Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.

Gloomy predictions abound that the applications of artificial intelligence and machine learning will put huge numbers of people out of work in the coming years. But the corollary is that these technologies create opportunities to develop new goods and services that will bring new jobs. What's certain is that advanced implementations of computer science are beginning to disrupt our lives. We must start thinking about how these technologies are applied and regulated if we are to reap the benefits and minimise potential harms. The introduction of the steam engine in the 18th century disrupted the life of the agricultural labourer and fuelled the rise of cities, creating new industries and new jobs. Traditional professions such as medicine and law were largely unchanged. But the latest industrial revolution has the potential to change almost every form of work.

To function effectively in this knowledge economy, you need to read through trillions of data points. You basically need to go back to school every day to answer the questions that hit your desk. That is the challenge of the knowledge economy. And that is where I believe we can do something different in security with AI and cognitive.

On Friday, Treasury Secretary Steve Mnuchin said he's "not worried at all" about artificial intelligence replacing human workers because it's "50-100 more years" off. In reality, data shows this is already happening -- with an estimated 38 percent of existing U.S. jobs at risk of being turned over to machines by 2030, according to research from PwC. Another study put out by the University of Oxford last year had similar estimates: The researchers found that 47 percent of US jobs were at risk of automation in the next two decades. But despite justified fears of obsolescence in the West, it is actually developing economies that are poised to be hit the hardest by fourth Industrial Revolution, or "Industry 4.0," where machines are networked together in "smart factories" that have little need for human input. This is already evident in China, where the domestic economy exploded in the last two decades thanks to Western companies that moved their manufacturing operations there.

TOKYO Toshiba Corp will likely miss a third deadline to report its quarterly business results, two sources told Reuters, forcing the Japanese conglomerate to ask for a fresh extension or face a possible delisting from the Tokyo Stock Exchange.

NASA MAY HAVE equipped its Mars Curiosity rover with an impressive array of scientific instruments, but the robot attaché's size and $2.5-billion price tag give its operators ample reason to steer clear of terrain that could jeopardize its mission. Which is a shame, because much of Mars' craggy, cave-ridden, boulder-strewn landscape is so treacherous (planetary geologists literally call it chaos terrain), that big, expensive robots like Curiosity can't risk accessing it. That's why NASA's Jet Propulsion Laboratory built Puffer.

JUDY WOODRUFF: Tonight, we conclude our series War on the Brain. Special correspondent Soledad O'Brien reports on the efforts of researchers to find the cause of post-traumatic stress disorder. JACOB FADLEY, U.S. Army Veteran: Having PTSD is -- it's like being in a room where you have no control and everything's going wrong. Triggers are, for me, when I'm in traffic. SOLEDAD O'BRIEN: Jacob Fadley served 12 years and four tours in Iraq and Afghanistan.

Last year, we learned about the remarkable scale of the FBI's facial-recognition technology, with its access to nearly 412 million photos--many originating from sources unrelated to crime, such as ID documents. The intelligence agency has been trying to create a system that can accurately identify criminals in, say, CCTV footage--though it wasn't then known how well the bureau's software worked, nor whether it actually improved investigations. Now, we have at least a little more insight into the program. The Guardian reports that a House oversight committee hearing last week revealed some interesting new details about the proliferation and abilities of the FBI's facial-recognition systems. First, there is a very good chance that you, as an American, appear within the database. Among those many millions of photos, it turns out, are the likenesses of around half the country's adult population.

As society enters an era where AI will take life or death decisions--spotting whether moles are cancerous and driving us to work--trusting these machines will become ever more important. The difficulty is that it's almost impossible for us to understand the inner workings of many modern AI systems that perform human-like tasks, such as recognizing real-life objects or understanding speech. The models produced by the deep-learning systems that have powered recent AI breakthroughs are largely opaque, functioning as black boxes that spit out a result but whose operation remains mysterious. This inscrutability stems from the complexity of the large neural networks that underpin deep-learning systems. These brain-inspired networks are interconnected layers of algorithms that feed data into each other and can be trained to carry out specific tasks.

An asteroid as big as a bus came closer to Earth than the moon last night. The object, dubbed 2017 FJ101, zoomed passed within 202,000 miles (325,087 km) of our planet. But the near-Earth asteroid posed no threat to our planet or the moon, scientists said. An asteroid as big as a bus came closer to Earth than the moon last night. According to an asteroid-impact simulator called'Impact Earth!' by Purdue University, if a porous rock asteroid of 111 feet (34 meters) long hit Earth at a 45-degree angle, the simulator found, it would have exploded as an air burst.