Podcast appearances and mentions of Scott Gray

dogs christmas special big finish panopticon bbc america raw dog chris chibnall peter davison paul cornell dwm jodie whitaker scott gray sarah sutton

Play Episode Listen Later Dec 14, 2024 127:01

Send us a textIn the first of the Too Hot For TV festive specials, Dylan is joined by Liam to do a commentary on 'Festive Thirteenth Doctor Yule Log' a 2 hour animated webcast from BBC America. As they endure 2 hours of very little activity they discuss all things festive and Doctor Who related with a sprinkle comic strips and Big Finish for good measure. And as always they answer the burning questions:Who is getting smuggled into Panopticon? What is the best 13th Doctor Christmas Special?Who are generation Alan?And if you want to raw dog the log with us you find a link herehttps://www.namasha.com/v/qLGkSECg/Festive_Thirteenth_Doctor_Yule_Log_Doctor_Who_BBC_Americaand you can find Liam's art on instagram https://www.instagram.com/artfullyliam/

Yoversion Podcast #134 – November 2024 with John Jones – Special Guestmix: Danism & Train

Yoversion Podcast with John Jones >> House Music with Vision

Play Episode Listen Later Nov 2, 2024 120:43

Yoversion Podcast #134 - November 2024 with John Jones - Special Guestmix: Danism & Train TRACKLISTING Outr Drive & Steffanie Christi'an - You Dont Know “Kon's 12 Let's Go Disco Mix” // Industry Standard Seamus Haji & Mike Dunn - Disco Dreams “Hatiras Remix” // Big Love THE HOTSPOT Dam Swindle - Touch Me Again // Heist Armand Van Helden, A-Trak, Duck Sauce - Fallin In Love // D4 D4NCE Robert Owens, Cinthie - Every Chapter // Fabric Records BACK IN THE BOX Alex Wann - My Love (2024) // Positiva Betical, Arper - Back On 74 (Rework) // White Label 3-ON-THE-SPIN Très Mortimer - At Night I Think Of You “Seth Troxler & Nick Morgan Remix” // Ministry of Sound Us Two - Breaking Dollars // Heavy House Smokin Jo, D. Ramirez - In The Night // Faith Marco Faraone, Deetron - The Answer // We Are Pyramid Your SHOUT! (Scott Gray, Melon Bomb Ibiza) Melon Bomb - Twilight // Basement Disco IDRIS ft. Mayra Andrade - Sima Agua // Defected THE CLASSIC TRACK Astrotrax - You Are My Everything // Astrotrax Special Guestmix: Danism & Train

train trak john jones danism scott gray special guest mix

Panel Borders: Fantasy and Horror Locations

Panel Borders – Panel Borders and other podcasts

Play Episode Listen Later Jul 3, 2024

Fantasy and Horror Locations: In an episode that looks at Fantasy and Horror Comics in memoriable locations, a trio of creators talk about their work in this area. Alex Fitch talks to John Dudley and Scott Gray about their kickstarted title Big Shoulders which mixes music and magic, dragons and immortal storytellers in contemporary and […]

S6 Ep8: Scott Gray - Commitment to Living The Dream

The Mojo Podcast

Play Episode Listen Later Jun 10, 2024 47:28

My guest this week is Scott Gray is an Artist and DJ from London. He is joint founder of Ibiza's iconic Melon Bomb DJ collective and creator of the art and design brand Chapter. The Island of Ibiza (where Scott now resides) is the main influence and really kick-started his art and musical career. We met at Scott's home in Ibiza - where the walls are filled with his favourite art and he serves a cracking cup of coffee. We talked about the incredible change Scott and his wife Mandy created in their lives - when whilst on holiday in Ibiza Scott has a near breakdown moment and realised he could not continue with his stressful if successful career in recruitment. Another life called them. Fast forward a few years and Scott followed his intuition again to bring together some fellow DJs to create the now globally renowned and frankly Island favourite - House Music Collective Melon Bomb. We talk about the life of a DJ, the joys the pressures the creative process and of course just what it's like to move a crowd to your rhythms. I really enjoyed getting to know Scott better through this chat and he says an abundance of wisdom and advice for anyone thinking about change and anyone finding their mojo not to be in the groove they'd like. Remember to subscribe to be notified about new episodes. And please do rate & review this episode on Apple Podcasts. And if you'd like to book a free 30 minute discovery call with me to talk about some coaching can help get your Mojo back - just drop me an email. Hope you love it Richard

dj artist island commitment djs ibiza mojo living the dream pacha defected soul survivor pikes glitterbox scott gray richard stokes melon bomb

Scott Gray - How Is Your Spiritual Walk?

WCBC Chapel Podcast

Play Episode Listen Later Apr 30, 2024 42:20

Scott Gray - How Is Your Spiritual Walk? by West Coast Baptist College

spiritual walk scott gray west coast baptist college

Sony Future Filmmaker Awards 2024 Announces Shortlist of 30 International Filmmakers

Irish Tech News Audio Articles

Play Episode Listen Later Apr 24, 2024 12:35

Creo is delighted to announce the shortlist for the Sony Future Filmmaker Awards 2024. The shortlist of 30 filmmakers across six categories are awarded the unique experience of attending a week of special events at the Sony Pictures Studios lot in Los Angeles, including a workshop program providing unparalleled access behind-the-scenes of the industry and culminating in the Awards ceremony on May 30, 2024 where the six category winners, chosen by a selection of expert judges, are announced. Established by Creo and sponsored by Sony, the sophomore edition of the major annual awards program for short films provides a gateway for the development of exceptional cinematic talent and sets out to elevate voices with an original perspective on storytelling. This year's shortlist was chosen from over 8,400 films by more than 5,000 filmmakers across 148 countries and territories submitted across six categories: Fiction, Non-Fiction, Environment, Animation, Student, and Future Format. The shortlisted stories range from a poignant documentary about two Holocaust survivors miraculously reunited after 80 years, a filmmaker's search for the last remaining gibbon in Kuala Lumpur, a homeless ballet dancer undertaking a life-changing audition, a spontaneous romance between two strangers thrown together by grounded flights, and much more. Representing a truly global perspective on filmmaking, the shortlist includes films from Australia, Brazil, Canada, Czech Republic, Egypt, Estonia, Germany, Ghana, Hong Kong, India, Malaysia, Mexico, Nigeria, South Africa, Thailand, the United Kingdom, Uruguay, and the United States. Working from a longlist of commended submissions, the 30 shortlisted filmmakers were chosen by Emmy-award winning cinematographer Robert Primes ASCand celebrated Australian filmmaker Unjoo Moon. At the second stage of the judging process, judges Michael Barker and Tom Bernard, Co-Founders and Co-Presidents of Sony Pictures Classics (Call Me By Your Name, The Father, Whiplash); Rob Hardy ASC, BSC, BAFTA award-winning cinematographer (Mission: Impossible - Fallout, Ex-Machina, Civil War) ; Kate Reid BSC, acclaimed British cinematographer (Game of Thrones, Great Expectations, Silo) will choose this year's category winners, awarding creative excellence and original approaches to narrative. Both stages of the judging process are chaired by award-winning director Justin Chadwick (Mandela: Long Walk to Freedom, The Other Boleyn Girl, Tulip Fever). The 30 shortlisted filmmakers will be flown to Los Angeles to attend a comprehensive four-day workshop program at the Sony Pictures Studios lot in Culver City from May 28 - 31, 2024, culminating in a black-tie Awards ceremony on May 30, 2024. Held at the Cary Grant Theater and hosted by Entertainment Tonight's Denny Directo, during the ceremony the six category winners will be announced, receiving a range of cash prizes and equipment. This immersive experience provides filmmakers with a one-of-a-kind opportunity to connect with fellow filmmakers and leaders in the field of cinema, and to gain exclusive access behind-the-scenes of the industry, with workshops led by Sony Pictures executives, and covering a range of topics from keynotes by major cinematographers, screenings and Q&A sessions, to insights into working with talent agencies and using cutting-edge technologies, to film scoring and music rights. Justin Chadwick, award-winning theater, television and film director and Chair of the Jury, says: "The level of submission and the international scope of new voices shortlisted for the Awards is thrilling. Across the 30 chosen filmmakers the perspectives told are manifold and captivating, charged with a passion and authenticity. I am delighted to once again lead this selection of filmmaking; a vision of storytelling and cinematic ingenuity and an aperture into the filmmakers who shall make up the future of our industry." Scott Gray, Founder and CEO, Creo, says: "With a staggering 8,400 films ...

Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Mar 6, 2024 80:00

Speaker CFPs and Sponsor Guides are now available for AIE World's Fair — join us on June 25-27 for the biggest AI Engineer conference of 2024!Soumith Chintala needs no introduction in the ML world — his insights are incredibly accessible across Twitter, LinkedIn, podcasts, and conference talks (in this pod we'll assume you'll have caught up on the History of PyTorch pod from last year and cover different topics). He's well known as the creator of PyTorch, but he's more broadly the Engineering Lead on AI Infra, PyTorch, and Generative AI at Meta.Soumith was one of the earliest supporters of Latent Space (and more recently AI News), and we were overjoyed to catch up with him on his latest SF visit for a braindump of the latest AI topics, reactions to some of our past guests, and why Open Source AI is personally so important to him.Life in the GPU-Rich LaneBack in January, Zuck went on Instagram to announce their GPU wealth: by the end of 2024, Meta will have 350k H100s. By adding all their GPU clusters, you'd get to 600k H100-equivalents of compute. At FP16 precision, that's ~1,200,000 PFLOPS. If we used George Hotz's (previous guest!) "Person of Compute" measure, Meta now has 60k humans of compute in their clusters. Occasionally we get glimpses into the GPU-rich life; on a recent ThursdAI chat, swyx prompted PaLM tech lead Yi Tay to write down what he missed most from Google, and he commented that UL2 20B was trained by accidentally leaving the training job running for a month, because hardware failures are so rare in Google.Meta AI's Epic LLM RunBefore Llama broke the internet, Meta released an open source LLM in May 2022, OPT-175B, which was notable for how “open” it was - right down to the logbook! They used only 16 NVIDIA V100 GPUs and Soumith agrees that, with hindsight, it was likely under-trained for its parameter size.In Feb 2023 (pre Latent Space pod), Llama was released, with a 7B version trained on 1T tokens alongside 65B and 33B versions trained on 1.4T tokens. The Llama authors included Guillaume Lample and Timothée Lacroix, who went on to start Mistral.July 2023 was Llama2 time (which we covered!): 3 model sizes, 7B, 13B, and 70B, all trained on 2T tokens. The three models accounted for a grand total of 3,311,616 GPU hours for all pre-training work. CodeLlama followed shortly after, a fine-tune of Llama2 specifically focused on code generation use cases. The family had models in the 7B, 13B, 34B, and 70B size, all trained with 500B extra tokens of code and code-related data, except for 70B which is trained on 1T.All of this on top of other open sourced models like Segment Anything (one of our early hits!), Detectron, Detectron 2, DensePose, and Seamless, and in one year, Meta transformed from a company people made fun of for its “metaverse” investments to one of the key players in the AI landscape and its stock has almost tripled since (about $830B in market value created in the past year).Why Open Source AIThe obvious question is why Meta would spend hundreds of millions on its AI efforts and then release them for free. Zuck has addressed this in public statements:But for Soumith, the motivation is even more personal:“I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India… And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for like zero dollars. And I think that was a strong reason why I ended up where I am. So like that, like the open source side of things, I always push regardless of like what I get paid for, like I think I would do that as a passion project on the side……I think at a fundamental level, the most beneficial value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me……Like, okay, I again always go back to like I'm a student in India with no money. What is my accessibility to any of these closed source models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control issue: I strongly believe if you want human aligned AI, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble.We like the way Soumith put it last year: Closed AI “rate-limits against people's imaginations and needs”!What It Takes For Open Source AI to WinHowever Soumith doesn't think Open Source will simply win by popular demand. There is a tremendous coordination problem with the decentralized nature of the open source AI development right now: nobody is collecting the valuable human feedback in the way that OpenAI or Midjourney are doing.“Open source in general always has a coordination problem. If there's a vertically integrated provider with more resources, they will just be better coordinated than open source. And so now open source has to figure out how to have coordinated benefits. And the reason you want coordinated benefits is because these models are getting better based on human feedback. And if you see with open source models, like if you go to the /r/localllama subreddit, like there's so many variations of models that are being produced from, say, Nous research. I mean, like there's like so many variations built by so many people. And one common theme is they're all using these fine-tuning or human preferences datasets that are very limited and they're not sufficiently diverse. And you look at the other side, say front-ends like Oobabooga or like Hugging Chat or Ollama, they don't really have feedback buttons. All the people using all these front-ends, they probably want to give feedback, but there's no way for them to give feedback… So we're just losing all of this feedback. Maybe open source models are being as used as GPT is at this point in like all kinds of, in a very fragmented way, like in aggregate all the open source models together are probably being used as much as GPT is, maybe close to that. But the amount of feedback that is driving back into the open source ecosystem is like negligible, maybe less than 1% of like the usage. So I think like some, like the blueprint here I think is you'd want someone to create a sinkhole for the feedback… I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having a runaway effect against OpenAI, I think like there's a clear chance we can take at truly winning open source.”If you're working on solving open source coordination, please get in touch!Show Notes* Soumith Chintala Twitter* History of PyTorch episode on Gradient Podcast* The Llama Ecosystem* Apple's MLX* Neural ODEs (Ordinary Differential Equations)* AlphaGo* LMSys arena* Dan Pink's "Drive"* Robotics projects:* Dobb-E* OK Robot* Yann LeCun* Yangqing Jia of Lepton AI* Ed Catmull* George Hotz on Latent Space* Chris Lattner on Latent Space* Guillaume Lample* Yannic Kilcher of OpenAssistant* LMSys* Alex Atallah of OpenRouter* Carlo Sferrazza's 3D tactile research* Alex Wiltschko of Osmo* Tangent by Alex Wiltschko* Lerrel Pinto - RoboticsTimestamps* [00:00:00] Introductions* [00:00:51] Extrinsic vs Intrinsic Success* [00:02:40] Importance of Open Source and Its Impact* [00:03:46] PyTorch vs TinyGrad* [00:08:33] Why PyTorch is the Switzerland of frameworks* [00:10:27] Modular's Mojo + PyTorch?* [00:13:32] PyTorch vs Apple's MLX* [00:16:27] FAIR / PyTorch Alumni* [00:18:50] How can AI inference providers differentiate?* [00:21:41] How to build good benchmarks and learnings from AnyScale's* [00:25:28] Most interesting unexplored ideas* [00:28:18] What people get wrong about synthetic data* [00:35:57] Meta AI's evolution* [00:38:42] How do you allocate 600,000 GPUs?* [00:42:05] Even the GPU Rich are GPU Poor* [00:47:31] Meta's MTIA silicon* [00:50:09] Why we need open source* [00:59:00] Open source's coordination problem for feedback gathering* [01:08:59] Beyond text generation* [01:15:37] Osmo and the Future of Smell Recognition TechnologyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:15]: Hey, and today we have in the studio Soumith Chintala, welcome.Soumith [00:00:17]: Thanks for having me.Swyx [00:00:18]: On one of your rare visits from New York where you live. You got your start in computer vision at NYU with Yann LeCun. That was a very fortuitous start. I was actually listening to your interview on the Gradient podcast. So if people want to know more about the history of Soumith, history of PyTorch, they can go to that podcast. We won't spend that much time there, but I just was marveling at your luck, or I don't know if it's your luck or your drive to find AI early and then find the right quality mentor because I guess Yan really sort of introduced you to that world.Soumith [00:00:51]: Yeah, I think you're talking about extrinsic success, right? A lot of people just have drive to do things that they think is fun, and a lot of those things might or might not be extrinsically perceived as good and successful. I think I just happened to like something that is now one of the coolest things in the world or whatever. But if I happen, the first thing I tried to become was a 3D VFX artist, and I was really interested in doing that, but I turned out to be very bad at it. So I ended up not doing that further. But even if I was good at that, whatever, and I ended up going down that path, I probably would have been equally happy. It's just like maybe like the perception of, oh, is this person successful or not might be different. I think like after a baseline, like your happiness is probably more correlated with your intrinsic stuff.Swyx [00:01:44]: Yes. I think Dan Pink has this book on drive that I often refer to about the power of intrinsic motivation versus extrinsic and how long extrinsic lasts. It's not very long at all. But anyway, now you are an investor in Runway, so in a way you're working on VFX. Yes.Soumith [00:02:01]: I mean, in a very convoluted way.Swyx [00:02:03]: It reminds me of Ed Catmull. I don't know if you guys know, but he actually tried to become an animator in his early years and failed or didn't get accepted by Disney and then went and created Pixar and then got bought by Disney and created Toy Story. So you joined Facebook in 2014 and eventually became a creator and maintainer of PyTorch. And there's this long story there you can refer to on the gradient. I think maybe people don't know that you also involved in more sort of hardware and cluster decision affair. And we can dive into more details there because we're all about hardware this month. Yeah. And then finally, I don't know what else, like what else should people know about you on a personal side or professional side?Soumith [00:02:40]: I think open source is definitely a big passion of mine and probably forms a little bit of my identity at this point. I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India. I didn't have internet for a while. In college, actually, I didn't have internet except for GPRS or whatever. And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for zero dollars. And I think that was a strong reason why I ended up where I am. So the open source side of things, I always push regardless of what I get paid for, like I think I would do that as a passion project on the side.Swyx [00:03:35]: Yeah, that's wonderful. Well, we'll talk about the challenges as well that open source has, open models versus closed models. Maybe you want to touch a little bit on PyTorch before we move on to the sort of Meta AI in general.PyTorch vs Tinygrad tradeoffsAlessio [00:03:46]: Yeah, we kind of touched on PyTorch in a lot of episodes. So we had George Hotz from TinyGrad. He called PyTorch a CISC and TinyGrad a RISC. I would love to get your thoughts on PyTorch design direction as far as, I know you talk a lot about kind of having a happy path to start with and then making complexity hidden away but then available to the end user. One of the things that George mentioned is I think you have like 250 primitive operators in PyTorch, I think TinyGrad is four. So how do you think about some of the learnings that maybe he's going to run into that you already had in the past seven, eight years almost of running PyTorch?Soumith [00:04:24]: Yeah, I think there's different models here, but I think it's two different models that people generally start with. Either they go like, I have a grand vision and I'm going to build a giant system that achieves this grand vision and maybe one is super feature complete or whatever. Or other people say they will get incrementally ambitious, right? And they say, oh, we'll start with something simple and then we'll slowly layer out complexity in a way that optimally applies Huffman coding or whatever. Like where the density of users are and what they're using, I would want to keep it in the easy, happy path and where the more niche advanced use