Podcasts about Roble

In Episode 2 of Season 2, Dr. Kenny Friedman & Rabbi Yisroel Bernath get back to basics, reviewing wines they have been drinking recently and discussing what makes them interesting.Kenny's Wines:Bodegas Vizar, Roble, 2023Chateau Les Graves de Lavaud, 2020Dalton, Galilee, Sauvignon Blanc, Fumé, 2024Clos 15, Brut Nature, Cava, NVDalton, Galilee, Alma, Deep Red, 2020R' Yisroel's Wines:J. de Villebois, Pouilly-Fumé, “Les Silex Blancs,” 2023El Orador, Rioja Alta, 2023Dalton, Pinot Gris, 2023Hajdu, Mendocino County, Pinot Blanc, 2024Recanati, Reserve, Marselan, 2019Kenny and R' Yisroel discuss genetic mutations creating white variants of red grapes, screw top wines, the impression labels make on customers, and the greater topic of "ambience" as it applies to restaurants and food, among many other topics.Support the showEmail your questions and comments to kosherwinepodcast@gmail.com

What does it take to develop an Olympic-level performance mindset? In this episode of The Better Leadership Team Show, I sit down with two-time Olympian Stephanie Roble, a world-class sailor, to explore how elite athletes build resilience, manage pressure, and develop the mindset needed to perform at the highest level.Stephanie shares her journey from childhood sailing in Wisconsin to competing on the world's biggest stage. We discuss how decision-making, communication, self-talk, and mindfulness impact both sports and business leadership. She also reveals how she overcame performance anxiety, burnout, and self-doubt to achieve international success.Now transitioning into coaching, Stephanie is using her experience to help high performers—both athletes and business leaders—reach their full potential.If you want to build a championship mindset in business, sports, or life, this is an episode you won't want to miss! Connect with Stephanie Roble on LinkedIn & Instagram.https://www.instagram.com/stephanieroble/https://www.linkedin.com/in/stephanieroble/ Thanks for listening! Connect with us at mike-goldman.com/blog and on Instagram@mikegoldmancoach and on YouTube @Mikegoldmancoach

Si hoy se siente como si estuviera en la temporada en la que nadie lo nota, recuerde que su fortaleza no está en lo que los demás, sino en lo que ha construido en su interior.

. ¡La cuarta parte de "El Zorrito y el Valor de la Familia" ya está disponible! En este capítulo, Nico descubre que el mayor tesoro no es solo explorar el mundo, sino compartir momentos con quienes ama. Una historia sobre la importancia del amor, la unión y la familia. Contamos con la participación especial de Tomás Perona como Roble. Escúchalo ahora en todas las plataformas. ¡No te lo pierdas! Disfruta del contenido completo en nuestra web: nekoeteurythmia.com. Conviértete en nuestro mecenas y recibe recompensas exclusivas a través de nuestro Patreon: patreon.com/NEKOETEURYTHMIA. Si quieres participar en el programa, envíanos la historia, eventos o novedades de tu grupo al correo: nekoeteurythmia@gmail.com.

- . ✨ ¡Ya está disponible la segunda parte de la fábula "El zorrito y el valor de la familia"! ✨ Acompaña a Nico en esta nueva aventura, donde descubrirá importantes lecciones sobre el amor, la protección y el verdadero significado de la familia. Con la participación especial de Tomás Perona como Roble y Levi como Tejón Escúchala ya en todas nuestras plataformas y disfruta de una historia que te emocionará y te dejará una bonita reflexión. Disponible en Spotify, iVoox, YouTube, Amazon Music y más. Cuéntanos, ¿Qué te ha parecido esta segunda parte? ¿Quieres escuchar más historias como esta?

Si bien algunos cambios pueden ocurrir rápidamente, como las flores que brotan rápidamente, los cambios más profundos y duraderos requieren tiempo y paciencia. Confiemos en el proceso y en el tiempo de Dios, sabiendo que Él está obrando en nuestras vidas de manera perfecta y amorosa.

Review các phim ra rạp từ ngày 10/01/2025: PHÁ ĐỊA NGỤC – T16 Đạo diễn:  Anselm Chan Diễn viên: Michael Hui, Dayo Wong, Michelle Wai Thể loại:  Tâm Lý Phá ngục cứu vong - một nghi thức đậm màu truyền thống nhưng cũng đầy tính tâm linh huyền bí. Theo niềm tin Đạo giáo, điệu múa “Phá địa ngục” sẽ giúp người quá cố thoát khỏi địa ngục và siêu thoát đến miền cực lạc. “Phá Địa Ngục” trở thành phim nội địa ăn khách nhất mọi thời đại tại Hong Kong. Những thông điệp vừa gần gũi vừa mang tính triết lý về đời sống và kiếp người sẽ được kể một cách khéo léo qua chuyện đời và nghiệp của sư phụ Văn (Hứa Quán Văn thủ vai - ảnh đế đầu tiên giải Kim Tượng) cùng Đạo Sinh (Huỳnh Tử Hoa thủ vai) HỎA THẦN – T13 Đạo diễn: Kwak Kyung-Taek Diễn viên: Joo Won, Kwak Do-won, Yoo Jae-myung Thể loại: Hành Động Đội cứu hoả khu Tây Seoul luôn phải làm việc trong điều kiện thiếu thốn và khắc nghiệt. Ngày nào họ cũng phải lặp lại câu hỏi: đây có phải là lần làm nhiệm vụ cuối cùng? Phải tập trung cứu người hay bảo vệ mạng sống của chính mình? Một ngày, đường dây khẩn báo động về một đám cháy tại Hongje-dong. Ngay lập tức, cả đội cảm nhận được sự nghiêm trọng của tình hình… LEMBAYUNG: OAN HỒN SẢN PHỤ - T18 Đạo diễn:  Baim Wong Diễn viên: Taskya Namya; Yasamin Jasem; Arya Saloka Thể loại: Kinh Dị Arum và Pica, những người muốn hoàn thành kỳ thực tập tại bệnh viện Lembayung, đã phải đối mặt với nỗi khiếp sợ bí ẩn từ một người phụ nữ mà họ nghi ngờ đã treo cổ trong nhà tắm. Tình huống trở nên gay cấn hơn khi họ nhờ người khác giúp đỡ đến mức đe dọa cả tính mạng của chính mình và những người thân cận nhất. NHỮNG KẺ BẤT BẠI 2: PANTERA - 18 Đạo diễn: Christian Gudegast Diễn viên: Gerard Butler, Jordan Bridges, O'Shea Jackson Jr., Thể loại: Hành Động, Hồi hộp, Tội phạm Là phần hậu truyện của Những Kẻ Bất Bại (2018), Những Kẻ Bất Bại 2: Pantera đánh dấu sự trở lại của Big Nick với cuộc săn trên những con phố đầy rẫy tội phạm ở châu Âu. Tại đây, anh tiếp cận Donnie, kẻ hiện đang dính líu tới thế giới nguy hiểm của những tên trộm kim cương và băng đảng mafia Pantera khét tiếng, khi chúng âm mưu một vụ cướp ở sàn giao dịch kim cương lớn nhất thế giới. Overlord: The Sacred Kingdom – T13 Đạo diễn: Naoyuki Ito Diễn viên: Satoshi Hino, Yumi Hara, Masayuki Kato, Thể loại: Hành Động, Hoạt Hình, Phiêu Lưu, Thần thoại Phim là câu chuyện về Thánh Quốc Roble, đứng đầu là Thánh Hậu Calca. Thánh Quốc đã trải qua một kỷ nguyên hòa bình với vùng đất được bảo vệ bởi một bức tường dài. Tuy nhiên, sự xuất hiện bất ngờ của Quỷ Hoàng Jaldabaoth và sự xâm lược của Liên minh Á nhân, hòa bình đã bị phá hủy. LỜI THÌ THẦM CỦA TRÁI TIM - K Đạo diễn:  Yoshifumi Kondô Diễn viên: Yoko Honna, Issei Takahashi, Takashi Tachibana, ... Thể loại:  Hoạt Hình, Tâm Lý, Tình cảm Phát hành tại Nhật Bản vào ngày 15 tháng 7 năm 1995 Dựa trên manga của Hiiragi Aoi Sản xuất, viết kịch bản và dàn dựng bởi Miyazaki Hayao Đạo diễn bởi Kondo Yoshifumi Tsukishima Shizuku, một cô bé lớp 9 mê đọc sách, luôn bị cuốn hút bởi những câu chuyện trên từng trang giấy. Vào một ngày, cô phát hiện ra rằng hầu hết các cuốn sách cô mượn đều có tên một người đọc trước đó – Amasawa Seiji . Hóa ra, Seiji cũng chính là một học sinh cùng trường, với ước mơ trở thành một nghệ nhân làm đàn violin. Cậu ấy đã mơ ước rằng mình có thể học nghề làm đàn vĩ cầm tại Cremona, Ý sau khi tốt nghiệp cấp 2. Cuộc gặp gỡ với Seiji, một người có ước mơ lớn lao nhưng vẫn rất hiện thực đã thổi bùng ngọn lửa nhiệt huyết trong Shizuku, một cô bé vẫn đang loay hoay chưa biết mình muốn làm gì trong cuộc sống...

País Reino Unido Dirección Ken Loach Guion Paul Laverty Reparto Música George Fenton Fotografía Robbie Ryan Sinopsis El futuro del último pub que queda, The Old Oak, en un pueblo del noreste de Inglaterra, donde la gente está abandonando la tierra a medida que se cierran las minas. Las casas son baratas y están disponibles, por lo que es un lugar ideal para los refugiados sirios.

