After a busy start to the year, regulatory and policy developments related to Artificial Intelligence and Automated Systems ("AI") have continued apace in the second quarter of 2021. Unlike the comprehensive regulatory framework proposed by the European Union ("EU") in April 2021,[1] more specific regulatory guidelines in the U.S. are still being proposed on an agency-by-agency basis. President Biden has so far sought to amplify the emerging U.S. AI strategy by continuing to grow the national research and monitoring infrastructure kick-started by the 2019 Trump Executive Order[2] and remain focused on innovation and competition with China in transformative innovations like AI, superconductors, and robotics. Most recently, the U.S. Innovation and Competition Act of 2021--sweeping, bipartisan R&D and science-policy legislation--moved rapidly through the Senate. While there has been no major shift away from the previous "hands off" regulatory approach at the federal level, we are closely monitoring efforts by the federal government and enforcers such as the FTC to make fairness and transparency central tenets of U.S. AI policy.

Read 3 articles daily and stand to win ST rewards, including the ST News Tablet worth $398. Artificial intelligence (AI) and concerns about its potential impact on humanity have been with us for more than half a century. The term was coined in 1956 at a Dartmouth College symposium. Please subscribe or log in to continue reading the full article. Join ST's Telegram channel here and get the latest breaking news delivered to you.

This piece was updated Aug. 11 to add information from Google. Recently, the Biden administration and a host of allies called out China for its massive Microsoft Exchange hack (among others), and threatened strengthened cyber defense measures and continued exposure of the PRC's malicious cyber activity. But despite a lot of hard talk about securing America's cyber defenses, action is wanting, and the government has failed to address a glaring boon to the PRC's cyber capabilities: our own companies' AI research centers in China. Housing the AI research labs of America's cutting-edge tech companies in authoritarian China was never a good idea. But given that the Chinese government uses foreign tech companies to help find and exploit security vulnerabilities, and that it is claiming ever more control over tech companies' operations and data, it looks more objectionable than ever. AI is an increasingly crucial element of cyber security and hacking, and Xi Jinping's China has demonstrated time and time again that China's high-tech sector serves the CCP, which sees AI technology in particular as a core tool of its future autocratic rule.

Israel's tech and innovation ecosystem has had a monumental year so far in 2021, breaking funding records and yielding 10 new unicorns – private companies valued at $1 billion or more -- just in the first three months of the year, more than any country in Europe. Israeli high-tech activity on public markets also increased significantly this year, a trend reflected in the number of IPOs, SPAC (special-purpose acquisition company) transactions, and follow-on offerings. "Year in and year out, Tel Aviv's startup community has proven that it can achieve more than whole countries within its 52km2, thanks to investment in world-class research facilities, robust government support, and an ever-reliable influx of investment," writes WIRED UK Contributor Allyssia Alleyne in a new post this week highlighting 10 "hottest startups from Tel Aviv as part of the UK edition of the American tech publication's annual round-up (except in 2020) of Europe's 100 hottest startups. They include startups and companies from London, Amsterdam, Stockholm, Barcelona, Dublin, Helsinki, Berlin, Paris, and Lisbon. These 100 companies "are a cohort like no other," says Greg Williams, the deputy global editorial director of WIRED. "They survived an unprecedented year, embodying what entrepreneurial spirit is all about." The companies, featured in the September/October issue on newsstands this month, are not necessarily "the largest, best-known or most-funded," but they "are generating buzz" and they are organizations "people are talking about and inspired by," added Williams. The Tel Aviv entry is a mix of established companies with prominent backers, high-flying unicorns, and determined startups. Many operate in the deep tech sector. "Tel Aviv has long been known as a place where founders have built innovative companies in verticals such as fintech and cybersecurity.

The World Health Organization issued its first global report on artificial intelligence in late June, highlighting concerns of algorithmic bias in health care applications of AI. It accompanies a growing number of news stories exposing AI's shortfalls. AI has come of age through the alchemy of cheap parallel (cloud) computing combined with the availability of big data and better algorithms. Problems that seemed unconquerable a few years ago are being solved, at times with startling gains -- think instant language translation capabilities, self-driving cars, and human-like robots. AI's arrival to health care, however, has been markedly slower.

Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable$^3$-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable$^3$-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable$^3$-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable$^3$-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.

Target detection systems identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. Deep Convolutional Neural Network (DCNN) classifiers have proven to be successful tools for computer vision applications. However,prior research confirms that even state of the art classifier models are susceptible to adversarial attacks. In this paper, we show how to generate adversarial infrared images by adding small perturbations to the targets region to deceive a DCNN-based target detector at remarkable levels. We demonstrate significant progress in developing visually imperceptible adversarial infrared images where the targets are visually recognizable by an expert but a DCNN-based target detector cannot detect the targets in the image.

The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development. Solving the technology will create social change which parallels the invention of the automobile itself. Today's autonomous driving technology is laudable, though rooted in decisions made a decade ago. We argue that a rethink is required, reconsidering the autonomous vehicle (AV) problem in the light of the body of knowledge that has been gained since the DARPA challenges which seeded the industry. What does AV2.0 look like? We present an alternative vision: a recipe for driving with machine learning, and grand challenges for research in driving.

Kevin Yager (front) and Masafumi Fukuto at Brookhaven Lab's National Synchrotron Light Source II, where they've been implementing a method of autonomous experimentation. In the popular view of traditional science, scientists are in the lab hovering over their experiments, micromanaging every little detail. For example, they may iteratively test a wide variety of material compositions, synthesis and processing protocols, and environmental conditions to see how these parameters influence material properties. In each iteration, they analyze the collected data, looking for patterns and relying on their scientific knowledge and intuition to select useful follow-on measurements. This manual approach consumes limited instrument time and the attention of human experts who could otherwise focus on the bigger picture.

A new material inspired by chain mail used in armour worn by medieval knights could be used to create spacesuits of the future, according to its developers. The material can hold over 30 times its own weight while also being flexible, and was created by experts from the California Institute of Technology in Pasadena. As well as helping astronauts walk on Mars in the future, the 3D printed material may also cover the spacecraft - shielding it from meteorites and other deep space perils. More down to Earth, it offers extra protection for sports men and women due to its ability to turn from soft and bendable to rigid and back again. The hi-tech garment also opens the door to a smart exoskeleton that enables paralysed people to walk again, or could provide armour for modern soldiers.