Podcasts about moes

MOPs & MOEs is ⁠⁠⁠⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.Drew is on a side quest to dive into niche areas of military human performance, and this episode is extremely niche. A few episodes ago we mentioned the fitness test for the Marine Corps Body Bearers (which includes bench press, squat, barbell curls, and behind the neck overhead presses). We got some of the details wrong, and thanks to the our audience we were put in touch with a former bearer to set the record straight.Billy Lashley served as a United States Marine and World Famous Body Bearer from 2019 to 2023. In that time, he performed 625 funerals — including high-profile state and joint services — and took part in Friday night parades and wreath-laying ceremonies at Marine Barracks Washington. His roles within the section included recruiter and instructor, giving him a front-row seat to both the weight of the mission and the responsibility of preparing others for it.This is small and extremely unique community that upholds some elite performance standards, and our conversation spans recruiting/testing standards, training protocols, and how leaders in the organization maintain the culture.Billy has much longer hair now, and is even more jacked. Follow him on Instagram at @blashley96.Here's some official Marine Corps media diving into the organization if you want more after listening to the episode:Marine Corps Body Bearers Part IMarine Corps Body Bearers Part II

Harmen en Marjolein zitten al vroeg aan het ontbijt vandaag. Toch lukt het om voor de drukte uit in gesprek te gaan over de wankele balans tussen beeld en inhoud in de politieke arena, het kinderlijke vocabulaire van Donald Trump, gratis kraamhulp voor Amsterdammers EN de nieuwe minister van lhtbg+ rechten Gouke Moes. Verder bespreken ze de gestaag groeiende stroom van luisteraar-reacties en moet hond Pijke geduld hebben voordat hij naar buiten kan. 

MOPs & MOEs is ⁠⁠⁠⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.In this week's episode we're breaking down some recently published research. Specifically, "Personalized, Evidence-Informed Training Plans and Exercise Prescriptions for Performance, Fitness and Health" by Henning Wackerhage and Brad Schoenfeld. Up front, the article itself is an opinion piece, but it's based on an extensive review of the literature, and provides thorough citations. It's a useful article specifically because it synthesizes so much evidence into some practical guidelines for coaches.The authors advocate for an athlete, client and patient-centered approach whereby an individual's needs and abilities are the main consideration behind all decision-making. They also lay out a subjective, pragmatic six-step approach that details how to write a training plan or exercise prescription that is partially based on scientific evidence.

MOPs & MOEs is ⁠⁠⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.Find ⁠Tactical Alpinism on Instagram here⁠You can find Mauri's podcast on ⁠Spotify⁠ or on ⁠Apple PodcastsWe recently had Mauri on to discuss his research on lactate threshold based training, but after he joined the conversation on our Discord we found out we missed an even more important topic. Fitness plays a huge role in ROTC cadets' ranking, and those rankings determine their choices of component and branch. As an instructor, Mauri's human performance focused approach dramatically enhanced his school's outcomes, so in the conversation we explore what worked.We discussed news of a cadet's death at Advanced Camp, you can find that story here.You can find coverage of the ROTC "rebalancing and optimization" (downgrading programs) here.

Miss Jill Moes from Divich Elementary just won a $50 Amazon Gift Card courtesy of Best Mattress to help clear her Amazon wish list!

On July 31st President Trump signed an executive order re-establishing the President's Council on Sports, Fitness, and Nutrition and directing the new council to develop a proposal on bringing back the Presidential Fitness Test. This test figures prominently in the childhood memories of many Americans, with pride for some and trauma for others. In this episode we break down the latest news within the decades of historical context that got us here. You can read "The Soft American" here (we consider it mandatory reading for MOPs & MOEs followers)For background on our mention of physical education in Europe (especially the Turnverein movement) check out our episode History of Army Fitness with Dr. EastFor some context on the President's Council on Sports, Fitness and Nutrition, check out our episode with former council member Rob WilkinsWe mentioned Maintenance Phase's episode on the PFT and you can find that hereWe also mentioned a similar perspective on the test presented in this article on VoxDrew referenced the official history of the council provided on the HHS websiteAlex referenced the FitnessGram teacher training which provides an overview of the program This article highlights the lack of academic scrutiny focused on physical education, including FitnessGramThe source of the claim that the average school budget for physical education is $764 annually is this article from TimeYou can read the La Sierra High School Physical Education handbook here, including the basic philosophies as well as the specific events and standards

MOPs & MOEs is ⁠⁠⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.This episode includes a reference to this Defense Health Agency report that found that the more H2F resources provided for Reserve soldiers, the better results they saw.If you follow the MOPs & MOEs blog, you already know this week's guests from things like his 5 part series "The Other 28 Days" on how to implement human performance for part time service members or his "Maximizing Fitness Efficiency" piece on minimal effective dose training. Members of our discord server know he's always bringing research citations to the conversations happening there.Mark Christiani is an Army Veteran who served in Ranger Regiment before transitioning into the human performance space. He currently works with O2X as an On-Site Human Performance Specialist at the 81st Readiness Division of the Army Reserve. Mark served as the Brigade Lead Strength and Conditioning Coach for GAP Solutions for not just any brigade, but 44th Medical Brigade where Drew works. He holds a Master of Science in Sports Medicine from Georgia Southern University and is a Certified Strength and Conditioning Specialist (CSCS) and Registered Strength and Conditioning Coach (RSCC). 

Derby weekend won't be the same next year, following the news that Churchill Downs is pushing the running of the Kentucky Oaks back to 8 p.m. or later.We chat about the impact of that on this week's Access Louisville podcast. Churchill Downs announced the change on Thursday, July 24.  Typically post time for the race, which runs the day before the Kentucky Derby, is scheduled shortly before 6 p.m. NBC executives say the race will move to NBC and Peacock, and the primetime post will allow for a “spectacular twilight finish.”Our next live podcast is July 28: Join us as we take look at Louisville's most important development projects. Registration here.The big impact will likely be how restaurants and bars evolve their dinner service. They're obviously losing a few hours — though it's hard to imagine that restaurants will actually be empty at dinner time on Oaks Day. We'll surely be watching how restaurants respond come next May.We also chat about how the Derby Week experience has evolved over the years as it becomes more of a "bucket list" type of event for the world. Oaks Day used to be known as the day for locals to come to the track but that hasn't been the case for some time. Even Thurby is a scene, nowadays. For better or worse, locals are much more likely to be spotted walking around at 502'sDay at the track.Reporter Joel Stinnett also gives us an update on a recent project at the track — albeit a behind the scenes one. Later in the show we shift gears to talk about gas station food. The popular gas station Wawa opened recently in Louisville — bringing out a number of fans. We also got the news that a Florida gas station, Nick & Moes, known for its fried chicken, is opening locally. That gets us talking about favorite gas station foods as well as the cult following that many of these chains have managed to cultivate. Access Louisville, sponsored by Baird, is a weekly podcast from Louisville Business First. It's available on popular podcast services including Apple Podcasts and Spotify (which are linked above.) You can also listen in the player above.

MOPs & MOEs is ⁠⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.Find Tactical Alpinism on Instagram hereYou can find Mauri's podcast on Spotify or on Apple PodcastsYou can download a copy of Mauri's thesis, “Relationship Between the Lactate Thresholds and Endurance Performance in Trained Runners” hereIn this episode we're returning to the topic of how mountain athletes and tactical athletes have similar fitness demands. Mauri is particularly qualified to discuss this topic since he is a bit of both. His perspectives include being an infantry officer, alpinist, coach to endurance athletes, certified mountain guide, and more.Mauri served in a multitude of leadership roles as an infantry officer in the US Army. During that service he applied many advanced planning and navigation techniques to make mountain missions successful. He adapted the military's operational planning process for use in the mountains by combining military planning and navigation techniques with mountain objectives. He now leads Tactical Alpinism, where he provides training and education for both tactical professionals and civilians pursuing high levels of performance in the mountains. This includes both physical training and technical mountain navigation.

Tydens sy streeksrede het goewerneur van Omaheke, Pijoo Nganate, onthul dat droogtehulp versprei is na die helfte van die streek se inwoners. Meer as 22 575 huishoudings is bereik wat 67 725 mense dek.

Die wêreld-nommer-een Jannik Sinner van Italië is deur na die kwarteindronde by Wimbledon nadat die Bulgaar, Grigor Dimitrov, weens ʼn besering moes onttrek terwyl hy met 6-3, 7-5, 2-2 voorgeloop het. Die Serwiër, Novak Djokovic, het die Australiër, Alex de Minaur, naelskraaps geklop om sy 16de Wimbledon-kwarteindronde te bereik. Die Amerikaner Ben Shelton en Flavio Cobolli van Italië is ook deur na die laaste agt. Sinner sê dis jammer die wedstryd het sleg geëindig:

MOPs & MOEs is ⁠⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠⁠ We have experts standing by to answer your questions.This week's episode is just the two of us, and we're discussing a topic that we've referenced a few times on social media: Nazareth Syndrome. One of the simplest ways to explain this phenomenon is "nobody trusts the hometown kid." The origins of this idea are biblical (Jesus was rejected by his own community because to them he was just the carpenter they knew), but the applications are very practical. Have you ever seen a leader latch onto an idea from a guest speaker or outside consultant that their subordinates have been trying to explain for ages? That's because human nature makes us more receptive to these messages from outsides than from people we're too familiar.In this conversation we break down how this affects the military, and specifically how it plays out in human performance settings (both within teams, and between the teams and the units they support).