Meditaciones y reflexiones para hacer la oración especialmente dirigidas a jóvenes. || Pásate por nuestra WEB y lee los testimonios, artículos y suscríbete a los Podcast diarios de rezarhoy en: https://www.jovenescatolicos.es/ Sigue el canal de Jóvenes Católicos en WhatsApp: https://whatsapp.com/channel/0029VaDQN04LY6d1sgDXEK3s Pásate por nuestra cuenta de Instagram: https://www.instagram.com/catolicos_es/ Twitter: https://twitter.com/catolicos_es Facebook: https://www.facebook.com/Catolicos.es/ Pásate por la página web de Cobel Ediciones: http://www.cobelediciones.com/

Temporada 06 | Episodio 42 de Mi Lado V | Radio Fecha de emisión: 12-noviembre-2024 Título: Compuertas Protagonista: María José del Amor Tema 'Vinventions': "Floodgates" by AndrewPlusJohn Tema 'Saint Felicien': "Roble" by Kobol #Wine #Jazz

Hi friends! En este episodio me siento con Nuria Robleño (@nurirobleno) para hablar de emprendimiento, proyectos y como intentar llegar a todo. Acaba de montar Pa eso estamos, un estudio creativo de comunicación. También charlamos de viajes, de encontrar tu estilo, qué hay en su wishlist, cómo buscar inspiración y mucho más. Es una conversación super inspiradora que sé que vais a disfrutar y acabar con las pilas puestas.  Espero que os guste!  Laura x  Podeis suscribiros a los episodios extra en Spotify: https://podcasters.spotify.com/pod/show/withlum/subscribe   Episodios en YouTube (uniros al canal para los extra):  https://www.youtube.com/@LauraUbeda Instagram - @laura_ubeda y @with_lum Mi marca - www.withlum.com Email - info@withlum.com   MUCHAS GRACIAS!!! ☁️✨

¡Vótame en los Premios iVoox 2024! Hoy en La Órbita De Endor recuperamos la sección Expediente Tolkien, para traeros toda la grandiosidad dramática de la guerra que duró media docena de años entre enanos y orcos y que culminó en la sangrienta Batalla del Arroyo Sombrío, también conocida como La Batalla de Azanulbizar. Desfilarán por el relato personajes como el caudillo orco Azog o el rey Thror, Dáin Pie de Hierro, así como otros enanos más conocidos pero que en aquel momento no gozaban de tal fama, como Thorin Escudo de Roble o los hermanos Balin y Dwalin. Pura y genuina épica hecha audio. Pero esto no será más que un entrante, porque nuestro plato fuerte de hoy será el análisis del manga y el anime de moda en estos momentos, la obra llegada desde el Japón de la que todo el mundo habla: Shingeki no Kyojin, conocida en nuestro país como ATAQUE A LOS TITANES. El examen en profundidad de la primera temporada del anime estará dividida en una primera parte sin spoilers donde conoceremos el mundo, la ambientación y los personajes principales, así como los creadores y los aspectos generales del título; y una segunda parte con spoilers (anunciada por la correspondiente Alarma Roja) donde se comentarán los momentos más fuertes de la trama y las sorpresas y asombrosos giros de un argumento arrollador. Estarán presentes en el programa Raúl Martín, Marc Rollán Funs y Antonio Runa. Enanos y gigantes en un audio sobrecargado de espectacularidad, adrenalina y emoción a raudales. ¿Estamos exagerando? COMPRUÉBALO AHORA. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

La Asociación de Apicultores de la Comunidad de Madrid ha celebrado la VI edición de los Premios a las Mejores Mieles de Madrid, y de nuevo la Miel Ecológica de Roble, Antonio Simón, ha sido galardonada. En concreto, ha recibido el Premio a la Mejor Miel Oscura de Madrid, un prestigioso reconocimiento que pone en valor la calidad y el sabor inigualable de las mieles producidas en la región.

Iniciamos la conversación con  Lenin Ocampo –reportero de El Sur de Guerrero– y Julián Andrade –periodista– quienes comparten información acerca del Alcalde de Chilpancingo Alejandro Arcos Catalán, quien fue decapitado y sus restos abandonados en una camioneta, en la Colonia Villa del Roble. Ahí fue hallada una credencial de elector que certificaba su identidad. Arturo Ángel –periodista independiente en EUA– comenta sobre los resultados del Censo Nacional de Procuración de Justicia Estatal y Federal, que fueron presentados la semana pasada.Con 526 mil averiguaciones previas pendientes, Jalisco está en primer lugar en rezago a nivel nacional, son datos del Censo Nacional de Procuración de Justicia Estatal y Federal 2024. Adán Escamilla –reportero en Mérida– y Joaquín Díaz Mena –gobernador de Yucatán– nos comparten sobre el estado del Huracán Miltón que se ubica a 240 km al oeste de Progreso, Yucatán y a 1.185 km al suroeste de Tampa,  Florida, en Estados Unidos y observa rachas de 315 km/h y desplazamiento hacia el este-sureste a 15 km/h.  Anahís Terán –comunicóloga– nos habla sobre la experiencia ‘Cata de los Otros Sentidos', una propuesta inclusiva diseñada para intensificar los sentidos al privar de la vista mediante el uso de antifaces, celebrada en el Restaurante La Hacienda, cuyo objetivo se centra en sensibilizar sobre la discapacidad visual, resaltando la importancia de la accesibilidad en la hostelería.  Programa transmitido el 07 de octubre de 2024. Escucha el Noticiero de Nacho Lozano, en vivo de lunes a viernes de 1:00 p.m. a 2:00 p.m. por el 105.3 de FM. Esta es una producción de Radio Chilango.

In a world often divided, a recent gathering brought together young adults to discuss unity and service. Last week, Boyd had the privilege of attending the Seminario Sudamericano, organized by the Roble del Sur Foundation, where he engaged in meaningful dialogue with Elder David A. Bednar, an apostle for the Church of Jesus Christ of Latter-day Saints. Their conversation centered on the conference's impact and the crucial role church members play in influencing communities globally through unifying principles. Through the power of individual action and the ripple effect of compassion, even small acts of kindness can create significant change.

In Peru, Boyd experienced a powerful reminder of the transformative potential that lies within each individual. It is critically important to bring one's authentic self to the public sphere, where genuine engagement can spark real change. In a world often paralyzed by division, the need to bridge our differences and focus on solutions that drive true progress and societal momentum can be found in each of us. Boyd’s experience in Peru created a compelling call to action, affirming that one person, armed with passion and purpose, can indeed make a profound difference in shaping our collective future.

Suele pasar que las comparaciones hacen mucho daño, sobre todo cuando se desconoce el propósito propio e individual. No podemos pretender ser igual que al resto

Es definitoria, sí. La barrica, sus grados de tostado, el tipo de madera elegida, el tiempo de crianza. Todo esto afecta de manera drástica en el perfil aromático y gustativo de un vino y, por ende, marca también sus complementos en la mesa.  ―――――――――――――――――――――― Esto es MeLoDijoBraga El Podcast. Yo soy Mariano Braga y te espero cada lunes, miércoles y viernes con un nuevo episodio lleno de charlas, experiencias, curiosidades y consejos desde mi mirada del mundo del vino.  Para más información, te invito a navegar estos enlaces: ➡ Recibe gratis “El Boletín Serial” ➡ Mi página web ➡ Sé parte del club ¡Me encantaría que seas parte de esta comunidad gigante de bebedores seriales, siguiéndome en las redes! ➡ Instagram  ➡ Facebook  ➡ Twitter  ➡ YouTube  ➡ LinkedIn  ➡ TikTok  ―――――――――――――――――――――― No te olvides valorar nuestro podcast ★★★★★ y suscribirte para no perderte nada y que sigamos construyendo juntos la mayor comunidad de bebedores seriales de habla hispana. ――――――――――――――――――――――

También conocido como Raid del Gran Sasso o Unternehmen Eiche), fue una operación de rescate ejecutada durante la Segunda Guerra Mundial en la que un comando de paracaidistas de la Wehrmacht alemana liberó al Duce italiano Benito Mussolini de su encierro en el Hotel Campo Imperatore en septiembre de 1943. La orden de Hitler de rescatar a Mussolini a cualquier precio, le fue encomendada al coronel Otto Skorzeny de las Waffen-SS, planificada por el comandante Harald Mors y aprobada por el general Kurt Student, jefe de operaciones de los paracaidistas de la Luftwaffe. Pero como siempre, hubieron varios cambios de planes. Te lo cuenta Antonio Gómez y Dani CarAn. ⭐️ ¿Qué es la Edición Especial de Verano? Se trata de reediciones revisadas de episodios relevantes de nuestro arsenal, para que no pases el verano sin tu ración de Historia Bélica. 🔗 Enlaces para Listas de Episodios Exclusivos para 💥 FANS 👉 CB FANS 💥 https://bit.ly/CBPListCBFans 👉 Histórico 📂 FANS Antes de la 2GM https://bit.ly/CBPListHis1 👉 Histórico 📂 FANS 2ª Guerra Mundial https://bit.ly/CBPListHis2 👉 Histórico 📂 FANS Guerra Fría https://bit.ly/CBPListHis3 👉 Histórico 📂 FANS Después de la G Fría https://bit.ly/CBPListHis4 Casus Belli Podcast pertenece a 🏭 Factoría Casus Belli. Casus Belli Podcast forma parte de 📀 Ivoox Originals. 📚 Zeppelin Books (Digital) y 📚 DCA Editor (Físico) http://zeppelinbooks.com son sellos editoriales de la 🏭 Factoría Casus Belli. Estamos en: 🆕 WhatsApp https://bit.ly/CasusBelliWhatsApp 👉 X/Twitter https://twitter.com/CasusBelliPod 👉 Facebook https://www.facebook.com/CasusBelliPodcast 👉 Instagram estamos https://www.instagram.com/casusbellipodcast 👉 Telegram Canal https://t.me/casusbellipodcast 👉 Telegram Grupo de Chat https://t.me/casusbellipod 📺 YouTube https://bit.ly/casusbelliyoutube 👉 TikTok https://www.tiktok.com/@casusbelli10 👉 https://podcastcasusbelli.com 👨‍💻Nuestro chat del canal es https://t.me/casusbellipod ⚛️ El logotipo de Casus Belli Podcasdt y el resto de la Factoría Casus Belli están diseñados por Publicidad Fabián publicidadfabian@yahoo.es 🎵 La música incluida en el programa es Ready for the war de Marc Corominas Pujadó bajo licencia CC. https://creativecommons.org/licenses/by-nd/3.0/ El resto de música es bajo licencia privada de Epidemic Music, Jamendo Music o SGAE SGAE RRDD/4/1074/1012 de Ivoox. 🎭Las opiniones expresadas en este programa de pódcast, son de exclusiva responsabilidad de quienes las trasmiten. Que cada palo aguante su vela. 📧¿Quieres contarnos algo? También puedes escribirnos a casus.belli.pod@gmail.com ¿Quieres anunciarte en este podcast, patrocinar un episodio o una serie? Hazlo a través de 👉 https://www.advoices.com/casus-belli-podcast-historia Si te ha gustado, y crees que nos lo merecemos, nos sirve mucho que nos des un like, ya que nos da mucha visibilidad. Muchas gracias por escucharnos, y hasta la próxima. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Two-time Olympian Stephanie Roble talks about the joys of sailing as well as her roots on Wisconsin’s Lake Beulah. The East Troy native is competing in the 2024 Summer Olympics Games in Paris in sailing on a 49erFX boat with teammate Maggie Shea of Illinois.

