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David Ross is in town for the Apple TV+ broadcast of the Braves & Red Sox making it a great time to catch up with our old friend. See omnystudio.com/listener for privacy information.
David Ross is in town for the Apple TV+ broadcast of the Braves & Red Sox making it a great time to catch up with our old friend. See omnystudio.com/listener for privacy information.
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 10a-2p! The 12 o'clock hour is brought to you by SCANA Energy, the Official Natural Gas Partner of Georgia Tech. Braves begin 3 game set in DC BTG Trae Young trolls his favorite peeps - Knicks fans See omnystudio.com/listener for privacy information.
GDP Script/ Top Stories for May 17th Publish Date: May 17th PRE-ROLL: From the BG AD Group Studio Welcome to the Gwinnett Daily Post Podcast. Today is Saturday, May 17th and Happy Birthday to Sugar Ray Leonard I’m Peyton Spurlock and here are your top stories presented by Gwinnett KIA Mall of Georgia. Collins Hill High School senior earns thousands for college Dino Safari Experience coming to Mall of Georgia this weekend Four Gwinnett seniors selected for National Merit Corporate Scholarships Plus, the Stripers report with Hurston Waldrep All of this and more is coming up on the Gwinnett Daily Post podcast, and if you are looking for community news, we encourage you to listen daily and subscribe! Break 1: 07.14.22 KIA MOG STORY 1: Collins Hill High School senior earns thousands for college Collins Hill High School senior Jacari Thomas has been awarded the 2025 Belli Scholarship, worth up to $20,000. This renewable scholarship supports Gwinnett County graduates with financial need who show potential for college success, even if they aren't top academic or athletic performers. Recipients must meet criteria like a minimum 2.5 GPA and submit teacher recommendations and an essay. Founded in memory of the Belli family, the scholarship provides $5,000 annually for up to four years, covering tuition and other college expenses, as long as recipients maintain a 2.0 GPA. STORY 2: Dino Safari Experience coming to Mall of Georgia this weekend LuminoCity returns to the Atlanta area with its Dino Safari Experience at the Mall of Georgia in Buford, running from May 15 to June 15. The event features over 70 life-sized animatronic dinosaurs, hundreds of light sculptures, and interactive attractions like the Dino Bounce House and Safari Train Ride. Weekly themed events, including a “Dino-Bash” and axe-throwing competition, blend fantasy, STEM storytelling, and Instagram-worthy visuals. Open daily from 5 p.m., tickets range from $17.99 to $19.99 depending on the session and day. STORY 3: Four Gwinnett seniors selected for National Merit Corporate Scholarships Four Gwinnett County Public Schools seniors have been awarded 2025 Corporate-Sponsored National Merit Scholarships. Winners include Hannah L. DeLong (Government Service), Logan G. Borneman (Undecided), Logan B. Whitehead (Electrical Engineering), and Damon E. Daley, Jr. (Writing). These scholarships, funded by corporate sponsors, support students pursuing specific careers or meeting sponsor criteria. Part of the 70th annual National Merit program, the awards are renewable for up to four years or offered as single payments between $1,000 and $10,000. The competition began with the 2023 PSAT, narrowing over 16,000 high-achieving students to finalists. We have opportunities for sponsors to get great engagement on these shows. Call 770.874.3200 for more info. We’ll be right back Break 2: STRIPERS INTERVIEW Break 3: STORY 6: Music @ the Mall, Movies Under the Stars highlight weekend at Mall of Georgia Music @ the Mall is coming to Gwinnett County's Mall of Georgia on Saturday, May 17, from noon to 7 p.m. This immersive event offers emerging Gen Z musicians a chance to be discovered by industry icons Randy Jackson and Paula Moore. Performances, meet-and-greets, and autograph sessions will feature rising stars like Jules Walcott and Kevin Woo. Later, the mall’s outdoor movie series, Movies Under the Stars, kicks off with a free screening of Disney’s *Encanto*. Pre-movie activities, including face painting and games, start at 5 p.m., with the film at dusk. STORY 7: Gwinnett Sports Hall of Famer Buck Lanford Enjoys Long Career in Hometown Broadcast journalist Buck Lanford, now a morning anchor on Fox 5’s *Good Day Atlanta*, took a career-defining leap in the late 1990s, leaving a full-time job in Savannah for a three-month contract at Fox 5. That stint turned into a 27-year career, including covering the Falcons’ Super Bowl run and other major sports events. A South Gwinnett High alum, Lanford cherishes covering local athletes who became stars, like Jeff Francoeur and Brian McCann. Recently surprised live on air, Lanford learned he’ll be inducted into the Gwinnett County Sports Hall of Fame on May 19, calling it the honor of a lifetime. We’ll have closing comments after this Break 4: Ingles Markets 6 Signoff – Thanks again for hanging out with us on today’s Gwinnett Daily Post Podcast. If you enjoy these shows, we encourage you to check out our other offerings, like the Cherokee Tribune Ledger podcast, the Marietta Daily Journal, or the Community Podcast for Rockdale Newton and Morgan Counties. Read more about all our stories and get other great content at www.gwinnettdailypost.com Did you know over 50% of Americans listen to podcasts weekly? Giving you important news about our community and telling great stories are what we do. Make sure you join us for our next episode and be sure to share this podcast on social media with your friends and family. Add us to your Alexa Flash Briefing or your Google Home Briefing and be sure to like, follow, and subscribe wherever you get your podcasts. Produced by the BG Podcast Network Show Sponsors: www.ingles-markets.com www.kiamallofga.com #NewsPodcast #CurrentEvents #TopHeadlines #BreakingNews #PodcastDiscussion #PodcastNews #InDepthAnalysis #NewsAnalysis #PodcastTrending #WorldNews #LocalNews #GlobalNews #PodcastInsights #NewsBrief #PodcastUpdate #NewsRoundup #WeeklyNews #DailyNews #PodcastInterviews #HotTopics #PodcastOpinions #InvestigativeJournalism #BehindTheHeadlines #PodcastMedia #NewsStories #PodcastReports #JournalismMatters #PodcastPerspectives #NewsCommentary #PodcastListeners #NewsPodcastCommunity #NewsSource #PodcastCuration #WorldAffairs #PodcastUpdates #AudioNews #PodcastJournalism #EmergingStories #NewsFlash #PodcastConversations See omnystudio.com/listener for privacy information.
Aaron Lupuloff & Buck Lanford/Gwinnett County Sports Hall of Fame The 14th Annual Gwinnett County Sports Hall of Fame Induction Ceremony will take place May 19, 2025 at the Gas South Convention Center. This years inductees are Jennifer Gardner (Basketball – Norcross), Will Hinton (Shooting – Dacula), Brian McCann (Baseball – Duluth), Dexter Wood (Athletic […]
Aaron Lupuloff & Buck Lanford/Gwinnett County Sports Hall of Fame The 14th Annual Gwinnett County Sports Hall of Fame Induction Ceremony will take place May 19, 2025 at the Gas South Convention Center. This years inductees are Jennifer Gardner (Basketball – Norcross), Will Hinton (Shooting – Dacula), Brian McCann (Baseball – Duluth), Dexter Wood (Athletic […] The post Aaron Lupuloff and Buck Lanford with the Gwinnett County Sports Hall of Fame appeared first on Business RadioX ®.
HR1 - Atlanta Drive win inaugural TGL Championship & take home Sofi Cup In hour one Tiffany, Mike, and Beau react to the the Atlanta Drive winning the inaugural TGL title thanks to Billy Horschel's clutch putt, the Atlanta Hawks losing on the road last night to the Houston Rockets 121-114 and having their three game winning streak snapped, let you hear Falcons' edge rusher Leonard Floyd talk wanting to get a career high in sacks this year, being able to do multiple things within a scheme, and having experience playing in multiple schemes, and react to what Floyd had to say as well. Finally, The Morning Shift crew closes out hour one by letting you hear some clips of Brian Snitker's interview with Steakhouse, and react to Snitker talking about Jurickson Profar fitting in from day one, keeping the pitching staff healthy, there not being a timeline or timetable on Ronald Acuna Jr.'s return, and Drake Baldwin being very impressive and Snitker comparing him to Brian McCann.
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 11a-2p! The 11am hour is presented by Findlay Roofing. Atlanta's most trusted and recommended roofer with 4 decades experience, and a lifetime warranty on their work...FindlayRoofing.com X Question of the Day on the Harrah's Cherokee X Feed Andruw Jones fell short again On Campus TGL was at it again See omnystudio.com/listener for privacy information.
Andruw Jones fell short of making the Hall of Fame yet again and Chipper Jones is not happy about it one bit! As long as the Braves are healthy, all will be fine. Trust AA See omnystudio.com/listener for privacy information.
-Andruw Jones, Billy Wagner & Brian McCann....what does Snit think about their Hall of Fame fates that were decided last night. -Offseason moves -Spring Training around the corner See omnystudio.com/listener for privacy information.
2025 BBWAA HOF Ballot Happy New Year! It's once again time to talk about the Hall of Fame, this time the writers' ballot. There are plenty of worthy candidates returning to the ballot, and two standout first year candidates to watch. Japanese icon Ichiro Suzuki is a lock for induction this year. Although he didn't debut until he was 27, the spry savant amassed over 3,000 hits, 1,400 runs, 10 All-Stars and Gold Gloves, not to mention two batting titles, a Rookie of the Year, and a MVP. He also holds the single season hit record, with 262 in the 2004 season. Although not the first Japanese player in MLB, his smashing success paved the way for other Japanese stars such as Hideki Matsui, Yu Darvish, and Shohei Ohtani. The only question about Ichiro is whether or not he'll be unanimous. Don't sleep on ace lefty C.C. Sabathia's chances on his debut ballot. As one of just three left handed pitchers with 3,000 strikeouts (Steve Carlton and Randy Johnson the other two), Sabathia was the definition of a workhorse. Add in 250 wins, a Cy Young, a World Series, and an ALCS MVP, and you've got a Hall of Famer, especially in this era. Among the other first-year candidates, there are a few who could survive the 5% threshold to remain on ballot for another year. Félix Hernández was one of the most dominant starters in baseball from 2007 - 2015, but that might not be enough for the writers. Dustin Pedroia and Ian Kinsler were both excellent second basemen that frankly lack the longevity needed for enshrinement, but are certainly worthy cases to discuss, particularly on the same ballot as sabermetric darling Chase Utley. Russell Martin and Brian McCann come up short in the traditional counting stats that writers demand from catchers, but their advanced framing metrics put them in similar ranks as Joe Mauer, Buster Posey, and Yadier Molina. Of the returning candidates, Billy Wagner came up just short last year (73.8%), and he will need a push to get over in his final year on the ballot. In his second to last year, Andruw Jones appears to be stalling around 60% of the vote, not far off from induction, but not close enough to be confident he'll make it this year or the next. Carlos Beltrán and Chase Utley appear to be on track for induction in a few years time, though they'll need to continue their growth. AROD, ManRam, and Andy Pettitte would be in the Hall if not for PEDs, and they're not getting in anytime soon. How many will join Dick Allen and Dave Parker for a July celebration? Find out January 21st when the results are announced. Audio clips from Youtube #3,000 for Ichiro - Fox Sports Florida Sun - Rich Waltz Laser Throw from Ichiro - Fox Sports Northwest - Rick Rizzs Single season hit record for Ichiro - Root Sports Northwest - Dave Niehaus CC gets 3K K's - YES Network King Felix Perfecto - RSNW - Dave Sims Dustin Pedroia Leadoff WS HR - FOX - Joe Buck and Tim McCarver As always, shout out to Baseball Reference and SABR for statistics, boxscores, background information, and much, much more. Catch you next time, P.C..O.
