In a new book, to be published on 8 March, Nello Cristianini explains the fundamental concepts of artificial intelligence (AI) and how it is changing culture and society. The Shortcut: Why Intelligent Machines Do Not Think Like Us is aimed at the general reader, providing an introduction to the concepts that underpin the technology and the wider implications for society. In the book, Nello provides practical advice on how we should approach AI in the future, including how to avoid the hype and the fears that tend to surround the technology today. We spoke to Nello about the "first draft of AI", the "shortcut", some of the questions he considered in the book, and important considerations we should bear in mind as the technology progresses. Building the first useful form of machine intelligence was not easy, but, as it turns out, that was not the most difficult part.

When Mike Koval, the police chief of Madison, Wisconsin, abruptly resigned on a Sunday in September 2019, the community's relationship with its men and women in blue was already strained. Use-of-force issues hung over the department after the killing of a Black teenager in 2015. Then, months before Koval left, another Black teenager, in the middle of a mental health crisis, was beaten on the head by an officer while being restrained by three others. The process of selecting a new police chief followed a standard formula. A five-person team of mayor-appointed, city-council-approved commissioners would make the ultimate decision, allowing for public comment beforehand.

Will your next doctor be a human being―or a machine? Will you have a choice? If you do, what should you know before making it? This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.

Autonomous mechanisms have been proposed to regulate certain aspects of society and are already being used to regulate business organisations. We take seriously recent proposals for algorithmic regulation of society, and we identify the existing technologies that can be used to implement them, most of them originally introduced in business contexts. We build on the notion of 'social machine' and we connect it to various ongoing trends and ideas, including crowdsourced task-work, social compiler, mechanism design, reputation management systems, and social scoring. After showing how all the building blocks of algorithmic regulation are already well in place, we discuss possible implications for human autonomy and social order. The main contribution of this paper is to identify convergent social and technical trends that are leading towards social regulation by algorithms, and to discuss the possible social, political, and ethical consequences of taking this path.

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.

If the fragmented media environment is a sick patient, machine learning may be the cure. That was the proposition Andrew Heyward, visiting scholar from MIT's Media Laboratory and former president of CBS News, outlined in his presentation, "Can Robots Solve Your PR Problems?" at the New York offices of agency Makovsky on Feb. 6. Heyward and his colleagues at MIT Media Lab's Laboratory for Social Machines are studying artificial intelligence solutions to modern plights of the PR practitioner: fake news, polarization, the public's lack of faith in journalism and short attention spans, to name a few. Heyward's group uses machine learning algorithms as their primary tool to map and track the overall health of the public sphere. And soon, PR pros may be able to use those AI insights to make better decisions--whether they're managing a crisis or planning a national campaign. Here are four PR applications of AI and machine learning shared by Heyward.

In 2016, the scientific community thrilled to news that the LIGO collaboration had detected gravitational waves for the first time. LIGO is the latest in a long line of revolutionary technologies in astronomy, from the ability to'see' the universe from radio waves to gamma rays, or from detecting cosmic rays and neutrinos (the Laser Interferometer Gravitational-Wave Observatory--LIGO--is an NSF-supported collaborative effort by the U.S National Science Foundation and is operated by Caltech and MIT). Each time a new technology is deployed, it can open up a new window on the cosmos, and major new theoretical developments can follow rapidly. These, in turn, can inform future technologies. This interplay of technological and fundamental theoretical advance is replicated across all the natural sciences--which include, we argue, computer science.

That's just one takeaway from a new 48-page report White House AI report it released to policy makers this week, according to Jim Hendler, AI expert, researcher and coauthor of the new book, Social Machines: The Coming Collision of Artificial Intelligence, Social Networking and Humanity. Addressing such concerns, the report says that fears about super-intelligent and evil computers, shouldn't have much impact on current US policy toward AI. "And it gets that part right … we all need to cut past the hype and look at what's really on in AI," he said, "so we can pay attention to the real risks and opposed to the science fiction risks," he said. The things to worry about aren't the frightening HAL 9000 or Skynet scenarios, Hendler says, "but the very real economic, societal ethical and safety concerns AI poses for the foreseeable future." The report does a fair job of addressing such issues overall, he says, "and it is good as far as it goes," Hendler says.

Will your next doctor be a human being--or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler--co-originator of the Semantic Web (Web 3.0)--and Alice Mulvehill--developer of AI-based operational systems for DARPA, the Air Force, and NASA--explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators.