Podcasts about Reka

Reka je tekla od izvira v osrčju oddaljenih gora skozi različne pokrajine, dokler ni prišla do puščavskega peska. Kakor je ...Iz knjige Drobne zgodbe za dušo, ki je izšla pri založbi Ognjišče.

This week, I'm talking to Reka and Dorina, the founders of Budapest Racquet Society—or BRS—in, you guessed it, Budapest.We're in the early days of this new tennis club movement, but something exciting is definitely happening at the intersection of social community and sports. Running and training have paved the way, and now tennis is stepping into the spotlight—and it's emerging fast.For a certain generation, the concept of social community is second nature. People are craving connection and movement, whether it's reconnecting with something they loved as kids or finally trying something they've always wanted to but found intimidating. Tennis has traditionally carried that air of exclusivity, but these new clubs are flipping the script. They're all about connection, community, and a genuine passion for the sport. BRS is a perfect example, making serious waves in Budapest's tennis scene.In this episode, we dive into Reka and Dorina's tennis backgrounds, the tennis culture in Hungary, and why Budapest seems especially welcoming to this new style of club. What I love about BRS and others like it is their focus on individuality and inclusivity. On court, with a racquet in hand, you're simply a tennis player—no clichés required. At its core, the draw of these clubs goes beyond the sport. It's about fun—the joy of being on court, moving, learning, and improving alongside others. Honestly, life doesn't get much better than that.A huge thanks to Dorina and Reka for sharing their time and for what they're doing for tennis in Budapest. I'm a big fan of the sport and all these new clubs popping up around the world.As always, share this pod with like-minded people or anyone who's thought about getting into tennis or racquet sports but didn't know where to start. Chances are, there's a club like BRS in a city near you—find them, get on court, and get involved.From Chris and me, peace.

Welcome to this episode of the Hungarian Heritage Podcast. We are back with another episode in our series of Celebrating Hungarian Heritage Together! Previously my guests on the podcast, Reka Bakos and Dr. Anna Fenyvesi, the editors of Hungarian Roots and American Dreams, will be my co-hosts for various episodes during season 3, as we celebrate Hungarian Heritage Together. This collaborative  series will feature guests who contributed their personal and family's stories in Reka and Anna's newly released book. In this episode I am thrilled to announce that my co-host with be Reka Bakos, and together we will be featuring Julie Galatocky's Hungarian heritage story which is featured in Hungarian Roots and American Dreams. As Julie shares her family's story, you will not only learn about her family's journey from Hungary to America, but you will learn about Julie's Hungarian upbringing, her passion for genealogy research, her full circle connection with Sunday Soup, and of course when she finally met her Hungarian relatives for the first time. You will certainly feel the Hungarian connection when listening to Julie's story, and you will realize that you are not alone in your Hungarian Heritage journey. Listen along as we Celebrate Hungarian Heritage Together.If you have feedback or questions about this episode or you would like to connect with me at the podcast, you will also find that information below. If you've enjoyed this episode and you're interested in learning more about this Hungarian Heritage community, please don't hesitate to reach out. I would love to hear from you. Our theme music is Hungarian Dance by Pony Music, used with special license from Envato Market. Don't forget to subscribe, rate and review wherever you listen to your podcasts. Thanks again for listening, and until next time, make sure you Stay Hungarian Heritage Strong! CONNECT with Reka Bakos Instagram: @hungarianroots_americandreamsFacebook : Hungarian Roots and American DreamsEmail : reka.bakos@rootstories.huPurchase a copy of Hungarian Roots and American Dreams through this email: hungarianrootsamericandreams@rootstories.huCONNECT with the Podcast Website: www.myhungarianheritage.com Email: Christine@myhungarianheritage.comInstagram: @hungarianheritagepodcastFacebook: Hungarian Heritage Podcast  *If you would like to get in touch with Julie Galatocky, you can reach out to either Reka or myself and we will connect you by email with Julie's permission.   

Im Interview mit Reka von der Grünen Liga geht um die Pläne der Leag nach dem Kohleausstieg die Tagebauen in der Lausitz zu fluten. Welche Bedeutung wird dies für die Wasserverorgung u.a. in Berlin haben? Welche Wasserverschmutzungen drohen? Darüberhinaus ging es um den tschechische Unternehmer Daniel Křetínský und dessen Pläne. Ein Haufen gute Gründe um […]

Is your organization just jumping on the AI bandwagon, or do you have a solution that will support your company's needs in the short and long term? Welcome to this episode, brought to you by Reka, a developer of industry-leading, multimodal, AI models that enable individuals and organizations to deploy generative AI applications. Today we're going to talk about the power of multimodal AI in the enterprise and why it is important for businesses to incorporate AI that is able to utilize multiple input sources, multiple languages, and flexible contexts to provide more intelligent insights. To help me discuss this topic, I'd like to welcome Dani Yogatama, CEO of Reka. RESOURCES Reka: https://www.reka.ai Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brightest minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

Is your organization just jumping on the AI bandwagon, or do you have a solution that will support your company's needs in the short and long term? Welcome to this episode, brought to you by Reka, a developer of industry-leading, multimodal, AI models that enable individuals and organizations to deploy generative AI applications. Today we're going to talk about the power of multimodal AI in the enterprise and why it is important for businesses to incorporate AI that is able to utilize multiple input sources, multiple languages, and flexible contexts to provide more intelligent insights. To help me discuss this topic, I'd like to welcome Dani Yogatama, CEO of Reka. RESOURCES Reka: https://www.reka.ai Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brightest minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

Reka Ljubljanica je zanimiva z več vidikov - od geološko geografskih, saj je tako rekoč sestavljena iz šestih ponikalnic širokega kraškega zaledja, do zgodovinsko antropoloških. V reki in ob njej so našli toliko arheoloških ostankov, da lahko arheologi iz njih spoznavajo, kako živahno je bilo življenje že pred tisočletji. Leto nastanka: 2023.

V Srbiji je pred današnjim novim velikim študentskim protestom v Beogradu napeto. Pol ure pred uradnim začetkom je središče mesta povsem polno in blokirano. Oblast, ki trdi, da je cilj opozicije nasilen prevzem oblasti, je prepovedala vstop v državo več novinarjem, predvsem iz Hrvaške, pa tudi našim kolegom s POP TV-ja. Predsednik Aleksandar Vučić zagotavlja, da bodo na, kot pravi, nezakonitem shodu storili vse za varnost udeležencev. V oddaji tudi: - Svetovni voditelji na virtualnem vrhu o namestivi vojske v Ukrajini in povečanju pritiska na Rusijo. - Ob močnem deževju Krka in Reka poplavljata, nekatere ceste so pod vodo. - Anamariji Lampič tretje mesto na Pokljuki, tekma skakalk v Vikersundu zaradi vetra preložena.

Fluent Fiction - Hungarian: Easter Harmony: A Tale of Friendship and Courage at Budapest High Find the full episode transcript, vocabulary words, and more:fluentfiction.com/hu/episode/2025-03-06-23-34-02-hu Story Transcript:Hu: Ahogy az iskola folyosói megéltek a tavaszi napfényt, a sarokban csodás húsvéti dekorációk figyeltek.En: As the school corridors basked in the spring sunlight, beautiful Easter decorations observed from the corners.Hu: Virágok illata töltötte be a helyiséget, ami egy különös varázst adott a budapesti középiskola légterének.En: The scent of flowers filled the place, giving a peculiar charm to the atmosphere of the Budapest high school.Hu: A tanulók izgatott suttogása visszhangzott a falaik között, mindeni a közelgő tavaszi tehetségkutatóra készülődött.En: The excited whispers of the students echoed within the walls, as everyone prepared for the upcoming spring talent show.Hu: Bence, a tehetséges énekes, akinek álma az volt, hogy lenyűgözi a feltételezett tehetségkutatókat, feszülten gyakorolt.En: Bence, the talented singer whose dream was to impress the presumed talent scouts, practiced tensely.Hu: Réka, a legjobb barátja, hűségesen mellette ült a zongoránál, de a szíve titkok salamoni pánikját zubogott.En: Réka, his best friend, sat faithfully by him at the piano, but inside her heart, the panic of unspeakable secrets raged.Hu: – Tudod, Reka – kezdte Bence, miközben takarította a torkát.En: "You know, Réka," began Bence, clearing his throat.Hu: – Nagy esély ez számunkra.En: "This is a big opportunity for us.Hu: Tökéletesnek kell lennie.En: It has to be perfect."Hu: Réka bólintott, bár kezei remegtek a billentyűkön.En: Réka nodded, though her hands trembled on the keys.Hu: Inkább élvezni szerette volna a fellépést, de belső félelmei újra felszínre törtek.En: She preferred to enjoy the performance, but her inner fears resurfaced again.Hu: Az ujjak tétován csúsztak a zongorabillentyűkön, szeme pedig idegesen követelt pihenést.En: Her fingers hesitated over the piano keys, and her eyes nervously demanded rest.Hu: Ahogy a próbák folytatódtak, Bence észrevette Réka nyugtalanságát.En: As the rehearsals continued, Bence noticed Réka's restlessness.Hu: Egy nap a próbateremben Réka hirtelen megállt.En: One day in the rehearsal room, Réka suddenly stopped.Hu: Lélegzete felgyorsult, majd kitört belőle:– Nem megy.En: Her breath accelerated, then she burst out: "I can't.Hu: Nem vagyok elég jó ehhez!En: I'm not good enough for this!"Hu: – Reka, várj!En: "Réka, wait!"Hu: – mondta Bence, átkarolva.En: said Bence, embracing her.Hu: – Nem tűnt fel, hogy ennyire aggódsz.En: "I didn't realize you were so worried.Hu: Segíteni akarok, nem nyomást helyezni rád.En: I want to help you, not pressure you."Hu: Ezután Bence kitalálta, hogyan támogathatná Rékát ahelyett, hogy tovább nyomná.En: Then, Bence figured out how he could support Réka instead of pushing her further.Hu: Napokig csak beszélgettek, nevettek, és kisebb fellépéseket tartottak az osztálytársak előtt, hogy Réka megszokja a nézőket.En: For days, they just talked, laughed, and held smaller performances in front of classmates, so Réka could get used to the audience.Hu: A nagy bemutató napja gyorsan elérkezett.En: The day of the big show arrived quickly.Hu: Az aula tele volt izguló diákokkal, tanárokkal és néhány kívülállóval, akik a falakat díszítő húsvéti koszorúkat csodálták.En: The auditorium was filled with excited students, teachers, and some outsiders who admired the Easter wreaths decorating the walls.Hu: Ahogy Bence és Réka színpadra léptek, a nézők elcsendesedtek.En: As Bence and Réka stepped onto the stage, the audience quieted.Hu: A lámpák fénye barátságosan érintette a duót.En: The light's glow touched the duo warmly.Hu: Bence felsóhajtott, majd Rékára nézett.En: Bence sighed, then looked at Réka.Hu: Egy bátorító mosoly kíséretében kezdte az éneklést, és Réka csatlakozott hozzá.En: With an encouraging smile, he started singing, and Réka joined him.Hu: Bár nem volt minden hang és akkord tökéletes, a közönség érezte a kettejük közötti harmóniát és örömöt.En: Although not every note and chord was perfect, the audience felt the harmony and joy between the two.Hu: A tapsvihar megérdemelten zúgott fel a terem falai között.En: The applause deservedly roared within the room's walls.Hu: Az esemény után Bence és Réka kicsit hátrébb húzódtak a tömegtől.En: After the event, Bence and Réka stepped back a bit from the crowd.Hu: Mindketten megkönnyebbülten nevettek, mert rájöttek, hogy a tökéletességnél sokkal fontosabb a közös élmény volt.En: Both laughed with relief, realizing that the shared experience was far more important than perfection.Hu: – Köszönöm, Bence – szólt Reka, mosollyal az arcán.En: "Thank you, Bence," said Réka, smiling.Hu: – Segítettél kiűzni a félelmet.En: "You helped me banish the fear."Hu: – Te voltál a legjobb partner – válaszolta Bence.En: "You were the best partner," replied Bence.Hu: – Rájöttem, hogy a csapatmunka és a barátság sokkal fontosabb.En: "I realized that teamwork and friendship are much more important."Hu: A tavasz újraélesztette a reményt és önbizalmat a szívükben, ahogy együtt indultak el az új kalandok felé a budapesti tavasz melegében.En: Spring rekindled hope and confidence in their hearts as they set out together on new adventures in the warmth of Budapest's spring. Vocabulary Words:corridor: folyosóbask: megéltdecoration: dekorációpeculiar: különöscharm: varázspresumed: feltételezetttensely: feszültenfaithfully: hűségesenunspeakable: salamonirage: zubogopportunity: esélytremble: remeghesitate: tétovázrestlessness: nyugtalanságaccelerate: felgyorsulembrace: átkarolpressure: nyomásauditorium: aulaoutsiders: kívülállókdecorate: díszítencouraging: bátorítóharmony: harmóniaapplause: tapsvihardeservedly: megérdemeltenrelief: megkönnyebbülésbanish: kiűzpartner: partnerteamwork: csapatmunkarekindle: újraélesztconfidence: önbizalom

Welcome to this episode of the Hungarian Heritage Podcast. We are continuing our series of Celebrating Hungarian Heritage Together! As I have mentioned before,  my previous guests on the podcast, Reka Bakos and Dr. Anna Fenyvesi, the editors of Hungarian Roots and American Dreams, will be my co-hosts for various episodes during Season 3, as we celebrate Hungarian Heritage Together. This collaborative series will feature guests who have contributed their personal and family's stories in Reka and Anna's newly released book. In this episode I am thrilled to announce that my co-host with be Dr. Anna Fenyvesi, and together we will be featuring Dr. Rózsavőlgyi's  Hungarian Heritage story titled, "The Story of a 20th Century American Peregrination and its Tragic Consequences."  Along with my cohost, Dr. Anna Fenyvesi, we will also have Dr. Don Peckham, who co-authored the story with Anna, and Nóra Szentgyörgi, who is Dr. Rózsavölgyi's great granddaughter.  Listen along while we unpack the layers of research, family histories, and serendipitous meetings, as we discover why Dr. Rózsavőlgyi emigrated to the United States with his family, and why he and his family eventually returned to Hungary years later, not to mention all of the interesting things that happened after he returned. If you are interested in hearing more Hungarian Heritage stories from the Hungarian Roots and American Dreams book, you can purchase a copy of the book either in English or in Hungarian. That information will be located below. If you are interested in joining the Facebook group that was mentioned in this episode, you will find that information below, as well as, how you can get in contact with Dr. Anna Fenyvesti or Reka Bakos, the editors of Hungarian Roots and American Dreams. If you've enjoyed this episode and you're interested in learning more about this Hungarian Heritage community, please don't hesitate to reach out. I would love to hear from you. Our theme music is Hungarian Dance by Pony Music, used with special license from Envato Market. Don't forget to subscribe, rate and review wherever you listen to your podcasts. Thanks again for listening, and until next time, make sure you Stay Hungarian Heritage Strong!  SziastokCONNECT with Dr. Anna Fenzvesi Instagram: @hungarianroots_americandreamsFacebook : Hungarian Roots and American DreamsEmail : fenyvesi@lit.u-szeged.huCONNECT with co-editor Reka Bakos Instagram: @hungarianroots_americandreamsFacebook : Hungarian Roots and American DreamsEmail : reka.bakos@rootstories.huPURCHASE A COPY  of Hungarian Roots and American Dreams through this email: hungarianrootsamericandreams@rootstories.huCONNECT with the Podcast Website: www.myhungarianheritage.com Email: Christine@myhungarianheritage.comInstagram: @hungarianheritagepodcastFacebook: Hungarian Heritage Podcast      

Présentée par Jeff - Partie CRUSH THE RATS - Convulsound Studio à 00:30:00   Gérer le son live pour des Benighted, Celeste ou Destinity, a dû donner l'envie à Thibault Bernard de rebrancher la guitare. L'ingénieur son, à qui l'on doit des productions d'albums de Kamizol-K ou Deathawaits, pour ne citer qu'eux, présente désormais son propre projet. CRUSH THE RATS est un condensé de violence qui puise principalement dans le grindcore, mais aussi le brutal death, la noise et le black metal. Ce premier EP Don't montre la couleur, en seulement seize minutes et très peu de secondes de respiration. Nous profitons de ce premier méfait pour discuter avec le musicien et l'ingé son sur l'ensemble de ses activités ! En première partie d'émission, nous évoquerons le deuxième album de CROSS BRINGER, formation avec des membres de PREDATORY VOID, REKA et HOARI. Nous allons aussi du côté de la Belgique avec RÄUM qui présente Emperor of the Sun paru chez Les Acteurs De L'Ombre Productions. Les britanniques d'ABDUCTION viennent aussi de sortir leur cinquième méfait, Existentialismus. Ils sont également au programme de notre macabre cérémonie…  

In this episode, we're bringing back Reka, Head of Community at RISC Zero, about the state of zero-knowledge (ZK) infrastructure and strategies for successful go-to-market campaigns for blockchain protocols. Reka shares her journey from being a founder to joining RISC Zero, her insights on the importance of ZK technology, and the challenges and opportunities it presents. She also talks about her experience advising various crypto projects and her thoughts on combining education and community engagement in the blockchain space. Reka previously appeared on Rehash S4 E1 alongside LDF: https://youtu.be/TXzEpbvSVo0?si=Nzj9Dvbq6yMU0CM_ ⏳ TIMESTAMPS: 0:00 Intro 01:49 Updates from Reka's past appearance in S4 E1 02:58 Why intent infrastructure? 06:17 Understanding ZK technology 13:44 Applications and benefits of ZK 18:21 Challenges and future of ZK 23:24 Joining RISC Zero and building Boundless Protocol 29:07 Education through memes 30:13 Marketing strategies for protocols 34:17 Balancing developer adoption and end user growth 37:13 Strategies for building a community from scratch 44:27 Questions from the community 50:09 Follow Reka 

Kuliah Tafsir Nurul Ehsan Jilid 3 yang berlangsung pada 20 Jun 2006. (Ini adalah siaran eksperimen. Kami mengalu-alukan sebarang cadangan dan komen membina.) -- ISI KANDUNGAN KULIAH : ~ Nabi Nampak benda pelik, seorang Yahudi di contengkan muka ~ Hukuman untuk orang yang berzina dalam kitab taurat ~ Jawapan alim taurat tentang hukum yang sebenarnya ~ Bercakap benar Sebab disuruh bersumpah dengan nama Allah ~ Golongan bangsawan dan berkedudukan dalam bangsa Yahudi banyak berzina ~ Beza hukuman zina untuk orang yang kaya berbanding orang miskin ~ Reka hukuman sendiri yang tak membezakan antara orang kaya dan miskin ~ Hukum yang telah ditinggalkan oleh pendeta yahudi ~ Nabi tak main-main dengan perintah Allah ~ Seorang perempuan dari golongan yang terhormat ditangkap mencuri ~ Mencuri kerana sengaja suka mencuri ~ Perempuan takkan nak potong tangan ~ Minta batalkan atau tangguhkan hukuman ~ Buah hati Rasulullah yang boleh pujuk Nabi ~ Berubah warna muka Nabi ~ Hang nak suruh aku pinda hukum hudud Allah? ~ Sesungguhnya umat Nabi dulu, binasa sebab mereka meninggalkan hukum Allah ~ Memilih bulu dalam mengenakan hukuman ~ Sekiranya Fatimah Binti Muhammad mencuri, aku akan potong tangan dia ~ Hukum Allah, Nabi tak buat main-main -- TAFSIR NURUL EHSAN JILID 3 MUKA SURAT 293 -- ~ Tafsir Surah Faathir Ayat 24 ~ Nabi Muhammad memang sebenarnya dilantik oleh Allah dan membawa agama yang benar ~ Nabi bagitahu berita gembira kepada orang yang ikut agama Allah ~ Ancaman dan berita buruk untuk orang yang ingkar ~ Tiada satu pun umat yang tidak ada Nabi ~ Allah jamin ketulenan Quran dan Quran akan terpelihara sehingga kiamat ~ Kitab-kitab samawi yang pernah diturunkan selain dari Quran ~ Dakwah orang tak mahu dengar ni perkara biasa ~ Laluan yang telah pernah dilalui oleh para Nabi dahulu ~ Kisah seorang perempuan telefon Ustaz ~ Orang yang kematian anak kecil ~ Anak kecil tak mahu masuk syurga ~ Apa jadi jika anak berumur 18 tahun mati dalam keadaan baik? ~ Sejak lahir tak pernah guris hati mak ~ Sabar dalam menerima apa saja ujian ~ Bantah Allah tidak akan terlepas daripada dihukum ~ Mungkinkah Allah akan mungkir janji? ~ Air hujan yang turun sama saja, tapi tumbuh daripada bumi, tumbuhan berbeza ~ Warna berbeza-beza, gunung dan bukit pun warna tak sama ~ Bukit Uhud tak sama dengan warna bukit yang berada disekelilingnya ~ Air laut juga lain-lain warnanya ~ Ambillah iktibar wahai orang-orang yang diberikan mata ~ Muka Melayu dengan Jawa tak sama ~ Orang yang takut Allah adalah orang alim dan betul kenal Allah ~ Allah bersedia untuk mengampunkan mana-mana orang yang bertaubat ~ Ciri-ciri orang baik ialah selalu baca kitab Allah ~ Tanya diri kita, berapa kali kita baca Quran dalam seminggu ~ Sejauh mana kita dengan perintah Allah ~ Orang yang sanggup nafkahkan rezeki di jalan Allah ~ Tanda kebaikan seorang manusia ~ Perniagaan yang tidak akan rugi ~ Tijaratan Lan Tabur ~ Kisah tiga orang pemuda yang terperangkap dalam gua ~ Cari satu amalan yang dibuat betul-betul ikhlas kerana Allah ~ Bertawasul dengan amal soleh dibolehkan ~ Mana-mana orang yang buat baik, Allah akan tambah kebaikan ~ Allah akan tambah kebaikan kepada orang yang bersyukur ~ Betapa banyaknya perkataan "Ghafur" diulang sebut dalam Quran ~ Allah tunggu hamba-hambaNya minta ampun ~ Quran yang sebenarnya daripada Allah ~ Quran tidak menafikan taurat dan injil ~ Allah maha mengetahui dan maha melihat ~ Perintah Allah sentiasa relevan sepanjang masa SETERUSNYA ~ Manusia ada tiga macam -- Dapatkan External SSD 512GB Sempena 11 Tahun Zonkuliah : https://toyyibpay.com/Zonkuliah-External-SSD-512GB -- Sokong Projek Zonkuliah Dengan Menyumbang Ke Akaun Berikut : ➢ https://payment.tngdigital.com.my/sc/bDLnYClrWk ➢ MAYBANK (Produksi Zonkita) - 557250054584 ➢ PAYPAL - paypal.me/DanaZK --- ☑● Doakan Dimurahkan Rezeki dan Diberikan Kesihatan Yang Baik Untuk Kami Teruskan Projek ZonKuliah ☑● ✚ Untuk update terkini sila like Facebook Page kami : www.facebook.com/zonkuliah

Why was there more matter than antimatter left over? Neil deGrasse Tyson and comedian Chuck Nice explore the quantum origins of the universe, charge parity violation, dark matter, and the many quarks that make up our world with CERN particle physicist Harry Cliff. NOTE: StarTalk+ Patrons can listen to this entire episode commercial-free here: https://startalkmedia.com/show/why-anything-with-harry-cliff/Thanks to our Patrons Diedre Austin, Robert R Able, Peter Onnasch, Valarie McCullar, tremayne johnston, Kurt Kwok, Gianfranco Iannotta, April007, Dale Frewaldt, Sergio Castañeda, Desiray Belcher, Steelfinger7 Steelfinger7, Arnav Madan, Jana, Stephan, Craig Cordwell, Emmanuel Nolasco, Micheal Dunthorn, Forgotten Glory, Thornman, Simba vortex, Justus Patrick, Joey Sandall, Ainsley Bhattan, Dan Teston, Nick Smith, Matt Curtis, Todd King, Reka, and Micheal Smith for supporting us this week. Subscribe to SiriusXM Podcasts+ on Apple Podcasts to listen to new episodes ad-free and a whole week early.

