Shaped
Qu'est-ce que Shaped ?
Shaped est une plateforme qui aide les entreprises à augmenter l'engagement pour les flux, les recommandations et les notifications en utilisant l'apprentissage automatique. Elle propose des solutions pour divers secteurs, notamment les places de marché, les médias sociaux, les plateformes de médias, le commerce électronique, et plus encore.
Comment utiliser Shaped ?
Pour utiliser Shaped, vous devez connecter vos données à la plateforme, former vos modèles de classement et les intégrer à votre application. Shaped propose des API faciles à utiliser et gère les complexités de l'ingénierie des données, de la formation des modèles et du déploiement. Elle vous permet de personnaliser les sessions utilisateur en temps réel, de vous adapter aux scénarios de démarrage à froid et d'utiliser l'ensemble de vos données pour le classement. Shaped est conçu en pensant aux développeurs, en offrant des options personnalisables et configurables pour l'expérimentation.
Fonctionnalités de base de Shaped
Les principales fonctionnalités de Shaped incluent: 1. Pages 'Pour Vous': Créez des pages personnalisées pour vos utilisateurs. 2. Flux: Personnalisez les flux pour afficher du contenu pertinent. 3. Recherche: Améliorez les résultats de recherche pour une meilleure expérience utilisateur. 4. Recommandations: Générez des recommandations personnalisées pour les utilisateurs. 5. Notifications: Personnalisez les notifications pour augmenter l'engagement. 6. Discussion: Améliorez les interactions utilisateur avec des expériences de discussion personnalisées. 7. Similaire: Fournissez des recommandations d'articles similaires aux utilisateurs. 8. IA en temps réel pour la personnalisation: S'adapte aux préférences de l'utilisateur en temps réel. 9. Infrastructure pour les recommandations, la recherche et la modélisation utilisateur: Utilisez l'infrastructure de Shaped pour gérer ces tâches facilement.
Les cas d'utilisation de Shaped
Shaped peut être utilisé pour divers cas d'utilisation de personnalisation, tels que: 1. Pages 'Pour Vous': Créez des pages de destination personnalisées pour les utilisateurs en fonction de leurs préférences. 2. Flux: Personnalisez les flux d'actualités, les flux de médias sociaux ou les flux de produits pour afficher du contenu pertinent à chaque utilisateur. 3. Meilleurs choix pour vous: Générez des recommandations personnalisées des meilleurs articles ou contenus pour les utilisateurs. 4. Actualités tendances: Affichez des articles d'actualités tendances en fonction des intérêts des utilisateurs. 5. Fréquemment acheté ensemble: Offrez des recommandations personnalisées d'articles fréquemment achetés ensemble. 6. Surmonter le démarrage à froid: Adaptez-vous aux nouveaux utilisateurs en personnalisant leur expérience en fonction de données limitées. 7. Améliorer les résultats de recherche: Améliorez la pertinence et la précision des résultats de recherche. 8. Améliorer la précision des recommandations: Ajustez les recommandations pour augmenter la satisfaction de l'utilisateur.
FAQ de Shaped
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Société Shaped
Shaped Nom de l'entreprise : Shaped .
Shaped Linkedin
Lien de Linkedin Shaped : https://www.linkedin.com/company/shapedai/
Shaped Twitter
Lien de Twitter Shaped : https://twitter.com/shapedai
Shaped Avis (0)
Analyse de Shaped
Shaped Analyse du trafic du site Web
Trafic géographique
Top 5 Régions
Shaped Lancer les intégrations
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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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