The AI Now Institute's Executive Director, Andrea Nill Sánchez, today testified before the European Parliament LIBE Committee Public Hearing on "Artificial Intelligence in Criminal Law and Its Use by the Police and Judicial Authorities in Criminal Matters." Her message was simple: "Predictive policing systems will never be safe… until the criminal justice system they're built on are reformed." Sanchez argued that predictive policing systems are built with "dirty data" compiled over decades of police misconduct, and that there's no current method by which this can be resolved with technology. Her testimony was based on a detailed study conducted by the AI Now Institute last year that detailed how predictive policing systems are inherently biased. In a recent study, my colleagues at the AI Now Institute examined 13 US police jurisdictions that had engaged in illegal, corrupt, or biased practices and subsequently built or acquired predictive policing systems. Specifically, my colleagues found that in nine of those jurisdictions, there was a high risk that the system's predictions reflected the biases embedded in the data.

The EU is pursuing a digital strategy that builds on our successful history of technology, innovation and ingenuity, vested in European values, and projecting them onto the international stage. The White Paper on Artificial Intelligence (AI) and the European data strategy presented today show that Europe can set global standards on technological development while putting people first. Digital technologies considerably improve our lives, from better access to knowledge and content to how we do business, communicate or buy goods and services. The EU must ensure that the digital transformation works for the benefit of all people, not just a few. Citizens should have the opportunity to flourish, choose freely, engage in society and at the same time feel safe online. Businesses should benefit from a framework that allows them to start up, scale up, pool data, innovate and compete with large companies on fair terms.

A scientist and a researcher, Brian Roemelle, once said that artificial intelligence is the electricity of the future. And it is difficult to disagree, for AI has a huge impact on many industries right now -- from banking to auto. But have you ever thought how AI works for a sextech? Great changes are happening right now, and although you may not even notice it, your sex experience is getting better. The sex industry is booming -- people accept themselves and their bodies, some of them open out, some start experimenting, and some identify themselves as digisexuals (people whose primary sexual identity comes through the use of technology -- they don't need other people to have sex to).

The pace of adoption for AI and cognitive technologies continues unabated with widespread, worldwide, rapid adoption. Adoption of AI by enterprises and organizations continues to grow, as evidenced by a recent survey showing growth across each of the seven patterns of AI. However, with this growth of adoption comes strain as existing regulation and laws struggle to deal with emerging challenges. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in the face of technology change and can deal with new, emerging challenges posed by AI. Research firm Cognilytica recently published a report on Worldwide AI Laws and Regulations that explores the latest legal and regulatory actions taken by countries around the world across nine different AI-relevant areas. Specifically, the report analyzed emerging laws and regulations pertaining to the use of facial recognition and computer vision, operation and development of autonomous vehicles, issues of AI-relevant data privacy, challenges arising from conversational systems and chatbots, the emergence of the possibility of lethal autonomous weapons systems (LAWS), concerns around AI ethics and bias, aspects of AI-supported decision making, the potential for malicious use of AI, and other regulations and laws pertaining to the use, creation, or interaction with AI systems.

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and easy access to vastly powerful computing resources. Particularly, the wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data, which provides the opportunity to derive accurate models and develop various intelligent applications at the network edge. However, such enormous data cannot all be sent from end devices to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative. AI at the edge requires close cooperation among edge devices, such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication overheads. In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. Specifically, we first identify key communication challenges in edge AI systems. We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge. Potential future research directions are also highlighted.

"Smart contracts" are a form of code, in the context of cryptocurrency and blockchain platforms, that is used to enforce security properties of multi-agent protocols. Often these protocols are for processes for which trust amongst the agents would typically have been provided through the use of legal contracts. The emergence of the area of "smart contracts" has given renewed motivation to study the formal representation of legal reasoning and legal processes. In the present paper, we consider questions of knowledge representation pertinent to a particular legal process: contract signature. In formation of legal contracts between two or more parties, all parties to the contract are required to sign in order for the contract to be considered valid. In some sensitive situations, this requires a physical meeting of the parties so that copies of the contract can be signed and immediately exchanged for co-signature. An example of such a sensitive situation is where one party may gain advantage in a negotiation with a third party by presentation of a partially signed contract. It is also frequently desirable to establish a state of common knowledge amongst the parties that the contract has been signed and that the signers were authenticated: a physical signing ceremony achieves this goal.

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In the wake of recent high-profile AI bias scandals, companies have begun to realize that they need to rethink their AI strategy to include not just AI Fairness, but also Algorithmic Fairness more broadly as a fundamental tenet. At the Pragmatic Institute, we educate Fortune 500 companies about data science and AI. Through our work, we've discovered that many companies struggle to form a clear definition of algorithmic fairness for their organization. Without a clear definition, well-meaning fairness initiatives languish in the realm of good intentions and never arrive at meaningful impact. But defining fairness is not as easy as it may seem. Two examples highlight just how challenging this can be.

Tucked away in an office on a quiet Los Angeles street, past hallways chockablock with miniature props and movie posters, is a cavernous motion-capture studio. And in that studio is the National Mall in Washington, D.C., in 1963, on the day Martin Luther King Jr. delivered his "I Have a Dream" speech. Or rather, it was inside that room that the visual-effects studio Digital Domain captured the expressions, movements and spirit of King, so that he could appear digitally in The March, a virtual reality experience that TIME has produced in partnership with the civil rights leader's estate. The experience, which is executive–produced and narrated by actor Viola Davis, draws on more than a decade of research in machine learning and human anatomy to create a visually striking re-creation of the country's National Mall circa 1963--and of King himself. When work on the project began more than three years ago, a big question needed answering.

Not as a defendant -- it would have been a novel case had it been so -- but as an aid to help the magistrate with sentencing. Trends elsewhere suggest something more. But if a machine should one day sit at the bench presiding over a court battle between men and men, then it will be a surrender most ominous. There are at least two reasons why we should not defer to machines. It is true that AI can do many complicated things.