SAN FRANCISCO - San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves. Efforts to restrict its use are getting pushback from law enforcement groups and the tech industry, though it's far from a united front. Microsoft, while opposed to an outright ban, has urged lawmakers to set limits on the technology, warning that leaving it unchecked could enable an oppressive dystopia reminiscent of George Orwell's novel "1984."

In this Oct. 31, 2018, file photo, a man, who declined to be identified, has his face painted to represent efforts to defeat facial recognition during a protest at Amazon headquarters over the company's facial recognition system, "Rekognition," in Seattle. San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies. SAN FRANCISCO – San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves.

Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. It used to take a lot of time and expertise to realistically falsify videos. For decades, authentic-looking video renderings were only seen in big-budget sci-fi movies films like "Star Wars." However, thanks to the rise in artificial intelligence, doctoring footage has become more accessible than ever, which researchers say poses a threat to national security.

This blogpost is an updated round up of the various sets of ethical principles of robotics and AI that have been proposed to date, ordered by date of first publication. I previously listed principles published before December 2017 here; this blogpost appends those principles drafted since January 2018 (plus one in October 2017 I had missed). The principles are listed here (in full or abridged) with links, notes and references but without critique. If there any (prominent) ones I've missed please let me know. I have included these to explicitly acknowledge, firstly, that Asimov undoubtedly established the principle that robots (and by extension AIs) should be governed by principles, and secondly that many subsequent principles have been drafted as a direct response.

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Therefore, reliable and accurate closure models for unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Benard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate.

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter's selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real-world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place. We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in Massive Open Online Courses (MOOCs).

Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population health survey (n=34,000) was used to identify the spatial distribution of health and wellbeing outcomes, including general health and social capital. For each outcome, the most and least desirable locations formed two domains. Streetscape design was sampled using around 80,000 Google Street View images per domain. Generative adversarial networks translated these images from one domain to the other, preserving the main structure of the input image, but transforming the `style' from locations where self-reported health was bad to locations where it was good. These translations indicate that areas in Melbourne with good general health are characterised by sufficient green space and compactness of the urban environment, whilst streetscape imagery related to high social capital contained more and wider footpaths, fewer fences and more grass. Beyond identifying relationships, the method is a first step towards computer-generated design interventions that have the potential to improve population health and wellbeing.

BEIJING - China's intensified tariff war with the Trump administration is threatening Beijing's ambition to transform itself into the dominant player in global technology. The United States is a vital customer and source of technology for Chinese makers of electronics, medical equipment and other high-tech exports -- industries that the ruling Communist Party sees as the heart of its economic future. Beijing managed to keep Chinese economic growth steady in the most recent quarter despite a drop in exports to the United States. It did so by boosting government spending and bank lending. But China's technology exporters suffered huge sales drops of up to 40 percent, which ate into profits that pay for technology research.

Imagine you're hiking through the woods near a border. Suddenly, you hear a mechanical buzzing, like a gigantic bee. Two quadcopters have spotted you and swoop in for a closer look. They send the signals to a central server, which triangulates your exact location and feeds it back to the drones. Cameras and other sensors on the machines recognize you as human and try to ascertain your intentions.

If there's one thing we've learned in recent years, it's that humans aren't great at predicting the consequences of technology. After all, social media platforms, which began as a way for friends to connect online, are today being used to radicalize terrorists and potentially swing presidential elections. Imagine, then, the chaos that could ensue with new technologies that don't even pretend to be friendly. The advent of lethal autonomous weapons--"killer robots" to detractors--has many analysts alarmed. Equipped with artificial intelligence, some of these weapons could, without proximate human control, select and eliminate targets with a speed and efficiency soldiers can't possibly match.