Information Engineering Distinguished Lecture Series: Recent Episodes
Cambridge University
This Lecture Series invites world-leading scientists to introduce today’s high-impact research areas.
Technical debt is incurred when complex systems are rapidly deployed without due thought as to how they will be maintained. Intellectual debt is incurred when complex systems are rapidly deployed without due thought to how they’ll be explained. Both problems are pervasive in the design and deployment of large scale algorithmic decision making engines.
In this talk, we’ll review the origin of the problem, and propose a roadmap for obtaining solutions. It’s a journey that will require collaboration between industry, academia, third sector, and government.
This talk is part of the Information Engineering Distinguished Lecture Series series.
This talk examines speech recognition issue, comparing and contrasting them to what is known about human perception. With recent advances in Deep Learning, it is suggested that it is now achievable for Word Error Rates to be comparable to human listeners. This talk specifically highlights issues with accented, noisy speech, different speaking styles, multilingual speech recognition and more. And through demonstrations in comparison to human perception, there is still significant work in speech recognition research from the community.
I will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups, including house-hunting honeybees and schooling fish, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment.
This talk focuses on active ML that close the loop on machine learning, sensing and data collection, and human labeling. Standard (passive) machine learning involves designing a classification rule based on a randomly selected training dataset. Active machine learning algorithms automatically and adaptively select the most informative data for labeling so that human time is not wasted labeling irrelevant or trivial examples. The aim is to make ML as efficient and robust as possible, with a minimal amount of human supervision and assistance. This talk describes ongoing theoretical and experimental work in several areas of active learning
Convex optimization has emerged as useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. After an overview of the mathematics, algorithms, and software frameworks for convex optimization, we turn to common themes that arise across applications, such as sparsity and relaxation. We describe recent work on real-time embedded convex optimization, in which small problems are solved repeatedly in millisecond or microsecond time frames, and large-scale distributed convex optimization, in which many solvers are coordinated to solve enormous problems.