Deep learning is a sub-field of machine learning that focuses on hierarchical representations of features or concepts, where high-level semantic-like features can emerge via automatic layer-by-layer learning from low-level features. In recent years, deep learning has achieved important successes in a variety of applied artificial intelligence tasks including speech recognition, computer vision, and natural language processing. The implications of such recent work have been prominently covered in recent media. Since 2009, in partnership with leading academic researchers, Microsoft Research has been pursuing deep learning research and technology transfer, and has pioneered the development of industry-scale deep learning technology for speech recognition and other applications, resulting in industry-wide adoption of deep learning in Windows Phones (Microsoft), Android Phones (Google), iPhones (Siri of Apple via Nuance/IBM), and Baidu and iFlyTech voice search products (China).
In this talk, I will provide a historical overview on how academic conceptualization of deep learning rapidly evolved into wide product deployment worldwide within only a few short years, and discuss what implications this recent triumphant history may have for future academic-industrial collaborations. I also plan to go into some technical depth in describing the current deep learning technology, and in particular the somewhat disparate approaches which industry and academia take in current pursuits of the technology. This talk will be concluded by analyzing future directions of deep learning, and speculating on what types of information processing and artificial intelligence applications may benefit most from deep learning technology in light of the known mechanisms of the human brain that grounds intelligence and extreme effectiveness in information processing.
Li Deng (IEEE SM'92;F'04) received the Bachelor degree from the University of Science and Technology of China, and received the Master and Ph.D. degrees from the University of Wisconsin-Madison. He was an assistant professor (1989-1992), tenured associate professor (1992-1996), and tenured Full Professor (1996-1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, WA, where he is currently a Principal Researcher. Since 2000, he has also been an Affiliate Full Professor and graduate committee member at the University of Washington, Seattle, teaching graduate course of Computer Speech Processing and supervising graduate students. Prior to MSR, he also worked or taught at Massachusetts Institute of Technology, ATR Interpreting Telecom. Research Lab. (Kyoto, Japan), and HKUST. He has been granted over 60 US or international patents in acoustics/audio, speech/language technology, and machine learning. He received numerous awards/honors bestowed by IEEE, ISCA, ASA, Microsoft, and other organizations.
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