Bodas de Oro papás del Padre Andrés García en Basílica del Roble

Hoy el invitado es Sergio Monsalve de Roble Ventures, un fondo centrado en el futuro del trabajo, que invierte en tecnologías que potencian a las organizaciones y equipos para alcanzar metas más ambiciosas. Sergio tiene una amplia experiencia viviendo en los Estados Unidos y trabajando en el sector de capital de riesgo. Comenzó como emprendedor, luego se desempeñó como socio en Norwest Venture, un fondo único en su tipo con un único inversor, y posteriormente fundó Roble Ventures, su propio fondo.Durante su trayectoria, ha realizado inversiones en empresas destacadas como Udemy, Kahoot, entre otras. Estoy seguro de que disfrutarás tanto como yo esta conversación. Libros recomendados:Outlive - Peter Attia Sobre el invitado:Conecta con Sergio en LinkedinVisita el sitio web de Roble VenturesFollow Us:NewsletterEscribe una ReseñaEncuesta de AudienciaTikTokInstagramTwitterLinkedinWeb Follow Us:NewsletterInvest in startups in a deal by deal basis, min 1k usd.Encuesta de AudienciaTikTokInstagramTwitterLinkedinWeb

Misa en Basílica del Roble por motivo de las elecciones

Misa día de las Madres en la Basílica del Roble

Una diferencia pequeña en el crecimiento de los dos tipos de árboles... y eso condiciona lo que encontramos en el vino después. Si creías haberlo escuchado todo sobre este tema, hoy seguro aprendés algo nuevo sobre el roble francés versus el americano. ―――――――――――――――――――――― Esto es MeLoDijoBraga El Podcast. Yo soy Mariano Braga y te espero cada lunes, miércoles y viernes con un nuevo episodio lleno de charlas, experiencias, curiosidades y consejos desde mi mirada del mundo del vino.  Para más información, te invito a navegar estos enlaces: ➡ Recibe gratis “El Boletín Serial” ➡ Mi página web ➡ Sé parte del club ¡Me encantaría que seas parte de esta comunidad gigante de bebedores seriales, siguiéndome en las redes! ➡ Instagram  ➡ Facebook  ➡ Twitter  ➡ YouTube  ➡ LinkedIn  ➡ TikTok  ―――――――――――――――――――――― No te olvides valorar nuestro podcast ★★★★★ y suscribirte para no perderte nada y que sigamos construyendo juntos la mayor comunidad de bebedores seriales de habla hispana. ――――――――――――――――――――――

La leyenda de una virgen protegida por un árbol de roble desencadena el origen de Monterrey. Conocida por sus fieles, desde chicos y grandes, quienes habrán escuchado aquella increíble historia que sucedió durante la fundación de la ciudad. Descubre cómo dicha historia se enlaza a pasadas anécdotas contadas en el programa. Ven y comparte con nosotros, un tentempié, para el hambre espiritual... que se arme la Machaca, la Machaca Espiritual!

Misa con Padres de Chiapas en Basílica del Roble

Escuche esta y más noticias de LA PATRIA Radio de lunes a viernes por los 1540 AM de Radio Cóndor en Manizales y en www.lapatria.com, encuentre videos de las transmisiones en nuestro Facebook Live: www.facebook.com/lapatria.manizales/videos

Misa Crismal en Básilica del Roble

The elevated plain of the Duero River valley offers deep, textured Tempranillo wines. No Spanish wine selection is complete without this region. Invest 10-minutes with me to find out the special attributes of this great wine region.Explore:Consejo ReguladorBodegas Portia

Misa de envío de consejos pastorales en la Basílica de Nuestra Señora del Roble

Bienvenida a Planeta Parto. Esta semana te traigo al podcast a Lidia, que nos cuenta los partos de sus hijos Minerva y Roble. Lidia empieza diciendo que en su primer parto pensaba que sabía mucho, pero que se dio cuenta después de que no había llegado bien preparada a la prueba. Su primer parto fue una inducción, incluyendo el propex, rotura de bolsa y oxitocina, y un expulsivo instrumentado con forceps. Para su segundo parto se preparó con Raquel de @aymimara con una preparación al parto en movimiento. Esta segunda experiencia fue muy distinta, con un parto que se desencadenó de manera espontánea, llegó al hospital dilatada de 6cm, y Lidia fue la que cogió a su bebé mientras nacía. Un episodio precioso que espero disfrutes tanto como yo!!! CLICA PLAY y empezamos.

El gerente (e) de la Industria Licorera de Caldas, Jaime Alberto Valencia Ramos, nos cuenta sobre los hitos más importantes de la ILC alcanzados durante los últimos cuatro años, además de las bondades de los productos y el desarrollo de nuevos que se posicionan en el mercado. Gracias por escucharnos.

Festividad en basilica de Ntra Señora del Roble en Monterrey

¿Por qué en el vino siempre hablamos de roble? ¿Qué pasa con los otros árboles, con las otras maderas? ¿No sirven, es moda, tiene que ver con algo técnico? ¿Por qué es roble o nada… y el resto es mala palabra? ―――――――――――――――――――――― Esto es MeLoDijoBraga El Podcast. Yo soy Mariano Braga y te espero cada lunes, miércoles y viernes con un nuevo episodio lleno de charlas, experiencias, curiosidades y consejos desde mi mirada del mundo del vino.  Para más información, te invito a navegar estos enlaces: ➡ Recibe gratis “El Boletín Serial” ➡ Mi página web ➡ Mis cursos online de vinos ¡Me encantaría que seas parte de esta comunidad gigante de bebedores seriales, siguiéndome en las redes! ➡ Instagram  ➡ Facebook  ➡ Twitter  ➡ YouTube  ➡ LinkedIn  ➡ TikTok  ―――――――――――――――――――――― No te olvides valorar nuestro podcast ★★★★★ y suscribirte para no perderte nada y que sigamos construyendo juntos la mayor comunidad de bebedores seriales de habla hispana. ――――――――――――――――――――――

Sergio Monsalve is the Founding Partner of Roble Ventures, an early-stage venture capital firm investing in entrepreneurs building human enablement technologies. Before founding Roble Ventures, Sergio was a Partner at Norwest Venture Partners and the Board Member and Investor of Udemy. He also invested in other billion-dollar companies such as Kahoot! and Adaptive Insights.  You can learn more about:  From working at a top fund to starting a fund. Insights into founding Roble Ventures and key strategies for success in venture capital. A look into future trends in work, learning, and AI, and what makes a successful ed-tech company. ===================== YouTube: @GraceGongCEO Newsletter: @SmartVenture LinkedIn: @GraceGong TikTok: @GraceGongCEO IG: @GraceGongCEO Twitter: @GraceGongGG ===================== Join the SVP fam with your host Grace Gong. In each episode, we are going to have conversations with some of the top investors, superstar founders, as well as well-known tech executives in silicon valley. We will have a coffee chat with them to learn their ways of thinking and actionable tips on how to build or invest in a successful company.

AA material y mi experencia. Fernando M. --- Send in a voice message: https://podcasters.spotify.com/pod/show/fernando-montes-de-oca/message Support this podcast: https://podcasters.spotify.com/pod/show/fernando-montes-de-oca/support

El Parke que esta en el oeste de la cuidad+ Occidental el Roble --- Support this podcast: https://podcasters.spotify.com/pod/show/fernando-m-de-oca/support

Mi experencias de Fernando M. --- Support this podcast: https://podcasters.spotify.com/pod/show/fernando-m-de-oca/support

Misa para Ministerios laicales (Acólitos) en Basílica Ntra. Sra. del Roble

En este episodio celebramos a la clase obrera. Lo hacemos con esperanza y cierto pesar con 'El viejo roble', la película con la que tiene toda la pinta se despedirá Ken Loach, el cineasta británico que mejor ha retratado y tratado a los trabajadores. Con él charlamos y también con la taxista de Malena Alterio de 'Que nadie duerma', la desconcertante propuesta de Antonio Méndez Esparza. Hay mucho cine español con 'La ermita', 'El sueño de la sultana' y 'La imagen permanente', pero atención, en la segunda parte os contamos todos los secretos detrás de 'La mesías' con Javier Calvo y Javier Ambrossi. Es solo un resumen de la primera parte de un especial de una hora que emitiremos en unos días.

Directors of research at France's National Centre of Scientific Research find huge reserves of hydrogen gas in France. Can this replace Russian oil? How much of it is there? Why haven't we found reserves like this before? They compare the rum to several other Diplomatico rums. They smoke the La Aurora Connecticut Preferidos 1903 Edition Corona with Roble Viejo Maestra Venezuelan rum.  https://www.accuweather.com/en/climate/they-went-hunting-for-fossil-fuels-what-they-found-could-help-save-the-world/1591853  

¿Qué hace único a Stanford?Para tratar de responder esta pregunta, hoy conversé con Sergio Monsalve, Socio Fundador de Roble Ventures, un fondo de venture capital que invierte en emprendedores creando tecnologías para la educación y el trabajo. Además, es profesor de emprendimiento en Stanford.Sergio ha sido inversionista en grandes compañías como Udemy y Kahoot!.Conversamos sobre el papel de Stanford como cuna de emprendedores, qué la hace tan especial y cómo puedes conectar con inversionistas en Silicon Valley si no atendiste a esta universidad. Este episodio condensa más de 20 años de experiencia en Silicon Valley. Espero que disfrutes mi conversación con Sergio Monsalve de Roble Ventures-La manera más sencilla de ayudarnos a crecer es dejando una reseña en Spotify o Apple Podcasts: https://ratethispodcast.com/startupeable---Notas del episodio: https://startupeable.com/roble-ventures/---Para más contenido síguenos en

En este episodio hablamos de la triste noticia del cierre de la cervecería Anchor Steam Beer, así que les hablamos un poco de historia de este lugar y les presentamos la receta clon de una de sus cervezas icónicas. Además hablamos de nuestra experiencia madurando diferentes cervezas en un barril de roble blamco que tenía […] The post EP086 – Anchor Steam Beer y maduración en barrica de roble first appeared on cervezatlan.

P. Federico (Guatemala)Somos la tierra (el terreno) en que Jesús siembra. También estamos llamados a ser lo que Él ha sembrado. Que estemos dispuestos a morir a nosotros mismos y hacer crecer eso que Dios sueña. ¡Sé quien eres!

Juan David Díaz, hijo del exalcalde del Roble, Eudaldo ‘Tito' Díaz, habló en 6AM Hoy por Hoy de Caracol Radio, sobre la decisión de la JEP de dejar en libertad al exgobernador Salvador Arana.

En 6AM Hoy por Hoy de Caracol Radio, estuvo Juan David Díaz, hijo del líder social y político asesinado en el 2003, Eudaldo “Tito” Díaz, hablando sobre las declaraciones del jefe exparamilitar, Salvatore Mancuso, ante la JEP.