Andri Rúnar Bjarnason, sóknarmaður Stjörnunnar, er sérstakur gestur í Enski boltinn hlaðvarpinu þennan föstudaginn. Andri er stuðningsmaður Manchester United sem hefur farið býsna vel af stað á árinu 2025. Man Utd vann endurkomusigur á Southampton í gær þar sem Amad Diallo skoraði þrennu. Guðmundur Aðalsteinn Ásgeirsson stýrir þættinum og Magnús Haukur Harðarson er að venju með. Farið er yfir síðustu leiki og stöðuna í deildinni.

Welcome to this episode of the Hungarian Heritage Podcast. We are back with another episode in our series of Celebrating Hungarian Heritage Together! Previously my guests on the podcast, Reka Bakos and Dr. Anna Fenyvesi, the editors of Hungarian Roots and American Dreams, will be my co-hosts for various episodes during season 3, as we celebrate Hungarian Heritage Together. This collaborative  series will feature guests who contributed their personal and family's stories in Reka and Anna's newly released book. In this episode I am thrilled to announce that my co-host with be Reka Bakos, and together we will be featuring Laura Csiszer's Hungarian heritage story, which is featured in Hungarian Roots and American Dreams. As Laura shares her family's story, you will not only learn about her family's journey from Hungary to America, but you will learn about Laura's decades of genealogy research, when she first had her breakthrough moment, her fantastic and fulfilling journey when she finally met her Hungarian relatives for the first time, as well as, some very interesting stories along the way. You will certainly feel the Hungarian connection when listening to Laura's story, and you will realize that you are not alone in your Hungarian Heritage journey. Listen along as we Celebrate Hungarian heritage together.If you're interested in purchasing a copy of Hungarian Roots and American Dreams,  either in English or in Hungarian, or if you are interested in joining the Facebook group that was mentioned in this episode, you will find that information below, as well as, how you can get in contact with Reka Bakos. If you have feedback or questions about this episode or you would like to connect with me at the podcast, you will also find that information below. If you've enjoyed this episode and you're interested in learning more about this Hungarian Heritage community, please don't hesitate to reach out. I would love to hear from you. Our theme music is Hungarian Dance by Pony Music, used with special license from Envato Market. Don't forget to subscribe, rate and review wherever you listen to your podcasts. Thanks again for listening, and until next time, make sure you Stay Hungarian Heritage Strong!  SziastokCONNECT with Reka Bakos Instagram: @hungarianroots_americandreamsFacebook : Hungarian Roots and American DreamsEmail : reka.bakos@rootstories.huPurchase a copy of Hungarian Roots and American Dreams through this email: hungarianrootsamericandreams@rootstories.huCONNECT with the Podcast Website: www.myhungarianheritage.com Email: Christine@myhungarianheritage.comInstagram: @hungarianheritagepodcastFacebook: Hungarian Heritage Podcast  *If you would like to get in touch with Laura Csiszer, you can reach out to either Reka or myself and we will connect you by email with Laura's permission.    

Bangunan ikonik di Singapura seperti Taman East Coast, Lapangan Terbang Changi, Terminal 2, dan Masjid Al-Falah telah menjadi destinasi popular yang sering dikunjungi ramai. Tapi siapa sebenarnya arkitek di sebalik reka bentuk bangunan-bangunan ini Episod NoTapis kali ini menampilkan dua arkitek berbakat yang terlibat dalam projek-projek ini — arkitek bersekutu, Encik Mohammed Madeni Jais, dan arkitek eksekutif, Cik Nur Syarafina Kamsani, dari RSP Architects, Planners & Engineers (Pte) Ltd. Mereka pengalaman serta kesan yang diharapkan sampai kepada masyarakat melalui reka bentuk bangunan dan ruang ini.See omnystudio.com/listener for privacy information.

Reka Una izvira na Hrvaškem in večinoma teče v Bosni in Hercegovini. Pred nekaj meseci se je na njenem izviru, ki sodi v zaščiteno območje Natura 2000, začela gradnja male hidroelektrarne. Reka je spremenila svoj tok, zaradi grobega posega v naravo je nekaj vasi ostalo brez pitne vode. Po protestih domačinov in ekologov, je Hrvaška gradnjo vendarle zaustavila, dogajanje pa odpira širša vprašanja varovanja in zaščite rek tudi na območju Evropske unije. Obiskali smo izvir reke Une, se pogovarjali z domačini in odgovornimi, in šli s turističnim tokom reke vse do Bihaća. Napiši komentar o reportaži! Piši na luka.hvalc@rtvslo.si in gasper.andrinek@rtvslo.si.

Welcome to this episode of the Hungarian Heritage Podcast. Staying true to the vision of connecting the circles of Hungarians around us, I would like to welcome all you to this new series of Celebrating Hungarian Heritage Together. Previously my guests on the podcast, Reka Bakos and Anna Fenyvesi, the authors of Hungarian Roots and American Dreams, will be my co-hosts for various episodes throughout season 3, as we celebrate Hungarian Heritage Together. This new series will feature guests who contributed their personal and family's stories in Reka and Anna's newly released book. In this episode I am thrilled to announce that my co-host with be Reka Bakos, and together we will be featuring Aaron Melville's Hungarian heritage story that was featured in Hungarian Roots and American Dreams. As Aaron shares his family's story, you will not only learn of his family's journey from Hungary to America, but you will learn about his clever genealogy research tactics that helped him connect more of the branches of his Hungarian family tree. Listen along as we Celebrate Hungarian heritage together. If you're interested in purchasing a copy of the book either in English or in Hungarian, or if you are interested in joining the Facebook groups that were mentioned in this episode, you will find that information below, as well as, how you can get in contact with Reka Bakos. If you have feedback or questions about this episode or you would like to connect with me at the podcast, you will also find that information below. If you've enjoyed this episode and you're interested in learning more about this Hungarian Heritage community, please don't hesitate to reach out. I would love to hear from you.  Our theme music is Hungarian Dance by Pony Music, used with special license from Envato Market. Don't forget to subscribe, rate and review wherever you listen to your podcasts. Thanks again for listening, and until next time, make sure you Stay Hungarian Heritage Strong!  SziasztokCONNECT with Reka Bakos Instagram: @hungarianroots_americandreamsFacebook : Hungarian Roots and American DreamsEmail : reka.bakos@rootstories.huPurchase a copy of Hungarian Roots and American Dreams through this email: hungarianrootsamericandreams@rootstories.huCONNECT with the Podcast Website: www.myhungarianheritage.com Email: Christine@myhungarianheritage.comInstagram: @hungarianheritagepodcastFacebook: Hungarian Heritage Podcast  *If you would like to get in touch with Aaron Melville, you can reach out to either Reka or myself and we will connect you by email with Aaron's permission.    

139 Mit dem Jahresende nahen die Jahresbestenlisten - natürlich auch bei Videospielen. Im FM4 Game Podcast blicken auch David Riegler und Rainer Sigl auf das ganze Spielejahr zurück, aber durch die rosa Brille - mit Absicht: Cozy Games, also Spiele, in denen es weniger ums Kämpfen und Überleben geht, sondern eher um Gemütlichkeit, Achtsamkeit und Kooperation. Klingt nach Hype-Trend, Biedermeier und "Stardew Valley" samt seinen 1000 Klonen? Ja - aber nicht nur. Entgegen dem Klischee hat das Cozy-Games-Jahr 2024 Spiele aus ganz verschiedenen Genres und Richtungen hervorgebracht - viele davon hat FM4 bereits bei Erscheinen gewürdigt. David und Rainer holen "Tiny Glade", "Trash Goblin", "REKA", "Little Kitty, Big City", "Core Keeper", "Oddsparks", "Towers of Aghasba", "Europa", "Smalland", "Botany Manor" und "Schnupferich: Die Melodie des Mumintals" nochmal auf Podcast-Bühne - es war ein gutes Jahr für Cozy Games. (Folge #139) Sendungshinweis: FM4 Game Podcast 5. Dezember 2024, 0-1 Uhr.

Singapore's GovTech is hosting an AI CTF challenge with ~$15,000 in prizes, starting October 26th, open to both local and virtual hackers. It will be hosted on Dreadnode's Crucible platform; signup here!It is common to say if you want to work in AI, you should come to San Francisco. Not everyone can. Not everyone should. If you can only do meaningful AI work in one city, then AI has failed to generalize meaningfully.As non-Americans working in the US, we know what it's like to see AI progress so rapidly here, and yet be at a loss for what our home countries can do. Through Latent Space we've tried to tell the story of AI outside of the Bay Area bubble; we talked to Notion in New York and Humanloop and Wondercraft in London and HuggingFace in Paris and ICLR in Vienna, and the Reka, RWKV, and Winds of AI Winter episodes were taped in Singapore (the World's Fair also had Latin America representation and we intend to at least add China, Japan, and India next year).The Role of Government with AIAs an intentionally technical resource, we've mostly steered clear of regulation and safety debates on the podcast; whether it is safety bills or technoalarmism, often at the cost of our engagement numbers or ability to book big name guests with a political agenda. When SOTA shifts 3x faster than it takes to pass a law, when nobody agrees on definitions of important things, when you can elicit never-before-seen behavior by slightly different prompting or sampling, it is hard enough to simply keep up to speed, so we are happy limiting our role to that. The story of AI progress has more often been achieved in the private sector, usually in spite of, rather than with thanks to, government intervention.But industrial policy is inextricably linked to the business of AI, which we do very much care about, has an explicitly accelerationist intent if not impact, and has a track record of success in correcting for legitimate market failures in private sector investment, particularly outside of the US. It is with this lens we approach today's episode and special guest, our first with a sitting Cabinet member.Singapore's National AI StrategyIt is well understood that much of Singapore's economic success is attributable to industrial policy, from direct efforts like the Jurong Town Corporation industrialization to indirect ones like going all in on English as national first language. Singapore's National AI Strategy grew out of its 2014 Smart Nation initiative, first launched in 2019 and then refreshed in 2023 by Minister Josephine Teo, our guest today.While Singapore is not often thought of as an AI leader, the National University ranks in the top 10 in publications (above Oxford/Harvard!), and many overseas Singaporeans work at the leading AI companies and institutions in the US (and some of us even run leading AI Substacks?). OpenAI has often publicly named the Singapore government as their model example of government collaborator and is opening an office in Singapore in time for DevDay 2024.AI Engineer NationsSwyx first pitched the AI Engineer Nation concept at a private Sovereign AI summit featuring Dr. He Ruimin, Chief AI Officer of Singapore, which eventually led to an invitation to discuss the concept with Minister Teo, the country's de-facto minister for tech (she calls it Digital Development, for good reasons she explains in the pod).This chat happened (with thanks to Jing Long, Joyce, and other folks from MDDI)!The central pitch for any country, not just Singapore, to emphasize and concentrate bets on AI Engineers, compared with other valuable efforts like training more researchers, releasing more government-approved data, or offering more AI funding, is a calculated one, based on the fact that: * GPU clusters and researchers have massive returns to scale and colocation, mostly concentrated in the US, that are irresponsibly expensive to replicate* Even if research stopped today and there was no progress for the next 30 years, there are far more capabilities to unlock and productize from existing foundation models and we

Vorweg: Reka befindet sich noch im Early Access, es kann sich also noch einiges ändern, gerade meine negativen Punkte könnten in der Zukunft noch wegfallen. Aktuell ist leider noch wenig Inhalt bei Reka zu finden, ca. 2 Stunden braucht man, um den Sandbox Modus freizuschalten und dann können die endlosen Stunden beginnen, die eigene Hexenhütte zu gestalten. Es gibt jetzt schon viele Deko Elemente für die Hütte, ebenso wie Möbel, die wir dann präzise platzieren können, einzig Böden und Wände finde ich ein bisschen schwierig. Hier wäre ein Flugmodus ganz praktisch. Die Mischung aus erkunden, Informationen beschaffen, indem wir mit Dorfbewohnern reden, bauen und craften ist meiner Meinung nach ganz gut gelungen bisher. Natürlich kann man noch nicht viel sagen, da es ja in einem frühen EA Zustand ist, aber was ich bisher erlebt habe, macht definitiv Lust auf mehr. Ich bin gespannt, wohin uns unsere Hexenhütte noch bringen wird.

Das Thurgauer Verwaltungsgericht hat eine Beschwerde gegen die Aufhebung eines bestehenden Gestaltungsplans als unbegründet beurteilt. Ein wichtiger Schritt für die weitere Planung des Reka-Feriendorfs direkt am Bodenseeufer, welches nun nach vielen Jahren Realität werden könnte. Weitere Themen: * Frauenfeld stimmt am 24. November über kommunale Veloinitiative ab und über die Verwendung von Neuberwertungsreserven im Wert von über 36 Millionen Franken. * Keine Defizitgarantie für die Freestyle-WM von der Engadiner Gemeinde Samedan.

Es wird kälter, die Bäume werden kahl, die Chilis auf der Fensterbank wachsen nicht mehr so gut. Der Frost kommt. Und wir müssen uns anpassen. Zum Beispiel indem wir die Kinder in die Kohleminen schicken, die Wohnung mit Asbest tapezieren und Schlägertrupps auf die Straße schicken, um zu gucken, ob jemand Fenster auf und Heizung an hat. Zumindest wenn es nach Frostpunk 2 geht. Der erste Teil hat das Survival-Aufbau-Genre richtig groß gemacht. Und der zweite Teil ist...kontrovers. Denn Frostpunk 2 verändert ganz viele Spielprinzipien und erweitert das Aufbauspiel um eine Politik-Simulations-Ebene. Aber ist das auch wirklich eine so gute Idee? Reden wir drüber! Außerdem: Baba Yaga im Early Access. Wir sprechen über das Berliner Indie Game Reka. Ein Cozy Game in der Hexenhütte mit Hühnerbeinen. Und: Wir müssen mal wieder über KI sprechen. Denn Marcus ist über ein Kickstarter-Projekt gestolpert und hat Fragen.

Welcome to this episode of the Hungarian Heritage Podcast. For this first episode of season 3, I had the pleasure of speaking with Reka Bakos, and Anna Fenyvesi, and they are the authors of the book, Hungarian Roots and American Dreams. Their book is a compilation of people telling stories about their Hungarian family members who emigrated to the United States from Hungary, and some who also returned to Hungary. We are going to discuss how their book started out as an idea about researching their own family trees and sharing stories about Hungarian Heritages. Then, shortly after Reka and Anna met,  the stars aligned for them and they collaborated to transform the collected stories into a new book that is dedicated to preserving and sharing the histories of Hungarian immigrants and their descendants. Listen along as we explore all of this, and so much more.Below, you will find information about how you can connect with Reka and Anna and for how you can purchase a copy of their new book. If you have feedback or questions about this episode or you would like to connect with me at the podcast, you will also find all of the ways to contact me below.  If you've enjoyed this episode and you're interested in learning more about this Hungarian Heritage community, please don't hesitate to reach out. I would love to hear from you. Thanks again for listening, and until next time, make sure you Stay Hungarian Heritage Strong!  CONNECT with Reka Bakos and Anna FenyvesiInstagram: @hungarianroots_americandreamsFacebook: Hungarian Roots and American DreamsAttend the Book Launch in Person  - https://www.eventbrite.com/e/hungarian-roots-and-american-dreams-tracing-personal-history-tickets-965370548297Attend the Book Launch Online - https://www.eventbrite.com/e/hungarian-roots-and-american-dreams-tracing-personal-history-tickets-991179974987CONNECT with the Podcast Website: www.myhungarianheritage.com Email: Christine@myhungarianheritage.comInstagram: @hungarianheritagepodcastFacebook: Hungarian Heritage Podcast  

Hey Folks, we are finally due for a "relaxing" week in AI, no more HUGE company announcements (if you don't consider Meta Movie Gen huge), no conferences or dev days, and some time for Open Source projects to shine. (while we all wait for Opus 3.5 to shake things up) This week was very multimodal on the show, we covered 2 new video models, one that's tiny and is open source, and one massive from Meta that is aiming for SORA's crown, and 2 new VLMs, one from our friends at REKA that understands videos and audio, while the other from Rhymes is apache 2 licensed and we had a chat with Kwindla Kramer about OpenAI RealTime API and it's shortcomings and voice AI's in general. ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.All right, let's TL;DR and show notes, and we'll start with the 2 Nobel prizes in AI

Welcome back to the NSG Indie Game Roundup, our monthly series featuring indie games news, plus game demos and new release discussions. September was PACKED with wild indie game news stories. From Annapurna Interactive's entire team leaving en masse, to Nintendo and The Pokémon Company finally announcing a lawsuit against Palworld's developer Pocket Pair, we discuss our thoughts on these big moves and shakes in the indie game world. We also discuss the demos we've been playing, plus new releases like Wild Bastards, Tiny Glade, REKA and more! Also, huge thanks to our newest Patreon supporters! In addition to other fun perks, members on Patreon get early access to all of the Indie Game Roundup episodes. If you love No Small Games and would like to support us in growing the show, while also unlocking fun, exclusive content, you can check out our Patreon page. Timestamps 00:00:00 - Intro 00:05:010 - Indie Game News 00:38:20 - Demos 00:48:55 - Full Releases Keep in touch with us on social media: Kate's Twitter ✦ https://twitter.com/katerblossom Emily's Twitter ✦ https://twitter.com/aSpecificEgg No Small Games Twitter ✦ https://twitter.com/NoSmallGames  No Small Games Instagram ✦ https://www.instagram.com/nosmallgames  Want to learn more and weigh in on what games we should play in future episodes? Check us out and leave a game suggestion at nosmallgames.com

Erik ten Hag getur ekki lifað mikið lengur í starfi hjá Manchester United. Það er bara svoleiðis, United hlýtur að fara að reka hann. United tapaði 0-3 gegn Tottenham í gær en það var farið vel yfir það í Enski boltinn hlaðvarpinu í dag. Tryggvi Páll Tryggvason var á línunni og fór yfir leikinn í gær. Chelsea stuðningsmennirnir Haraldur Örn Haraldsson og Stefán Marteinn Ólafsson eru þá gestir og fara yfir flotta byrjun Chelsea á tímabilinu. Cole (Cold) Palmer fór á kostum um helgina. Liverpool er á toppnum, Arsenal missti frá sér forystu en vann samt og Manchester City missteig sig án Rodri. Og já, Everton vann leik!

Castlevania: Dominus Collection, Enotria: The Last Song indie soulslike, New World: Aeternum beta, Harvest Moon: Home Sweet Home, Nightingale: Realms Rebuilt update, Reka indie "Witch Crafter", John's best bud McCarthy joins us to talk about COD, 4k FireStick Xbox streaming, and his trip to Japan, DeathSprint 66, Star Trek: Resurgence, a cricket from hell, Plucky Squire and UFO 50 preview, and more pumpkin spiced gaming news. Grab your rakes and join our Discord to chat about this episode, enter contests, or support us to get extra podcasts: https://discord.gg/Ab6pxpT! For more weekly bonus shows and the entire back catalog of VGO, support us for only 1.99 a month and download or listen on the web or on the free VGO mobile apps for iOS/Android! Sub and support on VideoGameOutsiders.com right now! We also have a Patreon.com/videogameoutsiders to be listed as a supporter, sponsor an episode, buy us a game, or more! You can also also check out Twitch.tv/johnANDmichelle and sub free with Amazon Prime each month!

