Artificial intelligence (AI) and machine learning will play a role in protecting the United States from malicious cyber actors. NSA's Jason Wang, technical director for the Computer and Analytic Sciences Research Group, forecasted a future in which AI will support the Intelligence Community's (IC) efforts to secure and defend our Nation's networks. "I think the next frontier for us is probably in the cybersecurity space," he said. "There's a lot of opportunity … to bring machines to this very low latency, highly dynamic problem in ways that really are not human-time kinds of responses." Mr. Wang was responding to a question about cybersecurity and AI posed by former Director of National Intelligence James Clapper, one of several hundred audience members attending a virtual web briefing on July 12th that also featured AI experts from the Central Intelligence Agency (CIA) and National Geospatial Intelligence Agency (NGA).

Hallam is an Associate Professor in the History Programme and in the School of Biological Sciences at Nanyang Technological University in Singapore. Daniel is an external PhD candidate at eLaw -- Center for Law and Digital Technologies, Leiden University, the Netherlands. This essay is part of our ongoing "AI Lexicon" project, a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. Much of the history, meaning, and imagination of AI is discussed in relation to the West, often against a backdrop of cybernetics, "AI winters," and Terminator androids. These narratives inform how we understand the risks and "social good" of AI.

An international group of researchers led by the Harvard astronomer who believes the first interstellar object discovered was a'lightsail' from another civilization have announced a new project to search for signs of'extraterrestrial technological civilizations' (ETCs) in space. Known as the Galileo Project, the researchers - led by Harvard astrophysicist Avi Loeb - will use artificial intelligence and look at data from existing and future astronomical surveys and high-resolution telescopes. The project will have three objectives: to search for unidentified aerial phenomena (UAP), other interstellar objects like'Oumuamua and satellites created by ETCs. A new project will search for'extraterrestrial technological civilizations' in space has been announced. Pictured is the famous Tic-Tac footage, which has been previously acknowledged as real by the Navy.

Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model explicitly and observe the evolution of system-level outcomes over time. However, performing simulation-based studies often requires researchers to build their own simulation environments from the ground up, which creates a high barrier to entry, introduces room for implementation error, and makes it difficult to disentangle whether observed outcomes are due to the model or the implementation. We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems and other types of sociotechnical systems in which an algorithm mediates the interactions between multiple stakeholders, such as users and content creators. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems. We additionally show how T-RECS can be used to generate novel insights with minimal overhead. Our tool promotes reproducibility in this area of research, provides a unified language for simulating sociotechnical systems, and removes the friction of implementing simulations from scratch.

Designers of online deliberative platforms aim to counter the degrading quality of online debates and eliminate online discrimination based on class, race or gender. Support technologies such as machine learning and natural language processing open avenues for widening the circle of people involved in deliberation, moving from small groups to ``crowd'' scale. Some design features of large-scale online discussion systems allow larger numbers of people to discuss shared problems, enhance critical thinking, and formulate solutions. However, scaling up deliberation is challenging. We review the transdisciplinary literature on the design of digital mass-deliberation platforms and examine the commonly featured design aspects (e.g., argumentation support, automated facilitation, and gamification). We find that the literature is heavily focused on developing technical fixes for scaling up deliberation, with a heavy western influence on design and test users skew young and highly educated. Contrastingly, there is a distinct lack of discussion on the nature of the design process, the inclusion of stakeholders and issues relating to inclusion, which may unwittingly perpetuate bias. Another tendency of deliberation platforms is to nudge participants to desired forms of argumentation, and simplifying definitions of good and bad arguments to fit algorithmic purposes. Few studies bridge disciplines between deliberative theory, design and engineering. As a result, scaling up deliberation will likely advance in separate systemic siloes. We make design and process recommendations to correct this course and suggest avenues for future research.

The high expectations of AI have triggered worldwide interest and concern, generating 400 policy documents on responsible AI. Intense discussions over the ethical issues lay a helpful foundation, preparing researchers, managers, policy makers, and educators for constructive discussions that will lead to clear recommendations for building the reliable, safe, and trustworthy systems6 that will be commercial success. This Viewpoint focuses on four themes that lead to 15 recommendations for moving forward. The four themes combine AI thinking with human-centered User Experience Design (UXD). Ethical discussions are a vital foundation, but raising the edifice of responsible AI requires design decisions to guide software engineering teams, business managers, industry leaders, and government policymakers.

In late 2018, thousands of workers walked out of Google offices around the globe to protest the company's handling of sexual harassment accusations against prominent executives. The same year, hundreds of Salesforce employees signed a letter to CEO Marc Benioff protesting the fact the company sold products to U.S. Customs and Border Protection. Also in the headlines was an effort by some Microsoft employees to protest the company's bid for work on the U.S. Department of Defense's Joint Enterprise Defense Infrastructure (JEDI) project. In a letter to Microsoft CEO Satya Nadella, the employees wrote, "many Microsoft employees don't believe that what we build should be used for waging war." Tech employee activism is nothing new, but the momentum generated by the 2018 wave of protests was.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.

Backpacks to track bees, bushfire modelling and sensors to detect broken water pipes are some of the technologies being developed in Australia as part of an artificial intelligence boom. Digital Economy Minister Jane Hume says artificial intelligence has the capacity to improve the lives of all Australians. Senator Hume will tell a Committee for Economic Development of Australia event on Tuesday the government had two roles to play in terms of AI: an enabler and a standards setter, especially in terms of ethics. Earlier this year the government announced a $1.2 billion digital economic strategy which included an additional $124 million commitment to the AI initiatives. The CSIRO estimates AI technology will contribute $22 trillion into the global economy by 2030.

The program schedule will cover six days from July 26 until July 31. Presentations each day will start at 10:30 and end at 12:30 US Eastern Time. We will start on July 26 with a presentation on the IEEE Entrepreneur Program and an overview of the Entrepreneur Process and the resources that are available to support the aspiring Entrepreneur. Each following day will provide a Speaker that can give their experience in creating a IoT based Start Up. On the last day, July 31, we will have a spirited competition of Start Ups making their "Pitches".