The are over 13,000 Magic: The Gathering cards, each of which fits uniquely into an incredibly rich, decades-old world of lore, rules, tokens, and tournaments. It takes years to master the game, and even then a new set of cards comes every few months to shake things up. That's why it's wonderful to see what kinds of innovation and oddity a days old artificial intelligence can come up with. MTGSalvation user Talcos is a PhD candidate researcher in computer science at the University of Alabama at Birmingham. Talcos stuides how "deep neural networks" handle classification and problem-solving tasks.

An emerging trend promises to bring the power of machine learning to mobile devices, opening the door to a plethora of valuable new applications. Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But recent developments may enable machine learning to be embedded into mobile devices, thus greatly expanding applications for its use. Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning. They are particularly well suited for performing perceptual tasks such as computer vision and speech recognition.

The creators of Apple's artificial intelligence assistant, Siri, have come out with a new product, Viv, which can handle more complicated tasks and partner with third party sites. Ordering things online is definitely going to become a helluva lot easier. Siri is great and all, but she's got some flaws (sorry baby). Most of what she can do is Google search things for you, which is helpful, but also not spectacular with a boat load of other requests. For instance, what if you wanted to order some flowers for your mom on Mother's Day? Siri wouldn't be able to do that since it doesn't play well with external sites.

The Internet of Things (IoT) and Machine Learning are two of the hottest technologies of our time. They have made possible things that were totally inconceivable a few years ago, and promise to continue transforming our lives in ways that are unimaginable at present. Smart appliances, cars, homes, cities and smart-about-everything are making our lives faster and more efficient. But are we considering the tradeoffs? Are we ready to embrace the dramatic changes that will overcome our lives in a few years? More precisely, are we ready for machines starting to decide for us and us becoming dumb people who rely on smart things to carry on with their lives?

Startup Blue River Technology is building agricultural robots that allow farmers to assess their crops plant by plant, aided by the deep learning technology from Nervana Systems. Nervana, in effect, is an AI-as-a-service company aimed at expanding deep learning beyond a few big companies and research institutions with the deep pockets and expertise to use it. Nervana recently launched a cloud service to offer developers and data scientists the tools and infrastructure to make machine intelligence dramatically faster, easier and more scalable. Blue River's robots are used in the field for tasks such as thinning lettuce. It had developed a 3-D system for phenotyping corn plants that became flummoxed by things like weeds.

Seattle is poised to be an epicenter for machine learning and artificial intelligence. That's one takeaway from the inaugural Machine Learning / Artificial Intelligence Summit hosted by Madrona Venture Group on Wednesday in downtown Seattle. Thanks to Amazon Web Services, Microsoft Azure and a wide range of startups, the Emerald City is already known as a hub for cloud computing technology development, with Madrona Managing Director Matt McIllwain calling Seattle the "cloud capital of the world" more than two years ago. There is also a long list of Silicon Valley tech companies who have established engineering outposts in the Seattle area, including Google, Facebook, Oracle, Apple, HP, Uber, Lyft, Twitter, Splunk, and many others. But looking beyond the infrastructure and services side of cloud computing, there is also momentum building from both tech giants and small startups in Seattle that are developing technologies related to machine learning, artificial intelligence, natural language processing, algorithms, data analytics, and more.

About Author The second phase of Delhi's odd-even rule ended Saturday, but restrictions on "surge pricing" used by cab aggregators Ola and Uber to meet demand and supply is not expected to end till the state government issues sector-specific guidelines. Maitreya One, a black futurist and hip-hop artist living in Harlem, steps off the Greyhound bus on a warm morning in Montgomery, Alabama. I walk up to him and give him a hug. Nightmare scenarios involving Artificial Intelligence typically involve computers that become too smart for their own good and turn against their creators. In 2001: A Space Odyssey, HAL 9000 famously refused to open the pod bay doors for Dave: Well, now we have an entirely different cause to be wary of AI, and the culprit is human rather than machine.

Computer scientists are not often invited to present their research at The Science of Consciousness annual conference, but University of California San Diego development engineer Stephen Deiss did just that. He spoke to the meeting in Tucson, AZ, in late April on the subject of "Romancing the Oxymoron: The'Hardware Problem' of Machine Consciousness." "I presented my view that our preconceptions – about causality and mechanisms – bias us against accepting the possibility that machines can be conscious," said Deiss (at left), who earned his M.S. in computer science at Purdue University. "It leaves us believing that there is something spooky and unnatural about our own awareness, but I argue that consciousness is fundamental and scale-free in nature." Supported by CSE since 2013, Deiss first worked with the Non-Volatile Systems Laboratory of CSE Prof. Steven Swanson, but now splits his time between the Integrated Systems Neuroengineering Lab of Bioengineering professor Gert Cauwenberghs, and the new Pattern Recognition Laboratory of the Qualcomm Institute (both with CSE support). "We are focused on neurally-inspired or otherwise non-von Neumann computing paradigms," said Deiss, referring to new architectures that, unlike most of today's computers, are not based on executing instructions sequentially.

An Arlington, Va.-based company called Deep Learning Analytics has developed a deep machine learning automatic target recognition (ATR) program prototype for DARPA that will help assist aircraft pilots in finding targets.Deep Learning Analytics is nine months into… You must be logged in as a subscriber to view this page. Please log in below to access the content. If you are already a Defense Daily subscriber or registered user, login here.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.