A quick note: I'm going to take a brief newsletter break, but will resume publishing the week of July 9. At that time, or shortly thereafter, I will likely begin publishing 4 times per week (Monday-Thursday). You'll receive twice as many emails, but each issue will be significantly less dense. This issue is particularly dense. There's a lot to talk about this week, but I want to begin with the topic of bias in artificial intelligence models. It's an issue that gets an awful lot of attention -- including in this feature from Fortune -- but that I think the general public misunderstands.

NEW DELHI: The Artificial Intelligence Task Force of the Ministry of Defence led by Tata Sons Chairman N Chandrasekaran on Saturday submitted its final report to Defence Minister Nirmala Sitharaman on using AI for military superiority. "The Task Force handed over the final report to Raksha Mantri Nirmala Sitharaman to accept it and to implement its recommendations," the Ministry of Defence said in a statement. The Task Force was constituted in February 2018 to study the strategic implications of AI in national security perspective and in global context. It is a multi-stakeholder group comprising members from government, services, academia, industry and start-ups. "AI has the potential to have transformative impact on national security. It is also seen that AI is essentially a dual use technology. While it can fuel technology driven economic growth, it also has potential to provide military superiority," the statement said.

On Tuesday this week, the U.S. House of Representatives Subcommittees on Research and Technology and Energy invited prominent academics, tech executives, and scientists to talk about the "game-changing" potential and implications of AI, as the hearing charter put it. It touched on a number of topics. Rep. Barbara Comstock (R-VA) sought suggestions from the panel on ways institutions and government might collaborate on AI systems development. And Rep. Marc Veasey (D-TX) asked earnestly about the potential for "doomsday" scenarios. "To what extent do you think [is it] something we should be concerned about?" he said.

Even amid the majority concerns, only 32 percent of Americans support the creation of a Federal Robotics Commission to regulate development and usage of robots. However, 39 percent of Americans between ages 18 and 34 were in favor of the robotics agency, compared to only 25 percent of older people (55 and over). That's the result that West found most interesting, suggesting that support for the idea may continue to increase. "If young people hold on to those views as they age, that would suggest we're headed towards more government regulation," West said. The Trump administration does have a major reorganization of federal agencies on its agenda, including a proposed combination of the Department of Education and Labor.

James A. Hendler was recognized with the AAAI Distinguished Service Award at AAAI-17 for his contributions to the field of artificial intelligence through sustained service to AAAI, other professional societies and government activities promoting the importance of artificial intelligence research. This article presents his recipe for success advice, with advice directed at newer AI researchers (with some notes for experienced ones as well).

The AAAI 2017 Fall Symposium Series was held Thursday through Saturday, November 9–11, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, DC. The titles of the six symposia were Artificial Intelligence for Human-Robot Interaction; Cognitive Assistance in Government and Public Sector Applications; Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; Human-Agent Groups: Studies, Algorithms and Challenges; Natural Communication for Human-Robot Collaboration; and A Standard Model of the Mind. The highlights of each symposium (except the Natural Communication for Human-Robot Collaboration symposium, whose organizers did not submit a report) are presented in this report.

Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

In 2016, amidst this landscape of uncertainty, the United States government launched a series of activities and actions to help the country better understand and prepare for the impacts of advancements in artificial intelligence (Felten 2016b). In one of those actions, the government called for the creation of a national strategic plan on AI that defines the federal role in AI research and development (R&D). Why was a strategic plan needed? If done thoughtfully, a national AI R&D strategic plan could help address these uncertainties by identifying the federal role in AI investments and defining open AI R&D challenges that must be solved before AI can be used in important societal applications.

Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.

Artificial Intelligence (AI) has the potential to have transformative impact on national security. It is also seen that AI is essentially a dual use technology.