Jürgen Schmidhuber will provide a comprehensive framework for thinking about artificial intelligence (AI). He will review past developments in artificial intelligence, and discuss its current (and expected future) effect on the work life, various industries, and infrastructure and traffic design.
Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab’s Deep Learning Neural Networks (since 1991) such as Long Short-Term Memory (LSTM) have transformed machine learning and AI, and are now (2017) available to billions of users through the world’s most valuable public companies, Deep Learning since 1991 – Winning Contests in Pattern Recognition and Sequence Learning Through Fast & Deep / Recurrent Neural Networks e.g., for greatly improved (CTC-based) speech recognition on over 2 billion Android phones (since mid 2015), greatly improved machine translation through Google Translate (since Nov 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon’s Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. His research group also established the field of mathematically rigorous universal AI and recursive self-improvement in universal problem solvers that learn to learn (since 1987). His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age’s extreme form of minimal art. He is recipient of numerous awards, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI.
2016
UC-hCoYyM17zKza9F50dIe_w
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