Disclaimer: We recorded this episode ~1.5 months ago, timing for the FastHTML release. It then got bottlenecked by Llama3.1, Winds of AI Winter, and SAM2 episodes, so we're a little late. Since then FastHTML was released, swyx is building an app in it for AINews, and Anthropic has also released their prompt caching API. Remember when Dylan Patel of SemiAnalysis coined the GPU Rich vs GPU Poor war? (if not, see our pod with him). The idea was that if you're GPU poor you shouldn't waste your time trying to solve GPU rich problems (i.e. pre-training large models) and are better off working on fine-tuning, optimized inference, etc. Jeremy Howard (see our “End of Finetuning” episode to catchup on his background) and Eric Ries founded Answer.AI to do exactly that: “Practical AI R&D”, which is very in-line with the GPU poor needs. For example, one of their first releases was a system based on FSDP + QLoRA that let anyone train a 70B model on two NVIDIA 4090s. Since then, they have come out with a long list of super useful projects (in no particular order, and non-exhaustive):* FSDP QDoRA: this is just as memory efficient and scalable as FSDP/QLoRA, and critically is also as accurate for continued pre-training as full weight training.* Cold Compress: a KV cache compression toolkit that lets you scale sequence length without impacting speed.* colbert-small: state of the art retriever at only 33M params* JaColBERTv2.5: a new state-of-the-art retrievers on all Japanese benchmarks.* gpu.cpp: portable GPU compute for C++ with WebGPU.* Claudette: a better Anthropic API SDK. They also recently released FastHTML, a new way to create modern interactive web apps. Jeremy recently released a 1 hour “Getting started” tutorial on YouTube; while this isn't AI related per se, but it's close to home for any AI Engineer who are looking to iterate quickly on new products: In this episode we broke down 1) how they recruit 2) how they organize what to research 3) and how the community comes together. At the end, Jeremy gave us a sneak peek at something new that he's working on that he calls dialogue engineering: So I've created a new approach. It's not called prompt engineering. I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it.He explains it a bit more ~44:53 in the pod, but we'll just have to wait for the public release to figure out exactly what he means.Timestamps* [00:00:00] Intro by Suno AI* [00:03:02] Continuous Pre-Training is Here* [00:06:07] Schedule-Free Optimizers and Learning Rate Schedules* [00:07:08] Governance and Structural Issues within OpenAI and Other AI Labs* [00:13:01] How Answer.ai works* [00:23:40] How to Recruit Productive Researchers* [00:27:45] Building a new BERT* [00:31:57] FSDP, QLoRA, and QDoRA: Innovations in Fine-Tuning Large Models* [00:36:36] Research and Development on Model Inference Optimization* [00:39:49] FastHTML for Web Application Development* [00:46:53] AI Magic & Dialogue Engineering* [00:52:19] AI wishlist & predictionsShow Notes* Jeremy Howard* Previously on Latent Space: The End of Finetuning, NeurIPS Startups* Answer.ai* Fast.ai* FastHTML* answerai-colbert-small-v1* gpu.cpp* Eric Ries* Aaron DeFazio* Yi Tai* Less Wright* Benjamin Warner* Benjamin Clavié* Jono Whitaker* Austin Huang* Eric Gilliam* Tim Dettmers* Colin Raffel* Sebastian Raschka* Carson Gross* Simon Willison* Sepp Hochreiter* Llama3.1 episode* Snowflake Arctic* Ranger Optimizer* Gemma.cpp* HTMX* UL2* BERT* DeBERTa* Efficient finetuning of Llama 3 with FSDP QDoRA* xLSTMTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: And today we're back with Jeremy Howard, I think your third appearance on Latent Space. Welcome.Jeremy [00:00:19]: Wait, third? Second?Swyx [00:00:21]: Well, I grabbed you at NeurIPS.Jeremy [00:00:23]: I see.Swyx [00:00:24]: Very fun, standing outside street episode.Jeremy [00:00:27]: I never heard that, by the way. You've got to send me a link. I've got to hear what it sounded like.Swyx [00:00:30]: Yeah. Yeah, it's a NeurIPS podcast.Alessio [00:00:32]: I think the two episodes are six hours, so there's plenty to listen, we'll make sure to send it over.Swyx [00:00:37]: Yeah, we're trying this thing where at the major ML conferences, we, you know, do a little audio tour of, give people a sense of what it's like. But the last time you were on, you declared the end of fine tuning. I hope that I sort of editorialized the title a little bit, and I know you were slightly uncomfortable with it, but you just own it anyway. I think you're very good at the hot takes. And we were just discussing in our pre-show that it's really happening, that the continued pre-training is really happening.Jeremy [00:01:02]: Yeah, absolutely. I think people are starting to understand that treating the three ULM FIT steps of like pre-training, you know, and then the kind of like what people now call instruction tuning, and then, I don't know if we've got a general term for this, DPO, RLHFE step, you know, or the task training, they're not actually as separate as we originally suggested they were in our paper, and when you treat it more as a continuum, and that you make sure that you have, you know, more of kind of the original data set incorporated into the later stages, and that, you know, we've also seen with LLAMA3, this idea that those later stages can be done for a lot longer. These are all of the things I was kind of trying to describe there. It wasn't the end of fine tuning, but more that we should treat it as a continuum, and we should have much higher expectations of how much you can do with an already trained model. You can really add a lot of behavior to it, you can change its behavior, you can do a lot. So a lot of our research has been around trying to figure out how to modify the model by a larger amount rather than starting from random weights, because I get very offended at the idea of starting from random weights.Swyx [00:02:14]: Yeah, I saw that in ICLR in Vienna, there was an outstanding paper about starting transformers from data-driven piers. I don't know if you saw that one, they called it sort of never trained from scratch, and I think it was kind of rebelling against like the sort of random initialization.Jeremy [00:02:28]: Yeah, I've, you know, that's been our kind of continuous message since we started Fast AI, is if you're training for random weights, you better have a really good reason, you know, because it seems so unlikely to me that nobody has ever trained on data that has any similarity whatsoever to the general class of data you're working with, and that's the only situation in which I think starting from random weights makes sense.Swyx [00:02:51]: The other trends since our last pod that I would point people to is I'm seeing a rise in multi-phase pre-training. So Snowflake released a large model called Snowflake Arctic, where they detailed three phases of training where they had like a different mixture of like, there was like 75% web in the first instance, and then they reduced the percentage of the web text by 10% each time and increased the amount of code in each phase. And I feel like multi-phase is being called out in papers more. I feel like it's always been a thing, like changing data mix is not something new, but calling it a distinct phase is new, and I wonder if there's something that you're seeingJeremy [00:03:32]: on your end. Well, so they're getting there, right? So the point at which they're doing proper continued pre-training is the point at which that becomes a continuum rather than a phase. So the only difference with what I was describing last time is to say like, oh, there's a function or whatever, which is happening every batch. It's not a huge difference. You know, I always used to get offended when people had learning rates that like jumped. And so one of the things I started doing early on in Fast.ai was to say to people like, no, you should actually have your learning rate schedule should be a function, not a list of numbers. So now I'm trying to give the same idea about training mix.Swyx [00:04:07]: There's been pretty public work from Meta on schedule-free optimizers. I don't know if you've been following Aaron DeFazio and what he's doing, just because you mentioned learning rate schedules, you know, what if you didn't have a schedule?Jeremy [00:04:18]: I don't care very much, honestly. I don't think that schedule-free optimizer is that exciting. It's fine. We've had non-scheduled optimizers for ages, like Less Wright, who's now at Meta, who was part of the Fast.ai community there, created something called the Ranger optimizer. I actually like having more hyperparameters. You know, as soon as you say schedule-free, then like, well, now I don't get to choose. And there isn't really a mathematically correct way of, like, I actually try to schedule more parameters rather than less. So like, I like scheduling my epsilon in my atom, for example. I schedule all the things. But then the other thing we always did with the Fast.ai library was make it so you don't have to set any schedules. So Fast.ai always supported, like, you didn't even have to pass a learning rate. Like, it would always just try to have good defaults and do the right thing. But to me, I like to have more parameters I can play with if I want to, but you don't have to.Alessio [00:05:08]: And then the more less technical side, I guess, of your issue, I guess, with the market was some of the large research labs taking all this innovation kind of behind closed doors and whether or not that's good, which it isn't. And now we could maybe make it more available to people. And then a month after we released the episode, there was the whole Sam Altman drama and like all the OpenAI governance issues. And maybe people started to think more, okay, what happens if some of these kind of labs, you know, start to break from within, so to speak? And the alignment of the humans is probably going to fall before the alignment of the models. So I'm curious, like, if you have any new thoughts and maybe we can also tie in some of the way that we've been building Answer as like a public benefit corp and some of those aspects.Jeremy [00:05:51]: Sure. So, yeah, I mean, it was kind of uncomfortable because two days before Altman got fired, I did a small public video interview in which I said, I'm quite sure that OpenAI's current governance structure can't continue and that it was definitely going to fall apart. And then it fell apart two days later and a bunch of people were like, what did you know, Jeremy?Alessio [00:06:13]: What did Jeremy see?Jeremy [00:06:15]: I didn't see anything. It's just obviously true. Yeah. So my friend Eric Ries and I spoke a lot before that about, you know, Eric's, I think probably most people would agree, the top expert in the world on startup and AI governance. And you know, we could both clearly see that this didn't make sense to have like a so-called non-profit where then there are people working at a company, a commercial company that's owned by or controlled nominally by the non-profit, where the people in the company are being given the equivalent of stock options, like everybody there was working there with expecting to make money largely from their equity. So the idea that then a board could exercise control by saying like, oh, we're worried about safety issues and so we're going to do something that decreases the profit of the company, when every stakeholder in the company, their remuneration pretty much is tied to their profit, it obviously couldn't work. So I mean, that was a huge oversight there by someone. I guess part of the problem is that the kind of people who work at non-profits and in this case the board, you know, who are kind of academics and, you know, people who are kind of true believers. I think it's hard for them to realize that 99.999% of the world is driven very heavily by money, especially huge amounts of money. So yeah, Eric and I had been talking for a long time before that about what could be done differently, because also companies are sociopathic by design and so the alignment problem as it relates to companies has not been solved. Like, companies become huge, they devour their founders, they devour their communities and they do things where even the CEOs, you know, often of big companies tell me like, I wish our company didn't do that thing. You know, I know that if I didn't do it, then I would just get fired and the board would put in somebody else and the board knows if they don't do it, then their shareholders can sue them because they're not maximizing profitability or whatever. So what Eric's spent a lot of time doing is trying to think about how do we make companies less sociopathic, you know, how to, or more, you know, maybe a better way to think of it is like, how do we make it so that the founders of companies can ensure that their companies continue to actually do the things they want them to do? You know, when we started a company, hey, we very explicitly decided we got to start a company, not a academic lab, not a nonprofit, you know, we created a Delaware Seacorp, you know, the most company kind of company. But when we did so, we told everybody, you know, including our first investors, which was you Alessio. They sound great. We are going to run this company on the basis of maximizing long-term value. And in fact, so when we did our second round, which was an angel round, we had everybody invest through a long-term SPV, which we set up where everybody had to agree to vote in line with long-term value principles. So like never enough just to say to people, okay, we're trying to create long-term value here for society as well as for ourselves and everybody's like, oh, yeah, yeah, I totally agree with that. But when it comes to like, okay, well, here's a specific decision we have to make, which will not maximize short-term value, people suddenly change their mind. So you know, it has to be written into the legal documents of everybody so that no question that that's the way the company has to be managed. So then you mentioned the PBC aspect, Public Benefit Corporation, which I never quite understood previously. And turns out it's incredibly simple, like it took, you know, like one paragraph added to our corporate documents to become a PBC. It was cheap, it was easy, but it's got this huge benefit, which is if you're not a public benefit corporation, then somebody can come along and offer to buy you with a stated description of like turning your company into the thing you most hate, right? And if they offer you more than the market value of your company and you don't accept it, then you are not necessarily meeting the kind of your fiduciary responsibilities. So the way like Eric always described it to me is like, if Philip Morris came along and said that you've got great technology for marketing cigarettes to children, so we're going to pivot your company to do that entirely, and we're going to pay you 50% more than the market value, you're going to have to say yes. If you have a PBC, then you are more than welcome to say no, if that offer is not in line with your stated public benefit. So our stated public benefit is to maximize the benefit to society through using AI. So given that more children smoking doesn't do that, then we can say like, no, we're not selling to you.Alessio [00:11:01]: I was looking back at some of our emails. You sent me an email on November 13th about talking and then on the 14th, I sent you an email working together to free AI was the subject line. And then that was kind of the start of the C round. And then two days later, someone got fired. So you know, you were having these thoughts even before we had like a public example of like why some of the current structures didn't work. So yeah, you were very ahead of the curve, so to speak. You know, people can read your awesome introduction blog and answer and the idea of having a R&D lab versus our lab and then a D lab somewhere else. I think to me, the most interesting thing has been hiring and some of the awesome people that you've been bringing on that maybe don't fit the central casting of Silicon Valley, so to speak. Like sometimes I got it like playing baseball cards, you know, people are like, oh, what teams was this person on, where did they work versus focusing on ability. So I would love for you to give a shout out to some of the awesome folks that you have on the team.Jeremy [00:11:58]: So, you know, there's like a graphic going around describing like the people at XAI, you know, Elon Musk thing. And like they are all connected to like multiple of Stanford, Meta, DeepMind, OpenAI, Berkeley, Oxford. Look, these are all great institutions and they have good people. And I'm definitely not at all against that, but damn, there's so many other people. And one of the things I found really interesting is almost any time I see something which I think like this is really high quality work and it's something I don't think would have been built if that person hadn't built the thing right now, I nearly always reach out to them and ask to chat. And I tend to dig in to find out like, okay, you know, why did you do that thing? Everybody else has done this other thing, your thing's much better, but it's not what other people are working on. And like 80% of the time, I find out the person has a really unusual background. So like often they'll have like, either they like came from poverty and didn't get an opportunity to go to a good school or had dyslexia and, you know, got kicked out of school in year 11, or they had a health issue that meant they couldn't go to university or something happened in their past and they ended up out of the mainstream. And then they kind of succeeded anyway. Those are the people that throughout my career, I've tended to kind of accidentally hire more of, but it's not exactly accidentally. It's like when I see somebody who's done, two people who have done extremely well, one of them did extremely well in exactly the normal way from the background entirely pointing in that direction and they achieved all the hurdles to get there. And like, okay, that's quite impressive, you know, but another person who did just as well, despite lots of constraints and doing things in really unusual ways and came up with different approaches. That's normally the person I'm likely to find useful to work with because they're often like risk-takers, they're often creative, they're often extremely tenacious, they're often very open-minded. So that's the kind of folks I tend to find myself hiring. So now at Answer.ai, it's a group of people that are strong enough that nearly every one of them has independently come to me in the past few weeks and told me that they have imposter syndrome and they're not convinced that they're good enough to be here. And I kind of heard it at the point where I was like, okay, I don't think it's possible that all of you are so far behind your peers that you shouldn't get to be here. But I think part of the problem is as an R&D lab, the great developers look at the great researchers and they're like, wow, these big-brained, crazy research people with all their math and s**t, they're too cool for me, oh my God. And then the researchers look at the developers and they're like, oh, they're killing it, making all this stuff with all these people using it and talking on Twitter about how great it is. I think they're both a bit intimidated by each other, you know. And so I have to kind of remind them like, okay, there are lots of things in this world where you suck compared to lots of other people in this company, but also vice versa, you know, for all things. And the reason you came here is because you wanted to learn about those other things from those other people and have an opportunity to like bring them all together into a single unit. You know, it's not reasonable to expect you're going to be better at everything than everybody else. I guess the other part of it is for nearly all of the people in the company, to be honest, they have nearly always been better than everybody else at nearly everything they're doing nearly everywhere they've been. So it's kind of weird to be in this situation now where it's like, gee, I can clearly see that I suck at this thing that I'm meant to be able to do compared to these other people where I'm like the worst in the company at this thing for some things. So I think that's a healthy place to be, you know, as long as you keep reminding each other about that's actually why we're here. And like, it's all a bit of an experiment, like we don't have any managers. We don't have any hierarchy from that point of view. So for example, I'm not a manager, which means I don't get to tell people what to do or how to do it or when to do it. Yeah, it's been a bit of an experiment to see how that would work out. And it's been great. So for instance, Ben Clavier, who you might have come across, he's the author of Ragatouille, he's the author of Rerankers, super strong information retrieval guy. And a few weeks ago, you know, this additional channel appeared on Discord, on our private Discord called Bert24. And these people started appearing, as in our collab sections, we have a collab section for like collaborating with outsiders. And these people started appearing, there are all these names that I recognize, like Bert24, and they're all talking about like the next generation of Bert. And I start following along, it's like, okay, Ben decided that I think, quite rightly, we need a new Bert. Because everybody, like so many people are still using Bert, and it's still the best at so many things, but it actually doesn't take advantage of lots of best practices. And so he just went out and found basically everybody who's created better Berts in the last four or five years, brought them all together, suddenly there's this huge collaboration going on. So yeah, I didn't tell him to do that. He didn't ask my permission to do that. And then, like, Benjamin Warner dived in, and he's like, oh, I created a whole transformers from scratch implementation designed to be maximally hackable. He originally did it largely as a teaching exercise to show other people, but he was like, I could, you know, use that to create a really hackable BERT implementation. In fact, he didn't say that. He said, I just did do that, you know, and I created a repo, and then everybody's like starts using it. They're like, oh my god, this is amazing. I can now implement all these other BERT things. And it's not just answer AI guys there, you know, there's lots of folks, you know, who have like contributed new data set mixes and blah, blah, blah. So, I mean, I can help in the same way that other people can help. So like, then Ben Clavier reached out to me at one point and said, can you help me, like, what have you learned over time about how to manage intimidatingly capable and large groups of people who you're nominally meant to be leading? And so, you know, I like to try to help, but I don't direct. Another great example was Kerem, who, after our FSTP QLORA work, decided quite correctly that it didn't really make sense to use LoRa in today's world. You want to use the normalized version, which is called Dora. Like two or three weeks after we did FSTP QLORA, he just popped up and said, okay, I've just converted the whole thing to Dora, and I've also created these VLLM extensions, and I've got all these benchmarks, and, you know, now I've got training of quantized models with adapters that are as fast as LoRa, and as actually better than, weirdly, fine tuning. Just like, okay, that's great, you know. And yeah, so the things we've done to try to help make these things happen as well is we don't have any required meetings, you know, but we do have a meeting for each pair of major time zones that everybody's invited to, and, you know, people see their colleagues doing stuff that looks really cool and say, like, oh, how can I help, you know, or how can I learn or whatever. So another example is Austin, who, you know, amazing background. He ran AI at Fidelity, he ran AI at Pfizer, he ran browsing and retrieval for Google's DeepMind stuff, created Jemma.cpp, and he's been working on a new system to make it easier to do web GPU programming, because, again, he quite correctly identified, yeah, so I said to him, like, okay, I want to learn about that. Not an area that I have much expertise in, so, you know, he's going to show me what he's working on and teach me a bit about it, and hopefully I can help contribute. I think one of the key things that's happened in all of these is everybody understands what Eric Gilliam, who wrote the second blog post in our series, the R&D historian, describes as a large yard with narrow fences. Everybody has total flexibility to do what they want. We all understand kind of roughly why we're here, you know, we agree with the premises around, like, everything's too expensive, everything's too complicated, people are building too many vanity foundation models rather than taking better advantage of fine-tuning, like, there's this kind of general, like, sense of we're all on the same wavelength about, you know, all the ways in which current research is fucked up, and, you know, all the ways in which we're worried about centralization. We all care a lot about not just research for the point of citations, but research that actually wouldn't have happened otherwise, and actually is going to lead to real-world outcomes. And so, yeah, with this kind of, like, shared vision, people understand, like, you know, so when I say, like, oh, well, you know, tell me, Ben, about BERT 24, what's that about? And he's like, you know, like, oh, well, you know, you can see from an accessibility point of view, or you can see from a kind of a actual practical impact point of view, there's far too much focus on decoder-only models, and, you know, like, BERT's used in all of these different places and industry, and so I can see, like, in terms of our basic principles, what we're trying to achieve, this seems like something important. And so I think that's, like, a really helpful that we have that kind of shared perspective, you know?Alessio [00:21:14]: Yeah. And before we maybe talk about some of the specific research, when you're, like, reaching out to people, interviewing them, what are some of the traits, like, how do these things come out, you know, usually? Is it working on side projects that you, you know, you're already familiar with? Is there anything, like, in the interview process that, like, helps you screen for people that are less pragmatic and more research-driven versus some of these folks that are just gonna do it, you know? They're not waiting for, like, the perfect process.Jeremy [00:21:40]: Everybody who comes through the recruiting is interviewed by everybody in the company. You know, our goal is 12 people, so it's not an unreasonable amount. So the other thing to say is everybody so far who's come into the recruiting pipeline, everybody bar one, has been hired. So which is to say our original curation has been good. And that's actually pretty easy, because nearly everybody who's come in through the recruiting pipeline are people I know pretty well. So Jono Whitaker and I, you know, he worked on the stable diffusion course we did. He's outrageously creative and talented, and he's super, like, enthusiastic tinkerer, just likes making things. Benjamin was one of the strongest parts of the fast.ai community, which is now the alumni. It's, like, hundreds of thousands of people. And you know, again, like, they're not people who a normal interview process would pick up, right? So Benjamin doesn't have any qualifications in math or computer science. Jono was living in Zimbabwe, you know, he was working on, like, helping some African startups, you know, but not FAANG kind of credentials. But yeah, I mean, when you actually see people doing real work and they stand out above, you know, we've got lots of Stanford graduates and open AI people and whatever in our alumni community as well. You know, when you stand out above all of those people anyway, obviously you've got something going for you. You know, Austin, him and I worked together on the masks study we did in the proceeding at the National Academy of Science. You know, we had worked together, and again, that was a group of, like, basically the 18 or 19 top experts in the world on public health and epidemiology and research design and so forth. And Austin, you know, one of the strongest people in that collaboration. So yeah, you know, like, I've been lucky enough to have had opportunities to work with some people who are great and, you know, I'm a very open-minded person, so I kind of am always happy to try working with pretty much anybody and some people stand out. You know, there have been some exceptions, people I haven't previously known, like Ben Clavier, actually, I didn't know before. But you know, with him, you just read his code, and I'm like, oh, that's really well-written code. And like, it's not written exactly the same way as everybody else's code, and it's not written to do exactly the same thing as everybody else's code. So yeah, and then when I chatted to him, it's just like, I don't know, I felt like we'd known each other for years, like we just were on the same wavelength, but I could pretty much tell that was going to happen just by reading his code. I think you express a lot in the code you choose to write and how you choose to write it, I guess. You know, or another example, a guy named Vic, who was previously the CEO of DataQuest, and like, in that case, you know, he's created a really successful startup. He won the first, basically, Kaggle NLP competition, which was automatic essay grading. He's got the current state-of-the-art OCR system, Surya. Again, he's just a guy who obviously just builds stuff, you know, he doesn't ask for permission, he doesn't need any, like, external resources. Actually, Karim's another great example of this, I mean, I already knew Karim very well because he was my best ever master's student, but it wasn't a surprise to me then when he then went off to create the world's state-of-the-art language model in Turkish on his own, in his spare time, with no budget, from scratch. This is not fine-tuning or whatever, he, like, went back to Common Crawl and did everything. Yeah, it's kind of, I don't know what I'd describe that process as, but it's not at all based on credentials.Swyx [00:25:17]: Assemble based on talent, yeah. We wanted to dive in a little bit more on, you know, turning from the people side of things into the technical bets that you're making. Just a little bit more on Bert. I was actually, we just did an interview with Yi Tay from Reka, I don't know if you're familiar with his work, but also another encoder-decoder bet, and one of his arguments was actually people kind of over-index on the decoder-only GPT-3 type paradigm. I wonder if you have thoughts there that is maybe non-consensus as well. Yeah, no, absolutely.Jeremy [00:25:45]: So I think it's a great example. So one of the people we're collaborating with a little bit with BERT24 is Colin Raffle, who is the guy behind, yeah, most of that stuff, you know, between that and UL2, there's a lot of really interesting work. And so one of the things I've been encouraging the BERT group to do, Colin has as well, is to consider using a T5 pre-trained encoder backbone as a thing you fine-tune, which I think would be really cool. You know, Colin was also saying actually just use encoder-decoder as your Bert, you know, why don't you like use that as a baseline, which I also think is a good idea. Yeah, look.Swyx [00:26:25]: What technical arguments are people under-weighting?Jeremy [00:26:27]: I mean, Colin would be able to describe this much better than I can, but I'll give my slightly non-expert attempt. Look, I mean, think about like diffusion models, right? Like in stable diffusion, like we use things like UNet. You have this kind of downward path and then in the upward path you have the cross connections, which it's not a tension, but it's like a similar idea, right? You're inputting the original encoding path into your decoding path. It's critical to make it work, right? Because otherwise in the decoding part, the model has to do so much kind of from scratch. So like if you're doing translation, like that's a classic kind of encoder-decoder example. If it's decoder only, you never get the opportunity to find the right, you know, feature engineering, the right feature encoding for the original sentence. And it kind of means then on every token that you generate, you have to recreate the whole thing, you know? So if you have an encoder, it's basically saying like, okay, this is your opportunity model to create a really useful feature representation for your input information. So I think there's really strong arguments for encoder-decoder models anywhere that there is this kind of like context or source thing. And then why encoder only? Well, because so much of the time what we actually care about is a classification, you know? It's like an output. It's like generating an arbitrary length sequence of tokens. So anytime you're not generating an arbitrary length sequence of tokens, decoder models don't seem to make much sense. Now the interesting thing is, you see on like Kaggle competitions, that decoder models still are at least competitive with things like Deberta v3. They have to be way bigger to be competitive with things like Deberta v3. And the only reason they are competitive is because people have put a lot more time and money and effort into training the decoder only ones, you know? There isn't a recent Deberta. There isn't a recent Bert. Yeah, it's a whole part of the world that people have slept on a little bit. And this is just what happens. This is how trends happen rather than like, to me, everybody should be like, oh, let's look at the thing that has shown signs of being useful in the past, but nobody really followed up with properly. That's the more interesting path, you know, where people tend to be like, oh, I need to get citations. So what's everybody else doing? Can I make it 0.1% better, you know, or 0.1% faster? That's what everybody tends to do. Yeah. So I think it's like, Itay's work commercially now is interesting because here's like a whole, here's a whole model that's been trained in a different way. So there's probably a whole lot of tasks it's probably better at than GPT and Gemini and Claude. So that should be a good commercial opportunity for them if they can figure out what those tasks are.Swyx [00:29:07]: Well, if rumors are to be believed, and he didn't comment on this, but, you know, Snowflake may figure out the commercialization for them. So we'll see.Jeremy [00:29:14]: Good.Alessio [00:29:16]: Let's talk about FSDP, Qlora, Qdora, and all of that awesome stuff. One of the things we talked about last time, some of these models are meant to run on systems that nobody can really own, no single person. And then you were like, well, what if you could fine tune a 70B model on like a 4090? And I was like, no, that sounds great, Jeremy, but like, can we actually do it? And then obviously you all figured it out. Can you maybe tell us some of the worst stories behind that, like the idea behind FSDP, which is kind of taking sharded data, parallel computation, and then Qlora, which is do not touch all the weights, just go quantize some of the model, and then within the quantized model only do certain layers instead of doing everything.Jeremy [00:29:57]: Well, do the adapters. Yeah.Alessio [00:29:59]: Yeah. Yeah. Do the adapters. Yeah. I will leave the floor to you. I think before you published it, nobody thought this was like a short term thing that we're just going to have. And now it's like, oh, obviously you can do it, but it's not that easy.Jeremy [00:30:12]: Yeah. I mean, to be honest, it was extremely unpleasant work to do. It's like not at all enjoyable. I kind of did version 0.1 of it myself before we had launched the company, or at least the kind of like the pieces. They're all pieces that are difficult to work with, right? So for the quantization, you know, I chatted to Tim Detmers quite a bit and, you know, he very much encouraged me by saying like, yeah, it's possible. He actually thought it'd be easy. It probably would be easy for him, but I'm not Tim Detmers. And, you know, so he wrote bits and bytes, which is his quantization library. You know, he wrote that for a paper. He didn't write that to be production like code. It's now like everybody's using it, at least the CUDA bits. So like, it's not particularly well structured. There's lots of code paths that never get used. There's multiple versions of the same thing. You have to try to figure it out. So trying to get my head around that was hard. And you know, because the interesting bits are all written in CUDA, it's hard to like to step through it and see what's happening. And then, you know, FSTP is this very complicated library and PyTorch, which not particularly well documented. So the only really, really way to understand it properly is again, just read the code and step through the code. And then like bits and bytes doesn't really work in practice unless it's used with PEF, the HuggingFace library and PEF doesn't really work in practice unless you use it with other things. And there's a lot of coupling in the HuggingFace ecosystem where like none of it works separately. You have to use it all together, which I don't love. So yeah, trying to just get a minimal example that I can play with was really hard. And so I ended up having to rewrite a lot of it myself to kind of create this like minimal script. One thing that helped a lot was Medec had this LlamaRecipes repo that came out just a little bit before I started working on that. And like they had a kind of role model example of like, here's how to train FSTP, LoRa, didn't work with QLoRa on Llama. A lot of the stuff I discovered, the interesting stuff would be put together by Les Wright, who's, he was actually the guy in the Fast.ai community I mentioned who created the Ranger Optimizer. So he's doing a lot of great stuff at Meta now. So yeah, I kind of, that helped get some minimum stuff going and then it was great once Benjamin and Jono joined full time. And so we basically hacked at that together and then Kerim joined like a month later or something. And it was like, gee, it was just a lot of like fiddly detailed engineering on like barely documented bits of obscure internals. So my focus was to see if it kind of could work and I kind of got a bit of a proof of concept working and then the rest of the guys actually did all the work to make it work properly. And, you know, every time we thought we had something, you know, we needed to have good benchmarks, right? So we'd like, it's very easy to convince yourself you've done the work when you haven't, you know, so then we'd actually try lots of things and be like, oh, and these like really important cases, the memory use is higher, you know, or it's actually slower. And we'd go in and we just find like all these things that were nothing to do with our library that just didn't work properly. And nobody had noticed they hadn't worked properly because nobody had really benchmarked it properly. So we ended up, you know, trying to fix a whole lot of different things. And even as we did so, new regressions were appearing in like transformers and stuff that Benjamin then had to go away and figure out like, oh, how come flash attention doesn't work in this version of transformers anymore with this set of models and like, oh, it turns out they accidentally changed this thing, so it doesn't work. You know, there's just, there's not a lot of really good performance type evals going on in the open source ecosystem. So there's an extraordinary amount of like things where people say like, oh, we built this thing and it has this result. And when you actually check it, so yeah, there's a shitload of war stories from getting that thing to work. And it did require a particularly like tenacious group of people and a group of people who don't mind doing a whole lot of kind of like really janitorial work, to be honest, to get the details right, to check them. Yeah.Alessio [00:34:09]: We had a trade out on the podcast and we talked about how a lot of it is like systems work to make some of these things work. It's not just like beautiful, pure math that you do on a blackboard. It's like, how do you get into the nitty gritty?Jeremy [00:34:22]: I mean, flash attention is a great example of that. Like it's, it basically is just like, oh, let's just take the attention and just do the tiled version of it, which sounds simple enough, you know, but then implementing that is challenging at lots of levels.Alessio [00:34:36]: Yeah. What about inference? You know, obviously you've done all this amazing work on fine tuning. Do you have any research you've been doing on the inference side, how to make local inference really fast on these models too?Jeremy [00:34:47]: We're doing quite a bit on that at the moment. We haven't released too much there yet. But one of the things I've been trying to do is also just to help other people. And one of the nice things that's happened is that a couple of folks at Meta, including Mark Seraphim, have done a nice job of creating this CUDA mode community of people working on like CUDA kernels or learning about that. And I tried to help get that going well as well and did some lessons to help people get into it. So there's a lot going on in both inference and fine tuning performance. And a lot of it's actually happening kind of related to that. So PyTorch team have created this Torch AO project on quantization. And so there's a big overlap now between kind of the FastAI and AnswerAI and CUDA mode communities of people working on stuff for both inference and fine tuning. But we're getting close now. You know, our goal is that nobody should be merging models, nobody should be downloading merged models, everybody should be using basically quantized plus adapters for almost everything and just downloading the adapters. And that should be much faster. So that's kind of the place we're trying to get to. It's difficult, you know, because like Karim's been doing a lot of work with VLM, for example. These inference engines are pretty complex bits of code. They have a whole lot of custom kernel stuff going on as well, as do the quantization libraries. So we've been working on, we're also quite a bit of collaborating with the folks who do HQQ, which is a really great quantization library and works super well. So yeah, there's a lot of other people outside AnswerAI that we're working with a lot who are really helping on all this performance optimization stuff, open source.Swyx [00:36:27]: Just to follow up on merging models, I picked up there that you said nobody should be merging models. That's interesting because obviously a lot of people are experimenting with this and finding interesting results. I would say in defense of merging models, you can do it without data. That's probably the only thing that's going for it.Jeremy [00:36:45]: To explain, it's not that you shouldn't merge models. You shouldn't be distributing a merged model. You should distribute a merged adapter 99% of the time. And actually often one of the best things happening in the model merging world is actually that often merging adapters works better anyway. The point is, Sean, that once you've got your new model, if you distribute it as an adapter that sits on top of a quantized model that somebody's already downloaded, then it's a much smaller download for them. And also the inference should be much faster because you're not having to transfer FB16 weights from HPM memory at all or ever load them off disk. You know, all the main weights are quantized and the only floating point weights are in the adapters. So that should make both inference and fine tuning faster. Okay, perfect.Swyx [00:37:33]: We're moving on a little bit to the rest of the fast universe. I would have thought that, you know, once you started Answer.ai, that the sort of fast universe would be kind of on hold. And then today you just dropped Fastlight and it looks like, you know, there's more activity going on in sort of Fastland.Jeremy [00:37:49]: Yeah. So Fastland and Answerland are not really distinct things. Answerland is kind of like the Fastland grown up and funded. They both have the same mission, which is to maximize the societal benefit of AI broadly. We want to create thousands of commercially successful products at Answer.ai. And we want to do that with like 12 people. So that means we need a pretty efficient stack, you know, like quite a few orders of magnitude more efficient, not just for creation, but for deployment and maintenance than anything that currently exists. People often forget about the D part of our R&D firm. So we've got to be extremely good at creating, deploying and maintaining applications, not just models. Much to my horror, the story around creating web applications is much worse now than it was 10 or 15 years ago in terms of, if I say to a data scientist, here's how to create and deploy a web application, you know, either you have to learn JavaScript or TypeScript and about all the complex libraries like React and stuff, and all the complex like details around security and web protocol stuff around how you then talk to a backend and then all the details about creating the backend. You know, if that's your job and, you know, you have specialists who work in just one of those areas, it is possible for that to all work. But compared to like, oh, write a PHP script and put it in the home directory that you get when you sign up to this shell provider, which is what it was like in the nineties, you know, here are those 25 lines of code and you're done and now you can pass that URL around to all your friends, or put this, you know, .pl file inside the CGI bin directory that you got when you signed up to this web host. So yeah, the thing I've been mainly working on the last few weeks is fixing all that. And I think I fixed it. I don't know if this is an announcement, but I tell you guys, so yeah, there's this thing called fastHTML, which basically lets you create a complete web application in a single Python file. Unlike excellent projects like Streamlit and Gradio, you're not working on top of a highly abstracted thing. That's got nothing to do with web foundations. You're working with web foundations directly, but you're able to do it by using pure Python. There's no template, there's no ginger, there's no separate like CSS and JavaScript files. It looks and behaves like a modern SPA web application. And you can create components for like daisy UI, or bootstrap, or shoelace, or whatever fancy JavaScript and or CSS tailwind etc library you like, but you can write it all in Python. You can pip install somebody else's set of components and use them entirely from Python. You can develop and prototype it all in a Jupyter notebook if you want to. It all displays correctly, so you can like interactively do that. And then you mentioned Fastlight, so specifically now if you're using SQLite in particular, it's like ridiculously easy to have that persistence, and all of your handlers will be passed database ready objects automatically, that you can just call dot delete dot update dot insert on. Yeah, you get session, you get security, you get all that. So again, like with most everything I do, it's very little code. It's mainly tying together really cool stuff that other people have written. You don't have to use it, but a lot of the best stuff comes from its incorporation of HTMX, which to me is basically the thing that changes your browser to make it work the way it always should have. So it just does four small things, but those four small things are the things that are basically unnecessary constraints that HTML should never have had, so it removes the constraints. It sits on top of Starlet, which is a very nice kind of lower level platform for building these kind of web applications. The actual interface matches as closely as possible to FastAPI, which is a really nice system for creating the kind of classic JavaScript type applications. And Sebastian, who wrote FastAPI, has been kind enough to help me think through some of these design decisions, and so forth. I mean, everybody involved has been super helpful. Actually, I chatted to Carson, who created HTMX, you know, so about it. Some of the folks involved in Django, like everybody in the community I've spoken to definitely realizes there's a big gap to be filled around, like, highly scalable, web foundation-based, pure Python framework with a minimum of fuss. So yeah, I'm getting a lot of support and trying to make sure that FastHTML works well for people.Swyx [00:42:38]: I would say, when I heard about this, I texted Alexio. I think this is going to be pretty huge. People consider Streamlit and Gradio to be the state of the art, but I think there's so much to improve, and having what you call web foundations and web fundamentals at the core of it, I think, would be really helpful.Jeremy [00:42:54]: I mean, it's based on 25 years of thinking and work for me. So like, FastML was built on a system much like this one, but that was of hell. And so I spent, you know, 10 years working on that. We had millions of people using that every day, really pushing it hard. And I really always enjoyed working in that. Yeah. So, you know, and obviously lots of other people have done like great stuff, and particularly HTMX. So I've been thinking about like, yeah, how do I pull together the best of the web framework I created for FastML with HTMX? There's also things like PicoCSS, which is the CSS system, which by default, FastHTML comes with. Although, as I say, you can pip install anything you want to, but it makes it like super easy to, you know, so we try to make it so that just out of the box, you don't have any choices to make. Yeah. You can make choices, but for most people, you just, you know, it's like the PHP in your home directory thing. You just start typing and just by default, you'll get something which looks and feels, you know, pretty okay. And if you want to then write a version of Gradio or Streamlit on top of that, you totally can. And then the nice thing is if you then write it in kind of the Gradio equivalent, which will be, you know, I imagine we'll create some kind of pip installable thing for that. Once you've outgrown, or if you outgrow that, it's not like, okay, throw that all away and start again. And this like whole separate language that it's like this kind of smooth, gentle path that you can take step-by-step because it's all just standard web foundations all the way, you know.Swyx [00:44:29]: Just to wrap up the sort of open source work that you're doing, you're aiming to create thousands of projects with a very, very small team. I haven't heard you mention once AI agents or AI developer tooling or AI code maintenance. I know you're very productive, but you know, what is the role of AI in your own work?Jeremy [00:44:47]: So I'm making something. I'm not sure how much I want to say just yet.Swyx [00:44:52]: Give us a nibble.Jeremy [00:44:53]: All right. I'll give you the key thing. So I've created a new approach. It's not called prompt engineering. It's called dialogue engineering. But I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it. So I always just build stuff for myself and hope that it'll be useful for somebody else. Think about chat GPT with code interpreter, right? The basic UX is the same as a 1970s teletype, right? So if you wrote APL on a teletype in the 1970s, you typed onto a thing, your words appeared at the bottom of a sheet of paper and you'd like hit enter and it would scroll up. And then the answer from APL would be printed out, scroll up, and then you would type the next thing. And like, which is also the way, for example, a shell works like bash or ZSH or whatever. It's not terrible, you know, like we all get a lot done in these like very, very basic teletype style REPL environments, but I've never felt like it's optimal and everybody else has just copied chat GPT. So it's also the way BART and Gemini work. It's also the way the Claude web app works. And then you add code interpreter. And the most you can do is to like plead with chat GPT to write the kind of code I want. It's pretty good for very, very, very beginner users who like can't code at all, like by default now the code's even hidden away, so you never even have to see it ever happened. But for somebody who's like wanting to learn to code or who already knows a bit of code or whatever, it's, it seems really not ideal. So okay, that's one end of the spectrum. The other end of the spectrum, which is where Sean's work comes in, is, oh, you want to do more than chat GPT? No worries. Here is Visual Studio Code. I run it. There's an empty screen with a flashing cursor. Okay, start coding, you know, and it's like, okay, you can use systems like Sean's or like cursor or whatever to be like, okay, Apple K in cursors, like a creative form that blah, blah, blah. But in the end, it's like a convenience over the top of this incredibly complicated system that full-time sophisticated software engineers have designed over the past few decades in a totally different environment as a way to build software, you know. And so we're trying to like shoehorn in AI into that. And it's not easy to do. And I think there are like much better ways of thinking about the craft of software development in a language model world to be much more interactive, you know. So the thing that I'm building is neither of those things. It's something between the two. And it's built around this idea of crafting a dialogue, you know, where the outcome of the dialogue is the artifacts that you want, whether it be a piece of analysis or whether it be a Python library or whether it be a technical blog post or whatever. So as part of building that, I've created something called Claudette, which is a library for Claude. I've created something called Cosette, which is a library for OpenAI. They're libraries which are designed to make those APIs much more usable, much easier to use, much more concise. And then I've written AI magic on top of those. And that's been an interesting exercise because I did Claudette first, and I was looking at what Simon Willison did with his fantastic LLM library. And his library is designed around like, let's make something that supports all the LLM inference engines and commercial providers. I thought, okay, what if I did something different, which is like make something that's as Claude friendly as possible and forget everything else. So that's what Claudette was. So for example, one of the really nice things in Claude is prefill. So by telling the assistant that this is what your response started with, there's a lot of powerful things you can take advantage of. So yeah, I created Claudette to be as Claude friendly as possible. And then after I did that, and then particularly with GPT 4.0 coming out, I kind of thought, okay, now let's create something that's as OpenAI friendly as possible. And then I tried to look to see, well, where are the similarities and where are the differences? And now can I make them compatible in places where it makes sense for them to be compatible without losing out on the things that make each one special for what they are. So yeah, those are some of the things I've been working on in that space. And I'm thinking we might launch AI magic via a course called how to solve it with code. The name is based on the classic Polya book, if you know how to solve it, which is, you know, one of the classic math books of all time, where we're basically going to try to show people how to solve challenging problems that they didn't think they could solve without doing a full computer science course, by taking advantage of a bit of AI and a bit of like practical skills, as particularly for this like whole generation of people who are learning to code with and because of ChatGPT. Like I love it, I know a lot of people who didn't really know how to code, but they've created things because they use ChatGPT, but they don't really know how to maintain them or fix them or add things to them that ChatGPT can't do, because they don't really know how to code. And so this course will be designed to show you how you can like either become a developer who can like supercharge their capabilities by using language models, or become a language model first developer who can supercharge their capabilities by understanding a bit about process and fundamentals.Alessio [00:50:19]: Nice. That's a great spoiler. You know, I guess the fourth time you're going to be on learning space, we're going to talk about AI magic. Jeremy, before we wrap, this was just a great run through everything. What are the things that when you next come on the podcast in nine, 12 months, we're going to be like, man, Jeremy was like really ahead of it. Like, is there anything that you see in the space that maybe people are not talking enough? You know, what's the next company that's going to fall, like have drama internally, anything in your mind?Jeremy [00:50:47]: You know, hopefully we'll be talking a lot about fast HTML and hopefully the international community that at that point has come up around that. And also about AI magic and about dialogue engineering. Hopefully dialogue engineering catches on because I think it's the right way to think about a lot of this stuff. What else? Just trying to think about all on the research side. Yeah. I think, you know, I mean, we've talked about a lot of it. Like I think encoder decoder architectures, encoder only architectures, hopefully we'll be talking about like the whole re-interest in BERT that BERT 24 stimulated.Swyx [00:51:17]: There's a safe space model that came out today that might be interesting for this general discussion. One thing that stood out to me with Cartesia's blog posts was that they were talking about real time ingestion, billions and trillions of tokens, and keeping that context, obviously in the state space that they have.Jeremy [00:51:34]: Yeah.Swyx [00:51:35]: I'm wondering what your thoughts are because you've been entirely transformers the whole time.Jeremy [00:51:38]: Yeah. No. So obviously my background is RNNs and LSTMs. Of course. And I'm still a believer in the idea that state is something you can update, you know? So obviously Sepp Hochreiter came up, came out with xLSTM recently. Oh my God. Okay. Another whole thing we haven't talked about, just somewhat related. I've been going crazy for like a long time about like, why can I not pay anybody to save my KV cash? I just ingested the Great Gatsby or the documentation for Starlet or whatever, you know, I'm sending it as my prompt context. Why are you redoing it every time? So Gemini is about to finally come out with KV caching, and this is something that Austin actually in Gemma.cpp had had on his roadmap for years, well not years, months, long time. The idea that the KV cache is like a thing that, it's a third thing, right? So there's RAG, you know, there's in-context learning, you know, and prompt engineering, and there's KV cache creation. I think it creates like a whole new class almost of applications or as techniques where, you know, for me, for example, I very often work with really new libraries or I've created my own library that I'm now writing with rather than on. So I want all the docs in my new library to be there all the time. So I want to upload them once, and then we have a whole discussion about building this application using FastHTML. Well nobody's got FastHTML in their language model yet, I don't want to send all the FastHTML docs across every time. So one of the things I'm looking at doing in AI Magic actually is taking advantage of some of these ideas so that you can have the documentation of the libraries you're working on be kind of always available. Something over the next 12 months people will be spending time thinking about is how to like, where to use RAG, where to use fine-tuning, where to use KV cache storage, you know. And how to use state, because in state models and XLSTM, again, state is something you update. So how do we combine the best of all of these worlds?Alessio [00:53:46]: And Jeremy, I know before you talked about how some of the autoregressive models are not maybe a great fit for agents. Any other thoughts on like JEPA, diffusion for text, any interesting thing that you've seen pop up?Jeremy [00:53:58]: In the same way that we probably ought to have state that you can update, i.e. XLSTM and state models, in the same way that a lot of things probably should have an encoder, JEPA and diffusion both seem like the right conceptual mapping for a lot of things we probably want to do. So the idea of like, there should be a piece of the generative pipeline, which is like thinking about the answer and coming up with a sketch of what the answer looks like before you start outputting tokens. That's where it kind of feels like diffusion ought to fit, you know. And diffusion is, because it's not autoregressive, it's like, let's try to like gradually de-blur the picture of how to solve this. So this is also where dialogue engineering fits in, by the way. So with dialogue engineering, one of the reasons it's working so well for me is I use it to kind of like craft the thought process before I generate the code, you know. So yeah, there's a lot of different pieces here and I don't know how they'll all kind of exactly fit together. I don't know if JEPA is going to actually end up working in the text world. I don't know if diffusion will end up working in the text world, but they seem to be like trying to solve a class of problem which is currently unsolved.Alessio [00:55:13]: Awesome, Jeremy. This was great, as usual. Thanks again for coming back on the pod and thank you all for listening. Yeah, that was fantastic. Get full access to Latent Space at www.latent.space/subscribe