In episode 177 of RizzoCast, we are joined by Caught Looking Podcast host Max Greenfield! We discuss the 2025 Hall of Fame ballot, every candidate in detail from Bobby Abreu to Billy Wagner, arguments surrounding Brian McCann and Russell Martin, CC Sabathia vs Andy Pettitte, Dustin Pedroia's debut, Manny and A-Rod's chances and more! Follow Max on X: https://x.com/greenfieldmax18?s=21&t=WlnLpfzmWn0ol34Bn7EELw Watch and listen to RizzoCast's full episodes: https://linktr.ee/RizzoCast Follow RizzoCast on Twitter: https://twitter.com/RizzoCast Follow RizzoCast on Instagram: https://www.instagram.com/rizzocast/ Follow Steven Rissotto on Twitter: https://twitter.com/StevenRissotto
Welcome to "The Locker Room" with "Hometeam" Brandon Leak, John Michaels and former Atlanta Falcon Brian Finneran. The guys talk all the top stories from the Braves, Falcons, Hawks, Bulldogs, Tech as well as across the nation. The 8am hour of The Locker Room is presented by Central Heating & Air, ,one of the few second generation locally owned HVAC companies in Atlanta. With Central, you're not just a number, you're a member of the family. SEC Championship Game not a burden? Championship Games hurtful or helpful? 2025 MLB Hall of Fame Ballot is out See omnystudio.com/listener for privacy information.
Welcome to "The Locker Room" with "Hometeam" Brandon Leak, John Michaels and former Atlanta Falcon Brian Finneran. The guys talk all the top stories from the Braves, Falcons, Hawks, Bulldogs, Tech as well as across the nation. The 6am hour of The Locker Room is brought to you by "Right From The Heart". Sign up and receive your daily devotionals by email at rightfromtheheart.org Starting 5 presented by Piedmont Bank Hometeam went to Denver but did the team? University of Georgia J Reid Parker Director of Athletics Josh Brooks OTP presented by Artisan Custom Closets 2 Braves on the 2025 Hall of Fame Ballot See omnystudio.com/listener for privacy information.
Jim is joined by Atlanta Braves reporter for 92.9 The Game and host of From the Diamond, Grant McAuley, to break down the hall of fame candidacy of former MLB catcher Brian McCann. First, Grant and Jim cover why McCann is not brought up in the same conversations with catching peers Buster Posey and Joe Mauer despite winning more silver sluggers and making the same/more number of all-star game appearances than both (8:40). Next, they discuss McCann's big postseason home runs during his rookie season, his all-star game MVP that did not result in a new car, and how he compares to Hall of Fame catcher Gary Cater (13:55). Finally, they debate the importance of framing and what to make of framing advanced metrics that highly favor McCann (28:15), before making a final call on whether or not McCann deserves a plaque in Cooperstown (37:10).
Join us today as we welcome former 6x Silver Slugger and 7x MLB All-Star catcher Brian McCann! Brian shares his experiences growing up as the son of a college baseball coach and how his dad played a key role in helping shape his sports journey, which ultimately led to a very successful career in the MLB. Brian gives his thoughts on the current state of youth baseball today and sheds some light on some of the struggles that he has with the evaluation process of youth baseball players.Brian also dives into his thoughts on the 2024 World Series as well as shares some entertaining stories during his own MLB career, including some of his favorite moments playing alongside Pure Athlete's own Jeff Francoeur. You don't want to miss this one!
"Focus on learning and being ultra curious, not on the status or the money when starting your sales career." - Brian McCann in today's Tip 1813 Learn more about Brian at DailySales.Tips/1813 Have feedback? Want to share a sales tip? Email: scott@top1.fm
Join us as Brian shares his journey from a fitness trainer to a leading recruiter, discussing the pivotal role of AI in sales and the importance of reaching the right person with authenticity and trust. He brings a unique perspective on leveraging small wins, learning from failures, and balancing competitiveness with personal growth.
9/11 anniversary, Braves Lopez injury, the letter 'Y' is indeed a vowel, Clemson O-lineman delusional about UGA, Auburn QB takes on gamblers, mocking the Falcons, my rant on the Falcons, deep dive on Kirk Cousins stats, ManningCast flopping and why, Dolphins liar Tyreek Hill just can't shut up, Ravens mad at Chiefs doc, Reggie Bush screams at burglar, Shannon Sharpe sex tape leaked, crazy deadly snake owner gets bit, how to injure yourself taking off a shirt, Florence Chadwick swims the Channel, Pistorius murders his gf, Bear Bryant bday, Tom Landry, Franz Beckenbauer, Don Slaught, Ed Reed, Johnny Unitas, Chris Schenkel, Mick Tingelhoff, Braves no-hitter, Ty Cobb vs Babe Ruth, Pete Rose vs Eric Show, a bunch of Malarkey, Eddie Collins, Eddie Plank, Denny Lemaster and son, Nolan Ryan, Pat "Bulldog" Jarvis, Tom Seaver, Brian McCann gets his hackles up, Ed Kranepool obit, Hugh Freeze joke, plus Pete's Tweets and baseball quotes from Ralph Kiner and Jay Leno!
My guest this week is B.B. Abbott. Over the past 25 years, BB has represented some of the generation's biggest baseball stars, including Chipper Jones, Brian McCann, and past guest Corey Kluber. This was a short but action-packed episode, where we talked about the increasing intensity of childhood athletics, the economics of the player-agent model, and the impact of Name, Image, and Likeness, or NIL on College Baseball. We end up with a fascinating riff on how analytics has enriched scouting. Please enjoy this episode with B.B. Abbott. For the full show notes, transcript, and links to the best content to learn more, check out the episode page HERE. ----- Making Markets is a property of Colossus, LLC. For more episodes of Making Markets, visit joincolossus.com/episodes. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @makingmkts | @ericgoldenx Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes (00:00:00) Welcome to Making Markets (00:01:04) Meet BB Abbott: Baseball's Top Agent (00:01:48) The Evolution of Youth Sports (00:02:24) Scouting and Specialization in Baseball (00:06:14) The Role of Advisors and Agents (00:08:29) Understanding NIL: Name, Image, and Likeness (00:11:31) The Legal Landscape of NIL (00:17:58) The Transfer Portal and Its Implications (00:20:34) NIL's Impact on Professional Draft Decisions (00:22:03) Evaluating and Valuing Players (00:26:39) The Economics of Being a Sports Agent (00:31:46) The Role of Data and Analytics in Scouting (00:35:44) Competing in the Sports Agency Industry Learn more about your ad choices. Visit megaphone.fm/adchoices
A veteran of 15 years in the business of minor league baseball, Tennessee native Chris Harris is experiencing perhaps the most unusual year of his career. We talk Mississippi Braves baseball, Atlanta Braves baseball and the College World Series.
The month of May has once again been a bit unkind to the Atlanta Braves, who seem like they are winning games in spite of their offense at the moment. Of course, the pitching staff has been pitching in more than its fair share to make it happen. Grant McAuley discussed the latest Braves news from the week that was, including some encouraging signs for Matt Olson at the plate, but a discouraging injury update for Austin Riley that seems to have the third baseman in limbo as far as a return. You'll hear Riley's comments from Saturday as he described the ailment that has him stuck on the bench for the time being. Meanwhile, Travis d'Arnaud also had a brush with injury but appears to have emerged unscathed while Sean Murphy's return to could be getting closer with an upcoming rehab assignment. Ronald Acuña Jr. is among those still searching for consistency and results, so did a day off help speed that process up? You'll also hear from Atlanta reliever Jesse Chavez, who shares some insight on the inner workings of the bullpen. And we'll discuss the latest Players Only telecast on Bally Sports South with Brian McCann and Jeff Francoeur, who reflect on their friendship and the chance to reunite on the 20th anniversary of their rookie season. Special guest Russell Dorsey of Yahoo Sports joins the show to discuss the state of the Toronto Blue Jays, a club that once seemed destined to become a powerhouse, but is now staring at the possibility that their homegrown stars are not only under-performing but could also bolt in free agency. David Adler of MLB.com checks in to share the inner workings of Statcast's new bat-tracking metrics, from concept to the first iteration which debuted this past week. This latest evolution allows us to take a deeper look at how the swing works and who has the most explosive swing in MLB. From The Diamond airs live on 92-9 The Game in Atlanta on Sundays from 5-7 pm ET. You can also subscribe wherever you get your podcasts. Every episode of the show and more great Braves coverage is available at FromTheDiamond.com.
Braves vs Padres, Austin Riley on I.L.? Scottie Scheffler goes rogue! Falcons rookie signs, Michigan fans can now legally drown their sorrows at games, KC mayor in hot water for doxxing Chiefs PK Harrison Butker, Mahomes family needs to shut the f up, Lionel Messi's very expensive napkin, Dick Butkus napkin, gamblers attacking college players on social media, Bat Speed analytics (kind of) explained, Paul Skenes is the new Kerry Wood, Tiger and Daly miss PGA cut, Tim Horton's donuts, Dale Jarrett's last race, FBI blows Nassar case, Zip Zabel, The Grey Eagle, Connie Mack, 4 straight days of rainouts, Hank Greenberg is Jackie Robinson's hero, Hank Aaron Mr. 3000, Luis Tiant's greatness, Ralph (Beep Beep) Garr sets MLB record, Bobby Valentine's career cut short, Dodgers trade gay player after he refuses sham marriage, David Wells perfecto, Brian McCann's big night, Fredi Gonzalez fired, Pete's Tweets, This Day in Sports History, Ripley's Believe It or Not!, plus baseball quotes from Leo Durocher and Dave Smith!