cases, I'll still want people to try them, but they need to take additional frictional steps. George, I think just like we started with PyTorch, George started with the incrementally ambitious thing. I remember TinyGrad used to be, like we would be limited to a thousand lines of code and I think now it's at 5,000. So I think there is no real magic to which why PyTorch has the kind of complexity. I think it's probably partly necessitated and partly because we built with the technology available under us at that time, PyTorch is like 190,000 lines of code or something at this point. I think if you had to rewrite it, we would probably think about ways to rewrite it in a vastly simplified way for sure. But a lot of that complexity comes from the fact that in a very simple, explainable way, you have memory hierarchies. You have CPU has three levels of caches and then you have DRAM and SSD and then you have network. Similarly, GPU has several levels of memory and then you have different levels of network hierarchies, NVLink plus InfiniBand or Rocky or something like that, right? And the way the flops are available on your hardware, they are available in a certain way and your computation is in a certain way and you have to retrofit your computation onto both the memory hierarchy and like the flops available. When you're doing this, it is actually a fairly hard mathematical problem to do this setup, like you find the optimal thing. And finding the optimal thing is, what is optimal depends on the input variables themselves. So like, okay, what is the shape of your input tensors and what is the operation you're trying to do and various things like that. Finding that optimal configuration and writing it down in code is not the same for every input configuration you have. Like for example, just as the shape of the tensors change, let's say you have three input tensors into a Sparstar product or something like that. The shape of each of these input tensors will vastly change how you do this optimally placing this operation onto the hardware in a way that will get you maximal throughput. So a lot of our complexity comes from writing out hundreds of configurations for each single PyTorch operator and templatizing these things and symbolically generating the final CUDA code or CPU code. There's no way to avoid it because mathematically we haven't found symbolic ways to do this that also keep compile time near zero. You can write a very simple framework, but then you also should be willing to eat the long compile time. So if searching for that optimal performance at runtime, but that's the trade off. There's no, like, I don't think unless we have great breakthroughs George's vision is achievable, he should be thinking about a narrower problem such as I'm only going to make this for work for self-driving car connets or I'm only going to make this work for LLM transformers of the llama style. Like if you start narrowing the problem down, you can make a vastly simpler framework. But if you don't, if you need the generality to power all of the AI research that is happening and keep zero compile time and in all these other factors, I think it's not easy to avoid the complexity.Pytorch vs MojoAlessio [00:08:33]: That's interesting. And we kind of touched on this with Chris Lattner when he was on the podcast. If you think about frameworks, they have the model target. They have the hardware target. They have different things to think about. He mentioned when he was at Google, TensorFlow trying to be optimized to make TPUs go brr, you know, and go as fast. I think George is trying to make especially AMD stack be better than ROCm. How come PyTorch has been such as Switzerland versus just making Meta hardware go brr?Soumith [00:09:00]: First, Meta is not in the business of selling hardware. Meta is not in the business of cloud compute. The way Meta thinks about funding PyTorch is we're funding it because it's net good for Meta to fund PyTorch because PyTorch has become a standard and a big open source project. And generally it gives us a timeline edge. It gives us leverage and all that within our own work. So why is PyTorch more of a Switzerland rather than being opinionated? I think the way we think about it is not in terms of Switzerland or not. We actually the way we articulate it to all hardware vendors and software vendors and all who come to us being we want to build a backend in core for PyTorch and ship it by default is we just only look at our user side of things. Like if users are using a particular piece of hardware, then we want to support it. We very much don't want to king make the hardware side of things. So as the MacBooks have GPUs and as that stuff started getting increasingly interesting, we pushed Apple to push some engineers and work on the NPS support and we spend significant time from Meta funded engineers on that as well because a lot of people are using the Apple GPUs and there's demand. So we kind of mostly look at it from the demand side. We never look at it from like oh which hardware should we start taking opinions on.Swyx [00:10:27]: Is there a future in which, because Mojo or Modular Mojo is kind of a superset of Python, is there a future in which PyTorch might use Mojo features optionally?Soumith [00:10:36]: I think it depends on how well integrated it is into the Python ecosystem. So if Mojo is like a pip install and it's readily available and users feel like they can use Mojo so smoothly within their workflows in a way that just is low friction, we would definitely look into that. Like in the same way PyTorch now depends on Triton, OpenAI Triton, and we never had a conversation that was like huh, that's like a dependency. Should we just build a Triton of our own or should we use Triton? It almost doesn't, like those conversations don't really come up for us. The conversations are more well does Triton have 10,000 dependencies and is it hard to install? We almost don't look at these things from a strategic leverage point of view. We look at these things from a user experience point of view, like is it easy to install? Is it smoothly integrated and does it give enough benefits for us to start depending on it? If so, yeah, we should consider it. That's how we think about it.Swyx [00:11:37]: You're inclusive by default as long as it meets the minimum bar of, yeah, but like maybe I phrased it wrongly. Maybe it's more like what problems would you look to solve that you have right now?Soumith [00:11:48]: I think it depends on what problems Mojo will be useful at.Swyx [00:11:52]: Mainly a performance pitch, some amount of cross compiling pitch.Soumith [00:11:56]: Yeah, I think the performance pitch for Mojo was like, we're going to be performant even if you have a lot of custom stuff, you're going to write arbitrary custom things and we will be performant. And that value proposition is not clear to us from the PyTorch side to consider it for PyTorch. So PyTorch, it's actually not 250 operators, it's like a thousand operators. PyTorch exposes about a thousand operators and people kind of write their ideas in the thousand operators of PyTorch. Mojo is like, well, maybe it's okay to completely sidestep those thousand operators of PyTorch and just write it in a more natural form. Just write raw Python, write for loops or whatever, right? So from the consideration of how do we intersect PyTorch with Mojo, I can see one use case where you have custom stuff for some parts of your program, but mostly it's PyTorch. And so we can probably figure out how to make it easier for say Torch.compile to smoothly also consume Mojo subgraphs and like, you know, the interoperability being actually usable, that I think is valuable. But Mojo as a fundamental front end would be replacing PyTorch, not augmenting PyTorch. So in that sense, I don't see a synergy in more deeply integrating Mojo.Pytorch vs MLXSwyx [00:13:21]: So call out to Mojo whenever they have written something in Mojo and there's some performance related thing going on. And then since you mentioned Apple, what should people think of PyTorch versus MLX?Soumith [00:13:32]: I mean, MLX is early and I know the folks well, Ani used to work at FAIR and I used to chat with him all the time. He used to be based out of New York as well. The way I think about MLX is that MLX is specialized for Apple right now. It has a happy path because it's defined its product in a narrow way. At some point MLX either says we will only be supporting Apple and we will just focus on enabling, you know, there's a framework if you use your MacBook, but once you like go server side or whatever, that's not my problem and I don't care. For MLS, it enters like the server side set of things as well. Like one of these two things will happen, right? If the first thing will happen, like MLX's overall addressable market will be small, but it probably do well within that addressable market. If it enters the second phase, they're going to run into all the same complexities that we have to deal with. They will not have any magic wand and they will have more complex work to do. They probably wouldn't be able to move as fast.Swyx [00:14:44]: Like having to deal with distributed compute?Soumith [00:14:48]: Distributed, NVIDIA and AMD GPUs, like just like having a generalization of the concept of a backend, how they treat compilation with plus overheads. Right now they're deeply assumed like the whole NPS graph thing. So they need to think about all these additional things if they end up expanding onto the server side and they'll probably build something like PyTorch as well, right? Like eventually that's where it will land. And I think there they will kind of fail on the lack of differentiation. Like it wouldn't be obvious to people why they would want to use it.Swyx [00:15:24]: I mean, there are some cloud companies offering M1 and M2 chips on servers. I feel like it might be interesting for Apple to pursue that market, but it's not their core strength.Soumith [00:15:33]: Yeah. If Apple can figure out their interconnect story, maybe, like then it can become a thing.Swyx [00:15:40]: Honestly, that's more interesting than the cars. Yes.Soumith [00:15:43]: I think the moat that NVIDIA has right now, I feel is that they have the interconnect that no one else has, like AMD GPUs are pretty good. I'm sure there's various silicon that is not bad at all, but the interconnect, like NVLink is uniquely awesome. I'm sure the other hardware providers are working on it, but-Swyx [00:16:04]: I feel like when you say it's uniquely awesome, you have some appreciation of it that the rest of us don't. I mean, the rest of us just like, you know, we hear marketing lines, but what do you mean when you say NVIDIA is very good at networking? Obviously they made the acquisition maybe like 15 years ago.Soumith [00:16:15]: Just the bandwidth it offers and the latency it offers. I mean, TPUs also have a good interconnect, but you can't buy them. So you have to go to Google to use it.PyTorch MafiaAlessio [00:16:27]: Who are some of the other FAIR PyTorch alumni that are building cool companies? I know you have Fireworks AI, Lightning AI, Lepton, and Yangqing, you knew since college when he was building Coffee?Soumith [00:16:40]: Yeah, so Yangqing and I used to be framework rivals, PyTorch, I mean, we were all a very small close-knit community back then. Caffe, Torch, Theano, Chainer, Keras, various frameworks. I mean, it used to be more like 20 frameworks. I can't remember all the names. CCV by Liu Liu, who is also based out of SF. And I would actually like, you know, one of the ways it was interesting is you went into the framework guts and saw if someone wrote their own convolution kernel or they were just copying someone else's. There were four or five convolution kernels that were unique and interesting. There was one from this guy out of Russia, I forgot the name, but I remembered who was awesome enough to have written their own kernel. And at some point there, I built out these benchmarks called ConNet benchmarks. They're just benchmarking all the convolution kernels that are available at that time. It hilariously became big enough that at that time AI was getting important, but not important enough that industrial strength players came in to do these kinds of benchmarking and standardization. Like we have MLPerf today. So a lot of the startups were using ConNet benchmarks in their pitch decks as like, oh, you know, on ConNet benchmarks, this is how we fare, so you should fund us. I remember Nirvana actually was at the top of the pack because Scott Gray wrote amazingly