MOPs & MOEs is ⁠⁠⁠⁠powered by TrainHeroic!⁠⁠⁠⁠To continue the conversation, ⁠⁠⁠⁠join our Discord!⁠⁠⁠⁠ We have experts (including Rachele!) standing by to answer your questions.In the final part of this series we cover several topics we didn't get enough clarity on in the first two segments: an update on MAHA, Lifestyle Medicine, and how research could be better communicated.Rachele Pojednic, PhD, EdM, FACSM, is the Director of Scientific Research & Education at Restore Hyper Wellness, an Adjunct Lecturer at Stanford University and the Director of Education at Stanford Lifestyle Medicine. In addition, she serves as a Research Associate at the Institute of Lifestyle Medicine at Harvard Medical School and is an award-winning Instructor at the Harvard Extension School. Previously, she was a tenure-track faculty member at Norwich University and Simmons University.For the past decade, Dr Pojednic's work has examined nutrition, supplementation and physical activity interventions on muscle physiology, performance and recovery, as well as muscle related chronic disease. She has received research funding from the National Institutes of Health (NIH) National Heart Lung and Blood Institute (NHLBI) and the Vermont Biomedical Research Network (VBRN) an NIH IDeA Network of Biomedical Research Excellence (INBRE) program. She has published extensively on vitamin D and cannabidiol (CBD) supplementation and their effects on skeletal muscle in health and disease, muscle physiology and aging with a focus on sarcopenia, physiologic metrics of muscle recovery in warfighters, the effects of nutrition and exercise interventions on diseases such as obesity and type 2 diabetes, and educational models for healthcare professionals focused on nutrition and exercise.Dr. Pojednic received her PhD in Biochemical and Molecular Nutrition & Exercise Physiology from Tufts University. She also holds a Masters in Education in Physical Education and Coaching from Boston University and a BS in Cardiopulmonary and Exercise Science from Northeastern University. She holds a Certified Strength and Conditioning Specialist (CSCS) certification from National Strength and Conditioning Association (NSCA) and is board certified Health Coach from the National Board of Health and Wellness Coaches (NBHWC).

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MOPs & MOEs is ⁠⁠⁠powered by TrainHeroic!⁠⁠⁠To continue the conversation, ⁠⁠⁠join our Discord!⁠⁠⁠ We have experts (including Rachele!) standing by to answer your questions.In part 2 of this 3 part series we finally get to the primary topic we invited Rachele onto the podcast to talk about: the gap between human performance research and the practitioners who work to implement it every day. Some specific topics include the the state of human performance research, consumer trends, recovery modalities, supplements, and how all these things get funded.Rachele Pojednic, PhD, EdM, FACSM, is the Director of Scientific Research & Education at Restore Hyper Wellness, an Adjunct Lecturer at Stanford University and the Director of Education at Stanford Lifestyle Medicine. In addition, she serves as a Research Associate at the Institute of Lifestyle Medicine at Harvard Medical School and is an award-winning Instructor at the Harvard Extension School. Previously, she was a tenure-track faculty member at Norwich University and Simmons University.For the past decade, Dr Pojednic's work has examined nutrition, supplementation and physical activity interventions on muscle physiology, performance and recovery, as well as muscle related chronic disease. She has received research funding from the National Institutes of Health (NIH) National Heart Lung and Blood Institute (NHLBI) and the Vermont Biomedical Research Network (VBRN) an NIH IDeA Network of Biomedical Research Excellence (INBRE) program. She has published extensively on vitamin D and cannabidiol (CBD) supplementation and their effects on skeletal muscle in health and disease, muscle physiology and aging with a focus on sarcopenia, physiologic metrics of muscle recovery in warfighters, the effects of nutrition and exercise interventions on diseases such as obesity and type 2 diabetes, and educational models for healthcare professionals focused on nutrition and exercise.Dr. Pojednic received her PhD in Biochemical and Molecular Nutrition & Exercise Physiology from Tufts University. She also holds a Masters in Education in Physical Education and Coaching from Boston University and a BS in Cardiopulmonary and Exercise Science from Northeastern University. She holds a Certified Strength and Conditioning Specialist (CSCS) certification from National Strength and Conditioning Association (NSCA) and is board certified Health Coach from the National Board of Health and Wellness Coaches (NBHWC).

MOPs & MOEs is ⁠⁠powered by TrainHeroic!⁠⁠To continue the conversation, ⁠⁠join our Discord!⁠⁠ We have experts (including Rachele!) standing by to answer your questions.When we hit record on this conversation, we thought it would be a single episode focused on bridging the gap between academia and practice, but we quickly realized that Rachele had a wealth of information to share. This will be part 1 of a 3 part series, and it all revolves around science communication. Some specific topics include the roles of different professions within human performance, lifestyle medicine, supplements, and the challenges of making sure research is relevant and useful.Rachele Pojednic, PhD, EdM, FACSM, is the Director of Scientific Research & Education at Restore Hyper Wellness, an Adjunct Lecturer at Stanford University and the Director of Education at Stanford Lifestyle Medicine. In addition, she serves as a Research Associate at the Institute of Lifestyle Medicine at Harvard Medical School and is an award-winning Instructor at the Harvard Extension School. Previously, she was a tenure-track faculty member at Norwich University and Simmons University.For the past decade, Dr Pojednic's work has examined nutrition, supplementation and physical activity interventions on muscle physiology, performance and recovery, as well as muscle related chronic disease. She has received research funding from the National Institutes of Health (NIH) National Heart Lung and Blood Institute (NHLBI) and the Vermont Biomedical Research Network (VBRN) an NIH IDeA Network of Biomedical Research Excellence (INBRE) program. She has published extensively on vitamin D and cannabidiol (CBD) supplementation and their effects on skeletal muscle in health and disease, muscle physiology and aging with a focus on sarcopenia, physiologic metrics of muscle recovery in warfighters, the effects of nutrition and exercise interventions on diseases such as obesity and type 2 diabetes, and educational models for healthcare professionals focused on nutrition and exercise.Dr. Pojednic received her PhD in Biochemical and Molecular Nutrition & Exercise Physiology from Tufts University. She also holds a Masters in Education in Physical Education and Coaching from Boston University and a BS in Cardiopulmonary and Exercise Science from Northeastern University. She holds a Certified Strength and Conditioning Specialist (CSCS) certification from National Strength and Conditioning Association (NSCA) and is board certified Health Coach from the National Board of Health and Wellness Coaches (NBHWC).

MOPs & MOEs is ⁠powered by TrainHeroic!⁠To continue the conversation, ⁠join our Discord!⁠ We have experts standing by to answer your questions.This is a quick hits episode! Instead of spending an hour on a specific topic, we hustle through a series of hot topics that either address current events, add to previous episodes we've done, or just don't quite make the cut for a full episode of their own. These topics include:- Athletic Trainers in the military- The nomination of Casey Means for Surgeon General- The validity of the Standing Power Throw- Some concerns with the new combat standards- Are frequent changes to fitness standards a problem?- How the side shuffle got added to the sprint drag carry- Do alternate cardio events need full scoring scales?

MOPs & MOEs is powered by TrainHeroic!To continue the conversation, join our Discord! We have experts standing by to answer your questions.One of the classic problems with tactical strength and conditioning is that, unlike many athletic settings, you can't assume a common baseline level of fitness. Even in combat arms units, it's normal to have extremely wide ranges of fitness levels in the same unit. The same platoon might have powerlifters, distance runners, and gamers who would rather not exercise at all. In this episode Nate Palin joins us to share some lessons learned about navigating this challenge. He has coached diverse populations ranging from civilians to special operators, all of whom have influenced his current perspective. We touch on a number of important topics: Novelty in training can significantly impact engagement Barbells are not always the primary tool in tactical trainingEngagement and enjoyment are crucial for participation in trainingCoaching relationships are essential for effective training outcomesLifestyle conversations often take precedence over strict programmingReal-time monitoring can enhance training effectiveness and safetyPerformance programs focus on pushing limits, while wellness programs aim to elevate the baselineWellness and high performance are interconnectedTactical professionals have diverse roles beyond physicalityKnowledge sharing is vital for effective coachingMotivation is a leader's responsibility, not just a coach'sHard work remains the foundation of success in training

Adv Jan Augustyn, senior operasionele bestuurder vir monitering, inspeksie en handhawing by die Finansiële Intelligensiesentrum, gesels oor die FIC-regulasies vir casinos. Volg RSG Geldsake op Twitter

With Terry Bradshaw

Send us a textWelcome to IoT Coffee Talk #246 where we have a chat about all things #IoT over a cup of coffee or two with some of the industry's leading business minds, thought leaders and technologists in a totally unscripted, organic format. Thanks for joining us. Sit back with a cup of Joe and enjoy the morning banter.This week, Pete, Tom, David, Bill, Debbie, Rob, and Leonard jump on Web3 to talk about:THE WORST KARAOKE! "Anyway You Want It", JourneyAI fatigue - Too much DeepSeek nonsense!All Chinese tech denial leads to a whining road to D.C.The great AI hypocrisyHow to build LLMs and 1.5 trillion parameter MoEs out of coconutsThe Week of DeepSeek - Dazed and ConfusedThe red AI pill or the blue AI pill - utopia or dystopia?The 3 Laws of Edge AIWhat does safe, reliable, trustworthy Edge AI look like?How to make your content LLM copyright protected - 80 percent nonsense ruleWhy IoT Coffee Talk doesn't fit in the attention span of 99.999 percent of humanityIt's a great episode. Grab an extraordinarily expensive latte at your local coffee shop and check out the whole thing. You will get all you need to survive another week in the world of IoT and greater tech!Thanks for listening to us! Watch episodes at http://iotcoffeetalk.com/. We support Elevate Our Kids to bridge the digital divide by bringing K-12 computing devices and connectivity to support kids' education in under-resourced communities. Please donate.

Hey everyone, Alex here

Though the steps involved in developing a good, evidence-based treatment plan are well documented on our podcast, what good is any of that hard work if the families you purport to use it with kinda, sorta hate your plan. Well, this week, rather than complaining about how unappreciated your procedures are, why not take a step back and ask yourself, “How can I better learn from families I work with what will meet their needs?” We take a run down to explore the ever-confusing and complex world of family services, take a qualitative look at social validity in treatment planning, and review some key contexts that spell the difference between a good plan and a plan that works. This episode is available for 1.0 CULTURAL (ETHICS) CEU. Articles discussed this episode: Russa, M.B., Matthews, A.L., & Owen-DeSchryver, J.S. (2015). Expanding supports to improve the lives of families of children with autism spectrum disorder. Journal of Positive Behavior Interventions, 17, 95-104. doi: 10.1177/1098300714532134 Moes, D.R. & Frea, W.D. Using family context to inform intervention planning for the treatment of a child with autism. (2000). Journal of Positive Behavior Interventions, 2, 40-46. doi: 10.1177/109830070000200 Guinness, K.E., Atkinson, R.S., & Feil, E.G. (2024). Evaluating social validity to inform intervention development: Qualitative analysis of caregiver interviews. Behavior Analysis in Practice, 17, 870-879. doi: 10.1007/s40617-023-00899-6 If you're interested in ordering CEs for listening to this episode, click here to go to the store page. You'll need to enter your name, BCBA #, and the two episode secret code words to complete the purchase. Email us at abainsidetrack@gmail.com for further assistance.