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 11a-2p! Notice cracked bricks, floor gaps or bowed basement walls...the expert from AquaGuard can provide a free inspection. If you're ready to fix your sagging, bouncy floors, visit AquaGuard.net Former Atlanta Braves C Brian McCann Dimino's Dump CrossTalk with Matt Chernoff & Carlos Medina Back In Time See omnystudio.com/listener for privacy information.
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 11a-2p! Notice cracked bricks, floor gaps or bowed basement walls...the expert from AquaGuard can provide a free inspection. If you're ready to fix your sagging, bouncy floors, visit AquaGuard.net Former Atlanta Braves C Brian McCann Dimino's Dump CrossTalk with Matt Chernoff & Carlos Medina Back In Time See omnystudio.com/listener for privacy information.
Former Atlanta Braes Catcher Brian McCann joined Nick Cellini and Chris Dimino to talk about last night PlayerCast on Bally's, his days paying, almost missing a tee time at Augusta National and moreSee omnystudio.com/listener for privacy information.
Former Atlanta Braes Catcher Brian McCann joined Nick Cellini and Chris Dimino to talk about last night PlayerCast on Bally's, his days paying, almost missing a tee time at Augusta National and moreSee omnystudio.com/listener for privacy information.
Sandra and Beau get into a conversation on the Braves and the broadcast team of Chipper Jones, Brian McCann, Jeff Francouer, and Tom Glavine and how well they did with storytelling, including a moment between Brian Snitker and the team on Snitker coaching third base with McCann running the bags.
David & Eric welcome on one of the great catchers in Braves history, Brian McCann, who appeared in seven All Star games and won six Silver Slugger Awards. They begin with his thoughts on how Atlanta stacks up with Los Angeles and Philadelphia in the National League pecking order. This leads to Brian's observations on the ill effects of the long layoff between the regular season and Division series as he recalls returning to the field after the All Star break. (10:00) McCann shares the best highlights of his career. He and Eric were teammates with the Braves from 2009-2013 and remember their fond collective memories from the clubhouse. Hear about his first big Postseason in his rookie season against an all time great pitcher. (29:45) Full discussion about the unforgettable evening when McCann and Carlos Gomez famously got into it in between third base and home plate in September of 2013. Brian explains what happened that night and why he has been unfairly tagged with the reputation as being a member of the "Fun Police". (43:00) "BMac" talks about the Braves loaded lineup and all the challenges that they present. This includes a conversation on just how special Ronald Acuña Jr. truly is. (50:00) The best player Brian McCann ever played with and an explanation as to why that is his choice. (57:30) Just how hard is it to face Chris Sale, especially as a left handed hitter? Learn more about your ad choices. Visit podcastchoices.com/adchoices
On Nov. 30, the Faison Center dedicated its newest building, Pioneer Hall, with the help of the Edmunds family: long-time Faison client Paige Edmunds and her parents, Debbie and Paul. Pioneer Hall – which boasts a clubhouse named for Paige – will house Faison's Adult Day Services and Employment Academy programs. The Paige Edmunds Clubhouse will serve as a home base for Faison's adult clients, who spend parts of the day in community-based activities. Above, Paige Edmunds huddles against the wind and Faison President and CEO Brian McCann looks on as Debbie Edmunds prepares to cut the ribbon. The ribbon-cutting...Article LinkSupport the show
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 11a-2p! On Campus presented by Subaru of Gwinnett Business of Sports Minute: Lemon Street Classic presented by Southern Company Zack Greinke & Brian McCann This Day in Atlanta Braves & MLB History Jon Rahm tells the truth we've all waited to hear Drive Around the Sports World presented by Marietta Toyota See omnystudio.com/listener for privacy information.
Nick Cellini and Chris Dimino talk everything Atlanta Sports, the National Sports picture and the current (and WAY back when) in pop culture! Get the latest and your fill of Atlanta Braves, Georgia Bulldogs, Atlanta Falcons, Atlanta Hawks daily from two "Southern" Yankees daily Mon-Fri from 11a-2p! On Campus presented by Subaru of Gwinnett Business of Sports Minute: Lemon Street Classic presented by Southern Company Zack Greinke & Brian McCann This Day in Atlanta Braves & MLB History Jon Rahm tells the truth we've all waited to hear Drive Around the Sports World presented by Marietta Toyota See omnystudio.com/listener for privacy information.
Today's #podcast had on @seueaglesbaseball #catcher @alexiajorge_26 & @noplacelikehome_bsbl who is a junior at the university. She's usually up 3:30am every morning. Always working on her craft. Besides being a student athlete she works two jobs. Commutes from home to school. She played @lyndhursthsbaseball for three years too. Has been playing #baseball since she was three when she joined her brother's #teeball team. Excelled at #littleleague where she hit for power and pitched three #nohitters too. Came up thru @mlbdevelops and won gold @copabe_wbsc with @usabaseball in 2019. She is one of 7 #women playing in #collegebaseball as is a teammate of hers @_oliviapichardo currently on @brownu_baseball team. Her goal is to play @wbsc in #thunderbay this summer. Her favorite player is #brianmccann who she got to #facetime recently from his good friend @markdero7 an awesome moment for her. Favorite current player is #bryceharper whose @phillies look like a strong bet to getting to #worldseries this year. Olivia is a role model and baseball has taught her many life lessons. Supportive family and a very hard worker. It's a grind and she is working extremely hard to make her goals a reality.All sports. One podcast. (even hockey) PODCAST LINK ON ITUNES: http://bit.ly/JDTSPODCAST
Thanks to the over 17,000 people who have joined the first AI Engineer Summit! A full recap is coming. Last call to fill out the State of AI Engineering survey! See our Community page for upcoming meetups in SF, Paris and NYC.This episode had good interest on Twitter.Fast.ai's “Practical Deep Learning” courses been watched by over >6,000,000 people, and the fastai library has over 25,000 stars on Github. Jeremy Howard, one of the creators of Fast, is now one of the most prominent and respected voices in the machine learning industry; but that wasn't always the case. Being non-consensus and right In 2018, Jeremy and Sebastian Ruder published a paper on ULMFiT (Universal Language Model Fine-tuning), a 3-step transfer learning technique for NLP tasks: The paper demonstrated that pre-trained language models could be fine-tuned on a specific task with a relatively small amount of data to achieve state-of-the-art results. They trained a 24M parameters model on WikiText-103 which was beat most benchmarks.While the paper had great results, the methods behind weren't taken seriously by the community: “Everybody hated fine tuning. Everybody hated transfer learning. I literally did tours trying to get people to start doing transfer learning and nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning […] which I was convinced was not the right direction, but who's going to listen to me, cause as you said, I don't have a PhD, not at a university… I don't have a big set of computers to fine tune huge transformer models.”Five years later, fine-tuning is at the center of most major discussion topics in AI (we covered some like fine tuning vs RAG and small models fine tuning), and we might have gotten here earlier if Jeremy had OpenAI-level access to compute and distribution. At heart, Jeremy has always been “GPU poor”:“I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use.”This story is a good reminder of how some of the best ideas are hiding in plain sight; we recently covered RWKV and will continue to highlight the most interesting research that isn't being done in the large labs. Replacing fine-tuning with continued pre-trainingEven though fine-tuning is now mainstream, we still have a lot to learn. The issue of “catastrophic forgetting” and potential solutions have been brought up in many papers: at the fine-tuning stage, the model can forget tasks it previously knew how to solve in favor of new ones. The other issue is apparent memorization of the dataset even after a single epoch, which Jeremy covered Can LLMs learn from a single example? but we still don't have the answer to. Despite being the creator of ULMFiT, Jeremy still professes that there are a lot of open questions on finetuning:“So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do.”He now advocates for "continued pre-training" - maintaining a diversity of data throughout the training process rather than separate pre-training and fine-tuning stages. Mixing instructional data, exercises, code, and other modalities while gradually curating higher quality data can avoid catastrophic forgetting and lead to more robust capabilities (something we covered in Datasets 101).“Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it… the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training. And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data….So yeah, that's now my view, is I think ULMFiT is the wrong approach. And that's why we're seeing a lot of these so-called alignment tax… I think it's actually because people are training them wrong.An example of this phenomena is CodeLlama, a LLaMA2 model finetuned on 500B tokens of code: while the model is much better at code, it's worse on generic tasks that LLaMA2 knew how to solve well before the fine-tuning. In the episode we also dive into all the places where open source model development and research is happening (academia vs Discords - tracked on our Communities list and on our survey), and how Jeremy recommends getting the most out of these diffuse, pseudonymous communities (similar to the Eleuther AI Mafia).Show Notes* Jeremy's Background* FastMail* Optimal Decisions* Kaggle* Enlitic* fast.ai* Rachel Thomas* Practical Deep Learning* fastai for PyTorch* nbdev* fastec2 (the underrated library we describe)* Can LLMs learn from a single example?* the Kaggle LLM Science Exam competition, which “challenges participants to answer difficult science-based questions written by a Large Language Model”.* Sebastian Ruder* Alec Radford* Sylvain Gugger* Stephen Merity* Chris Lattner* Modular.ai / Mojo* Jono Whittaker* Zeiler and Fergus paper* ULM Fit* DAWNBench* Phi-1* Code Llama* AlexNetTimestamps* [00:00:00] Intros and Jeremy's background* [00:05:28] Creating ULM Fit - a breakthrough in NLP using transfer learning* [00:06:32] The rise of GPT and the appeal of few-shot learning over fine-tuning* [00:10:00] Starting Fast.ai to distribute AI capabilities beyond elite academics* [00:14:30] How modern LMs like ChatGPT still follow the ULM Fit 3-step approach* [00:17:23] Meeting with Chris Lattner on Swift for TensorFlow at Google* [00:20:00] Continued pre-training as a fine-tuning alternative* [00:22:16] Fast.ai and looking for impact vs profit maximization* [00:26:39] Using Fast.ai to create an "army" of AI experts to improve their domains* [00:29:32] Fast.ai's 3 focus areas - research, software, and courses* [00:38:42] Fine-tuning memorization and training curve "clunks" before each epoch* [00:46:47] Poor training and fine-tuning practices may be causing alignment failures* [00:48:38] Academia vs Discords* [00:53:41] Jeremy's high hopes for Chris Lattner's Mojo and its potential* [01:05:00] Adding capabilities like SQL generation through quick fine-tuning* [01:10:12] Rethinking Fast.ai courses for the AI-assisted coding era* [01:14:53] Rapid model development has created major technical debt* [01:17:08] Lightning RoundAI Summary (beta)This is the first episode we're trying this. Here's an overview of the main topics before you dive in the transcript. * Jeremy's background and philosophies on AI* Studied philosophy and cognitive science in college* Focused on ethics and thinking about AI even 30 years ago* Believes AI should be accessible to more people, not just elite academics/programmers* Created fast.ai to make deep learning more accessible* Development of transfer learning and ULMFit* Idea of transfer learning critical for making deep learning accessible* ULMFit pioneered transfer learning for NLP* Proposed training general language models on large corpora then fine-tuning - this became standard practice* Faced skepticism that this approach would work from NLP community* Showed state-of-the-art results on text classification soon after trying it* Current open questions around fine-tuning LLMs* Models appear to memorize training