter - https://github.com/philipbrown/ (https://github.com/philipbrown/?utm_source=thinkingelixir&utm_medium=shownotes) – on Github - https://yflag.com (https://yflag.com?utm_source=thinkingelixir&utm_medium=shownotes) – Page about his consulting services - https://culttt.com (https://culttt.com?utm_source=thinkingelixir&utm_medium=shownotes) – Blog - https://elixirmerge.com (https://elixirmerge.com?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir Merge newsletter Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel - @bernheisel (https://twitter.com/bernheisel) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern) - Cade Ward - @cadebward (https://twitter.com/cadebward) - Cade Ward on Fediverse - @cadebward@genserver.social (https://genserver.social/cadebward)

Happy leap year day everyone, very excited to bring you a special once-in-a-4 year edition of ThursdAI

An airhacks.fm conversation with Paul Sandoz (@paulsandoz) about: Project Babylon aims to enable Java integration with GPUs and accelerators through Code Reflection, building on Project Panama, Code Model can be used to optimize machine learning models, generate SQL, Java and GPUs, Project Babylon, Project Sumatra, CUDA, OpenCL, MLIR, Edge Computing, WebAssembly, Project Leyden, project valhalla, Code reflection, SQL optimization, edge functions, WebAssembly, WASM, BLIS presentation, Code Reflection presentation Paul Sandoz on twitter: @paulsandoz