2023 is the year of Multimodal AI, and Latent Space is going multimodal too! * This podcast comes with a video demo at the 1hr mark and it's a good excuse to launch our YouTube - please subscribe! * We are also holding two events in San Francisco — the first AI | UX meetup next week (already full; we'll send a recap here on the newsletter) and Latent Space Liftoff Day on May 4th (signup here; but get in touch if you have a high profile launch you'd like to make). * We also joined the Chroma/OpenAI ChatGPT Plugins Hackathon last week where we won the Turing and Replit awards and met some of you in person!This post featured on Hacker News.Out of the five senses of the human body, I'd put sight at the very top. But weirdly when it comes to AI, Computer Vision has felt left out of the recent wave compared to image generation, text reasoning, and even audio transcription. We got our first taste of it with the OCR capabilities demo in the GPT-4 Developer Livestream, but to date GPT-4's vision capability has not yet been released. Meta AI leapfrogged OpenAI and everyone else by fully open sourcing their Segment Anything Model (SAM) last week, complete with paper, model, weights, data (6x more images and 400x more masks than OpenImages), and a very slick demo website. This is a marked change to their previous LLaMA release, which was not commercially licensed. The response has been ecstatic:SAM was the talk of the town at the ChatGPT Plugins Hackathon and I was fortunate enough to book Joseph Nelson who was frantically integrating SAM into Roboflow this past weekend. As a passionate instructor, hacker, and founder, Joseph is possibly the single best person in the world to bring the rest of us up to speed on the state of Computer Vision and the implications of SAM. I was already a fan of him from his previous pod with (hopefully future guest) Beyang Liu of Sourcegraph, so this served as a personal catchup as well. Enjoy! and let us know what other news/models/guests you'd like to have us discuss! - swyxRecorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold.Show Notes* Joseph's links: Twitter, Linkedin, Personal* Sourcegraph Podcast and Game Theory Story* Represently* Roboflow at Pioneer and YCombinator* Udacity Self Driving Car dataset story* Computer Vision Annotation Formats* SAM recap - top things to know for those living in a cave* https://segment-anything.com/* https://segment-anything.com/demo* https://arxiv.org/pdf/2304.02643.pdf * https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/* https://blog.roboflow.com/segment-anything-breakdown/* https://ai.facebook.com/datasets/segment-anything/* Ask Roboflow https://ask.roboflow.ai/* GPT-4 Multimodal https://blog.roboflow.com/gpt-4-impact-speculation/Cut for time:* WSJ mention* Des Moines Register story* All In Pod: timestamped mention* In Forbes: underrepresented investors in Series A* Roboflow greatest hits* https://blog.roboflow.com/mountain-dew-contest-computer-vision/* https://blog.roboflow.com/self-driving-car-dataset-missing-pedestrians/* https://blog.roboflow.com/nerualhash-collision/ and Apple CSAM issue * https://www.rf100.org/Timestamps* [00:00:19] Introducing Joseph* [00:02:28] Why Iowa* [00:05:52] Origin of Roboflow* [00:16:12] Why Computer Vision* [00:17:50] Computer Vision Use Cases* [00:26:15] The Economics of Annotation/Segmentation* [00:32:17] Computer Vision Annotation Formats* [00:36:41] Intro to Computer Vision & Segmentation* [00:39:08] YOLO* [00:44:44] World Knowledge of Foundation Models* [00:46:21] Segment Anything Model* [00:51:29] SAM: Zero Shot Transfer* [00:51:53] SAM: Promptability* [00:53:24] SAM: Model Assisted Labeling* [00:56:03] SAM doesn't have labels* [00:59:23] Labeling on the Browser* [01:00:28] Roboflow + SAM Video Demo * [01:07:27] Future Predictions* [01:08:04] GPT4 Multimodality* [01:09:27] Remaining Hard Problems* [01:13:57] Ask Roboflow (2019)* [01:15:26] How to keep up in AITranscripts[00:00:00] Hello everyone. It is me swyx and I'm here with Joseph Nelson. Hey, welcome to the studio. It's nice. Thanks so much having me. We, uh, have a professional setup in here.[00:00:19] Introducing Joseph[00:00:19] Joseph, you and I have known each other online for a little bit. I first heard about you on the Source Graph podcast with bian and I highly, highly recommend that there's a really good game theory story that is the best YC application story I've ever heard and I won't tease further cuz they should go listen to that.[00:00:36] What do you think? It's a good story. It's a good story. It's a good story. So you got your Bachelor of Economics from George Washington, by the way. Fun fact. I'm also an econ major as well. You are very politically active, I guess you, you did a lot of, um, interning in political offices and you were responding to, um, the, the, the sheer amount of load that the Congress people have in terms of the, the support.[00:01:00] So you built, representing, which is Zendesk for Congress. And, uh, I liked in your source guide podcast how you talked about how being more responsive to, to constituents is always a good thing no matter what side of the aisle you're on. You also had a sideline as a data science instructor at General Assembly.[00:01:18] As a consultant in your own consultancy, and you also did a bunch of hackathon stuff with Magic Sudoku, which is your transition from N L P into computer vision. And apparently at TechCrunch Disrupt, disrupt in 2019, you tried to add chess and that was your whole villain origin story for, Hey, computer vision's too hard.[00:01:36] That's full, the platform to do that. Uh, and now you're co-founder c e o of RoboFlow. So that's your bio. Um, what's not in there that[00:01:43] people should know about you? One key thing that people realize within maybe five minutes of meeting me, uh, I'm from Iowa. Yes. And it's like a funnily novel thing. I mean, you know, growing up in Iowa, it's like everyone you know is from Iowa.[00:01:56] But then when I left to go to school, there was not that many Iowans at gw and people were like, oh, like you're, you're Iowa Joe. Like, you know, how'd you find out about this school out here? I was like, oh, well the Pony Express was running that day, so I was able to send. So I really like to lean into it.[00:02:11] And so you kind of become a default ambassador for places that. People don't meet a lot of other people from, so I've kind of taken that upon myself to just make it be a, a part of my identity. So, you know, my handle everywhere Joseph of Iowa, like I I, you can probably find my social security number just from knowing that that's my handle.[00:02:25] Cuz I put it plastered everywhere. So that's, that's probably like one thing.[00:02:28] Why Iowa[00:02:28] What's your best pitch for Iowa? Like why is[00:02:30] Iowa awesome? The people Iowa's filled with people that genuinely care. You know, if you're waiting a long line, someone's gonna strike up a conversation, kinda ask how you were Devrel and it's just like a really genuine place.[00:02:40] It was a wonderful place to grow up too at the time, you know, I thought it was like, uh, yeah, I was kind of embarrassed and then be from there. And then I actually kinda looking back it's like, wow, you know, there's good schools, smart people friendly. The, uh, high school that I went to actually Ben Silverman, the CEO and, or I guess former CEO and co-founder of Pinterest and I have the same teachers in high school at different.[00:03:01] The co-founder, or excuse me, the creator of crispr, the gene editing technique, Dr. Jennifer. Doudna. Oh, so that's the patent debate. There's Doudna. Oh, and then there's Fang Zang. Uh, okay. Yeah. Yeah. So Dr. Fang Zang, who I think ultimately won the patent war, uh, but is also from the same high school.[00:03:18] Well, she won the patent, but Jennifer won the[00:03:20] prize.[00:03:21] I think that's probably, I think that's probably, I, I mean I looked into it a little closely. I think it was something like she won the patent for CRISPR first existing and then Feng got it for, uh, first use on humans, which I guess for commercial reasons is the, perhaps more, more interesting one. But I dunno, biolife Sciences, is that my area of expertise?[00:03:38] Yep. Knowing people that came from Iowa that do cool things, certainly is. Yes. So I'll claim it. Um, but yeah, I, I, we, um, at Roble actually, we're, we're bringing the full team to Iowa for the very first time this last week of, of April. And, well, folks from like Scotland all over, that's your company[00:03:54] retreat.[00:03:54] The Iowa,[00:03:55] yeah. Nice. Well, so we do two a year. You know, we've done Miami, we've done. Some of the smaller teams have done like Nashville or Austin or these sorts of places, but we said, you know, let's bring it back to kinda the origin and the roots. Uh, and we'll, we'll bring the full team to, to Des Moines, Iowa.[00:04:13] So, yeah, like I was mentioning, folks from California to Scotland and many places in between are all gonna descend upon Des Moines for a week of, uh, learning and working. So maybe you can check in with those folks. If, what do they, what do they decide and interpret about what's cool. Our state. Well, one thing, are you actually headquartered in Des Moines on paper?[00:04:30] Yes. Yeah.[00:04:30] Isn't that amazing? That's like everyone's Delaware and you're like,[00:04:33] so doing research. Well, we're, we're incorporated in Delaware. Okay. We we're Delaware Sea like, uh, most companies, but our headquarters Yeah. Is in Des Moines. And part of that's a few things. One, it's like, you know, there's this nice Iowa pride.[00:04:43] And second is, uh, Brad and I both grew up in Brad Mc, co-founder and I grew up in, in Des Moines. And we met each other in the year 2000. We looked it up for the, the YC app. So, you know, I think, I guess more of my life I've known Brad than not, uh, which is kind of crazy. Wow. And during yc, we did it during 2020, so it was like the height of Covid.[00:05:01] And so we actually got a house in Des Moines and lived, worked outta there. I mean, more credit to. So I moved back. I was living in DC at the time, I moved back to to Des Moines. Brad was living in Des Moines, but he moved out of a house with his. To move into what we called our hacker house. And then we had one, uh, member of the team as well, Jacob Sorowitz, who moved from Minneapolis down to Des Moines for the summer.[00:05:21] And frankly, uh, code was a great time to, to build a YC company cuz there wasn't much else to do. I mean, it's kinda like wash your groceries and code. It's sort of the, that was the routine[00:05:30] and you can use, uh, computer vision to help with your groceries as well.[00:05:33] That's exactly right. Tell me what to make.[00:05:35] What's in my fridge? What should I cook? Oh, we'll, we'll, we'll cover[00:05:37] that for with the G P T four, uh, stuff. Exactly. Okay. So you have been featured with in a lot of press events. Uh, but maybe we'll just cover the origin story a little bit in a little bit more detail. So we'll, we'll cover robo flow and then we'll cover, we'll go into segment anything.[00:05:52] Origin of Roboflow[00:05:52] But, uh, I think it's important for people to understand. Robo just because it gives people context for what you're about to show us at the end of the podcast. So Magic Sudoku tc, uh, techers Disrupt, and then you go, you join Pioneer, which is Dan Gross's, um, YC before yc.[00:06:07] Yeah. That's how I think about it.[00:06:08] Yeah, that's a good way. That's a good description of it. Yeah. So I mean, robo flow kind of starts as you mentioned with this magic Sudoku thing. So you mentioned one of my prior business was a company called Represent, and you nailed it. I mean, US Congress gets 80 million messages a year. We built tools that auto sorted them.[00:06:23] They didn't use any intelligent auto sorting. And this is somewhat a solved problem in natural language processing of doing topic modeling or grouping together similar sentiment and things like this. And as you mentioned, I'd like, I worked in DC for a bit and been exposed to some of these problems and when I was like, oh, you know, with programming you can build solutions.[00:06:40] And I think the US Congress is, you know, the US kind of United States is a support center, if you will, and the United States is sports center runs on pretty old software, so mm-hmm. We, um, we built a product for that. It was actually at the time when I was working on representing. Brad, his prior business, um, is a social games company called Hatchlings.[00:07:00] Uh, he phoned me in, in 2017, apple had released augmented reality kit AR kit. And Brad and I are both kind of serial hackers, like I like to go to hackathons, don't really understand new technology until he build something with them type folks. And when AR Kit came out, Brad decided he wanted to build a game with it that would solve Sudoku puzzles.