fast convolution kernels at that time. Very interesting, but separate times. But to answer your question, Alessio, I think mainly Lepton, Fireworks are the two most obvious ones, but I'm sure the fingerprints are a lot wider. They're just people who worked within the PyTorch Cafe2 cohort of things and now end up at various other places.Swyx [00:18:50]: I think as a, both as an investor and a people looking to build on top of their services, it's a uncomfortable slash like, I don't know what I don't know pitch. Because I've met Yang Tsing and I've met Lin Chao. Yeah, I've met these folks and they're like, you know, we are deep in the PyTorch ecosystem and we serve billions of inferences a day or whatever at Facebook and now we can do it for you. And I'm like, okay, that's great. Like, what should I be wary of or cautious of when these things happen? Because I'm like, obviously this experience is extremely powerful and valuable. I just don't know what I don't know. Like, what should people know about like these sort of new inference as a service companies?Soumith [00:19:32]: I think at that point you would be investing in them for their expertise of one kind. So if they've been at a large company, but they've been doing amazing work, you would be thinking about it as what these people bring to the table is that they're really good at like GPU programming or understanding the complexity of serving models once it hits a certain scale. You know, various expertise like from the infra and AI and GPUs point of view. What you would obviously want to figure out is whether their understanding of the external markets is clear, whether they know and understand how to think about running a business, understanding how to be disciplined about making money or, you know, various things like that.Swyx [00:20:23]: Maybe I'll put it like, actually I will de-emphasize the investing bit and just more as a potential customer. Oh, okay. Like, it's more okay, you know, you have PyTorch gods, of course. Like, what else should I know?Soumith [00:20:37]: I mean, I would not care about who's building something. If I'm trying to be a customer, I would care about whether...Swyx [00:20:44]: Benchmarks.Soumith [00:20:44]: Yeah, I use it and it's usability and reliability and speed, right?Swyx [00:20:51]: Quality as well.Soumith [00:20:51]: Yeah, if someone from some random unknown place came to me and say, user stuff is great. Like, and I have the bandwidth, I probably will give it a shot. And if it turns out to be great, like I'll just use it.Benchmark dramaSwyx [00:21:07]: Okay, great. And then maybe one more thing about benchmarks, since we already brought it up and you brought up Confident Benchmarks. There was some recent drama around AnyScale. AnyScale released their own benchmarks and obviously they look great on their own benchmarks, but maybe didn't give the other... I feel there are two lines of criticism. One, which is they didn't test some apples for apples on the kind of endpoints that the other providers, that they are competitors with, on their benchmarks and that is due diligence baseline. And then the second would be more just optimizing for the right thing. You had some commentary on it. I'll just kind of let you riff.Soumith [00:21:41]: Yeah, I mean, in summary, basically my criticism of that was AnyScale built these benchmarks for end users to just understand what they should pick, right? And that's a very good thing to do. I think what they didn't do a good job of is give that end user a full understanding of what they should pick. Like they just gave them a very narrow slice of understanding. I think they just gave them latency numbers and that's not sufficient, right? You need to understand your total cost of ownership at some reasonable scale. Not oh, one API call is one cent, but a thousand API calls are 10 cents. Like people can misprice to cheat on those benchmarks. So you want to understand, okay, like how much is it going to cost me if I actually subscribe to you and do like a million API calls a month or something? And then you want to understand the latency and reliability, not just from one call you made, but an aggregate of calls you've made over several various times of the day and times of the week. And the nature of the workloads, is it just some generic single paragraph that you're sending that is cashable? Or is it like testing of real world workload? I think that kind of rigor, like in presenting that benchmark wasn't there. It was a much more narrow sliver of what should have been a good benchmark. That was my main criticism. And I'm pretty sure if before they released it, they showed it to their other stakeholders who would be caring about this benchmark because they are present in it, they would have easily just pointed out these gaps. And I think they didn't do that and they just released it. So I think those were the two main criticisms. I think they were fair and Robert took it well.Swyx [00:23:40]: And he took it very well. And we'll have him on at some point and we'll discuss it. But I think it's important for, I think the market being maturing enough that people start caring and competing on these kinds of things means that we need to establish what best practice is because otherwise everyone's going to play dirty.Soumith [00:23:55]: Yeah, absolutely. My view of the LLM inference market in general is that it's the laundromat model. Like the margins are going to drive down towards the bare minimum. It's going to be all kinds of arbitrage between how much you can get the hardware for and then how much you sell the API and how much latency your customers are willing to let go. You need to figure out how to squeeze your margins. Like what is your unique thing here? Like I think Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, you know, hardware kernels in general. But those modes only last for a month or two. These ideas quickly propagate.Swyx [00:24:38]: Even if they're not published?Soumith [00:24:39]: Even if they're not published, the idea space is small. So even if they're not published, the discovery rate is going to be pretty high. It's not like we're talking about a combinatorial thing that is really large. You're talking about Llama style LLM models. And we're going to beat those to death on a few different hardware SKUs, right? Like it's not even we have a huge diversity of hardware you're going to aim to run it on. Now when you have such a narrow problem and you have a lot of people working on it, the rate at which these ideas are going to get figured out is going to be pretty rapid.Swyx [00:25:15]: Is it a standard bag of tricks? Like the standard one that I know of is, you know, fusing operators and-Soumith [00:25:22]: Yeah, it's the standard bag of tricks on figuring out how to improve your memory bandwidth and all that, yeah.Alessio [00:25:28]: Any ideas instead of things that are not being beaten to death that people should be paying more attention to?Novel PyTorch ApplicationsSwyx [00:25:34]: One thing I was like, you know, you have a thousand operators, right? Like what's the most interesting usage of PyTorch that you're seeing maybe outside of this little bubble?Soumith [00:25:41]: So PyTorch, it's very interesting and scary at the same time, but basically it's used in a lot of exotic ways, like from the ML angle, what kind of models are being built? And you get all the way from state-based models and all of these things to stuff nth order differentiable models, like neural ODEs and stuff like that. I think there's one set of interestingness factor from the ML side of things. And then there's the other set of interesting factor from the applications point of view. It's used in Mars Rover simulations, to drug discovery, to Tesla cars. And there's a huge diversity of applications in which it is used. So in terms of the most interesting application side of things, I think I'm scared at how many interesting things that are also very critical and really important it is used in. I think the scariest was when I went to visit CERN at some point and they said they were using PyTorch and they were using GANs at the same time for particle physics research. And I was scared more about the fact that they were using GANs than they were using PyTorch, because at that time I was a researcher focusing on GANs. But the diversity is probably the most interesting. How many different things it is being used in. I think that's the most interesting to me from the applications perspective. From the models perspective, I think I've seen a lot of them. Like the really interesting ones to me are where we're starting to combine search and symbolic stuff with differentiable models, like the whole AlphaGo style models is one example. And then I think we're attempting to do it for LLMs as well, with various reward models and search. I mean, I don't think PyTorch is being used in this, but the whole alpha geometry thing was interesting because again, it's an example of combining the symbolic models with the gradient based ones. But there are stuff like alpha geometry that PyTorch is used at, especially when you intersect biology and chemistry with ML. In those areas, you want stronger guarantees on the output. So yeah, maybe from the ML side, those things to me are very interesting right now.Swyx [00:28:03]: Yeah. People are very excited about the alpha geometry thing. And it's kind of like, for me, it's theoretical. It's great. You can solve some Olympia questions. I'm not sure how to make that bridge over into the real world applications, but I'm sure people smarter than me will figure it out.Synthetic Data vs Symbolic ModelsSoumith [00:28:18]: Let me give you an example of it. You know how the whole thing about synthetic data will be the next rage in LLMs is a thing?Swyx [00:28:27]: Already is a rage.Soumith [00:28:28]: Which I think is fairly misplaced in how people perceive it. People think synthetic data is some kind of magic wand that you wave and it's going to be amazing. Synthetic data is useful in neural networks right now because we as humans have figured out a bunch of symbolic models of the world or made up certain symbolic models because of human innate biases. So we've figured out how to ground particle physics in a 30 parameter model. And it's just very hard to compute as in it takes a lot of flops to compute, but it only has 30 parameters or so. I mean, I'm not a physics expert, but it's a very low rank model. We built mathematics as a field that basically is very low rank. Language, a deep understanding of language, like the whole syntactic parse trees and just understanding how language can be broken down and into a formal symbolism is something that we figured out. So we basically as humans have accumulated all this knowledge on these subjects, either synthetic, we created those subjects in our heads, or we grounded some real world phenomenon into a set of symbols. But we haven't figured out how to teach neural networks symbolic world models directly. The only way we have to teach them is generating a bunch of inputs and outputs and gradient dissenting over them. So in areas where we have the symbolic models and we need to teach all the knowledge we have that is better encoded in the symbolic models, what we're doing is we're generating a bunch of synthetic data, a bunch of input output pairs, and then giving that to the neural network and asking it to learn the same thing that we already have a better low rank model of in gradient descent in a much more over-parameterized way. Outside of this, like where we don't have good symbolic models, like synthetic data obviously doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases in every case or whatever. It's just where we as humans already have good symbolic models off. We need to impart that knowledge to neural networks and we figured out the synthetic data is a vehicle to impart this knowledge to. So, but people, because maybe they don't know enough about synthetic data as a notion, but they hear, you know, the next wave of data revolution is synthetic data. They think it's some kind of magic where we just create a bunch of random data somehow. They don't think about how, and then they think that's just a revolution. And I think that's maybe a gap in understanding most people have in this hype cycle.Swyx [00:31:23]: Yeah, well, it's a relatively new concept, so. Oh, there's two more that I'll put in front of you and then you can see what you respond. One is, you know, I have this joke that it's, you know, it's only synthetic data if it's from the Mistral region of