Thanks to the over 17,000 people who have joined the first AI Engineer Summit! A full recap is coming. Last call to fill out the State of AI Engineering survey! See our Community page for upcoming meetups in SF, Paris and NYC.This episode had good interest on Twitter.Fast.ai's “Practical Deep Learning” courses been watched by over >6,000,000 people, and the fastai library has over 25,000 stars on Github. Jeremy Howard, one of the creators of Fast, is now one of the most prominent and respected voices in the machine learning industry; but that wasn't always the case. Being non-consensus and right In 2018, Jeremy and Sebastian Ruder published a paper on ULMFiT (Universal Language Model Fine-tuning), a 3-step transfer learning technique for NLP tasks: The paper demonstrated that pre-trained language models could be fine-tuned on a specific task with a relatively small amount of data to achieve state-of-the-art results. They trained a 24M parameters model on WikiText-103 which was beat most benchmarks.While the paper had great results, the methods behind weren't taken seriously by the community: “Everybody hated fine tuning. Everybody hated transfer learning. I literally did tours trying to get people to start doing transfer learning and nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning […] which I was convinced was not the right direction, but who's going to listen to me, cause as you said, I don't have a PhD, not at a university… I don't have a big set of computers to fine tune huge transformer models.”Five years later, fine-tuning is at the center of most major discussion topics in AI (we covered some like fine tuning vs RAG and small models fine tuning), and we might have gotten here earlier if Jeremy had OpenAI-level access to compute and distribution. At heart, Jeremy has always been “GPU poor”:“I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use.”This story is a good reminder of how some of the best ideas are hiding in plain sight; we recently covered RWKV and will continue to highlight the most interesting research that isn't being done in the large labs. Replacing fine-tuning with continued pre-trainingEven though fine-tuning is now mainstream, we still have a lot to learn. The issue of “catastrophic forgetting” and potential solutions have been brought up in many papers: at the fine-tuning stage, the model can forget tasks it previously knew how to solve in favor of new ones. The other issue is apparent memorization of the dataset even after a single epoch, which Jeremy covered Can LLMs learn from a single example? but we still don't have the answer to. Despite being the creator of ULMFiT, Jeremy still professes that there are a lot of open questions on finetuning:“So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do.”He now advocates for "continued pre-training" - maintaining a diversity of data throughout the training process rather than separate pre-training and fine-tuning stages. Mixing instructional data, exercises, code, and other modalities while gradually curating higher quality data can avoid catastrophic forgetting and lead to more robust capabilities (something we covered in Datasets 101).“Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it… the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training. And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data….So yeah, that's now my view, is I think ULMFiT is the wrong approach. And that's why we're seeing a lot of these so-called alignment tax… I think it's actually because people are training them wrong.An example of this phenomena is CodeLlama, a LLaMA2 model finetuned on 500B tokens of code: while the model is much better at code, it's worse on generic tasks that LLaMA2 knew how to solve well before the fine-tuning. In the episode we also dive into all the places where open source model development and research is happening (academia vs Discords - tracked on our Communities list and on our survey), and how Jeremy recommends getting the most out of these diffuse, pseudonymous communities (similar to the Eleuther AI Mafia).Show Notes* Jeremy's Background* FastMail* Optimal Decisions* Kaggle* Enlitic* fast.ai* Rachel Thomas* Practical Deep Learning* fastai for PyTorch* nbdev* fastec2 (the underrated library we describe)* Can LLMs learn from a single example?* the Kaggle LLM Science Exam competition, which “challenges participants to answer difficult science-based questions written by a Large Language Model”.* Sebastian Ruder* Alec Radford* Sylvain Gugger* Stephen Merity* Chris Lattner* Modular.ai / Mojo* Jono Whittaker* Zeiler and Fergus paper* ULM Fit* DAWNBench* Phi-1* Code Llama* AlexNetTimestamps* [00:00:00] Intros and Jeremy's background* [00:05:28] Creating ULM Fit - a breakthrough in NLP using transfer learning* [00:06:32] The rise of GPT and the appeal of few-shot learning over fine-tuning* [00:10:00] Starting Fast.ai to distribute AI capabilities beyond elite academics* [00:14:30] How modern LMs like ChatGPT still follow the ULM Fit 3-step approach* [00:17:23] Meeting with Chris Lattner on Swift for TensorFlow at Google* [00:20:00] Continued pre-training as a fine-tuning alternative* [00:22:16] Fast.ai and looking for impact vs profit maximization* [00:26:39] Using Fast.ai to create an "army" of AI experts to improve their domains* [00:29:32] Fast.ai's 3 focus areas - research, software, and courses* [00:38:42] Fine-tuning memorization and training curve "clunks" before each epoch* [00:46:47] Poor training and fine-tuning practices may be causing alignment failures* [00:48:38] Academia vs Discords* [00:53:41] Jeremy's high hopes for Chris Lattner's Mojo and its potential* [01:05:00] Adding capabilities like SQL generation through quick fine-tuning* [01:10:12] Rethinking Fast.ai courses for the AI-assisted coding era* [01:14:53] Rapid model development has created major technical debt* [01:17:08] Lightning RoundAI Summary (beta)This is the first episode we're trying this. Here's an overview of the main topics before you dive in the transcript. * Jeremy's background and philosophies on AI* Studied philosophy and cognitive science in college* Focused on ethics and thinking about AI even 30 years ago* Believes AI should be accessible to more people, not just elite academics/programmers* Created fast.ai to make deep learning more accessible* Development of transfer learning and ULMFit* Idea of transfer learning critical for making deep learning accessible* ULMFit pioneered transfer learning for NLP* Proposed training general language models on large corpora then fine-tuning - this became standard practice* Faced skepticism that this approach would work from NLP community* Showed state-of-the-art results on text classification soon after trying it* Current open questions around fine-tuning LLMs* Models appear to memorize training data extremely quickly (after 1 epoch)* This may hurt training dynamics and cause catastrophic forgetting* Unclear how best to fine-tune models to incorporate new information/capabilities* Need more research on model training dynamics and ideal data mixing* Exciting new developments* Mojo and new programming languages like Swift could enable faster model innovation* Still lots of room for improvements in computer vision-like innovations in transformers* Small models with fine-tuning may be surprisingly capable for many real-world tasks* Prompting strategies enable models like GPT-3 to achieve new skills like playing chess at superhuman levels* LLMs are like computer vision in 2013 - on the cusp of huge new breakthroughs in capabilities* Access to AI research* Many key convos happen in private Discord channels and forums* Becoming part of these communities can provide great learning opportunities* Being willing to do real work, not just talk about ideas, is key to gaining access* The future of practical AI* Coding becoming more accessible to non-programmers through AI assistance* Pre-requisite programming experience for learning AI may no longer be needed* Huge open questions remain about how to best train, fine-tune, and prompt LLMsTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:21]Swyx: Hey, and today we have in the remote studio, Jeremy Howard all the way from Australia. Good morning. [00:00:27]Jeremy: The remote studio, also known as my house. Good morning. Nice to see you. [00:00:32]Swyx: Nice to see you too. I'm actually very used to seeing you in your mask as a message to people, but today we're mostly audio. But thank you for doing the very important public service of COVID awareness. It was a pleasure. [00:00:46]Jeremy: It was all very annoying and frustrating and tedious, but somebody had to do it. [00:00:52]Swyx: Somebody had to do it, especially somebody with your profile. I think it really drives home the message. So we tend to introduce people for them and then ask people to fill in the blanks on the personal side. Something I did not know about you was that you graduated with a BA in philosophy from the University of Melbourne. I assumed you had a PhD. [00:01:14]Jeremy: No, I mean, I barely got through my BA because I was working 80 to 100 hour weeks at McKinsey and Company from 19 years old onwards. So I actually didn't attend any lectures in second and third year university. [00:01:35]Swyx: Well, I guess you didn't need it or you're very sort of self-driven and self-motivated. [00:01:39]Jeremy: I took two weeks off before each exam period when I was working at McKinsey. And then, I mean, I can't believe I got away with this in hindsight, I would go to all my professors and say, oh, I was meant to be in your class this semester and I didn't quite turn up. Were there any assignments I was meant to have done, whatever. I can't believe all of them let me basically have it. They basically always would say like, okay, well, if you can have this written by tomorrow, I'll accept it. So yeah, stressful way to get through university, but. [00:02:12]Swyx: Well, it shows that, I guess, you min-maxed the opportunities. That definitely was a precursor. [00:02:18]Jeremy: I mean, funnily, like in as much as I, you know, in philosophy, the things I found interesting and focused on in the little bit of time I did spend on it was ethics and cognitive science. And it's kind of really amazing that it's now come back around and those are actually genuinely useful things to know about, which I never thought would happen. [00:02:38]Swyx: A lot of, yeah, a lot of relevant conversations there. So you were a consultant for a while and then in the magical month of June 1989, you founded both Optimal Decisions and Fastmeal, which I also briefly used. So thank you for that. [00:02:53]Jeremy: Oh, good for you. Yeah. Cause I had read the statistics, which is that like 90% or something of small businesses fail. So I thought if I start two businesses, I have a higher chance. In hindsight, I was thinking of it as some kind of stochastic thing I didn't have control over, but it's a bit odd, but anyway. [00:03:10]Swyx: And then you were president and chief scientist at Kaggle, which obviously is the sort of composition platform of machine learning. And then Enlitic, where you were working on using deep learning to improve medical diagnostics and clinical decisions. Yeah. [00:03:28]Jeremy: I was actually the first company to use deep learning in medicine, so I kind of founded the field. [00:03:33]Swyx: And even now that's still like a pretty early phase. And I actually heard you on your new podcast with Tanish, where you went very, very deep into the stuff, the kind of work that he's doing, such a young prodigy at his age. [00:03:47]Jeremy: Maybe he's too old to be called a prodigy now, ex-prodigy. No, no. [00:03:51]Swyx: I think he still counts. And anyway, just to round out the bio, you have a lot more other credentials, obviously, but most recently you started Fast.ai, which is still, I guess, your primary identity with Rachel Thomas. So welcome. [00:04:05]Jeremy: Yep. [00:04:06]Swyx: Thanks to my wife. Thank you. Yeah. Doing a lot of public service there with getting people involved in AI, and I can't imagine a better way to describe it than fast, fast.ai. You teach people from nothing to stable diffusion in seven weeks or something, and that's amazing. Yeah, yeah. [00:04:22]Jeremy: I mean, it's funny, you know, when we started that, what was that, like 2016 or something, the idea that deep learning was something that you could make more accessible was generally considered stupid. Everybody knew that deep learning was a thing that you got a math or a computer science PhD, you know, there was one of five labs that could give you the appropriate skills and that you would join, yeah, basically from one of those labs, you might be able to write some papers. So yeah, the idea that normal people could use that technology to do good work was considered kind of ridiculous when we started it. And we weren't sure if it was possible either, but we kind of felt like we had to give it a go because the alternative was we were pretty sure that deep learning was on its way to becoming, you know, the most or one of the most, you know, important technologies in human history. And if the only people that could use it were a handful of computer science PhDs, that seemed like A, a big waste and B, kind of dangerous. [00:05:28]Swyx: Yeah. [00:05:29]Alessio: And, you know, well, I just wanted to know one thing on your bio that at Kaggle, you were also the top rank participant in both 2010 and 2011. So sometimes you see a lot of founders running companies that are not really in touch with the problem, but you were clearly building something that you knew a lot about, which is awesome. Talking about deep learning, you created, published a paper on ULM fit, which was kind of the predecessor to multitask learning and a lot of the groundwork that then went to into Transformers. I've read back on the paper and you turned this model, AWD LSTM, which I did the math and it was like 24 to 33 million parameters, depending on what training data set you use today. That's kind of like not even small, it's like super small. What were some of the kind of like contrarian takes that you had at the time and maybe set the stage a little bit for the rest of the audience on what was kind of like the state of the art, so to speak, at the time and what people were working towards? [00:06:32]Jeremy: Yeah, the whole thing was a contrarian take, you know. So okay, so we started Fast.ai, my wife and I, and we thought, yeah, so we're trying to think, okay, how do we make it more accessible? So when we started thinking about it, it was probably 2015 and then 2016, we started doing something about it. Why is it inaccessible? Okay, well, A, no one knows how to do it other than a few number of people. And then when we asked those few number of people, well, how do you actually get good results? They would say like, oh, it's like, you know, a box of tricks that aren't published. So you have to join one of the labs and learn the tricks. So a bunch of unpublished tricks, not much software around, but thankfully there was Theano and rappers and particularly Lasagna, the rapper, but yeah, not much software around, not much in the way of data sets, you know, very hard to get started in terms of the compute. Like how do you get that set up? So yeah, no, everything was kind of inaccessible. And you know, as we started looking into it, we had a key insight, which was like, you know what, most of the compute and data for image recognition, for example, we don't need to do it. You know, there's this thing which nobody knows about, nobody talks about called transfer learning, where you take somebody else's model, where they already figured out like how to detect edges and gradients and corners and text and whatever else, and then you can fine tune it to do the thing you want