Deep Learning has benefited primarily and continues to do so thanks to the pioneering works of Geoff Hinton, Yann Lecun and Yoshua Bengio in the late 1980s. Contributions of Yann Lecun, especially in developing convolutional neural networks and their applications in computer vision and other areas of artificial intelligence form the basis of many products and services deployed across most technology companies today. Here are a few of Yann's groundbreaking research papers that have contributed greatly to this field: The ability of neural networks to generalize can be greatly enhanced by providing constraints from the task domain. As a follow up to his widely popular work on back-prop, in this paper, Yann and his peers demonstrate how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the US Postal Service.

Join the Technology Association of Oregon as we discuss Artificial Intelligence and the profound impacts it will have on multiple industries, healthcare among them. Attendees will have an opportunity to hear a keynote presentation from Dr. Jose Morey, Chief Medical Innovation Officer of Liberty BioSecurity and Singularity University faculty-member, on trends to watch as AI adoption increases, and what this means for healthcare patients and practitioners. Following Dr. Morey's presentation, we have assembled a panel of local digital health executives from local startups and established companies who will talk about their groundbreaking work in the area of AI and healthcare. AI and Digital Health Professionals interested in expanding their knowledge on how some of the industry's leading experts anticipate new trends, technological advances, and emerging insights will affect an ever-expanding topic.

Idemia National Security Solutions (NSS) has announced the appointment of Dr. Mark J. Burge, who is known in the artificial intelligence community for leading federal, industrial, and academic teams developing machine learning programs to address difficult biometric and computer vision challenges, to the newly created position of Chief Artificial Intelligence Officer. Burge brings experience working in academia with ETH Zurich, OSU, USNA, in government for IARPA and the NSF, and in industry for Mitre and Noblis, to the new office, according to the announcement. "As Chief AI Officer, Mark will lend insights and expertise to high-impact government and commercial R&D programs, drive groundbreaking research for AI/ML applications, and strengthen NSS's strategic positioning as the leader of identity intelligence solutions for the national security community," comments Idemia NSS President and CEO Scott Swan. The intricacies of deploying biometric facial recognition to enhance national security were explored by Swan fellow NSS executives B. Scott Swann and James Loudermilk in a guest post for Biometric Update earlier this year.

In one form or another, competitive organizations are already using artificial intelligence to automate tasks, improve efficiencies, or drive more revenue. For sales and marketing teams, AI and its subfields have finally fused science into the art of selling. Natural language processing (NLP) for example, can help determine customer intent. Machine learning (ML) can recommend the best response for sellers, and A/B testing can identify the most effective playbook for a scenario or customer demographic. Indeed, the time for pitching AI's business case in sales has long since passed.

The Indian manufacturing sector is witnessing massive transformation due to explosion of smart technologies including artificial intelligence, machine learning, 3D printing, Big Data, and 5G. Major manufacturing companies are shifting gears and investing heavily in modern technologies to meet evolving expectations of customers and partners, reduce costs, timely delivery, real-time monitoring, decision making, predictive maintenance and more. Furthering the perspective of adoption of next gen technology, Cisco in association with CNBC-TV18 has initiated a'Cisco Idea Lab' series to throw open insightful discussions with industry leaders on tech innovations' impact across businesses in India, starting with the manufacturing sector. In the first episode of the series, industry leaders Nishant Arya, ED, JBM Group; Sanjay Bhutani, MD-India & SAARC, Bausch Lomb; Mahesh Gupta, CMD, Kent RO Systems; Vijay Sethi, CIO, Hero MotoCorp; and Daisy Chittilapilly, MD- Digital Transformation Officer, Cisco India & SAARC spoke about how tech that can unlock the true potential of the manufacturing sector. In a nutshell, robotics, IoT, Analytics, 5G, smart factories, and more will drive the manufacturing sector.