France, otherwise it's just a sparkling distillation, which is what news research is doing. Like they're distilling GPT-4 by creating synthetic data from GPT-4, creating mock textbooks inspired by Phi 2 and then fine tuning open source models like Llama. And so I don't know, I mean, I think that's, should we call that synthetic data? Should we call it something else? I don't know.Soumith [00:31:57]: Yeah, I mean, the outputs of LLMs, are they synthetic data? They probably are, but I think it depends on the goal you have. If your goal is you're creating synthetic data with the goal of trying to distill GPT-4's superiority into another model, I guess you can call it synthetic data, but it also feels like disingenuous because your goal is I need to copy the behavior of GPT-4 and-Swyx [00:32:25]: It's also not just behavior, but data set. So I've often thought of this as data set washing. Like you need one model at the top of the chain, you know, unnamed French company that has that, you know, makes a model that has all the data in it that we don't know where it's from, but it's open source, hey, and then we distill from that and it's great. To be fair, they also use larger models as judges for preference ranking, right? So that is, I think, a very, very accepted use of synthetic.Soumith [00:32:53]: Correct. I think it's a very interesting time where we don't really have good social models of what is acceptable depending on how many bits of information you use from someone else, right? It's like, okay, you use one bit. Is that okay? Yeah, let's accept it to be okay. Okay, what about if you use 20 bits? Is that okay? I don't know. What if you use 200 bits? I don't think we as society have ever been in this conundrum where we have to be like, where is the boundary of copyright or where is the boundary of socially accepted understanding of copying someone else? We haven't been tested this mathematically before,Swyx [00:33:38]: in my opinion. Whether it's transformative use. Yes. So yeah, I think this New York Times opening eye case is gonna go to the Supreme Court and we'll have to decide it because I think we never had to deal with it before. And then finally, for synthetic data, the thing that I'm personally exploring is solving this great stark paradigm difference between rag and fine tuning, where you can kind of create synthetic data off of your retrieved documents and then fine tune on that. That's kind of synthetic. All you need is variation or diversity of samples for you to fine tune on. And then you can fine tune new knowledge into your model. I don't know if you've seen that as a direction for synthetic data.Soumith [00:34:13]: I think you're basically trying to, what you're doing is you're saying, well, language, I know how to parametrize language to an extent. And I need to teach my model variations of this input data so that it's resilient or invariant to language uses of that data.Swyx [00:34:32]: Yeah, it doesn't overfit on the wrong source documents.Soumith [00:34:33]: So I think that's 100% synthetic. You understand, the key is you create variations of your documents and you know how to do that because you have a symbolic model or like some implicit symbolic model of language.Swyx [00:34:48]: Okay.Alessio [00:34:49]: Do you think the issue with symbolic models is just the architecture of the language models that we're building? I think maybe the thing that people grasp is the inability of transformers to deal with numbers because of the tokenizer. Is it a fundamental issue there too? And do you see alternative architectures that will be better with symbolic understanding?Soumith [00:35:09]: I am not sure if it's a fundamental issue or not. I think we just don't understand transformers enough. I don't even mean transformers as an architecture. I mean the use of transformers today, like combining the tokenizer and transformers and the dynamics of training, when you show math heavy questions versus not. I don't have a good calibration of whether I know the answer or not. I, you know, there's common criticisms that are, you know, transformers will just fail at X. But then when you scale them up to sufficient scale, they actually don't fail at that X. I think there's this entire subfield where they're trying to figure out these answers called like the science of deep learning or something. So we'll get to know more. I don't know the answer.Meta AI and Llama 2/3Swyx [00:35:57]: Got it. Let's touch a little bit on just Meta AI and you know, stuff that's going on there. Maybe, I don't know how deeply you're personally involved in it, but you're our first guest with Meta AI, which is really fantastic. And Llama 1 was, you know, you are such a believer in open source. Llama 1 was more or less the real breakthrough in open source AI. The most interesting thing for us covering on this, in this podcast was the death of Chinchilla, as people say. Any interesting insights there around the scaling models for open source models or smaller models or whatever that design decision was when you guys were doing it?Soumith [00:36:31]: So Llama 1 was Guillaume Lample and team. There was OPT before, which I think I'm also very proud of because we bridged the gap in understanding of how complex it is to train these models to the world. Like until then, no one really in gory detail published.Swyx [00:36:50]: The logs.Soumith [00:36:51]: Yeah. Like, why is it complex? And everyone says, oh, it's complex. But no one really talked about why it's complex. I think OPT was cool.Swyx [00:37:02]: I met Susan and she's very, very outspoken. Yeah.Soumith [00:37:05]: We probably, I think, didn't train it for long enough, right? That's kind of obvious in retrospect.Swyx [00:37:12]: For a 175B. Yeah. You trained it according to Chinchilla at the time or?Soumith [00:37:17]: I can't remember the details, but I think it's a commonly held belief at this point that if we trained OPT longer, it would actually end up being better. Llama 1, I think, was Guillaume Lample and team Guillaume is fantastic and went on to build Mistral. I wasn't too involved in that side of things. So I don't know what you're asking me, which is how did they think about scaling loss and all of that? Llama 2, I was more closely involved in. I helped them a reasonable amount with their infrastructure needs and stuff. And Llama 2, I think, was more like, let's get to the evolution. At that point, we kind of understood what we were missing from the industry's understanding of LLMs. And we needed more data and we needed more to train the models for longer. And we made, I think, a few tweaks to the architecture and we scaled up more. And that was Llama 2. I think Llama 2, you can think of it as after Guillaume left, the team kind of rebuilt their muscle around Llama 2. And Hugo, I think, who's the first author is fantastic. And I think he did play a reasonable big role in Llama 1 as well.Soumith [00:38:35]: And he overlaps between Llama 1 and 2. So in Llama 3, obviously, hopefully, it'll be awesome.Alessio [00:38:42]: Just one question on Llama 2, and then we'll try and fish Llama 3 spoilers out of you. In the Llama 2 paper, the loss curves of the 34 and 70B parameter, they still seem kind of steep. Like they could go lower. How, from an infrastructure level, how do you allocate resources? Could they have just gone longer or were you just, hey, this is all the GPUs that we can burn and let's just move on to Llama 3 and then make that one better?Soumith [00:39:07]: Instead of answering specifically about that Llama 2 situation or whatever, I'll tell you how we think about things. Generally, we're, I mean, Mark really is some numbers, right?Swyx [00:39:20]: So let's cite those things again. All I remember is like 600K GPUs.Soumith [00:39:24]: That is by the end of this year and 600K H100 equivalents. With 250K H100s, including all of our other GPU or accelerator stuff, it would be 600-and-something-K aggregate capacity.Swyx [00:39:38]: That's a lot of GPUs.Soumith [00:39:39]: We'll talk about that separately. But the way we think about it is we have a train of models, right? Llama 1, 2, 3, 4. And we have a bunch of GPUs. I don't think we're short of GPUs. Like-Swyx [00:39:54]: Yeah, no, I wouldn't say so. Yeah, so it's all a matter of time.Soumith [00:39:56]: I think time is the biggest bottleneck. It's like, when do you stop training the previous one and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, better clean data for the next one in a way that it's not worth really focusing on the previous one? It's just a standard iterative product. You're like, when is the iPhone 1? When do you start working on iPhone 2? Where is the iPhone? And so on, right? So mostly the considerations are time and generation, rather than GPUs, in my opinion.Alessio [00:40:31]: So one of the things with the scaling loss, like Chinchilla is optimal to balance training and inference costs. I think at Meta's scale, you would rather pay a lot more maybe at training and then save on inference. How do you think about that from infrastructure perspective? I think in your tweet, you say you can try and guess on like how we're using these GPUs. Can you just give people a bit of understanding? It's like, because I've already seen a lot of VCs say, Llama 3 has been trained on 600,000 GPUs and that's obviously not true, I'm sure. How do you allocate between the research, FAIR and the Llama training, the inference on Instagram suggestions that get me to scroll, like AI-generated stickers on WhatsApp and all of that?Soumith [00:41:11]: Yeah, we haven't talked about any of this publicly, but as a broad stroke, it's like how we would allocate resources of any other kinds at any company. You run a VC portfolio, how do you allocate your investments between different companies or whatever? You kind of make various trade-offs and you kind of decide, should I invest in this project or this other project, or how much should I invest in this project? It's very much a zero sum of trade-offs. And it also comes into play, how are your clusters configured, like overall, what you can fit of what size and what cluster and so on. So broadly, there's no magic sauce here. I mean, I think the details would add more spice, but also wouldn't add more understanding. It's just gonna be like, oh, okay, I mean, this looks like they just think about this as I would normally do.Alessio [00:42:05]: So even the GPU rich run through the same struggles of having to decide where to allocate things.Soumith [00:42:11]: Yeah, I mean, at some point I forgot who said it, but you kind of fit your models to the amount of compute you have. If you don't have enough compute, you figure out how to make do with smaller models. But no one as of today, I think would feel like they have enough compute. I don't think I've heard any company within the AI space be like, oh yeah, like we feel like we have sufficient compute and we couldn't have done better. So that conversation, I don't think I've heard from any of my friends at other companies.EleutherSwyx [00:42:47]: Stella from Eleuther sometimes says that because she has a lot of donated compute. She's trying to put it to interesting uses, but for some reason she's decided to stop making large models.Soumith [00:42:57]: I mean, that's a cool, high conviction opinion that might pay out.Swyx [00:43:01]: Why?Soumith [00:43:02]: I mean, she's taking a path that most people don't care to take about in this climate and she probably will have very differentiated ideas. I mean, think about the correlation of ideas in AI right now. It's so bad, right? So everyone's fighting for the same pie. In some weird sense, that's partly why I don't really directly work on LLMs. I used to do image models and stuff and I actually stopped doing GANs because GANs were getting so hot that I didn't have any calibration of whether my work would be useful or not because, oh yeah, someone else did the same thing you did. It's like, there's so much to do, I don't understand why I need to fight for the same pie. So I think Stella's decision is very smart.Making BetsAlessio [00:43:53]: And how do you reconcile that with how we started the discussion about intrinsic versus extrinsic kind of like accomplishment or success? How should people think about that especially when they're doing a PhD or early in their career? I think in Europe, I walked through a lot of the posters and whatnot, there seems to be mode collapse in a way in the research, a lot of people working on the same things. Is it worth for a PhD to not take a bet on something that is maybe not as interesting just