Remarkable new applications of artificial intelligence (AI) seem to hit the headlines at an ever-increasing frequency these days. Whether enabling faster, more accurate diagnoses for cancer patients or powering conservation efforts for endangered animal species, the uses of AI are expanding and becoming more impactful. As well as the impressive technological progress, these examples are important because they contribute to increasing public acceptance of, and trust in, AI. These vital positive stories run counter to the negative headlines published by some media outlets. For context, it's helpful to compare the reception AI receives from some quarters with the historic response to other now-familiar technologies.

An attorney's computer program offers to screen potential jurors based on their ethnicity, political views and occupation to find a jury most favorable to a defense lawyer's case. Momus Analytics, the company was founded by attorney Alex Alvarez, trawls potential jurors' social media accounts and uses the findings to predict whether or not they should be chosen. The program includes a racially-biased algorithm that suggests Asian, Central American, and South American people are more likely to be leaders - a quality the program appears to prize. People who described their race as'other' were found to be likely to be leaders. Alvarez, who worked with Texas-based software designer Frogslayer to develop the program, has a pending patent application for the program.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

The controversial facial-recognition company that contracts with law-enforcement agencies announced that attackers have gained unauthorized access to its entire client list. The company already informed its customers of the security breach. The startup came under scrutiny after media reported that it had scraped more than 3 billion photos from social media (Facebook, YouTube, and Twitter) for facial recognition purposes. The company has been hit with class-action lawsuits by American citizens, but the company refused any accusation remarking that it was authorized by the First Amendment to scrape public data. "In the notification, which The Daily Beast reviewed, the startup Clearview AI disclosed to its customers that an intruder "gained unauthorized access" to its list of customers, to the number of user accounts those customers had set up, and to the number of searches its customers have conducted."

Agnieszka Kurant's lower Manhattan studio stands among a scattering of cultural outposts that represent some of the most recent efforts of the avant guard to grapple with our cultural moment. When I visited in late January, a gallery two doors down was hosting a reproductive rights-themed show with works listed for upwards of $30,000. Across the street, four floors of the windowless New Museum were taken over by a retrospective of artist Hans Haacke, which included a demographic survey, a portrait of Ronald Reagan and a grass-covered mound of dirt. The seventh floor was occupied by a "mixed reality pop-up," sponsored by Ruinart champagne, in which visitors could wander about in augmented reality glasses. Minders politely asked those without reservations to "step away from the experience."

Federal guidance on artificial intelligence needs additions to ensure the U.S. has a seat at the international table. The rapid proliferation of applications of artificial intelligence and machine learning--or AI, for short--coupled with the potential for significant societal impact has spurred calls around the world for new regulation. The European Union and China are developing their own rules, and the Organization for Economic Cooperation and Development has developed principles that enjoy the support of its members plus a handful of other countries. In January, the U.S. Office of Management and Budget (OMB) also issued its own draft guidance, ensuring the United States a seat at the table during this ongoing, multi-year, international conversation. The U.S. guidance--covering "weak" or narrow AI applications of the kind we experience today--reflects a light-touch approach to regulation, consistent with a desire to reward U.S. ingenuity.

Dr. Andrew Toole is the Chief Economist at the U.S. Patent and Trademark Office (USPTO) and a Research Associate at the Centre for European Economic Research (ZEW). Dr. Toole joined the USPTO with experience in the private sector, academia, and government. While completing his PhD in economics at Michigan State University, Andrew Toole was a Senior Economist for Laurits R. Christensen Associates where he conducted studies on total factor productivity, cost and price analysis, and competitive strategy. In 1998, Dr. Toole went to Stanford University as a postdoctoral student before becoming a faculty member at Illinois State University and Rutgers University in New Jersey. As an academic researcher, Dr. Toole was asked to advise on science and technology policy issues for institutions such as the U.S. National Academies of Science, U.S. National Institutes of Health, and the U.S. Department of Agriculture (USDA).

This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step towards understanding the relationship between neural network pruning and differential privacy, this paper proves that pruning a given layer of the neural network is equivalent to adding a certain amount of differentially private noise to its hidden-layer activations. The paper also presents experimental results to show the practical implications of the theoretical finding and the key parameter values in a simple practical setting. These results show that neural network pruning can be a more effective alternative to adding differentially private noise for neural networks.

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.