data extremely quickly (after 1 epoch)* This may hurt training dynamics and cause catastrophic forgetting* Unclear how best to fine-tune models to incorporate new information/capabilities* Need more research on model training dynamics and ideal data mixing* Exciting new developments* Mojo and new programming languages like Swift could enable faster model innovation* Still lots of room for improvements in computer vision-like innovations in transformers* Small models with fine-tuning may be surprisingly capable for many real-world tasks* Prompting strategies enable models like GPT-3 to achieve new skills like playing chess at superhuman levels* LLMs are like computer vision in 2013 - on the cusp of huge new breakthroughs in capabilities* Access to AI research* Many key convos happen in private Discord channels and forums* Becoming part of these communities can provide great learning opportunities* Being willing to do real work, not just talk about ideas, is key to gaining access* The future of practical AI* Coding becoming more accessible to non-programmers through AI assistance* Pre-requisite programming experience for learning AI may no longer be needed* Huge open questions remain about how to best train, fine-tune, and prompt LLMsTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:21]Swyx: Hey, and today we have in the remote studio, Jeremy Howard all the way from Australia. Good morning. [00:00:27]Jeremy: The remote studio, also known as my house. Good morning. Nice to see you. [00:00:32]Swyx: Nice to see you too. I'm actually very used to seeing you in your mask as a message to people, but today we're mostly audio. But thank you for doing the very important public service of COVID awareness. It was a pleasure. [00:00:46]Jeremy: It was all very annoying and frustrating and tedious, but somebody had to do it. [00:00:52]Swyx: Somebody had to do it, especially somebody with your profile. I think it really drives home the message. So we tend to introduce people for them and then ask people to fill in the blanks on the personal side. Something I did not know about you was that you graduated with a BA in philosophy from the University of Melbourne. I assumed you had a PhD. [00:01:14]Jeremy: No, I mean, I barely got through my BA because I was working 80 to 100 hour weeks at McKinsey and Company from 19 years old onwards. So I actually didn't attend any lectures in second and third year university. [00:01:35]Swyx: Well, I guess you didn't need it or you're very sort of self-driven and self-motivated. [00:01:39]Jeremy: I took two weeks off before each exam period when I was working at McKinsey. And then, I mean, I can't believe I got away with this in hindsight, I would go to all my professors and say, oh, I was meant to be in your class this semester and I didn't quite turn up. Were there any assignments I was meant to have done, whatever. I can't believe all of them let me basically have it. They basically always would say like, okay, well, if you can have this written by tomorrow, I'll accept it. So yeah, stressful way to get through university, but. [00:02:12]Swyx: Well, it shows that, I guess, you min-maxed the opportunities. That definitely was a precursor. [00:02:18]Jeremy: I mean, funnily, like in as much as I, you know, in philosophy, the things I found interesting and focused on in the little bit of time I did spend on it was ethics and cognitive science. And it's kind of really amazing that it's now come back around and those are actually genuinely useful things to know about, which I never thought would happen. [00:02:38]Swyx: A lot of, yeah, a lot of relevant conversations there. So you were a consultant for a while and then in the magical month of June 1989, you founded both Optimal Decisions and Fastmeal, which I also briefly used. So thank you for that. [00:02:53]Jeremy: Oh, good for you. Yeah. Cause I had read the statistics, which is that like 90% or something of small businesses fail. So I thought if I start two businesses, I have a higher chance. In hindsight, I was thinking of it as some kind of stochastic thing I didn't have control over, but it's a bit odd, but anyway. [00:03:10]Swyx: And then you were president and chief scientist at Kaggle, which obviously is the sort of composition platform of machine learning. And then Enlitic, where you were working on using deep learning to improve medical diagnostics and clinical decisions. Yeah. [00:03:28]Jeremy: I was actually the first company to use deep learning in medicine, so I kind of founded the field. [00:03:33]Swyx: And even now that's still like a pretty early phase. And I actually heard you on your new podcast with Tanish, where you went very, very deep into the stuff, the kind of work that he's doing, such a young prodigy at his age. [00:03:47]Jeremy: Maybe he's too old to be called a prodigy now, ex-prodigy. No, no. [00:03:51]Swyx: I think he still counts. And anyway, just to round out the bio, you have a lot more other credentials, obviously, but most recently you started Fast.ai, which is still, I guess, your primary identity with Rachel Thomas. So welcome. [00:04:05]Jeremy: Yep. [00:04:06]Swyx: Thanks to my wife. Thank you. Yeah. Doing a lot of public service there with getting people involved in AI, and I can't imagine a better way to describe it than fast, fast.ai. You teach people from nothing to stable diffusion in seven weeks or something, and that's amazing. Yeah, yeah. [00:04:22]Jeremy: I mean, it's funny, you know, when we started that, what was that, like 2016 or something, the idea that deep learning was something that you could make more accessible was generally considered stupid. Everybody knew that deep learning was a thing that you got a math or a computer science PhD, you know, there was one of five labs that could give you the appropriate skills and that you would join, yeah, basically from one of those labs, you might be able to write some papers. So yeah, the idea that normal people could use that technology to do good work was considered kind of ridiculous when we started it. And we weren't sure if it was possible either, but we kind of felt like we had to give it a go because the alternative was we were pretty sure that deep learning was on its way to becoming, you know, the most or one of the most, you know, important technologies in human history. And if the only people that could use it were a handful of computer science PhDs, that seemed like A, a big waste and B, kind of dangerous. [00:05:28]Swyx: Yeah. [00:05:29]Alessio: And, you know, well, I just wanted to know one thing on your bio that at Kaggle, you were also the top rank participant in both 2010 and 2011. So sometimes you see a lot of founders running companies that are not really in touch with the problem, but you were clearly building something that you knew a lot about, which is awesome. Talking about deep learning, you created, published a paper on ULM fit, which was kind of the predecessor to multitask learning and a lot of the groundwork that then went to into Transformers. I've read back on the paper and you turned this model, AWD LSTM, which I did the math and it was like 24 to 33 million parameters, depending on what training data set you use today. That's kind of like not even small, it's like super small. What were some of the kind of like contrarian takes that you had at the time and maybe set the stage a little bit for the rest of the audience on what was kind of like the state of the art, so to speak, at the time and what people were working towards? [00:06:32]Jeremy: Yeah, the whole thing was a contrarian take, you know. So okay, so we started Fast.ai, my wife and I, and we thought, yeah, so we're trying to think, okay, how do we make it more accessible? So when we started thinking about it, it was probably 2015 and then 2016, we started doing something about it. Why is it inaccessible? Okay, well, A, no one knows how to do it other than a few number of people. And then when we asked those few number of people, well, how do you actually get good results? They would say like, oh, it's like, you know, a box of tricks that aren't published. So you have to join one of the labs and learn the tricks. So a bunch of unpublished tricks, not much software around, but thankfully there was Theano and rappers and particularly Lasagna, the rapper, but yeah, not much software around, not much in the way of data sets, you know, very hard to get started in terms of the compute. Like how do you get that set up? So yeah, no, everything was kind of inaccessible. And you know, as we started looking into it, we had a key insight, which was like, you know what, most of the compute and data for image recognition, for example, we don't need to do it. You know, there's this thing which nobody knows about, nobody talks about called transfer learning, where you take somebody else's model, where they already figured out like how to detect edges and gradients and corners and text and whatever else, and then you can fine tune it to do the thing you want to do. And we thought that's the key. That's the key to becoming more accessible in terms of compute and data requirements. So when we started Fast.ai, we focused from day one on transfer learning. Lesson one, in fact, was transfer learning, literally lesson one, something not normally even mentioned in, I mean, there wasn't much in the way of courses, you know, the courses out there were PhD programs that had happened to have recorded their lessons and they would rarely mention it at all. We wanted to show how to do four things that seemed really useful. You know, work with vision, work with tables of data, work with kind of recommendation systems and collaborative filtering and work with text, because we felt like those four kind of modalities covered a lot of the stuff that, you know, are useful in real life. And no one was doing anything much useful with text. Everybody was talking about word2vec, you know, like king plus queen minus woman and blah, blah, blah. It was like cool experiments, but nobody's doing anything like useful with it. NLP was all like lemmatization and stop words and topic models and bigrams and SPMs. And it was really academic and not practical. But I mean, to be honest, I've been thinking about this crazy idea for nearly 30 years since I had done cognitive science at university, where we talked a lot about the CELS Chinese room experiment. This idea of like, what if there was somebody that could kind of like, knew all of the symbolic manipulations required to answer questions in Chinese, but they didn't speak Chinese and they were kind of inside a room with no other way to talk to the outside world other than taking in slips of paper with Chinese written on them and then they do all their rules and then they pass back a piece of paper with Chinese back. And this room with a person in is actually fantastically good at answering any question you give them written in Chinese. You know, do they understand Chinese? And is this, you know, something that's intelligently working with Chinese? Ever since that time, I'd say the most thought, to me, the most thoughtful and compelling philosophical response is yes. You know, intuitively it feels like no, because that's just because we can't imagine such a large kind of system. But you know, if it looks like a duck and acts like a duck, it's a duck, you know, or to all intents and purposes. And so I always kind of thought, you know, so this is basically a kind of analysis of the limits of text. And I kind of felt like, yeah, if something could ingest enough text and could use the patterns it saw to then generate text in response to text, it could appear to be intelligent, you know. And whether that means it is intelligent or not is a different discussion and not one I find very interesting. Yeah. And then when I came across neural nets when I was about 20, you know, what I learned about the universal approximation theorem and stuff, and I started thinking like, oh, I wonder if like a neural net could ever get big enough and take in enough data to be a Chinese room experiment. You know, with that background and this kind of like interest in transfer learning, you know, I'd been thinking about this thing for kind of 30 years and I thought like, oh, I wonder if we're there yet, you know, because we have a lot of text. Like I can literally download Wikipedia, which is a lot of text. And I thought, you know, how would something learn to kind of answer questions or, you know, respond to text? And I thought, well, what if we used a language model? So language models are already a thing, you know, they were not a popular or well-known thing, but they were a thing. But language models exist to this idea that you could train a model to fill in the gaps. Or actually in those days it wasn't fill in the gaps, it was finish a string. And in fact, Andrej