because of funding and visibility and whatnot? Or yeah, what suggestions would you give?Soumith [00:44:28]: I think there's a baseline level of compatibility you need to have with the field. Basically, you need to figure out if you will get paid enough to eat, right? Like whatever reasonable normal lifestyle you want to have as a baseline. So you at least have to pick a problem within the neighborhood of fundable. Like you wouldn't wanna be doing something so obscure that people are like, I don't know, like you can work on it.Swyx [00:44:59]: Would a limit on fundability, I'm just observing something like three months of compute, right? That's the top line, that's the like max that you can spend on any one project.Soumith [00:45:09]: But like, I think that's very ill specified, like how much compute, right? I think that the notion of fundability is broader. It's more like, hey, are these family of models within the acceptable set of, you're not crazy or something, right? Even something like neural or DS, which is a very boundary pushing thing or states-based models or whatever. Like all of these things I think are still in fundable territory. When you're talking about, I'm gonna do one of the neuromorphic models and then apply image classification to them or something, then it becomes a bit questionable. Again, it depends on your motivation. Maybe if you're a neuroscientist, it actually is feasible. But if you're an AI engineer, like the audience of these podcasts, then it's more questionable. The way I think about it is, you need to figure out how you can be in the baseline level of fundability just so that you can just live. And then after that, really focus on intrinsic motivation and depends on your strengths, like how you can play to your strengths and your interests at the same time. Like I try to look at a bunch of ideas that are interesting to me, but also try to play to my strengths. I'm not gonna go work on theoretical ML. I'm interested in it, but when I want to work on something like that, I try to partner with someone who is actually a good theoretical ML person and see if I actually have any value to provide. And if they think I do, then I come in. So I think you'd want to find that intersection of ideas you like, and that also play to your strengths. And I'd go from there. Everything else, like actually finding extrinsic success and all of that, I think is the way I think about it is like somewhat immaterial. When you're talking about building ecosystems and stuff, slightly different considerations come into play, but that's a different conversation.Swyx [00:47:06]: We're gonna pivot a little bit to just talking about open source AI. But one more thing I wanted to establish for Meta is this 600K number, just kind of rounding out the discussion, that's for all Meta. So including your own inference needs, right? It's not just about training.Soumith [00:47:19]: It's gonna be the number in our data centers for all of Meta, yeah.Swyx [00:47:23]: Yeah, so there's a decent amount of workload serving Facebook and Instagram and whatever. And then is there interest in like your own hardware?MTIASoumith [00:47:31]: We already talked about our own hardware. It's called MTIA. Our own silicon, I think we've even showed the standard photograph of you holding the chip that doesn't work. Like as in the chip that you basically just get like-Swyx [00:47:51]: As a test, right?Soumith [00:47:52]: Yeah, a test chip or whatever. So we are working on our silicon and we'll probably talk more about it when the time is right, but-Swyx [00:48:00]: Like what gaps do you have that the market doesn't offer?Soumith [00:48:04]: Okay, I mean, this is easy to answer. So basically, remember how I told you about there's this memory hierarchy and like sweet spots and all of that? Fundamentally, when you build a hardware, you make it general enough that a wide set of customers and a wide set of workloads can use it effectively while trying to get the maximum level of performance they can. The more specialized you make the chip, the more hardware efficient it's going to be, the more power efficient it's gonna be, the more easier it's going to be to find the software, like the kernel's right to just map that one or two workloads to that hardware and so on. So it's pretty well understood across the industry that if you have a sufficiently large volume, enough workload, you can specialize it and get some efficiency gains, like power gains and so on. So the way you can think about everyone building, every large company building silicon, I think a bunch of the other large companies are building their own silicon as well, is they, each large company has a sufficient enough set of verticalized workloads that can be specialized that have a pattern to them that say a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the table by not exploiting that. And you have sufficient scale and you have sufficient forecasted stability that those workloads will exist in the same form, that it's worth spending the time to build out a chip to exploit that sweet spot. Like obviously something like this is only useful if you hit a certain scale and that your forecasted prediction of those kind of workloads being in the same kind of specializable exploitable way is true. So yeah, that's why we're building our own chips.Swyx [00:50:08]: Awesome.Open Source AIAlessio [00:50:09]: Yeah, I know we've been talking a lot on a lot of different topics and going back to open source, you had a very good tweet. You said that a single company's closed source effort rate limits against people's imaginations and needs. How do you think about all the impact that some of the Meta AI work in open source has been doing and maybe directions of the whole open source AI space?Soumith [00:50:32]: Yeah, in general, I think first, I think it's worth talking about this in terms of open and not just open source, because like with the whole notion of model weights, no one even knows what source means for these things. But just for the discussion, when I say open source, you can assume it's just I'm talking about open. And then there's the whole notion of licensing and all that, commercial, non-commercial, commercial with clauses and all that. I think at a fundamental level, the most benefited value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me. Like I got this thing in a very accessible way. And then it's various degrees, right? And then if it's open source, but it's actually a commercial license, then a lot of companies are gonna benefit from gaining value that they didn't previously have, that they maybe had to pay a closed source company for it. So open source is just a very interesting tool that you can use in various ways. So there's, again, two kinds of open source. One is some large company doing a lot of work and then open sourcing it. And that kind of effort is not really feasible by say a band of volunteers doing it the same way. So there's both a capital and operational expenditure that the large company just decided to ignore and give it away to the world for some benefits of some kind. They're not as tangible as direct revenue. So in that part, Meta has been doing incredibly good things. They fund a huge amount of the PyTorch development. They've open sourced Llama and those family of models and several other fairly transformative projects. FICE is one, Segment Anything, Detectron, Detectron 2. Dense Pose. I mean, it's-Swyx [00:52:52]: Seamless. Yeah, seamless.Soumith [00:52:53]: Like it's just the list is so long that we're not gonna cover. So I think Meta comes into that category where we spend a lot of CapEx and OpEx and we have a high talent density of great AI people and we open our stuff. And the thesis for that, I remember when FAIR was started, the common thing was like, wait, why would Meta wanna start a open AI lab? Like what exactly is a benefit from a commercial perspective? And for then the thesis was very simple. It was AI is currently rate limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like AI was the limiting factor and we just wanted AI to advance more and we didn't care if the IP of the AI was uniquely in our possession or not. However the field advances, that accelerates Meta's ability to build a better product. So we just built an open AI lab and we said, if this helps accelerate the progress of AI, that's strictly great for us. But very easy, rational, right? Still the same to a large extent with the Llama stuff. And it's the same values, but the argument, it's a bit more nuanced. And then there's a second kind of open source, which is, oh, we built this project, nights and weekends and we're very smart people and we open sourced it and then we built a community around it. This is the Linux kernel and various software projects like that. So I think about open source, like both of these things being beneficial and both of these things being different. They're different and beneficial in their own ways. The second one is really useful when there's an active arbitrage to be done. If someone's not really looking at a particular space because it's not commercially viable or whatever, like a band of volunteers can just coordinate online and do something and then make that happen. And that's great.Open Source LLMsI wanna cover a little bit about open source LLMs maybe. So open source LLMs have been very interesting because I think we were trending towards an increase in open source in AI from 2010 all the way to 2017 or something. Like where more and more pressure within the community was to open source their stuff so that their methods and stuff get adopted. And then the LLMs revolution kind of took the opposite effect OpenAI stopped open sourcing their stuff and DeepMind kind of didn't, like all the other cloud and all these other providers, they didn't open source their stuff. And it was not good in the sense that first science done in isolation probably will just form its own bubble where people believe their own b******t or whatever. So there's that problem. And then there was the other problem which was the accessibility part. Like, okay, I again always go back to I'm a student in India with no money. What is my accessibility to any of these closers models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control thing. I strongly believe if you want human aligned stuff, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble. Like all the friends I hang out with talk about some random thing like Dyson Spheres or whatever, that's a thing. And most of the world doesn't know or care about any of this stuff. It's definitely a bubble and bubbles can form very easily. And when you make a lot of decisions because you're in a bubble, they're probably not globally optimal decisions. So I think open source, the distribution of open source powers a certain kind of non-falsifiability that I think is very important. I think on the open source models, like it's going great in the fact that LoRa I think came out of the necessity of open source models needing to be fine-tunable in some way. Yeah, and I think DPO also came out of the academic open source side of things. So do any of the closed source labs, did any of them already have LoRa or DPO internally? Maybe, but that does not advance humanity in any way. It advances some companies probability of doing the winner takes all that I talked about earlier in the podcast.Open Source and TrustI don't know, it just feels fundamentally good. Like when people try to, you know, people are like, well, what are the ways in which it is not okay? I find most of these arguments, and this might be a little controversial, but I find a lot of arguments based on whether closed source models are safer or open source models are safer very much related to what kind of culture they grew up in, what kind of society they grew up in. If they grew up in a society that they trusted, then I think they take the closed source argument. And if they grew up in a society that they couldn't trust, where the norm was that you didn't trust your government, obviously it's corrupt or whatever, then I think the open source argument is what they take. I think there's a deep connection to like people's innate biases from their childhood and their trust in society and governmental aspects that push them towards one opinion or the other. And I'm definitely in the camp of open source is definitely going to actually have better outcomes for society. Closed source to me just means that centralization of power, which, you know, is really hard to trust. So I think it's going well

