Facebook CEO Mark Zuckerberg's testimony before Congress made one thing clear: the government needs an Federal Artificial Intelligence Agency. Facebook FB, -0.26% is a canary in the proverbial AI coal mine. AI is going to play an enormous role in our lives and in the global economy. It is the key to self-driving cars, the Amazon AMZN, -0.63% Alexa in your home, autonomous trading desks on Wall Street, innovation in medicine, and cyberwar defenses. Technology is rarely good nor evil -- it's all in how humans use it.

A group from the University of Otago has called for the implementation of laws to regulate and govern the development and use of AI and machine learning in New Zealand. Colin Gavaghan has spoken out as a representative of the Artificial Intelligence and Law in New Zealand Project (AILNZP) - he is an Associate Professor at Otago's Faculty of Law and the director of the NZ Law Foundation sponsored Centre for Law and Policy in Emerging Technologies. In an article published recently, Gavaghan cites the concerns around Immigration New Zealand, ACC, and The Ministry for Social Development's use of predictive analytics systems as reasons that now is the time to consider a regulatory body to oversee the rising use of artificial intelligence (AI) systems in New Zealand Government departments. "These systems can be of great use, but there must be more transparency about how predictive systems are being used in government," says Gavaghan in the article. Considering the amount of data that business and industry are collecting about their clients and customers, there seemed to be a lack of discussion in the article around whether this oversight should extend into the private sphere.

These days, when presidents want to make policy, they often do it through their powers to regulate. The recent move by the Trump administration to relax fuel-efficiency standards for automobiles is one of many recent, high-profile examples. But federal agencies cannot just do whatever they want: There are legal rules that have been in place since the 1940s that require agencies to solicit and consider public input. Unfortunately, those rules have mostly been frozen in the mid-20th century and have not adapted to the new technological environment. For example, opportunities to learn about and comment on regulation abound online, leading to an explosion of public participation.

The U.S. Army's Autonomous Remote Engagement System is mounted on the Picatinny Lightweight Remote Weapon System and coupled with an M240B machine gun. It's part of a program that reduces the time to identify targets using automatic target detection and user-specified target selection. The U.S. Army's Autonomous Remote Engagement System is mounted on the Picatinny Lightweight Remote Weapon System and coupled with an M240B machine gun. It's part of a program that reduces the time to identify targets using automatic target detection and user-specified target selection. Killer robots have been a staple of TV and movies for decades, from Westworld to The Terminator series. But in the real world, killer robots are officially known as "autonomous weapons."

Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the number of events. As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena. In order to improve existing approaches, one possibility is to make use of the readily available, partially annotated data (P as in partial) that cover more documents. However, missing annotations in P are known to hurt, rather than help, existing systems. This work is a case study in exploring various usages of P for TempRel extraction. Results show that despite missing annotations, P is still a useful supervision signal for this task within a constrained bootstrapping learning framework. The system described in this system is publicly available.

Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.

Human spies will soon be relics of the past, and the CIA knows it. Dawn Meyerriecks, the Agency's deputy director for technology development, recently told an audience at an intelligence conference in Florida the CIA was adapting to a new landscape where its primary adversary is a machine, not a foreign agent. Meyerriecks, speaking to CNN after the conference, said other countries have relied on AI to track enemy agents for years. She went on to explain the difficulties encountered by current CIA spies trying to live under an assumed identity in the era of digital tracking and social media, indicating the modern world is becoming an inhospitable environment to human spies. But the CIA isn't about to give up.

This interdisciplinary event teamed data science, engineering, and policy students to explore solutions to real societal challenges submitted by sponsor organizations. The hackathon, subtitled "Data to Decisions," was organized and run by students from MIT's Institute for Data, Systems, and Society (IDSS). Participants used datasets provided by nonprofit, education, and government institutions to pitch solutions to complex challenges in cybersecurity, health, energy and climate, transportation, and the future of work. A panel of judges evaluated the pitches and read final policy proposals. "It's a different type of hackathon in that it is focused on public policy outcomes," says Amy Umaretiya, a student organizer with IDSS's Master's program in Technology and Policy (TPP).

This interdisciplinary event teamed data science, engineering, and policy students to explore solutions to real societal challenges submitted by sponsor organizations. The hackathon, subtitled "Data to Decisions," was organized and run by students from MIT's Institute for Data, Systems, and Society (IDSS). Participants used datasets provided by nonprofit, education, and government institutions to pitch solutions to complex challenges in cybersecurity, health, energy and climate, transportation, and the future of work. A panel of judges evaluated the pitches and read final policy proposals. "It's a different type of hackathon in that it is focused on public policy outcomes," says Amy Umaretiya, a student organizer with IDSS's Master's program in Technology and Policy (TPP).

Cybersecurity incidents are among the greatest concerns of businesses, government agencies, and private citizens today. In the modern world, protecting our data and information assets is nearly as important as maintaining the security of our physical assets. It should not be surprising, then, that data analytics play a key role in cybersecurity. Analytics and machine intelligence, a field concerned with producing machines able to autonomously perform tasks that would normally require human intelligence, can drive an organization from reactive to proactive when coupled with organizational change. This capability enables organizations of all types to move from simply measuring signals (data), to creating sentinels (machine learning algorithms), and then moving ahead to sense-making (actionable machine intelligence).