Shaped
¿Qué es Shaped?
Shaped es una plataforma que ayuda a las empresas a aumentar la participación en sus feeds, recomendaciones y notificaciones utilizando el aprendizaje automático. Proporciona soluciones para diversos sectores, incluyendo mercados, redes sociales, plataformas de medios, comercio electrónico y más.
¿Cómo usar Shaped?
Para utilizar Shaped, debes conectar tus datos a la plataforma, entrenar tus modelos de clasificación e integrarlos en tu aplicación. Shaped ofrece APIs fáciles de usar y se encarga de las complejidades de la ingeniería de datos, el entrenamiento de modelos y la implementación. Te permite personalizar sesiones de usuario en tiempo real, adaptarte a escenarios de inicio en frío y utilizar todos tus datos para la clasificación. Shaped está diseñado pensando en los desarrolladores, proporcionando opciones personalizables y configurables para experimentación.
Características principales de Shaped
Las características principales de Shaped incluyen: 1. Páginas 'Para Ti': Crea páginas personalizadas para tus usuarios. 2. Feeds: Personaliza los feeds para mostrar contenido relevante. 3. Búsqueda: Mejora los resultados de búsqueda para una mejor experiencia de usuario. 4. Recomendaciones: Genera recomendaciones personalizadas para los usuarios. 5. Notificaciones: Personaliza las notificaciones para aumentar la participación. 6. Chat: Mejora las interacciones de los usuarios con experiencias de chat personalizadas. 7. Similares: Proporciona recomendaciones de artículos similares a los usuarios. 8. IA en tiempo real para la personalización: Adáptate a las preferencias de los usuarios en tiempo real. 9. Infraestructura para recomendaciones, búsqueda y modelado de usuarios: Utiliza la infraestructura de Shaped para gestionar fácilmente estas tareas.
Casos de uso de Shaped
Shaped se puede utilizar para diversos casos de personalización, como: 1. Páginas 'Para Ti': Crea páginas de destino personalizadas para los usuarios basadas en sus preferencias. 2. Feeds: Personaliza los feeds de noticias, redes sociales o productos para mostrar contenido relevante a cada usuario. 3. Mejores opciones para ti: Genera recomendaciones personalizadas de los mejores artículos o contenido para los usuarios. 4. Noticias destacadas: Muestra artículos de noticias populares basados en los intereses de los usuarios. 5. Comprados frecuentemente juntos: Ofrece recomendaciones personalizadas de artículos que se compran con frecuencia en conjunto. 6. Supera el inicio en frío: Adáptate a los nuevos usuarios personalizando su experiencia basada en datos limitados. 7. Mejora los resultados de búsqueda: Mejora la relevancia y precisión de los resultados de búsqueda. 8. Mejora la precisión de las recomendaciones: Ajusta las recomendaciones para aumentar la satisfacción del usuario.
FAQ de Shaped
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Shaped empresa
Shaped Nombre de la empresa: Shaped .
Linkedin de Shaped
Enlace de Linkedin de Shaped: https://www.linkedin.com/company/shapedai/
Twitter de Shaped
Enlace de Twitter de Shaped: https://twitter.com/shapedai
Shaped Reseñas (0)
Analítica de Shaped
Shaped Análisis del tráfico del sitio web
Tráfico geográfico
Top 5 Regiones
Shaped Iniciar incrustaciones
Utiliza las insignias del sitio web para impulsar el apoyo de tu comunidad para el lanzamiento de Toolify. Son fáciles de incrustar en tu página de inicio o pie de página.
