AI has become part of the fabric of modern life, with applications in sectors ranging from agriculture to retail to health to education. We believe that AI, used appropriately, can deliver great benefit for economies and society, and help people to make decisions that are fairer, safer, and more inclusive and informed. As with other technologies, there are new policy questions that arise with the use of AI, and governments and civil society groups worldwide have a key role to play in the AI governance discussion. In a white paper we're publishing today, we outline five areas where government can work with civil society and AI practitioners to provide important guidance on responsible AI development and use: explainability standards, fairness appraisal, safety considerations, human-AI collaboration and liability frameworks. There are many trade-offs within each of these areas and the details are paramount for responsible implementation.

In business today, AI is a shorthand used to refer to technological processes that automate services. Attorneys will generally encounter two kinds of AI: reactive and limited memory. Reactive AIs respond to human input using predetermined algorithms, like playing chess against a computer. Limited memory AIs rely on both preprogrammed inputs and the AI's own observations over time, like self-driving cars, natural language processors (e.g., Siri), and machine learning. The most popular among them, and the most disruptive for the legal industry, is machine learning.

A year ago, I picked seven technologies that would play significant roles in 2018. Some of my predictions were correct, and some of them reappear on this list -- 5G hasn't quite happened yet, but we're getting closer. As George Saville, the 17th-century English statesman and essayist, once wrote, "The best qualification of a prophet is to have a good memory." It's a fancy way of saying the past is prologue, and no vision for the near-future is possible without analyzing past trends. That's what I've done to concoct these informed guesses about the state of tech in 2019.

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is twofold, firstly we present a structured and comprehensive overviewof research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques inreal application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.

While artificial intelligence (AI) technology is making headlines in a wide range of industries, legal AI may not come to mind for many. However, AI is already transforming the legal sector in many ways, primarily because it is streamlining traditionally cumbersome processes and allowing professionals to focus on higher-level tasks. In case you missed our previous dataset compilations, you can find them all here. Still can't find the custom data you need to train your model? Gengo provides custom AI training data in 37 languages for your specific machine learning project needs. Contact us to learn more about how Gengo can work for you.

President Trump's plans to trade record-high budget cuts at the Environmental Protection Agency (EPA) for increases in defense spending made headlines after the release of his proposed budget in early March 2017. At the Environmental Defense Fund (EDF), our marketing and communications teams wanted to see how the EPA budget cuts were being covered in the media to study which messages were most engaging to constituents. Preliminary survey results that we collected suggested that the general public was more receptive to health-related messaging; however, we wanted to better understand the entire media landscape around the topic. To explore the health angle and other prominent themes around cuts to the EPA's budget, EDF used Quid to analyze relevant articles within a four-month period following Trump's 2017 announcement. Quid leverages natural language processing and machine learning algorithms to read thousands of news articles and blog posts and group them based on common language and keywords.

Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. The subject was artificial intelligence, and his students last week were mainly senior policymakers from countries in the 36-nation Organization for Economic Cooperation and Development.

"We don't have enough people to keep up." "We have to go through miles of case law on this one." "The paperwork is killing our productivity." "We don't know because we can't track events like that." Spend enough time in or around government agencies, and these are the kinds of pressures you're likely to hear about.

When a class in Mandarin Chinese starts next summer at Rensselaer Polytechnic Institute, students will be practicing their spoken dialogues with a different sort of teaching assistant: an artificial intelligence chatbot. Capable of conversing with students in simulated settings -- a restaurant, garden or even a Tai Chi class -- the bot is part of a future where artificial intelligence (AI) will perform more of the tasks, and potentially the jobs, now done by humans. Part of a so-called "situations room" at RPI, the chatbot is an example of what are called "cognitive and immersive systems," in which the burgeoning field of AI is melded with rapidly growing torrents of financial, health and education information as well as so-called "unstructured data" like social media posts spreading across an expanding constellation of networked computers, smartphones and other electronic devices. RPI is developing the room under a partnership with the technology giant IBM and its supercomputer Watson, which first gained worldwide attention in 2011 when it beat humans in the TV game show "Jeopardy." It's too early to predict how much impact AI will have on how New Yorkers work, but a recent report by the Albany-based Rockefeller Institute of Government projects that large numbers of jobs being replaced or changed -- particularly in jobs that involve basic, repetitive actions.