Dialogue between Sir C. Antony R. Hoare and Leslie Lamport

Presidents of the award-granting institutions interview the laureates that were acknowledged most recently. Find out what led them to their field of expertise, how they reacted to the news of becoming a laureate and how the award has changed their life. Gabriele Kotsis of the ACM sits down with Edwin E. Catmull and Patrick M. Hanrahan who received the 2019 ACM A.M. Turing Award, and 2019 ACM Prize in Computing recipient David Silver. Carlos Kenig of the IMU interviews the 2018 Fields Medalists Caucher Birkar, Alessio Figalli and Peter Scholze, as well as Constantinos Daskalakis who was awarded the 2018 Nevanlinna Prize. On behalf of the DNVA, Alex Bellos talks with Gregory Margulis and Hillel Furstenberg who received the 2020 Abel Prize.

followed by Q&A

Artificial Intelligence is one of the most exciting technologies of our time, but how exactly does it work? In which areas is it used and what are its limitations?

I AM A.I. is an interactive science exhibition dedicated to explaining methods and concepts of Artificial Intelligence to a general audience. It started with a digital format and will launch in 2021 as an open source traveling exhibition in Germany and then expands to international venues.

Featuring interactive videos and so-called “trails” with integrated apps and games that let you explore gradient descent or neural networks, I AM A.I. explores novel formats for science communication. The exhibition is rounded off with a graphic novel about the current state of AI and a do-it-yourself-tutorial on how to build a simple AI experiment using only cups and paper. 

I AM A.I. was developed by IMAGINARY in collaboration with researchers. It is financed by the Carl Zeiss Foundation.

More information at: https://www.i-am.ai

About IMAGINARY: https://about.imaginary.org

IMAGINARY is a non-profit organization for the communication of modern mathematics. It offers a platform for open and interactive mathematics with a variety of content that can be used in schools, at home, in museums, at exhibitions or for events and media activities. The main contents of IMAGINARY are its interactive programs and its picture galleries. IMAGINARY was initiated at the Mathematisches Forschungsinstitut Oberwolfach (MFO), an institute of the Leibniz Association.

Machine learning is now driving AI, and the first word in Machine Learning is machine.  Thus, we need even faster computers to enhance AI, but the slowing of Moore’s Law means conventional machines are barely improving. In order to  expand the impact of AI, we are forced to design machines specifically for AI.
Google is a leader on this new architectural path with the announcement of three generations of Tensor Processing Units (TPUs), with the first deployed for the cloud  in 2015. The co-design of machine learning specific software and hardware let production machine learning applications scale to 1000 chips with almost 1000 times the performance and deliver 10 times the performance/Watt of the most efficient general-purpose supercomputers.

Dialogue between Robert Endre Tarjan and Donald Ervin Knuth

Envisioning how the future of healthcare could be transformed by technology, sensors, data and machine learning can be very exciting. Even though many innovations and promising new methods are theoretically possible, they also come with inherent obstacles that can be easily overlooked. Starting with the right questions, researching the real problems and challenges with the infrastructure such as the Internet or electricity, as well as questions of privacy and the needs of the population are important to consider.

In order to thoroughly examine eHealth’s influence, the session has been broken down into two panels. The first half, “Global Health and Tech,” will be devoted to the question of what the real conditions are like in different parts of the world and what the largest challenges are. The second panel, “Concrete Innovations, Apps and Challenges,” is set unpack current solutions and forecast the potential of future developments.

Panel 1: Stefanie Friedhoff, Arunan Skandarajah, Ziad Obermayer

Panel 2: Katherine Chou, Aisha Walcott, Shwetak Patel

Moderator: Eva Wolfangel

Karen Uhlenbeck became the first woman to receive the Abel Prize in 2019, making her a tremendous inspiration not just for mathematicians everywhere but for anyone with a pioneering spirit. For the Virtual HLF, two alumnae had the unique opportunity to interview Uhlenbeck about professional obstacles specifically facing women and how to overcome them. They also discuss Uhlenbeck’s background, how to define success, nurturing motivation and the importance of the scientific environment.

