I like to think that while I may have misgivings about much of what the current technological revolution is visiting on us, I yet manage to resist that dread ascription "luddite". It's one Bridle also wishes to avoid; but such is the pessimism about the machines that informs his argument, that his calls for a new "partnership" between them and us seem like special pleading. As futile, in fact, as a weaver believing that by smashing a Jacquard loom he'll stop the industrial revolution in its tracks. If we're in ignorance of what our robots are doing, how can we know if we're being harmed? At the core of our thinking about new technology there lies, Bridle suggests, a dangerous fallacy: we both model our own minds on our understanding of computers, and believe they can solve all our problems – if, that is, we supply them with enough data, and make them fast enough to deliver real-time analyses.

Monitoring transactions for suspicious ones can be more efficient. All it takes is doing it intelligently. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand.

A top Air Force commander has assured reporters that no, the military's experiments with artificial intelligence are not the first step toward "Skynet," the evil defense network in the "Terminator" movies that tried to wipe out humanity. Google recently announced that it will not work with the Pentagon beyond its 2017 contract on Project Maven, an effort to have artificial intelligence help analyze footage from drones. Speaking at a Defense Writers Group breakfast on Thursday morning, Air Force Gen. Mike Holmes, head of Air Combat Command, said that the purpose of Project Maven is to determine whether "machines can learn to do the things that people are doing." Holmes went on to explain that the Air Force does not have enough people to watch all of the full motion video taken from drones, so airmen are using so-called "learning algorithms" to teach machines what to look for in the endless footage. "The way we've been doing that kind of the same way I watch 3-year-olds learn things on their iPads"," Holmes said. "Pick all the green things.

Airbus and IBM have collaborated to create a basketball-sized sphere with a computer screen to assist astronauts in the International Space Station. Called CIMON, or Crew Interactive MObile companioN, the floating orb uses artificial intelligence to listen for commands and then display experiment or repair instructions. It can even search for objects and document experiments, according to the German Aerospace Center (DLR). However, CIMON looks undeniably dumb. CIMON is joining the ISS as a part of the SpaceX launch today (June 29), alongside six other experiments.

Ask federal prognosticators what technology is going to fix all the government's problems and most often you'll hear, "artificial intelligence." The Internal Revenue Service is curious whether that's truly the case when it comes to securing its internal systems. The tax agency issued a request for information June 27 looking for AI and machine learning cloud cybersecurity solutions. The agency is looking for more than just a threat intelligence platform, according to request. The ideal software "automatically and continuously learns the environment," "triages alerts to reduce false positives," "identifies previously unknown threats," and analyzes all that data to provide actionable context for security officials. The analytic machine learning tools should include multiple, diverse behavioral modes, be able to support near real time and streaming data sources, manage data from different technological sources--such as operational technology, internet of things devices and industrial control systems--and identify new threats without human intervention.

WASHINGTON – General Motors Co. warned on Friday that higher tariffs on imported vehicles under consideration by the Trump administration could cost jobs and lead to "a smaller GM" while isolating U.S. businesses from the global market. The administration in May launched an investigation into whether imported vehicles pose a national security threat, and U.S. President Donald Trump has repeatedly threatened to impose a 20 percent vehicle import tariff. The largest U.S. automaker said in comments filed with the U.S. Commerce Department that overly broad tariffs could "lead to a smaller GM, a reduced presence at home and abroad for this iconic American company, and risk less -- not more -- U.S. jobs." Higher tariffs could also hike vehicle prices and reduce sales, GM said. Its comments echoed those from two major U.S. auto trade groups on Wednesday, when they warned that tariffs of up to 25 percent on imported vehicles would cost hundreds of thousands of auto jobs, dramatically raise prices on vehicles and threaten industry spending on self-driving cars.

In 2014 Chinese President Xi Jinping called for a "robot revolution" in manufacturing. It's now under way and boosting productivity, but there are adverse consequences. After decades of growth, rising wages are consuming profits and pushing manufacturing to Southeast Asia. Shanghai's minimum monthly wage, for example, the highest in China, is 2,420 yuan (US$366.62), "They realize you cannot just compete with cheap labor. You have to elevate the manufacturing capabilities as a whole," said Jing Bing Zhang, research director of market intelligence and consulting firm IDC.

A NASA laboratory is working with Google, Intel and other industry heavyweights to launch applied AI technologies into space. The space agency's Frontier Development Lab is targeting four space applications that would use AI and machine learning technologies, including improved prediction of space weather and accelerating the discovery of exoplanets. The results of a public-private partnership to launch AI-based technologies will be announced at Intel (NASDAQ: INTC) headquarters in Santa Clara, Calif., on Aug. 16. The NASA lab also is working with the SETI Institute, the non-profit group searching for signs of life in the universe. SETI was an early adopter of data analytics, machine learning and signal detection technologies.

What's happening: Explainable AI, also sometimes called transparent AI, has become a top priority for nearly all the big companies in the AI field, including Microsoft, Google, Intel, IBM and Oracle. The topic is also expected to come up in Thursday's White House meeting on AI. That sounds straightforward, even obvious. But it actually isn't a feature built into many of the deep learning systems that are currently available. No one size fits all: AI was a huge topic at Google's I/O developer conference this week, with some focus on explainability as well.

Interpretability has arisen as a key desideratum of machine learning models alongside performance. Approaches so far have been primarily concerned with fixed dimensional inputs emphasizing feature relevance or selection. In contrast, we focus on temporal modeling and the problem of tailoring the predictor, functionally, towards an interpretable family. To this end, we propose a co-operative game between the predictor and an explainer without any a priori restrictions on the functional class of the predictor. The goal of the explainer is to highlight, locally, how well the predictor conforms to the chosen interpretable family of temporal models. Our co-operative game is setup asymmetrically in terms of information sets for efficiency reasons. We develop and illustrate the framework in the context of temporal sequence models with examples.