President Netumbo Nandi-Ndaitwah het Sondag gesê sy het landbouminister Mac Hengari amptelik van sy pligte onthef, effektief van verlede Woensdag. Hengari is Saterdagmiddag in hegtenis geneem ná hy glo ‘n 21-jarige vrou wat hom van verkragting beskuldig, met 230 000 Namibiese dollar in kontant probeer omkoop het om die saak te laat vaar. Die polisie ondersoek verskeie klagte teen Hengari, insluitend verkragting, geslagsgebaseerde geweld en onwettige aborsie. Kosmos 94.1 Nuus het reaksie by die politieke ontleder, Rui Tyitende, gekry wat meen daar moet 'n keuringproses in plek wees vir ministers:

We are breaking the silence to discuss what it means to be a truth-seeker in a family of secret-keepers. Let's take a deeper look at the impact of generational trauma, the emotional weight of family dysfunction, and the courage it takes to become a cycle breaker.We talk about how silence & secrecy are often survival strategies passed down through generations and how confronting those patterns can feel isolating, painful, and necessary. If you've ever felt like the black sheep, the emotional translator, or the one who sees what others pretend not to, this episode is for you.

Erika Kemp talks finding consistency by staying the course, flip-phones, why she values the small wins, the importance of representation and being the fastest U.S.-born Black female marathoner, the value of a dollar, Chipotle and Moes, early 2000's commercials and more!Episode link in bio.Be sure to follow Lactic Acid on the following platforms: YouTube: Lactic Acid Podcast Twitter: Lacticacid_pod Instagram: Lacticacidpodcast Click here for more information on Marrow CoutureJoin our official Facebook group here: https://www.facebook.com/groups/303650599433289/If you're loving the show, please subscribe and leave a rating and review on Apple Podcasts, and share it with your friends and family!

Gesprek met Josef Moes (https://orthodoxia.be/nl/enoria/parochie-van-de-heilige-nektarios-te-eindhoven/) n.a.v. het boek "Geraakt door Gods liefde", leven en wijsheid van oudvader Porfyrios Eem uitgave van uitgevrij Orthodox Logos in Tilburg (https://orthodoxlogos.com/store/geraakt-door-gods-liefde/) Van hun site: Vader Porfyrios, gestorven in 1991, was een Griekse monnik en priester. Hij stond in de lange traditie van geestelijke leiders, beginnend in de Apostolische tijden tot moderne heiligen zoals Serafim van Sarov en Vader Silouan. In dit boek vertelt hij zijn levensverhaal en in eenvoudige, wijze woorden legt hij het christelijk geloof uit voor de huidige mens…

Anger isn't the enemy - it's a messenger. And today, we're learning how to listen. In this episode, we dive deep into the complexities of anger - how it's perceived, suppressed, and ultimately, how it can be reclaimed as a tool for empowerment. Our guest, Anger Management Expert Alistair Moes, unpacks all of the ways anger has been misunderstood, how it can be used for good, and healing through anger.

It's time for The Truth!Today, Sam and Dougie are looking at Arsenal and in particular, a big reshuffle in the boardroom that has seen Andrea Berta succeed Edu Gaspar as their Sporting Director. Berta was formerly at Atletico Madrid, forming a (mostly) impressive team with Diego Simeone, and also previously worked at Genoa and Parma in his native Italy. We examine what his record was at Atleti in terms of overseeing signings, discuss which positions and players Arsenal may look to in the summer under his stewardship, and scrutinise how the Manager-SD relationship works at Arsenal in particular with Mikel Arteta. There's a little bit of time too to examine the role of a Sporting Director in the modern game, and look at exactly what falls under their remit, before we round things off. So, is this the appointment that helps steer Arsenal through that final step where they lift a Premier League trophy? Will he mesh with what Arteta wants and needs on the pitch? Or is this simply background shuffles that bark louder than they actually bite? Well, The Truth is somewhere in the middle... And remember, if you'd like more from the Rank Squad, including extra podcasts every Monday and Friday (including our weekly Postbox taking a look at the whole weekend of football) and access to our brilliant Discord community, then why not join us here on Patreon?

Spring has sprung on us with a bunch of freezing rain. So what better time than now to get set for a cozy crop of new podcasts for April. First up, as visit from our favorite mythical bunny with a grab bag of goodies in the form of new articles to discuss. Then finally wrap up our (winter!) Listener Choice episode with a tutorial on token economies before coming up with new ways to finish our paperwork and create meaningful family supports. Then, for patrons-only, our Spring Book Club looking at the female neurodivergent-supporting book, Divergent Mind. By the time you've listened to all of these episodes, the flowers will definitely be in bloom. Articles for April 2025 Hoppin' Down the Grab Bag Trail (Spring 2025 Grab Bag) Nevill, R.E., Crawford, M.F., Zarcone, J.R., Maquera, E., Rooker, G.W., Schmidt, J.D. (2024). A retrospective consecutive controlled case series analysis of the assessment and treatment of elopement in children with autism in an inpatient setting. Behavior Analysis in Practice. doi: 10.1007/s40617-024-00979-1 Santa Cruz, H. A. C.,  MIltenburger, R. G. & Baruni., R. R. (2024). Evaluating remote behavioral skills training of online gaming safety skills. Behavior Analysis in Practice, 17, 246-256. doi: 10.1007/s40617-023-00830-z Kelly-Sisken, S., Reeve, K. F., McPheters, C. J., Vladescu, J. C, Reeve, S. A., & Jennings, A. M. (2025). Comparing equivalence-based instruction to a PowerPoint video lecture to teach differential reinforcement descriptors to college students. Behavioral Interventions, 40, online first publication. doi: 10.1002/bin.70002 Tutorial: Token Economies (Spring 2025 Listener Choice) Ackerman, K. B., Samudre, M., & Allday, R. A. (2020). Practical components for getting the most from a token economy.Teaching Exceptional Children, 52(4), 242-249. doi: 10.1177/0040059919892022 Kazdin, A.E. (1982). The token economy: A decade later. Journal of Applied Behavior Analysis, 15, 431-445. doi: 10.1901/jaba.1982.15-431. doi: 10.1901/jaba.1982.15-431 Degli Espinosa, F. & Hackenberg, T.D. (2024). Token economies: Evidence-based recommendations for practitioners. Behavioral Interventions. doi: 10.1002/bin.2051 You Forgot to Do Your Paperwork Luna, O. & Rapp, J.T. (2019). Using a checklist to increase objective session note writing: Preliminary results. Behavior Analysis in Practice, 12, 622-626. doi: 10.1007/s40617-018-00315-4 Halbur, M., Reidy, J., Kodak, T., Cowan, L., & Harman, M. (2024). Comparison of enhanced and standard data sheets on treatment fidelity and data collection for tact training. Behavior Analysis in Practice, 17, 533-543. doi: 10.1007/s40617-023-00869-y Brown, K.J. (2022). The use of a pictorially enhanced self-instruction packet ot improve weekly time sheet completion in an ABA clinic. Journal of Organizational Behavior Management. doi: 10.1080/01608061.2022.2063221 Family Supports and Contextualized Treatment Planning Russa, M.B., Matthews, A.L., & Owen-DeSchryver, J.S. (2015). Expanding supports to improve the lives of families of children with autism spectrum disorder. Journal of Positive Behavior Interventions, 17, 95-104. doi: 10.1177/1098300714532134 Moes, D.R. & Frea, W.D. Using family context to inform intervention planning for the treatment of a child with autism. (2000). Journal of Positive Behavior Interventions, 2, 40-46. doi: 10.1177/109830070000200 Guinness, K.E., Atkinson, R.S., & Feil, E.G. (2024). Evaluating social validity to inform intervention development: Qualitative analysis of caregiver interviews. Behavior Analysis in Practice, 17, 870-879. doi: 10.1007/s40617-023-00899-6 Divergent Mind Book Club (PATRONS ONLY) Nerenberg, J. (2020). Divergent mind: Thriving in a world that wasn't designed for you. Harper One.  

Die politieke ontleder, Daniel Silke, sê dis kommerwekkend om die verbrokkelende verhouding tussen Suid-Afrika en die VSA dop te hou. Dit volg nadat Suid-Afrika se ambassadeur in Amerika, Ebrahim Rasool, as persona non grata verklaar en uit die land gesit is. Hy het president Donald Trump tydens ʼn webinaar beskuldig dat hy die leier van ʼn wit heerssugtige beweging is. Silke sê Rasool moes van beter geweet het as om die verkeerde knoppies te druk terwyl ekonomiese en politieke bande slim hanteer moet word:

Benno & JP talk the mess of a segment on all time bad Smackdown this weekend as The Rock returns (and turns) again  to add more confusion to the road to WrestleMania, plus Tony Khan and CM Punk's legal woes (or lack thereof) and AEW's apparent return to form with another solid week of TV.They also talk Miro in Qatar updates, Scott Steiner's massive son, Lex Luger's road to recovery, a bit of Gladiators and of course the big news of the week, Shane McMahon's grand vision for AEW.SHOWNOTES:0:00 Intro13:01 Dealer's Choice Plugs - Straight Edge Society, WCW Spring Stampede 199919:55 Rock/Cody, Smackdown, WWE on Netflix, Vince McMahon16:50 Ryan Nemeth & CM Punk case 1:09:03 AEW Dynamite Collision, Dynamite, positive creative directions1:52:28 Shane McMahon, Smallman at Progress, Gladiators, Miro in Qatar, Lex Luger, Misc NewsGRAPPL Spotlight is produced with support from our Patrons and YouTube members, with special thanks to King & Queen Of The Mountain Patrons - Conor O'Loughlin, Eddie Sideburns, Chris Platt, Carl Gac & Sophia Hitchcock! You can find all of our live shows on YouTube by becoming a Member at ⁠http://www.Youtube.com/@GRAPPL,⁠ or join us on Patreon for both live video and audio replays at ⁠http://www.patreon.com/GRAPPL!⁠ Get the the new line of GRAPPL merchandise with FREE SHIPPING to the UK, EU, US, Canada, Australia & New Zealand at https://chopped-tees.com/en-uk/collections/grapplYou can also join us on the GRAPPL Discord for free at https://discord.gg/KqeVAcwctS⁠