Karpathy did his fantastic RNN demonstration from this at a similar time where he showed like you can have it ingest Shakespeare and it will generate something that looks a bit like Shakespeare. I thought, okay, so if I do this at a much bigger scale, using all of Wikipedia, what would it need to be able to do to finish a sentence in Wikipedia effectively, to do it quite accurately quite often? I thought, geez, it would actually have to know a lot about the world, you know, it'd have to know that there is a world and that there are objects and that objects relate to each other through time and cause each other to react in ways and that causes proceed effects and that, you know, when there are animals and there are people and that people can be in certain positions during certain timeframes and then you could, you know, all that together, you can then finish a sentence like this was signed into law in 2016 by US President X and it would fill in the gap, you know. So that's why I tried to create what in those days was considered a big language model trained on the entirety on Wikipedia, which is that was, you know, a bit unheard of. And my interest was not in, you know, just having a language model. My interest was in like, what latent capabilities would such a system have that would allow it to finish those kind of sentences? Because I was pretty sure, based on our work with transfer learning and vision, that I could then suck out those latent capabilities by transfer learning, you know, by fine-tuning it on a task data set or whatever. So we generated this three-step system. So step one was train a language model on a big corpus. Step two was fine-tune a language model on a more curated corpus. And step three was further fine-tune that model on a task. And of course, that's what everybody still does today, right? That's what ChatGPT is. And so the first time I tried it within hours, I had a new state-of-the-art academic result on IMDB. And I was like, holy s**t, it does work. And so you asked, to what degree was this kind of like pushing against the established wisdom? You know, every way. Like the reason it took me so long to try it was because I asked all my friends in NLP if this could work. And everybody said, no, it definitely won't work. It wasn't like, oh, maybe. Everybody was like, it definitely won't work. NLP is much more complicated than vision. Language is a much more vastly complicated domain. You know, and you've got problems like the grounding problem. We know from like philosophy and theory of mind that it's actually impossible for it to work. So yeah, so don't waste your time. [00:15:10]Alessio: Jeremy, had people not tried because it was like too complicated to actually get the data and like set up the training? Or like, were people just lazy and kind of like, hey, this is just not going to work? [00:15:20]Jeremy: No, everybody wasn't lazy. So like, so the person I thought at that time who, you know, there were two people I thought at that time, actually, who were the strongest at language models were Stephen Merity and Alec Radford. And at the time I didn't know Alec, but I, after we had both, after I'd released ULM Fit and he had released GPT, I organized a chat for both of us with Kate Metz in the New York Times. And Kate Metz answered, sorry, and Alec answered this question for Kate. And Kate was like, so how did, you know, GPT come about? And he said, well, I was pretty sure that pre-training on a general large corpus wouldn't work. So I hadn't tried it. And then I read ULM Fit and turns out it did work. And so I did it, you know, bigger and it worked even better. And similar with, with Stephen, you know, I asked Stephen Merity, like, why don't we just find, you know, take your AWD-ASTLM and like train it on all of Wikipedia and fine tune it? And he's kind of like, well, I don't think that's going to really lie. Like two years before I did a very popular talk at KDD, the conference where everybody in NLP was in the audience. I recognized half the faces, you know, and I told them all this, I'm sure transfer learning is the key. I'm sure ImageNet, you know, is going to be an NLP thing as well. And, you know, everybody was interested and people asked me questions afterwards and, but not just, yeah, nobody followed up because everybody knew that it didn't work. I mean, even like, so we were scooped a little bit by Dai and Lee, Kwok Lee at Google. They had, they had, I already, I didn't even realize this, which is a bit embarrassing. They had already done a large language model and fine tuned it. But again, they didn't create a general purpose, large language model on a general purpose corpus. They only ever tested a domain specific corpus. And I haven't spoken to Kwok actually about that, but I assume that the reason was the same. It probably just didn't occur to them that the general approach could work. So maybe it was that kind of 30 years of mulling over the, the cell Chinese room experiment that had convinced me that it probably would work. I don't know. Yeah. [00:17:48]Alessio: Interesting. I just dug up Alec announcement tweet from 2018. He said, inspired by Cobe, Elmo, and Yola, I'm fit. We should have a single transformer language model can be fine tuned to a wide variety. It's interesting because, you know, today people think of AI as the leader, kind of kind of like the research lab pushing forward the field. What was that at the time? You know, like kind of like going back five years, people think of it as an overnight success, but obviously it took a while. [00:18:16]Swyx: Yeah. Yeah. [00:18:17]Jeremy: No, I mean, absolutely. And I'll say like, you know, it's interesting that it mentioned Elmo because in some ways that was kind of diametrically opposed to, to ULM fit. You know, there was these kind of like, so there was a lot of, there was a lot of activity at the same time as ULM fits released. So there was, um, so before it, as Brian McCann, I think at Salesforce had come out with this neat model that did a kind of multitask learning, but again, they didn't create a general fine tune language model first. There was Elmo, um, which I think was a lip, you know, actually quite a few months after the first ULM fit example, I think. Um, but yeah, there was a bit of this stuff going on. And the problem was everybody was doing, and particularly after GPT came out, then everybody wanted to focus on zero shot and few shot learning. You know, everybody hated fine tuning. Everybody hated transfer learning. And like, I literally did tours trying to get people to start doing transfer learning and people, you know, nobody was interested, particularly after GPT showed such good results with zero shot and few shot learning. And so I actually feel like we kind of went backwards for years and, and not to be honest, I mean, I'm a bit sad about this now, but I kind of got so disappointed and dissuaded by like, it felt like these bigger lab, much bigger labs, you know, like fast AI had only ever been just me and Rachel were getting all of this attention for an approach I thought was the wrong way to do it. You know, I was convinced was the wrong way to do it. And so, yeah, for years people were really focused on getting better at zero shot and few shots and it wasn't until, you know, this key idea of like, well, let's take the ULM fit approach, but for step two, rather than fine tuning on a kind of a domain corpus, let's fine tune on an instruction corpus. And then in step three, rather than fine tuning on a reasonably specific task classification, let's fine tune on a, on a RLHF task classification. And so that was really, that was really key, you know, so I was kind of like out of the NLP field for a few years there because yeah, it just felt like, I don't know, pushing uphill against this vast tide, which I was convinced was not the right direction, but who's going to listen to me, you know, cause I, as you said, I don't have a PhD, not at a university, or at least I wasn't then. I don't have a big set of computers to fine tune huge transformer models. So yeah, it was definitely difficult. It's always been hard. You know, it's always been hard. Like I've always been somebody who does not want to build stuff on lots of big computers because most people don't have lots of big computers and I hate creating stuff that most people can't use, you know, and also stuff that's created on lots of big computers has always been like much more media friendly. So like, it might seem like a recent thing, but actually throughout my 30 years in data science, the attention's always been on, you know, the big iron results. So when I first started, everybody was talking about data warehouses and it was all about Teradata and it'd be like, oh, this big bank has this huge room full of computers and they have like terabytes of data available, you know, at the press of a button. And yeah, that's always what people want to talk about, what people want to write about. And then of course, students coming out of their PhDs and stuff, that's where they want to go work because that's where they read about. And to me, it's a huge distraction, you know, because like I say, most people don't have unlimited compute and I want to help most people, not the small subset of the most well-off people. [00:22:16]Alessio: That's awesome. And it's great to hear, you do such a great job educating that a lot of times you're not telling your own story, you know? So I love this conversation. And the other thing before we jump into Fast.AI, actually, a lot of people that I know, they run across a new architecture and whatnot, they're like, I got to start a company and raise a bunch of money and do all of this stuff. And say, you were like, I want everybody to have access to this. Why was that the case for you? Was it because you already had a successful venture in like FastMail and you were more interested in that? What was the reasoning? [00:22:52]Jeremy: It's a really good question. So I guess the answer is yes, that's the reason why. So when I was a teenager, I thought it would be really cool to like have my own company. You know, I didn't know the word startup. I didn't know the word entrepreneur. I didn't know the word VC. And I didn't really know what any of those things were really until after we started Kaggle, to be honest. Even the way it started to what we now call startups. I just thought they were just small businesses. You know, they were just companies. So yeah, so those two companies were FastMail and Optimal Decisions. FastMail was the first kind of synchronized email provider for non-businesses. So something you can get your same email at home, on your laptop, at work, on your phone, whatever. And then Optimal Decisions invented a new approach to insurance pricing. Something called profit-optimized insurance pricing. So I saw both of those companies, you know, after 10 years. And at that point, I had achieved the thing that as a teenager I had wanted to do. You know, it took a lot longer than it should have because I spent way longer in management consulting than I should have because I got caught up in that stupid rat race. But, you know, eventually I got there and I remember my mom saying to me, you must be so proud. You know, because she remembered my dream. She's like, you've done it. And I kind of reflected and I was like, I'm not proud at all. You know, like people quite liked FastMail. You know, it's quite nice to have synchronized email. It probably would have happened anyway. Yeah, I'm certainly not proud that I've helped some insurance companies suck more money out of their customers. Yeah, no, I'm not proud. You know, it's actually, I haven't really helped the world very much. You know, maybe in the insurance case I've made it a little bit worse. I don't know. So, yeah, I was determined to not waste more years of my life doing things, working hard to do things which I could not be reasonably sure would have a lot of value. So, you know, I took some time off. I wasn't sure if I'd ever work again, actually. I didn't particularly want to, because it felt like, yeah, it felt like such a disappointment. And, but, you know, and I didn't need to. I had enough money. Like, I wasn't super rich, but I had enough money. I didn't need to work. And I certainly recognized that amongst the other people I knew who had enough money that they didn't need to work, they all worked ridiculously hard, you know, and constantly put themselves in extremely stressful situations. And I thought, I don't want to be one of those idiots who's tied to, you know, buying a bigger plane than the next guy or whatever. You know, Kaggle came along and I mainly kind of did that just because it was fun and interesting to hang out with interesting people. But, you know, with Fast.ai in particular, you know, Rachel and I had a very explicit, you