Much of the fundamental research in computer science has been driven by the needs of those attempting to utilize computing for various applications, such as health. Shwetak Patel will describe a collection of his research projects and those from the research community in global health that is now having an influence on how to approach the current global COVID Pandemic. These projects follow the theme of finding unique signals and biomarkers in order to enable access and scale by leveraging existing hardware and data. His remarks will underscore the potential advances in health and clinical science through the convergence of sensing, machine learning, public policy, ethics, and human-computer interaction.

Maryam Mirzakhani is an inspiration for many reasons and the panel aims to analyze a few of them. Vincent Delecroix (France) will present one of the theorems he proved recently with his collaborators using Maryam Mirzakhani's ideas. Hélène Barcelo (USA) will present the Mathematical Science Research Institute’s initiative to create a special chair named after Maryam Mirzakhani, and Andrea Vera-Gajardo (Chile) will describe the May 12 Initiative, celebrating women in mathematics every year on Maryam Mirzakhani’s birthday. Finally, two young researchers Sorelle Toukam Tchoumegne (Cameroon) and Georges-Philippe Gadoury-Sansfaçon (Canada) will give testimonies of her influence on their lives. The panel discussion is hosted by Marie-Françoise Roy (France).

How can the public trust science it cannot understand? The case of science communication on research in mathematics and computer science is a very tough call because the essence of formal languages and theoretical concepts cannot be “explained” to nonscientific audiences. So, how do researchers or science journalists deal with this dilemma: informing the public while the underlying scientific concepts remain in a black box?

The panelists will discuss ways how science journalists craft their stories, what mathematicians try to in order to reach nonscientific audiences. Does the digital revolution allow for new ways to handle science communication worldwide? Is there actually a problem if people cannot understand the science, but can nevertheless establish trust with scientists?

Panelists:

Ilka Agricola is full professor of pure mathematics at the University of Marburg

Susan D'Agostino is a mathematician who became author and science writer

John Hopcroft is a computer scientist at Cornell University and a recipient of the ACM A.M. Turing Award 1986

Eva Wolfangel is an award-winning freelance science, tech and reportage journalist

Moderator:

Volker Stollorz, Chief Editor Science Media Center Germany

Panel discussion with Vinton Cerf, Alessio Figalli and Efim Zelmanov

This talk will present a plan for the use of AI and Machine Learning Technology to eliminate Covid Lockdowns. This talk is not a solution to the coronavirus problem, but rather a technological option to the problem of Lockdowns (or Shutdowns) of the economy and quarantining of Everyone, instead of just the Infected. To contain the economic and social disruption, we need to control the exponential spread of the virus. There would no need for a 100% Lockdown if we can reliably identify and quarantine a majority of the 2% that are infected.

Recently, several smart watches have appeared on the market that are capable of measuring COVID-19 symptoms such as temperature, oxygen level, heart rate, breathing rate and blood pressure. Using such sensors, Machine Learning technology can be used to classify if a person is potentially COVID infected. Infected people can be quarantined while others can go about their lives. Routine monitoring of all the people on the Planet will improve our ability to predict and prevent most illnesses, not just Viral Infections.

Why is self-driving so hard? Despite the enthusiastic involvement of big technological companies and the massive investment of many billions of dollars, all the optimistic predictions about self-driving cars “being around the corner” went utterly wrong.

I argue that these difficulties emblematically illustrate the challenges raised by the vision for trustworthy autonomous systems. These are critical systems intended to replace human operators in complex organizations, very different from other intelligent systems such as game-playing robots or intelligent personal assistants. They have to understand dynamically changing situations in unpredictable dynamically changing environments. They have to manage many different potentially conflicting goals and plan actions for achieving them. Finally yet importantly, they have to interact safely with human operators.

I discuss complexity limitations inherent to autonomic behavior but also to integration in complex cyber-physical and human environments. I argue that traditional model-based critical systems engineering techniques fall short of meeting the complexity challenge. I also argue that emerging end-to-end AI-enabled solutions currently developed by industry fail to provide the required strong trustworthiness guarantees.
 
I conclude that building trustworthy autonomous systems goes far beyond the current AI vision and advocate a new scientific and engineering foundation addressing this unique and groundbreaking challenge.

