In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the "Concerned Home Office Associates." While it's not unusual for journalists to receive anonymous tips, they don't usually come with their own slickly produced videos. The employees said they were "past their breaking point," with Everseen, a small artificial intelligence firm based in Cork, Ireland, whose technology Walmart began using in 2017. Walmart uses Everseen in thousands of stores to prevent shoplifting at registers and self-checkout kiosks.

Google warned on Thursday that the EU's definition of artificial intelligence was too broad and that Brussels must refrain from over-regulating a crucial technology. The search and advertising giant made its argument in feedback to the European Commission, the EU's powerful regulator that has reached out to big tech as it draws up ways to set new rules for AI. The EU has not decided yet on how to regulate AI, but is putting most of its focus on what it calls "high risk" sectors, such as healthcare and transport. It's plans, to be spearheaded by EU commissioners Margrethe Vestager and Thierry Breton, are not expected until the end of the year. "A clear and widely understood definition of AI will be a critical foundational element for an effective AI regulatory framework," the company said in its 45-page submission.

Clearview AI is about to deal with more pushback beyond corporate objections and occasional bans. The American Civil Liberties Union has sued Clearview AI for allegedly violating Illinois' Biometric Information Privacy Act with its combination of facial recognition and internet data scraping. The ACLU claimed that the real-time identification technology infringed privacy rights by collecting faceprints from state residents without notifying them or obtaining consent. This facial data harvesting is bad for everyone, but it's particularly harmful to "Latinas and survivors," according to Mujeres Latinas en Acción's Linda Xóchitl Tortolero. She argued that it enables stalkers, abusers, "predatory companies" and immigration agents to illegally track and target people.

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.

We propose an adaptation of the Random Forest algorithm to estimate the conditional distribution of a possibly multivariate response. We suggest a new splitting criterion based on the MMD two-sample test, which is suitable for detecting heterogeneity in multivariate distributions. The weights provided by the forest can be conveniently used as an input to other methods in order to locally solve various learning problems. The code is available as \texttt{R}-package \texttt{drf}.

But it won't replace lawyers, yet.

Having been collected for its primary purpose in patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics. However, the potential for secondary usage of OHD continues to be hampered by the fiercely private nature of patient-related data. Generative Adversarial Networks (GAN) have Generative Adversarial Networks (GAN) have recently emerged as a groundbreaking approach to efficiently learn generative models that produce realistic Synthetic Data (SD). However, the application of GAN to OHD seems to have been lagging in comparison to other fields. We conducted a review of GAN algorithms for OHD in the published literature, and report our findings here.

The statistical imprecision of quarterly gross domestic product (GDP) estimates, along with the fact that the first estimate is available with a delay of nearly a month, pose a significant challenge to policy makers, market participants, and other observers with an interest in monitoring the state of the economy in real time; see, e.g., Ghysels, Horan, and Moench (2018) for a recent discussion of macroeconomic data revision and publication delays. A term originated in meteorology, nowcasting pertains to the prediction of the present and very near future. Nowcasting is intrinsically a mixed frequency data problem as the object of interest is a low-frequency data series (e.g., quarterly GDP), whereas the real-time information (e.g., daily, weekly, or monthly) can be used to update the state, or to put it differently, to nowcast the low-frequency series of interest. Traditional methods used for nowcasting rely on dynamic factor models that treat the underlying low frequency series of interest as a latent process with high frequency data noisy observations. These models are naturally cast in a state-space form and inference can be performed using likelihood-based methods and Kalman filtering techniques; see Bańbura, Giannone, Modugno, and Reichlin (2013) for a recent survey.

Street lamps constitute the densest electrically operated public infrastructure in urban areas. Their changeover to energy-friendly LED light quickly amortizes and is increasingly leveraged for smart city projects, where LED street lamps double, for example, as wireless networking or sensor infrastructure. We make the case for a new paradigm called SLaaP--street lamps as a platform. SLaaP is proposed as an open, enabling platform, fostering innovative citywide services for the full range of stakeholders and end users--seamlessly extending from everyday use to emergency response. In this article, we first describe the role and potential of street lamps and introduce one novel base service as a running example. We then discuss citywide infrastructure design and operation, followed by addressing the major layers of a SLaaP infrastructure: hardware, distributed software platform, base services, value-added services and applications for users and'things.' Finally, we discuss the crucial roles and participation of major stakeholders: citizens, city, government, and economy. Recent years have seen the emergence of smart street lamps, with very different meanings of'smart'--sometimes related to the original purpose as with usage-dependent lighting, but mostly as add-on capabilities like urban sensing, monitoring, digital signage, WiFi access, or e-vehicle charging.a The future holds even more use cases: for example, after a first wave of 5G mobile network rollouts from 2020 onward, a second wave shall apply mm-wave frequencies for which densely deployed light poles can be appropriate'cell towers.'

"This is a bill being sold as a privacy bill, but it's a wolf in sheep's clothing," Matt Cagle, an attorney for the American Civil Liberties Union of Northern California, said in an interview. The ACLU, Electronic Frontier Foundation and other civil liberties groups held a virtual rally Thursday night to rail against the bill, calling it vaguely worded and potentially dangerous for low-income communities hit hard by the coronavirus. Their remarks were the latest shots fired from a campaign to halt the legislation. The bill's fate in California--which has pushed for more aggressive privacy protections in recent years--could foreshadow how a potentially huge market for facial recognition technology is regulated by other states. The bill calls for companies and agencies that use facial recognition tools in areas accessible to the public to "provide a conspicuous and contextually appropriate notice" that faces may get scanned.