This week I welcome on the show two of the most important technologists ever, in any field.Jeff Dean is Google's Chief Scientist, and through 25 years at the company, has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip, to Gemini.Noam Shazeer invented or co-invented all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to Gemini and many other things.We talk about their 25 years at Google, going from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and maybe soon to ASI.My favorite part was Jeff's vision for Pathways, Google's grand plan for a mutually-reinforcing loop of hardware and algorithmic design and for going past autoregression. That culminates in us imagining *all* of Google-the-company, going through one huge MoE model.And Noam just bites every bullet: 100x world GDP soon; let's get a million automated researchers running in the Google datacenter; living to see the year 3000.SponsorsScale partners with major AI labs like Meta, Google Deepmind, and OpenAI. Through Scale's Data Foundry, labs get access to high-quality data to fuel post-training, including advanced reasoning capabilities. If you're an AI researcher or engineer, learn about how Scale's Data Foundry and research lab, SEAL, can help you go beyond the current frontier at scale.com/dwarkesh.Curious how Jane Street teaches their new traders? They use Figgie, a rapid-fire card game that simulates the most exciting parts of markets and trading. It's become so popular that Jane Street hosts an inter-office Figgie championship every year. Download from the app store or play on your desktop at figgie.com.Meter wants to radically improve the digital world we take for granted. They're developing a foundation model that automates network management end-to-end. To do this, they just announced a long-term partnership with Microsoft for tens of thousands of GPUs, and they're recruiting a world class AI research team. To learn more, go to meter.com/dwarkesh.Advertisers:To sponsor a future episode, visit: dwarkeshpatel.com/p/advertise.Timestamps00:00:00 - Intro00:02:44 - Joining Google in 199900:05:36 - Future of Moore's Law00:10:21 - Future TPUs00:13:13 - Jeff's undergrad thesis: parallel backprop00:15:10 - LLMs in 200700:23:07 - “Holy s**t” moments00:29:46 - AI fulfills Google's original mission00:34:19 - Doing Search in-context00:38:32 - The internal coding model00:39:49 - What will 2027 models do?00:46:00 - A new architecture every day?00:49:21 - Automated chip design and intelligence explosion00:57:31 - Future of inference scaling01:03:56 - Already doing multi-datacenter runs01:22:33 - Debugging at scale01:26:05 - Fast takeoff and superalignment01:34:40 - A million evil Jeff Deans01:38:16 - Fun times at Google01:41:50 - World compute demand in 203001:48:21 - Getting back to modularity01:59:13 - Keeping a giga-MoE in-memory02:04:09 - All of Google in one model02:12:43 - What's missing from distillation02:18:03 - Open research, pros and cons02:24:54 - Going the distance Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

This week we're bringing you an episode from Part-Time Hitters, where Eric Evans interviewed us about all things military human performance. We discussed H2F, MOPs & MOEs, Leg Tuck Nation, and how to improve performance in the part-time military.Go check out more from Part-Time Hitters and their supporters!Part-Time Hitters Website (a podcast about the reservist life)The Fratty Guard on Instagram (a lifestyle brand for part-time hitters)Friendly Forces Website (a 501c3 non-profit committed to helping reserve component members seamlessly integrate their military service with rewarding civilian careers)

Die sekretaris van die kabinet dr. George Simataa sê ingevolge die Kieswet moet alle ampsdraers wat verkies is tot die Nasionale Vergadering op die dag van die aankondiging moet bedank. Hy sê in ‘n omsendskrywe dat sommiges nog nie bedank het nie en owerhede moet toesien dat dit wel gebeur.

Build One South Africa sê president Cyril Ramaphosa se bywoning van die inhuldiging van Mosambiek se verkose president, Daniel Chapo dra 'n gevaarlike boodskap oor rakende Suid-Afrika se standpunt oor demokrasie. Die party voer aan die Mosambiekse verkiesing is geteister deur geweld en ongerymdhede, insluitend sluipmoordpogings op opposisieleiers, wat hul geloofwaardigheid ondermyn. BOSA-woordvoerder, Roger Solomons waarsku hierdie optrede hou die gevaar in om die SAOG-streek te destabiliseer en demokratiese waardes te verbrokkel:

This is a rerun of an episode from 2022, if you joined us recently it's a great introduction to building smarter physical training plans to improve performance and reduce injuries. We'll be back in a couple weeks with fresh content. Until then, happy holidays! No guest this time, just Alex and Drew trying to answer one of the most commonly asked questions we get here at MOPs & MOEs. Many of you are tactical professionals out there leading your teams without access to professional coaches. Or there are a lot of you training on your own with no guidance at all. So how do you build a plan that will produce results? This conversation will provide you with a few foundational principles you can apply to make sure you're on the right track. We discuss foundational movement patterns, conditioning modalities, frequencies for different types of training, balancing intensity and volume, and more. But we start with the most important thing, which too many people seem to forget: how to set a good goal.

Happy holidays! This is a rerun of an episode we published back in March 2023, but this topic has been getting a lot of discussion again recently so we wanted to revisit it! MOPs & MOEs merch is now for sale on our website! Check out the shop for tees, hoodies, stickers, and more. Job Fransen is a skill acquisition specialist working at the University Medical Centre Groningen in the Netherlands and an adjunct fellow at the University of Technology Sydney's School of Sport, Exercise, and Rehabilitation. His research focuses on optimizing skill acquisition in athletes. He has worked with high-performance athletes and individuals from around the world, across elite sport, esports and gaming, and the military. Job is also a skill acquisition consultant, assisting some of the world's best coaches to design practice that optimizes learning across a range of sports, most notably rugby, Australian football, soccer, and basketball. We discovered Job's work because of a preprint article he released that provides extensively resourced evidence to argue two main points: A far transfer of skills is something we all think we do yet it is very difficult to achieve. Instead, we mostly achieve near transfers of skills between very similar or related tasks. Cognitive training is evidenced not to have a far transfer in robust scientific research in psychology, yet numerous tech companies claim to have the ‘next best cognitive or perceptual training tool' for improving sports performance while these transfers are exceptionally difficult to achieve and there is no evidence these tools can even achieve them. In this episode, we start off by defining the concepts of "near transfer" and "far transfer" and then set off on a wide-ranging conversation about how to better deliver actual evidence-based cognitive training. We address the heated debate among researchers in this space, critique some of the popular technologies, and arrive at some pretty valuable insights on how to integrate skill acquisition principles into the ways we train, such as the optimal challenge point model. If this is a topic that excites you, you're in luck. Both ahead of and during our conversation Job pointed us toward a wealth of resources. We'll include links to numerous references below, but if you want to contact Job directly he is very open to that. You can email him at Job.Fransen@gmail.com or reach him on his LinkedIn. References: A critical systematic review of the Neurotracker perceptual-cognitive training tool Near and Far Transfer in Cognitive Training: A Second-Order Meta-Analysis Far Transfer: Does it Exist? Do “Brain-Training” Programs Work? Business leaders praised Lumosity's success then just two years later Lumosity settles for millions and admits lack of evidence for their claims

Die Verenigde Nasies sê 'n wapenstilstand in Gaza moes lankal gebeur het, met meer as 45 000 Palestyne wat volgens berigte dood is. Dit kom te midde van Egipte wat gasheer is vir die leiers van agt lande met ʼn Moslem-meerderheid. Die adjunk-sekretaris-generaal van die VN, Mohamed Khaled Khiari, veroordeel die bombardering deur Israelse magte. Hy sê bemiddelingspogings deur Amerika, Katar en Egipte toon belofte maar gevegte duur voort en eis onskuldige lewens:

Moe's Valley access is under threat! Todd Perkins returns to the show to talk about what is happening with Moe's Valley, what actions are being taken to protect it, and what we can do to help. You can sign the petition here!Sign the Petition:Petition to Permanently Protect the Greater Moe's Valley Area(https://docs.google.com/forms/d/e/1FAIpQLSf3winkzQEwb-NI9TPPIW0yaEo1iLcifw43N0sCS5X9sW3nhQ/viewform)More Links:stgeorgeclimberscoalition.orgShow Notes:  thenuggetclimbing.com/episodes/todd-perkins-returnsNuggets:(00:00:00) – A few thoughts about my political episode with Kaizen(00:02:16) – Intro(00:03:34) – A splash of cold water(00:04:33) – What's going on with Moe's Valley(00:10:24) – Todd's early days in Moe's(00:13:30) – Moe's has it's place(00:14:04) – The petition & upcoming hearings(00:17:46) – Fundraising(00:21:18) – Stories from Todd(00:27:56) – Striking a balance(00:30:58) – Todd's health & climbing(00:33:00) – Top secret information(00:34:02) – Wrap up