For this year’s Virtual HLF, our HLF alumni have the opportunity to present one of their current projects or institution, exhibiting a few examples of how careers can develop. Devised not only to offer a glimpse into what some of the HLF’s former participants are working on, these presentations also hope to strengthen the alumni network by opening up their research to a wider audience.

For this year’s Virtual HLF, our HLF alumni have the opportunity to present one of their current projects or institution, exhibiting a few examples of how careers can develop. Devised not only to offer a glimpse into what some of the HLF’s former participants are working on, these presentations also hope to strengthen the alumni network by opening up their research to a wider audience.

Dialogue between Jon Kleinberg and Yoshua Bengio

Panel discussion with Alessio Figalli, Jon Kleinberg, Madhu Sudan and Efim Zelmanov

It was important to retain many aspects of the actual forum and select institutions are offering virtual lab tours to participating young researchers. The underlying purpose of these excursions serves to potentially expand the young researchers’ occupational network and allow them to learn more about employment and research possibilities. Traditionally, these tours would exclusively be in the Heidelberg area, but this year has afforded participants the opportunity to visit international locations. Renowned labs have opened their doors, from local fixtures such as HITS and MPIA, to international companies like SAP, NEC, EMBL, IBM Research and SAS.

It was important to retain many aspects of the actual forum and select institutions are offering virtual lab tours to participating young researchers. The underlying purpose of these excursions serves to potentially expand the young researchers’ occupational network and allow them to learn more about employment and research possibilities. Traditionally, these tours would exclusively be in the Heidelberg area, but this year has afforded participants the opportunity to visit international locations. Renowned labs have opened their doors, from local fixtures such as HITS and MPIA, to international companies like SAP, NEC, EMBL, IBM Research and SAS.

Recent discussion in the public sphere has explored some of the way in which prediction algorithms trained on data might exhibit bias in their decision-making. This discussion, drawing on input from a wide range of communities, has involved a number of crucial trade-offs that can be formulated in precise terms. We will survey some of these trade-offs, including tensions between the simplicity of a classification rule and its equity guarantees, and tensions between competing definitions of what it means for a prediction algorithm to be fair to different groups.

The talk will be based on joint work with Jens Ludwig, Sendhil Mullainathan, Manish Raghavan, and Cass Sunstein.

Research in programming methodology led to the development of the principles and methods that underlie how modern software systems are designed and structured.  At the center of this work are the notions of abstraction and modularity. These ideas are related: design is the process of inventing and identifying abstractions, and the implementations of the abstractions become the modules that make up the program.  This talk will discuss our current understanding of abstraction and modularity and the research that got us to where we are today.

In my talk, I will recall my experiences since my student days.

There were several instances which drew my attention to algebraic geometry, and after all, I became an algebraic geometer. The first was a famous example of complete non-projective algebraic variety shown to me by Prof. H. Hironaka when I was a junior. One can see it in R. Hartshorne's book "Algebraic Geometry," but he wrote it a few years later during his stay in Kyoto. I will explain about a few more instances in my talk.

During this year‘s HLF scientific interaction, a workshop will offer insights into a current survey among HLF alumni that was devised as a springboard for the upcoming guiding minds community platform. We will discuss the upcoming initiative in an online plenary session and will continue with two highly interactive workshops on career pathways in mathematics and computer science.

In these virtual sessions, the participants gain insight into how the skills of mathematicians and computer scientists are employed in the worlds of business and academia. The survey results will be analyzed to identify possibilities where the HLF community can offer career development support. Additionally, guidance will be offered on how to apply for interesting jobs at exciting companies or how to establish oneself in academia.

During this year‘s HLF scientific interaction, a workshop will offer insights into a current survey among HLF alumni that was devised as a springboard for the upcoming guiding minds community platform. We will discuss the upcoming initiative in an online plenary session and will continue with two highly interactive workshops on career pathways in mathematics and computer science.

In these virtual sessions, the participants gain insight into how the skills of mathematicians and computer scientists are employed in the worlds of business and academia. The survey results will be analyzed to identify possibilities where the HLF community can offer career development support. Additionally, guidance will be offered on how to apply for interesting jobs at exciting companies or how to establish oneself in academia.