A former Google engineer has been sentenced to 18 months in prison after pleading guilty to stealing trade secrets before joining Uber's effort to build robotic vehicles for its ride-hailing service. The sentence handed down Tuesday by U.S. District Judge William Alsup came more than four months after former Google engineer Anthony Levandowski reached a plea agreement with the federal prosecutors who brought a criminal case against him last August. Levandowski, who helped steer Google's self-driving car project before landing at Uber, was also ordered to pay more than $850,000. Alsup had taken the unusual step of recommending the Justice Department open a criminal investigation into Levandowski while presiding over a high-profile civil trial between Uber and Waymo, a spinoff from a self-driving car project that Google began in 2007 after hiring Levandowski to be part of its team. Levandowski eventually became disillusioned with Google and left the company in early 2016 to start his own self-driving truck company, called Otto, which Uber eventually bought for $680 million. He wound up pleading guilty to one count, culminating in Tuesday's sentencing.

Technologies powered by artificial intelligence, such as chatbots and personalized shopping suggestions, have become more common in recent years, leading many consumers to embrace artificial intelligence. Such human-centered AI analyzes data through the lens of human behavior, which in turn allows companies to better understand their customer base. As this technology develops and becomes more integrated into our daily lives, the future of human-centered AI is looking brighter than ever. Below, the members of Forbes Technology Council share 13 exciting future uses of human-centered AI to keep an eye on. Because we have the opportunity to teach and train the AI of the future, we have a unique opportunity to define AI for all.

Machine learning best practice statements have proliferated, but there is a lack of consensus on what the standards should be. For fairness standards in particular, there is little guidance on how fairness might be achieved in practice. Specifically, fairness in errors (both false negatives and false positives) can pose a problem of how to set weights, how to make unavoidable tradeoffs, and how to judge models that present different kinds of errors across racial groups. This paper considers the consequences of having higher rates of false positives for one racial group and higher rates of false negatives for another racial group. The paper examines how different errors in justice settings can present problems for machine learning applications, the limits of computation for resolving tradeoffs, and how solutions might have to be crafted through courageous conversations with leadership, line workers, stakeholders, and impacted communities.

The Council of Europe is working on a future legal framework to regulate the use of artificial intelligence (AI) across all 47 member states. The Council's Ad hoc Committee on Artificial Intelligence (CAHAI) held a three-day meeting on 6-8 July attended by around 150 international experts. The purpose of the meeting was to draw up "concrete proposals on the feasibility study of a future legal framework on artificial intelligence based on human rights, democracy and the rule of law," according to the Council. Representatives from all 47 member states, including Russia, attended the online meeting alongside delegates from'observer states' (USA, Canada, Japan, Mexico, the Vatican and Israel) and AI experts drawn from civil society, academia, and business. Other international organisations such as the EU, OECD and the UN will also contribute to CAHAI's work on potential AI regulation.

"I personally think that no matter which approach you use, you lose," said Emily Wenger, a Ph.D. student who helped create Fawkes. "You can have these technological solutions, but it's a cat-and-mouse game. And you can have a law, but there will always be illegal actors." Ms. Wenger thinks "a two-prong approach" is needed, where individuals have technological tools and a privacy law to protect themselves. Elizabeth Joh, a law professor at the University of California, Davis, has written about tools like Fawkes as "privacy protests," where individuals want to thwart surveillance but not for criminal reasons.

Every time Congress holds a hearing about Silicon Valley companies, people mock the legislators for being out of their depth. Last week's effort by the antitrust subcommittee of the House Judiciary Committee was no exception. "The technological ignorance demonstrated by our elected officials ... was truly stunning," Shelly Palmer, CEO at the Palmer Group, a tech strategy advisory group, told USA Today. "People who are this clueless about the economic forces shaping our world should not be tasked with leading us into the age of AI," he said. "The data elite are playing a different game with a different set of rules. Apparently, Congress can't even find the ballpark."

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.

A framework is proposed that seeks to identify and establish a set of robust autonomous levels articulating the realm of Artificial Intelligence and Legal Reasoning (AILR). Doing so provides a sound and parsimonious basis for being able to assess progress in the application of AI to the law, and can be utilized by scholars in academic pursuits of AI legal reasoning, along with being used by law practitioners and legal professionals in gauging how advances in AI are aiding the practice of law and the realization of aspirational versus achieved results. A set of seven levels of autonomy for AI and Legal Reasoning are meticulously proffered and mindfully discussed.

Parking is an inevitable issue in the fast-growing developing countries. Increasing number of vehicles require more and more urban land to be allocated for parking. However, a little attention has been conferred to the parking issues in developing countries like India. This study proposes a model for analysing the influence of car users' socioeconomic and travel characteristics on parking duration. Specifically, artificial neural networks (ANNs) is deployed to capture the interrelationship between driver characteristics and parking duration. ANNs are highly efficient in learning and recognizing connections between parameters for best prediction of an outcome. Since, utility of ANNs has been critically limited due to its Black Box nature, the study involves the use of Garson algorithm and Local interpretable model-agnostic explanations (LIME) for model interpretations. LIME shows the prediction for any classification, by approximating it locally with the developed interpretable model. This study is based on microdata collected on-site through interview surveys considering two land-uses: office-business and market/shopping. Results revealed the higher probability of prediction through LIME and therefore, the methodology can be adopted ubiquitously. Further, the policy implications are discussed based on the results for both land-uses. This unique study could lead to enhanced parking policy and management to achieve the sustainability goals.

Chinese artificial intelligence company Shanghai Zhizhen Intelligent Network Technology Co., Ltd., also known as Xiao-i, has filed a lawsuit against Apple Inc, alleging it has infringed on its patents. The company is calling for 10 billion yuan ($1.43 billion) in damages and demands that Apple cease "manufacturing, using, promising to sell, selling, and importing" products that infringe on the patent, it said in a social media post. Xiao-i argued that Apple's voice-recognition technology Siri infringes on a patent that it applied for in 2004 and was granted in 2009. Apple did not respond to a request for comment. Reuters was not immediately available to find a copy of the court filing.