SAN FRANCISCO – Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the threat that artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said.

Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

And how can businesses, as well as members of the public, best keep themselves informed about the extent to which advances in AI may impact on the economy, as well as our society? A recent consultation by the UK House of Lords Select Committee on Artificial Intelligence has called for evidence on the economic, ethical and social implications of advances in artificial intelligence [call for evidence PDF]. The consultation poses a range of questions in particular topic areas, such as the impact of AI on society and the public perception of it, as well as ethical considerations and the role of the governemnt in responding to AI's development and use. For example, one question, targeted at experts in the field, asks "What is the current state of artificial intelligence and what factors have contributed to this?". Another, that could be answered by a much wider audience, seeks to explore the extent to which "efforts [should be] be made to improve the public's understanding of, and engagement with, artificial intelligence" and how they should be pursued.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories . But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

The U.S. Naval Research Lab has been working on its CICADA (Close-In Covert Autonomous Disposable Aircraft) drones since at least 2011. The tiny drones are designed to be carried aloft by other aircraft and dropped, whereupon they'll use GPS and little fins to glide to within 15 feet of their destination. They can carry a small sensor payload, and they're designed to be cheap enough that you can use a whole bunch of them all at once. At the Sea Air Space Expo back in April, we checked out the latest MK5 CICADA prototypes, along with a new delivery system that'll launch 32 of them out of a standardized sonobuoy tube all at once. The CICADA drones themselves consist mostly of a printed circuit board, which makes up the wings and also contains a custom autopilot system that can recover from the crazy tumbling that you can see when the CICADAs are initially deployed. A 3D-printed fuselage minimizes the amount of hands-on assembly time required, and the general idea is that eventually, these things will be created and assembled entirely by robots.

Much has been made of the "food boom" in Pittsburgh, and the city has long had a thriving arts scene. But perhaps the secret, underlying driver for both the economy and the cool factor -- the reason Pittsburgh now gets mentioned alongside Brooklyn and Portland, Ore., as an urban hot spot for millennials -- isn't chefs or artists but geeks. In a 2014 article in The Pittsburgh Post-Gazette, Mayor Bill Peduto compared Carnegie Mellon, along with the University of Pittsburgh, to the iron ore factories that made this city an industrial power in the 19th century. The schools are the local resource "churning out that talent" from which the city is fueled. Because of the top students and research professors at Carnegie Mellon, tech companies like Apple, Facebook, Google and Uber have opened offices here.

Don Ingabire (L), 16 and Audrey Mwamikazi, 17, members of a teenage robotics team from the African nation of Burundi, who were reported missing after taking part in an international competition and later spotted crossing the United States border into Canada, are seen in pictures released by the Metropolitan Police Department in Washington, D.C., U.S. July 20, 2017.

From ship-hunting Tomahawk missiles and sub-spying drone ships to semi-autonomous UAV swarms and situationally-aware reconnaissance robots, the Pentagon has long sought to protect its human forces with the use of robotic weapons. But as these systems gain ever-greater degrees of intelligence and independence, their increasing autonomy has some critics worried that humans are ceding too much power to devices whose decision-making processes we don't fully understand (and which we may not be entirely able to control). What constitutes an Autonomous Weapon System (AWS) depends on who you ask, as these systems exhibit varying degrees of independence. Sense and React to Military Objects (SARMO) weapons like the Phalanx and C-RAM are able to react to incoming artillery and missile threats, targeting and engaging them without human oversight. However these aren't fully-autonomous, per se -- they simply perform a set automated task.

In 2016, Stanford students started hacking for defense--that is, they took on real projects from National Security Agency, the Army, the Navy, the Air Force, the Army Cyber Command, the Veterans Administration, and other agencies with defense-related problems. The students actually came up with prototype solutions. The innovative Hacking For Defense (H4D) class, which requires each student team to conduct at least 100 interviews with defense industry "clients," caught on quickly. Today, according to Steve Blank, an instructor at Stanford and one of the creators of the curriculum, eight universities in addition to Stanford have offered or will offer a Hacking for Defense class this year: Boise State, Columbia, Georgetown, James Madison, the University of California at San Diego, the University of Pittsburgh, the University of Southern California, and the University of Southern Mississippi. At Stanford, the class has spun out Hacking for Diplomacy, Hacking for Energy, and other targeted classes that use the same methodology.