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Wednesday Evening Worship with Scott Gray

HealthBiz with David E. Williams

Play Episode Listen Later Jan 24, 2024

worship scott gray

Interview with Clincierge CEO Scott Gray

Play Episode Listen Later Dec 21, 2023 24:40 Transcription Available

Clinical trials must recruit and retain patients to succeed. But it's easier said than done –and as a result many trials are delayed, scaled back or even scrapped completely especially when they involve international travel. Today's guest, Scott Gray founded Clinicierge to reduce logistical and financial challenges for patient to enhance enrollment and reduce attrition.Support the showHost David E. Williams is president of healthcare strategy consulting firm Health Business Group. Produced by Dafna Williams.

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HealthBiz Brief: Clincierge's Keys to Clinical Trial Success w/ CEO, Scott Gray

CareTalk Podcast: Healthcare. Unfiltered.

Play Episode Listen Later Dec 20, 2023 4:52 Transcription Available

Clincierge's CEO, Scott Gray joins David Williams to discuss the challenges of clinical trial retention and Clincierge's innovative approach to addressing them.

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Your Region Pod: How does an airport fuel innovation?

Your Region Pod

Play Episode Play 42 sec Highlight Listen Later Dec 12, 2023 14:44

For many of us here in The Region of Waterloo, YKF is a convenient place to fly in and out of. In fact, the airport has become so popular that the number of passengers flying out of YKF is projected to reach 1 million in the coming years. However, YKF is more than a convenient travel hub, it is an engine for innovation, it connects our local economy to the global economy, and it creates jobs. In this episode, we explore the advantages that YKF brings to the Region, and we'll even look at the potential for the airport to help address climate change.Joining us on this episode:Rod Regier, Commissioner, Planning, Development & Legislative Services, Region of Waterloo Rod is responsible for planning the future growth of our community and creating strategies to support our continued prosperity. This includes the operation of the Region of Waterloo International Airport, the Region's museums and libraries and the management of Regional Forests, as well as oversight of the Regional Official Plan.https://www.waterlooairport.ca/@regierr Scott Gray, CEO of Avidrone Since 2007, AVIDRONE Aerospace has been maturing and developing some of the world's best UAV technologies. AVIDRONE was founded on over 20 years of National and World Championship winning experience, combined with thousands of commercial flight operation missions, and a veteran team of highly motivated innovators in high-tech automation excellence.https://www.avidroneaerospace.com/2018/01/15/avidrone-cicer-one/ Dr. Suzanne Kearns, University of Waterloo Dr. Kearns is an aviation academic with a focus on education and improving pilot performance. Her research explores how to optimize the next generation of aviation professionals (NGAP) by analyzing processes to attract people to the field of aviation. An accomplished educator both in the classroom and through electronic courseware, she has taught thousands of aviation professionals worldwide. She is a former airplane and helicopter pilot and is internationally recognized within the aviation industry. https://uwaterloo.ca/sustainable-aeronautics/profiles/suzanne-kearns https://wwfc.ca/ Find out more about Your Region Pod at our website: Website: https://yourregionpod.buzzsprout.com Spotify: Your Region Pod | Podcast on SpotifyiTunes: Your Region Pod on Apple Podcasts Google: Your Region Pod on GoogleAmazon: Your Region Pod on AmazonWe want to hear from you! X: Region of Waterloo (@RegionWaterloo) / X (twitter.com) Instagram: Regional Municipality of Waterloo | Kitchener, Ontario | Instagram p

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Removing Obstacles to Clinical Trial Access with Scott Gray

Passionate Pioneers with Mike Biselli

Play Episode Listen Later Nov 13, 2023 27:27

This episode's Community Champion Sponsor is Catalyst. To virtually tour Catalyst and claim your space on campus, or host an upcoming event: CLICK HERE---Episode Overview: During this episode, we spend time with Scott Gray, CEO of Clincierge, pioneering concierge-level support services to enhance clinical trials. Bringing extensive experience in pharmaceutical events, Scott is driven to remove obstacles patients face in accessing potentially life-changing trials. While together, Scott shares how Clincierge manages logistics and finances so patients can focus on treatment and why his team is so passionate in building trust and customized care by aligning coordinators directly with participants throughout lengthy trial regimens. Additionally, we discuss innovations enabling continuation of research and patient safety amidst the pandemic's disruption. Join us as Scott advocates for considering the immense value of improving trial experiences to speed novel therapies to market and how his team at Clinciege is doing exactly this! Let's go! Episode Highlights:Scott brings pharmaceutical events expertise to transform clinical trial experiences.Clincierge manages logistics and finances so patients can focus on treatment.Custom care and trust stem from aligned coordinators supporting patients.Scott drove creative solutions enabling trials to continue safely despite COVID.Scott exemplifies the immense value of improving experiences to progress faster.About our Guest: Scott Gray is the co-founder and CEO of Clincierge, a provider of patient support services for clinical trials. Since 2015, Clincierge patient coordinators have managed logistics and reimbursements in more than 300 clinical trials worldwide.Links Supporting This Episode:Clincierge Website: CLICK HEREScott Gray LinkedIn page: CLICK HEREClincierge Twitter page: CLICK HERE Mike Biselli LinkedIn page: CLICK HEREMike Biselli Twitter page: CLICK HEREVisit our website: CLICK HERESubscribe to newsletter: CLICK HEREGuest nomination form: CLICK HERE

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Scott Gray and Wednesday Service

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Play Episode Listen Later Aug 16, 2023

service scott gray

Scott Gray, Sr. Client Center Manager at St. Mary's Food Bank

Mac & Gaydos Show Audio

Play Episode Listen Later Jun 16, 2023 4:37

Scott Gray describes giving food to a hungry child, and why it's so important to donate to the food bank. See omnystudio.com/listener for privacy information.

Episode 105: Changing the faces of clinical trials with Clincierge's Scott Gray

The Collective Voice of Health IT, A WEDI Podcast

Play Episode Listen Later Jun 2, 2023 22:56

Michael welcomes Scott Gray, CEO of Clincierge, a patient concierge services provider in clinical trials. The two discuss the current state regarding diversity in clinical trials and how the need for minority participation can help serve a more accurate, realistic population, establish trust between patients and providers, and improve the patient experience.

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Wednesday Service With Scott Gray

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Play Episode Listen Later May 10, 2023

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Addressing the Barriers to Patient Participation in Clinical Trials

RARECast

Play Episode Listen Later Mar 23, 2023 26:04

A significant obstacle to getting patients to participate in rare disease clinical trials, particularly children, is the burden placed on patients and their families to address the logistical challenges of arranging travel, fronting expenses, and completing paperwork for reimbursement. In fact, nearly two-third of patients and caregivers say travel stopped them from participating in a clinical trial. Clincierge seeks to remove the burden of participation in clinical trials on patients and caregivers by managing the logistics of travel and reimbursement, as well as assigning coordinators to them during the life of a study. We spoke to Scott Gray, co-founder and CEO of Clincierge, about the burdens placed on patients who want to participate in a clinical trial, how Clincierge works to eliminate those, and the impact its work has on recruitment and retention of patients in clinical studies.

Coordinating Patient Participation in Rare Disease Clinical Trials with Scott Gray Clincierge TRANSCRIPT

ceo president patients monitoring participation clinical trials rare diseases coordinating rare disease day scott gray

Play Episode Listen Later Feb 21, 2023

Scott Gray is the President and CEO of Clincierge and supporting the activities related to the observation of Rare Disease Day on February 28. Clincierge is focused on coordinating logistical, financial, caregiving, and translation services to facilitate participation in clinical trials by patients with rare diseases. With a personalized, local approach, they support sponsors of research in the recruitment and retention of patients by removing obstacles and reducing stress. Scott explains, "The intent is to provide an energy and a focal point that enables rare disease advocacy work to progress on the local, national, and international levels. The intent of improving access to treatment and medical representation for the many individuals who struggle with a rare disease and include their families who support and care for them. Since it was created in 2008, Rare Disease Day has played a critical part in building an international rare disease community that is multi-disease, global, and diverse but truly united in its purpose of expanding access for these unique patients." "From what we've observed in the rare disease space, there hasn't been a great effect by technology. Monitoring can happen, but a lot of the observational reviews that have to happen during the visit are only able to happen with going to the site. During the pandemic, as you mentioned, we were asked in some instances to move the healthcare provider to the home of the rare disease patient so that they didn't have to travel, especially if it was a patient who was immunocompromised. And if that wasn't possible, there were also instances where the sponsor budgeted for private jet travel so that an immunocompromised patient could visit the site." @Clincierge #RareDiseaseDay #ShowYourStripes #RareDiseases #ClinicalTrials clincierge.com Listen to the podcast here

Coordinating Patient Participation in Rare Disease Clinical Trials with Scott Gray Clincierge

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Play Episode Listen Later Feb 21, 2023 16:52

Gallifrey's Most Wanted Presents: Comic Shop IX The Flood

Gallifrey's Most Wanted Podcast

Play Episode Listen Later Dec 10, 2022 53:26

Ross is joined by Mark McManus from Trap One Podcast to talk Doctor Who comics again. This time it is the final 8th Doctor story from Doctor Who Magazine The Flood by Scott Gray, Martin Geragthy, and David A. Roach. The 8th Doctor and Destrii battle time traveling Cybermen from the future. This epic closes out Classic Who Doctor tale before the book is handed over the the Modern Doctors Thanks again to Mark for joining in on the coversation @TrapOne_ @QuarkMcMalus @DMWTweets @Scott1Gray #DoctorWho #TARDIS #Panani #Cybermen #ComicBooks #PaulMcGann #8thDoctor

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Wednesday Service With Scott Gray

Play Episode Listen Later Nov 30, 2022

service scott gray

S3 E6 - The MacGuffin of Time

ceo challenges patients retention irb scott gray

Play Episode Listen Later Nov 26, 2022 57:32

In the second part of this anniversary celebration, Dylan is once again joined by Mark Donaldson from On The Timelash. Together they discuss three DWM anniversary comic strips.Time & Time AgainWriter: Paul CornellArtist: John RidgwayHappy DeathdayWriter: Scott GrayArtist: Roger LangridgeTV Action!Writer: Alan BarnesArtist: Roger LangridgeThey meander around these three releases and all things multi doctor, while answering these burning questions: Who wasn't invited to Ian Levine's house?Do we actually need an excuse to see a Mandrel?What is it like to be housemates with William Hartnell?

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Patient Retention Challenges & Solutions

SCRS Talks

Play Episode Listen Later Nov 21, 2022 11:33

Scott Gray, CEO of Clincierge, highlights statistics from an independent, IRB-approved survey of patients and caregivers regarding participation in clinical trials. Learn about the financial risks of patient dropouts and trial delays, plus practical strategies for making trial participation easier and encouraging patient retention.

Maximizing the Impact of Church Meetings and Events - Scott Gray

For Ministry Leaders Podcast

Play Episode Listen Later Nov 9, 2022 47:01

This session is from the Spiritual Leadership Conference in Lancaster, CA. Audio Download Download 2022-09-21-gray-scott-maximizing-the-impact-of-church-meetings-and-events.pdf (188.24 KB)

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Scott Gray - Wednesday Evening, October 26, 2022

First Baptist Church of Hammond

Play Episode Listen Later Nov 7, 2022 29:00

Scott Gray - Wednesday Evening, October 26, 2022 by First Baptist Church of Hammond

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EP57 "Back In Motion" with Dr. Scott Gray

The Motiv8d Mindset Podcast

Play Episode Listen Later Oct 13, 2022 22:05

Welcome into the latest episode of the Motiv8d Mindset Podcast and to my next guest a world renowned physical therapist, published author and speaker who specializes in the conservative, non-invasive treatment of athletic, sport and spine injury. He has invented a revolutionary approach to rehabilitation called the Gray Method, a form of treatment to accurately diagnose the condition and address the cause rather than symptoms alone. Thanks for Listening!~

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S4E10: 6/22/94 — Scott Gray

Undermine

Play Episode Listen Later Oct 10, 2022 34:35

Today we go to Columbus for the amazing 6/22/94 show, with a person who not only was there, but who describes it as one of the two most important days of his life (aside from his birth). Scott Gray enlightens us on this life-changing experience, and some insight into why Trey was talking about tall people during Icculus. We also get into the merits of jam length, the top 5 shows of all time, and a lot more. It's a fun ride. Thanks to our partners at Green Future Wealth—they can help with all of your financial planning needs.Undermine is brought to you by Osiris Media. Executive Producers are Tom Marshall, RJ Bee, Brian Brinkman, Matt Dwyer, and Benjy Eisen. Produced and edited by Brian Brinkman and Eric Limarenko. Mixed and Mastered by Matt Dwyer. Production assistance from Christina Collins and Nick Cejas. Original Music by Amar Sastry. Art by Mark Dowd. Hosted on Acast. See acast.com/privacy for more information.