to do. And we thought that's the key. That's the key to becoming more accessible in terms of compute and data requirements. So when we started Fast.ai, we focused from day one on transfer learning. Lesson one, in fact, was transfer learning, literally lesson one, something not normally even mentioned in, I mean, there wasn't much in the way of courses, you know, the courses out there were PhD programs that had happened to have recorded their lessons and they would rarely mention it at all. We wanted to show how to do four things that seemed really useful. You know, work with vision, work with tables of data, work with kind of recommendation systems and collaborative filtering and work with text, because we felt like those four kind of modalities covered a lot of the stuff that, you know, are useful in real life. And no one was doing anything much useful with text. Everybody was talking about word2vec, you know, like king plus queen minus woman and blah, blah, blah. It was like cool experiments, but nobody's doing anything like useful with it. NLP was all like lemmatization and stop words and topic models and bigrams and SPMs. And it was really academic and not practical. But I mean, to be honest, I've been thinking about this crazy idea for nearly 30 years since I had done cognitive science at university, where we talked a lot about the CELS Chinese room experiment. This idea of like, what if there was somebody that could kind of like, knew all of the symbolic manipulations required to answer questions in Chinese, but they didn't speak Chinese and they were kind of inside a room with no other way to talk to the outside world other than taking in slips of paper with Chinese written on them and then they do all their rules and then they pass back a piece of paper with Chinese back. And this room with a person in is actually fantastically good at answering any question you give them written in Chinese. You know, do they understand Chinese? And is this, you know, something that's intelligently working with Chinese? Ever since that time, I'd say the most thought, to me, the most thoughtful and compelling philosophical response is yes. You know, intuitively it feels like no, because that's just because we can't imagine such a large kind of system. But you know, if it looks like a duck and acts like a duck, it's a duck, you know, or to all intents and purposes. And so I always kind of thought, you know, so this is basically a kind of analysis of the limits of text. And I kind of felt like, yeah, if something could ingest enough text and could use the patterns it saw to then generate text in response to text, it could appear to be intelligent, you know. And whether that means it is intelligent or not is a different discussion and not one I find very interesting. Yeah. And then when I came across neural nets when I was about 20, you know, what I learned about the universal approximation theorem and stuff, and I started thinking like, oh, I wonder if like a neural net could ever get big enough and take in enough data to be a Chinese room experiment. You know, with that background and this kind of like interest in transfer learning, you know, I'd been thinking about this thing for kind of 30 years and I thought like, oh, I wonder if we're there yet, you know, because we have a lot of text. Like I can literally download Wikipedia, which is a lot of text. And I thought, you know, how would something learn to kind of answer questions or, you know, respond to text? And I thought, well, what if we used a language model? So language models are already a thing, you know, they were not a popular or well-known thing, but they were a thing. But language models exist to this idea that you could train a model to fill in the gaps. Or actually in those days it wasn't fill in the gaps, it was finish a string. And in fact, Andrej Karpathy did his fantastic RNN demonstration from this at a similar time where he showed like you can have it ingest Shakespeare and it will generate something that looks a bit like Shakespeare. I thought, okay, so if I do this at a much bigger scale, using all of Wikipedia, what would it need to be able to do to finish a sentence in Wikipedia effectively, to do it quite accurately quite often? I thought, geez, it would actually have to know a lot about the world, you know, it'd have to know that there is a world and that there are objects and that objects relate to each other through time and cause each other to react in ways and that causes proceed effects and that, you know, when there are animals and there are people and that people can be in certain positions during certain timeframes and then you could, you know, all that together, you can then finish a sentence like this was signed into law in 2016 by US President X and it would fill in the gap, you know. So that's why I tried to create what in those days was considered a big language model trained on the entirety on Wikipedia, which is that was, you know, a bit unheard of. And my interest was not in, you know, just having a language model. My interest was in like, what latent capabilities would such a system have that would allow it to finish those kind of sentences? Because I was pretty sure, based on our work with transfer learning and vision, that I could then suck out those latent capabilities by transfer learning, you know, by fine-tuning it on a task data set or whatever. So we generated this three-step system. So step one was train a language model on a big corpus. Step two was fine-tune a language model on a more curated corpus. And step three was further fine-tune that model on a task. And of course, that's what everybody still does today, right? That's what ChatGPT is. And so the first time I tried it within hours, I had a new state-of-the-art academic result on IMDB. And I was like, holy s**t, it does work. And so you asked, to what degree was this kind of like pushing against the established wisdom? You know, every way. Like the reason it took me so long to try it was because I asked all my friends in NLP if this could work. And everybody said, no, it definitely won't work. It wasn't like, oh, maybe. Everybody was like, it definitely won't work. NLP is much more complicated than vision. Language is a much more vastly complicated domain. You know, and you've got problems like the grounding problem. We know from like philosophy and theory of mind that it's actually impossible for it to work. So yeah, so don't waste your time. [00:15:10]Alessio: Jeremy, had people not tried because it was like too complicated to actually get the data and like set up the training? Or like, were people just lazy and kind of like, hey, this is just not going to work? [00:15:20]Jeremy: No, everybody wasn't lazy. So like, so the person I thought at that time who, you know, there were two people I thought at that time, actually, who were the strongest at language models were Stephen Merity and Alec Radford. And at the time I didn't know Alec, but I, after we had both, after I'd released ULM Fit and he had released GPT, I organized a chat for both of us with Kate Metz in the New York Times. And Kate Metz answered, sorry, and Alec answered this question for Kate. And Kate was like, so how did, you know, GPT come about? And he said, well, I was pretty sure that pre-training on a general large corpus wouldn't work. So I hadn't tried it. And then I read ULM Fit and turns out it did work. And so I did it, you know, bigger and it worked even better. And similar with, with Stephen, you know, I asked Stephen Merity, like, why don't we just find, you know, take your AWD-ASTLM and like train it on all of Wikipedia and fine tune it? And he's kind of like, well, I don't think that's going to really lie. Like two years before I did a very popular talk at KDD, the conference where everybody in NLP was in the audience. I recognized half the faces, you know, and I told them all this, I'm sure transfer learning is the key. I'm sure ImageNet, you know, is going to be an NLP thing as well. And, you know, everybody was interested and people asked me questions afterwards and, but not just, yeah, nobody followed up because everybody knew that it didn't work. I mean, even like, so we were scooped a little bit by Dai and Lee, Kwok Lee at Google. They had, they had, I already, I didn't even realize this, which is a bit embarrassing. They had already done a large language model and fine tuned it. But again, they didn't create a general purpose, large language model on a general purpose corpus. They only ever tested a domain specific corpus. And I haven't spoken to Kwok actually about that, but I assume that the reason was the same. It probably just didn't occur to them that the general approach could work. So maybe it was that kind of 30 years of mulling over the, the cell Chinese room experiment that had convinced me that it probably would work. I don't know. Yeah. [00:17:48]Alessio: Interesting. I just dug up Alec announcement tweet from 2018. He said, inspired by Cobe, Elmo, and Yola, I'm fit. We should have a single transformer language model can be fine tuned to a wide variety. It's interesting because, you know, today people think of AI as the leader, kind of kind of like the research lab pushing forward the field. What was that at the time? You know, like kind of like going back five years, people think of it as an overnight success, but obviously it took a while. [00:18:16]Swyx: Yeah. Yeah. [00:18:17]Jeremy: No, I mean, absolutely. And I'll say like, you know, it's interesting that it mentioned Elmo because in some ways that was kind of diametrically opposed to, to ULM fit. You know, there was these kind of like, so there was a lot of, there was a lot of activity at the same time as ULM fits released. So there was, um, so before it, as Brian McCann, I think at Salesforce had come out with this neat model that did a kind of multitask learning, but again, they didn't create a general fine tune language model first. There was Elmo, um, which I think was a lip, you know, actually quite a few months after the first ULM fit example, I think. Um, but yeah, there was a bit of this stuff going on. And the problem was everybody was doing, and particularly after GPT came out, then everybody wanted to focus on zero shot and few shot learning. You know, everybody hated fine tuning. Everybody hated transfer learning. And like, I literally did tours trying to get people to start doing transfer learning and people, you know, nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning. And so I actually feel like we kind of went backwards for years and, and not to be honest, I mean, I'm a bit sad about this now, but I kind of got so disappointed and dissuaded by like, it felt like these bigger lab, much bigger labs, you know, like fast AI had only ever been just me and Rachel were getting all of this attention for an approach I thought was the wrong way to do it. You know, I was convinced was the wrong way to do it. And so, yeah, for years people were really focused on getting better at zero shot and few shots and it wasn't until, you know, this key idea of like, well, let's take the ULM fit approach, but for step two, rather than fine tuning on a kind of a domain corpus, let's fine tune on an instruction corpus. And then in step three, rather than fine tuning on a reasonably specific task classification, let's fine tune on a, on a RLHF task classification. And so that was really, that was really key, you know, so I was kind of like out of the NLP field for a few years there because yeah, it just felt like, I don't know, pushing uphill against this vast tide, which I was convinced was not the right direction, but who's going to listen to me, you know, cause I, as you said, I don't have a PhD, not at a university, or at least I wasn't then. I don't have a big set of computers to fine tune huge transformer models. So yeah, it was definitely difficult. It's always been hard. You know, it's always been hard. Like I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use, you know, and also stuff that's created on lots of big computers has always been like much more media friendly. So like, it might seem like a recent thing, but actually throughout my 30 years in data science, the attention's always been on, you know, the big iron results. So when I first started, everybody was talking about data warehouses and it was all about Teradata and it'd be like, oh, this big bank has this huge room full of computers and they have like terabytes of data available, you know, at the press of a button. And yeah, that's always what people want to talk about, what people want to write about. And then of course, students coming out of their PhDs and stuff, that's where they want to go work because that's where they read about. And to me, it's a huge distraction, you know, because like I say, most people don't have unlimited compute and I want to help most people, not the small subset of the most well-off people. [00:22:16]Alessio: That's awesome. And it's great to hear, you do such a great job educating that a lot of times you're not telling your own story, you know? So I love this conversation. And the other thing before we jump into Fast.AI, actually, a lot of people that I know, they run across a new architecture and whatnot, they're like, I got to start a company and raise a bunch of money and do all of this stuff. And say, you were like, I want everybody to have access to this. Why was that the case for you? Was it because you already had a successful venture in like FastMail and you were more interested in that? What was the reasoning? [00:22:52]Jeremy: It's a really good question. So I guess the answer is yes, that's the reason why. So when I was a teenager, I thought it would be really cool to like have my own company. You know, I didn't know the word startup. I didn't know the word entrepreneur. I didn't know the word VC. And I didn't really know what any of those things were really until after we started Kaggle, to be honest. Even the way it started to what we now call startups. I just thought they were just small businesses. You know, they were just companies. So yeah, so those two companies were FastMail and Optimal Decisions. FastMail was the first kind of synchronized email provider for non-businesses. So something you can get your same email at home, on your laptop, at work, on your phone, whatever. And then Optimal Decisions invented a new approach to insurance pricing. Something called profit-optimized insurance pricing. So I saw both of those companies, you know, after 10 years. And at that point, I had achieved the thing that as a teenager I had wanted to do. You know, it took a lot longer than it should have because I spent way longer in management consulting than I should have because I got caught up in that stupid rat race. But, you know, eventually I got there and I remember my mom saying to me, you must be so proud. You know, because she remembered my dream. She's like, you've done it. And I kind of reflected and I was like, I'm not proud at all. You know, like people quite liked FastMail. You know, it's quite nice to have synchronized email. It probably would have happened anyway. Yeah, I'm certainly not proud that I've helped some insurance companies suck more money out of their customers. Yeah, no, I'm not proud. You know, it's actually, I haven't really helped the world very much. You know, maybe in the insurance case I've made it a little bit worse. I don't know. So, yeah, I was determined to not waste more years of my life doing things, working hard to do things which I could not be reasonably sure would have a lot of value. So, you know, I took some time off. I wasn't sure if I'd ever work again, actually. I didn't particularly want to, because it felt like, yeah, it felt like such a disappointment. And, but, you