Apologies for lower audio quality; we lost recordings and had to use backup tracks. Our guests today are Anastasios Angelopoulos and Wei-Lin Chiang, leads of Chatbot Arena, fka LMSYS, the crowdsourced AI evaluation platform developed by the LMSys student club at Berkeley, which became the de facto standard for comparing language models. Arena ELO is often more cited than MMLU scores to many folks, and they have attracted >1,000,000 people to cast votes since its launch, leading top model trainers to cite them over their own formal academic benchmarks:The Limits of Static BenchmarksWe've done two benchmarks episodes: Benchmarks 101 and Benchmarks 201. One issue we've always brought up with static benchmarks is that 1) many are getting saturated, with models scoring almost perfectly on them 2) they often don't reflect production use cases, making it hard for developers and users to use them as guidance. The fundamental challenge in AI evaluation isn't technical - it's philosophical. How do you measure something that increasingly resembles human intelligence? Rather than trying to define intelligence upfront, Arena let users interact naturally with models and collect comparative feedback. It's messy and subjective, but that's precisely the point - it captures the full spectrum of what people actually care about when using AI.The Pareto Frontier of Cost vs IntelligenceBecause the Elo scores are remarkably stable over time, we can put all the chat models on a map against their respective cost to gain a view of at least 3 orders of magnitude of model sizes/costs and observe the remarkable shift in intelligence per dollar over the past year:This frontier stood remarkably firm through the recent releases of o1-preview and price cuts of Gemini 1.5:The Statistics of SubjectivityIn our Benchmarks 201 episode, Clémentine Fourrier from HuggingFace thought this design choice was one of shortcomings of arenas: they aren't reproducible. You don't know who ranked what and what exactly the outcome was at the time of ranking. That same person might rank the same pair of outputs differently on a different day, or might ask harder questions to better models compared to smaller ones, making it imbalanced. Another argument that people have brought up is confirmation bias. We know humans prefer longer responses and are swayed by formatting - Rob Mulla from Dreadnode had found some interesting data on this in May:The approach LMArena is taking is to use logistic regression to decompose human preferences into constituent factors. As Anastasios explains: "We can say what components of style contribute to human preference and how they contribute." By adding these style components as parameters, they can mathematically "suck out" their influence and isolate the core model capabilities.This extends beyond just style - they can control for any measurable factor: "What if I want to look at the cost adjusted performance? Parameter count? We can ex post facto measure that." This is one of the most interesting things about Arena: You have a data generation engine which you can clean and turn into leaderboards later. If you wanted to create a leaderboard for poetry writing, you could get existing data from Arena, normalize it by identifying these style components. Whether or not it's possible to really understand WHAT bias the voters have, that's a different question.Private EvalsOne of the most delicate challenges LMSYS faces is maintaining trust while collaborating with AI labs. The concern is that labs could game the system by testing multiple variants privately and only releasing the best performer. This was brought up when 4o-mini released and it ranked as the second best model on the leaderboard:But this fear misunderstands how Arena works. Unlike static benchmarks where selection bias is a major issue, Arena's live nature means any initial bias gets washed out by ongoing evaluation. As Anastasios explains: "In the long run, there's way more fresh data than there is data that was used to compare these five models." The other big question is WHAT model is actually being tested; as people often talk about on X / Discord, the same endpoint will randomly feel “nerfed” like it happened for “Claude European summer” and corresponding conspiracy theories:It's hard to keep track of these performance changes in Arena as these changes (if real…?) are not observable.The Future of EvaluationThe team's latest work on RouteLLM points to an interesting future where evaluation becomes more granular and task-specific. But they maintain that even simple routing strategies can be powerful - like directing complex queries to larger models while handling simple tasks with smaller ones.Arena is now going to expand beyond text into multimodal evaluation and specialized domains like code execution and red teaming. But their core insight remains: the best way to evaluate intelligence isn't to simplify it into metrics, but to embrace its complexity and find rigorous ways to analyze it. To go after this vision, they are spinning out Arena from LMSys, which will stay as an academia-driven group at Berkeley.Full Video PodcastChapters* 00:00:00 - Introductions* 00:01:16 - Origin and development of Chatbot Arena* 00:05:41 - Static benchmarks vs. Arenas* 00:09:03 - Community building* 00:13:32 - Biases in human preference evaluation* 00:18:27 - Style Control and Model Categories* 00:26:06 - Impact of o1* 00:29:15 - Collaborating with AI labs* 00:34:51 - RouteLLM and router models* 00:38:09 - Future of LMSys / ArenaShow Notes* Anastasios Angelopoulos* Anastasios' NeurIPS Paper Conformal Risk Control* Wei-Lin Chiang* Chatbot Arena* LMSys* MTBench* ShareGPT dataset* Stanford's Alpaca project* LLMRouter* E2B* DreadnodeTranscriptAlessio [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 [00:00:14]: Hey, and today we're very happy and excited to welcome Anastasios and Wei Lin from LMSys. Welcome guys.Wei Lin [00:00:21]: Hey, how's it going? Nice to see you.Anastasios [00:00:23]: Thanks for having us.Swyx [00:00:24]: Anastasios, I actually saw you, I think at last year's NeurIPS. You were presenting a paper, which I don't really super understand, but it was some theory paper about how your method was very dominating over other sort of search methods. I don't remember what it was, but I remember that you were a very confident speaker.Anastasios [00:00:40]: Oh, I totally remember you. Didn't ever connect that, but yes, that's definitely true. Yeah. Nice to see you again.Swyx [00:00:46]: Yeah. I was frantically looking for the name of your paper and I couldn't find it. Basically I had to cut it because I didn't understand it.Anastasios [00:00:51]: Is this conformal PID control or was this the online control?Wei Lin [00:00:55]: Blast from the past, man.Swyx [00:00:57]: Blast from the past. It's always interesting how NeurIPS and all these academic conferences are sort of six months behind what people are actually doing, but conformal risk control, I would recommend people check it out. I have the recording. I just never published it just because I was like, I don't understand this enough to explain it.Anastasios [00:01:14]: People won't be interested.Wei Lin [00:01:15]: It's all good.Swyx [00:01:16]: But ELO scores, ELO scores are very easy to understand. You guys are responsible for the biggest revolution in language model benchmarking in the last few years. Maybe you guys want to introduce yourselves and maybe tell a little bit of the brief history of LMSysWei Lin [00:01:32]: Hey, I'm Wei Lin. I'm a fifth year PhD student at UC Berkeley, working on Chatbot Arena these days, doing crowdsourcing AI benchmarking.Anastasios [00:01:43]: I'm Anastasios. I'm a sixth year PhD student here at Berkeley. I did most of my PhD on like theoretical statistics and sort of foundations of model evaluation and testing. And now I'm working 150% on this Chatbot Arena stuff. It's great.Alessio [00:02:00]: And what was the origin of it? How did you come up with the idea? How did you get people to buy in? And then maybe what were one or two of the pivotal moments early on that kind of made it the standard for these things?Wei Lin [00:02:12]: Yeah, yeah. Chatbot Arena project was started last year in April, May, around that. Before that, we were basically experimenting in a lab how to fine tune a chatbot open source based on the Llama 1 model that I released. At that time, Lama 1 was like a base model and people didn't really know how to fine tune it. So we were doing some explorations. We were inspired by Stanford's Alpaca project. So we basically, yeah, grow a data set from the internet, which is called ShareGPT data set, which is like a dialogue data set between user and chat GPT conversation. It turns out to be like pretty high quality data, dialogue data. So we fine tune on it and then we train it and release the model called V2. And people were very excited about it because it kind of like demonstrate open way model can reach this conversation capability similar to chat GPT. And then we basically release the model with and also build a demo website for the model. People were very excited about it. But during the development, the biggest challenge to us at the time was like, how do we even evaluate it? How do we even argue this model we trained is better than others? And then what's the gap between this open source model that other proprietary offering? At that time, it was like GPT-4 was just announced and it's like Cloud One. What's the difference between them? And then after that, like every week, there's a new model being fine tuned, released. So even until still now, right? And then we have that demo website for V2 now. And then we thought like, okay, maybe we can add a few more of the model as well, like API model as well. And then we quickly realized that people need a tool to compare between different models. So we have like a side by side UI implemented on the website to that people choose, you know, compare. And we quickly realized that maybe we can do something like, like a battle on top of ECLMs, like just anonymize it, anonymize the identity, and that people vote which one is better. So the community decides which one is better, not us, not us arguing, you know, our model is better or what. And that turns out to be like, people are very excited about this idea. And then we tweet, we launch, and that's, yeah, that's April, May. And then it was like first two, three weeks, like just a few hundred thousand views tweet on our launch tweets. And then we have regularly double update weekly, beginning at a time, adding new model GPT-4 as well. So it was like, that was the, you know, the initial.Anastasios [00:04:58]: Another pivotal moment, just to jump in, would be private models, like the GPT, I'm a little,Wei Lin [00:05:04]: I'm a little chatty. That was this year. That was this year.Anastasios [00:05:07]: Huge.Wei Lin [00:05:08]: That was also huge.Alessio [00:05:09]: In the beginning, I saw the initial release was May 3rd of the beta board. On April 6, we did a benchmarks 101 episode for a podcast, just kind of talking about, you know, how so much of the data is like in the pre-training corpus and blah, blah, blah. And like the benchmarks are really not what we need to evaluate whether or not a model is good. Why did you not make a benchmark? Maybe at the time, you know, it was just like, Hey, let's just put together a whole bunch of data again, run a, make a score that seems much easier than coming out with a whole website where like users need to vote. Any thoughts behind that?Wei Lin [00:05:41]: I think it's more like fundamentally, we don't know how to automate this kind of benchmarks when it's more like, you know, conversational, multi-turn, and more open-ended task that may not come with a ground truth. So let's say if you ask a model to help you write an email for you for whatever purpose, there's no ground truth. How do you score them? Or write a story or a creative story or many other things like how we use ChatterBee these days. It's more open-ended. You know, we need human in the loop to give us feedback, which one is better. And I think nuance here is like, sometimes it's also hard for human to give the absolute rating. So that's why we have this kind of pairwise comparison, easier for people to choose which one is better. So from that, we use these pairwise comparison, those to calculate the leaderboard. Yeah. You can add more about this methodology.Anastasios [00:06:40]: Yeah. I think the point is that, and you guys probably also talked about this at some point, but static benchmarks are intrinsically, to some extent, unable to measure generative model performance. And the reason is because you cannot pre-annotate all the outputs of a generative model. You change the model, it's like the distribution of your data is changing. New labels to deal with that. New labels are great automated labeling, right? Which is why people are pursuing both. And yeah, static benchmarks, they allow you to zoom in to particular types of information like factuality, historical facts. We can build the best benchmark of historical facts, and we will then know that the model is great at historical facts. But ultimately, that's not the only axis, right? And we can build 50 of them, and we can evaluate 50 axes. But it's just so, the problem of generative model evaluation is just so expansive, and it's so subjective, that it's just maybe non-intrinsically impossible, but at least we don't see a way. We didn't see a way of encoding that into a fixed benchmark.Wei Lin [00:07:47]: But on the other hand, I think there's a challenge where this kind of online dynamic benchmark is more expensive than static benchmark, offline benchmark, where people still need it. Like when they build models, they need static benchmark to track where they are.Anastasios [00:08:03]: It's not like our benchmark is uniformly better than all other benchmarks, right? It just measures a different kind of performance that has proved to be useful.Swyx [00:08:14]: You guys also published MTBench as well, which is a static version, let's say, of Chatbot Arena, right? That people can actually use in their development of models.Wei Lin [00:08:25]: Right. I think one of the reasons we still do this static benchmark, we still wanted to explore, experiment whether we can automate this, because people, eventually, model developers need it to fast iterate their model. So that's why we explored LM as a judge, and ArenaHard, trying to filter, select high-quality data we collected from Chatbot Arena, the high-quality subset, and use that as a question and then automate the judge pipeline, so that people can quickly get high-quality signal, benchmark signals, using this online benchmark.Swyx [00:09:03]: As a community builder, I'm curious about just the initial early days. Obviously when you offer effectively free A-B testing inference for people, people will come and use your arena. What do you think were the key unlocks for you? Was it funding for this arena? Was it marketing? When people came in, do you see a noticeable skew in the data? Which obviously now you have enough data sets, you can separate things out, like coding and hard prompts, but in the early days, it was just all sorts of things.Anastasios [00:09:31]: Yeah, maybe one thing to establish at first is that our philosophy has always been to maximize organic use. I think that really does speak to your point, which is, yeah, why do people come? They came to use free LLM inference, right? And also, a lot of users just come to the website to use direct chat, because you can chat with the model for free. And then you could think about it like, hey, let's just be kind of like more on the selfish or conservative or protectionist side and say, no, we're only giving credits for people that battle or so on and so forth. Strategy wouldn't work, right? Because what we're trying to build is like a big funnel, a big funnel that can direct people. And some people are passionate and interested and they battle. And yes, the distribution of the people that do that is different. It's like, as you're pointing out, it's like, that's not as they're enthusiastic.Wei Lin [00:10:24]: They're early adopters of this technology.Anastasios [00:10:27]: Or they like games, you know, people like this. And we've run a couple of surveys that indicate this as well, of our user base.Wei Lin [00:10:36]: We do see a lot of developers come to the site asking polling questions, 20-30%. Yeah, 20-30%.Anastasios [00:10:42]: It's obviously not reflective of the general population, but it's reflective of some corner of the world of people that really care. And to some extent, maybe that's all right, because those are like the power users. And you know, we're not trying to claim that we represent the world, right? We represent the people that come and vote.Swyx [00:11:02]: Did you have to do anything marketing-wise? Was anything effective? Did you struggle at all? Was it success from day one?Wei Lin [00:11:09]: At some point, almost done. Okay. Because as you can imagine, this leaderboard depends on community engagement participation. If no one comes to vote tomorrow, then no leaderboard.Anastasios [00:11:23]: So we had some period of time when the number of users was just, after the initial launch, it went lower. Yeah. And, you know, at some point, it did not look promising. Actually, I joined the project a couple months in to do the statistical aspects, right? As you can imagine, that's how it kind of hooked into my previous work. At that time, it wasn't like, you know, it definitely wasn't clear that this was like going to be the eval or something. It was just like, oh, this is a cool project. Like Wayland seems awesome, you know, and that's it.Wei Lin [00:11:56]: Definitely. There's in the beginning, because people don't know us, people don't know what this is for. So we had a hard time. But I think we were lucky enough that we have some initial momentum. And as well as the competition between model providers just becoming, you know, became very intense. Intense. And then that makes the eval onto us, right? Because always number one is number one.Anastasios [00:12:23]: There's also an element of trust. Our main priority in everything we do is trust. We want to make sure we're doing everything like all the I's are dotted and the T's are crossed and nobody gets unfair treatment and people can see from our profiles and from our previous work and from whatever, you know, we're trustworthy people. We're not like trying to make a buck and we're not trying to become famous off of this or that. It's just, we're trying to provide a great public leaderboard community venture project.Wei Lin [00:12:51]: Yeah.Swyx [00:12:52]: Yes. I mean, you are kind of famous now, you know, that's fine. Just to dive in more into biases and, you know, some of this is like statistical control. The classic one for human preference evaluation is humans demonstrably prefer longer contexts or longer outputs, which is actually something that we don't necessarily want. You guys, I think maybe two months ago put out some length control studies. Apart from that, there are just other documented biases. Like, I'd just be interested in your review of what you've learned about biases and maybe a little bit about how you've controlled for them.Anastasios [00:13:32]: At a very high level, yeah. Humans are biased. Totally agree. Like in various ways. It's not clear whether that's good or bad, you know, we try not to make value judgments about these things. We just try to describe them as they are. And our approach is always as follows. We collect organic data and then we take that data and we mine it to get whatever insights we can get. And, you know, we have many millions of data points that we can now use to extract insights from. Now, one of those insights is to ask the question, what is the effect of style, right? You have a bunch of data, you have votes, people are voting either which way. We have all the conversations. We can say what components of style contribute to human preference and how do they contribute? Now, that's an important question. Why is that an important question? It's important because some people want to see which model would be better if the lengths of the responses were the same, were to be the same, right? People want to see the causal effect of the model's identity controlled for length or controlled for markdown, number of headers, bulleted lists, is the text bold? Some people don't, they just don't care about that. The idea is not to impose the judgment that this is not important, but rather to say ex post facto, can we analyze our data in a way that decouples all the different factors that go into human preference? Now, the way we do this is via statistical regression. That is to say the arena score that we show on our leaderboard is a particular type of linear model, right? It's a linear model that takes, it's a logistic regression that takes model identities and fits them against human preference, right? So it regresses human preference against model identity. What you get at the end of that logistic regression is a parameter vector of coefficients. And when the coefficient is large, it tells you that GPT 4.0 or whatever, very large coefficient, that means it's strong. And that's exactly what we report in the table. It's just the predictive effect of the model identity on the vote. The other thing that you can do is you can take that vector, let's say we have M models, that is an M dimensional vector of coefficients. What you can do is you say, hey, I also want to understand what the effect of length is. So I'll add another entry to that vector, which is trying to predict the vote, right? That tells me the difference in length between two model responses. So we have that for all of our data. We can compute it ex post facto. We added it into the regression and we look at that predictive effect. And then the idea, and this is formally true under certain conditions, not always verifiable ones, but the idea is that adding that extra coefficient to this vector will kind of suck out the predictive power of length and put it into that M plus first coefficient and quote, unquote, de-bias the rest so that the effect of length is not included. And that's what we do in style control. Now we don't just do it for M plus one. We have, you know, five, six different style components that have to do with markdown headers and bulleted lists and so on that we add here. Now, where is this going? You guys see the idea. It's a general methodology. If you have something that's sort of like a nuisance parameter, something that exists and provides predictive value, but you really don't want to estimate that. You want to remove its effect. In causal inference, these things are called like confounders often. What you can do is you can model the effect. You can put them into your model and try to adjust for them. So another one of those things might be cost. You know, what if I want to look at the cost adjusted performance of my model, which models are punching above their weight, parameter count, which models are punching above their weight in terms of parameter count, we can ex post facto measure that. We can do it without introducing anything that compromises the organic nature of theWei Lin [00:17:17]: data that we collect.Anastasios [00:17:18]: Hopefully that answers the question.Wei Lin [00:17:20]: It does.Swyx [00:17:21]: So I guess with a background in econometrics, this is super familiar.Anastasios [00:17:25]: You're probably better at this than me for sure.Swyx [00:17:27]: Well, I mean, so I used to be, you know, a quantitative trader and so, you know, controlling for multiple effects on stock price is effectively the job. So it's interesting. Obviously the problem is proving causation, which is hard, but you don't have to do that.Anastasios [00:17:45]: Yes. Yes, that's right. And causal inference is a hard problem and it goes beyond statistics, right? It's like you have to build the right causal model and so on and so forth. But we think that this is a good first step and we're sort of looking forward to learning from more people. You know, there's some good people at Berkeley that work on causal inference for the learning from them on like, what are the really most contemporary techniques that we can use in order to estimate true causal effects if possible.Swyx [00:18:10]: Maybe we could take a step through the other categories. So style control is a category. It is not a default. I have thought that when you wrote that blog post, actually, I thought it would be the new default because it seems like the most obvious thing to control for. But you also have other categories, you have coding, you have hard prompts. We consider that.Anastasios [00:18:27]: We're still actively considering it. It's just, you know, once you make that step, once you take that step, you're introducing your opinion and I'm not, you know, why should our opinion be the one? That's kind of a community choice. We could put it to a vote.Wei Lin [00:18:39]: We could pass.Anastasios [00:18:40]: Yeah, maybe do a poll. Maybe do a poll.Swyx [00:18:42]: I don't know. No opinion is an opinion.Wei Lin [00:18:44]: You know what I mean?Swyx [00:18:45]: Yeah.Wei Lin [00:18:46]: There's no neutral choice here.Swyx [00:18:47]: Yeah. You have all these others. You have instruction following too. What are your favorite categories that you like to talk about? Maybe you tell a little bit of the stories, tell a little bit of like the hard choices that you had to make.Wei Lin [00:18:57]: Yeah. Yeah. Yeah. I think the, uh, initially the reason why we want to add these new categories is essentially to answer some of the questions from our community, which is we won't have a single leaderboard for everything. So these models behave very differently in different domains. Let's say this model is trend for coding, this model trend for more technical questions and so on. On the other hand, to answer people's question about like, okay, what if all these low quality, you know, because we crowdsource data from the internet, there will be noise. So how do we de-noise? How do we filter out these low quality data effectively? So that was like, you know, some questions we want to answer. So basically we spent a few months, like really diving into these questions to understand how do we filter all these data because these are like medias of data points. And then if you want to re-label yourself, it's possible, but we need to kind of like to automate this kind of data classification pipeline for us to effectively categorize them to different categories, say coding, math, structure, and also harder problems. So that was like, the hope is when we slice the data into these meaningful categories to give people more like better signals, more direct signals, and that's also to clarify what we are actually measuring for, because I think that's the core part of the benchmark. That was the initial motivation. Does that make sense?Anastasios [00:20:27]: Yeah. Also, I'll just say, this does like get back to the point that the philosophy is to like mine organic, to take organic data and then mine it x plus factor.Alessio [00:20:35]: Is the data cage-free too, or just organic?Anastasios [00:20:39]: It's cage-free.Wei Lin [00:20:40]: No GMO. Yeah. And all of these efforts are like open