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Gallifrey's Most Wanted Presents: Comic Shop VI

Gallifrey's Most Wanted Podcast

Play Episode Listen Later Sep 18, 2022 85:25

Ross is joined once again by Mark from the Trap One Podcast to talk about Doctor Who comics. This time it's the Doctor Magazine trade titled The Hunters of the Burning Stone. This volume features three stories, The Broken Man, Imaginary Enemies, and The Hunters of Burning Stone the 50th anniversary story featuring the return of Ian and Barbara. These tales feature the talents of, Scott Gray, Martin Geraghty, MIke Collins, David A. Roach, James Offredi, and Roger Langridge. Shout outs!: Trap One Podcast @TrapOne_ Mark McManus @QuarkMcMalus Doctor Who Magazine @DWMTweets https://doctorwhomagazine.com/

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Improving Recruitment Retention and Outcomes of Clinical Trials with Scott Gray Clincierge TRANSCRIPT

ceo president improving outcomes recruitment retention clinical trials scott gray

Play Episode Listen Later Aug 31, 2022

Scott Gray is the President and CEO of Clincierge and is focused on streamlining clinical trial recruitment, retention, and completion and improving the results of trials to speed time to approval. Clincierge is most concerned about the challenges presented by travel needs and logistics for patients, caregivers, and family members. Scott explains, "Our mission is to lead the global clinical trial performance improvement market. So a lot of my experience has been in leadership development with my pharma customers and performance improvement. And as we created Clincierge over the last seven years, we felt that what we could do in the space, being non-medical in our approach, that we could help with the patient experience and thereby influence the overall outcome to the improvement of clinical trials." "We positioned it in the space of clinical trials and helping those who want to conduct clinical trials, especially sponsors, to better understand what patients are asking for or to better understand the challenges that patients face when they want to participate in a clinical trial. As an example, 62% of patients and 59% of the caregivers said that travel stopped them from participating in a clinical trial. And 42% of patients and 47% of caregivers said financial issues stop them from participating." "Well, with patients having a better experience, there's a lot of building of trust. Because some other reasons that patients drop out are not only the financial and travel burdens, but they may feel underappreciated. They have work pressures, losing work, losing wages from not being at work." @Clincierge #ClinicalTrials #ClinicalResearch #ClinicalTrialRecruitment #PatientExperience #RareDiseases #Pharma #Biotech Clincierge.com Listen to the podcast here

Improving Recruitment Retention and Outcomes of Clinical Trials with Scott Gray Clincierge

ceo president improving outcomes recruitment retention clinical trials scott gray

Play Episode Listen Later Aug 31, 2022 17:46

Save Jesus Only: Scott Gray

Turning Point Sermons

Play Episode Listen Later Aug 4, 2022 44:19

Matthew 17:1-8

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Evaluating Large Language Models Trained on Code

Papers Read on AI

Play Episode Listen Later Jun 28, 2022 53:01

We introduce Codex, a GPT language model ﬁne-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Fur-thermore, we ﬁnd that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difﬁcult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difﬁculty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics. 2021: Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, F. Such, D. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, I. Babuschkin, S. Balaji, Shantanu Jain, A. Carr, J. Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, M. Knight, Miles Brundage, Mira Murati, Katie Mayer, P. Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba https://arxiv.org/pdf/2107.03374v2.pdf

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Mastering Dungeons – Designing and Running Fun D&D Combats with Scott Gray

Down With DnD

Play Episode Listen Later Jun 16, 2022 81:45

While Teos is off on another adventure, Scott Fitzgerald Gray joins Shawn to talk about ways to make combats interesting and thrilling for everyone at your D&D table. With Shawn […]

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Live from Howard Hill 2022

BreakingTrad

Play Episode Listen Later Jun 16, 2022 41:52

In this episode we recap some of our previous shoots and cut up a little with Scott Gray!Make Every Shot Count

How the Clinical Treatment Act is Increasing Diverse Access to Trials

SCRS Talks

Play Episode Listen Later Apr 18, 2022 14:29

In January 2022, the U.S. enacted legislation supporting our industry's ability to sponsor more diverse, equitable, and inclusive clinical trials via the Clinical Treatment Act. This law aims to create equal and increased access to clinical trials for more than 76 million Americans enrolled in Medicaid. SCRS sat down with Scott Gray, CEO of Clincierge, who shares more insight into this new law and how it may affect enrollment of diverse communities in trials.

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S2 E5 - A Cosmic Sphincter of Basic Fanwank - Part 1

Play Episode Listen Later Mar 13, 2022 51:22

In episode 5 of series 2, Dylan and Iain Martin (@theIainMartin) of the All Of Time And Space Podcast (@TimeNSpacePod) do a deep dive into the wilderness years. In this episode they look at the DWM comic strip featuring the 7th Doctor and Ace 'Ground Zero', written by Scott Gray, with art from Martin Geraghty and Bambos Georgio.They will be back next week to look at 'Death Comes to Time'

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S1 E24 - Nick Briggs IS the Doctor.... Sort of!

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Play Episode Listen Later Sep 25, 2021 115:17

With Jack still passed out in his delta wave augmenter, Dylan is joined by Mark from 'On The Timelash' to look at an epic run of eighth Doctor, Fey and Izzy comic strips. First up is 'The Final Chapter' by Alan Barnes, with art from Martin Geraghty and Robin Smith. Then Nick Briggs is temporarily the Doctor in 'Wormwood' by Scott Gray with art from Martin Geraghty & Sean Longcroft.If you haven't heard of 'On the Timelash' (shame on you!), you can listen to them at https://onthetimelash.wordpress.com or wherever you get your podcasts.

Colours

Against the Lore

Play Episode Listen Later Sep 5, 2021 30:34

In which we welcome Scott Gray to talk about colours in the ancient world! Meg and Scott discuss whether the ancient Greeks were colour-blind, Zenia tells us about how statues in the ancient world were painted in bright colours, and the colours of the rainbow confuse everyone, from Homer to Aristotle...

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Gray Matter with Scott Gray

BreakingTrad

Play Episode Listen Later Jul 22, 2021 31:41

Alright, Alright, Alright In this weeks episode we talk to Scott Gray, a long time shooter and proponent of Traditional Archery. We discuss arrow spin, bow weight and a tone of other things. Most of all, I would like to point out how Scott said the podcast was great and you should like it to!! LOL!! Please fell free to like, subscribe, follow, rate, and review. All the good things that come with supporting you local podcast and shooters. We really appreciate every ones support so far!Remember "MAKE EVERY SHOT COUNT"

1.3 Living Legend

Doctor Who - Pieces of Eighth

Play Episode Listen Later Jun 4, 2021 20:02

We take our first tip into Big Finish Eighth Doctor territory, with a look back to a little gem from 2003. Becca and Kenny are joined by producer/director Gary Russell, as well as Conrad Westmaas, who recalls his time in studio as Thon, in Scott Gray's witty 22-minute long episode.

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Scott Gray

Monocle 24: The Monocle Weekly

Play Episode Listen Later Apr 16, 2021 19:01

Monocle's Culture editor, Chiara Rimella, is joined by the CEO and founder of the World Photography Organisation, Scott Gray. They discuss the recently held 2021 Sony World Photography awards and the impact of the pandemic on photography and the events industry at large.See omnystudio.com/listener for privacy information.

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Scott Gray: Why adding a strategist to a digital project increases the chance of success

Digital Surfing

Play Episode Listen Later Feb 24, 2021 43:22

Our guest this week is Scott Gray, Experience Director at health club chain, Virgin Active South Africa.Scott has worked in digital business for around 20 years - from BMW South Africa, where he helped launch them into social media in 2009, to co-founding his side hustle CancerDojo which aims to help and empower those with Cancer.He is a digital transformation specialist with a wealth of experience in digital product, design and user experience known for his user-centred design thinking and innovation. Out of work, you'll find him climbing, fly-fishing and in 2008, Scott even played for the SA beach ultimate frisbee team in Italy...It's Scott Gray!Follow Daryn on LinkedIn: linkedin.com/in/darynsmith/Links referenced:Product Hunt newsletter: https://www.producthunt.com/newsletterCancer Dojo: https://cancerdojo.com/

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Episode 4: The Centipede Temple (part 2)

Centaur Party: Kids Playing D & D in the Mythic Odysseys of Theros Campaign Setting

Play Episode Listen Later Nov 4, 2020 31:16

The Centaurs continue their adventure in the Centipede Temple and face off against the High Priest of the Children of a Thousand Legs! This episode uses some of the playtest material from Fantastic Lairs by Scott Gray, James Introcaso, and Mike Shea. You can preorder a copy of Fantastic Lairs here: http://fantasticlairs.com/

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S1 E12 - Cosplay and smudgy face

Centaur Party: Kids Playing D & D in the Mythic Odysseys of Theros Campaign Setting

Play Episode Listen Later Nov 1, 2020 78:02

In Episode 8 Dylan and Jack look at two stories featuring the Master. The eighth Doctor DWM comic strip 'The Glorious Dead' by Scott Gray and the seventh Doctor Big Finish play 'Master' by Joseph Lidster.

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Episode 3: The Centipede Temple (part 1)

Play Episode Listen Later Oct 28, 2020 24:01

Artemesia the Dryad comes to June and Lama and asks them to investigate the disappearance of a Moura named Amantha Crystalgust. A mysterious carapace of a Giant Centipede is found in Amantha's home. The investigation leads our heroes to a cave that contains an abandoned Temple to the god Erebos. This episode uses some of the playtest material from Fantastic Lairs by Scott Gray, James Introcaso, and Mike Shea. You can preorder a copy of Fantastic Lairs here: http://fantasticlairs.com/

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S1 E08 - Breathless Manic Pixie