Because of the nature of SAM, this is more video heavy than usual. See our YouTube!Because vision is first among equals in multimodality, and yet SOTA vision language models are closed, we've always had an interest in learning what's next in vision. Our first viral episode was Segment Anything 1, and we have since covered LLaVA, IDEFICS, Adept, and Reka. But just like with Llama 3, FAIR holds a special place in our hearts as the New Kings of Open Source AI.The list of sequels better than the originals is usually very short, but SAM 2 delighted us by not only being a better image segmentation model than SAM 1, it also conclusively and inexpensively solved video segmentation in just an elegant a way as SAM 1 did for images, and releasing everything to the community as Apache 2/CC by 4.0.“In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM).”Surprisingly EfficientThe paper reports that SAM 2 was trained on 256 A100 GPUs for 108 hours (59% more than SAM 1). Taking the upper end $2 A100 cost off gpulist.ai means SAM2 cost ~$50k to train if it had an external market-rate cost - surprisingly cheap for adding video understanding!The newly released SA-V dataset is also the largest video segment dataset to date, with careful attention given to scene/object/geographical diversity, including that of annotators. In some ways, we are surprised that SOTA video segmentation can be done on only ~50,000 videos (and 640k masklet annotations). Model-in-the-loop Data Engine for Annotations and Demo-first DevelopmentSimilar to SAM 1, a 3 Phase Data Engine helped greatly in bootstrapping this dataset. As Nikhila says in the episode, the demo you see wasn't just for show, they actually used this same tool to do annotations for the model that is now demoed in the tool:“With the original SAM, we put a lot of effort in building a high-quality demo. And the other piece here is that the demo is actually the annotation tool. So we actually use the demo as a way to improve our annotation tool. And so then it becomes very natural to invest in building a good demo because it speeds up your annotation. and improve the data quality, and that will improve the model quality. With this approach, we found it to be really successful.”An incredible 90% speedup in annotation happened due to this virtuous cycle which helped SA-V reach this incredible scale.Building the demo also helped the team live the context that their own downstream users, like Roboflow, would experience, and forced them to make choices accordingly.As Nikhila says:“It's a really encouraging trend for not thinking about only the new model capability, but what sort of applications folks want to build with models as a result of that downstream.I think it also really forces you to think about many things that you might postpone. For example, efficiency. For a good demo experience, making it real time is super important. No one wants to wait. And so it really forces you to think about these things much sooner and actually makes us think about what kind of image encoder we want to use or other things. hardware efficiency improvements. So those kind of things, I think, become a first-class citizen when you put the demo first.”Indeed, the team swapped out standard ViT-H Vision Transformers for Hiera (Hierarchical) Vision Transformers as a result of efficiency considerations.Memory AttentionSpeaking of architecture, the model design is probably the sleeper hit of a project filled with hits. The team adapted SAM 1 to video by adding streaming memory for real-time video processing:Specifically adding memory attention, memory encoder, and memory bank, which surprisingly ablated better than more intuitive but complex architectures like Gated Recurrent Units.One has to wonder if streaming memory can be added to pure language models with a similar approach… (pls comment if there's an obvious one we haven't come across yet!)Video PodcastTune in to Latent Space TV for the video demos mentioned in this video podcast!Timestamps* [00:00:00] The Rise of SAM by Udio (David Ding Edit)* [00:03:07] Introducing Nikhila* [00:06:38] The Impact of SAM 1 in 2023* [00:12:15] Do People Finetune SAM?* [00:16:05] Video Demo of SAM* [00:20:01] Why the Demo is so Important* [00:23:23] SAM 1 vs SAM 2 Architecture* [00:26:46] Video Demo of SAM on Roboflow* [00:32:44] Extending SAM 2 with other models* [00:35:00] Limitations of SAM: Screenshots* [00:38:56] SAM 2 Paper* [00:39:15] SA-V Dataset and SAM Data Engine* [00:43:15] Memory Attention to solve Video* [00:47:24] "Context Length" in Memory Attention* [00:48:17] Object Tracking* [00:50:52] The Future of FAIR* [00:52:23] CVPR, Trends in Vision* [01:02:04] Calls to ActionTranscript[00:00:00] [music intro][00:02:11] AI Charlie: Happy Yoga! This is your AI co host Charlie. Thank you for all the love for our special 1 million downloads Wins of AI Winter episode last week, especially Sam, Archie, Trellis, Morgan, Shrey, Han, and more. For this episode, we have to go all the way back to the first viral episode of the podcast Segment Anything Model and the Hard Problems of Computer Vision, which we discussed with Joseph Nelson of Roboflow.[00:02:39] AI Charlie: Since Meta released SAM 2 last week, we are delighted to welcome Joseph back as our fourth guest co host to chat with Nikhila Ravi, Research Engineering Manager at Facebook AI Research and lead author of SAM 2. Just like our SAM 1 podcast, this is a multimodal pod because of the vision element, so we definitely encourage you to hop over to our YouTube at least for the demos, if not our faces.[00:03:04] AI Charlie: Watch out and take care.[00:03:10] Introducing Nikhila[00:03:10] swyx: Welcome to the latest podcast. I'm delighted to do segment anything to our first, one of our very first viral podcasts was segment anything one with Joseph. Welcome back. Thanks so much. And this time we are joined by the lead author of Segment Anything 2, Nikki Ravi, welcome.[00:03:25] Nikhila Ravi: Thank you. Thanks for having me.[00:03:26] swyx: There's a whole story that we can refer people back to episode of the podcast way back when for the story of Segment Anything, but I think we're interested in just introducing you as a researcher, as a, on the human side what was your path into AI research? Why, you know, why did you choose computer vision coming out of your specialization at Cambridge?[00:03:46] Nikhila Ravi: So I did my undergraduate. Degree in engineering at Cambridge university. The engineering program is very general. So first couple of years, you sort of study everything from mechanical engineering to fluid mechanics, structural mechanics, material science, and also computer science.[00:04:04] Nikhila Ravi: Towards the end of my degree, I started taking more classes in machine learning and computational neuroscience, and I really enjoyed it. And actually after graduating from undergrad, I had a place at Oxford to study medicine. And so I was. Initially planning on becoming a doctor, had everything planned and then decided to take a gap year after finishing undergrad.[00:04:28] Nikhila Ravi: And actually that was around the time that sort of deep learning was emerging. And in my machine learning class in undergrad, I remember one day our professor came in and that was when Google acquired DeepMind. And so that became like a huge thing. We talked about it for the whole class. It kind of really stuck.[00:04:48] Nikhila Ravi: And I was kicked off thinking about, okay, maybe I want to try something different other than medicine. Maybe this is a different path I want to take. And then in the gap year, I did a bunch of coding, worked on a number of projects. Did some sort of freelance contracting work. And then I got a scholarship to come and study in America.[00:05:06] Nikhila Ravi: So I went to Harvard for a year, took a bunch of computer science classes at Harvard and MIT, worked on a number of AI projects, especially in computer vision. I really, really enjoyed working in computer vision. I applied to Facebook and got this job at Facebook, and I've now at Facebook at the time, now Meta, and I've been here for seven years, so very circuitous path, probably not a very unconventional, I didn't do a PhD, I'm not like a research, typical research scientist, definitely came from more of an engineering background, but since being at Meta, Have had amazing opportunities to work across so many different interesting problems in computer vision from 3D computer vision.[00:05:50] Nikhila Ravi: How can you go from images of objects to 3D structures and then going back to 2D computer vision and actually understanding the objects and the pixels and the images themselves. So it's been a very interesting journey over the past seven years.[00:06:05] swyx: It's weird because like, I guess with segment anything too, it's like 4D because you solve time, you know, you started with 3D and now you're solving the 4D.[00:06:14] Nikhila Ravi: Yeah, it's just going from 3D to images to video. It's really covering the full spectrum. And actually, one of the nice things has been, so I think I mentioned I, Wanted to become a doctor, but actually Sam is having so much impact in medicine, probably more than I could have ever had as a doctor myself. So I think, you know, hopefully Sam too can also have a similar sort of impact in medicine and other fields.[00:06:39] The Impact of SAM 1 in 2023[00:06:39] swyx: Yeah. I want to give Joseph a chance to comment. Does that also mirror your, we know your story about going into, into vision, but like in the past year, since we did our podcast on Sam what's been the impact that you've seen?[00:06:51] Joseph Nelson: Segment anything. Set a new standard in computer vision, you know recapping from from the first release to present Sam introduces the ability for models to near zero shot meaning without any training identify kind of perfect polygons and outlines of items and objects inside images and that capability previously required a Lots of manual labeling, lots of manual preparation, clicking very meticulously to create outlines of individuals and people.[00:07:25] Joseph Nelson: And there were some models that attempted to do zero shot segmentation. of items inside images, though none were as high quality as segment anything. And with the introduction of segment anything, you can pass an image with SAM1, SAM2 videos as well, and get perfect pixel perfect outlines of most everything inside the images.[00:07:52] Joseph Nelson: Now there are some edge cases across domains and Similar to the human eye, sometimes you need to say, like, which item maybe you most care about for the downstream task and problem you're working on. Though, SAM has accelerated the rate at which developers are able to use computer vision in production applications.[00:08:13] Joseph Nelson: So, at RoboFlow, we were very quick to enable the community of computer vision developers and engineers to use SAM and apply it to their problems. The principle ways of using SAM, you could kind of use SAM as is to like pass an image and receive back masks. Another use case for SAM is in preparation of data for other types of problems.[00:08:37] Joseph Nelson: So, for example, in the medical domain, let's say that you're working on a problem where you have a bunch of images from a wet lab experiment. And from each of those images, you need to count the presence of a particular protein that reacts to some experiment. To count all the individual protein reactions, You can go in and lab assistants to this day will still like kind of individually count and say what are the presence of all those proteins.[00:09:07] Joseph Nelson: With Segment Anything, it's able to identify all of those individual items correctly. But often you may need to also add like a class name to what the protein is. Or you may need to say, hey, like, I care about the protein portion of this. I don't care about the rest of the portion of this in the image.[00:09:26] Joseph Nelson: And, or what it encourages and asks for the user to do is to provide some visual prompting to say, hey, which part, like, Sam says, hey, I can find segments of anything, but which segments do you care about? And so you can do visual prompting, which is kind of a new primitive that Sam introduced. And so at RoboFlow, we have one portion of our tool stack enables users to very quickly label data.[00:09:48] Joseph Nelson: With segment anything, Sam can already provide, hey, here's where I see the outlines of objects. Or a user can click to prompt to say, Hey, here's where the outlines of objects matter. And I recently pulled statistics from the usage of SAM in RoboFlow over the course of the last year. And users have labeled about 49 million images using segment anything on the hosted side of the RoboFlow platform.[00:10:12] Joseph Nelson: And that's like 5 million in the last 30 days alone. And of those images, We did kind of like a rough bafka napkin calculation of like how much time that has saved. Because, again, the alternative is you're clicking individual points to create a polygon, and with SAM you just click once and it guesses where the polygon is.[00:10:32] Joseph Nelson: And I'm sure in a bit we can maybe screen share and show some examples of what this experience is like. And in that time estimation, it's like, On average saves, you know, maybe a dozen or so seconds. And we estimate that this is probably saved on the order of magnitude of 35 years of time for users.[00:10:53] Nikhila Ravi: That's incredible.[00:10:54] Joseph Nelson: So, I mean, basically like in the first, the first year of a model being available, not only can you say, Hey, I'm just going to go use this model, those numbers that like 49 million images. is an estimate directly related to just the hosted side. So imagine all of the users that are self hosting or using SAM for robotics applications or out in the field or offline where it's not even, like, the time or the image counts are tabulated.[00:11:20] Joseph Nelson: And we're probably talking about, you know, just a fraction of the amount of value that's actually being produced for a number of downstream tasks. So to say that the impact has been You know, people use terms like game changing and these sorts of things. It has changed the industry. It's set a new standard.[00:11:36] Joseph Nelson: And with the release of SAM 2, I think we're about to see an acceleration of those capabilities for a lot of reasons.[00:11:42] Nikhila Ravi: That's really great to hear. I think one of the, really SAM 1 was. How many fields actually rely on manual segmentation? I think we're not really exposed to that. Maybe you are at Roboflow because you get to see all the users of these tools.[00:11:57] Nikhila Ravi: But for me, it was, you know, people working on understanding coral reef bleaching or farmers counting their cows and so many different applications that as a researcher. You never get exposed to, but you can have impact towards. So I think that was really awesome to hear.[00:12:15] Do People Finetune SAM?[00:12:15] swyx: So as sort of audience surrogate, who knows less than the two of you, I'm going to ask a really dumb question maybe, but is everyone using stock, a segment, anything?[00:12:23] swyx: Are they fine tuning for the medical domain? Like how on earth could it work for the medical field without fine tuning, right? Like, is that a thing?[00:12:32] Nikhila Ravi: So I mean, I can give a quick perspective from the research side. So one of the things, design decisions we made in SAM was to not have class labels. And so all the data is annotated in a class agnostic way.[00:12:48] Nikhila Ravi: So anything that has a boundary, we consider to be an object. So for example, in any image, there's lots of small objects. We might not know what the name of them are, but they're If you can draw a boundary around it, so you can imagine that we have 11 million images in the SA 1B dataset, we annotated all the objects, there's many, many small objects.[00:13:12] Nikhila Ravi: And so if you think about cells, they're also kind of small objects, there's probably things in the training data. That looked like it, but we didn't have to label it. And so that means that even when you use SAM for applications that it wasn't really trained for, because we didn't restrict it to a certain set of categories, you can actually use it out of the box without custom adaptation.[00:13:35] Nikhila Ravi: But having said that, there's probably certain domains where you need some expertise in order to be able to segment something properly. And for those use cases, Having some extra fine tuning data would probably help, and we've sort of seen that there's some papers that have come out that do this, and, you know, we'd love to hear, Joseph, how people are collecting data with SAM and fine tuning for their use cases.[00:13:59] Joseph Nelson: Once SAM came out, there were adaptations that said, could we use SAM to be, you know, like, efficient SAM? Like, basically take SAM and maybe accelerate it. And then there were domain adapted SAMs, like CellSAM, for example, out of the UC system. Now, what's interesting is, there's, like, adapting SAM to a domain, there's kind of two ways by which that's done.[00:14:21] Joseph Nelson: One is, as you mentioned, like, potentially SAM doesn't have a good concept of The objects of interest. And so you need to do domain adaptation and increase the accuracy for zero shot prediction. The second way though, is it's not fine tuning. It's actually just prompting. It's just guiding the model existing knowledge.[00:14:42] Joseph Nelson: to say which segments you care about. And both those are actually kind of equally important on the application side. You need to, like, a priori ensure that the objects of interest can be correctly segmented and maybe collect data to do that. But even if you had, like, a perfect SAM, like an omniscient SAM that could see every segment in every domain with all pixels perfectly outlined, in production, you would still need some way to Almost like signal to the model what you care about like to paint this picture if you are like a retailer and you are providing Photos of models wearing your clothing on your retail site You may care about you know only the shirt and Sam by default might segment the full person And so there's you know visual prompting that you can do to ensure that you only outline Maybe the shirt for the purposes of swapping in and out different shirts for displaying a given model on a retail page You And so I think what's interesting is that's where, like I wouldn't call it domain adaptation, but that's where, like, when you apply to industry, like, one thing that's particularly important with tooling and enabling SAM to reach its full potential.[00:15:51] swyx: That's really encouraging to hear. I should also think, like, you know, the last time we talked about this, we wanted to, the very natural addition on the class labeling side is the grounding Dino work, right? So I think people, built a grounding SAM and all the other extensions.[00:16:05] Video Demo of SAM[00:16:05] swyx: I think it's, it's probably a good time to cut to a quick demo of SAM2 for people who are, who are tuning in for SAM2 and who better to demo SAM2 than Nikki.[00:16:15] Nikhila Ravi: Sure. So I'll try to narrate what I'm what I'm doing. So audio listeners can also understand. So we have a web demo where anyone can try SAM2 on a video. Here we have a video of someone kicking a football, and I'm going to click on the football to select the object in the first frame. But you can actually select the object in any frame of the video, and this will work.[00:16:40] Nikhila Ravi: The next step is to hit track. So the model's now tracking this in real time. We don't save any of this, it's all running in real time. And now you can see the ball has been tracked throughout the entire video. There's even like a little bit of a challenging case here where the shoe covers the football.[00:16:59] Nikhila Ravi: And actually, you know, the model makes a little bit of a mistake, but that's okay. Because we can actually, here, the model makes a little bit of a mistake here. But you know, we can actually add a refinement click. You can add negative clicks until we get the mask that we want on this frame. And then you can hit track again, and the model will track the object, taking into account the additional information I've provided at that frame.[00:17:25] Nikhila Ravi: We've also added a couple of other fun things you can do on top of the track, like add effects. We can add you know, foreground effects, background effects. And these are just ways of showing how we can use the output from SAM2 as part of other tools like video editing tools. Other systems, so this is just a preview of what you can do with SAM2, but the really cool use cases are places where we might not have even imagined SAM2 being useful.[00:17:54] Nikhila Ravi: So we have a number of examples of things you might want to use it for. There's like underwater videos that it works actually really well for even though we, models never really seen an octopus before and octopus have a lot of moving parts that SAM2 can actually quite effectively. Keep track of all the different tentacles and we can probably see it more clearly if I desaturate the background.[00:18:18] Nikhila Ravi: We can see that actually the tracking of all the different tentacles is Quite accurate. Another challenge with video is that objects can actually become occluded. They can disappear from view and reappear. And a really fun example here is the shuffling cup game, which many of you might have seen. And so here I can click on the ball in the first frame.[00:18:41] Nikhila Ravi: I can also, You know, click on a different cup. And so here, the additional challenge is that there's three cups that look exactly the same. And then there's the ball that will get occluded by the cup. So the ball's no longer visible, the cups are all moving around, they all look the same. But the model actually keeps track of the cup that we selected.[00:19:02] Nikhila Ravi: And, as you can see at the end, here I'll jump to the end so you can see. It actually finds the cup again. I wanted to point out a couple of fun demo UX features that we added that actually really helped with this. So if you can see at the bottom, there's these swim lanes and then the swim lanes, actually the thickness of the swim lane tells you if the object's visible or not.[00:19:22] Nikhila Ravi: So at the beginning, the object's visible,[00:19:25] swyx: the object[00:19:26] Nikhila Ravi: disappears, and then the object comes back. So you can actually visually tell. When the object's being occluded and when it's not, and so it's a nice way of like, knowing if you need to go in and fix the model prediction or not. And so these are some of the UX innovations that we came up with, as well as the model innovations.[00:19:46] Joseph Nelson: One thing that I think is really notable here, there's two things. One is that like, I'd love to have a little bit of a discussion about how the models keeping track of the embedded scene to keep track of the ball and the cup in different places. Put a pause on that for a second.[00:19:59] Why the Demo is so Important[00:19:59] Joseph Nelson: One thing that Meta has put an emphasis on here in a much greater degree than other model releases is the demo experience of recognizing that in addition to having a model that can do zero shot segmentation, you've created a web experience that allows folks to kind of experience both the video effects but the types of UX innovations that encourage usage and adoption.[00:20:23] Joseph Nelson: It's actually kind of reminiscent of The underlying technology of ChatGPT was available prior to the web experience of ChatGPT. Can you talk a bit about why that was a consideration to your team and how you thought about the creation of The demo experience in tandem with training and releasing a new model.[00:20:41] Nikhila Ravi: Yeah, absolutely. I think that's a really great example of how, you know, Chad, GPT was really more of a UX innovation. Obviously it was like a number of research innovations that helped to get to this point. But as you said, like the underlying technology was around for a while. And, you know, putting this UX around as a chat interface helped tremendously with the.[00:21:03] Nikhila Ravi: Adoption and people understanding how it could be useful for real world use cases. And in computer vision, especially, it's so visual. The best way to show how these models work. Is by trying it on your own image or your own video with the original SAM, we put a lot of effort in building like a high quality demo.[00:21:23] Nikhila Ravi: And the other piece here is that the demo is actually the annotation tool. So we actually. Use the demo as a way to improve our annotation tool. And so then it becomes very natural to invest in building a good demo because it speeds up your annotation and improves the data quality and that will improve the model quality.[00:21:43] Nikhila Ravi: With this approach, we found it to be really successful. And obviously externally, people really liked being able to try it. I think, you know, people in fields outside of machine learning would never have tried SAM if we didn't have that demo. And I think that definitely led to a lot of the adoption in, like, diverse fields.[00:22:05] Nikhila Ravi: And so because we saw that with SAM 2, like, the demo was a priority first class citizen from day one. And so we really invested in making that. And I think with SAM2 as well, we wanted to have like a step change in the demo experience. Interactive video segmentation, I think that experience is something that maybe has not had much thought given to it.[00:22:27] Nikhila Ravi: And we really wanted to be like, okay, if we are to design a step changing video segmentation experience, what would that look like? And that really did influence our model. And annotation design as well.[00:22:40] Joseph Nelson: It's a really encouraging trend for not thinking about only the new model capability, but what sort of applications folks want to build with models as a result of that downstream.[00:22:49] Nikhila Ravi: I think it also really forces you to think about many things that you might postpone, for example, efficiency.[00:22:55] Joseph Nelson: Yes.[00:22:55] Nikhila Ravi: For a good demo experience. Making it real time is super important. No one wants to wait. And so it really forces you to think about these things much sooner and actually makes us think about how to, what kind of image encoder we want to use or like other hardware efficiency improvements.[00:23:13] Nikhila Ravi: So those kinds of things, I think, become a first class citizen when you put the demo first.[00:23:19] SAM 1 vs SAM 2 Architecture[00:23:19] Joseph Nelson: That's one thing I was going to ask about, and this is related to the architecture change. So SAM1 and the SAM1 demo experience. You have the encoder that's creating the embeddings of all the potential spaces.[00:23:31] Joseph Nelson: That needs to be run on a GPU. That's a relatively intensive operation. But then the query of those embeddings can be run independently and on a cheaper process. So in the SAM1 demo, the way that it was structured, and also this is the way that we have our SAM tool structured in Robloflow as well, is images go to a GPU to get all the SAM based embeddings.[00:23:53] Joseph Nelson: But then for querying those embeddings, we do that client side, in the browser, so that the user can very quickly, you know, you can move your mouse over and you get the proposed candidate masks that Sam found for that region of the image. In SAM 2 you dropped that in the web demo. And I think that's because you made some notable improvements to the rate at which encoding happens.[00:24:16] Joseph Nelson: Can you talk a bit about what led to those speed increases and, again, how that interplays with providing a fast encryption? user experience for interacting with the model.[00:24:29] Nikhila Ravi: Yeah. So the SAM2 web demo is primarily focused on video. We, we decided to just keep it simple and focus on video and on GitHub, we have a Colab notebook that shows how to run SAM2 on images.[00:24:41] Nikhila Ravi: So if you're interested in using, replacing SAM with SAM2 for images, check out GitHub, but on the SAM2 demo, it's not as straightforward to adopt the same architecture as SAM. For video, because we can't send the per frame image embeddings for an entire video back to the front end. In SAM, each frame embedding was like four megabytes, but if you have a long video and that's like per frame, it would become impossible to send that back to the front end.[00:25:11] Nikhila Ravi: So, SAM 2 actually, in terms of the architecture details, I was actually just looking at this earlier, but SAM1 model was around 630 million parameters. It's a fraction of the size of these large language models, but very small. Actually, SAM2, the largest model, is around 224 million parameters. So it's actually One third the size of the SAM original model.[00:25:38] Nikhila Ravi: So we changed the imaging coder from A-V-I-T-H and SAM to a higher model, which has also developed by by meta. So that definitely was something that helped. And in terms of the efficiency compared to sam, so if we were to run SAM per frame on a video or run SAM two, it's around six times faster to run SAM two versus run SAM per frame.[00:26:03] Nikhila Ravi: A number of things improved the efficiency of SAM2 such that we were actually able to run this entirely on the server and not have any component in the front end. But I am very curious to see who puts this on device, like I'm pretty sure soon we'll see like an on device SAM2 or, you know, maybe even running in the browser or something, so.[00:26:25] Nikhila Ravi: I think that could definitely unlock some of these edge use cases that we were able to make a compelling web demo without having to do that.[00:26:34] swyx: Hugging face is probably already working on Transformers. js version of it, but totally makes sense. I want to talk about more about things from the paper, but I think we're still in this sort of demo section.[00:26:42] Video Demo of SAM on Roboflow[00:26:42] swyx: And so I want to hand it to Joseph for his demo to see what the RoboFlow site looks like.[00:26:47] Joseph Nelson: So I can, I can give some context into one key area that Nicola, you mentioned earlier, which is. Sam has made the decision, both Sam 1 and Sam 2, to be class agnostic in terms of its predictions. And that, you then have the ability to have a generalizable, model for zero shot capability.[00:27:05] Joseph Nelson: However, in a lot of domain applications, you do want the class wise name. And so a lot of the challenge can be adding that class wise name for the, at least the annotation to an experience that we've created. That's one of the key considerations. So I will similarly Share my screen and show an example.[00:27:27] Joseph Nelson: Here, I have a bunch of images, and there's a number of ways that I could annotate things, like I could prompt a large multimodal model with like grounding capabilities, you know, you could outsource it, or I can do manual labeling. And with the manual labeling, this is where we make use of models like segment anything.[00:27:45] Joseph Nelson: to propose candidate masks and make it faster. So we have, you know, this annotation pane and what we call the smart poly tool, which is powered by Segment Anything. This is currently Segment Anything 1. We're accelerating and seeing improvements from similar to what the paper shows of Segment Anything 2 performed better on E3.[00:28:06] Joseph Nelson: Images as well as video, but with a segment, anything I'm able to basically prompt regions of my image of interest. So for example, if like, I wanted to say, I want to like add the drum set. You'll see here that like, the original candidate proposal is just the base drum, but let's say I wanted the whole drum set.[00:28:26] Joseph Nelson: So the UX primitive of being able to add and subtract candidate regions of interest is really intuitive here. And now, great, I have this outline, but in fact what I want is, I want to name that as a class. Because maybe for the model that I'm building, I want to build like a task specific model, you know, like an object detection model or an instant segmentation model.