source, like we open source all of the data cleaning pipeline, filtering pipeline. Yeah.Swyx [00:20:50]: I love the notebooks you guys publish. Actually really good just for learning statistics.Wei Lin [00:20:54]: Yeah. I'll share this insights with everyone.Alessio [00:20:59]: I agree on the initial premise of, Hey, writing an email, writing a story, there's like no ground truth. But I think as you move into like coding and like red teaming, some of these things, there's like kind of like skill levels. So I'm curious how you think about the distribution of skill of the users. Like maybe the top 1% of red teamers is just not participating in the arena. So how do you guys think about adjusting for it? And like feels like this where there's kind of like big differences between the average and the top. Yeah.Anastasios [00:21:29]: Red teaming, of course, red teaming is quite challenging. So, okay. Moving back. There's definitely like some tasks that are not as subjective that like pairwise human preference feedback is not the only signal that you would want to measure. And to some extent, maybe it's useful, but it may be more useful if you give people better tools. For example, it'd be great if we could execute code with an arena, be fantastic.Wei Lin [00:21:52]: We want to do it.Anastasios [00:21:53]: There's also this idea of constructing a user leaderboard. What does that mean? That means some users are better than others. And how do we measure that? How do we quantify that? Hard in chatbot arena, but where it is easier is in red teaming, because in red teaming, there's an explicit game. You're trying to break the model, you either win or you lose. So what you can do is you can say, Hey, what's really happening here is that the models and humans are playing a game against one another. And then you can use the same sort of Bradley Terry methodology with some, some extensions that we came up with in one of you can read one of our recent blog posts for, for the sort of theoretical extensions. You can attribute like strength back to individual players and jointly attribute strength to like the models that are in this jailbreaking game, along with the target tasks, like what types of jailbreaks you want.Wei Lin [00:22:44]: So yeah.Anastasios [00:22:45]: And I think that this is, this is a hugely important and interesting avenue that we want to continue researching. We have some initial ideas, but you know, all thoughts are welcome.Wei Lin [00:22:54]: Yeah.Alessio [00:22:55]: So first of all, on the code execution, the E2B guys, I'm sure they'll be happy to helpWei Lin [00:22:59]: you.Alessio [00:23:00]: I'll please set that up. They're big fans. We're investors in a company called Dreadnought, which we do a lot in AI red teaming. I think to me, the most interesting thing has been, how do you do sure? Like the model jailbreak is one side. We also had Nicola Scarlini from DeepMind on the podcast, and he was talking about, for example, like, you know, context stealing and like a weight stealing. So there's kind of like a lot more that goes around it. I'm curious just how you think about the model and then maybe like the broader system, even with Red Team Arena, you're just focused on like jailbreaking of the model, right? You're not doing kind of like any testing on the more system level thing of the model where like, maybe you can get the training data back, you're going to exfiltrate some of the layers and the weights and things like that.Wei Lin [00:23:43]: So right now, as you can see, the Red Team Arena is at a very early stage and we are still exploring what could be the potential new games we can introduce to the platform. So the idea is still the same, right? And we build a community driven project platform for people. They can have fun with this website, for sure. That's one thing, and then help everyone to test these models. So one of the aspects you mentioned is stealing secrets, stealing training sets. That could be one, you know, it could be designed as a game. Say, can you still use their credential, you know, we hide, maybe we can hide the credential into system prompts and so on. So there are like a few potential ideas we want to explore for sure. Do you want to add more?Anastasios [00:24:28]: I think that this is great. This idea is a great one. There's a lot of great ideas in the Red Teaming space. You know, I'm not personally like a Red Teamer. I don't like go around and Red Team models, but there are people that do that and they're awesome. They're super skilled. When I think about the Red Team arena, I think those are really the people that we're building it for. Like, we want to make them excited and happy, build tools that they like. And just like chatbot arena, we'll trust that this will end up being useful for the world. And all these people are, you know, I won't say all these people in this community are actually good hearted, right? They're not doing it because they want to like see the world burn. They're doing it because they like, think it's fun and cool. And yeah. Okay. Maybe they want to see, maybe they want a little bit.Wei Lin [00:25:13]: I don't know. Majority.Anastasios [00:25:15]: Yeah.Wei Lin [00:25:16]: You know what I'm saying.Anastasios [00:25:17]: So, you know, trying to figure out how to serve them best, I think, I don't know where that fits. I just, I'm not expressing. And give them credits, right?Wei Lin [00:25:24]: And give them credit.Anastasios [00:25:25]: Yeah. Yeah. So I'm not trying to express any particular value judgment here as to whether that's the right next step. It's just, that's sort of the way that I think we would think about it.Swyx [00:25:35]: Yeah. We also talked to Sander Schulhoff of the HackerPrompt competition, and he's pretty interested in Red Teaming at scale. Let's just call it that. You guys maybe want to talk with him.Wei Lin [00:25:45]: Oh, nice.Swyx [00:25:46]: We wanted to cover a little, a few topical things and then go into the other stuff that your group is doing. You know, you're not just running Chatbot Arena. We can also talk about the new website and your future plans, but I just wanted to briefly focus on O1. It is the hottest, latest model. Obviously, you guys already have it on the leaderboard. What is the impact of O1 on your evals?Wei Lin [00:26:06]: Made our interface slower.Anastasios [00:26:07]: It made it slower.Swyx [00:26:08]: Yeah.Wei Lin [00:26:10]: Because it needs like 30, 60 seconds, sometimes even more to, the latency is like higher. So that's one. Sure. But I think we observe very interesting things from this model as well. Like we observe like significant improvement in certain categories, like more technical or math. Yeah.Anastasios [00:26:32]: I think actually like one takeaway that was encouraging is that I think a lot of people before the O1 release were thinking, oh, like this benchmark is saturated. And why were they thinking that? They were thinking that because there was a bunch of models that were kind of at the same level. They were just kind of like incrementally competing and it sort of wasn't immediately obvious that any of them were any better. Nobody, including any individual person, it's hard to tell. But what O1 did is it was, it's clearly a better model for certain tasks. I mean, I used it for like proving some theorems and you know, there's some theorems that like only I know because I still do a little bit of theory. Right. So it's like, I can go in there and ask like, oh, how would you prove this exact thing? Which I can tell you has never been in the public domain. It'll do it. It's like, what?Wei Lin [00:27:19]: Okay.Anastasios [00:27:20]: So there's this model and it crushed the benchmark. You know, it's just like really like a big gap. And what that's telling us is that it's not saturated yet. It's still measuring some signal. That was encouraging. The point, the takeaway is that the benchmark is comparative. There's no absolute number. There's no maximum ELO. It's just like, if you're better than the rest, then you win. I think that was actually quite helpful to us.Swyx [00:27:46]: I think people were criticizing, I saw some of the academics criticizing it as not apples to apples. Right. Like, because it can take more time to reason, it's basically doing some search, doing some chain of thought that if you actually let the other models do that same thing, they might do better.Wei Lin [00:28:03]: Absolutely.Anastasios [00:28:04]: To be clear, none of the leaderboard currently is apples to apples because you have like Gemini Flash, you have, you know, all sorts of tiny models like Lama 8B, like 8B and 405B are not apples to apples.Wei Lin [00:28:19]: Totally agree. They have different latencies.Anastasios [00:28:21]: Different latencies.Wei Lin [00:28:22]: Control for latency. Yeah.Anastasios [00:28:24]: Latency control. That's another thing. We can do style control, but latency control. You know, things like this are important if you want to understand the trade-offs involved in using AI.Swyx [00:28:34]: O1 is a developing story. We still haven't seen the full model yet, but it's definitely a very exciting new paradigm. I think one community controversy I just wanted to give you guys space to address is the collaboration between you and the large model labs. People have been suspicious, let's just say, about how they choose to A-B test on you. I'll state the argument and let you respond, which is basically they run like five anonymous models and basically argmax their Elo on LMSYS or chatbot arena, and they release the best one. Right? What has been your end of the controversy? How have you decided to clarify your policy going forward?Wei Lin [00:29:15]: On a high level, I think our goal here is to build a fast eval for everyone, and including everyone in the community can see the data board and understand, compare the models. More importantly, I think we want to build the best eval also for model builders, like all these frontier labs building models. They're also internally facing a challenge, which is how do they eval the model? That's the reason why we want to partner with all the frontier lab people, and then to help them testing. That's one of the... We want to solve this technical challenge, which is eval. Yeah.Anastasios [00:29:54]: I mean, ideally, it benefits everyone, right?Wei Lin [00:29:56]: Yeah.Anastasios [00:29:57]: And people also are interested in seeing the leading edge of the models. People in the community seem to like that. Oh, there's a new model up. Is this strawberry? People are excited. People are interested. Yeah. And then there's this question that you bring up of, is it actually causing harm?Wei Lin [00:30:15]: Right?Anastasios [00:30:16]: Is it causing harm to the benchmark that we are allowing this private testing to happen? Maybe stepping back, why do you have that instinct? The reason why you and others in the community have that instinct is because when you look at something like a benchmark, like an image net, a static benchmark, what happens is that if I give you a million different models that are all slightly different, and I pick the best one, there's something called selection bias that plays in, which is that the performance of the winning model is overstated. This is also sometimes called the winner's curse. And that's because statistical fluctuations in the evaluation, they're driving which model gets selected as the top. So this selection bias can be a problem. Now there's a couple of things that make this benchmark slightly different. So first of all, the selection bias that you include when you're only testing five models is normally empirically small.Wei Lin [00:31:12]: And that's why we have these confidence intervals constructed.Anastasios [00:31:16]: That's right. Yeah. Our confidence intervals are actually not multiplicity adjusted. One thing that we could do immediately tomorrow in order to address this concern is if a model provider is testing five models and they want to release one, and we're constructing the models at level one minus alpha, we can just construct the intervals instead at level one minus alpha divided by five. That's called Bonferroni correction. What that'll tell you is that the final performance of the model, the interval that gets constructed, is actually formally correct. We don't do that right now, partially because we know from simulations that the amount of selection bias you incur with these five things is just not huge. It's not huge in comparison to the variability that you get from just regular human voters. So that's one thing. But then the second thing is the benchmark is live, right? So what ends up happening is it'll be a small magnitude, but even if you suffer from the winner's curse after testing these five models, what'll happen is that over time, because we're getting new data, it'll get adjusted down. So if there's any bias that gets introduced at that stage, in the long run, it actually doesn't matter. Because asymptotically, basically in the long run, there's way more fresh data than there is data that was used to compare these five models against these private models.Swyx [00:32:35]: The announcement effect is only just the first phase and it has a long tail.Anastasios [00:32:39]: Yeah, that's right. And it sort of like automatically corrects itself for this selection adjustment.Swyx [00:32:45]: Every month, I do a little chart of Ellim's ELO versus cost, just to track the price per dollar, the amount of like, how much money do I have to pay for one incremental point in ELO? And so I actually observe an interesting stability in most of the ELO numbers, except for some of them. For example, GPT-4-O August has fallen from 12.90

