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September 4, 2024 6:00 AM Bryan McCann, left, CTO of You.com, and Richard Socher, the company's CEO, at their office in Palo Alto, Calif. The co-founders are spearheading the development of AI-powered "productivity engines" for enterprise use. (Credit: You.com) Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreYou.com, the AI-powered search and productivity platform, announced today it has raised $50 million in Series B funding, marking a significant shift in the enterprise AI landscape. Georgian led the round, with participation from tech giants Nvidia and Salesforce Ventures, in addition to Day One Ventures, bringing You.com’s total funding to $99 million. This investment highlights the growing demand for AI solutions that can demonstrably enhance workplace productivity.Richard Socher, former chief scientist at Salesforce and a renowned figure in natural language processing, founded You.com in 2021. The company is pioneering a new category in the AI space: “productivity engines.” These tools aim to revolutionize how knowledge workers interact with information and complete complex tasks.“We started you.com to do more than search. We saw an opportunity to reinvent the gateway to everyone’s online journey,” Socher told VentureBeat. “We now help millions of knowledge workers be more productive, whether it’s through research and analysis, problem-solving, or content creation.”You.com’s versatile AI platform, shown here, enables users to perform diverse tasks from plotting financial data to exploring complex scientific concepts. The interface allows seamless switching between different AI models and the creation of custom agents, demonstrating the company’s vision for a comprehensive ‘productivity engine’ in enterprise settings. (Credit: You.com)You.com’s growth trajectory is impressive. The company reports serving over 1 billion queries since its launch and claims a staggering 500% revenue growth since January. This rapid expansion comes at a time when enterprises are grappling with “AI sprawl” — the proliferation of disparate AI tools and subscriptions across organizations.To address this challenge, You.com offers access to multiple leading AI models through a single platform, enhanced with live web access. The company also places a strong emphasis on accuracy, a critical factor for enterprise adoption.“It’s easy to make a quick prototype with an LLM. It’s difficult to make them accurate at scale,” Socher explained. “Our focus at you.com has been making LLMs more trustworthy. In December 2022, we were the first consumer-facing LLM with internet access, providing up-to-date answers with verifiable citations. Since then, we’ve built an entire AI operating system that’s model agnostic to provide the most comprehensive and accurate responses.”This focus on accuracy distinguishes You.com in a market saturated with AI solutions that often prioritize speed over reliability. The company’s approach includes features like the “Research Assistant,” which provides comprehensive reports with verifiable citations, and the “Genius Assistant,” which uses Python code and chain-of-thought reasoning to solve complex problems.You.com is also introducing “multiplayer AI” features, expanding beyond individual productivity to enable team collaboration and custom AI assistant sharing within organizations. This shift towards enterprise-focused solutions is strategic, as Mr. Socher explains:“Where we really shine, where we provide a 10x better experience is for knowledge workers in business,” he said. “When you’re that AE who needs to get a quick brief on a company before they go into meeting — that’s when it really shines.”The company’s move to a consumption-based pricing model represents another significant development. Mr. Socher argues this approach better aligns incentives to drive enterprise-wide AI adoption.“Even companies that are trying to use this technology often struggle with it, and there’s low adoption in their organizations,” he said. “If you’re selling them 1000 seats and then only 20 use it, you’re still making money, right? And

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It as a key player in the next phase of enterprise AI adoption. The company’s success will hinge on its ability to deliver on its promises of accuracy, customization, and tangible productivity gains in real-world business environments.“In the end, we will likely have more AI agents surfing the web than people, and that’s going to start next year,” Mr. Socher predicted. If this vision materializes, it could mark a fundamental shift in how knowledge work is performed and how businesses operate in the AI-enabled future.As the enterprise AI market continues to evolve rapidly, You.com’s focus on productivity and accuracy positions it as a company to watch. The coming months will reveal whether its approach can truly deliver the transformative impact on knowledge work that Mr. Socher and his team envision. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.. Search Socher family obituaries and memoriams on Legacy.com. There are 66 obituaries and memoriams for the surname Socher. Thursday, Decem. Brian

