FILE - In this June 4, 1942 file photo provided by the U.S. Navy shows the USS Yorktown listing heavily to port after being struck by Japanese bombers and torpedo planes in the Battle of Midway. Researchers scouring the world's oceans for sunken World War II ships are honing in on debris fields deep in the Pacific.(AP MIDWAY ATOLL, Northwestern Hawaiian Islands (AP) -- Deep-sea explorers scouring the world's oceans for sunken World War II ships are focusing on debris fields deep in the Pacific, in an area where one of the most decisive battles of the time took place. Hundreds of miles off Midway Atoll, nearly halfway between the United States and Japan, a research vessel is launching underwater robots miles into the abyss to look for warships from the famed Battle of Midway. Weeks of grid searches around the Northwestern Hawaiian Islands have already led the crew of the Petrel to one sunken warship, the Japanese ship the Kaga.

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its ap - pearance in a bigger font and sometimes in colour print. In this paper, we sug - gest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers includ - ing children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are indepen - dent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

In wondering what can be done to steer civilization away from the abyss, I confess to being increasingly puzzled by the central enigma of contemporary cognitive psychology: To what degree are we consciously capable of changing our minds? I don't mean changing our minds as to who is the best NFL quarterback, but changing our convictions about major personal and social issues that should unite but invariably divide us. As a senior neurologist whose career began before CAT and MRI scans, I have come to feel that conscious reasoning, the commonly believed remedy for our social ills, is an illusion, an epiphenomenon supported by age-old mythology rather than convincing scientific evidence. If so, it's time for us to consider alternate ways of thinking about thinking that are more consistent with what little we do understand about brain function. I'm no apologist for artificial intelligence, but if we are going to solve the world's greatest problems, there are several major advantages in abandoning the notion of conscious reason in favor of seeing humans as having an AI-like "black-box" intelligence. To believe that we can accurately determine whether or not consciousness contains causal properties is sheer folly. But first, a brief overview as to why I feel so strongly that purely conscious thought isn't physiologically likely.

On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects. A 5-feet 7-inch tall white robot, basically a spindly arm affixed with a claw of the sort customarily found in an arcade vending machine, glided around the kitchen on its two Segway wheels. Following the command of a research scientist sitting at a nearby computer, the robot grabbed a Cheez-It box on the counter and extended its limb to gently place the snacks inside a cabinet. "What's deceptive is that what's simple to us in the kitchen is challenging for a robot," said University of Washington Computer Science and Engineering Professor Dieter Fox, who also serves as the lab's senior director of robotics research. The Silicon Valley-based technology company opened the robotics lab last fall to harness the UW's talent in a sector where Seattle plays a central role. Still, paranoia around the capabilities of AI technology persist.

Visual 1st, the executive conference focused on promoting innovation and partnerships in the photo and video ecosystem, will bring AI, AR, computational photography, and the future of digital cameras to the center stage, Oct. 3-4, at the Golden Gate Club, San Francisco, Calif. AI is already everywhere in imaging, from recognition to enhancement to auto-editing – and of course, there's much more to come. In parallel, AR solutions are proliferating at a rapid pace, serving use cases ranging from having lots of fun to being highly productive. As these two technologies evolve in mutually reinforcing ways, we, as an industry, must take the imaging solutions they enable to the next level of value and profitability, while also keeping things safe, secure and private for our customers – but how? Alexander Schiffhauer recently left his role as Technical Advisor to Google's CEO Sundar Pichai to take product management responsibility for the company's computational photography teams. Under his leadership, these teams have pioneered innovation on Pixel Camera, leveraging AI and computer vision techniques to create photos unimaginable only a few years ago.

Turn AI cameras on your employees and you can measure their productivity. Fly them over the Pacific Ocean and you've got yourself an automated shark-warning system. What's happening: UC Santa Barbara, with the help of a few AI experts from Salesforce, is using drones to monitor sharks near California beaches in real time.

October 14, 2019 A BSTRACT Expressivity is one of the most significant issues in assessing neural networks. In this paper, we provide a quantitative analysis of the expressivity from dynamic models, where Hilbert space is employed to analyze its convergence and criticality. From the feature mapping of several widely used activation functions made by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation. Moreover, we analyze the information transfer of deep neural networks, emphasizing the convergence problem caused by the mismatch between input and topological structure. We also study the effects of input perturbations and regularization operators on critical expressivity. Finally, we verified the proposed method by multivariate time series prediction. The results show that the optimized DeepESN provides higher predictive performance, especially for long-term prediction. Our theoretical analysis reveals that deep neural networks use spatial domains for information representation and evolve to the edge of chaos as depth increases. In actual training, whether a particular network can ultimately arrive that depends on its ability to overcome convergence and pass information to the required network depth. K eywords Deep neural networks; expressivity; criticality theory; convergence; activation function; Hilbert transform 1 Introduction Deep neural networks (DNNs) have achieved outstanding performance in many fields, from the automatic translation to speech and image recognition [1, 2].

We develop a novel approximate Bayesian computation (ABC) framework, ABCDP, that obeys the notion of differential privacy (DP). Under our framework, simply performing ABC inference with a mild modification yields differentially private posterior samples. We theoretically analyze the interplay between the ABC similarity threshold $\epsilon_{abc}$ (for comparing the similarity between real and simulated data) and the resulting privacy level $\epsilon_{dp}$ of the posterior samples, in two types of frequently-used ABC algorithms. We apply ABCDP to simulated data as well as privacy-sensitive real data. The results suggest that tuning the similarity threshold $\epsilon_{abc}$ helps us obtain better privacy and accuracy trade-off.

The Crew Interactive Mobile CompaniON (CIMON) mobile astronaut assistant, which is equipped with artificial intelligence (AI), returned to Earth on 27 August 2019. The SpaceX CRS-18 Dragon spacecraft carrying CIMON was undocked from the International Space Station (ISS) at 16:59 CEST; the capsule splashed down in the Pacific Ocean approximately 480 kilometres southwest of Los Angeles and was recovered at 22:21 CEST. "We expect CIMON to return to Germany at the end of October," reports Christian Karrasch, CIMON Project Manager at the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) Space Administration. He looks back on the past few months: "CIMON is a technology demonstration that has completely met our expectations. During its initial operation in space – a 90-minute mission with the German ESA astronaut Alexander Gerst on the ISS in November 2018 – it showed that it functions well in microgravity conditions and can interact successfully with astronauts. We are very proud to have been the first to use AI on the Space Station and have been working for several months on an improved successor model. With CIMON, we were able to lay the foundations for human assistance systems in space to support astronauts in their tasks and perhaps, in the future, to take over some of their work."

For decades, sci-fi movies have predicted a future in which humans lose control of intelligent machines and chaos ensues. Those apocalyptic portrayals of artificial intelligence may seem like a distant or unrealistic future. But the seeds of a reality in which we lose control of the machines we build are being sown today. "What is the biggest software-related issue to impact the economy in Puget Sound in 2019?" "Software in the cockpit of an airplane, software that the pilots couldn't turn off," Smith said. Smith was referring to the multi-billion dollar fallout from Boeing's faulty 737 Max software that resulted in two crashes killing 346 people. Boeing's manufacturing center is based in Renton, Wash.