know, and I didn't need to. I had enough money. Like, I wasn't super rich, but I had enough money. I didn't need to work. And I certainly recognized that amongst the other people I knew who had enough money that they didn't need to work, they all worked ridiculously hard, you know, and constantly put themselves in extremely stressful situations. And I thought, I don't want to be one of those idiots who's tied to, you know, buying a bigger plane than the next guy or whatever. You know, Kaggle came along and I mainly kind of did that just because it was fun and interesting to hang out with interesting people. But, you know, with Fast.ai in particular, you know, Rachel and I had a very explicit, you know, long series of conversations over a long period of time about like, well, how can we be the most helpful to society as a whole, and particularly to those people who maybe need more help, you know? And so we definitely saw the world going in a potentially pretty dystopian direction if the world's most powerful technology was controlled by a small group of elites. So we thought, yeah, we should focus on trying to help that not happen. You know, sadly, it looks like it still is likely to happen. But I mean, I feel like we've helped make it a little bit less likely. So we've done our bit. [00:26:39]Swyx: You've shown that it's possible. And I think your constant advocacy, your courses, your research that you publish, you know, just the other day you published a finding on, you know, learning that I think is still something that people are still talking about quite a lot. I think that that is the origin story of a lot of people who are going to be, you know, little Jeremy Howards, furthering your mission with, you know, you don't have to do everything by yourself is what I'm saying. No, definitely. Definitely. [00:27:10]Jeremy: You know, that was a big takeaway from like, analytic was analytic. It definitely felt like we had to do everything ourselves. And I kind of, I wanted to solve medicine. I'll say, yeah, okay, solving medicine is actually quite difficult. And I can't do it on my own. And there's a lot of other things I'd like to solve, and I can't do those either. So that was definitely the other piece was like, yeah, you know, can we create an army of passionate domain experts who can change their little part of the world? And that's definitely happened. Like I find nowadays, at least half the time, probably quite a bit more that I get in contact with somebody who's done really interesting work in some domain. Most of the time I'd say, they say, yeah, I got my start with fast.ai. So it's definitely, I can see that. And I also know from talking to folks at places like Amazon and Adobe and stuff, which, you know, there's lots of alumni there. And they say, oh my God, I got here. And like half of the people are fast.ai alumni. So it's fantastic. [00:28:13]Swyx: Yeah. [00:28:14]Jeremy: Actually, Andre Kapathy grabbed me when I saw him at NeurIPS a few years ago. And he was like, I have to tell you, thanks for the fast.ai courses. When people come to Tesla and they need to know more about deep learning, we always send them to your course. And the OpenAI Scholars Program was doing the same thing. So it's kind of like, yeah, it's had a surprising impact, you know, that's just one of like three things we do is the course, you know. [00:28:40]Swyx: Yes. [00:28:40]Jeremy: And it's only ever been at most two people, either me and Rachel or me and Sylvia nowadays, it's just me. So yeah, I think it shows you don't necessarily need a huge amount of money and a huge team of people to make an impact. [00:28:56]Swyx: Yeah. So just to reintroduce fast.ai for people who may not have dived into it much, there is the courses that you do. There is the library that is very well loved. And I kind of think of it as a nicer layer on top of PyTorch that people should start with by default and use it as the basis for a lot of your courses. And then you have like NBDev, which I don't know, is that the third one? [00:29:27]Jeremy: Oh, so the three areas were research, software, and courses. [00:29:32]Swyx: Oh, sorry. [00:29:32]Jeremy: So then in software, you know, fast.ai is the main thing, but NBDev is not far behind. But then there's also things like FastCore, GHAPI, I mean, dozens of open source projects that I've created and some of them have been pretty popular and some of them are still a little bit hidden, actually. Some of them I should try to do a better job of telling people about. [00:30:01]Swyx: What are you thinking about? Yeah, what's on the course of my way? Oh, I don't know, just like little things. [00:30:04]Jeremy: Like, for example, for working with EC2 and AWS, I created a FastEC2 library, which I think is like way more convenient and nice to use than anything else out there. And it's literally got a whole autocomplete, dynamic autocomplete that works both on the command line and in notebooks that'll like auto-complete your instance names and everything like that. You know, just little things like that. I try to make like, when I work with some domain, I try to make it like, I want to make it as enjoyable as possible for me to do that. So I always try to kind of like, like with GHAPI, for example, I think that GitHub API is incredibly powerful, but I didn't find it good to work with because I didn't particularly like the libraries that are out there. So like GHAPI, like FastEC2, it like autocompletes both at the command line or in a notebook or whatever, like literally the entire GitHub API. The entire thing is like, I think it's like less than 100K of code because it actually, as far as I know, the only one that grabs it directly from the official open API spec that GitHub produces. And like if you're in GitHub and you just type an API, you know, autocomplete API method and hit enter, it prints out the docs with brief docs and then gives you a link to the actual documentation page. You know, GitHub Actions, I can write now in Python, which is just so much easier than writing them in TypeScript and stuff. So, you know, just little things like that. [00:31:40]Swyx: I think that's an approach which more developers took to publish some of their work along the way. You described the third arm of FastAI as research. It's not something I see often. Obviously, you do do some research. And how do you run your research? What are your research interests? [00:31:59]Jeremy: Yeah, so research is what I spend the vast majority of my time on. And the artifacts that come out of that are largely software and courses. You know, so to me, the main artifact shouldn't be papers because papers are things read by a small exclusive group of people. You know, to me, the main artifacts should be like something teaching people, here's how to use this insight and here's software you can use that builds it in. So I think I've only ever done three first-person papers in my life, you know, and none of those are ones I wanted to do. You know, they were all ones that, like, so one was ULM Fit, where Sebastian Ruder reached out to me after seeing the course and said, like, you have to publish this as a paper, you know. And he said, I'll write it. He said, I want to write it because if I do, I can put it on my PhD and that would be great. And it's like, okay, well, I want to help you with your PhD. And that sounds great. So like, you know, one was the masks paper, which just had to exist and nobody else was writing it. And then the third was the Fast.ai library paper, which again, somebody reached out and said, please, please write this. We will waive the fee for the journal and everything and actually help you get it through publishing and stuff. So yeah, so I don't, other than that, I've never written a first author paper. So the research is like, well, so for example, you know, Dawn Bench was a competition, which Stanford ran a few years ago. It was kind of the first big competition of like, who can train neural nets the fastest rather than the most accurate. And specifically it was who can train ImageNet the fastest. And again, this was like one of these things where it was created by necessity. So Google had just released their TPUs. And so I heard from my friends at Google that they had put together this big team to smash Dawn Bench so that they could prove to people that they had to use Google Cloud and use their TPUs and show how good their TPUs were. And we kind of thought, oh s**t, this would be a disaster if they do that, because then everybody's going to be like, oh, deep learning is not accessible. [00:34:20]Swyx: You know, to actually be good at it, [00:34:21]Jeremy: you have to be Google and you have to use special silicon. And so, you know, we only found out about this 10 days before the competition finished. But, you know, we basically got together an emergency bunch of our students and Rachel and I and sat for the next 10 days and just tried to crunch through and try to use all of our best ideas that had come from our research. And so particularly progressive resizing, just basically train mainly on small things, train on non-square things, you know, stuff like that. And so, yeah, we ended up winning, thank God. And so, you know, we turned it around from being like, like, oh s**t, you know, this is going to show that you have to be Google and have TPUs to being like, oh my God, even the little guy can do deep learning. So that's an example of the kind of like research artifacts we do. And yeah, so all of my research is always, how do we do more with less, you know? So how do we get better results with less data, with less compute, with less complexity, with less education, you know, stuff like that. So ULM fits obviously a good example of that. [00:35:37]Swyx: And most recently you published, can LLMs learn from a single example? Maybe could you tell the story a little bit behind that? And maybe that goes a little bit too far into the learning of very low resource, the literature. [00:35:52]Jeremy: Yeah, yeah. So me and my friend, Jono Whittaker, basically had been playing around with this fun Kaggle competition, which is actually still running as we speak, which is, can you create a model which can answer multiple choice questions about anything that's in Wikipedia? And the thing that makes it interesting is that your model has to run on Kaggle within nine hours. And Kaggle's very, very limited. So you've only got 14 gig RAM, only two CPUs, and a small, very old GPU. So this is cool, you know, if you can do well at this, then this is a good example of like, oh, you can do more with less. So yeah, Jono and I were playing around with fine tuning, of course, transfer learning, pre-trained language models. And we saw this, like, so we always, you know, plot our losses as we go. So here's another thing we created. Actually, Sylvain Guuger, when he worked with us, created called fast progress, which is kind of like TQEDM, but we think a lot better. So we look at our fast progress curves, and they kind of go down, down, down, down, down, down, down, a little bit, little bit, little bit. And then suddenly go clunk, and they drop. And then down, down, down, down, down a little bit, and then suddenly clunk, they drop. We're like, what the hell? These clunks are occurring at the end of each epoch. So normally in deep learning, this would be, this is, you know, I've seen this before. It's always been a bug. It's always turned out that like, oh, we accidentally forgot to turn on eval mode during the validation set. So I was actually learning then, or, oh, we accidentally were calculating moving average statistics throughout the epoch. So, you know, so it's recently moving average or whatever. And so we were using Hugging Face Trainer. So, you know, I did not give my friends at Hugging Face the benefit of the doubt. I thought, oh, they've fucked up Hugging Face Trainer, you know, idiots. Well, you'll use the Fast AI Trainer instead. So we switched over to Learner. We still saw the clunks and, you know, that's, yeah, it shouldn't really happen because semantically speaking in the epoch, isn't like, it's not a thing, you know, like nothing happens. Well, nothing's meant to happen when you go from ending one epoch to starting the next one. So there shouldn't be a clunk, you know. So I kind of asked around on the open source discords. That's like, what's going on here? And everybody was just like, oh, that's just what, that's just what these training curves look like. Those all look like that. Don't worry about it. And I was like, oh, are you all using Trainer? Yes. Oh, well, there must be some bug with Trainer. And I was like, well, we also saw it in Learner [00:38:42]Swyx: and somebody else is like, [00:38:42]Jeremy: no, we've got our own Trainer. We get it as well. They're just like, don't worry about it. It's just something we see. It's just normal. [00:38:48]Swyx: I can't do that. [00:38:49]Jeremy: I can't just be like, here's something that's like in the previous 30 years of neural networks, nobody ever saw it. And now suddenly we see it. [00:38:57]Swyx: So don't worry about it. [00:38:59]Jeremy: I just, I have to know why. [00:39:01]Swyx: Can I clarify? This is, was everyone that you're talking to, were they all seeing it for the same dataset or in different datasets? [00:39:08]Jeremy: Different datasets, different Trainers. They're just like, no, this is just, this is just what it looks like when you fine tune language models. Don't worry about it. You know, I hadn't seen it before, but I'd been kind of like, as I say, I, you know, I kept working on them for a couple of years after ULM fit. And then I kind of moved on to other things, partly out of frustration. So I hadn't been fine tuning, you know, I mean, Lama's only been out for a few months, right? But I wasn't one of those people who jumped straight into it, you know? So I was relatively new to the kind of Lama fine tuning world, where else these guys had been, you know, doing it since day one. [00:39:49]Swyx: It was only a few months ago, [00:39:51]Jeremy: but it's still quite a bit of time. So, so yeah, they're just like, no, this is all what we see. [00:39:56]Swyx: Don't worry about it. [00:39:56]Jeremy: So yeah, I, I've got a very kind of like, I don't know, I've just got this brain where I have to know why things are. And so I kind of, I ask people like, well, why, why do you think it's happening? And they'd be like, oh, it would pretty obviously, cause it's like memorize the data set. It's just like, that can't be right. It's only seen it once. Like, look at this, the loss has dropped by 0.3, 0.3, which is like, basically it knows the answer. And like, no, no, it's just, it is, it's just memorize the data set. So yeah. So look, Jono and I did not discover this and Jono and I did not come up with a hypothesis. You know, I guess we were just the ones, I guess, who had been around for long enough to recognize that like, this, this isn't how it's meant to work. And so we, we, you know, and so we went back and like, okay, let's just run some experiments, you know, cause nobody seems to have actually published anything about this. [00:40:51]Well, not quite true.Some people had published things, but nobody ever actually stepped back and said like, what the hell, you know, how can this be possible? Is it possible? Is this what's happening? And so, yeah, we created a bunch of experiments where we basically predicted ahead of time. It's like, okay, if this hypothesis is correct, that it's memorized in the training set, then we ought to see blah, under conditions, blah, but not under these conditions. And so we ran a bunch of experiments and all of them supported the hypothesis that it was memorizing the data set in a single thing at once. And it's a pretty big data set, you know, which in hindsight, it's