know, long series of conversations over a long period of time about like, well, how can we be the most helpful to society as a whole, and particularly to those people who maybe need more help, you know? And so we definitely saw the world going in a potentially pretty dystopian direction if the world's most powerful technology was controlled by a small group of elites. So we thought, yeah, we should focus on trying to help that not happen. You know, sadly, it looks like it still is likely to happen. But I mean, I feel like we've helped make it a little bit less likely. So we've done our bit. [00:26:39]Swyx: You've shown that it's possible. And I think your constant advocacy, your courses, your research that you publish, you know, just the other day you published a finding on, you know, learning that I think is still something that people are still talking about quite a lot. I think that that is the origin story of a lot of people who are going to be, you know, little Jeremy Howards, furthering your mission with, you know, you don't have to do everything by yourself is what I'm saying. No, definitely. Definitely. [00:27:10]Jeremy: You know, that was a big takeaway from like, analytic was analytic. It definitely felt like we had to do everything ourselves. And I kind of, I wanted to solve medicine. I'll say, yeah, okay, solving medicine is actually quite difficult. And I can't do it on my own. And there's a lot of other things I'd like to solve, and I can't do those either. So that was definitely the other piece was like, yeah, you know, can we create an army of passionate domain experts who can change their little part of the world? And that's definitely happened. Like I find nowadays, at least half the time, probably quite a bit more that I get in contact with somebody who's done really interesting work in some domain. Most of the time I'd say, they say, yeah, I got my start with fast.ai. So it's definitely, I can see that. And I also know from talking to folks at places like Amazon and Adobe and stuff, which, you know, there's lots of alumni there. And they say, oh my God, I got here. And like half of the people are fast.ai alumni. So it's fantastic. [00:28:13]Swyx: Yeah. [00:28:14]Jeremy: Actually, Andre Kapathy grabbed me when I saw him at NeurIPS a few years ago. And he was like, I have to tell you, thanks for the fast.ai courses. When people come to Tesla and they need to know more about deep learning, we always send them to your course. And the OpenAI Scholars Program was doing the same thing. So it's kind of like, yeah, it's had a surprising impact, you know, that's just one of like three things we do is the course, you know. [00:28:40]Swyx: Yes. [00:28:40]Jeremy: And it's only ever been at most two people, either me and Rachel or me and Sylvia nowadays, it's just me. So yeah, I think it shows you don't necessarily need a huge amount of money and a huge team of people to make an impact. [00:28:56]Swyx: Yeah. So just to reintroduce fast.ai for people who may not have dived into it much, there is the courses that you do. There is the library that is very well loved. And I kind of think of it as a nicer layer on top of PyTorch that people should start with by default and use it as the basis for a lot of your courses. And then you have like NBDev, which I don't know, is that the third one? [00:29:27]Jeremy: Oh, so the three areas were research, software, and courses. [00:29:32]Swyx: Oh, sorry. [00:29:32]Jeremy: So then in software, you know, fast.ai is the main thing, but NBDev is not far behind. But then there's also things like FastCore, GHAPI, I mean, dozens of open source projects that I've created and some of them have been pretty popular and some of them are still a little bit hidden, actually. Some of them I should try to do a better job of telling people about. [00:30:01]Swyx: What are you thinking about? Yeah, what's on the course of my way? Oh, I don't know, just like little things. [00:30:04]Jeremy: Like, for example, for working with EC2 and AWS, I created a FastEC2 library, which I think is like way more convenient and nice to use than anything else out there. And it's literally got a whole autocomplete, dynamic autocomplete that works both on the command line and in notebooks that'll like auto-complete your instance names and everything like that. You know, just little things like that. I try to make like, when I work with some domain, I try to make it like, I want to make it as enjoyable as possible for me to do that. So I always try to kind of like, like with GHAPI, for example, I think that GitHub API is incredibly powerful, but I didn't find it good to work with because I didn't particularly like the libraries that are out there. So like GHAPI, like FastEC2, it like autocompletes both at the command line or in a notebook or whatever, like literally the entire GitHub API. The entire thing is like, I think it's like less than 100K of code because it actually, as far as I know, the only one that grabs it directly from the official open API spec that GitHub produces. And like if you're in GitHub and you just type an API, you know, autocomplete API method and hit enter, it prints out the docs with brief docs and then gives you a link to the actual documentation page. You know, GitHub Actions, I can write now in Python, which is just so much easier than writing them in TypeScript and stuff. So, you know, just little things like that. [00:31:40]Swyx: I think that's an approach which more developers took to publish some of their work along the way. You described the third arm of FastAI as research. It's not something I see often. Obviously, you do do some research. And how do you run your research? What are your research interests? [00:31:59]Jeremy: Yeah, so research is what I spend the vast majority of my time on. And the artifacts that come out of that are largely software and courses. You know, so to me, the main artifact shouldn't be papers because papers are things read by a small exclusive group of people. You know, to me, the main artifacts should be like something teaching people, here's how to use this insight and here's software you can use that builds it in. So I think I've only ever done three first-person papers in my life, you know, and none of those are ones I wanted to do. You know, they were all ones that, like, so one was ULM Fit, where Sebastian Ruder reached out to me after seeing the course and said, like, you have to publish this as a paper, you know. And he said, I'll write it. He said, I want to write it because if I do, I can put it on my PhD and that would be great. And it's like, okay, well, I want to help you with your PhD. And that sounds great. So like, you know, one was the masks paper, which just had to exist and nobody else was writing it. And then the third was the Fast.ai library paper, which again, somebody reached out and said, please, please write this. We will waive the fee for the journal and everything and actually help you get it through publishing and stuff. So yeah, so I don't, other than that, I've never written a first author paper. So the research is like, well, so for example, you know, Dawn Bench was a competition, which Stanford ran a few years ago. It was kind of the first big competition of like, who can train neural nets the fastest rather than the most accurate. And specifically it was who can train ImageNet the fastest. And again, this was like one of these things where it was created by necessity. So Google had just released their TPUs. And so I heard from my friends at Google that they had put together this big team to smash Dawn Bench so that they could prove to people that they had to use Google Cloud and use their TPUs and show how good their TPUs were. And we kind of thought, oh s**t, this would be a disaster if they do that, because then everybody's going to be like, oh, deep learning is not accessible. [00:34:20]Swyx: You know, to actually be good at it, [00:34:21]Jeremy: you have to be Google and you have to use special silicon. And so, you know, we only found out about this 10 days before the competition finished. But, you know, we basically got together an emergency bunch of our students and Rachel and I and sat for the next 10 days and just tried to crunch through and try to use all of our best ideas that had come from our research. And so particularly progressive resizing, just basically train mainly on small things, train on non-square things, you know, stuff like that. And so, yeah, we ended up winning, thank God. And so, you know, we turned it around from being like, like, oh s**t, you know, this is going to show that you have to be Google and have TPUs to being like, oh my God, even the little guy can do deep learning. So that's an example of the kind of like research artifacts we do. And yeah, so all of my research is always, how do we do more with less, you know? So how do we get better results with less data, with less compute, with less complexity, with less education, you know, stuff like that. So ULM fits obviously a good example of that. [00:35:37]Swyx: And most recently you published, can LLMs learn from a single example? Maybe could you tell the story a little bit behind that? And maybe that goes a little bit too far into the learning of very low resource, the literature. [00:35:52]Jeremy: Yeah, yeah. So me and my friend, Jono Whittaker, basically had been playing around with this fun Kaggle competition, which is actually still running as we speak, which is, can you create a model which can answer multiple choice questions about anything that's in Wikipedia? And the thing that makes it interesting is that your model has to run on Kaggle within nine hours. And Kaggle's very, very limited. So you've only got 14 gig RAM, only two CPUs, and a small, very old GPU. So this is cool, you know, if you can do well at this, then this is a good example of like, oh, you can do more with less. So yeah, Jono and I were playing around with fine tuning, of course, transfer learning, pre-trained language models. And we saw this, like, so we always, you know, plot our losses as we go. So here's another thing we created. Actually, Sylvain Guuger, when he worked with us, created called fast progress, which is kind of like TQEDM, but we think a lot better. So we look at our fast progress curves, and they kind of go down, down, down, down, down, down, down, a little bit, little bit, little bit. And then suddenly go clunk, and they drop. And then down, down, down, down, down a little bit, and then suddenly clunk, they drop. We're like, what the hell? These clunks are occurring at the end of each epoch. So normally in deep learning, this would be, this is, you know, I've seen this before. It's always been a bug. It's always turned out that like, oh, we accidentally forgot to turn on eval mode during the validation set. So I was actually learning then, or, oh, we accidentally were calculating moving average statistics throughout the epoch. So, you know, so it's recently moving average or whatever. And so we were using Hugging Face Trainer. So, you know, I did not give my friends at Hugging Face the benefit of the doubt. I thought, oh, they've fucked up Hugging Face Trainer, you know, idiots. Well, you'll use the Fast AI Trainer instead. So we switched over to Learner. We still saw the clunks and, you know, that's, yeah, it shouldn't really happen because semantically speaking in the epoch, isn't like, it's not a thing, you know, like nothing happens. Well, nothing's meant to happen when you go from ending one epoch to starting the next one. So there shouldn't be a clunk, you know. So I kind of asked around on the open source discords. That's like, what's going on here? And everybody was just like, oh, that's just what, that's just what these training curves look like. Those all look like that. Don't worry about it. And I was like, oh, are you all using Trainer? Yes. Oh, well, there must be some bug with Trainer. And I was like, well, we also saw it in Learner [00:38:42]Swyx: and somebody else is like, [00:38:42]Jeremy: no, we've got our own Trainer. We get it as well. They're just like, don't worry about it. It's just something we see. It's just normal. [00:38:48]Swyx: I can't do that. [00:38:49]Jeremy: I can't just be like, here's something that's like in the previous 30 years of neural networks, nobody ever saw it. And now suddenly we see it. [00:38:57]Swyx: So don't worry about it. [00:38:59]Jeremy: I just, I have to know why. [00:39:01]Swyx: Can I clarify? This is, was everyone that you're talking to, were they all seeing it for the same dataset or in different datasets? [00:39:08]Jeremy: Different datasets, different Trainers. They're just like, no, this is just, this is just what it looks like when you fine tune language models. Don't worry about it. You know, I hadn't seen it before, but I'd been kind of like, as I say, I, you