The European Commission recently announced plans to increase that funding, to make more data available for use in AI, and to work with EU member states on a strategy for deploying AI in the European economy. But at the same time, the EU's new General Data Protection Regulation (GDPR) puts tight restrictions on uses of AI that involve personal data, and EU policymakers continue to search for additional restrictions on AI to address their remaining fears. Unlike tobacco, AI has many beneficial uses, and the potential risks depend on how it is developed and used over the long-term. The irony is that if Europe over-regulates AI now, it will miss its chance for global influence over the technology's future. The commission does not see a contradiction because it believes that stringent regulation will engender consumer trust in AI. But that reasoning is flawed.

We propose a very simple modification of gradient descent and stochastic gradient descent. We show that when applied to a variety of machine learning models including softmax regression, convolutional neural nets, generative adversarial nets, and deep reinforcement learning, this very simple surrogate can dramatically reduce the variance and improve the accuracy of the generalization. The new algorithm, (which depends on one nonnegative parameter) when applied to non-convex minimization, tends to avoid sharp local minima. Instead it seeks somewhat flatter local (and often global) minima. The method only involves preconditioning the gradient by the inverse of a tri-diagonal matrix that is positive definite. The motivation comes from the theory of Hamilton-Jacobi partial differential equations. This theory demonstrates that the new algorithm is almost the same as doing gradient descent on a new function which (a) has the same global minima as the original function and (b) is "more convex". Again, the programming effort in doing this is minimal, in cost, complexity and effort. We implement our algorithm into both PyTorch and Tensorflow platforms, which will be made publicly available.

Regional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.

It seems as if everybody is starting to look at artificial intelligence as some sort of make-or-break technology for the human race. Where the fate of the planet is concerned, there is an increasing collision between the nationalistic view that AI's overriding purpose is to help countries hold their own in geopolitical struggles and the humanitarian view that AI should deliver the benefits of material prosperity to all peoples, serving as an activist force in the universal struggle for equality, free expression, personal autonomy and democratic governance. The nationalistic perspective keeps popping out in headlines. For example, there are the sentiments expressed in this recent article by Horacio Rozanski, chief executive of Booz Allen Hamilton Inc. He discusses what he regards as a "close race" between the United States and China in developing and exploiting AI. I've been exploring AI benchmarking initiatives recently, and I take issue with the assumption that we can validly benchmark one nation against another in this regard.

The application of artificial intelligence algorithms in the justice system - for example to decide which offenders are eligible for alternatives to custodial sentences - will be among the first items on the agenda of a year-long investigation into the impact of technology opened by the Law Society. The Public Policy Technology and Law Commission - Algorithms in the Justice System, will meet in public three times, its chair Christina Blacklaws, who next month assumes the presidency of the Law Society, announced last night. The commmission's formation reflects growing concern about the advent of so-called'Schrodinger's justice' - in which decisions are taken by self-learning systems impervious to examination or challenge. Pressure group Big Brother Watch revealed yesterday that it has instructed human rights firm Leigh Day to take action against the Metropolitan Police over to demand the withdrawal of'dangerously authoritarian' automated technology for recognising faces at public events such as the Notting Hill Carnival. Blacklaws told an event at Chancery Lane last night that facial recognition systems in effect require'a degree of privacy to be surrendered in return for a promise of greater security' - but that the technology had so far failed to work.

Before joining Waymo, Waydo was a longtime engineer at NASA's Jet Propulsion laboratory, according to her LinkedIn profile. At Waymo, she oversaw systems engineering - the process of ensuring hardware and software work well together - and helped make key decisions about when to remove human safety drivers from the company's test fleet in Arizona, The Information reported.

A spacecraft, spinning in Earth's orbit, reaches inside itself. One of its four arms pulls out a length of polymer pipe that has been 3D-printed inside the body of the machines. All four of the spacecraft's arms are securing pieces together as it builds a new space station right there in orbit. This surreal project, called Archinaut, is the future vision of space manufacturing company Made In Space. The company promises a future of large imaging arrays, kilometer-scale communications tools, and big space stations all built off-planet by smart robots.

Last week's White House summit on artificial intelligence (AI) is an encouraging sign of American government and industry working collaboratively to advance this transformative technology. Defense Secretary Jim Mattis recently told a congressional committee that the Department of Defense (DoD) is "not going to have more papers, we're going to move on [AI]." DoD is broadly pursuing AI, not just as another set of programs, but also as a powerful enabler for nearly every defense mission and function. Strategic competitors are not standing idly by, either, as they reshape their economies to more service-based industries bolstered by technology. The U.S. commercial sector has a sense of urgency in adopting AI in the face of increasing international competition.

White woman cyborg on blurred background scanning human DNA 3D rendering. Artificial Intelligence (AI) has been much talked about in recent times and especially as regards the technology disrupting the jobs market. Some have even suggested it might not be too long before one won't be able to tell the difference at work between colleagues who are human or a digital replication. And, so it was that I went off to New York's financial district in recent days to hear the movers and shakers in the field. The presentations and keynote addresses from academics and industry players around AI as well as the "elephant in the room" - what happens once machines outsmart humans at all tasks - certainly gave much food for thought.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. On May 9, the Department of Transportation announced the first 10 project sites it chose to participate in its new three-year Drone Integration Pilot Program aimed at expanding the testing of new drone technology in a select number of local, state, and tribal jurisdictions. Selected from 149 lead applicants and over 2,800 private sector "interested parties," they're an eclectic bunch: the Choctaw Nation of Oklahoma; projects in the city of San Diego; the Innovation and Entrepreneurship Investment Authority in Herndon, Virginia; the Lee County Mosquito Control District in Florida; the Memphis–Shelby County Airport Authority in Tennessee; the North Carolina, Kansas, and North Dakota departments of transportation; the city of Reno, Nevada; and the University of Alaska–Fairbanks all saw their specific public-private partnership proposals get the greenlight. The projects include plans to test various kinds of unmanned aircraft systems (UAS for short, as they are formally known), including drone-based mapping, inspections, traffic and weather monitoring, commercial and medical delivery, and law enforcement surveillance systems. Selected applicants will be given special attention from the Federal Aviation Administration.