[00:07:19] And the idea of the game would be you take your phone, you hover hold it over top of a Sudoku puzzle, it recognizes the state of the board where it is, and then it fills it all in just right before your eyes. And he phoned me and I was like, Brad, this sounds awesome and sounds like you kinda got it figured out.[00:07:34] What, what's, uh, what, what do you think I can do here? It's like, well, the machine learning piece of this is the part that I'm most uncertain about. Uh, doing the digit recognition and, um, filling in some of those results. I was like, well, I mean digit recognition's like the hell of world of, of computer vision.[00:07:48] That's Yeah, yeah, MNIST, right. So I was like, that that part should be the, the easy part. I was like, ah, I'm, he's like, I'm not so super sure, but. You know, the other parts, the mobile ar game mechanics, I've got pretty well figured out. I was like, I, I think you're wrong. I think you're thinking about the hard part is the easy part.[00:08:02] And he is like, no, you're wrong. The hard part is the easy part. And so long story short, we built this thing and released Magic Sudoku and it kind of caught the Internet's attention of what you could do with augmented reality and, and with computer vision. It, you know, made it to the front ofer and some subreddits it run Product Hunt Air app of the year.[00:08:20] And it was really a, a flash in the pan type app, right? Like we were both running separate companies at the time and mostly wanted to toy around with, with new technology. And, um, kind of a fun fact about Magic Sudoku winning product Hunt Air app of the year. That was the same year that I think the model three came out.[00:08:34] And so Elon Musk won a Golden Kitty who we joked that we share an award with, with Elon Musk. Um, the thinking there was that this is gonna set off a, a revolution of if two random engineers can put together something that makes something, makes a game programmable and at interactive, then surely lots of other engineers will.[00:08:53] Do similar of adding programmable layers on top of real world objects around us. Earlier we were joking about objects in your fridge, you know, and automatically generating recipes and these sorts of things. And like I said, that was 2017. Roboflow was actually co-found, or I guess like incorporated in, in 2019.[00:09:09] So we put this out there, nothing really happened. We went back to our day jobs of, of running our respective businesses, I sold Represently and then as you mentioned, kind of did like consulting stuff to figure out the next sort of thing to, to work on, to get exposed to various problems. Brad appointed a new CEO at his prior business and we got together that summer of 2019.[00:09:27] We said, Hey, you know, maybe we should return to that idea that caught a lot of people's attention and shows what's possible. And you know what, what kind of gives, like the future is here. And we have no one's done anything since. No one's done anything. So why is, why are there not these, these apps proliferated everywhere.[00:09:42] Yeah. And so we said, you know, what we'll do is, um, to add this software layer to the real world. Will build, um, kinda like a super app where if you pointed it at anything, it will recognize it and then you can interact with it. We'll release a developer platform and allow people to make their own interfaces, interactivity for whatever object they're looking at.[00:10:04] And we decided to start with board games because one, we had a little bit of history there with, with Sudoku two, there's social by default. So if one person, you know finds it, then they'd probably share it among their friend. Group three. There's actually relatively few barriers to entry aside from like, you know, using someone else's brand name in your, your marketing materials.[00:10:19] Yeah. But other than that, there's no real, uh, inhibitors to getting things going and, and four, it's, it's just fun. It would be something that'd be bring us enjoyment to work on. So we spent that summer making, uh, boggle the four by four word game provable, where, you know, unlike Magic Sudoku, which to be clear, totally ruins the game, uh, you, you have to solve Sudoku puzzle.[00:10:40] You don't need to do anything else. But with Boggle, if you and I are playing, we might not find all of the words that adjacent letter tiles. Unveil. So if we have a, an AI tell us, Hey, here's like the best combination of letters that make high scoring words. And so we, we made boggle and released it and that, and that did okay.[00:10:56] I mean maybe the most interesting story was there's a English as a second language program in, in Canada that picked it up and used it as a part of their curriculum to like build vocabulary, which I thought was kind of inspiring. Example, and what happens just when you put things on the internet and then.[00:11:09] We wanted to build one for chess. So this is where you mentioned we went to 2019. TechCrunch Disrupt TechCrunch. Disrupt holds a Hackathon. And this is actually, you know, when Brad and I say we really became co-founders, because we fly out to San Francisco, we rent a hotel room in the Tenderloin. We, uh, we, we, uh, have one room and there's like one, there's room for one bed, and then we're like, oh, you said there was a cot, you know, on the, on the listing.[00:11:32] So they like give us a little, a little cot, the end of the cot, like bled and over into like the bathroom. So like there I am sleeping on the cot with like my head in the bathroom and the Tenderloin, you know, fortunately we're at a hackathon glamorous. Yeah. There wasn't, there wasn't a ton of sleep to be had.[00:11:46] There is, you know, we're, we're just like making and, and shipping these, these sorts of many[00:11:50] people with this hack. So I've never been to one of these things, but[00:11:52] they're huge. Right? Yeah. The Disrupt Hackathon, um, I don't, I don't know numbers, but few hundreds, you know, classically had been a place where it launched a lot of famous Yeah.[00:12:01] Sort of flare. Yeah. And I think it's, you know, kind of slowed down as a place for true company generation. But for us, Brad and I, who likes just doing hackathons, being, making things in compressed time skills, it seemed like a, a fun thing to do. And like I said, we'd been working on things, but it was only there that like, you're, you're stuck in a maybe not so great glamorous situation together and you're just there to make a, a program and you wanna make it be the best and compete against others.[00:12:26] And so we add support to the app that we were called was called Board Boss. We couldn't call it anything with Boggle cause of IP rights were called. So we called it Board Boss and it supported Boggle and then we were gonna support chess, which, you know, has no IP rights around it. Uh, it's an open game.[00:12:39] And we did so in 48 hours, we built an app that, or added fit capability to. Point your phone at a chess board. It understands the state of the chess board and converts it to um, a known notation. Then it passes that to stock fish, the open source chess engine for making move recommendations and it makes move recommendations to, to players.[00:13:00] So you could either play against like an ammunition to AI or improve your own game. We learn that one of the key ways users like to use this was just to record their games. Cuz it's almost like reviewing game film of what you should have done differently. Game. Yeah, yeah, exactly. And I guess the highlight of, uh, of chess Boss was, you know, we get to the first round of judging, we get to the second round of judging.[00:13:16] And during the second round of judging, that's when like, TechCrunch kind of brings around like some like celebs and stuff. They'll come by. Evan Spiegel drops by Ooh. Oh, and he uh, he comes up to our, our, our booth and um, he's like, oh, so what does, what does this all do? And you know, he takes an interest in it cuz the underpinnings of, of AR interacting with the.[00:13:33] And, uh, he is kinda like, you know, I could use this to like cheat on chess with my friends. And we're like, well, you know, that wasn't exactly the, the thesis of why we made it, but glad that, uh, at least you think it's kind of neat. Um, wait, but he already started Snapchat by then? Oh, yeah. Oh yeah. This, this is 2019, I think.[00:13:49] Oh, okay, okay. Yeah, he was kind of just checking out things that were new and, and judging didn't end up winning any, um, awards within Disrupt, but I think what we won was actually. Maybe more important maybe like the, the quote, like the co-founders medal along the way. Yep. The friends we made along the way there we go to, to play to the meme.[00:14:06] I would've preferred to win, to be clear. Yes. You played a win. So you did win, uh,[00:14:11] $15,000 from some Des Moines, uh, con[00:14:14] contest. Yeah. Yeah. The, uh, that was nice. Yeah. Slightly after that we did, we did win. Um, some, some grants and some other things for some of the work that we've been doing. John Papa John supporting the, uh, the local tech scene.[00:14:24] Yeah. Well, so there's not the one you're thinking of. Okay. Uh, there's a guy whose name is Papa John, like that's his, that's his, that's his last name. His first name is John. So it's not the Papa John's you're thinking of that has some problematic undertones. It's like this guy who's totally different. I feel bad for him.[00:14:38] His press must just be like, oh, uh, all over the place. But yeah, he's this figure in the Iowa entrepreneurial scene who, um, he actually was like doing SPACs before they were cool and these sorts of things, but yeah, he funds like grants that encourage entrepreneurship in the state. And since we'd done YC and in the state, we were eligible for some of the awards that they were providing.[00:14:56] But yeah, it was disrupt that we realized, you know, um, the tools that we made, you know, it took us better part of a summer to add Boggle support and it took us 48 hours to add chest support. So adding the ability for programmable interfaces for any object, we built a lot of those internal tools and our apps were kind of doing like the very famous shark fin where like it picks up really fast, then it kind of like slowly peters off.[00:15:20] Mm-hmm. And so we're like, okay, if we're getting these like shark fin graphs, we gotta try something different. Um, there's something different. I remember like the week before Thanksgiving 2019 sitting down and we wrote this Readme for, actually it's still the Readme at the base repo of Robo Flow today has spent relatively unedited of the manifesto.[00:15:36] Like, we're gonna build tools that enable people to make the world programmable. And there's like six phases and, you know, there's still, uh, many, many, many phases to go into what we wrote even at that time to, to present. But it's largely been, um, right in line with what we thought we would, we would do, which is give engineers the tools to add software to real world objects, which is largely predicated on computer vision. So finding the right images, getting the right sorts of video frames, maybe annotating them, uh, finding the right sort of models to use to do this, monitoring the performance, all these sorts of things. And that from, I mean, we released that in early 2020, and it's kind of, that's what's really started to click.[00:16:12] Why Computer Vision[00:16:12] Awesome. I think we should just kind[00:16:13] of[00:16:14] go right into where you are today and like the, the products that you offer, just just to give people an overview and then we can go into the, the SAM stuff. So what is the clear, concise elevator pitch? I think you mentioned a bunch of things like make the world programmable so you don't ha like computer vision is a means to an end.[00:16:30] Like there's, there's something beyond that. Yeah.[00:16:32] I mean, the, the big picture mission for the business and the company and what we're working on is, is making the world programmable, making it read and write and interactive, kind of more entertaining, more e. More fun and computer vision is the technology by which we can achieve that pretty quickly.[00:16:48] So like the one liner for the, the product in, in the company is providing engineers with the tools for data and models to build programmable interfaces. Um, and that can be workflows, that could be the, uh, data processing, it could be the actual model training. But yeah, Rob helps you use production ready computer vision workflows fast.[00:17:10] And I like that.[00:17:11] In part of your other pitch that I've heard, uh, is that you basically scale from the very smallest scales to the very largest scales, right? Like the sort of microbiology use case all the way to[00:17:20] astronomy. Yeah. Yeah. The, the joke that I like to make is like anything, um, underneath a microscope and, and through a telescope and everything in between needs to, needs to be seen.