[00:28:50] Joseph Nelson: Or, you know, maybe I'm even using like a multimodal model and I want that multimodal model to refer to regions of interest in the images as a specific thing. And so I think what's, you know, really powerful is, of course, like, I get this really rich zero shot prediction. And here we have our friend Rick.[00:29:10] Joseph Nelson: So I get this really rich candidate set of predictions. But then by adding the class wise label, I can, you know, very quickly make sure that any downstream tasks are aware not just of the segment, but also of the, what is inside that segment. Which actually takes me to A separate point of something that I predict that's probably going to happen and Nikhil, I'm actually kind of interested why maybe your team made a conscious decision to not do this initially with SAM2.[00:29:40] Joseph Nelson: There's been an emergent set of models that are also adding open text prompting capabilities to grounding models. So for example, like you've seen models like Grounding Dino or Owlvit, which, you know, you can do. Even image to image or text to image based prompting to find regions of interest. And maybe maybe I can actually give an example of that even in the context of this same data.[00:30:05] Joseph Nelson: So if I wanted to try out, you know, grounding dino on this same set of images, I could try out, you know, prompting grounding dino for a set of different classes. And what's notable is let's do, I don't know, let's prompt for person and we'll prompt for person and prompt for I don't know, microphone.[00:30:26] Joseph Nelson: NLASC or microphone. Here I can text prompt the image and then the understanding, in this case Grounding Dino's understanding, of where people are in this image allows me to create, in this case, bounding boxes, but, you know, soon you can do segmentations or in tandem with SAM do segmentations. And, you know, we've already seen applications of using SAM2 in tandem with models like Grounding Dino or Florence 2.[00:30:54] Joseph Nelson: So that people can basically text prompt and then get the benefits of the zero shot segmentation at the same time as getting the open form querying. And in doing so, you know, we maintain a framework called like autodistill so like folks can very quickly, you know, bring some images and then using autodistill to find some ontology and then prompt and say what you want from that ontology.[00:31:19] Nikhila Ravi: So you already do this for video as well?[00:31:21] Joseph Nelson: You can apply videos or groups of images, yes. So this is using a project called Autodistill. And the concept of Autodistill is, use a base model, like a big base model, which could be like SAM or Grounding Dino, and then you pass a directory of images, which also could be video, broken into individual frames, and you pass an ontology as well.[00:31:43] Joseph Nelson: So an example I was just showing was like the hello world we have, which is like a shipping container. And then the combination of the grounding capabilities of, in the example I was showing, Florence 2 plus SAM, looks for the concept of container, and then SAM does the rich segmentation of turning that concept of container into the candidate proposal of the region, so that a user could just say, hey, I want all the shipping containers, run this across a bunch of images or video frames, And then get back the class wise labels plus the regions of interest.[00:32:17] Joseph Nelson: And this feels like a natural extension. And in fact, like the open form grounding capabilities between SAM1 and SAM2 became something the field was broadly doing. So I'm curious, like, from your perspective, one of the things I thought maybe SAM2 would do is actually add this capability natively. So I'm curious to hear, like, the conscious decision to say, hey, we want to continue to be class agnostic.[00:32:39] Extending SAM 2 with other models[00:32:39] Joseph Nelson: We don't want to add yet maybe open form text prompting as a part of finding the segments and parts of images. And I'd love to hear about like the decision to think about it that way. And if you are encouraged or if you want kind of like what's happening here where people are naturally combining these capabilities as something that you would expect and encourage to happen despite not having it.[00:33:00] Joseph Nelson: In the base model itself.[00:33:02] Nikhila Ravi: Yeah, it's a great question. So I think it's really cool that the community is taking SAM and taking SAM 2 and building on top of it and coming up with cool applications. We love to see that. That's exactly why we open source our work. And then in terms of why we didn't put it into SAM 2, so as you've probably seen with SAM and SAM 2, it's a fairly narrow problem.[00:33:25] Nikhila Ravi: But we really tried to make it a step change in the capability. And so with each version, we are trying to limit the focus on one thing that we can know we can do really well. And in this case, like the first SAM, it was class agnostic segmentation, but can we do it so well that it's effectively solved?[00:33:47] Nikhila Ravi: And similarly, can we do that same thing, but with Video segmentation. So one step at a time, we are working on each of these problems one at a time so that we can actually deliver something that's really world class and step changing.[00:34:03] Joseph Nelson: So does that mean SAM 3 will have the text prompting? Problem is like the next challenge.[00:34:09] Nikhila Ravi: Who knows, who knows? Maybe the community will, will we'll build that too. So[00:34:15] Joseph Nelson: it makes sense to like very narrowly do something very well. And that's, I think, proven to be well accomplished.[00:34:21] Nikhila Ravi: It's like taking the, the, both the data, the model and the demo, and how can we push all three towards solving one thing really well?[00:34:30] Nikhila Ravi: So we found that. That's like a good recipe and that's what we've limited the focus of these, of each of these models.[00:34:38] swyx: This development reminds me of how, you know, when you do, and you break out the interpretability of ConvNets and you can see like, Oh, this is the edge detection one. I feel like SAM is the edge detection version equivalent.[00:34:51] swyx: And then you build up to whatever the next feature is on top of that.[00:34:54] Limitations of SAM: Screenshots[00:34:54] Joseph Nelson: Can I bring up one? Limitation of SAM. So like we've like even SAM one, SAM two, and the monitor is released at 4 PM Pacific on Monday. We're recording this on 11 AM Pacific on, on, on Thursday. So the, it's very fresh for a lot of the capabilities and.[00:35:09] Joseph Nelson: It is so clear that it is a stepwise change in the capability that, Nikhila, you mentioned your team wants to do, which is extend SAM's zero shot class agnostic capability to video, like, A plus, kind of mission accomplished. One thing that's interesting is finding, like, domain problems where there might be still domain applicability and domain adaptation that is available.[00:35:32] Joseph Nelson: One benchmark that we introduced at CBPR is this thing called RF100, which is like, seven different domain type problems that the industry commonly is working on in vision, like underwater document processing, aerial examples, medicine examples. And one place where interestingly segment anything maybe less performant than other models is handling screenshots.[00:35:57] Joseph Nelson: For example, like a lot of folks that are building agents to interact with the web are particularly interested in that challenge of given a screenshot of a computer, what are all the buttons. And how could I autonomously navigate and prompt and tell it to click? And I can show an example of like maybe what, how like Sam kind of performs on this challenge just to outline some of the context of this problem.[00:36:23] Joseph Nelson: But I'm curious like how you think about limitations like this and what you would expect to want to be the case. So here I just have a notebook where I run Sam on the source image on the left. Or the source image on the left and then Sam output is on the right. And this is just a screenshot of, of a website where we just grab like the top 100 websites by traffic and grab screenshots from them.[00:36:42] Joseph Nelson: One example of a place where I could see the community improving on Sam, and I'm curious how you think about this challenge and maybe why Sam is less well adapted for this type of problem. Is processing screenshots. So I'll share my screen to give an example for, for viewers that are participating here, you see like an example, a screenshot of a website on the left, and then right is SAM two running on that image.[00:37:06] Joseph Nelson: And in the context of agents, folks usually want to have like, Hey, tell me all of the buttons that a, an agent could press. Tell me like maybe the headlines of the articles tell me the individual images and Sam two behaves perhaps predictably, where it outlines like people in the images and like some of like the, the screen text.[00:37:22] Joseph Nelson: I'm curious, like, how you think about a challenge like this for a model that sees everything in the world, what about handling digital contexts? And Why maybe it could perform better here and how you would expect to see improvement for domains that might have been out of distribution from the training data?[00:37:40] Nikhila Ravi: Yeah, this is a good question. So fair, we don't really build with a specific use case in mind. We try to build like these foundational models that can be applied to lots of different use cases out of the box. So I think in this kind of example, potentially people might want to annotate some data.[00:37:59] Nikhila Ravi: Fine tune on top of what we release. I think we probably won't build things that are very custom for different use cases. I think that's not a direction we'll go in, but as you said, like the model is an annotation tool to improve the model. And so I think that's definitely the approach we want to take is we provide the tools for you to improve the model as well as the model itself.[00:38:27] Joseph Nelson: That makes sense. Focus on like as many. Multi or zero shot problems and then allow the community to pick up the torch for domain adaptation.[00:38:34] Nikhila Ravi: Yeah, absolutely. Like, we can't solve all the problems ourselves. Like, we can't solve all the different domains. But if we can provide a sort of base hammer tool, and then people can apply it to all their different problems.[00:38:48] SAM 2 Paper[00:38:48] swyx: If you don't mind, I guess we want to transition to a little bit on like asking more questions about the paper.[00:38:53] Udio AI: Sure.[00:38:54] swyx: There's a lot in here. I love the transparency from Meta recently with like LLAMA 3 last week and then, and was it last week? Maybe, maybe a little bit less than last week. But just like just really, really well written and a lot of disclosures, including the data set as well.[00:39:08] SA-V Dataset and SAM Data Engine[00:39:08] swyx: I think the top question that people had on the data set, you know, you release a diverse videos and there was, there's a lot of discussion about the data engine as well, which I really love. And I think it's innovative if you wanted. I think the top question is like, how do you decide the size of data set?[00:39:22] swyx: You know, what were you constrained by? People are asking about scaling laws. You had some ablations, but as a research manager for this whole thing, like how do you decide what you need?[00:39:32] Nikhila Ravi: Yeah. I mean, it's a great question. I think it's, as with all papers, you write them at the end of the project, so we can put these nice plots at the end, but going into it, I think, you know, the data engine design really follows.[00:39:47] Nikhila Ravi: So, this is sort of the model design, how we thought about the task, how we thought of the model capabilities. You can really see it's reflected in the different phases of the data engine. We started with just SAM, we apply SAM per frame. That's like the most basic way of extending SAM to video. Then the most obvious thing to do is to take the output masks from SAM and then provide it as input into a video object segmentation model that takes the mask as the first frame input.[00:40:19] Nikhila Ravi: And that's exactly what we did. We had SAM plus a version of SAM2 that only had mask as input. And then in the last phase, we got rid of SAM entirely and just had this one unified model that can do both image. And video segmentation. And I can do everything in just one model. And we found that, you know, going from each phase, it both improved the efficiency and it improved the data quality.[00:40:46] Nikhila Ravi: And in particular, when you get rid of this two part model, one of the advantages is that when you make refinement clicks, so, You prompt the model in one frame to select an object, then you propagate those predictions to all the other frames of the video to track the object. But if the model makes a mistake and you want to correct it, when you have this unified model, you only need to provide refinement clicks.[00:41:14] Nikhila Ravi: So you can provide maybe a negative click to remove a region or a positive click to add a region. But if you had this decoupled model, you would have to Delete that frame prediction and re annotate from scratch. And so you can imagine for more complex objects, this is actually adding like a lot of extra time to redefine that object every time you want to make a correction.[00:41:39] Nikhila Ravi: So both the data and the data engine phases really follow, like how we thought about the model design and the evolution of the capabilities, because it really helped us to do that. improve the data quality and the annotation efficiency as well.[00:41:54] swyx: Yeah, you had a really nice table with like time taken to annotate and it was just going down and down.[00:41:58] swyx: I think it was like down by like 90 percent by the time you hit stage[00:42:02] Joseph Nelson: three, which is kind of cool. We joke that when SAM 1 came out at RoboFlow, we're like, was this purpose built for our software? Like you have like the embedding, you have the embedding take like a big model and the querying of the embeddings A smaller model that happens in browser, which felt remarkably aligned.[00:42:18] Joseph Nelson: Now hearing you talk about how you think about building models with a demo in mind, it makes sense. Like, you're thinking about the ways that folks downstream are going to be consuming and creating value. So, what felt like maybe a coincidence was perhaps a deliberate choice by Meta to take into account how industry is going to take Seminal advances and apply them.[00:42:36] Nikhila Ravi: Yeah. And it's not just humans. Like it could also be a model that outputs boxes that then get fed into this model. So really thinking about this as a component that could be used by a human or as a component, as part of a, of a larger AI system. And that has, you know, a number of design requirements. It needs to be promptable.[00:42:56] Nikhila Ravi: It needs to be, have the zero shot generalization capability. We, you know, need it to be real time and. Those requirements really are very core to how we think about these models.[00:43:08] Memory Attention to solve Video[00:43:08] swyx: I cannot end this podcast without talking about the architecture, because this is your, effectively the sort of research level, architecture level innovation that enabled what I've been calling object permanence for SAM.[00:43:22] swyx: And it's memory retention. What was the inspiration going into it? And you know, what did you find?[00:43:27] Nikhila Ravi: Yeah, so at a high level, the way we think about extending SAM to video is that an image is just a special case of a video that just has one frame. With that idea in mind, we can extend the SAM architecture to be able to support segmentation across videos.[00:43:45] Nikhila Ravi: So this is a quick video that shows how this works. So SAM architecture, we have the image encoder, we have a prompt encoder, we have a mask decoder. You can click on an image. And that basically is a prompt, we use that prompt along with the image embedding to make a mask prediction for that image. Going to SAM2, we can also apply SAM2 to images because we can, you know, as I said, treat an image as a video with a single frame.[00:44:15] Nikhila Ravi: And so when we, in the SAM2 architecture, we introduce this new memory mechanism that consists of three main components. There's memory attention, there's a memory encoder, and then there's a memory bank. And when we apply SAM2 to images, these are effectively not used. And the architecture just collapses down to the original SAM architecture.[00:44:35] Nikhila Ravi: But when we do apply this to video, the memory components become really useful because they provide the context of the target object from Other frames. And so this could be from past frames. It can be from, there's two types of memory. So there's like the condition, conditional frames or the prompted frames, which are basically the frames at which a user or a model provides input like clicks.[00:45:01] Nikhila Ravi: And then there's like the surrounding frames. And say we use six frames around the current frame as memory of the object. So there's, there's those, those, both those types of memory that we use to make the prediction. Going into a little bit more detail about that, there's like two kinds of memory that we use.[00:45:18] Nikhila Ravi: So one is like spatial memory. So it's like this high resolution memory that captures the spatial details. And then we also have this like longer term object pointer memory that captures some of the sort of higher level concepts. And I think Swyx, you had a comment about how does this relate to sort of context window and LLMs.[00:45:37] Nikhila Ravi: And both of these types of memories have some relation to context window, so they both provide different types of information on the spatial side or in terms of the concept of the objects that we want to track. And so we found that having like six frame length for the spatial memory, Coupled with this longer period of the object pointer memory provides strong video segmentation accuracy at high speed.[00:46:01] Nikhila Ravi: So, as I mentioned, the real time aspect is really important. We have to find this speed accuracy trade off. And one way in which we sort of circumvent this is by allowing additional prompts on subsequent frames. So even if the model makes a mistake, maybe it loses the object. After an occlusion, you can provide another prompt, which actually goes into the memory.[00:46:24] Nikhila Ravi: And so the prompted frames are always in the memory. And so if you provide a prompt on a frame, we will, or the model will always remember what you provided. And so that's a way in which we can sort of avoid some of the model failure cases that actually is a big limitation of current models, current video object segmentation models.[00:46:45] Nikhila Ravi: Don't allow any way to recover if the model makes a mistake. And so, Joseph, going back to your point about the demo, that's something that we found just by playing with these models. There's no way to make a correction, and in many real world use cases, like, it's not going to be a one time prediction, but you actually want to be able to intervene, like, if an LLM makes a mistake, you can actually be like, no, actually do it this way, and provide feedback, and so, We really want to bring some of that thinking into how we build these computer vision models as well.[00:47:16] "Context Length" in Memory Attention[00:47:16] swyx: Amazing. My main reaction to finding out about the context length of eight input frames and six pass frames as their default is why not 60? Why not 600? In text language models, we're very used to severely extending context windows. And what does that do to the memory of your model?[00:47:35] Nikhila Ravi: So I think maybe one, one thing that's different is that the object in video, it is challenging.[00:47:41] Nikhila Ravi: Objects can, you know, change in appearance. There's different lighting conditions. They can deform, but I think a difference to language models is probably the amount of context that you need is significantly less than maintaining a long multi time conversation. And so, you know, coupling this. Short term spatial memory with this, like, longer term object pointers we found was enough.[00:48:03] Nikhila Ravi: So, I think that's probably one difference between vision models and LLMs.[00:48:09] Object Tracking[00:48:09] Joseph Nelson: I think so. If one wanted to be really precise with how literature refers to object re identification, object re identification is not only what SAM does for identifying that an object is similar across frames, It's also assigning a unique ID.[00:48:25] Joseph Nelson: How do you think about models keeping track of occurrences of objects in addition to seeing that the same looking thing is present in multiple places?[00:48:37] Nikhila Ravi: Yeah, it's a good question. I think, you know, SAM2 definitely isn't perfect and there's many limitations that, you know, we'd love to see. People in the community help us address, but one definitely challenging case is where there are multiple similar looking objects, especially if that's like a crowded scene with multiple similar looking objects, keeping track of the target object is a challenge.[00:49:03] Nikhila Ravi: That's still something that I don't know if we've solved perfectly, but again, the ability to provide refinement clicks. That's one way to sort of circumvent that problem. In most cases, when there's lots of similar looking objects, if you add enough refinement clicks, you can get the perfect track throughout the video.[00:49:22] Nikhila Ravi: So definitely that's one way to, to solve that problem. You know, we could have better motion estimation. We could do other things in the model to be able to disambiguate similar looking objects more effectively.[00:49:35] swyx: I'm just interested in leaving breadcrumbs for other researchers, anyone interested in this kind of architecture.[00:49:41] swyx: Like, are there papers that you would refer people to that are influential in your thinking or, you know, have, have other interesting alternative approaches?[00:49:49] Nikhila Ravi: I think there's other ways in which you can do tracking and video. You might not even need the full mask. I think that's it. Some other works that just track like points on objects.[00:49:59] Nikhila Ravi: It really, really depends on what your application is. Like if you don't care about the entire mask, you could just track a bounding box. You could just track a point on an object. And so having the high fidelity mask might not actually be necessary for certain use cases. From that perspective, you might not need the full capabilities.[00:50:19] Nikhila Ravi: of SAM or SAM2. There's many different approaches to tracking, I think I would encourage people to think about like what actually they need for their use case and then try to find something that that fits versus, yeah, maybe SAM2 is too much, you know, maybe you don't even need the full mask.[00:50:37] swyx: Makes total sense, but you have solved the problem that you set out to solve, which is no mean feat, which is something that we're still appreciating even today.[00:50:44] The Future of FAIR[00:50:44] swyx: If there are no further questions, I would just transition to sort of forward looking, future looking stuff. Joseph already hinted at, like, you know, our interest in SAM and the future of SAM, and obviously you're the best person to ask about that. I'm also interested in, like, How should external people think about FAIR, you know, like there's this stuff going on, this llama, this chameleon, this voice box, this image bind, like, how is, how are things organized?[00:51:09] swyx: And, you know, where are things trending?[00:51:11] Nikhila Ravi: Yeah, so in FAIR, we, you know, we have a number of different research areas. I work in an area called perception. So we built vision systems that solve basically, Look at all the fundamental problems in Compute Division. Can we build a step change in all of these different capabilities?[00:51:29] Nikhila Ravi: SAM was one example. SAM2 is another example. There are tons of other problems in Compute Division where we've made a lot of progress, but can we really say that they're solved? And so that's really the area in which I work on. And then there's a number of other research areas in language and in embodied AI.[00:51:49] Nikhila Ravi: And more efficient models and various other topics. So fair in general is still very much pushing the boundaries on solving these foundational problems across different domains. Well,[00:52:07] swyx: fair enough, maybe just outside of fair, just the future of computer vision, right?[00:52:10] CVPR, Trends in Vision[00:52:10] swyx: Like you are very involved in the community. What's the talk of the town at CVPR? Both of you went, who's doing the most interesting work? It's a question for both of you.[00:52:19] Joseph Nelson: I think the trends we're seeing towards more zero shot capability for common examples will accelerate. I think Mutu modality, meaning using, you know, images in tandem with text for richer understanding or images and video in tandem with audio and other mixed media will be a continued acceleration trend.[00:52:43] Joseph Nelson: The way I kind of see the field continuing to progress, the problem statement of computer vision is making sense of visual input. And I think about the world as the things that need to be observed follow your traditional bell curve, where like things that most frequently exist out in the world are on the center of that bell curve.[00:53:05] Joseph Nelson: And then there's things that are less frequently occurring that are in those long tails. For example, you know, as back as like 2014, you have the Cocoa data set, which sets out to say, Hey, can we find 80 common objects in context, like silverware and fridge and these sorts of things. And we also conceptualized the challenge of computer vision in terms of breaking it down into individual task types, because that's like the tools we had for the day.[00:53:29] Joseph Nelson: So that's why, you know, you have the origination of classification, object detection, instant segmentation. And then as you see things continue to progress. You have models and things that need to observe areas in the long tails. And so if you think of the Cocoa dataset as the center of that bell curve, I think of like the long tails, like really edge case problems.[00:53:49] Joseph Nelson: Some of our customers like Rivian, for example, only Rivian knows what the inside of like a Rivian should look like as it's assembled and put together before it makes its way to a customer and they're making custom parts. Right? So how could a model you've been trained on the things that go inside the componentry of producing a vehicle and Andreesen, What's kind of happening with computer vision is you're seeing models that generalize in the middle of the bell curve push outward faster.[00:54:17] Joseph Nelson: That's where you see the advent of like open text models or the richness of understanding of multimodal models. To allow richer understanding without perhaps any training, or maybe just using pre training and applying it to a given problem. And then, there's like, you know, kind of like the messy middle in between those two, right?[00:54:38] Joseph Nelson: So like, Akila kind of talked about examples where SAM does well out of distribution, where like, it finds an octopus, even though there wasn't octopi in the training data. I showed an example where, like, screenshots, where Sam isn't yet super great at screenshots, so maybe that's, like, in the messy middle or in the longer tails for now.[00:54:54] Joseph Nelson: But what's going to happen is there needs to be systems of validating the point of view that I think about, like, tooling to also validate that models are doing what we want them to do, adapting to datasets that we want them to adapt to. And so there's a lot of things on a forward looking basis that allow propelling that expansion of generalizability.[00:55:14] Joseph Nelson: That's for open text problems. That's where scaling up of training, of dataset curation, continues to play a massive role. Something that's notable, I think, about SAM2 is it's, what, 57, 000 videos? 51,[00:55:30] Nikhila Ravi: 000 videos? About 51, 000, yeah.[00:55:32] Joseph Nelson: And 100, 000 internal datasets. That's, like, not Massive, right? And the model size also isn't, you know, the largest, largest model being a couple hundred million parameters.[00:55:43] Joseph Nelson: The smallest model is 38 million parameters and can run at 45 FPS on an A100, right? Like the capabilities of, we're going to see more capable, more generalizable models. Being able to run on a higher wide array of problems with zero or multi shot capability on a faster, a faster rate. And I think the architecture innovations and things like SAM2 of memory, of increasingly like transformers making their way into division and probably blended architectures increasingly too.[00:56:15] Joseph Nelson: So my viewpoint of like on a go forward basis is we will have that bell curve of what humans can see both in the center of that curve and the long tails. And architectural changes allow richer understanding, multi and zero shot, and putting those into systems and putting those into industry and putting those into contexts that allow using them in practical and pragmatic ways.[00:56:38] Joseph Nelson: Nicola, I'd love to hear like your thought and perspective of like how you think the research trends map or don't map to that. And like maybe some of the key innovations that you saw at CVPR this year that, you know, Got you excited about the direction and maybe some promising early directions that you're thinking about researching or pushing the boundaries of further.[00:56:56] Nikhila Ravi: Yeah, I just wanted to actually reply to a couple of things that you said about so actually in video object segmentation, the number of classes. that are annotated in these, and then the size of these datasets are really small. So with SAM, it's, you know, we had a billion masks, we had 11 million images, didn't have class labels.[00:57:17] Nikhila Ravi: But even before that, there were a lot of datasets that have class labels and are annotated. With significantly more with, with like a lot of class labels, whereas in video datasets, the number of class labels are very small. So there's like YouTube VOS, which has 94 object categories, there's Mose, which has around like 30 or so object categories.[00:57:38] Nikhila Ravi: And they're usually like people, there's cars, there's dogs and cats and all these common objects, but not really, they don't really cover a very large number of object categories. And so while Sam learned this general notion of what an object is in an image. These video tracking models actually don't have that knowledge at all.[00:58:01] Nikhila Ravi: And so that's why having this data set is really important for the segment anything capability in video because if you just provide the mask as the input to an off the shelf Video object segmentation model. It might not actually be able to track that arbitrary object mask as effectively as a SAM2 model that's actually trained to track.[00:58:24] Nikhila Ravi: Any object across the entire video. So doing these sort of combining two models together to try to get a capability that will actually only get you so far and being able to actually create that the dataset to enable that anything capability, it was actually really important and we can actually see that when we do comparisons with baselines where we provide some two with the same input mask and the baseline model with the same input mask.[00:58:53] Nikhila Ravi: For example, the t shirt of a person, SAM2 can track the t shirt effectively across the entire video, whereas these baselines might actually start tracking the entire person, because that's what they're used to doing, and isolating it to just one part of the person is not something they were ever trained to do, and so those are sort of some of the limitations.