know, I kept working on them for a couple of years after ULM fit. And then I kind of moved on to other things, partly out of frustration. So I hadn't been fine tuning, you know, I mean, Lama's only been out for a few months, right? But I wasn't one of those people who jumped straight into it, you know? So I was relatively new to the kind of Lama fine tuning world, where else these guys had been, you know, doing it since day one. [00:39:49]Swyx: It was only a few months ago, [00:39:51]Jeremy: but it's still quite a bit of time. So, so yeah, they're just like, no, this is all what we see. [00:39:56]Swyx: Don't worry about it. [00:39:56]Jeremy: So yeah, I, I've got a very kind of like, I don't know, I've just got this brain where I have to know why things are. And so I kind of, I ask people like, well, why, why do you think it's happening? And they'd be like, oh, it would pretty obviously, cause it's like memorize the data set. It's just like, that can't be right. It's only seen it once. Like, look at this, the loss has dropped by 0.3, 0.3, which is like, basically it knows the answer. And like, no, no, it's just, it is, it's just memorize the data set. So yeah. So look, Jono and I did not discover this and Jono and I did not come up with a hypothesis. You know, I guess we were just the ones, I guess, who had been around for long enough to recognize that like, this, this isn't how it's meant to work. And so we, we, you know, and so we went back and like, okay, let's just run some experiments, you know, cause nobody seems to have actually published anything about this. [00:40:51]Well, not quite true.Some people had published things, but nobody ever actually stepped back and said like, what the hell, you know, how can this be possible? Is it possible? Is this what's happening? And so, yeah, we created a bunch of experiments where we basically predicted ahead of time. It's like, okay, if this hypothesis is correct, that it's memorized in the training set, then we ought to see blah, under conditions, blah, but not under these conditions. And so we ran a bunch of experiments and all of them supported the hypothesis that it was memorizing the data set in a single thing at once. And it's a pretty big data set, you know, which in hindsight, it's not totally surprising because the theory, remember, of the ULMFiT theory was like, well, it's kind of creating all these latent capabilities to make it easier for it to predict the next token. So if it's got all this kind of latent capability, it ought to also be really good at compressing new tokens because it can immediately recognize it as like, oh, that's just a version of this. So it's not so crazy, you know, but it is, it requires us to rethink everything because like, and nobody knows like, okay, so how do we fine tune these things? Because like, it doesn't even matter. Like maybe it's fine. Like maybe it's fine that it's memorized the data set after one go and you do a second go and okay, the validation loss is terrible because it's now really overconfident. [00:42:20]Swyx: That's fine. [00:42:22]Jeremy: Don't, you know, don't, I keep telling people, don't track validation loss, track validation accuracy because at least that will still be useful. Just another thing that's got lost since ULMFiT, nobody tracks accuracy of language models anymore. But you know, it'll still keep learning and it does, it does keep improving. But is it worse? You know, like, is it like, now that it's kind of memorized it, it's probably getting a less strong signal, you know, I don't know. So I still don't know how to fine tune language models properly and I haven't found anybody who feels like they do, like nobody really knows whether this memorization thing is, it's probably a feature in some ways. It's probably some things that you can do usefully with it. It's probably, yeah, I have a feeling it's messing up training dynamics as well. [00:43:13]Swyx: And does it come at the cost of catastrophic forgetting as well, right? Like, which is the other side of the coin. [00:43:18]Jeremy: It does to some extent, like we know it does, like look at Code Llama, for example. So Code Llama was a, I think it was like a 500 billion token fine tuning of Llama 2 using code. And also pros about code that Meta did. And honestly, they kind of blew it because Code Llama is good at coding, but it's bad at everything else, you know, and it used to be good. Yeah, I was pretty sure it was like, before they released it, me and lots of people in the open source discords were like, oh my God, you know, we know this is coming, Jan Lukinsk saying it's coming. I hope they kept at least like 50% non-code data because otherwise it's going to forget everything else. And they didn't, only like 0.3% of their epochs were non-code data. So it did, it forgot everything else. So now it's good at code and it's bad at everything else. So we definitely have catastrophic forgetting. It's fixable, just somebody has to do, you know, somebody has to spend their time training a model on a good mix of data. Like, so, okay, so here's the thing. Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it. [00:44:36]Jeremy: And that's because people are using it in a way different to why I created it. You know, I created it thinking the task-specific models would be more specific. You know, it's like, oh, this is like a sentiment classifier as an example of a task, you know, but the tasks now are like a, you know, RLHF, which is basically like answer questions that make people feel happy about your answer. So that's a much more general task and it's a really cool approach. And so we see, for example, RLHF also breaks models like, you know, like GPT-4, RLHDEFT, we know from kind of the work that Microsoft did, you know, the pre, the earlier, less aligned version was better. And these are all kind of examples of catastrophic forgetting. And so to me, the right way to do this is to fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training. And pre-training is something where from the very start, you try to include all the kinds of data that you care about, all the kinds of problems that you care about, instructions, exercises, code, general purpose document completion, whatever. And then as you train, you gradually curate that, you know, you gradually make that higher and higher quality and more and more specific to the kinds of tasks you want it to do. But you never throw away any data. You always keep all of the data types there in reasonably high quantities. You know, maybe the quality filter, you stop training on low quality data, because that's probably fine to forget how to write badly, maybe. So yeah, that's now my view, is I think ULM fit is the wrong approach. And that's why we're seeing a lot of these, you know, so-called alignment tacks and this view of like, oh, a model can't both code and do other things. And, you know, I think it's actually because people are training them wrong. [00:46:47]Swyx: Yeah, well, I think you have a clear [00:46:51]Alessio: anti-laziness approach. I think other people are not as good hearted, you know, they're like, [00:46:57]Swyx: hey, they told me this thing works. [00:46:59]Alessio: And if I release a model this way, people will appreciate it, I'll get promoted and I'll kind of make more money. [00:47:06]Jeremy: Yeah, and it's not just money. It's like, this is how citations work most badly, you know, so if you want to get cited, you need to write a paper that people in your field recognize as an advancement on things that we know are good. And so we've seen this happen again and again. So like I say, like zero shot and few shot learning, everybody was writing about that. Or, you know, with image generation, everybody just was writing about GANs, you know, and I was trying to say like, no, GANs are not the right approach. You know, and I showed again through research that we demonstrated in our videos that you can do better than GANs, much faster and with much less data. And nobody cared because again, like if you want to get published, you write a GAN paper that slightly improves this part of GANs and this tiny field, you'll get published, you know. So it's, yeah, it's not set up for real innovation. It's, you know, again, it's really helpful for me, you know, I have my own research lab with nobody telling me what to do and I don't even publish. So it doesn't matter if I get citations. And so I just write what I think actually matters. I wish there was, and, you know, and actually places like OpenAI, you know, the researchers there can do that as well. It's a shame, you know, I wish there was more academic, open venues in which people can focus on like genuine innovation. [00:48:38]Swyx: Twitter, which is unironically has become a little bit of that forum. I wanted to follow up on one thing that you mentioned, which is that you checked around the open source discords. I don't know if it's too, I don't know if it's a pusher to ask like what discords are lively or useful right now. I think that something I definitely felt like I missed out on was the early days of Luther AI, which is a very hard bit. And, you know, like what is the new Luther? And you actually shouted out the alignment lab AI discord in your blog post. And that was the first time I even knew, like I saw them on Twitter, never knew they had a discord, never knew that there was actually substantive discussions going on in there and that you were an active member of it. Okay, yeah. [00:49:23]Jeremy: And then even then, if you do know about that and you go there, it'll look like it's totally dead. And that's because unfortunately, nearly all the discords, nearly all of the conversation happens in private channels. You know, and that's, I guess. [00:49:35]Swyx: How does someone get into that world? Because it's obviously very, very instructive, right? [00:49:42]Jeremy: You could just come to the first AI discord, which I'll be honest with you, it's less bustling than some of the others, but it's not terrible. And so like, at least, to be fair, one of Emma's bustling channels is private. [00:49:57]Swyx: I guess. [00:49:59]Jeremy: So I'm just thinking. [00:50:01]Swyx: It's just the nature of quality discussion, right? Yeah, I guess when I think about it, [00:50:05]Jeremy: I didn't have any private discussions on our discord for years, but there was a lot of people who came in with like, oh, I just had this amazing idea for AGI. If you just thought about like, if you imagine that AI is a brain, then we, you know, this just, I don't want to talk about it. You know, I don't want to like, you don't want to be dismissive or whatever. And it's like, oh, well, that's an interesting comment, but maybe you should like, try training some models first to see if that aligns with your intuition. Like, oh, but how could I possibly learn? It's like, well, we have a course, just actually spend time learning. Like, you know, anyway. And there's like, okay, I know the people who always have good answers there. And so I created a private channel and put them all in it. And I got to admit, that's where I post more often because there's much less, you know, flight of fancy views about how we could solve AGI, blah, blah, blah. So there is a bit of that. But having said that, like, I think the bar is pretty low. Like if you join a Discord and you can hit the like participants or community or whatever button, you can see who's in it. And then you'll see at the top, who the admins or moderators or people in the dev role are. And just DM one of them and say like, oh, here's my GitHub. Well, here's some blog posts I wrote. You know, I'm interested in talking about this, you know, can I join the private channels? And I've never heard of anybody saying no. I will say, you know, Alutha's all pretty open. So you can do the Alutha Discord still. You know, one problem with the Alutha Discord is it's been going on for so long that it's like, it's very inside baseball. It's quite hard to get started. Yeah. Carpa AI looks, I think it's all open. That's just less stability. That's more accessible. [00:52:03]Swyx: Yeah. [00:52:04]Jeremy: There's also just recently, now it's research that does like the Hermes models and data set just opened. They've got some private channels, but it's pretty open, I think. You mentioned Alignment Lab, that one it's all the interesting stuff is on private channels. So just ask. If you know me, ask me, cause I've got admin on that one. There's also, yeah, OS Skunkworks, OS Skunkworks AI is a good Discord, which I think it's open. So yeah, they're all pretty good. [00:52:40]Swyx: I don't want you to leak any, you know, Discords that don't want any publicity, but this is all helpful. [00:52:46]Jeremy: We all want people, like we all want people. [00:52:49]Swyx: We just want people who like, [00:52:51]Jeremy: want to build stuff, rather