Cosatu het die skuldigbevinding en vonnisoplegging van die voormalige finansiële hoof van Steinhoff, Ben la Grange, verwelkom. Hy is tot tien jaar tronkstraf gevonnis waarvan vyf jaar opgeskort is na hy skuldig gepleit het op 'n aanklag van bedrog van meer as 300 miljoen Suid-Afrikaanse rand. Hy is die tweede persoon wat in die Steinhoff-skandaal vervolg is, na die arrestasie van die Gerhardus Burger. Matthew Parks van Cosatu sê La Grange se vonnis moes swaarder gewees het vir een van die grootste finansiële misdade in die Suid-Afrikaanse geskiedenis.

Prof. Jannie Rossouw, professor by die Wits-sakeskool sê hy glo dat die regering van nasionale eenheid ekonomiese groei sal herstel. Volg RSG Geldsake op Twitter

Reaksie op Swapo se pot-uitslae word steeds ontvang. Die lys verras met talle jeugdiges wat ingesluit is en verskeie strydrosse wat op uitgesluit is of, so laag op die lys is dat hulle moontlik nie die parlement sal haal nie. Die regeringskenner en ontleder dr. Marius Kudumo sê balans is nodig, veral in verband met vaardighede.

On this episode we discuss

On this episode we discuss

If you see this in time, join our emergency LLM paper club on the Llama 3 paper!For everyone else, join our special AI in Action club on the Latent Space Discord for a special feature with the Cursor cofounders on Composer, their newest coding agent!Today, Meta is officially releasing the largest and most capable open model to date, Llama3-405B, a dense transformer trained on 15T tokens that beats GPT-4 on all major benchmarks:The 8B and 70B models from the April Llama 3 release have also received serious spec bumps, warranting the new label of Llama 3.1.If you are curious about the infra / hardware side, go check out our episode with Soumith Chintala, one of the AI infra leads at Meta. Today we have Thomas Scialom, who led Llama2 and now Llama3 post-training, so we spent most of our time on pre-training (synthetic data, data pipelines, scaling laws, etc) and post-training (RLHF vs instruction tuning, evals, tool calling).Synthetic data is all you needLlama3 was trained on 15T tokens, 7x more than Llama2 and with 4 times as much code and 30 different languages represented. But as Thomas beautifully put it:“My intuition is that the web is full of s**t in terms of text, and training on those tokens is a waste of compute.” “Llama 3 post-training doesn't have any human written answers there basically… It's just leveraging pure synthetic data from Llama 2.”While it is well speculated that the 8B and 70B were "offline distillations" of the 405B, there are a good deal more synthetic data elements to Llama 3.1 than the expected. The paper explicitly calls out:* SFT for Code: 3 approaches for synthetic data for the 405B bootstrapping itself with code execution feedback, programming language translation, and docs backtranslation.* SFT for Math: The Llama 3 paper credits the Let's Verify Step By Step authors, who we interviewed at ICLR:* SFT for Multilinguality: "To collect higher quality human annotations in non-English languages, we train a multilingual expert by branching off the pre-training run and continuing to pre-train on a data mix that consists of 90% multilingualtokens."* SFT for Long Context: "It is largely impractical to get humans to annotate such examples due to the tedious and time-consuming nature of reading lengthy contexts, so we predominantly rely on synthetic data to fill this gap. We use earlier versions of Llama 3 to generate synthetic data based on the key long-context use-cases: (possibly multi-turn) question-answering, summarization for long documents, and reasoning over code repositories, and describe them in greater detail below"* SFT for Tool Use: trained for Brave Search, Wolfram Alpha, and a Python Interpreter (a special new ipython role) for single, nested, parallel, and multiturn function calling.* RLHF: DPO preference data was used extensively on Llama 2 generations. This is something we partially covered in RLHF 201: humans are often better at judging between two options (i.e. which of two poems they prefer) than creating one (writing one from scratch). Similarly, models might not be great at creating text but they can be good at classifying their quality.Last but not least, Llama 3.1 received a license update explicitly allowing its use for synthetic data generation.Llama2 was also used as a classifier for all pre-training data that went into the model. It both labelled it by quality so that bad tokens were removed, but also used type (i.e. science, law, politics) to achieve a balanced data mix. Tokenizer size mattersThe tokens vocab of a model is the collection of all tokens that the model uses. Llama2 had a 34,000 tokens vocab, GPT-4 has 100,000, and 4o went up to 200,000. Llama3 went up 4x to 128,000 tokens. You can find the GPT-4 vocab list on Github.This is something that people gloss over, but there are many reason why a large vocab matters:* More tokens allow it to represent more concepts, and then be better at understanding the nuances.* The larger the tokenizer, the less tokens you need for the same amount of text, extending the perceived context size. In Llama3's case, that's ~30% more text due to the tokenizer upgrade. * With the same amount of compute you can train more knowledge into the model as you need fewer steps.The smaller the model, the larger the impact that the tokenizer size will have on it. You can listen at 55:24 for a deeper explanation.Dense models = 1 Expert MoEsMany people on X asked “why not MoE?”, and Thomas' answer was pretty clever: dense models are just MoEs with 1 expert :)[00:28:06]: I heard that question a lot, different aspects there. Why not MoE in the future? The other thing is, I think a dense model is just one specific variation of the model for an hyperparameter for an MOE with basically one expert. So it's just an hyperparameter we haven't optimized a lot yet, but we have some stuff ongoing and that's an hyperparameter we'll explore in the future.Basically… wait and see!Llama4Meta already started training Llama4 in June, and it sounds like one of the big focuses will be around agents. Thomas was one of the authors behind GAIA (listen to our interview with Thomas in our ICLR recap) and has been working on agent tooling for a while with things like Toolformer. Current models have “a gap of intelligence” when it comes to agentic workflows, as they are unable to plan without the user relying on prompting techniques and loops like ReAct, Chain of Thought, or frameworks like Autogen and Crew. That may be fixed soon?

On this week's episode we're handing over the reins to our guest Brendon Huttmann. Brendon was our guest on episode 3 almost two years ago, where we learned about his transition from Major League Baseball to the Army's H2F program. This time, though, the tables are turned and Brendon is asking the questions. He came much more prepared for hosting duties than we normally do, and he asked some really insightful questions about our origin story, what we're trying to accomplish with this platform, and what we've learned in the process. Whether you're a new listener who isn't sure what we're all about or a long time fan who wants the full history of where MOPs & MOEs came from, this one should answer a few of your questions.

On this episode we each brought five books that have shaped the way we think about human performance and discussed why they had such an impact. And in classic form, we each also brought a few honorable mentions as well. Drew's books are a little more focused on strength and conditioning, while Alex's books (somewhat unexpectedly) are largely focused on mental health and how exercise affects our brains. If you're looking for reading suggestions in the human performance space, you have come to the right place. This list spans a lot of different topics, so there's something for everyone. If you want to get any of them, here is the full list: Drew's Top 5 Practical Programming for Strength Training (Andy Baker/Mark Rippetoe) The Science of Running (Steve Magness) The Structure of Scientific Revolutions (Thomas Kuhn) Training Talk (Martin Bingisser) Endure (Alex Hutchinson) Alex's Top 5 Spark: The Revolutionary New Science of Exercise and the Brain (John Ratey) Man's Search for Meaning (Viktor Frankl)   How Minds Change (David Mcraney) Saving Normal (Allen Frances) Tribe (Sebastian Junger) Honorable Mentions Training for the New Alpinism (Scott Johnston/Steve House) Starting Strength (Mark Rippetoe) Strongest Shall Survive (Bill Starr) 80/20 Running (Matt Fitzgerald) Winning (Clive Woodward) Reactive Training Systems Manual (Mike Tuscherer) John Kiely Papers: A New Understanding of Stress, Periodization Paradigms in the 21st Century, and Periodization Theory: Confronting an Inconvenient Truth Michael Pollan books: This is Your Mind on Plants, In Defense of Food, and Omnivore's Dilemma Go Wild (John Ratey) The Nature Fix (Florence Williams) Why Zebra's Don't Get Ulcers (Robert Sapolsky)

We're so back. This week, Jimmy and Larry are reeling from vacation and James' birthday to talk Oakley mules chicken or the egg flow, a new perfect pair of trouser emerges, Italian pervert pleats, a brief history of slouch socks, Amadeus (1984) as a master class in hating and Mozart the cooze hound, beefing with your dry cleaner, Spanto forever, the launch of Threads and what it could all mean for Twitter's future and Zuck vs. Elon, shining a spotlight on social media hang ups, media brands as forever losers, vacation moments of euphoria, including but not limited to: getting the urchin twerkin', horny wives watching Bridgerton, Italian style driving and turbo Scottish beer hitting back, Peroni hitting diffy in the motherland for a change, vertical pours, top 3 meals, nonna whipping work in pajamas, Antonio Ciongoli's cousin's best in class pizza, horse meat, dining in a damn cave, who knew rabbit slaps and much more before finally doing a dramatic reading of the 15 best worst entries from Drake's new poetry book Titles Ruin Everything. For more Throwing Fits, check us out on Patreon: www.patreon.com/throwingfits.