[00:17:27] I mean, we have people that run models in outer space, uh, underwater remote places under supervision and, and known places. The crazy thing is that like, All parts of, of not just the world, but the universe need to be observed and understood and acted upon. So vision is gonna be, I dunno, I feel like we're in the very, very, very beginnings of all the ways we're gonna see it.[00:17:50] Computer Vision Use Cases[00:17:50] Awesome. Let's go into a lo a few like top use cases, cuz I think that really helps to like highlight the big names that you've, big logos that you've already got. I've got Walmart and Cardinal Health, but I don't, I don't know if you wanna pull out any other names, like, just to illustrate, because the reason by the way, the reason I think that a lot of developers don't get into computer vision is because they think they don't need it.[00:18:11] Um, or they think like, oh, like when I do robotics, I'll do it. But I think if, if you see like the breadth of use cases, then you get a little bit more inspiration as to like, oh, I can use[00:18:19] CVS lfa. Yeah. It's kind of like, um, you know, by giving, by making it be so straightforward to use vision, it becomes almost like a given that it's a set of features that you could power on top of it.[00:18:32] And like you mentioned, there's, yeah, there's Fortune One there over half the Fortune 100. I've used the, the tools that Robel provides just as much as 250,000 developers. And so over a quarter million engineers finding and developing and creating various apps, and I mean, those apps are, are, are far and wide.[00:18:49] Just as you mentioned. I mean everything from say, like, one I like to talk about was like sushi detection of like finding the like right sorts of fish and ingredients that are in a given piece of, of sushi that you're looking at to say like roof estimation of like finding. If there's like, uh, hail damage on, on a given roof, of course, self-driving cars and understanding the scenes around us is sort of the, you know, very early computer vision everywhere.[00:19:13] Use case hardhat detection, like finding out if like a given workplace is, is, is safe, uh, disseminate, have the right p p p on or p p e on, are there the right distance from various machines? A huge place that vision has been used is environmental monitoring. Uh, what's the count of species? Can we verify that the environment's not changing in unexpected ways or like river banks are become, uh, becoming recessed in ways that we anticipate from satellite imagery, plant phenotyping.[00:19:37] I mean, people have used these apps for like understanding their plants and identifying them. And that dataset that's actually largely open, which is what's given a proliferation to the iNaturalist, is, is that whole, uh, hub of, of products. Lots of, um, people that do manufacturing. So, like Rivian for example, is a Rubal customer, and you know, they're trying to scale from 1000 cars to 25,000 cars to a hundred thousand cars in very short order.[00:20:00] And that relies on having the. Ability to visually ensure that every part that they're making is produced correctly and right in time. Medical use cases. You know, there's actually, this morning I was emailing with a user who's accelerating early cancer detection through breaking apart various parts of cells and doing counts of those cells.[00:20:23] And actually a lot of wet lab work that folks that are doing their PhDs or have done their PhDs are deeply familiar with that is often required to do very manually of, of counting, uh, micro plasms or, or things like this. There's. All sorts of, um, like traffic counting and smart cities use cases of understanding curb utilization to which sort of vehicles are, are present.[00:20:44] Uh, ooh. That can be[00:20:46] really good for city planning actually.[00:20:47] Yeah. I mean, one of our customers does exactly this. They, they measure and do they call it like smart curb utilization, where uhhuh, they wanna basically make a curb be almost like a dynamic space where like during these amounts of time, it's zoned for this during these amounts of times.[00:20:59] It's zoned for this based on the flows and e ebbs and flows of traffic throughout the day. So yeah, I mean the, the, the truth is that like, you're right, it's like a developer might be like, oh, how would I use vision? And then all of a sudden it's like, oh man, all these things are at my fingertips. Like I can just, everything you can see.[00:21:13] Yeah. Right. I can just, I can just add functionality for my app to understand and ingest the way, like, and usually the way that someone gets like almost nerd sniped into this is like, they have like a home automation project, so it's like send Yeah. Give us a few. Yeah. So send me a text when, um, a package shows up so I can like prevent package theft so I can like go down and grab it right away or.[00:21:29] We had a, uh, this one's pretty, pretty niche, but it's pretty funny. There was this guy who, during the pandemic wa, wanted to make sure his cat had like the proper, uh, workout. And so I've shared the story where he basically decided that. He'd make a cat workout machine with computer vision, you might be alone.[00:21:43] You're like, what does that look like? Well, what he decided was he would take a robotic arm strap, a laser pointer to it, and then train a machine to recognize his cat and his cat only, and point the laser pointer consistently 10 feet away from the cat. There's actually a video of you if you type an YouTube cat laser turret, you'll find Dave's video.[00:22:01] Uh, and hopefully Dave's cat has, has lost the weight that it needs to, cuz that's just the, that's an intense workout I have to say. But yeah, so like, that's like a, um, you know, these, uh, home automation projects are pretty common places for people to get into smart bird feeders. I've seen people that like are, are logging and understanding what sort of birds are, uh, in their background.[00:22:18] There's a member of our team that was working on actually this as, as a whole company and has open sourced a lot of the data for doing bird species identification. And now there's, I think there's even a company that's, uh, founded to create like a smart bird feeder, like captures photos and tells you which ones you've attracted to your yard.[00:22:32] I met that. Do, you know, get around the, uh, car sharing company that heard it? Them never used them. They did a SPAC last year and they had raised at like, They're unicorn. They raised at like 1.2 billion, I think in the, the prior round and inspected a similar price. I met the CTO of, of Getaround because he was, uh, using Rob Flow to hack into his Tesla cameras to identify other vehicles that are like often nearby him.[00:22:56] So he's basically building his own custom license plate recognition, and he just wanted like, keep, like, keep tabs of like, when he drives by his friends or when he sees like regular sorts of folks. And so he was doing like automated license plate recognition by tapping into his, uh, camera feeds. And by the way, Elliot's like one of the like OG hackers, he was, I think one of the very first people to like, um, she break iPhones and, and these sorts of things.[00:23:14] Mm-hmm. So yeah, the project that I want, uh, that I'm gonna work on right now for my new place in San Francisco is. There's two doors. There's like a gate and then the other door. And sometimes we like forget to close, close the gate. So like, basically if it sees that the gate is open, it'll like send us all a text or something like this to make sure that the gate is, is closed at the front of our house.[00:23:32] That's[00:23:32] really cool. And I'll, I'll call out one thing that readers and listeners can, uh, read out on, on your history. One of your most popular initial, um, viral blog post was about, um, autonomous vehicle data sets and how, uh, the one that Udacity was using was missing like one third of humans. And, uh, it's not, it's pretty problematic for cars to miss humans.[00:23:53] Yeah, yeah, actually, so yeah, the Udacity self-driving car data set, which look to their credit, it was just meant to be used for, for academic use. Um, and like as a part of courses on, on Udacity, right? Yeah. But the, the team that released it, kind of hastily labeled and let it go out there to just start to use and train some models.[00:24:11] I think that likely some, some, uh, maybe commercial use cases maybe may have come and, and used, uh, the dataset, who's to say? But Brad and I discovered this dataset. And when we were working on dataset improvement tools at Rob Flow, we ran through our tools and identified some like pretty, as you mentioned, key issues.[00:24:26] Like for example, a lot of strollers weren't labeled and I hope our self-driving cars do those, these sorts of things. And so we relabeled the whole dataset by hand. I have this very fond memory is February, 2020. Brad and I are in Taiwan. So like Covid is actually just, just getting going. And the reason we were there is we were like, Hey, we can work on this from anywhere for a little bit.[00:24:44] And so we spent like a, uh, let's go closer to Covid. Well, you know, I like to say we uh, we got early indicators of, uh, how bad it was gonna be. I bought a bunch of like N 90 fives before going o I remember going to the, the like buying a bunch of N 95 s and getting this craziest look like this like crazy tin hat guy.[00:25:04] Wow. What is he doing? And then here's how you knew. I, I also got got by how bad it was gonna be. I left all of them in Taiwan cuz it's like, oh, you all need these. We'll be fine over in the us. And then come to find out, of course that Taiwan was a lot better in terms of, um, I think, yeah. Safety. But anyway, we were in Taiwan because we had planned this trip and you know, at the time we weren't super sure about the, uh, covid, these sorts of things.[00:25:22] We always canceled it. We didn't, but I have this, this very specific time. Brad and I were riding on the train from Clay back to Taipei. It's like a four hour ride. And you mentioned Pioneer earlier, we were competing in Pioneer, which is almost like a gamified to-do list. Mm-hmm. Every week you say what you're gonna do and then other people evaluate.[00:25:37] Did you actually do the things you said you were going to do? One of the things we said we were gonna do was like this, I think re-release of this data set. And so it's like late, we'd had a whole week, like, you know, weekend behind us and, uh, we're on this train and it was very unpleasant situation, but we relabeled this, this data set, and one sitting got it submitted before like the Sunday, Sunday countdown clock starts voting for, for.[00:25:57] And, um, once that data got out back out there, just as you mentioned, it kind of picked up and Venture beat, um, noticed and wrote some stories about it. And we really rereleased of course, the data set that we did our best job of labeling. And now if anyone's listening, they can probably go out and like find some errors that we surely still have and maybe call us out and, you know, put us, put us on blast.[00:26:15] The Economics of Annotation (Segmentation)[00:26:15] But,[00:26:16] um, well, well the reason I like this story is because it, it draws attention to the idea that annotation is difficult and basically anyone looking to use computer vision in their business who may not have an off-the-shelf data set is going to have to get involved in annotation. And I don't know what it costs.[00:26:34] And that's probably one of the biggest hurdles for me to estimate how big a task this is. Right? So my question at a higher level is tell the customers, how do you tell customers to estimate the economics of annotation? Like how many images do, do we need? How much, how long is it gonna take? That, that kinda stuff.[00:26:50] How much money and then what are the nuances to doing it well, right? Like, cuz obviously Udacity had a poor quality job, you guys had proved it, and there's errors every everywhere. Like where do[00:26:59] these things go wrong? The really good news about annotation in general is that like annotation of course is a means to an end to have a model be able to recognize a thing.[00:27:08] Increasingly there's models that are coming out that can recognize things zero shot without any annotation, which we're gonna talk about. Yeah. Which, we'll, we'll talk more about that in a moment. But in general, the good news is that like the trend is that annotation is gonna become decreasingly a blocker to starting to use computer vision in meaningful ways.[00:27:24] Now that said, just as you mentioned, there's a lot of places where you still need to do. Annotation. I mean, even with these zero shot models, they might have of blind spots, or maybe you're a business, as you mentioned, that you know, it's proprietary data. Like only Rivian knows what a rivian is supposed to look like, right?