than people who, and like, it's fine to not know anything as well, but if you don't know anything, but you want to tell everybody else what to do and how to do it, that's annoying. If you don't know anything and want to be told like, here's a really small kind of task that as somebody who doesn't know anything is going to take you a really long time to do, but it would still be helpful. Then, and then you go and do it. That would be great. The truth is, yeah, [00:53:19]Swyx: like, I don't know, [00:53:20]Jeremy: maybe 5% of people who come in with great enthusiasm and saying that they want to learn and they'll do anything. [00:53:25]Swyx: And then somebody says like, [00:53:25]Jeremy: okay, here's some work you can do. Almost nobody does that work. So if you're somebody who actually does the work and follows up, you will massively stand out. That's an extreme rarity. And everybody will then want to help you do more work. [00:53:41]Swyx: So yeah. [00:53:41]Jeremy: So just, yeah, just do work and people will want to support you. [00:53:47]Alessio: Our Discord used to be referral only for a long time. We didn't have a public invite and then we opened it and they're kind of like channel gating. Yeah. A lot of people just want to do, I remember it used to be like, you know, a forum moderator. [00:54:00]Swyx: It's like people just want to do [00:54:01]Alessio: like drive-by posting, [00:54:03]Swyx: you know, and like, [00:54:03]Alessio: they don't want to help the community. They just want to get their question answered. [00:54:07]Jeremy: I mean, the funny thing is our forum community does not have any of that garbage. You know, there's something specific about the low latency thing where people like expect an instant answer. And yeah, we're all somehow in a forum thread where they know it's like there forever. People are a bit more thoughtful, but then the forums are less active than they used to be because Discord has got more popular, you know? So it's all a bit of a compromise, you know, running a healthy community is, yeah, it's always a bit of a challenge. All right, we got so many more things [00:54:47]Alessio: we want to dive in, but I don't want to keep you here for hours. [00:54:50]Swyx: This is not the Lex Friedman podcast [00:54:52]Alessio: we always like to say. One topic I would love to maybe chat a bit about is Mojo, modular, you know, CrystalLiner, not many of you on the podcast. So we want to spend a little time there. You recently did a hacker's guide to language models and you ran through everything from quantized model to like smaller models, larger models, and all of that. But obviously modular is taking its own approach. Yeah, what got you excited? I know you and Chris have been talking about this for like years and a lot of the ideas you had, so. [00:55:23]Jeremy: Yeah, yeah, yeah, yeah, no, absolutely. So I met Chris, I think it was at the first TensorFlow Dev Summit. And I don't think he had even like, I'm not sure if he'd even officially started his employment with Google at that point. So I don't know, you know, certainly nothing had been mentioned. So I, you know, I admired him from afar with LLVM and Swift and whatever. And so I saw him walk into the courtyard at Google. It's just like, oh s**t, man, that's Chris Latner. I wonder if he would lower his standards enough to talk to me. Well, worth a try. So I caught up my courage because like nobody was talking to him. He looked a bit lost and I wandered over and it's like, oh, you're Chris Latner, right? It's like, what are you doing here? What are you doing here? And I was like, yeah, yeah, yeah. It's like, oh, I'm Jeremy Howard. It's like, oh, do you do some of this AI stuff? And I was like, yeah, yeah, I like this AI stuff. Are you doing AI stuff? It's like, well, I'm thinking about starting to do some AI stuff. Yeah, I think it's going to be cool. And it's like, wow. So like, I spent the next half hour just basically brain dumping all the ways in which AI was stupid to him. And he listened patiently. And I thought he probably wasn't even remember or care or whatever. But yeah, then I kind of like, I guess I re-caught up with him a few months later. And it's like, I've been thinking about everything you said in that conversation. And he like narrated back his response to every part of it, projects he was planning to do. And it's just like, oh, this dude follows up. Holy s**t. And I was like, wow, okay. And he was like, yeah, so we're going to create this new thing called Swift for TensorFlow. And it's going to be like, it's going to be a compiler with auto differentiation built in. And blah, blah, blah. And I was like, why would that help? [00:57:10]Swyx: You know, why would you? [00:57:10]Jeremy: And he was like, okay, with a compiler during the forward pass, you don't have to worry about saving context, you know, because a lot will be optimized in the backward. But I was like, oh my God. Because I didn't really know much about compilers. You know, I spent enough to kind of like, understand the ideas, but it hadn't occurred to me that a compiler basically solves a lot of the problems we have as end users. I was like, wow, that's amazing. Okay, you do know, right, that nobody's going to use this unless it's like usable. It's like, yeah, I know, right. So I was thinking you should create like a fast AI for this. So, okay, but I don't even know Swift. And he was like, well, why don't you start learning it? And if you have any questions, ask me. It's just like, holy s**t. Like, not only has Chris Latner lowered his standards enough to talk to me, but he's offering me personal tutoring on the programming language that he made. So I was just like, I'm not g
Ronald Acuña Jr attacked in Coors Field as Braves win, former Braves coach Pat Corrales passes, 2 women shot at White Sox game, Sonny Seiler of UGA fame dies, Gators flee Florida, Georgia high school football coach sucker-punches player, Wisconsin bar hates Aaron Rodgers a lot, Browns send mixed messages, Djokovic rules, funny MiLB corn dog, anniversary of the passing of Olympic bomb hero Richard Jewell and how the fbi & ajc screwed him over, Babe Ruth gets drunk & fined, Yankees make dumb trade with Braves, Beatles last concert, Hank Aaron milestone, Lou Brock beats Cobb, Brian McCann's baseball first, plus Pete's Tweets and This Day in Baseball History
Brewers great Carlos Gomez walked LOUDLY so today's players can run with authentic entertaining emotion on the diamond. "Go-Go" names the star in today's game who is most fun to watch.The 2-time All Star tells Scott Braun and Erik Kratz why he got up for playing the Yankees, reveals the background story on the nixed Wilmer Flores trade & spills all of the juicy soundbites from his famous exchange with Brian McCann.Tap here to watch all Legends Territory episodes and clips on Foul Territory's Youtube channel.Follow @MLBPAA on Twitter and IG or go to baseballalumni.com for more info on your favorite former players!
Brewers great Carlos Gomez walked LOUDLY so today's players can run with authentic entertaining emotion on the diamond. "Go-Go" names the star in today's game who is most fun to watch. The 2-time All Star tells Scott Braun and Erik Kratz why he got up for playing the Yankees, reveals the background story on the nixed Wilmer Flores trade & spills all of the juicy soundbites from his famous exchange with Brian McCann. Tap here to watch all Legends Territory episodes and clips on Foul Territory's Youtube channel. Follow @MLBPAA on Twitter and IG or go to baseballalumni.com for more info on your favorite former players! Learn more about your ad choices. Visit megaphone.fm/adchoices
Brewers great Carlos Gomez walked LOUDLY so today's players can run with authentic entertaining emotion on the diamond. "Go-Go" names the star in today's game who is most fun to watch. The 2-time All Star tells Scott Braun and Erik Kratz why he got up for playing the Yankees, reveals the background story on the nixed Wilmer Flores trade & spills all of the juicy soundbites from his famous exchange with Brian McCann. Tap here to watch all Legends Territory episodes and clips on Foul Territory's Youtube channel. Follow @MLBPAA on Twitter and IG or go to baseballalumni.com for more info on your favorite former players! Learn more about your ad choices. Visit megaphone.fm/adchoices
Brewers great Carlos Gomez walked LOUDLY so today's players can run with authentic entertaining emotion on the diamond. "Go-Go" names the star in today's game who is most fun to watch.The 2-time All Star tells Scott Braun and Erik Kratz why he got up for playing the Yankees, reveals the background story on the nixed Wilmer Flores trade & spills all of the juicy soundbites from his famous exchange with Brian McCann.Tap here to watch all Legends Territory episodes and clips on Foul Territory's Youtube channel.Follow @MLBPAA on Twitter and IG or go to baseballalumni.com for more info on your favorite former players!
WBC USA Manager, MLB Networks, former Atlanta Brave and Friend of Show Mark DeRosa talks Atlanta Braves, World Baseball Classic and more with Nick Cellini and Chris Dimino See omnystudio.com/listener for privacy information.
WBC USA Manager, MLB Networks, former Atlanta Brave and Friend of Show Mark DeRosa talks Atlanta Braves, World Baseball Classic and more with Nick Cellini and Chris Dimino See omnystudio.com/listener for privacy information.
Cuba Superfan blowing loud horn during WBC. Do you hate fun like Brian McCann if you dont like the WBC? Is noon conference tournament iPad worthy at the office?
A "Drive Around The Sports World" presented by Marietta Toyota Vegas won the Super Bowl Someone needs to check on Chiefs Rookie Jaylen Watson Could Jimmy G be coming to the NFC South Latest on the Michael Irving Hotel Lawsuit Want to own the game winning Field Goal ball from Super Bowl LVII What do Charlie Morton, Jeff Francoeur & Brain McCann have in common Patrick Mahomes wife again... See omnystudio.com/listener for privacy information.
0:00 - York HS, Chicago media and fake news 53:12 - Jim Iuorio, analyst for FOX Business, weighs in as Bahman police bust Sam Bankman-Fried. For more from Jim @jimiuorio 01:06:26 - Brian McCann, with Advocates for Victims of Illegal Alien Crime, has an update on the case of his brother Denny who was killed by an illegal alien in 2011. Brian has also memorialized his brother in a book Death in Chicago A Sanctuary City: The Sad Saga of the Untimely Death of Denny McGurn in 2011 01:24:11 - Ted Dabrowski, president at Wirepoints, continues to warn of the SAFE-T Act as no cash bail goes statewide Jan 1 01:39:43 - Dan Henninger, deputy editor of The Wall Street Journal's editorial page, offers his take on the Twitter Files and Putin's Heist of Ukraine's National Identity 01:55:53 - John Holecek, former Loyola Academy football coach, on coaching an elite HS football team and his recent retirementSee omnystudio.com/listener for privacy information.
Judge ties Maris - Should players playoff stats intertwine with the regular season? - Brian McCann: Superstar or not?
Strider breaks Smoltz's K record in Braves win, Dodgers still suck, La Russa ill, MLB offers free prostate exams, Falcons moves, why Tom Brady is acting strange, CFB's big night, Sir Big Spur off the chopping block, CFP expansion by 2024? Assassination attempt stops soccer matches, Nadal nearly knocks himself out, Boxing legend dies, Gallinari ACL tear, Pete's Tweets, This Day in Sports History. Come for Francoeur poking fun at Brian McCann, stay for ESPN not knowing the difference between Dionne Warwick & Gladys Knight plus lots of Ernie Banks trivia. petedavis.buzzsprout.com
DOB and EOF catch up with Braves pitcher, Collin McHugh! The trio discusses Collin's college and pre-Atlanta career, the two rookies in Spencer Strider and Michael Harris II, Charlie Morton, the art of adding spin to a baseball, Brian McCann, Snitker, and more! Watch today's episode on YouTube: https://youtu.be/4iYKxk70Y0g Follow David on Twitter: @DOBrienATL Follow Eric on Twitter: @EOF34 Follow Collin on Twitter: @Collin_McHugh Learn more about your ad choices. Visit megaphone.fm/adchoices
Braves blow right now, but not as much as the Suns & Chris Paul, NBA player disses Paul on ESPN, Dawgs recruiting, Drew Brees in Seattle? another former Phillies player dies of brain cancer, NY Post columnist goes after NBA's love of thug music, Steve Spurrier on how to improve the SEC, General Booty? Yankees lefty vilified for teenage tweets, fat load on cover of SI's Swimsuit Issue, Rajon Rondo may have pulled a gun on a woman & child, petedavis.buzzsprout.com, Pete's Tweets, This Day in Sports History. Come for Bob Costas trying not to cuss during broadcast, stay for Hank Aaron & Brian McCann & Ralph Garr highlights. Plus the Dodgers trade a player for being gay
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