Fluent Fiction - Hungarian: Summer Adventures: Life Lessons from Budapest Market Find the full episode transcript, vocabulary words, and more:fluentfiction.org/summer-adventures-life-lessons-from-budapest-market Story Transcript:Hu: A nap ragyogott a budapesti piac felett.En: The sun shone over the Budapest market.Hu: Erős illatok, hangos kiáltások és színes standok mindenütt.En: Strong scents, loud shouts, and colorful stalls were everywhere.Hu: Reka, Marcell és Zsófia izgatottan sétáltak a zsúfolt piac központjában.En: Reka, Marcell, and Zsófia walked excitedly through the crowded market center.Hu: Nyár volt, és végre eljött a vakáció ideje.En: It was summer, and the vacation time had finally arrived.Hu: Reka kíváncsi volt, mint mindig, és egy különleges szuvenírt keresett, hogy emlékeztesse erre az útra.En: Reka was curious, as always, and was searching for a special souvenir to remind her of this trip.Hu: Marcell, bár védelmező bátyja volt, titokban saját kalandra vágyott.En: Marcell, although a protective brother, secretly yearned for his own adventure.Hu: Zsófia, a gyermekkori barátjuk, gyakorlati gondolkodású volt, de szenvedélyesen rajongott a művészet iránt.En: Zsófia, their childhood friend, was practical-minded but had a passionate love for art.Hu: "Azt hiszem, külön kell válnunk" mondta Reka.En: "I think we should split up," Reka suggested.Hu: "Így könnyebben találok valami különlegeset."En: "It will be easier for me to find something special that way."Hu: Marcell bólintott. „Rendben, de megadom a találkozóhelyet.”En: Marcell nodded. "Alright, but I'll set a meeting point."Hu: Zsófia is egyetértett. „Én megtalállak benneteket később, addig rajzolok.”En: Zsófia agreed as well. "I'll find you both later; in the meantime, I'll sketch."Hu: Reka elindult a szűk utcákon, színes standok között bújkálva.En: Reka set off through the narrow streets, ducking between colorful stalls.Hu: Ahogy egy eldugott sarkon fordult be, felfedezett egy apró, kézzel készített tárgyakat árusító standot.En: As she turned a hidden corner, she discovered a small stand selling handmade items.Hu: Tudta, hogy megtalálta, amit keresett.En: She knew she had found what she was looking for.Hu: Közben Marcell egy másik részén a piacnak új dolgokat fedezett fel, amikor hirtelen hangos szóváltás kezdődött.En: Meanwhile, in another part of the market, Marcell was discovering new things when a loud argument suddenly broke out.Hu: Egy helyi árus félreértette Marcell szándékait, és kisebb konfliktus alakult ki.En: A local vendor had misunderstood Marcell's intentions, leading to a minor conflict.Hu: Marcell segítségért kiáltott, és hallotta Reka hangját a távolban.En: Marcell called out for help and heard Reka's voice in the distance.Hu: Reka gyorsan odaért, és megpróbálta megmagyarázni a helyzetet.En: Reka quickly arrived and tried to explain the situation.Hu: Sikerrel jártak; az árus megnyugodott és bocsánatot kért.En: They succeeded; the vendor calmed down and apologized.Hu: Marcell megkönnyebbült, hogy Reka ott volt, amikor szüksége volt rá.En: Marcell was relieved that Reka was there when he needed her.Hu: Később találkoztak Zsófiával, aki csak mosolygott és mutatta a vázlatfüzetét.En: Later, they met up with Zsófia, who was smiling and showing them her sketchbook.Hu: Tele volt színes és élénk rajzokkal a piacról.En: It was filled with colorful and lively drawings of the market.Hu: Mindannyian büszkék voltak saját kis kalandjukra és a saját, egyedi emlékeikre.En: They were all proud of their little adventures and their own unique memories.Hu: Reka megtanulta, mennyire fontos a bizalom és az együttműködés.En: Reka learned the importance of trust and cooperation.Hu: Marcell rájött, hogy egyensúlyt kell találnia a védelmező természet és a személyes vágyak között.En: Marcell realized he needed to balance his protective nature with his personal desires.Hu: Zsófia pedig magabiztosabban osztotta meg művészetét.En: Zsófia, in turn, gained confidence in sharing her art.Hu: A három barát boldogan távozott a piacról, kezükben különleges emlékekkel és szívükben mélyebb megértéssel egymás iránt.En: The three friends left the market happily, holding special mementos in their hands and a deeper understanding of each other in their hearts.Hu: A fényes piac nemcsak különleges tárgyakat, hanem egy életre szóló élményt és tanulságot is adott nekik.En: The bright market had not only given them unique items but also a lifetime experience and valuable lessons. Vocabulary Words:shone: ragyogottscents: illatokstalls: standokcrowded: zsúfoltvacation: vakációcurious: kíváncsisouvenir: szuvenírprotective: védelmezőadventure: kalandpractical-minded: gyakorlati gondolkodásúpassionate: szenvedélyesensuggested: mondtanodded: bólintottmeeting point: találkozóhelyducking: bújkálvahidden: eldugotthandmade: kézzel készítettcalling out: kiáltottargument: szóváltásconflict: konfliktusvendor: árusintentions: szándékaitexplained: megmagyaráznirelieved: megkönnyebbültsketchbook: vázlatfüzetproud: büszkéktrust: bizalomcooperation: együttműködésbalance: egyensúlytmementos: emlékek

