Issue #159: AI Won't Save Us From Coronavirus

In the News

Artificial Intelligence Won't Save Us From Coronavirus

The hype is real, but the potential is not: Approach claims around AI and Covid-19 with skepticism.

In The News

To Preserve Jobs for Humans, Some Propose a Robot Tax

Slowly but surely, human workers are being pushed into the shallow end of the job pool. Some say a robot tax could help.

Introducing the AI Economist: Why Salesforce Researchers are Applying Machine Learning to Economics

This new AI framework is designed to simulate millions of years of economies – in parallel – to help economists, governments and others design tax policies that optimize social outcomes in the real world. We sat down with Chief Scientist and Head of Salesforce Research, Richard Socher, and his team...

Applied use cases

Doctors are using AI to triage covid-19 patients. The tools may be here to stay

He identified a promising AI-based chest x-ray system called qXR from the Mumbai-based company Qure.ai. Within weeks, Qure.ai retooled qXR to detect covid-induced pneumonia.

Useful Comparison Tables for AI, Data Science, IoT & Cloud

The main differences between Data Science and Business Intelligence Data Science Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Ethics

Urban Auscultation; or, Perceiving the Action of the Heart

46 Some players in the predictive analytics field build training sets with sound samples of well-behaved machines, while others listen across a wide array of systems, identify anomalies, and then invite human engineers to help them analyze and classify the aberrant sounds.

Fairness and AI

Sandra Wachter, a Faculty Associate at the Berkman Klein Center, Visiting Professor at Harvard Law School and Associate Professor and Senior Research Fellow in Law and Ethics of AI, Big Data, Robotics and Internet Regulation at the Oxford Internet Institute (OII) at the University of Oxford, joined...

IEEE Initiative on Ethical Design Is Making Headway

The effort was headed by the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (A/IS), with John C. Havens as Executive Director, who spoke to AI Trends for an Executive Interview.

Robotics

Where are the robots when you need them!

We also heard from Robin Murphy, Expert in Rescue Robotics & Raytheon Professor at Texas A&M University, who updated her slides on the way in which robots are being used in COVID-19 response.

Boston Dynamics’ robot dog is chipping in to help COVID-19 patients

We’ve seen Boston Dynamics’ four-legged Spot robot navigate an office, hold the door for a friend, pull a rickshaw and haul a box truck, but its most impressive feat yet might be protecting healthcare workers on the frontlines of the COVID-19 pandemic.

Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems

Testbed system: A continuous Game of Life We are interested in developing algorithms to autonomously explore a given target system which is characterized by a set of initial conditions (controllable system parameters) and a set of update rules (iteratively applied to evolve the state of the system...

Research

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

The AI Economist learns dynamic tax policies that optimize equality along with productivity in simulated economies, outperforming alternative tax systems.

Exponential Learning Rate Schedules for Deep Learning (Part 1)

Our paper gives a rigorous proof of the power of ExpLR by showing the following about the end-to-end function being computed (see Main Thm in the paper): (Informal Theorem) For commonly used values of the paremeters, every net produced by Weight Decay + Constant LR + BN + Momentum can also be...

AI Infrastructure for Everyone, Now Open Source

Working closely with cutting-edge deep learning teams across a variety of industries, a clear narrative emerged: without better infrastructure, training deep learning models at scale remains extremely difficult, as organizations move from research to production.