Advances in machine learning and artificial intelligence mean that predictions and decisions of algorithms are already in use in many important situations under legal or regulatory control, and this is likely to increase dramatically in the near future. Examples include deciding whether to approve a bank loan, driving an autonomous car, or even predicting whether a prison inmate is likely to offend again if released. This symposium will explore the key themes of privacy, liability, transparency and fairness specifically as they relate to the legal treatment and regulation of algorithms and data. Our primary goals are (i) to inform our community about important current and ongoing legislation (e.g. the EU’s GDPR https://en.wikipedia.org/wiki/GeneralDataProtection_Regulation which introduces a "right to explanation"); and (ii) to bring together the legal and technical communities to help form better policy in the future.

Deep Learning algorithms attempt to discover good representations, at multiple levels of abstraction. Deep Learning is a topic of broad interest, both to researchers who develop new algorithms and theories, as well as to the rapidly growing number of practitioners who apply these algorithms to a wider range of applications, from vision and speech processing, to natural language understanding, neuroscience, health, etc. Major conferences in these fields often dedicate several sessions to this topic, attesting the widespread interest of our community in this area of research.

There has been very rapid and impressive progress in this area in recent years, in terms of both algorithms and applications, but many challenges remain. This symposium aims at bringing together researchers in Deep Learning and related areas to discuss the new advances, the challenges we face, and to brainstorm about new solutions and directions.

Soon after the birth of modern computer science in the 1930s, two fundamental questions arose: 1. How can computers learn useful programs from experience, as opposed to being programmed by human programmers? 2. How to program parallel multiprocessor machines, as opposed to traditional serial architectures? Both questions found natural answers in the field of Recurrent Neural Networks (RNNs), which are brain-inspired general purpose computers that can learn parallel-sequential programs or algorithms encoded as weight matrices.

Our first RNNaissance NIPS workshop dates back to 2003: http://people.idsia.ch/~juergen/rnnaissance.html . Since then, a lot has happened. Some of the most successful applications in machine learning (including deep learning) are now driven by RNNs such as Long Short-Term Memory, e.g., speech recognition, video recognition, natural language processing, image captioning, time series prediction, etc. Through the world's most valuable public companies, billions of people have now access to this technology through their smartphones and other devices, e.g., in the form of Google Voice or on Apple's iOS. Reinforcement-learning and evolutionary RNNs are solving complex control tasks from raw video input. Many RNN-based methods learn sequential attention strategies.

Here we will review the latest developments in all of these fields, and focus not only on RNNs, but also on learning machines in which RNNs interact with external memory such as neural Turing machines, memory networks, and related memory architectures such as fast weight networks and neural stack machines. In this context we will also will discuss asymptotically optimal program search methods and their practical relevance.

Our target audience has heard a bit about recurrent neural networks but will happy to hear again a summary of the basics, and then delve into the latest advanced stuff, to see and understand what has recently become possible. We are hoping for thousands of attendees.

All talks (mostly by famous experts in the field who have already agreed to speak) will be followed by open discussions. We will also have a call for posters. Selected posters will adorn the environment of the lecture hall. We will also have a panel discussion on the bright future of RNNs, and their pros and cons.