Research on the Edge of the Expanding Sphere, V. 2.0 *
Dr. Alfred Z. Spector
Senior Vice Chancellor for Research Lecture
Science 2019, 18-October-2019
The multi-trillion-fold (!) increase in the capability of computation over the past 60 years, when coupled with global connectivity and vast data has made for vibrant fields of research that are growing with no end in sight.
- This explosive growth in computing and data has led to very excellent results in the core of the field: e.g., ever more capable and creative algorithms; the capability to build vast, globally networked systems that support most of the world’s population; and the effective solutions to grand challenge problems, such as speech and image recognition. However, many challenges remain in the the core. For example, we still have trouble designing and building robust, large scale systems, and we need breakthroughs in knowledge representation and inferencing. Both of these are needed to achieve the potential to have, for example, Level 5 autonomous vehicles.
- There are also immense opportunities on the edge of the field’s Expanding Sphere, at the border of Computer Science and X, for all fields X. Great creativity will be required to adapt our technologies to new application domains in healthcare, education, and manufacturing, to name a few.
- Finally, with the very growing import of computing in all aspects of society, important research areas abound at the intersection of computing and ethics/public policy.
In this talk, I’ll discuss the breadth of challenges that we have. I’ll illustrate the breadth of my points with many examples from my experience leading research teams in academia and industry.
* V. 1.0 of this talk was given on 11/8/04 at Harvard Center for Research on Computation and Society.
Opportunities and Perils in Data Science
Dr. Alfred Z. Spector
Presentations at Berkeley, Caltech, Cornell, Harvard, MIT, and Rice
Over the last few decades, empiricism has become the third leg of computer science, adding to the field’s traditional bases in mathematical analysis and engineering. This shift has occurred due to the sheer growth in the scale of computation, networking, and usage as well as progress in machine learning and related technologies. Resulting data-driven approaches have led to extremely powerful prediction and optimization techniques and hold great promise, even in the humanities and social sciences. However, no new technology arrives without complications. In this presentation, I will balance the opportunities provided by big data and associated A.I. approaches with a discussion of the various challenges. I’ll enumerate ten plus one categories including those which are technical (e.g., resilience and complexity), societal (e.g., difficulties in setting objective functions or understanding causation), and humanist (e.g., issues relating to free will or privacy). I’ll provide many example problems, and make suggestions on how to address some of the unanticipated consequences of big data.