Livestreams for the AI Engineer World's Fair (Multimodality ft. the new GPT-4o demo, GPUs and Inference (ft. Cognition/Devin), CodeGen, Open Models tracks) are now live! Subscribe to @aidotEngineer to get notifications of the other workshops and tracks!It's easy to get de-sensitized to new models topping leaderboards every other week — however, the top of the LMsys leaderboard has typically been the exclusive domain of very large, very very well funded model labs like OpenAI, Anthropic, Google, and Meta. OpenAI had about 600 people at the time of GPT-4, and Google Gemini had 950 co-authors. This is why Reka Core made waves in May - not only debuting at #7 on the leaderboard, but doing so with all-new GPU infrastructure and 20 employees with

Episode 186 was recorded June 13, 2024. Published June 25, 2024 Once in a while, you come across a name you know you want to remember and Réka Shikli is one to keep an eye on.  She began writing and making films at an early age with her parents camera and evolved into writing, producing and directing films. A unique story how seeking a film grant brought her family together through a smartphone and storytelling.  Never judge a filmmaker by the length of a film shot with a smartphone and never underestimate the filmmakers you meet in San Diego at the International Mobile Film Festival.  Listen to this in-depth conversation with Reka Shikli from Canada, talking to us from Hungary.  Disquiet was shot on iPhone 11. Director, writer, editor &  producer: Réka Shikli; Cinematographer: Kinga Shikli; Cast: Sofi Shikli, Laszlo Shikli; Composer: Frank Dormani Mentioned in this episode:  Disquiet Movie Trailer: https://vimeo.com/918944065  Reka Shikli: Instagram: https://www.instagram.com/rekashikli/  IMDb: https://www.imdb.com/name/nm12958441/  Website: https://rekashikli.github.io/ SBP Podcast Mobile Filmmaking: The Voice of Mobile Film™ is for everyone who ever wanted to or is curious about making movies and videos using smartphones.  Are you enjoying our free podcast? Share some love.  BuyMeACoffee: https://www.buymeacoffee.com/susybotello  Patreon: https://www.patreon.com/sbppodcast  Sign up for our Podcast Newsletter: http://eepurl.com/iwK-dM Subscribe to listen in your own app: https://www.podbean.com/site/podcatcher/index/blog/kOpp1Xtzvu6l  Our Links:  SBP Podcast Website: http://sbppodcast.studio  Smartphone Filmmaking Publication: https://medium.com/smartphonefilmmaking    Susy's Substack: https://susybotello.substack.com  Podcast Twitter: http://twitter.com/sbppodcast    Facebook: http://facebook.com/sbppodcast       Instagram: https://www.instagram.com/mobilefilmsd/  Susy on Threads: https://www.threads.net/@susybotelloofficial  Susy on Twitter: http://twitter.com/susybotello  Susy on Instagram: https://www.instagram.com/susybotelloofficial/  Apple Podcasts: https://itunes.apple.com/us/podcast/sbp-podcast/id1296673665  © 2024 S. Botello Productions. All rights reserved.

