When the Defense Department launched Project Maven, an effort to remake American military technology through artificial intelligence, it leaned on a team of about a dozen engineers working at Google. Many of them, according to two people familiar with the arrangement, were Chinese citizens. The Pentagon was fine with that, they said, even amid rising tensions between Washington and Beijing. Classified data was not involved, the Pentagon reasoned, and the American military needed the most qualified minds for the job. The Trump administration is now moving to limit Chinese access to advanced American research, as relations between the United States and China reach their worst point in decades.

To some in the tech industry, facial recognition increasingly looks like toxic technology. IBM is the latest company to declare facial recognition too troubling. CEO Arvind Krishna told members of Congress Monday that IBM would no longer offer the technology, citing the potential for racial profiling and human rights abuse. In a letter, Krishna also called for police reforms aimed at increasing scrutiny and accountability for misconduct. "We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies," wrote Krishna, the first non-white CEO in the company's 109-year history.

India is gearing up for the next generation computers. This move also reinforces the Digital India and Make in India vision of the Prime Minister Narendra Modi. The Government of India (GoI) has launched the National Supercomputing Mission (NSM) to connect the national academia with research institutes. With its peak computing power and high memory compute nodes supercomputers are expected to open out new avenues of scientific research and innovation. The first phase of NSM has already begun.

Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable decision-making in sensitive and uncertain environments. In this work, we propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects under the potential outcome framework. For completely randomized or stratified randomized experiments with perfect compliance, the intervals have guaranteed average coverage in finite samples regardless of the unknown data generating mechanism. For randomized experiments with ignorable compliance and general observational studies obeying the strong ignorability assumption, the intervals satisfy a doubly robust property which states the following: the average coverage is approximately controlled if either the propensity score or the conditional quantiles of potential outcomes can be estimated accurately. Numerical studies on both synthetic and real datasets empirically demonstrate that existing methods suffer from a significant coverage deficit even in simple models. In contrast, our methods achieve the desired coverage with reasonably short intervals.

This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National Science Foundation. It took place in Pittsburgh Pennsylvania on October 24 and 25, 2019 and was sponsored by National Science Foundation Grant Number IIS-1934222. Any opinions, findings and conclusions or future directions expressed in this document are those of the authors and do not necessarily reflect the views of the National Science Foundation. The 27 participants presented their individual research interests and their personal research goals. In the breakout sessions that followed, the participants defined the main research areas within the domain of intelligent agents and they discussed the major future directions that the research in each area of this domain should take.

The implementation of efficient demand response (DR) programs for household electricity consumption would benefit from data-driven methods capable of simulating the impact of different tariffs schemes. This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with exogenous weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. First, a large set of consumers is gathered into clusters according to their consumption behavior and price-responsiveness. The clustering method is based on a causality model that measures the effect of a specific tariff on the consumption level. Then, daily electrical energy consumption profiles are generated for each cluster with CVAE. This non-parametric approach is compared to a semi-parametric data generator based on generalized additive models and that uses prior knowledge of energy consumption. Experiments in a publicly available data set show that, the proposed method presents comparable performance to the semi-parametric one when it comes to generating the average value of the original data. The main contribution from this new method is the capacity to reproduce rebound and side effects in the generated consumption profiles. Indeed, the application of a special electricity tariff over a time window may also affect consumption outside this time window. Another contribution is that the clustering approach segments consumers according to their daily consumption profile and elasticity to tariff changes. These two results combined are very relevant for an ex-ante testing of future DR policies by system operators, retailers and energy regulators.

Facial recognition software is nothing if not fallible. In 2019, the National Institute of Standards and Technology demonstrated this with a study on A.I. systems used by police departments to identify alleged criminals. The study found that these algorithms falsely identified Asian and black faces 10 to 100 times more often than Caucasian faces. It is these sorts of findings that have led activists to call for bans on facial recognition technology and for technology companies not to develop such products. That movement scored a win on Monday, when IBM CEO Arvind Krishna announced in a letter to Congress that the company will no longer develop, research, or sell facial recognition technology.

U. S. Representatives Stephen F. Lynch and Ayanna Pressley along with a group of other House Democrats have launched an investigation into the Trump administration's surveillance of people protesting last month's killing of an African-American man by a white Minneapolis police officer. "We write with grave concern about the use of Department of Homeland Security (DHS) resources--including drones and armed uniformed officers--to surveil and intimidate peaceful protesters who were exercising their First Amendment rights to protest the murder of George Floyd by the Minneapolis Police Department," the members of the Committee of Oversight and Reform wrote. Lynch, writing as chairman of the Subcommittee on National Security, and Pressley, a member of the Subcommittee on Civil Rights and Civil Liberties, joined Committee Chairwoman Carolyn B. Maloney of New York; Jamie Raskin of Maryland; and Alexandria Ocasio-Cortez of New York in sending a letter to the Department of Homeland Security demanding the Trump Administration explain its use of Customs and Border Patrol resources to conduct surveillance of people protesting George Floyd's killing. CBP admitted to flying a surveillance drone, commonly known as a "Predator B," over protests in Minneapolis on May 29. The drone reportedly was far outside the bounds of CBP's jurisdiction.

A bipartisan cadre of tech-focused legislators in the House and Senate have introduced legislation that would direct the federal government to develop a national cloud computing infrastructure for artificial intelligence research.Introduced by Sens. Rob Portman, R-Ohio, and Martin Heinrich, D-N.M., Thursday, the National Cloud Computing Task Force Act would convene a mix of technical experts across academic, industry and government. The group would develop a nuanced roadmap for how the nation should build, deploy, govern and sustain a national research cloud for AI."With China focused on toppling the United States' leadership in AI, we need to redouble our efforts with a sustained commitment to the best and brightest by developing a national research cloud to ensure our technical researchers get the tools they need to succeed," Portman said in a statement. "By democratizing access to computing power we ensure that any American with computer science talent can pursue their good ideas."The A report submitted to Congress by the National Security Commission on Artificial Intelligence outlined how quickly China is closing in on the United States' tech research hold.

When an apple begins to rot it creates a chemical called ethylene. If that apple happens to be in a barrel with a bunch of other apples, and the rotting causes its skin to break, the ethylene will immediately cause the other apples to start rotting. That's why the proverb "one bad apple spoils the bunch" is meant as a warning. If you find one bad apple, all the apples around it are already rotting. Obviously, the smartest thing to do is to locate, isolate, and remove bad apples before they can poison others.