More than 2,400 AI researchers recently signed a pledge promising not to build so-called autonomous weapons--systems that would decide on their own whom to kill. This follows Google's decision not to renew a contract to supply the Pentagon with AI for analysis of drone footage after the company came under pressure from many employees opposed to its work on a project known as Maven. Paul Scharre, the author of a new book, Army of None: Autonomous Weapons and the Future of War, believes that AI researchers need to do more than opt out if they want to bring about change. An Army Ranger in Iraq and Afghanistan and now a senior fellow at the Center for a New American Security, Scharre argues that AI experts should engage with policymakers and military professionals to explain why researchers are concerned and help them understand the limitations of AI systems. Scharre spoke with MITTechnology Review senior editor Will Knight about the best way to halt a potentially dangerous AI arms race.

As automation, Machine Learning and AI leave their indelible imprint on multiple and diverse fields, including image analytics, workflow management, construction, autonomous vehicles, agriculture and the future of communication systems, it does seem that very soon these technologies will blast us off to the stratosphere. And the metaphor is quite fitting! AI and Machine Learning solutions are being increasingly researched and implemented in the space sector for a space age of the future, whose mainstay would be advanced robotics and which might resemble a robotic inter-galactic adventure. Application of AI is being extensively researched in the domain of satellite operations, especially in supporting the operational mechanism of huge satellite constellations, which usually includes many facets – relative positioning, communication, if cycle management etc. Machine Learning is being used for analyzing and processing high-resolution satellite imagery and for getting exact and precise visual representations.

DeepMind and its partners in the research, London's Moorfields Eye Hospital and the University College London Institute of Ophthalmology, said they plan prospective clinical trials of the technology in 2019. If those trials are successful, DeepMind said it would seek to create a regulator-approved product that Moorfields could roll out across the U.K. It said the product would be free for an initial five-year period. The software would be the first time a DeepMind AI algorithm using machine learning has ended up in a healthcare product. Alphabet has several initiatives aimed at using artificial intelligence to improve healthcare. Earlier this year, Verily, an Alphabet company that says its goal is to extend human lifespans, teamed up with AI experts from Alphabet's Google, to develop an algorithm that could spot a range of cardiovascular issues from a different kind of retinal image.

BOGOTA, Colombia – The exiled opposition leader accused by Venezuelan authorities of directing a failed plot to assassinate President Nicolas Maduro says the greatest threat to the embattled socialist leader may be his detractors in uniform standing quietly behind him. Julio Borges, who once led Venezuela's opposition-controlled National Assembly, said Tuesday that the arrests of two high-ranking military officers in connection with the attack using drones loaded with plastic explosives is yet another signal that fractures within the nation's armed forces are growing. "The conflict today is within the government -- not just at the political level, but more importantly within the armed forces," Borges said in an interview with The Associated Press in Colombia's capital. His comments came hours after Venezuela's chief prosecutor announced the arrest of Gen. Alejandro Perez and Col. Pedro Zambrano from Venezuela's National Guard as part of the investigation into the Aug. 4 attack. Their alleged roles were not described.

Artificial intelligence is one of the most influential forces in information technology. It can help drive cars, fly unmanned aircraft and protect networks. But artificial intelligence also can be a dark force, one that adversaries use to learn new ways to hack systems, shut down networks and deny access to crucial information. The challenge is to prepare for a future where autonomous cyber attacks powered by artificial intelligence (AI) will threaten cyberspace and could endanger human life. This prospect is so significant that the Japanese Cabinet Secretariat tasked with developing the country's cybersecurity initiatives has created a research and development focus group to craft plans to counter cybersecurity threats, including those designed with AI.

Climate change has been bleaching coral reefs, decimating the local marine species that call them home, since at least the first major observations were recorded in the Caribbean in 1980. Thankfully, new A.I. cataloguing designed to identify the geographic regions where coral is still thriving hopes to reverse the trend, saving some of the world's most dense and varied aquatic ecosystems from all-but-certain extinction. There are numerous reasons why we need to care about saving coral reefs, from the ethical to the economic. In addition to housing about a quarter of marine species, these reefs provide $375 billion USD in revenue to the world economy, according to the Guardian, and food security to half a billion people. Without them, researchers say countless species and the entire ocean fishing industry that depends on them would simply evaporate.

Give Jason Holmberg 10,000 zebra photos and he'll find the specific individual zebra you're looking for, no problem. "It could take two minutes," he said. Holmberg is executive director of the nonprofit Wild Me. The Portland-based organization has developed a digital tool called Wildbook that uses artificial intelligence and machine learning to expedite wildlife identification. In tandem with citizen science, Wildbook is able to condense years of human work -- like photographing thousands of animals and identifying each by hand -- into a matter of weeks.

Naturally: Kris Kobach was the Kansas secretary of state for almost eight years, and it was his job to make sure the state's election procedures were shipshape, Mark Joseph Stern writes. So of course, because Kris Kobach is a disaster, the aftermath of the Aug. 7 primary contest between him and the state's incumbent governor has become a comedy of errors. Fruitless: Does Omarosa have a tape of Donald Trump using the N-word? It doesn't matter, Christina Cauterucci writes. She's got no credibility, for one thing. Could be good: Sex robots might help us be more flexible in conceptualizing what a good marriage looks like, Marina Adshade writes.

We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this problem, we employ an actor-critic framework to enhance the training procedure. For the actor, we employ the typical attention based sequence-to-sequence (seq2seq) framework as the policy network for summary generation. For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones. Policy gradient method is used to conduct the parameter learning. An alternating training strategy is proposed to conduct the joint training of the actor and critic models. Extensive experiments on some benchmark datasets in different languages show that our framework achieves improvements over the state-of-the-art methods.

Textmining is an exciting topic. There is tremendous potential to gain insights from textual analysis. See for example Gentzko, Kelly and Taddy's Text as Data. While text mining may be quite advanced in other fields, in finance and economics the application of these techniques is still in its infancy. In order to take advantage of text as data, economists and financial analysts need tools to help them.