[00:27:39] Uh, at the time of, at the time of it being produced, like underneath the hood and, and all these sorts of things. And so, yeah, that's gonna necessarily require annotation. So your question of how long is it gonna take, how do you estimate these sorts of things, it really comes down to the complexity of the problem that you're solving and the amount of variance in the scene.[00:27:57] So let's give some contextual examples. If you're trying to recognize, we'll say a scratch on one specific part and you have very strong lighting. You might need fewer images because you control the lighting, you know the exact part and maybe you're lucky in the scratch. Happens more often than not in similar parts or similar, uh, portions of the given part.[00:28:17] So in that context, you, you, the function of variance, the variance is, is, is lower. So the number of images you need is also lower to start getting up to work. Now the orders of magnitude we're talking about is that like you can have an initial like working model from like 30 to 50 images. Yeah. In this context, which is shockingly low.[00:28:32] Like I feel like there's kind of an open secret in computer vision now, the general heuristic that often. Users, is that like, you know, maybe 200 images per class is when you start to have a model that you can rely[00:28:45] on? Rely meaning like 90, 99, 90, 90%, um,[00:28:50] uh, like what's 85 plus 85? Okay. Um, that's good. Again, these are very, very finger in the wind estimates cuz the variance we're talking about.[00:28:59] But the real question is like, at what point, like the framing is not like at what point do it get to 99, right? The framing is at what point can I use this thing to be better than the alternative, which is humans, which maybe humans or maybe like this problem wasn't possible at all. And so usually the question isn't like, how do I get to 99?[00:29:15] A hundred percent? It's how do I ensure that like the value I am able to get from putting this thing in production is greater than the alternative? In fact, even if you have a model that's less accurate than humans, there might be some circumstances where you can tolerate, uh, a greater amount of inaccuracy.[00:29:32] And if you look at the accuracy relative to the cost, Using a model is extremely cheap. Using a human for the same sort of task can be very expensive. Now, in terms of the actual accuracy of of what you get, there's probably some point at which the cost, but relative accuracy exceeds of a model, exceeds the high cost and hopefully high accuracy of, of a human comparable, like for example, there's like cameras that will track soccer balls or track events happening during sporting matches.[00:30:02] And you can go through and you know, we actually have users that work in sports analytics. You can go through and have a human. Hours and hours of footage. Cuz not just watching their team, they're watching every other team, they're watching scouting teams, they're watching junior teams, they're watching competitors.[00:30:15] And you could have them like, you know, track and follow every single time the ball goes within blank region of the field or every time blank player goes into, uh, this portion of the field. And you could have, you know, exact, like a hundred percent accuracy if that person, maybe, maybe not a hundred, a human may be like 95, 90 7% accuracy of every single time the ball is in this region or this player is on the field.[00:30:36] Truthfully, maybe if you're scouting analytics, you actually don't need 97% accuracy of knowing that that player is on the field. And in fact, if you can just have a model run at a 1000th, a 10000th of the cost and goes through and finds all the times that Messi was present on the field mm-hmm. That the ball was in this region of the.[00:30:54] Then even if that model is slightly less accurate, the cost is just so orders of magnitude different. And the stakes like the stakes of this problem, of knowing like the total number of minutes that Messi played will say are such that we have a higher air tolerance, that it's a no-brainer to start to use Yeah, a computer vision model in this context.[00:31:12] So not every problem requires equivalent or greater human performance. Even when it does, you'd be surprised at how fast models get there. And in the times when you, uh, really look at a problem, the question is, how much accuracy do I need to start to get value from this? This thing, like the package example is a great one, right?[00:31:27] Like I could in theory set up a camera that's constantly watching in front of my porch and I could watch the camera whenever I have a package and then go down. But of course, I'm not gonna do that. I value my time to do other sorts of things instead. And so like there, there's this net new capability of, oh, great, I can have an always on thing that tells me when a package shows up, even if you know the, the thing that's gonna text me.[00:31:46] When a package shows up, let's say a flat pack shows up instead of a box and it doesn't know what a flat pack likes, looks like initially. Doesn't matter. It doesn't matter because I didn't have this capability at all before. And I think that's the true case where a lot of computer vision problems exist is like it.[00:32:00] It's like you didn't even have this capability, this superpower before at all, let alone assigning a given human to do the task. And that's where we see like this explosion of, of value.[00:32:10] Awesome. Awesome. That was a really good overview. I want to leave time for the others, but I, I really want to dive into a couple more things with regards to Robo Flow.[00:32:17] Computer Vision Annotation Formats[00:32:17] So one is, apparently your original pitch for Robo Flow was with regards to conversion tools for computer vision data sets. And I'm sure as, as a result of your job, you have a lot of rants. I've been digging for rants basically on like the best or the worst annotation formats. What do we know? Cause most of us, oh my gosh, we only know, like, you know, I like,[00:32:38] okay, so when we talk about computer vision annotation formats, what we're talking about is if you have an image and you, you picture a boing box around my face on that image.[00:32:46] Yeah. How do you describe where that Monty box is? X, Y, Z X Y coordinates. Okay. X, y coordinates. How, what do you mean from the top lefts.[00:32:52] Okay. You, you, you, you take X and Y and then, and then the. The length and, and the width of the, the[00:32:58] box. Okay. So you got like a top left coordinate and like the bottom right coordinate or like the, the center of the bottom.[00:33:02] Yeah. Yeah. Top, left, bottom right. Yeah. That's one type of format. Okay. But then, um, I come along and I'm like, you know what? I want to do a different format where I wanna just put the center of the box, right. And give the length and width. Right. And by the way, we didn't even talk about what X and Y we're talking about.[00:33:14] Is X a pixel count? Is a relative pixel count? Is it an absolute pixel count? So the point is, the number of ways to describe where a box lives in a freaking image is endless, uh, seemingly and. Everyone decided to kind of create their own different ways of describing the coordinates and positions of where in this context of bounding Box is present.[00:33:39] Uh, so there's some formats, for example, that like use re, so for the x and y, like Y is, uh, like the left, most part of the image is zero. And the right most part of the image is one. So the, the coordinate is like anywhere from zero to one. So 0.6 is, you know, 60% of your way right up the image to describe the coordinate.[00:33:53] I guess that was, that was X instead of Y. But the point is there, of the zero to one is the way that we determined where that was in the position, or we're gonna do an absolute pixel position anyway. We got sick, we got sick of all these different annotation formats. So why do you even have to convert between formats?[00:34:07] Is is another part of this, this story. So different training frameworks, like if you're using TensorFlow, you need like TF Records. If you're using PyTorch, it's probably gonna be, well it depends on like what model you're using, but someone might use Coco JSON with PyTorch. Someone else might use like a, just a YAML file and a text file.[00:34:21] And to describe the cor it's point is everyone that creates a model. Or creates a dataset rather, has created different ways of describing where and how a bounding box is present in the image. And we got sick of all these different formats and doing these in writing all these different converter scripts.[00:34:39] And so we made a tool that just converts from one script, one type of format to another. And the, the key thing is that like if you get that converter script wrong, your model doesn't not work. It just fails silently. Yeah. Because the bounding boxes are now all in the wrong places. And so you need a way to visualize and be sure that your converter script, blah, blah blah.[00:34:54] So that was the very first tool we released of robo. It was just a converter script, you know, like these, like these PDF to word converters that you find. It was basically that for computer vision, like dead simple, really annoying thing. And we put it out there and people found some, some value in, in that.[00:35:08] And you know, to this day that's still like a surprisingly painful[00:35:11] problem. Um, yeah, so you and I met at the Dall-E Hackathon at OpenAI, and we were, I was trying to implement this like face masking thing, and I immediately ran into that problem because, um, you know, the, the parameters that Dall-E expected were different from the one that I got from my face, uh, facial detection thing.[00:35:28] One day it'll go away, but that day is not today. Uh, the worst format that we work with is, is. The mart form, it just makes no sense. And it's like, I think, I think it's a one off annotation format that this university in China started to use to describe where annotations exist in a book mart. I, I don't know, I dunno why that So best[00:35:45] would be TF record or some something similar.[00:35:48] Yeah, I think like, here's your chance to like tell everybody to use one one standard and like, let's, let's, can[00:35:53] I just tell them to use, we have a package that does this for you. I'm just gonna tell you to use the row full package that converts them all, uh, for you. So you don't have to think about this. I mean, Coco JSON is pretty good.[00:36:04] It's like one of the larger industry norms and you know, it's in JS O compared to like V xml, which is an XML format and Coco json is pretty descriptive, but you know, it has, has its own sort of drawbacks and flaws and has random like, attribute, I dunno. Um, yeah, I think the best way to handle this problem is to not have to think about it, which is what we did.[00:36:21] We just created a, uh, library that, that converts and uses things. Uh, for us. We've double checked the heck out of it. There's been hundreds of thousands of people that have used the library and battle tested all these different formats to find those silent errors. So I feel pretty good about no longer having to have a favorite format and instead just rely on.[00:36:38] Dot load in the format that I need. Great[00:36:41] Intro to Computer Vision Segmentation[00:36:41] service to the community. Yeah. Let's go into segmentation because is at the top of everyone's minds, but before we get into segment, anything, I feel like we need a little bit of context on the state-of-the-art prior to Sam, which seems to be YOLO and uh, you are the leading expert as far as I know.[00:36:56] Yeah.[00:36:57] Computer vision, there's various task types. There's classification problems where we just like assign tags to images, like, you know, maybe safe work, not safe work, sort of tagging sort of stuff. Or we have object detection, which are the boing boxes that you see and all the formats I was mentioning in ranting about there's instant segmentation, which is the polygon shapes and produces really, really good looking demos.[00:37:19] So a lot of people like instant segmentation.[00:37:21] This would be like counting pills when you point 'em out on the, on the table. Yeah. So, or[00:37:25] soccer players on the field. So interestingly, um, counting you could do with bounding boxes. Okay. Cause you could just say, you know, a box around a person. Well, I could count, you know, 12 players on the field.[00:37:35] Masks are most useful. Polygons are most useful if you need very precise area measurements. So you have an aerial photo of a home and you want to know, and the home's not a perfect box, and you want to know the rough square footage of that home. Well, if you know the distance between like the drone and, and the ground.