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And Sanjeev Khudanpur. Recurrent neural network based language model. In Proceedings of the 11th Annual Conference of the International Speech Communication Association, pages 1045–1048, 2010. Google Scholar Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the 27th Annual Conference on Neural Information Processing Systems, pages 3111–3119, 2013. Google Scholar Tom M. Mitchell, William W. Cohen, Estevam R. Hruschka Jr., Partha Pratim Talukdar, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matthew Gardner, Bryan Kisiel, Jayant Krishnamurthy, Ni Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapandula Nakashole, Emmanouil A. Platanios, Alan Ritter, Mehdi Samadi, Burr Settles, Richard C. Wang, Derry Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, and Joel Welling. Never-ending learning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pages 2302–2310, 2015. Google Scholar Bhaskar Mitra and Nick Craswell. Query auto-completion for rare prefixes. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pages 1755–1758, 2015. Google Scholar Bhaskar Mitra, Fernando Diaz, and Nick Craswell. Learning to match using local and distributed representations of text for web search. In Proceedings of the 26th International Conference on World Wide Web, pages 1291–1299, 2017. Google Scholar Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana. Improving document ranking with dual word embeddings. In Proceedings of the 25th International Conference on World Wide Web, pages 83–84, 2016. Google Scholar Rodrigo Nogueira and Kyunghyun Cho. Task-oriented query reformulation with reinforcement learning. CoRR, abs/1704.04572, 2017. Google Scholar Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab K. Ward. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Trans. Audio, Speech and Language Processing, 24 (4): 694–707, 2016.Article Google Scholar Dae Hoon Park and Rikio Chiba. A neural language model for query auto-completion. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1189–1192, 2017. Google Scholar Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1532–1543, 2014. Google Scholar Francesco Piccinno and Paolo Ferragina. From TagME to WAT: a new entity annotator. In Proceedings of the First ACM International Workshop on Entity Recognition and Disambiguation, pages 55–62, 2014. Google Scholar James E. Pitkow, Hinrich ACM SIGCOMM Comput. Commun. Rev. 41(1), 53–53 (2011) Google Scholar Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Predictable 802.11 packet delivery from wireless channel measurements. ACM SIGCOMM Comput. Commun. Rev. 40, 159–170 (2010) Google Scholar Hristov, H.D.: Fresnal Zones in Wireless Links, Zone Plate Lenses and Antennas. Artech House, Inc., Boston (2000) Google Scholar Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009) Google Scholar Li, H., Chan, E.C., Guo, X., Xiao, J., Wu, K., Ni, L.M.: Wi-counter: smartphone-based people counter using crowdsourced wi-fi signal data. IEEE Trans. Human-Machine Syst. 45(4), 442–452 (2015)Article Google Scholar Liu, C., Zhang, L., Liu, Z., Liu, K., Li, X., Liu, Y.: Lasagna: towards deep hierarchical understanding and searching over mobile sensing data. In: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking (ACM MobiCom), pp. 334–347 (2016) Google Scholar Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Res. 37(23), 3311–3325 (1997)Article CAS PubMed Google Scholar Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing and Networking (ACM MobiCom), pp. 27–38 (2013) Google Scholar Sadanand, S., Corso, J.J.: Action bank: a high-level representation of activity in video. In: IEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR), pp. 1234–1241 (2012) Google Scholar Sarikaya, R., Hinton, G.E., Deoras, A.: Application of deep belief networks for natural language understanding. IEEE/ACM Trans. Audio Speech Language Process. (TASLP) 22(4), 778–784 (2014) Google Scholar Socher, R., Bengio, Y., Manning, C.: Deep learning for nlp. Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013) Google Scholar Su, C.J., Chiang, C.Y., Huang, J.Y.: Kinect-enabled home-based rehabilitation system using dynamic time warping and fuzzy logic. Appl. Soft Comput. 22, 652–666 (2014)Article Google Scholar Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes.

