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A Google service that automatically labels images produced starkly different results depending on skin tone on a given image. The company fixed the issue, but the problem is likely much broader. In the fight against the novel coronavirus, many countries ordered that citizens have their temperature checked at train stations or airports. The device needed in such situations, a hand-held thermometer, has risen from a specialist item to a common sight. A branch of Artificial Intelligence known as "computer vision" focuses on automated image labeling.

Artificial intelligence (AI) algorithms are generally hungry for data, a trend which is accelerating. A new breed of AI approaches, called lifelong learning machines, are being designed to pull data continually and indefinitely. But this is already happening with other AI approaches, albeit with human intervention. A steady stream of data is the fuel for coveted results. But, with the ever-increasing importance of data, the stakes of data bias are growing ever higher.

MOSCOW, April 14 (RAPSI) – The State Duma has approved in the second and third reading a bill envisaging that since July 1 an experiment concerning the introduction of a legal framework governing implementation of artificial intelligence (AI) technologies in Moscow for a 5-year period is to be launched, according to a statement on the official website of the lower house of Russia's parliament. The AI technologies include computer vision, natural language processing, speech recognition and synthesis. The experimental legal regime is to govern only those participating in the experiment: legal entities and individual entrepreneurs listed on a respective register on the basis of their applications. The complex of technologies to be used includes such components as systems and means for processing of information and software. The document also regulates the issues relating to the storage, use, and destruction of anonymized personal data.

I welcome you all to the Cyber Society of Today--a wondrous place where'what' is a possibility, 'how' is full of options, and'when' is a mystery. Despite what you may think, this is a real place. It is here, it is now, and most certainly you are in it. So, buckle up, be open-minded, and enjoy the ride--the doors are locked, and there is no place to hide. In this podcast, Sean and I are following up on an exciting story that we started during one of the panels we hosted at the RSA Conference in San Francisco a few weeks ago.

This article is a guest post on NoCamels and has been contributed by a third party. NoCamels assumes no responsibility for the content, including facts, visuals, and opinions presented by the author(s). Ryan E. Long is a non-residential fellow of Stanford Law School's Center for Internet and Society and Vice-Chair of the CA Lawyers Association, IP Licensing Interest Group. In addition, he has written for or been interviewed by publications such as The Nordic Blockchain Association, El Pais, Cognitive Times, and Digital Trends about new tech subjects such as artificial intelligence, blockchain and "deep fake" videos. Currently, he is an adjunct professor of media law at Pepperdine Law School in Malibu, California.

Deep Tech is a new subscriber-only podcast that brings alive the people and ideas in our print magazine. Episodes are released every two weeks. We're making the first four installments, built around our 10 Breakthrough Technologies issue, available for free. Every two weeks, give or take, SpaceX puts another 60 Starlink communications satellites into low Earth orbit. Its initial goal is to launch 12,000 of these small mass-produced satellites--six times the number of operating satellites currently in orbit--with another 42,000 possibly to follow. Other companies such as Amazon, Telesat, and Planet are planning their own satellite "mega-constellations." The result could be a welter of new space-based services, from Internet connectivity to continuous mapping. But there's also growing attention to the potential downsides, including an increased risk of collisions that could end up littering low Earth orbit with dangerous debris and rendering it unusable. In this episode of Deep Tech, we hear from OneWeb founder Greg Wyler and science writer and former astrophysicist Ramin Skibba about efforts to mitigate the hazards.

We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.

Understanding cross-cultural differences is an important application of natural language understanding. This problem is difficult due to the relativism between cultures. We present a computational approach to learn cultural models that encode the general opinions and values of cultures from multi-lingual Wikipedia. Specifically, we assume a language is a symbol of a culture and different languages represent different cultures. Our model can automatically identify statements that potentially reflect cultural differences. Experiments on English and Chinese languages show that on a held out set of diverse topics, including marriage, gun control, democracy, etc., our model achieves high correlation with human judgements regarding within-culture values and cultural differences.

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