not totally surprising because the theory, remember, of the ULMFiT theory was like, well, it's kind of creating all these latent capabilities to make it easier for it to predict the next token. So if it's got all this kind of latent capability, it ought to also be really good at compressing new tokens because it can immediately recognize it as like, oh, that's just a version of this. So it's not so crazy, you know, but it is, it requires us to rethink everything because like, and nobody knows like, okay, so how do we fine tune these things? Because like, it doesn't even matter. Like maybe it's fine. Like maybe it's fine that it's memorized the data set after one go and you do a second go and okay, the validation loss is terrible because it's now really overconfident. [00:42:20]Swyx: That's fine. [00:42:22]Jeremy: Don't, you know, don't, I keep telling people, don't track validation loss, track validation accuracy because at least that will still be useful. Just another thing that's got lost since ULMFiT, nobody tracks accuracy of language models anymore. But you know, it'll still keep learning and it does, it does keep improving. But is it worse? You know, like, is it like, now that it's kind of memorized it, it's probably getting a less strong signal, you know, I don't know. So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do, like nobody really knows whether this memorization thing is, it's probably a feature in some ways. It's probably some things that you can do usefully with it. It's probably, yeah, I have a feeling it's messing up training dynamics as well. [00:43:13]Swyx: And does it come at the cost of catastrophic forgetting as well, right? Like, which is the other side of the coin. [00:43:18]Jeremy: It does to some extent, like we know it does, like look at Code Llama, for example. So Code Llama was a, I think it was like a 500 billion token fine tuning of Llama 2 using code. And also pros about code that Meta did. And honestly, they kind of blew it because Code Llama is good at coding, but it's bad at everything else, you know, and it used to be good. Yeah, I was pretty sure it was like, before they released it, me and lots of people in the open source discords were like, oh my God, you know, we know this is coming, Jan Lukinsk saying it's coming. I hope they kept at least like 50% non-code data because otherwise it's going to forget everything else. And they didn't, only like 0.3% of their epochs were non-code data. So it did, it forgot everything else. So now it's good at code and it's bad at everything else. So we definitely have catastrophic forgetting. It's fixable, just somebody has to do, you know, somebody has to spend their time training a model on a good mix of data. Like, so, okay, so here's the thing. Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it. [00:44:36]Jeremy: And that's because people are using it in a way different to why I created it. You know, I created it thinking the task-specific models would be more specific. You know, it's like, oh, this is like a sentiment classifier as an example of a task, you know, but the tasks now are like a, you know, RLHF, which is basically like answer questions that make people feel happy about your answer. So that's a much more general task and it's a really cool approach. And so we see, for example, RLHF also breaks models like, you know, like GPT-4, RLHDEFT, we know from kind of the work that Microsoft did, you know, the pre, the earlier, less aligned version was better. And these are all kind of examples of catastrophic forgetting. And so to me, the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training. And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data. You always keep all of the data types there in reasonably high quantities. You know, maybe the quality filter, you stop training on low quality data, because that's probably fine to forget how to write badly, maybe. So yeah, that's now my view, is I think ULM fit is the wrong approach. And that's why we're seeing a lot of these, you know, so-called alignment tacks and this view of like, oh, a model can't both code and do other things. And, you know, I think it's actually because people are training them wrong. [00:46:47]Swyx: Yeah, well, I think you have a clear [00:46:51]Alessio: anti-laziness approach. I think other people are not as good hearted, you know, they're like, [00:46:57]Swyx: hey, they told me this thing works. [00:46:59]Alessio: And if I release a model this way, people will appreciate it, I'll get promoted and I'll kind of make more money. [00:47:06]Jeremy: Yeah, and it's not just money. It's like, this is how citations work most badly, you know, so if you want to get cited, you need to write a paper that people in your field recognize as an advancement on things that we know are good. And so we've seen this happen again and again. So like I say, like zero shot and few shot learning, everybody was writing about that. Or, you know, with image generation, everybody just was writing about GANs, you know, and I was trying to say like, no, GANs are not the right approach. You know, and I showed again through research that we demonstrated in our videos that you can do better than GANs, much faster and with much less data. And nobody cared because again, like if you want to get published, you write a GAN paper that slightly improves this part of GANs and this tiny field, you'll get published, you know. So it's, yeah, it's not set up for real innovation. It's, you know, again, it's really helpful for me, you know, I have my own research lab with nobody telling me what to do and I don't even publish. So it doesn't matter if I get citations. And so I just write what I think actually matters. I wish there was, and, you know, and actually places like OpenAI, you know, the researchers there can do that as well. It's a shame, you know, I wish there was more academic, open venues in which people can focus on like genuine innovation. [00:48:38]Swyx: Twitter, which is unironically has become a little bit of that forum. I wanted to follow up on one thing that you mentioned, which is that you checked around the open source discords. I don't know if it's too, I don't know if it's a pusher to ask like what discords are lively or useful right now. I think that something I definitely felt like I missed out on was the early days of Luther AI, which is a very hard bit. And, you know, like what is the new Luther? And you actually shouted out the alignment lab AI discord in your blog post. And that was the first time I even knew, like I saw them on Twitter, never knew they had a discord, never knew that there was actually substantive discussions going on in there and that you were an active member of it. Okay, yeah. [00:49:23]Jeremy: And then even then, if you do know about that and you go there, it'll look like it's totally dead. And that's because unfortunately, nearly all the discords, nearly all of the conversation happens in private channels. You know, and that's, I guess. [00:49:35]Swyx: How does someone get into that world? Because it's obviously very, very instructive, right? [00:49:42]Jeremy: You could just come to the first AI discord, which I'll be honest with you, it's less bustling than some of the others, but it's not terrible. And so like, at least, to be fair, one of Emma's bustling channels is private. [00:49:57]Swyx: I guess. [00:49:59]Jeremy: So I'm just thinking. [00:50:01]Swyx: It's just the nature of quality discussion, right? Yeah, I guess when I think about it, [00:50:05]Jeremy: I didn't have any private discussions on our discord for years, but there was a lot of people who came in with like, oh, I just had this amazing idea for AGI. If you just thought about like, if you imagine that AI is a brain, then we, you know, this just, I don't want to talk about it. You know, I don't want to like, you don't want to be dismissive or whatever. And it's like, oh, well, that's an interesting comment, but maybe you should like, try training some models first to see if that aligns with your intuition. Like, oh, but how could I possibly learn? It's like, well, we have a course, just actually spend time learning. Like, you know, anyway. And there's like, okay, I know the people who always have good answers there. And so I created a private channel and put them all in it. And I got to admit, that's where I post more often because there's much less, you know, flight of fancy views about how we could solve AGI, blah, blah, blah. So there is a bit of that. But having said that, like, I think the bar is pretty low. Like if you join a Discord and you can hit the like participants or community or whatever button, you can see who's in it. And then you'll see at the top, who the admins or moderators or people in the dev role are. And just DM one of them and say like, oh, here's my GitHub. Well, here's some blog posts I wrote. You know, I'm interested in talking about this, you know, can I join the private channels? And I've never heard of anybody saying no. I will say, you know, Alutha's all pretty open. So you can do the Alutha Discord still. You know, one problem with the Alutha Discord is it's been going on for so long that it's like, it's very inside baseball. It's quite hard to get started. Yeah. Carpa AI looks, I think it's all open. That's just less stability. That's more accessible. [00:52:03]Swyx: Yeah. [00:52:04]Jeremy: There's also just recently, now it's research that does like the Hermes models and data set just opened. They've got some private channels, but it's pretty open, I think. You mentioned Alignment Lab, that one it's all the interesting stuff is on private channels. So just ask. If you know me, ask me, cause I've got admin on that one. There's also, yeah, OS Skunkworks, OS Skunkworks AI is a good Discord, which I think it's open. So yeah, they're all pretty good. [00:52:40]Swyx: I don't want you to leak any, you know, Discords that don't want any publicity, but this is all helpful. [00:52:46]Jeremy: We all want people, like we all want people. [00:52:49]Swyx: We just want people who like, [00:52:51]Jeremy: want to build stuff, rather than people who, and like, it's fine to not know anything as well, but if you don't know anything, but you want to tell everybody else what to do and how to do it, that's annoying. If you don't know anything and want to be told like, here's a really small kind of task that as somebody who doesn't know anything is going to take you a really long time to do, but it would still be helpful. Then, and then you go and do it. That would be great. The truth is, yeah, [00:53:19]Swyx: like, I don't know, [00:53:20]Jeremy: maybe 5% of people who come in with great enthusiasm and saying that they want to learn and they'll do anything. [00:53:25]Swyx: And then somebody says like, [00:53:25]Jeremy: okay, here's some work you can do. Almost nobody does that work. So if you're somebody who actually does the work and follows up, you will massively stand out. That's an extreme rarity. And everybody will then want to help you do more work. [00:53:41]Swyx: So yeah. [00:53:41]Jeremy: So just, yeah, just do work and people will want to support you. [00:53:47]Alessio: Our Discord used to be referral only for a long time. We didn't have a public invite and then we opened it and they're kind of like channel gating. Yeah. A lot of people just want to do, I remember it used to be like, you know, a forum moderator. [00:54:00]Swyx: It's like people just want to do [00:54:01]Alessio: like drive-by posting, [00:54:03]Swyx: you know, and like, [00:54:03]Alessio: they don't want to help the community. They just want to get their question answered. [00:54:07]Jeremy: I mean, the funny thing is our forum community does not have any of that garbage. You know, there's something specific about the low latency thing where people like expect an instant answer. And yeah, we're all somehow in a forum thread where they know it's like there forever. People are a bit more thoughtful, but then the forums are less active than they used to be because Discord has got more popular, you know? So it's all a bit of a compromise, you know, running a healthy community is, yeah, it's always a bit of a challenge. All right, we got so many more things [00:54:47]Alessio: we want to dive in, but I don't want to keep you here for hours. [00:54:50]Swyx: This is not the Lex Friedman podcast [00:54:52]Alessio: we always like to say. One topic I would love to maybe chat a bit about is Mojo, modular, you know, CrystalLiner, not many of you on the podcast. So we want to spend a little time there. You recently did a hacker's guide to language models and you ran through everything from quantized model to like smaller models, larger models, and all of that. But obviously modular is taking its own approach. Yeah, what got you excited? I know you and Chris have been talking about this for like years and a lot of the ideas you had, so. [00:55:23]Jeremy: Yeah, yeah, yeah, yeah, no, absolutely. So I met Chris, I think it was at the first TensorFlow Dev Summit. And I don't think he had even like, I'm not sure if he'd even officially started his employment with Google at that point. So I don't know, you know, certainly nothing had been mentioned. So I, you know, I admired him from afar with LLVM and Swift and whatever. And so I saw him walk into the courtyard at Google. It's just like, oh s**t, man, that's Chris Latner. I wonder if he would lower his standards enough to talk to me. Well, worth a try. So I caught up my courage because like nobody was talking to him. He looked a bit lost and I wandered over and it's like, oh, you're Chris Latner, right? It's like, what are you doing here? What are you doing here? And I was like, yeah, yeah, yeah. It's like, oh, I'm Jeremy Howard. It's like, oh, do you do some of this AI stuff? And I was like, yeah, yeah, I like this AI stuff. Are you doing AI stuff? It's like, well, I'm thinking about starting to do some AI stuff. Yeah, I think it's going to be cool. And it's like, wow. So like, I spent the next half hour just basically brain dumping all the ways in which AI was stupid to him. And he listened patiently. And I thought he probably wasn't even remember or care or whatever. But yeah, then I kind of like, I guess I re-caught up with him a few months later. And it's like, I've been thinking about everything you said in that conversation. And he like narrated back his response to every part of it, projects he was planning to do. And it's just like, oh, this dude follows up. Holy s**t. And I was like, wow, okay. And he was like, yeah, so we're going to create this new thing called Swift for TensorFlow. And it's going to be like, it's going to be a compiler with auto differentiation built in. And blah, blah, blah. And I was like, why would that help? [00:57:10]Swyx: You know, why would you? [00:57:10]Jeremy: And he was like, okay, with a compiler during the forward pass, you don't have to worry about saving context, you know, because a lot will be optimized in the backward. But I was like, oh my God. Because I didn't really know much about compilers. You know, I spent enough to kind of like, understand the ideas, but it hadn't occurred to me that a compiler basically solves a lot of the problems we have as end users. I was like, wow, that's amazing. Okay, you do know, right, that nobody's going to use this unless it's like usable. It's like, yeah, I know, right. So I was thinking you should create like a fast AI for this. So, okay, but I don't even know Swift. And he was like, well, why don't you start learning it? And if you have any questions, ask me. It's just like, holy s**t. Like, not only has Chris Latner lowered his standards enough to talk to me, but he's offering me personal tutoring on the programming language that he made. So I was just like, I'm not g