[00:37:53] And you have the precise polygon shape of the home, then you can calculate how big that home is from aerial photos. And then insurers can, you know, provide say accurate estimates and that's maybe why this is useful. So polygons and, and instant segmentation are, are those types of tasks? There's a key point detection task and key point is, you know, if you've seen those demos of like all the joints on like a hand kind of, kind of outlined, there's visual question answering tasks, visual q and a.[00:38:21] And that's like, you know, some of the stuff that multi-modality is absolutely crushing for, you know, here's an image, tell me what food is in this image. And then you can pass that and you can make a recipe out of it. But like, um, yeah, the visual question in answering task type is where multi-modality is gonna have and is already having an enormous impact.[00:38:40] So that's not a comprehensive survey, very problem type, but it's enough to, to go into why SAM is significant. So these various task types, you know, which model to use for which given circumstance. Most things is highly dependent on what you're ultimately aiming to do. Like if you need to run a model on the edge, you're gonna need a smaller model, cuz it is gonna run on edge, compute and process in, in, in real time.[00:39:01] If you're gonna run a model on the cloud, then of course you, uh, generally have more compute at your disposal Considerations like this now, uh,[00:39:08] YOLO[00:39:08] just to pause. Yeah. Do you have to explain YOLO first before you go to Sam, or[00:39:11] Yeah, yeah, sure. So, yeah. Yeah, we should. So object detection world. So for a while I talked about various different task types and you can kinda think about a slide scale of like classification, then obvious detection.[00:39:20] And on the right, at most point you have like segmentation tasks. Object detection. The bounding boxes is especially useful for a wide, like it's, it's surprisingly versatile. Whereas like classification is kind of brittle. Like you only have a tag for the whole image. Well, that doesn't, you can't count things with tags.[00:39:35] And on the other hand, like the mask side of things, like drawing masks is painstaking. And so like labeling is just a bit more difficult. Plus like the processing to produce masks requires more compute. And so usually a lot of folks kind of landed for a long time on obvious detection being a really happy medium of affording you with rich capabilities because you can do things like count, track, measure.[00:39:56] In some CAGR context with bounding boxes, you can see how many things are present. You can actually get a sense of how fast something's moving by tracking the object or bounding box across multiple frames and comparing the timestamp of where it was across those frames. So obviously detection is a very common task type that solves lots of things that you want do with a given model.[00:40:15] In obviously detection. There's been various model frameworks over time. So kind of really early on there's like R-CNN uh, then there's faster rc n n and these sorts of family models, which are based on like resnet kind of architectures. And then a big thing happens, and that is single shot detectors. So faster, rc n n despite its name is, is very slow cuz it takes two passes on the image.[00:40:37] Uh, the first pass is, it finds par pixels in the image that are most interesting to, uh, create a bounding box candidate out of. And then it passes that to a, a classifier that then does classification of the bounding box of interest. Right. Yeah. You can see, you can see why that would be slow. Yeah. Cause you have to do two passes.[00:40:53] You know, kind of actually led by, uh, like mobile net was I think the first large, uh, single shot detector. And as its name implies, it was meant to be run on edge devices and mobile devices and Google released mobile net. So it's a popular implementation that you find in TensorFlow. And what single shot detectors did is they said, Hey, instead of looking at the image twice, what if we just kind of have a, a backbone that finds candidate bounding boxes?[00:41:19] And then we, we set loss functions for objectness. We set loss function. That's a real thing. We set loss functions for objectness, like how much obj, how object do this part of the images. We send a loss function for classification, and then we run the image through the model on a single pass. And that saves lots of compute time and you know, it's not necessarily as accurate, but if you have lesser compute, it can be extremely useful.[00:41:42] And then the advances in both modeling techniques in compute and data quality, single shot detectors, SSDs has become, uh, really, really popular. One of the biggest SSDs that has become really popular is the YOLO family models, as you described. And so YOLO stands for you only look once. Yeah, right, of course.[00:42:02] Uh, Drake's, uh, other album, um, so Joseph Redman introduces YOLO at the University of Washington. And Joseph Redman is, uh, kind of a, a fun guy. So for listeners, for an Easter egg, I'm gonna tell you to Google Joseph Redman resume, and you'll find, you'll find My Little Pony. That's all I'll say. And so he introduces the very first YOLO architecture, which is a single shot detector, and he also does it in a framework called Darknet, which is like this, this own framework that compiles the Cs, frankly, kind of tough to work with, but allows you to benefit from the speedups that advance when you operate in a low level language like.[00:42:36] And then he releases, well, what colloquially is known as YOLO V two, but a paper's called YOLO 9,000 cuz Joseph Redmond thought it'd be funny to have something over 9,000. So get a sense for, yeah, some fun. And then he releases, uh, YOLO V three and YOLO V three is kind of like where things really start to click because it goes from being an SSD that's very limited to competitive and, and, and superior to actually mobile That and some of these other single shot detectors, which is awesome because you have this sort of solo, I mean, him and and his advisor, Ali, at University of Washington have these, uh, models that are becoming really, really powerful and capable and competitive with these large research organizations.[00:43:09] Joseph Edmond leaves Computer Vision Research, but there had been Alexia ab, one of the maintainers of Darknet released Yola VI four. And another, uh, researcher, Glenn Yer, uh, jocker had been working on YOLO V three, but in a PyTorch implementation, cuz remember YOLO is in a dark implementation. And so then, you know, YOLO V three and then Glenn continues to make additional improvements to YOLO V three and pretty soon his improvements on Yolov theory, he's like, oh, this is kind of its own things.[00:43:36] Then he releases YOLO V five[00:43:38] with some naming[00:43:39] controversy that we don't have Big naming controversy. The, the too long didn't read on the naming controversy is because Glen was not originally involved with Darknet. How is he allowed to use the YOLO moniker? Roe got in a lot of trouble cuz we wrote a bunch of content about YOLO V five and people were like, ah, why are you naming it that we're not?[00:43:55] Um, but you know,[00:43:56] cool. But anyway, so state-of-the-art goes to v8. Is what I gather.[00:44:00] Yeah, yeah. So yeah. Yeah. You're, you're just like, okay, I got V five. I'll skip to the end. Uh, unless, unless there's something, I mean, I don't want, well, so I mean, there's some interesting things. Um, in the yolo, there's like, there's like a bunch of YOLO variants.[00:44:10] So YOLOs become this, like this, this catchall for various single shot, yeah. For various single shot, basically like runs on the edge, it's quick detection framework. And so there's, um, like YOLO R, there's YOLO S, which is a transformer based, uh, yolo, yet look like you only look at one sequence is what s stands were.[00:44:27] Um, the pp yo, which, uh, is PAT Paddle implementation, which is by, which Chinese Google is, is their implementation of, of TensorFlow, if you will. So basically YOLO has like all these variants. And now, um, yo vii, which is Glen has been working on, is now I think kind of like, uh, one of the choice models to use for single shot detection.[00:44:44] World Knowledge of Foundation Models[00:44:44] Well, I think a lot of those models, you know, Asking the first principal's question, like let's say you wanna find like a bus detector. Do you need to like go find a bunch of photos of buses or maybe like a chair detector? Do you need to go find a bunch of photos of chairs? It's like, oh no. You know, actually those images are present not only in the cocoa data set, but those are objects that exist like kind of broadly on the internet.[00:45:02] And so computer visions kind of been like us included, have been like really pushing for and encouraging models that already possess a lot of context about the world. And so, you know, if GB T's idea and i's idea OpenAI was okay, models can only understand things that are in their corpus. What if we just make their corpus the size of everything on the internet?[00:45:20] The same thing that happened in imagery, what's happening now? And that's kinda what Sam represents, which is kind of a new evolution of, earlier on we were talking about the cost of annotation and I said, well, good news. Annotations then become decreasingly necessary to start to get to value. Now you gotta think about it more, kind of like, you'll probably need to do some annotation because you might want to find a custom object, or Sam might not be perfect, but what's about to happen is a big opportunity where you want the benefits of a yolo, right?[00:45:47] Where it can run really fast, it can run on the edge, it's very cheap. But you want the knowledge of a large foundation model that already knows everything about buses and knows everything about shoes, knows everything about real, if the name is true, anything segment, anything model. And so there's gonna be this novel opportunity to take what these large models know, and I guess it's kind of like a form of distilling, like distill them down into smaller architectures that you can use in versatile ways to run in real time to run on the edge.[00:46:13] And that's now happening. And what we're seeing in actually kind of like pulling that, that future forward with, with, with Robo Flow.[00:46:21] Segment Anything Model[00:46:21] So we could talk a bit about, um, about SAM and what it represents maybe into, in relation to like these, these YOLO models. So Sam is Facebook segment Everything Model. It came out last week, um, the first week of April.[00:46:34] It has 24,000 GitHub stars at the time of, of this recording within its first week. And why, what does it do? Segment? Everything is a zero shot segmentation model. And as we're describing, creating masks is a very arduous task. Creating masks of objects that are not already represented means you have to go label a bunch of masks and then train a model and then hope that it finds those masks in new images.[00:47:00] And the promise of Segment anything is that in fact you just pass at any image and it finds all of the masks of relevant things that you might be curious about finding in a given image. And it works remarkably. Segment anything in credit to Facebook and the fair Facebook research team, they not only released the model permissive license to move things forward, they released the full data set, all 11 million images and 1.1 billion segmentation masks and three model sizes.[00:47:29] The largest ones like 2.5 gigabytes, which is not enormous. Medium ones like 1.2 and the smallest one is like 400, 3 75 megabytes. And for context,[00:47:38] for, for people listening, that's six times more than the previous alternative, which, which is apparently open images, uh, in terms of number images, and then 400 times more masks than open[00:47:47] images as well.[00:47:48] Exactly, yeah. So huge, huge order magnitude gain in terms of dataset accessibility plus like the model and how it works. And so the question becomes, okay, so like segment. What, what do I do with this? Like, what does it allow me to do? And it didn't Rob float well. Yeah, you should. Yeah. Um, it's already there.[00:48:04] You um, that part's done. Uh, but the thing that you can do with segment anything is you can almost, like, I almost think about like this, kinda like this model arbitrage where you can basically like distill down a giant model. So let's say like, like let's return to the package example. Okay. The package problem of, I wanna get a text when a package appears on my front porch before segment anything.[00:48:25] The way that I would go solve this problem is I would go collect some images of packages on my porch and I would label them, uh, with bounding boxes or maybe masks in that part. As you mentioned, it can be a long process and I would train a model. And that model it actually probably worked pretty well cause it's purpose-built.[00:48:44] The camera position, my porch, the packages I'm receiving. But that's gonna take some time, like everything that I just mentioned the

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