Despite its role in early 20th-century fiction, AI has been part of the professional conversation for barely 70 years. AI was first studied at a Dartmouth College conference in 1956. The 1960's saw gains in machine translation and analysis. But AI underwent a "winter" from the 1970s through the early '90s. Researchers shelved their work largely because of the problem of "combinatorial explosion." A U.K. professor who first described the AI concept worried that too many variables would make it useless outside of lab settings. In the early '70s, groups like the U.S. Defense Advanced Research Projects Agency pulled funding. Research failures had become the norm. Interest in AI grew during the 1990s and early 2000's. Processing power and data volumes increased. At the same time, data sets grew massively. Algorithms gained more "meat" on which to train. Advances in game theory and data modeling led to new approaches. Today, best-in-class infrastructures can support 100,000 or more computers. Two-and-a-half quintillion bytes of data are now generated every day. Globally, private firms are spending tens of billions of dollars per year researching and improving AI initiatives. In fact, 2018's investment amount is more than 50 percent larger than last year's alone. Add it all up, and AI seems ready for a leap forward unlike any seen in its history. But, after slow decades followed by speedy discoveries, few outside the field feel they truly understand it. A recent Dell Technologies report found that 67 percent of leaders said their companies were struggling to implement AI. A similar two out of three consumers don't even realize they're using it, according to a HubSpot survey. "By far the greatest danger of artificial intelligence is that people conclude too early that they understand it."

The concept of Artificial General Intelligence (AGI) that is able to carry out tasks and understand the world in the way that humans do has been around since 2005 when it was first mooted by Dr Ben Goertzel and Cassio Pennachin in their book Artificial General Intelligence. A new collaboration between network specialist Cisco and AI company SingularityNET brings practical AGI a step closer, with a commitment to developing applied technologies and customer solutions. SingularityNET's AGI technologies include a custom version of the OpenCog AGI engine, along with a variety of unique deep neural net technologies for vision, language and other data types, and a decentralized blockchain-based platform suited for deployment of AI technologies across all markets. "These corporate investments into AGI are occurring not only out of a desire to spur rapid progress toward important research and humanitarian goals, but also because AGI capability is expected to provide tremendous commercial benefit to whomever develops it," says Dr Goertzel. "This benefit may initially take the form of a generation of'Narrow AGI' systems that infuse general intelligence into products in specific vertical markets like, say, advertising, medical research, computer networking or financial analytics."

Fox News Flash top headlines for Sept. 23 are here. Check out what's clicking on Foxnews.com Drone fighter jets, hypersonic attack planes, artificial intelligence, lasers, electronic warfare and sensors woven into the fuselage of an aircraft are all areas of current technological exploration for the Air Force as it begins early prototyping for a new, 6th-Generation fighter jet to emerge in the 2030s and 2040s. While the initiative, called Next Generation Air Dominance(NGAD), has been largely conceptual for years, Air Force officials say current "prototyping" and "demonstrations" are informing which technologies the service will invest in for the future. "We have completed an analysis of alternatives and our acquisition team is working on the requirements. We are pretty deep into experimenting with hardware and software technologies that will help us control and exploit air power into the future," Gen. James Holmes, Commander, Air Combat Command, told reporters at the Association of the Air Force Air, Space and Cyber Conference.

Fifty years ago, Mike Sanders watched with awe and anticipation as the crew of Apollo 11--Neil Armstrong, Buzz Aldrin, and Michael Collins--splashed down in the Pacific Ocean. Landing men on the moon and returning them safely to the earth was a seminal moment in the history of flight, and it had a profound effect on then 7-year-old Sanders, who now heads the Lone Star UAS Center of Excellence & Innovation at Texas A&M University–Corpus Christi. Looking back, Sanders says he never expected the day to come when he would be working with NASA on anything, let alone another chapter in the history of flight. But this year, he landed in the middle of one of the most important aeronautical projects of this generation: an effort to build a safe and effective unmanned aircraft system traffic management (UTM) platform. In August, Texas A&M–Corpus Christi's Lone Star UAS Center of Excellence and its partners' workers stood alongside NASA scientists and engineers as they flew 22 small physical and digital drones above and between tall buildings in five areas of Corpus Christi. The low-altitude test culminated a five-year effort to learn what it would take to build a nationwide system for managing low-altitude drone traffic.

We've trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization--all without task-specific training. Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.