Just a week after it was announced, Google's new AI ethics board is already in trouble. The board, founded to guide "responsible development of AI" at Google, would have had eight members and met four times over the course of 2019 to consider concerns about Google's AI program. Those concerns include how AI can enable authoritarian states, how AI algorithms produce disparate outcomes, whether to work on military applications of AI, and more. Of the eight people listed in Google's initial announcement, one (privacy researcher Alessandro Acquisti) has announced on Twitter that he won't serve, and two others are the subject of petitions calling for their removal -- Kay Coles James, president of the conservative Heritage Foundation think tank, and Dyan Gibbens, CEO of drone company Trumbull Unmanned. Thousands of Google employees have signed onto the petition calling for James's removal.

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water droplets, we are able to observe an order of magnitude more variety of droplet behaviours than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the discovery of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplets motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how target free search can significantly increase the rate of unpredictable observations leading to new discoveries with potential applications in formulation chemistry.

Many applications of machine learning, such as human health research, involve processing private or sensitive information. Privacy concerns may impose significant hurdles to collaboration in scenarios where there are multiple sites holding data and the goal is to estimate properties jointly across all datasets. Differentially private decentralized algorithms can provide strong privacy guarantees. However, the accuracy of the joint estimates may be poor when the datasets at each site are small. This paper proposes a new framework, Correlation Assisted Private Estimation (CAPE), for designing privacy-preserving decentralized algorithms with better accuracy guarantees in an honest-but-curious model. CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems. A tighter characterization of the functional mechanism is provided that allows CAPE to achieve the same performance as a centralized algorithm in the decentralized setting using all datasets. Empirical results on regression and neural network problems for both synthetic and real datasets show that differentially private methods can be competitive with non-private algorithms in many scenarios of interest.

The International Federation of Robotics (IFR), at a press conference here last week, announced preliminary 2018 figures for the industrial sector of the robotics industry. Last year set another record -- but just barely. It was only up 1% over 2017. No information was given about service and field robotics. It's true that 2017 was a banner year, with a 30% year-over-year gain.

There's no denying that Japan, amid a severe labor crunch and a shrinking population, will need to rely more on foreign workers in the coming years, and that's especially true for small and midsize companies. Because of language issues and cultural differences, smaller firms often struggle to integrate foreign workers. But once they overcome those hurdles, many find that the addition of foreign perspectives can lead to new opportunities. Sakae Casting Co., a small aluminum cast manufacturer in Hachioji in western Tokyo, learned this the hard way. But its experience may be an example of what other firms will have to go through in the coming years.

AI-powered loan and credit approval processes have been marred by unforeseen bias. Smart speakers have secretly turned on and recorded thousands of minutes of audio of their owners. Unfortunately, there's no industry-standard, best-practices handbook on AI ethics for companies to follow--at least not yet. Some large companies, including Microsoft and Google, are developing their own internal ethical frameworks. A number of think tanks, research organizations, and advocacy groups, meanwhile, have been developing a wide variety of ethical frameworks and guidelines for AI. Below is a brief roundup of some of the more influential models to emerge--from the Asilomar Principles to best-practice recommendations from the AI Now Institute.

We study voting games on binary issues, where voters hold an objective over the outcome of the collective decision and are allowed, before the vote takes place, to negotiate their ballots with the other participants. We analyse the voters' rational behaviour in the resulting two-phase game when ballots are aggregated via non-manipulable rules and, more specifically, quota rules. We show under what conditions undesirable equilibria can be removed and desirable ones sustained as a consequence of the pre-vote phase.

The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.

A pillar box red electric train connects Paris, Verona and Grenada via Budapest's Liberty Bridge and on to Heidelberg Castle in a 120-hectare fantasy business park dreamt up by the Chinese billionaire Ren Zhengfei. Ren, 74, a former Red Army engineer who founded the telecommunications company Huawei in 1987 and still owns a 1.14% stake, asked the Japanese architect Kengo Kuma to recreate some of Europe's most historic cities. He hoped to inspire an army of 25,000 research and development staff to challenge Apple, Google and Samsung. While its US competitors keep their research facilities on lockdown to prevent corporate espionage (oft allegedly by the Chinese), Huawei is inviting the world's media into its labs and factories in an attempt to dispel the US government's claims that the privately held company is an arm of the Chinese state and that its technology could be used to hack into western governments. US politicians allege that Huawei's forthcoming 5G mobile phone networks could be hacked by Chinese spies to eavesdrop on sensitive phone calls, gain access to counter-terrorist operations – and potentially even kill targets by crashing driverless cars.

The FBI has failed to appease concerns about the use of its facial recognition technology in criminal investigations. Multiple issues were raised three years ago after a congressional watchdog urged the bureau to improve its practices in order to meet privacy and accuracy standards. The FBI - and other US law enforcement agencies - have been using the Next Generation Identification-Interstate Photo System since 2015. It uses facial recognition software to link potential suspects to crimes from a vast database of 30 million pictures, including mugshots. The report slamming the FBI for its failure to moderate the software comes as the bureau increases its use of the technology.