A PwC study in 2017 estimated the world would gain $15.7 trillion by 2030 if artificial intelligence (AI) was adopted across nations. The study said that AI would first lead to productivity enhancement, and a major portion of gains would accrue from consumer-side effects. China, it had said, could see its GDP rising by around a fourth as it was using AI more aggressively. Although the study did not estimate how much India would gain from using AI, new research by Icrier along with Nasscom and Google shows that even a marginal increase in artificial intelligence adoption may add 2.5% to GDP in the immediate term. Moreover, it highlights that if the government spends the Rs 7,000 crore it had envisaged for the national AI programme, GDP could get boosted by as much as $86 billion.

To explore all newsletters, click here. By signing, you agree to the terms of service and privacy policy. Self-driving cars, single-pilot commercial planes, robotic soldiers, and widespread gene editing may still be things of the future, but a new research center in San Antonio is working to bring these and other artificial intelligence innovations to life. The University of Texas at San Antonio officially launched its newest research center, the UTSA Matrix AI Consortium, on Thursday morning via a livestream kickoff event. The consortium will bring together experts studying artificial intelligence to expand the use and deployment of AI. "This initiative is a concerted effort to promote AI innovation, something I'm a big fan about these days," UTSA President Taylor Eighmy said.

Robin Kelly, D-Illinois, to author a detailed report on how to keep the U.S. from falling behind China on artificial intelligence.

The University of Florida and NVIDIA Tuesday unveiled a plan to build the world's fastest AI supercomputer in academia, delivering 700 petaflops of AI performance. The effort is anchored by a $50 million gift: $25 million from alumnus and NVIDIA co-founder Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA. "We've created a replicable, powerful model of public-private cooperation for everyone's benefit," said Malachowsky, who serves as an NVIDIA Fellow, in an online event featuring leaders from both the UF and NVIDIA. UF will invest an additional $20 million to create an AI-centric supercomputing and data center. The $70 million public-private partnership promises to make UF one of the leading AI universities in the country, advance academic research and help address some of the state's most complex challenges.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Racing over bridges, supporting advancing infantry with suppressive fire, shooting vehicle-launched counter-drone missiles and engaging enemy tanks from safe standoff ranges are all operations the Army anticipates for its new fleet of armored combat vehicles. A decided emphasis for the Army's family of Next Generation Combat Vehicles is speed, maneuverability and expeditionary warfare, all key components of the service's effort to bring supportive fires to advancing infantry, cross bridges, engage in mechanized maneuver warfare and leverage a new generation of sensor technology and drone coordination. The NGCV effort consists of an interesting mixture of new platforms, to include the fast-emerging Optionally Manned Fighting Vehicle infantry carrier, Robotic Combat Vehicle and some kind of future tank-like platform.

Nine philosophers explore the various issues and questions raised by the newly released language model, GPT-3, in this edition of Philosophers On, guest edited by Annette Zimmermann. Introduction Annette Zimmermann, guest editor GPT-3, a powerful, 175 billion parameter language model developed recently by OpenAI, has been galvanizing public debate and controversy. As the MIT Technology Review puts it: “OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless”. Parts of the technology community hope (and fear) that GPT-3 could brings us one step closer to the hypothetical future possibility of human-like, highly sophisticated artificial general intelligence (AGI). Meanwhile, others (including OpenAI’s own CEO) have critiqued claims about GPT-3’s ostensible proximity to AGI, arguing that they are vastly overstated. Why the hype? As is turns out, GPT-3 is unlike other natural language processing (NLP) systems, the latter of which often struggle with what comes comparatively easily to humans: performing entirely new language tasks based on a few simple instructions and examples. Instead, NLP systems usually have to be pre-trained on a large corpus of text, and then fine-tuned in order to successfully perform a specific task. GPT-3, by contrast, does not require fine tuning of this kind: it seems to be able to perform a whole range of tasks reasonably well, from producing fiction, poetry, and press releases to functioning code, and from music, jokes, and technical manuals, to “news articles which human evaluators have difficulty distinguishing from articles written by humans”. The Philosophers On series contains group posts on issues of current interest, with the aim being to show what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. Contributors present not fully worked out position papers but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are Amanda Askell (Research Scientist, OpenAI), David Chalmers (Professor of Philosophy, New York University), Justin Khoo (Associate Professor of Philosophy, Massachusetts Institute of Technology), Carlos Montemayor (Professor of Philosophy, San Francisco State University), C. Thi Nguyen (Associate Professor of Philosophy, University of Utah), Regina Rini (Canada Research Chair in Philosophy of Moral and Social Cognition, York University), Henry Shevlin (Research Associate, Leverhulme Centre for..

When the average person thinks about AI and robots what often comes to mind are post-apocalyptic visions of scary, super-intelligent machines taking over the world, or even the universe. The Terminator movie series is a good reflection of this fear of AI, with the core technology behind the intelligent machines powered by Skynet, referred to as an "artificial neural network-based conscious group mind and artificial general superintelligence system". However, the AI of today looks nothing like the worrisome science fiction representation. Rather, AI is performing many tedious and manual tasks and providing value from recognition and conversation systems to predictive analytics pattern matching and autonomous systems. In that context, the fact that governments and military organizations are investing heavily in AI shouldn't be as much concerning as it is intriguing. The ways that machine learning and AI are being implemented are both mundane from the perspective of enabling humans to do their existing tasks better, and very interesting seeing how machines are being made more intelligent to give humans better understanding and control of the environment around them.

TORONTO, July 31, 2020 (GLOBE NEWSWIRE) -- Datametrex AI Limited (the "Company" or "Datametrex") (TSXV: DM, FSE: D4G) is pleased to announce that it has been successfully awarded a second contract in the United States defense industry, United States Air Force via Wright State Applied Research Corp. ("WSARC") on July 29, 2020. WSARC is provides contracting, security and research administration services for Wright State Research Institute, the University and the state of Ohio, and will head the U.S. Air Force Academic Partnership and Engagement Experiment (APEX) program. "This is a great example of renewable business for Datametrex. Getting a one-year contract extension shows that our technology is valued by the client, and marks progress in our plan to expand our mandate with the organization. Datametrex will continue to solidify our position as a trusted solution provider within the U.S. military departments."

Machine vision is the classification and tracking of real-world objects based on visual, x-ray, laser, or other signals. Optical character recognition was an early success of machine vision, but deciphering handwritten text remains a work in progress. The quality of machine vision depends on human labeling of a large quantity of reference images. The simplest way for machines to start learning is through access to this labeled data. Within the next five years, video-based computer vision will be able to recognize actions and predict motion--for example, in surveillance systems.

Locates ethical analysis of artificial intelligence in the context of other modes of normative analysis, including legal, regulatory, philosophical, and policy approaches Interrogates artificial intelligence within the context of related fields of technological innovation, including machine learning, blockchain, big data, and robotics Broadens the conversation about the ethics of artificial intelligence beyond computer science and related fields to include many other fields of scholarly endeavour, including the social sciences, humanities, and the professions (law, medicine, engineering, etc.) Invites critical analysis of all aspects of-and participants in-the wide and continuously expanding artificial intelligence complex, from production to commercialization to consumption, from technical experts to venture capitalists to self-regulating professionals to government officials to journalists to the general public Broadens the conversation about the ethics of artificial intelligence beyond computer science and related fields to include many other fields of scholarly endeavour, including the social sciences, humanities, and the professions (law, medicine, engineering, etc.)