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Da Wikipedia, l'enciclopedia libera. You.comsito webLogoURLyou.com/Tipo di sitoMotore di ricercaLinguaMultilingueRegistrazioneOpzionaleProprietarioSuSea, Inc.Lancio9 novembre 2021Stato attualeAttivoModifica dati su Wikidata · ManualeYou.com è un motore di ricerca orientato alla privacy, con sede a Palo Alto, California; a differenza dei tradizionali motori di ricerca, i risultati vengono classificati in categorie piuttosto che una lista di link. Il motore di ricerca è stato distribuito in versione Beta il 9 novembre 2021.[1] In un'intervista, il cofondatore Richard Socher ha annunciato di volere un motore di ricerca con un bilanciamento fra privacy e personalizzazione.Il sito web è stato fondato da Bryan McCann e Richard Socher nel 2020, ex dipendenti di Salesforce.[2] Dopo l'annuncio dell'apertura al pubblico in versione Beta nel 2021, l'azienda ha ricevuto 20 milioni di dollari da Marc Benioff, fondatore di Salesforce.[3] A marzo 2022 l'azienda ha lanciato YouWrite, un modello GPT-3 per la scrittura di email e altri documenti. A dicembre 2022 è stata rilasciata anche YouChat, un evoluto chat bot che, a differenza di ChatGPT di OpenAI ha accesso ad Internet con le informazioni aggiornate e offre anche le fonti da cui le informazioni sono estratte.[4] È possibile anche la scrittura di codice di programmazione.I risultati di ricerca sono forniti tramite Microsoft Bing, oltre che Reddit e Twitter.[5] Per quanto riguarda la privacy, You.com non memorizza l'indirizzo IP e non raccoglie informazioni degli utenti per mostrare annunci in target. La registrazione è opzionale, sono possibili quindi due modalità di utilizzo, personale e privata, in quest'ultima non viene memorizzata alcuna informazione riguardante la ricerca.^ "You.com wants to remake the search engine", su theverge.com.^ "Former Salesforce chief scientist announces new search engine to take on Google", su techcrunch.com.^ "AI-driven search engine You.com takes on Google with $20M", su venturebeat.com.^ "you.com vs ChatGPT differenze", su ethicalhacking.freeflarum.com.^ "You.com search challenges Google with a new look and private mode", su cnet.com. Wikimedia Commons contiene immagini o altri file su You.com

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You.com’s Richard Socher: AI Search Domination

Running back at the University of Delaware and a professional creative. A regular search might return pages for several Will Knights, but the chatbot conflated them into a single person.Another issue with a system like ChatGPT is that its responses are based on only the data it was trained on. Retraining the model in its entirety can cost millions of dollars because of its size and the scale of the data. YouChat is confused when asked for the latest sports scores but knows what the weather is like in New York at the moment. Socher doesn’t want to disclose how up-to-date information is incorporated, seeing it as a competitive advantage.“I think right now a lot of these chat interfaces are way superior to the search experience in some ways, but in others they’re clearly still much worse,” Socher says. “We’re working on reducing all these issues.”Aravind Srinivas, founder and CEO of search startup Perplexity AI, who previously worked at OpenAI, says the challenge of updating a ChatGPT-like system with recent information means that they need to be combined with something else. “Alone they’ll never be able to be good search engines,” he says.Saam Motamedi, a venture capitalist at Greylock Partners who has invested in the AI-based search company Neeva, says it is also unclear how compatible chat interfaces are with the primary revenue model for search engines—advertising. Google and Bing use search queries to select ads that appear on top of the list of links served up in response. Motamedi suspects that new forms of advertising might need to emerge for chat-style search interfaces to be viable, but it isn’t altogether clear what those will be. Neeva charges a subscription fee for unlimited ad-free searches.The cost of running a model like ChatGPT on the scale of Google might also prove problematic. Luis Ceze, cofounder and CEO of OctoML, a company that helps companies lower the cost of deploying machine learning algorithms, estimates that it may be 10 times more expensive to run a ChatGPT search than a Google search, because each answer requires running a large and complex AI model.The scale of ChatGPT mania has taken some coders and AI researchers familiar with the underlying technology by surprise. The algorithm at the core of the bot, called GPT, was first developed by OpenAI in 2018, and a more powerful version, GPT-2, was revealed in 2019. It is a machine learning model designed to take in text and then predict what comes next, which OpenAI showed can perform impressively if trained with huge volumes of text. The first commercial version of the technology, GPT-3, has been available for developers to use since June 2020 and can accomplish many of the things ChatGPT has recently been feted for.ChatGPT uses an improved version of the underlying algorithm, but the biggest leap in its abilities comes from OpenAI having humans provide feedback to the system on what makes a satisfying answer. But like the text-generation systems before it, ChatGPT is still prone to reproducing