Want to help define the AI Engineer stack? Have opinions on the top tools, communities and builders? We're collaborating with friends at Amplify to launch the first State of AI Engineering survey! Please fill it out (and tell your friends)!If AI is so important, why is its software so bad?This was the motivating question for Chris Lattner as he reconnected with his product counterpart on Tensorflow, Tim Davis, and started working on a modular solution to the problem of sprawling, monolithic, fragmented platforms in AI development. They announced a $30m seed in 2022 and, following their successful double launch of Modular/Mojo

Hey ya'll, welcome to yet another ThursdAI, this is Alex coming at you every ThursdAI, including a live recording this time! Which was incredible, we chatted about Falcon 180B,had a great interview in the end with 3 authors of the YaRN scaling paper and LLongMa 128K context, had 3 breaking news! in the middle, MOJO

Chris Lattner is a legendary software and hardware engineer, leading projects at Apple, Tesla, Google, SiFive, and Modular AI, including the development of Swift, LLVM, Clang, MLIR, CIRCT, TPUs, and Mojo. Please support this podcast by checking out our sponsors: - iHerb: https://lexfridman.com/iherb and use code LEX to get 22% off your order - Numerai: https://numer.ai/lex - InsideTracker: https://insidetracker.com/lex to get 20% off EPISODE LINKS: Chris's Twitter: https://twitter.com/clattner_llvm Chris's Website: http://nondot.org/sabre/ Mojo programming language: https://www.modular.com/mojo Modular AI: https://modular.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Medium: https://medium.com/@lexfridman 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 (06:38) - Mojo programming language (16:55) - Code indentation (25:22) - The power of autotuning (35:12) - Typed programming languages (51:56) - Immutability (1:04:14) - Distributed deployment (1:38:41) - Mojo vs CPython (1:54:30) - Guido van Rossum (2:01:31) - Mojo vs PyTorch vs TensorFlow (2:04:55) - Swift programming language (2:10:27) - Julia programming language (2:15:32) - Switching programming languages (2:24:58) - Mojo playground (2:29:48) - Jeremy Howard (2:40:34) - Function overloading (2:48:59) - Error vs Exception (2:56:39) - Mojo roadmap (3:09:41) - Building a company (3:21:27) - ChatGPT (3:27:50) - Danger of AI (3:31:44) - Future of programming (3:35:01) - Advice for young people