With Steam Next Fest demos and the Xbox Games Showcase, we've got a ton to talk about. Both Ben and Ed have been playing a bunch of demos, and thoroughly enjoyed the showing at the Xbox Games Showcase. Join us for a debrief and chatter about some really cool indies coming out in the next year or two. Plus, what you've been playing! 00:00:00 - Intro 00:06:27 - Sonic X Shadow Generations 00:13:48 - Tiny Bookshop 00:18:01 - Albatroz 00:22:14 - Dungeons of Hinterberg 00:27:23 - Demonschool 00:32:56 - EbiTapes 00:38:56 - REKA 00:44:37 - Aero GPX 00:50:57 - Fruitbus 00:56:34 - Minami Lane 01:02:45 - What you've been playing 01:06:15 - Xbox Games Showcase 01:29:50 - Outro   ▼ Swapping Joysticks ▼ ● All previous episodes available at http://swappingjoysticks.com

00:16 | OpenAI GPT-4o's multi-modal AI changed the game- GPT-4o release last Monday; capable of handling text, speech, and video (multi-modal)- Best use cases; real-time translation, tutoring, Mac app- Mac app is game-changer, Google Workspace with AI is a game-changer- What will the world look like in 5 years after multi-modal AI?- $97b secondary market valuation, +13% vs last round (Apr 2024)15:52 | $20b IPO for Klarna!, is it an AI play?- $20b IPO could come as early as Q1 2025- 150m global customers, 40m US customers- UK based holding company established, sign IPO is coming- $10b secondary market valuation, +49% vs last round (Jul 2022)- Investors could make a quick 100% return if entering in the secondary and $20b IPO plays out- Affirm (close competitor) has 4.9x price to sales ratio, Klarna's 2023 revenue is $2.17b implying a $10b valuation- Could an AI valuation multiple be used to value Klarna? Or … is Klarna an AI play?23:27 | $625m for Reka AI founders/employees in

Pre-IPO stock valuations = www.x.com/aarongdillon (see pinned post)Pre-IPO stock index fact sheet = www.agdillon.com/index00:07 | OpenAI impresses with GPT-4o- Capable of handling text, speech, and video- Refreshed ChatGPT UI and a macOS desktop app- Verbal conversation improvement was the big surprise, in my opinion- Watch live demos here = https://x.com/OpenAI- $97b secondary market valuation, +13% vs last round (Apr 2024)01:53 | Reddit + OpenAI data deal- OpenAI to license Reddit data for training- $ amount OpenAI pays Reddit not disclosed- Follows OpenAI data deals with Dotdash Meredith, Financial Times02:53 | Klarna IPO rumored at +100% vs 2ndary mrkt- $20b IPO could come as early as Q1 2025- 150m global customers, 40m US customers- UK based holding company established, sign IPO is coming- $10b secondary market valuation, +49% vs last round (Jul 2022)- Investors could make a quick 100% return if entering in the secondary and $20b IPO plays out03:55 | Anthropic hires new CPO, launches in Europe- Mike Krieger is new chief product officer- Krieger co-founded Instagram and sold to Facebook for $1.0b- Anthropic also launched in Europe; full product suite- $18.1b secondary market valuation, +0.6% vs last round (Jan 2024)05:22 | Chime vs payday lenders- cash advance of $500- customer must have completed two payroll cycles- $2 fee, 1-2 day wait to access advance- 7m customers, profitable in Q1 2024- $5.3b secondary market valuation, -79% vs last round (Sep 2021)06:14 | $625m cash for Reka AI founders/employees- Snowflake rumored to acquire Reka AI for $1.0b- Reka AI investors; DST Global, Snowflake's venture arm- Reka AI founded in 2022, founders/employees still own 62.5% of company (my math)- $625m for Reka AI's founders/employees if Snowflake deal goes through07:14 | Starlink live in Indonesia, Carnival ships- Starlink live in Indonesia on May 19; Starlink available in 75+ countries- Starlink live on all 90 Carnival Cruises ships; $15 to $19 per person per day- $192b secondary market valuation, +6.4% vs last round (Jan 2024)08:41 | Rippling launches performance mgmt product- New product to help employers monitor performance continuously and free up HR for complex tasks- $14.9b secondary market valuation, +11% vs last round (Apr 2024)09:54 | Figma $12.5b tender- $600m to $900m deal size- Current investors, including current and former employees, allowed to sell- a16z, Sequoia, Kleiner Perkins to invest- Adobe $20b deal blocked by European regulators, paid Figma $1b breakup fee10:53 | Pre-IPO -0.67% for week, +30.73% for last 1yr- Week winners: Rippling +5.9%, Chime +4.2%, Discord +4.1%, Bytedance +2.2%, Revolut +1.3%- Week losers: Flexport -27.5%, Canva -6.6%, OpenAI -2.4%, Epic Games -2.0%, Anthropic -1.1%- Top valuations: ByteDance $295b, SpaceX $192b, OpenAI $97b, Stripe $74b, Databricks $43b11:39 | +0.26% 2024 Pre-IPO Stock Vintage Index- www.agdillon.com/index for fact sheet pdf- 2024 Vintage Index top contributors since inception: Epic Games +172%, Rippling +100%, Revolut +42%, Klarna +35%, Anduril +33%- Looking at all 20 vintages … here are the winners and losers for the week; winners = Rippling +5.9%, Chime +4.2%, Discord +4.1% and losers = Tanium -10.9%, OpenAI -2.4%, Epic Games -2.0%- Key metric averages for all Vintage Indexes 5 years old or older……3.31 distributed paid in capital (DPI)…2.05 residual value to paid in capital (RVPI)…5.36 total value to paid in capital (TVPI)…4.1 years to “return the fund”

Our 163rd episode with a summary and discussion of last week's big AI news! Note: apology for this one coming out a few days late, got delayed in editing it -Andrey Read out our text newsletter and comment on the podcast at https://lastweekin.ai/ Email us your questions and feedback at contact@lastweekin.ai and/or hello@gladstone.ai Timestamps + links: Intro / Banter Tools & Apps (00:02:16) Meta releases Llama 3, claims it's among the best open models available (00:14:01) Elon Musk's xAI Unveils Grok-1.5 Vision, Beats OpenAI's GPT-4V (00:17:55) Reka releases Reka Core, its multimodal language model to rival GPT-4 and Claude 3 Opus (00:21:50) Cohere Compass Private Beta: A New Multi-Aspect Embedding Model (00:23:48) Amazon Music's Maestro lets listeners make AI playlists (00:24:36) Snap plans to add watermarks to images created with its AI-powered tools Applications & Business (00:25:52) Boston Dynamics unveils new Atlas robot for commercial use (00:30:32) TSMC's $65 billion bet still leaves US missing piece of chip puzzle (00:36:30) U.S. blacklists Intel's and Nvidia's key partner in China — three other Chinese firms also included in the blacklist for helping the military (00:38:37) Elon Musk says the next-generation Grok 3 model will require 100,000 Nvidia H100 GPUs to train (00:40:22) Dr. Andrew Ng appointed to Amazon's Board of Directors (00:41:55) Collaborative Robotics Locks Up $100M, Latest Robot Startup To Raise Big Projects & Open Source (00:44:08) OpenEQA: Embodied Question Answering in the Era of Foundation Models (00:50:03) Introducing Idefics2: A Powerful 8B Vision-Language Model for the community Research & Advancements (00:51:21) RHO-1: Not All Tokens Are What You Need (00:57:21) Scaling Laws for Fine-Grained Mixture of Experts (01:03:20) Chinchilla Scaling: A replication attempt (01:07:18) China develops new light-based chiplet that could power artificial general intelligence — where AI is smarter than humans (01:10:45) OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments Policy & Safety (01:13:44) U.S. Commerce Secretary Gina Raimondo Announces Expansion of U.S. AI Safety Institute Leadership Team (01:17:18) NSA Publishes Guidance for Strengthening AI System Security (01:19:19) Foundational Challenges in Assuring Alignment and Safety of Large Language Models (01:24:11) Former OpenAI Board Member Calls for Audits of Top AI Companies (01:27:35) SoA survey reveals a third of translators and quarter of illustrators losing work to AI Synthetic Media & Art (01:30:25) Medium bans AI-generated content from its paid Partner Program

Kampf gegen KI-Deepfakes Das nächste multimodale KI-Modell Reka Core Forscher warnt vor "Wissenskollaps" durch große Sprachmodelle und Abschied von Atlas heise.de/ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.heiseplus.de/podcast https://www.ct.de/ki https://www.deutscher-podcastpreis.de/podcasts/ki-update/

Reka Zalan is a Berlin-based Techno DJ and promoter, co-hosting the ://elements series at ://about blank, where she's resident and part of the booking team. Her activities in the local music and club culture scene also include working at Hard Wax, one of Berlin's essential record store addresses. Reka's sound is a palpable blend of cosmic and driving Techno, combining deep, gloomy vibes and trippy elements as well as percussion-heavy, groovy and tooly facets. Besides her passion for Techno realms, she loves various forms of (UK) Bass music, Electro (Wave) or experimental beatless soundscapes. She also plays under the moniker No Life Signal, a B2B duo with ://elements colleague and DJ fellow THNTS. Since early 2023 now, she's been a member of the local residents team of well-known Berlin Tresor club. Furthermore she's part of the Coppi collective, a project space with weekly community-based studio sessions in an off-location between Friedrichshain and Lichtenberg. Tracklist via -Spotify: http://bit.ly/SRonSpotify -Reddit: www.reddit.com/r/Slam_Radio/ -Facebook: bit.ly/SlamRadioGroup Archive on Mixcloud: www.mixcloud.com/slam/ Subscribe to our podcast on -iTunes: apple.co/2RQ1xdh -Amazon Music: amzn.to/2RPYnX3 -Google Podcasts: bit.ly/SRGooglePodcasts -Deezer: bit.ly/SlamRadioDeezer Keep up with SLAM: fanlink.to/Slam Keep up with Soma Records: fanlink.to/SomaRecords For syndication or radio queries: harry@somarecords.com & conor@glowcast.co.uk Slam Radio is produced at www.glowcast.co.uk

Amid the pandemic, the concept of a human-centric workplace soared in Google searches, with HR institutions posting 2-3 articles weekly on the topic. Studies underscore its importance: PwC notes 83% of executives see it vital for innovation; University of Warwick research shows 12% productivity gains from happy employees; Gallup finds 21% higher profitability in companies with engaged staff. These findings emphasize the significance of prioritizing employee well-being and engagement for organizational success.   Despite these numbers many are still unsure that investing on wellbeing is worth.   About Reka Deak (founder of Wellbeing Designers) Reka Deak is the visionary Founder of Wellbeing Designers, a pioneering company dedicated to fostering human-centric organizations through Strategic Wellbeing Education. With a senior advisory role at The Josh Bersin Company and previous experience as a management consultant for industry giants like KPMG and Accenture, Reka brings a wealth of expertise in enabling Wellbeing Culture.  Reaching Reka Deak Reka's personal site https://www.rekadeak.com/ Wellbeing Designers https://www.wellbeing.design/ Reka's LinkedIn https://www.linkedin.com/in/rekadeak/ What We Discussed in this Episode on Is Wellbeing a Profit Center or a Sunk Cost? - Defining Wellbeing in Corporate Settings - Post-COVID Investments in Employee Wellbeing - Unveiling the Science behind Wellbeing Programs - Assessing the Impact of Employee Wellbeing Interventions - Uncovering Truths about Employee Wellbeing - Expert Perspectives on Emerging Trends - Consequences of Ignoring Employee Wellbeing - Profit Center or Expense? The Economic Outlook of Corporate Wellbeing   ### Sign up for the Simply Human Newsletter (monthly email newsletter): https://simplyhuman.substack.com Follow the Growth Hacking Culture Podcast: https://www.peoplekult.com/podcast-work-culture Follow Ivan Palomino on Twitter: https://twitter.com/ivanpalomino_ Follow Ivan Palomino on LinkedIn: https://www.linkedin.com/in/ipalomino/    About the Growth Hacking Culture Podcast The Growth Hacking Culture Podcast is a series of insightful interviews with prominent experts on mindsets, skills and mental resources to grow individually, lead motivated teams and create human-centric work cultures. These episodes are about thought provoking ideas to scale up and growth hack human-centric and performing work cultures. Hosted by Ivan Palomino.

In this episode of the Thoughtful Entrepreneur, your host Josh Elledge speaks with the President of Entrepreneurial Advisors, Kristopher Kluver. Kris Kluver has carved a niche in strategic advising, guiding CEOs and entrepreneurs to envision and execute their goals precisely. Kris emphasized the importance of having a clear roadmap and the role of a strategic advisor in helping business leaders navigate the often-tumultuous waters of entrepreneurship. He shared practical examples and tools that have allowed his clients to succeed, highlighting the transformative power of a well-crafted strategy.As a seasoned CEO coach, Kris understands the unique challenges that business leaders face. He shed light on the common pitfalls and how a coach can catalyze personal and professional growth. Kris discussed the value of having an external perspective to challenge your thinking, hold you accountable, and push you beyond your comfort zone. Kris's approach to coaching is not just about business growth but fostering leaders equipped to make impactful decisions.Kris highlighted its significance in every aspect of leadership and personal development. He discussed how self-awareness can lead to better decision-making, improved relationships, and a more satisfying life. Kris shared insights on how to cultivate self-awareness and the impact it can have on your business and personal life.Key Points from the Episode:Interview with Kris Kluver, founder of Entrepreneurial AdvisorsWork in strategic advising and CEO coachingDiscussion of his book "Life on Your Terms: Discovering What's Next"Challenges of work-life balance for business leadersImportance of self-awareness for business leadersAbout Kristopher Kluver:Kris Kluver is a distinguished facilitator, acclaimed author of "The Aspiring Solopreneur, Your Business Start-Up Bible," and a seasoned entrepreneur with over 30 years of experience. His life's purpose revolves around aiding individuals, leadership teams, and business owners to achieve their dreams. Kluver's commitment to relationship-building is evident through his 100% referral-based business model, emphasizing client success and fostering trust. As the founder of Entrepreneurial Advisors, a strategic advisory firm in the United States and Europe, Kluver guides organizations of all sizes in priority-setting, collaboration, issue resolution, and improved communication. With a holistic approach, he employs simple tools and concepts to help businesses and individuals discover shared visions, master accountability, and form cohesive leadership teams for collective success. Kluver's diverse entrepreneurial background includes ventures in business consulting, real estate, online services, counseling, advertising, and financial services. Harvard Business School-educated in Entrepreneurial Strategy, Kluver is also an Honorary Fellow at York University in the United Kingdom. He and his wife, Reka, also conduct couples retreats focused on strategic planning methodologies to align couples and help them achieve their ideal life. Outside of his professional pursuits, Kluver channels his passionate and adventurous spirit into hobbies such as hiking, adventure travel, ultra-marathons, and global networking.About Entrepreneurial Advisors:Entrepreneurial Advisors (EA) is a dedicated strategy, facilitation, and coaching firm committed to...

This Week in Startups is brought to you by: OpenPhone. Create business phone numbers for you and your team that work through an app on your smartphone or desktop. TWiST listeners can get an extra 20% off any plan for your first 6 months at http://www.openphone.com/twist Imagine AI LIVE is an AI conference where you'll learn how to apply AI in YOUR business directly from the people who build and use these tools. It's taking place March 27th and 28th in Las Vegas, and TWiST listeners can get 20% off tickets at http://imagineai.live/twist Scalable Path. Want to speed up your product development without breaking the bank? Since 2010, Scalable Path has helped over 300 companies hire deeply vetted engineers in their time zone. Visit ⁠http://www.scalablepath.com/twist⁠ to get 20% off your first month. Todays show: Sunny Madra joins Jason to discuss how Google's “woke AI” emergency came to be (1:17), Apple's lowkey AI integrations (33:51), what OpenAI's incredible Sora model means for Hollywood (39:39), and much more! Viewers! How are you enjoying the demos? What grades do you give these AI companies? Tell us what we got wrong and right and what demos you'd like to see on the podcast. Let us know by mentioning us on ⁠X.com⁠. ⁠https://x.com/Sundeep⁠ ⁠https://x.com/Jason⁠ ⁠https://x.com/twistartups⁠ See the full list of all AI demos from the show here: ⁠thisweekinstartups.com/AI⁠ Timestamps: (0:00) Sunny Madra joins Jason! (1:17) What went wrong with Google's AI: Model training, RLHF, or guardrails? Plus, how Google can look to Meta for a solution (13:35) OpenPhone - Get 20% off your first six months at http://www.openphone.com/twist (15:00) More examples of bias in Google's Gemini model (20:19) Explorer.Globe.Engineer: an AI-powered research assistant (27:45) Imagine AI LIVE - Get 20% off tickets at http://imagineai.live/twist (29:01) Reka's impressive multimodal functionality (33:51) Apple starts slowly releasing AI-powered features in its most popular apps (38:19) Scalable Path - Get 20% off your first month at http://www.scalablepath.com/twist (39:39) Sora demos from OpenAI, and what this means for the film industry Links: Check out Explorer.Globe: https://explorer.globe.engineer Check out Reka: ⁠https://reka.ai Check out Sora: https://openai.com/sora Follow Sunny X: ⁠https://twitter.com/sundeep⁠⁠ Check out Definitive: ⁠https://www.definitive.io Follow Jason: X: ⁠⁠https://twitter.com/jason⁠⁠ Instagram: ⁠⁠https://www.instagram.com/jason⁠⁠ LinkedIn: ⁠⁠https://www.linkedin.com/in/jasoncalacanis⁠ Thank you to our partners: (13:35) OpenPhone - Get 20% off your first six months at http://www.openphone.com/twist (27:45) Imagine AI LIVE - Get 20% off tickets at http://imagineai.live/twist (38:19) Scalable Path - Get 20% off your first month at http://www.scalablepath.com/twist Check out the Launch Accelerator: ⁠https://launchaccelerator.co⁠ Check out Founder University: ⁠https://www.founder.university⁠ Subscribe to This Week in Startups on Apple: ⁠https://rb.gy/v19fcp⁠

Tune in this week to be inspired in all the ways! Join us and hear Boss Babe, Reka, share all about her booming Baddie Brownie business, her health, fitness and career journey and what keeps her motivated. This episode is for anyone who has found themself feeling stuck and ready to unleash your inner Baddie! Connect with Reka on Instagram at @baddie.brownie_ You can work with me by booking a complimentary clarity call: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.mary-howard.com/book-online⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Let's talk about what is possible for you. Connect on IG ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@maryhoward.mc⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ *Human experiences are unique and nuanced. This podcast is intended for exploration and inspiration. Mary speaks from a heteronormative lens but this information is applicable and helpful for any human healing their heart and learning to love again. All are welcome here and we love you already. oo --- Send in a voice message: https://podcasters.spotify.com/pod/show/maryhowardmc/message

I denne lytterepisoden møter vi Reka (29) som kom til Norge i 2016. Huner ungarsk, fra Romania og kom til Norge som au pair for en tyskfamilie. 7 år senere er Reka fortsatt i Norge, bosatt på et småbruk.Hvilke spareteknikker og pengetankesett har hun hatt for å klare seg pålave lønninger i et nytt land?Hun forteller om alle jobbene hun har hatt og hvordan det føles nå,etter fullførte studier å ha en fast, 100 % jobb. Føler hun seg rik nå?Annonse/rabattkode hellofresh.no: FRESHPENGE  See omnystudio.com/listener for privacy information.

Our 140th episode with a summary and discussion of last week's big AI news! Read out our text newsletter and comment on the podcast at https://lastweekin.ai/ Email us your questions and feedback at contact@lastweekin.ai Note: the CEO of GitHub disputes "Report: GitHub Copilot Loses an Average of $20 Per User Per Month" Timestamps + Links: (00:00)  Intro / Banter Tools & Apps (03:00) Reka launches Yasa-1, a multimodal AI assistant to take on ChatGPT (06:48) Arc browser's new AI-powered features combine OpenAI and Anthropic's models (11:00) ElevenLabs Launches Voice Translation Tool to Break Down Language Barriers for Content (13:11) Canva's new AI tools automate boring, labor-intensive design tasks (16:32) Adobe previews AI upscaling to make old, fuzzy videos and GIFs look fresh (20:45) Google Bard is gaining a new 'Memory' toggle to remember key details (22:22) Assistant with Bard: A step toward a more personal assistant (24:14) Android 14's AI-generated wallpapers might be its coolest new feature Applications & Business (24:50) Waymo's robotaxi service is now available to tens of thousands of people in San Francisco (28:54) Report: GitHub Copilot Loses an Average of $20 Per User Per Month (32:34) TSMC Sales Fell Less Than Feared as AI Demand Offsets Slump (34:54) Microsoft could debut its AI chip next month: Report (37:06) Exclusive: ChatGPT-owner OpenAI is exploring making its own AI chips (39:08) Google announces new generative AI search capabilities for doctors Projects & Open Source (40:05) Replit's new AI Model now available on Hugging Face (43:46) Introducing Stable LM 3B: Bringing Sustainable, High-Performance Language Models to Smart Devices (46:30)  Protesters Decry Meta's “Irreversible Proliferation” of AI Research & Advancements (52:10) Scaling up learning across many different robot types (59:35) Decomposing Language Models Into Understandable Components (01:06:54) Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution (01:11:45) China's First 28nm Lithography Tool to Be Delivered This Year (01:15:03) LLMs can't self-correct in reasoning tasks, DeepMind study finds (01:18:07) Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! Policy & Safety (01:22:08) RISC-V technology emerges as battleground in US-China tech war (01:26:48) Meta and X questioned by lawmakers over lack of rules against AI-generated political deepfakes (01:33:44) Five Takeaways From Bellwether AI Copyright Case (01:36:19) A.I. Could Soon Need as Much Electricity as an Entire Country (01:38:12) Governments race to regulate AI tools (01:40:09) US curbs on chip tools to China nearly finalized, government posting shows Synthetic Media & Art (01:45:35) WGA Ratifies Three-Year Deal With Studios, Officially Ending Hollywood Strike (01:47:50)  AI Watermarks Are No Match for Attackers (01:50:00) Stable Signature: A new method for watermarking images created by open source generative AI (01:51:36) How an AI deepfake ad of MrBeast ended up on TikTok (01:53:53) Disney's Loki faces backlash over reported use of generative AI

Pravljico vsak večer lahko slišijo vsi otroci in tisti odrasli, ki si niso pozabili umiti ušes … Pripovedujeta: Gregor Zorc in Martina Maurič Lazar. Napisal: Mitja Šegina. Pravljica z natečaja za izvirno slovensko pravljico 2010. Posneto v studiih Radia Slovenij 2011.

Relationships, Statistics, and Self-Exploration: Unveiling Truths about Dating (each other) for Black Men and Women. An argument as to why we should give the bus driver a chance.  In this thought-provoking podcast episode hosted by Reka Robinson, she engages in a candid conversation with J. Hall about the complexities of relationships, the influence of statistics in dating, and the dynamics surrounding Black men and women. Together, they delve into the following thought-provoking questions: Should we base our choices on relationship statistics or on the reality we experience? Reka and J. Hall ponder the implications of relying solely on statistical data when it comes to dating and whether it aligns with personal experiences and individual needs. And where does faith come in? Nurturing Black men: Reka and J. Hall examine the idea of supporting and uplifting Black men. They question whether assumptions about their strengths and comfort levels hinder our ability to truly understand their needs and desires. The importance of open communication and asking questions is emphasized. Room for questions: The conversation navigates around whether Black men allow space for questioning, or if their ego obstructs genuine dialogue. Exploring the facets of self-discovery, they wonder if Black men truly explore the potential of the man they aspire to become. Why can't we call our men up?  Acknowledging collective struggles: J. Hall shares his newfound understanding that the challenges faced by women in their interactions with men impact society as a whole. “I use to say that sucks for you, now I know it sucks for us.” The path to healing: Reka opens up about her personal journey, revealing that she believes true healing can only be achieved by embracing the discomfort and vulnerability that comes with building relationships. She wonders if she will be able to reach that point with a Black man. Can we get past that wall? Financial stability and trust: The discussion shifts to the topic of financial stability and its impact on dating within the Black community. Reka and J. Hall question whether Black men should refrain from dating until they achieve financial stability and whether Black women are expected to meet them at the bottom as a way to gain their trust. Reflecting truths: Reka shares her belief that she has the ability to make men confront their own selves, which can be uncomfortable for them, leading to avoidance. Which is why they run and why relationships never work out for her.  Throughout the episode, Reka and J. Hall provide listeners with an engaging and thought-provoking dialogue that challenges preconceived notions about relationships, explores the dynamics between Black men and women, and encourages a deeper understanding of oneself and others. You can find J.Hall here. His Blog here. The episode of 'The Good Girl Podcast' we mention is here. The history of being Black; here. --- At this time I am not coaching but My DM is always open! Find me on IG ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠ Twitter ⁠⁠⁠⁠⁠⁠⁠here.⁠⁠⁠⁠⁠⁠⁠ Facebook ⁠⁠⁠⁠⁠⁠⁠here.⁠⁠⁠⁠⁠⁠⁠ TikTok ⁠⁠⁠⁠⁠⁠⁠here.⁠⁠⁠⁠⁠⁠⁠ Email = Reka@justmeReka.com Xo