Deep Learning: Applications and Implications

 

The Hot Topic is coordinated and moderated by Katherine Gorman.

Abstract

The field of Deep Learning has seen some incredible achievements in the past few years. Thanks to improvements in computing power and an incredible foundation of research, advancements within machine learning have enabled the creation of powerful applications. The success of these tools has garnered much attention, from both the practitioner community and those who would seek to gain an edge in their industry by harnessing the advantages of machine learning.

While simply applying these tools seems to revolutionize a great number of industries and fields, the implications of using tools that can be hard to parse – even for their creators – should not be ignored. With such promise and excitement surrounding the field for both those who build the tools and those who wish to use them, how will this area of research continue to develop?

In the 2022 Hot Topic, we will explore the applications and implications of Deep Learning. The conversation will open with a panel of experts in the field, including the recipients of the 2018 ACM A.M. Turing Award, who received the prize for their foundational work in deep neural networks. Touching on the science behind the technology, how the field has evolved, and the societal and ethical implications of the tools that Deep Learning enables, the panel will open up the larger conversation and highlight the topics that are key in this field for researchers, engineers, and those who are touched by these tools. Next, the audience will have the opportunity to ask questions. Afterwards, we will invite the audience into small group discussions and reflections during the break. Attendees will have the opportunity to engage with the topic in-person or online.  Finally, we will reconvene to discuss the ideas surfaced in a debriefing onstage.

Moderator

• Katherine Gorman is the Executive Producer for Collective Next where she helps groups to communicate about the issues that matter to them. Trained in journalism and production in the American public radio system, Katherine has worked for terrestrial public radio stations across the United States. Her work has appeared on many public radio outlets including NPR and the BBC. In 2014, she co-founded the machine intelligence podcast, Talking Machines. In 2016 and 2017, she served as a curator and host for TedXBoston which focused on issues around and research in machine learning and artificial intelligence.

Panelists

• Sanjeev Arora
  ACM Prize in Computing - 2011
  For contributions to computational complexity, algorithms, and optimization that have helped reshape our understanding of computation.
• Yoshua Bengio
  ACM A.M. Turing Award - 2018
  Together with Geoffrey E. Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
• Geoffrey E. Hinton
  ACM A.M. Turing Award - 2018
  For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
• Raj Reddy
  ACM A.M. Turing Award - 1994
  For pioneering the design and construction of large scale artificial intelligence systems, demonstrating the practical importance and potential commercial impact of artificial intelligence technology.
• Been Kim is a staff research scientist at Google Brain. Her research focuses on helping humans to communicate with complex machine learning models: not only by building tools (and tools to criticize them) but also by studying their nature compared to humans. She is a steering committee member of FAccT conference and former executive board member and VP of Women in Machine Learning.
• Dina Machuve is the Co-Founder and CTO of DevData Analytics, a data science consulting startup. She was until recently a Senior Lecturer and Researcher at the Nelson Mandela African Institution of Science and Technology in Arusha, Tanzania. Her research focuses on developing data-driven solutions in agriculture and health. For her PhD, she investigated the information logistics of small and medium-sized food processors.
• Shakir Mohamed works on technical and sociotechnical questions in machine learning research, working on problems in machine learning principles, applied problems in healthcare and environment, as well as ethics and diversity. Shakir is a Director for research at DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and an Honorary Professor of University College London.
• Shannon Vallor is the Baillie Gifford Professor in the Ethics of Data and Artificial Intelligence at the University of Edinburgh, where she serves as Director of the Centre for Technomoral Futures in the Edinburgh Futures Institute, and as a Turing Fellow of the Alan Turing Institute. Professor Vallor's research explores how emerging technologies reshape human moral and intellectual character, and maps the ethical challenges and opportunities posed by new uses of data and artificial intelligence.