The Federal Trade Commission has sued to block Nvidia's acquisition of Arm, the semiconductor design firm, saying that the blockbuster deal would unfairly stifle competition. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "The FTC is suing to block the largest semiconductor chip merger in history to prevent a chip conglomerate from stifling the innovation pipeline for next-generation technologies," Holly Vedova, director of the FTC's competition bureau, said in a statement. "Tomorrow's technologies depend on preserving today's competitive, cutting-edge chip markets. This proposed deal would distort Arm's incentives in chip markets and allow the combined firm to unfairly undermine Nvidia's rivals."

The parents of a 15-year-old accused of killing four students and wounding seven other people at a Michigan high school have been charged with four counts of involuntary manslaughter, according to court documents. The latest: Lawyers for James and Jennifer Crumbley told the Detroit News they are "returning to the area to be arraigned," after law enforcement officials announced a search for the Crumbleys had been initiated.

An unprecedented number of first-time investigators have secured viewing time on NASA's Hubble Space Telescope in the years since the agency overhauled the application process to reduce systemic biases. In 2018, NASA changed the way it evaluates requests for observing time on Hubble by introducing a'double-blind' system, in which neither the applicants nor the reviewers assessing their proposals know each other's identities. All the agency's other telescopes followed suit the next year. The move was intended to cut discrimination on the basis of gender and other factors, including bias against scientists who are at small research institutions, or who haven't received NASA grants before. Data from the Space Telescope Science Institute (STScI) in Baltimore, Maryland, which manages Hubble, show that since the change was introduced, more first-time principal investigators have been securing viewing time on Hubble. How do mathematicians come up with new theories?

This week, the Partnership on AI (PAI), a nonprofit committed to responsible AI use, released a paper addressing how technology -- particularly AI -- can accentuate various forms of biases. While most proposals to mitigate algorithmic discrimination require the collection of data on so-called sensitive attributes -- which usually include things like race, gender, sexuality, and nationality -- the coauthors of the PAI report argue that these efforts can actually cause harm to marginalized people and groups. Rather than trying to overcome historical patterns of discrimination and social inequity with more data and "clever algorithms," they say, the value assumptions and trade-offs associated with the use of demographic data must be acknowledged. "Harmful biases have been found in algorithmic decision-making systems in contexts such as health care, hiring, criminal justice, and education, prompting increasing social concern regarding the impact these systems are having on the wellbeing ...

By Paresh Dave Timnit Gebru, the computer scientist whose disputed exit from Google's artificial intelligence research team prompted debate across the tech industry about diversity and censorship, said on Thursday she has launched a small lab to continue her work freely. The Distributed AI Research Institute has raised $3.7 million from foundations and aims to critically study services from big tech companies as well as propose AI-based solutions to issues such as food insecurity and climate change, Gebru said. It joins several non-governmental projects such as the Algorithmic Justice League that are advancing ethical use of AI. Critics worry that without proper safeguards systems including for facial recognition and credit scoring could lead to mass surveillance and racial discrimination. Gebru has hired a fellow based in South Africa and expects to add other researchers next year.

Blockchain, machine learning, and artificial intelligence are revolutionizing the legal industry. Digital transformation in the legal industry has been slow, but as benefits are becoming more obvious, legal firms are adopting digital technologies to improve their services. Since privacy is of utmost importance in this field, the use of technology has not yet extensively pervaded, due to gradual and cautious implementation. However, digitization, although initially slow to catch on in the legal industry, is seeing a steady increase in its adoption. The digital transformation in the legal industry is already proving to be highly beneficial and is promising even greater benefits once fully realized.

Facial-recognition technology (FRT) is mainly deployed in the cybersecurity and surveillance sectors. It has long been in use at airport borders and on smartphones, and as a tool to help police identify criminals. But it is now creeping further into private and public spaces. From Quito to Nairobi, Moscow to Detroit, hundreds of municipalities have installed cameras equipped with FRT, sometimes promising to feed data to central command centres as part of'safe city' or'smart city' solutions to crime. The COVID-19 pandemic might accelerate their spread.

US regulators filed a lawsuit Thursday to block the $40-billion merger of graphics chip star Nvidia with mobile chip technology powerhouse Arm Ltd, fearing it would undermine competition. The move comes as US President Joe Biden strives to ramp up domestic chip production to ease American industry's reliance on imports. "The proposed vertical deal would give one of the largest chip companies control over the computing technology and designs that rival firms rely on to develop their own competing chips," the Federal Trade Commission said in a release, calling chips "critical infrastructure." The world faces a global shortage of semiconductors, choking production of a wide range of products including automobiles, sending new and used car prices surging. The FTC echoed concerns expressed about the merger by regulators in the United Kingdom, who recently ordered an in-depth probe of the take-over.

China is pursuing what its leaders call a "first-mover advantage" in artificial intelligence (AI), facilitated by a state-backed plan to achieve breakthroughs by modeling human cognition. While not unique to China, the research warrants concern since it raises the bar on AI safety, leverages ongoing U.S. research, and exposes U.S. deficiencies in tracking foreign technological threats. The article begins with a review of the statutory basis for China's AI-brain program, examines related scholarship, and analyzes the supporting science. China's advantages are discussed along with the implications of this brain-inspired research. Recommendations to address our concerns are offered in conclusion. All claims are based on primary Chinese data.1 Analysts familiar with China's technical development programs understand that in China things happen by plan, and that China is not reticent about announcing these plans. On July 8, 2017 China's State Council released its "New Generation AI Development Plan"2 to advance Chinese artificial intelligence in three stages, at the end of which, in 2030, China would lead the world in AI theory, technology, and applications.3

Good training data is a prerequisite to develop useful Machine Learning applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We explore the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network. As such, neuronal excitation can be used to generate synthetic stimuli. The stimuli data is used to train new classification models. Furthermore, we extend this framework to inhibit representations that are associated with specific individuals. We use sleep monitoring data from both an open and a large closed clinical study, and Electroencephalogram sleep stage classification data, to evaluate whether (1) end-users can create and successfully use customized classification models, and (2) the identity of participants in the study is protected. Extensive comparative empirical investigation shows that different algorithms trained on the stimuli are able to generalize successfully on the same task as the original model. Architectural and algorithmic similarity between new and original models play an important role in performance. For similar architectures, the performance is close to that of using the original data (e.g., Accuracy difference of 0.56%-3.82%, Kappa coefficient difference of 0.02-0.08). Further experiments show that the stimuli can provide state-ofthe-art resilience against adversarial association and membership inference attacks.