Host Chris Adams is joined by the GSF's Asim Hussain on this episode of The Week in Green Software. They discuss some interesting news about Amazon, AWS and their scope 3 GHG protocol emission data. We also find out how Python has got its Mojo back and we have a very exciting tool from Catchpoint WebpageTest for measuring site's carbon footprint. Finally, some great green software events that you can be part of!

Watch on YouTube About the show Sponsored by InfluxDB from Influxdata. Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Michael #1: Introducing 'Trusted Publishers' PyPI package maintainers can adopt a new, more secure publishing method that does not require long-lived passwords or API tokens to be shared with external systems. Our term for using the OpenID Connect (OIDC) standard to exchange short-lived identity tokens between a trusted third-party service and PyPI. Instead, PyPI maintainers can configure PyPI to trust an identity provided by a given OpenID Connect Identity Provider (IdP). These API tokens never need to be stored or shared rotate automatically by expiring quickly provide a verifiable link between a published package and its source Additional security hardening is available Brian #2: Mojo : a new programming language for all AI developers. Mojo may be the biggest programming language advance in decades - fast.ai blog Suggested by many listeners “Mojo combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models.” A programming language compatible with Python, with performance similar to C++/Rust. “Mojo is designed to become a superset of Python over time by preserving Python's dynamic features while adding new primitives for systems programming.” - emphasis from Brian It's not there yet, but still super cool Built on a MLIR, not LLVM “How compatible is Mojo with Python really? Mojo already supports many core features of Python including async/await, error handling, variadics, etc, but… it is still very early and missing many features - so today it isn't very compatible. Mojo doesn't even support classes yet!” Michael #3: django-prose Wonderful rich-text editing for your Django project. Rendering rich-text in templates Small rich-text content (as model fields) Django Prose is using Bleach to only allow certain tags and attributes See the website for a screenshot of it in action Brian #4: pylyzer is a static code analyzer / language server for Python, written in Rust. Shunsuke Shibayama Suggested by Owen Features fast detailed analysis type checking plus things like out-of-bounds accesses to lists, and non-existent key references to dicts more readable reports and a VS Code extension pylyzer vs ruff “Ruff, like pylyzer, is a static code analysis tool for Python written in Rust, but Ruff is a linter and pylyzer is a type checker & language server. pylyzer does not perform linting, and Ruff does not perform type checking.” Some limitations and incomplete “todo list”. See README for more details. Joke: Escape Room

Mojo, un revolucionario lenguaje de programación presentado por Modular.ai, una startup confundada por Chris Lattner, creador de Swift. Diseñado como super clase de Python pero para superar las limitaciones de este, Mojo ofrece un rendimiento excepcional gracias a su compilación de código y capacidad para trabajar en multihilo. Con una infraestructura de compilación llamada MLIR, Mojo puede adaptarse eficientemente a cualquier hardware y ejecutar tareas en paralelo. ¿Lo mejor de todo? Mojo es hasta 35.000 veces más rápido que Python en ciertos algoritmos y se integra sin problemas con todo el ecosistema de Python. Analicemos este y su importancia.  Descubre nuestro canal de Twitch en: twitch.tv/applecoding. Descubre nuestras ofertas para oyentes: - Cursos en Udemy (con código de oferta) - Apple Coding Academy - Suscríbete a Apple Coding en nuestro Patreon. - Canal de Telegram de Swift. Acceso al canal. --------------- Consigue las camisetas oficiales de Apple Coding con los logos de Swift y Apple Coding así como todo tipo de merchadising como tazas o fundas. - Tienda de merchandising de Apple Coding. --------------- Tema musical: "Final Frontier", compuesto por Thomas Bergensen. Usado con permisos de fair use. Escúchalo en Apple Music o Spotify.

Escopinyes, vermutet, un raig de sol furtiu que ens escalfa les neurones... El nostre vermut electr

Escopinyes, vermutet, un raig de sol furtiu que ens escalfa les neurones... El nostre vermut electr