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From Facebook AI to Shaped: Tullie Murrell's Journey to Personalized Ai
In this episode of "Inside Scoop with Sean Emory," Sean, the founder of Avory & Company, interviews Tullie Morrell, co-founder of Shaped and former researcher at Facebook, Palantir, Google, and Uber. They discuss the winners and losers in the rapidly evolving AI landscape, including the role of Meta and its AI capabilities. They also explore whether big players will dominate the field or if there is room for smaller companies to thrive. Tune in to gain insights into the world of AI and discover more about Tullie Murrell and his company, Shaped. Shaped: https://www.shaped.ai/ [00:00:00] Intro [00:05:31] Challenges in ranking and recommendations. [00:10:28] Tick tock "for you" pages. [00:12:40] AI tools within governance and privacy. [00:14:23] Data sovereignty and compliance. [00:19:26] Chat as a discovery tool. [00:22:11] Winners and losers in AI. [00:25:10] Hyper-focused and personalized chat apps. [00:28:49] The New York AI community. [00:31:26] CEO, co-founder of Shape.ai Tullie Murrell, the CEO and co-founder of Shaped brings a wealth of experience in AI research, having worked at esteemed institutions such as Facebook Research Labs, Palantir, Google, and Uber. With a strong background in the field, Tullie Murrell co-founded Shaped, an AI personalized engine that has been successfully operating for two years. For those interested in exploring the company and its products further, the official website can be found a thttps://www.shaped.ai/, as mentioned in the transcript at 00:30:59. Listeners are encouraged to visit the website to gain more insights into the company's offerings. To facilitate discussions on the latest AI techniques, Tullie is open to communication and can be reached via email at Tullie@shaped.ai. This contact information is provided in the episode transcript at 00:31:15 and 00:31:17. ___________________________ Disclaimer Avory & Co. is a Registered Investment Adviser. This platform is solely for informational purposes. Advisory services are only offered to clients or prospective clients where Avory & Co. and its representatives are properly licensed or exempt from licensure. Past performance is no guarantee of future returns. Investing involves risk and possible loss of principal capital. No advice may be rendered by Avory & Co. unless a client service agreement is in place. Listeners and viewers are encouraged to seek advice from a qualified tax, legal, or investment adviser to determine whether any information presented may be suitable for their specific situation. Past performance is not indicative of future performance. “Likes” are not intended to be endorsements of our firm, our advisors or our services. Please be aware that while we monitor comments and “likes” left on this page, we do not endorse or necessarily share the same opinions expressed by site users. While we appreciate your comments and feedback, please be aware that any form of testimony from current or past clients about their experience with our firm is strictly forbidden under current securities laws. Please honor our request to limit your posts to industry-related educational information and comments. Third-party rankings and recognitions are no guarantee of future investment success and do not ensure that a client or prospective client will experience a higher level of performance or results. These ratings should not be construed as an endorsement of the advisor by any client nor are they representative of any one client's evaluation. Please reach out to Houston Hess our head of Compliance and Operations for any further details. Find more here https://www.avory.xyz/disclaimer-page Keywords winners, losers, AI world, embedded, companies, big, small, meta, AI capabilities, best in class, Hugging Face, models, uploaded, meta stand, big players, dominate, leadership role, smaller, shift, moat, Sean Emory, founder, Avory & Company, conversation, Tullie Murrell Facebook research labs, Palantir, Google, Uber, engines of AI, data, co-founded, Shaped, AI personalized engine, pre, chat GPT hype, conversation, Inside Scoop, episode, CEO, co-founder, Shaped, New York scene, building a company, AI, hype, fake, real,background, Shaped, AI, Facebook, computer vision,commercial value, revenue, TikTok, ranking, consumer shift,ingest data, recommendation ranking models, optimization procedure, personalization, e-commerce marketplace,dynamics, APIs, backend, technical skills, lower code, developers, integration, databases, non-technical person, governance, privacy, AI tools, attitudes, mid-market, compliance, security, scale,liability, compliance frameworks, SOC2, GDPR, PII data,generative AI, models, thinking, concept, AI,embeddings, LLMs, vector search, hug and face models, language interface,debate, succeed, AI, infrastructure layer, data capabilities, applications, winners, losers, faster paced, embedded, companies, big, small, meta, AI capabilities, best in class, embeddings, moat, OpenAI,
Meta ImageBind: Holistic AI learning across six modalities?
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation Tell us what you think and stay tuned for our video running this AI on an nVidia RTX 3090. Demo Page: https://imagebind.metademolab.com/ Paper: https://facebookresearch.github.io/ImageBind/paper Github: https://github.com/facebookresearch/ImageBind AGI Paper: https://www.shaped.ai/blog/yann-lecun-a-path-towards-autonomous-machine-intelligence #meta #ai #aiart #stablediffusion #nvidia #multimodal #metaverse #gaming #text2image #text2video #openai #chatgpt #artificialintelligence #nvidiagpu
Unlocking reasoning in Large language models | Logesh Kumar Umapathi | Conf42 ML 2023
Read the abstract ➤ https://www.conf42.com/Machine_Learning_2023_Logesh_Kumar_Umapathi_reasoning_planning_abilities_language_models Other sessions at this event ➤ https://www.conf42.com/ml2023 Join Discord ➤ https://discord.gg/DnyHgrC7jC Further read ➤ https://pakodas.substack.com/p/how-to-leverage-emergent-abilities?utm_source=post-email-title&publication_id=54538&post_id=117114977&isFreemail=true&utm_medium=email ➤ https://www.shaped.ai/blog/do-large-language-models-llms-reason Chapters 0:00 intro 0:22 preface 0:45 about logesh 1:16 agenda 2:14 what is reasoning? 2:56 how is reasoning measured (in the literature)? 5:32 eliciting reasoning 7:47 chain of thought prompting and self consistency 10:53 program-aided language models 13:01 plan-and-solve prompting 18:07 star: self-taught reasoner bootstrapping reasoning with reasoning 19:33 specializing smaller language models towards multi-step reasoning 20:48 distilling step-by-step 22:34 recursive and iterative prompting 23:16 least-to-most prompting 25:11 plan, eliminate, and track 27:23 describe, explain, plan and select 30:06 tool usage 30:43 react: reason and act 33:02 chameleon 37:52 acknowledgement & further reading
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