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Panel Discussion
Jacob Andreas · Edward Gibson · Stefan Lee · Noga Zaslavsky · Jason Eisner · Jürgen Schmidhuber

Sat Dec 14 05:00 PM -- 05:55 PM (PST) @ None

Author Information

Jacob Andreas (MIT)
Edward Gibson (MIT)
Stefan Lee (Oregon State University)
Noga Zaslavsky (MIT)
Jason Eisner (Johns Hopkins University + Microsoft Semantic Machines)

Jason Eisner is Professor of Computer Science at Johns Hopkins University, as well as Director of Research at Microsoft Semantic Machines. He is a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Machine Learning Group, the Cognitive Science Department, and the national Center of Excellence in Human Language Technology. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. His 135+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; and unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation. He is also the lead designer of Dyna, a new declarative programming language that provides an infrastructure for AI research. He has received two school-wide awards for excellence in teaching, as well as recent Best Paper Awards at ACL 2017 and EMNLP 2019.

Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)

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 based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through users of the world's most valuable public companies, e.g., for greatly improved (CTC-LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTM-based translations per day), Apple's Siri and Quicktype on all iPhones, 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. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of mathematically rigorous universal AI and recursive self-improvement in metalearning machines that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game to achieve artificial curiosity (GANs are a special case). In 1991, he introduced very deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what's now called linear Transformers. 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, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and advising various governments on AI strategies.

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