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In the new paradigm, lawyers are expendable, and partners may jump to a competitor for the right amount of money, taking clients with them on the way out.
International Business Machines is betting that its strategy of giving big companies the flexibility to run their data and applications anywhere they want will help it win over enterprises concerned about cybersecurity.
Research team , (IBM): Peri Tarr, Yasu Yamada, Laxmi Parida, Niina Haiminen, Yannis Katsis, Peter Fay, Ban Kawas, Anna Paola Carrieri; (UCSD): Colin Depp, Danielle Glorioso, Camille Nebeker, A’verria Martin, Elizabeth Twamley, Sandrine Miller-Montgomery, Austin Swafford, Chun-Han Hsu, Siavash Mir Arabbaygi Goal Developing next-generation deep learning algorithms and techniques to help computers improve their understanding and interpretation of language, speech, and vision.
Technologies Deep learning, generative models, adversarial learning, reinforcement learning, natural language processing, dialogue, speech recognition, hardware acceleration of inference with deep models.
The company was founded by Charles Ranlett Flint and Thomas J. on June 16, 1911 and is headquartered in Armonk, NY.
Technologies Multi-model semantics graph integration, NLP of health forum, Personal health data wrangling, Automatic behavior phenotypes identification and analysis, Watson powered conversation agents, Engagement Apps.It operates through the following segments: Cognitive Solutions, Global Business Services, Technology Services & Cloud Platforms, Systems, and Global Financing.The Cognitive Solutions segment comprises a portfolio of capabilities that help IBM's clients to identify actionable insights and inform decision making for competitive advantage.Research team Goal To develop and evaluate AI technology solutions that enable older adults to live independently longer and have a higher quality of life; to develop machine learning methods and software tools, and generate novel findings, that implicate the human microbiome in health and disease.Technologies Behavior prediction models and prevention innovation from ML by using datasets of batteries, video, audio, accelerometer, and other wearables; Human gut bacteria-disease association knowledge base built by Natural Language Processing, ML methods for analysis of microbiome datasets and phenotype prediction.
Research team Goal Developing machines that can emulate the human ability to understand inputs from multiple video streams and predict potential future events in real-time.