FEATURED VOICES IN THIS EPISODEDan GuidoDan Guido is the CEO of Trail of Bits, a cybersecurity firm he founded in 2012 to address software security challenges with cutting-edge research. In his tenure leading Trail of Bits, Dan has grown the team to 80 engineers, led the team to compete in the DARPA Cyber Grand Challenge, built an industry-leading blockchain security practice, and refined open-source tools for the endpoint security market. In addition to his work at Trail of Bits, he's active on the boards of four early-stage technology companies. Dan contributes to cybersecurity policy papers from RAND, CNAS, and Harvard. He runs Empire Hacking, a 1,500-member meetup group focused on NYC-area cybersecurity professionals. His latest hobby coding project -- AlgoVPN -- is the Internet's most recommended self-hosted VPN. In prior roles, Dan taught a capstone course on software exploitation at NYU as a faculty member and the Hacker in Residence, consulted at iSEC Partners (now NCC Group), and worked as an incident responder for the Federal Reserve System.Nat ChinNat Chin is a security engineer 2 at Trail of Bits, where she performs security reviews of blockchain projects, and develops tools that are useful when working with Ethereum. She is the author of solc-select, a tool to help switch Solidity versions. She worked as a smart contract developer and taught as a Blockchain Professor at George Brown College, before transitioning to blockchain security when she joined Trail of Bits.Opal WrightOpal Wright is a cryptography analyst at Trail of Bits. Two of the following three statements about her are true: (a) she's a long-distance unicyclist; (b) she invented a public-key cryptosystem; (c) she designed and built an award-winning sex toy.Jim MillerJim Miller is the cryptography team lead at Trail of Bits. Before joining Trail of Bits, Jim attended graduate programs at both Cambridge and Yale, where he studied and researched both Number Theory and Cryptography, focusing on topics such as lattice-based cryptography and zero-knowledge proofs. During his time at Trail of Bits, Jim has led several security reviews across a wide variety of cryptographic applications and has helped lead the development of multiple projects, such as ZKDocs and PrivacyRaven.Josselin FeistJosselin Feist is a principal security engineer at Trail of Bits where he participates in assessments of blockchain software and designs automated bug-finding tools for smart contracts. He holds a Ph.D. in static analysis and symbolic execution and regularly speaks at both academic and industrial conferences. He is the author of various security tools, including Slither - a static analyzer framework for Ethereum smart contracts and Tealer - a static analyzer for Algorand contracts.Peter GoodmanPeter Goodman is a Staff Engineer in the Research and Engineering practice at Trail of Bits, where he leads all de/compilation efforts. He is the creator of various static and dynamic program analysis tools, ranging from the Remill library for lifting machine code into LLVM bitcode, to the GRR snapshot/record/replay-based fuzzer. When Peter isn't writing code, he's mentoring a fleet of interns to push the envelope. Peter holds a Master's in Computer Science from the University of Toronto.Host: Nick SelbyAn accomplished information and physical security professional, Nick leads the Software Assurance practice at Trail of Bits, giving customers at some of the world's most targeted companies a comprehensive understanding of their security landscape. He is the creator of the Trail of Bits podcast, and does everything from writing scripts to conducting interviews to audio engineering to Foley (e.g. biting into pickles). Prior to Trail of Bits, Nick was Director of Cyber Intelligence and Investigations at the NYPD; the CSO of a blockchain startup; and VP of Operations at an industry analysis firm.Production StaffStory Editor: Chris JulinAssociate Editor: Emily HaavikExecutive Producer: Nick SelbyExecutive Producer: Dan GuidoRecordingRocky Hill Studios, Ghent, New York. Nick Selby, EngineerPreuss-Projekt Tonstudio, Salzburg, Austria. Christian Höll, EngineerRemote recordings:Whistler, BC, Canada; (Nick Selby) Queens, NY; Brooklyn, NY; Rochester, NY (Emily Haavik);Toronto, ON, Canada. TAPES//TYPES, Russell W. Gragg, EngineerTrail of Bits supports and adheres to the Tape Syncers United Fair Rates CardEdited by Emily Haavik and Chris JulinMastered by Chris JulinMusicDISPATCHES FROM TECHNOLOGY'S FUTURE, THE TRAIL OF BITS THEME, Chris JulinOPEN WINGS, Liron MeyuhasNEW WORLD, Ian PostFUNKYMANIA, Omri Smadar, The Original OrchestraGOOD AS GONE, INSTRUMENTAL VERSION, Bunker Buster ALL IN YOUR STRIDE, AbeBREATHE EASY, Omri SmadarTREEHOUSE, LingerwellLIKE THAT, Tobias BergsonSCAPES,  Gray NorthReproductionWith the exception of any Copyrighted music herein, Trail of Bits Season 1 Episode 0; Immutable © 2022 by Trail of Bits is licensed under Attribution-NonCommercial-NoDerivatives 4.0 International. This license allows reuse: reusers may copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only (noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation), provided that reusers give credit to Trail of Bits as the creator. No derivatives or adaptations of this work are permitted. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.Meet the Team:CHRIS JULINChris Julin has spent years telling audio stories and helping other people tell theirs. These days he works as a story editor and producer for news outlets like APM Reports, West Virginia Public Broadcasting, and Marketplace. He has also taught and mentored hundreds of young journalists as a professor. For the Trail of Bits podcast, he serves as story and music editor, sound designer, and mixing and mastering engineer.EMILY HAAVIKFor the past 10 years Emily Haavik has worked as a broadcast journalist in radio, television, and digital media. She's spent time writing, reporting, covering courts, producing investigative podcasts, and serving as an editorial manager. She now works as an audio producer for several production shops including Us & Them from West Virginia Public Broadcasting and PRX, and APM Reports. For the Trail of Bits podcast, she helps with scripting, interviews, story concepts, and audio production.

Dr. Aart Bik is a Staff Software Engineer at Google where he works on MLIR, a compiler architecture project that uses extensible compiler infrastructure alongside LLVM to build better compilers for Machine Learning.Timestamps:1:10 - An explanation of MLIR and LLVM compilers2:30 - Compilers are more than just translating high level code to assembly6:00 - Progressive Lowering in LLVM and MLIR7:40 - MLIR is largely agnostic of hardware9:00 - TPUs9:40 - AlphaZero, the ML and TPU powered chess engine, explained13:00 - The ML behind language translation16:30 - Will the future advancements in ML come hardware or from software?20:39 - Performance improvements with MLIR22:43 - Using fuzz testing to build better software including compilers31:00 - Code up a hobbyist project based on LLVM if you want to learn more about compilers32:28 - In your opinion, what is the best piece of software ever built?

Mixé avec des mains propres en distanciation sociale à Villeray Dr. Alex Paterson - Loving You Live - Warp Charles Trees - - Sunrise On Belle Isle - Portage Garag Sounds Lord of The Isles - Inheritance ( feat. Ellen Renton) - Whities FIT Siegel - Exist On - Fit Sound Frak - Arcitect - Höga Nord Rekords DJ City - Important Message - Public Possession Marius Circus - Monumental Effort - In The Garden MLiR & Velmondo - Mad Honey - Internasjonal Kleeer - Open Your Mind (Joey Negro Seeekret Mix) - Z L.E.B. Harmony ‎– Feeling Love - Carrousel  

♫ Info + Tracklist ♫ www.deephouseamsterdam.com/dha-fm-mix-456-by-mlir MLiR http://facebook.com/WeAre.MLiR https://www.instagram.com/modernlife.isrubbish/ https://soundcloud.com/m-l-i-r

Comedian Mark Hughes drops in on the MLIR podcast to chat about his past life as a heroin addict and armed robber. Mark tells us his story about growing up in Vancouver, getting involved in a life of drugs and crime, which ultimately leads him to be in and out of prison for most of his life. After a seven year stint, Mark is sober and finds a new life with the help of stand up comedy, and AA. We break down the struggles and successes of being an ex convict trying to find a 'normal' existence in a new unknown world. Follow Mark @markhughescomic on all social media platforms If you are in Toronto check out this great show Mark is producing on July 12th (Laughs on Lives Terms) which will also feature me! https://www.facebook.com/events/383778092216836/

Magic Teapot Records is an exclusive online record store in Barcelona, but also a recently launched record label (run by Daniel Baughman— A&R at Hivern Discs, booker at audiophile venue Nica —, Marco Gegenheimer - who releases on Studio Barnhus as MLiR - and Jens Ingelstedt). The DJ duo though is made up of Baughman and Gegenheimer, and when they come together they unleash hidden vinyl gems that get you dancing and romancing. Their sound is one that has a lot to say and draws on steamy tropical delights and intoxicating world music that could be thirty years old or brand new. Their mix for us is a 90 minute live recording of them doing what they do best at Nica, namely taking us around the globe on a real adventure that has a sense of musical wanderlust running right through it. There are intimate moments of acoustic heaven bleeding into soft, drunken synths and glorious passages of churning reggae and twinkling disco along the way. It’s music with meaning threaded together by a air of expert curators.

Sign up for the Inspiration With Val newsletter (and the Carpe Diem Daily 12 Month Challenge) by texting VAL to 38470 - no spam, just inspiration!  Find more content like this, read blog posts, and learning more about Valiant Coaching at www.InspirationWithVal.com. PODCAST INTERVIEW WITH  KIRSTEN ROSS   SUMMARY: Kirsten E. Ross, MLIR, SPHR is known to many as the “Drama Free Queen”.  She’s the undisputed expert of transformation. Kirsten will move you from Resigned, Resentful and Overwhelmed to Focused, Engaged and Empowered.  She has provided individual and group coaching for privately held and family businesses and non-profits, and her insightful approach has helped leaders find their fuel, ignite their teams, speak with tenacity and become empowered in their lives and work.  Kirsten’s education and experience includes a Master’s Degree in Human Resource Management, Senior Human Resource Certification, the Coach Training Alliance curriculum, and over 20 years of hands-on Human Resources.  She authored the leadership books “Defeat the Drama!” and “From People Problems to Productivity” and has been featured as an expert for media such as: NBC Nightly News, Fox 2 News, National Public Radio, and for publications such as Entrepreneur Magazine, Working Mother Magazine and Crain’s.   About Kirsten Ross: (3:22) Kirsten E. Ross was born and raised in Petoskey, Michigan, a small town near the Mackinac bridge. She has resided in the Detroit area as an adult but she still maintains those small-town values. Kirsten brings a unique blend of energy, insight and compassion to her work with clients. Her passion is helping  transform leaders and their relationships, create drama-free work zones, enhance customer loyalty, and improve profits. How she fell into her own business: (4:18) Kirsten said that the catalyst to start her own business was actually being in an abusive marriage. Ten years ago, she worked in the field of Human Resource when she was a stay at home mom for a period and her marriage became more and more abusive. She did some soul-searching and come up with a plan to get out, and to take care of herself and her two boys. Trying to restore order in her marriage life: (5:55) Kirsten Ross really tried everything, and nothing worked. After she got a call from each of her two sons’ teachers telling her that they were not happy in school, she was 100% certain that she needed to make a decision and start making a plan to end her marriage and leave. Making the move to get out: (8:40) Kirsten stayed 100% focused on her business as the catalyst to power the transition. Kirsten  got trusted advisers to help create a plan for her business and then she launched her business. And also decided that once she got out, she would take her two boys and create a home that’s filled with laughter. Her full focus was directed to what was in her control for herself and the two kids.   Best Advice: 40:29 “Speak your mind even if your voice shakes;  speak up and it know what you want.”   Links: www.defeatthedrama.com Defeat the Drama:  The Podcast Defeat the Drama:  The Book