SYS-CON Events announced today that TechTarget has been named "Media Sponsor" of SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. TechTarget storage websites are the best online information resource for news, tips and expert advice for the storage, backup and disaster recovery markets. By creating abundant, high-quality editorial content across more than 140 highly targeted technology-specific websites, TechTarget attracts and nurtures communities of technology buyers researching their companies' information technology needs. By understanding these buyers' content consumption behaviors, TechTarget creates the purchase intent insights that fuel efficient and effective marketing and sales activities for clients around the world. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020.

If you use a computer, you've probably seen that little "Intel Inside" sticker on it somewhere, letting you know that your Facebook posts and cat videos are happening due to Intel's dominance in the computer chip business. But there's another computer chip maker, Advanced Micro Devices, or AMD, that we don't hear about very much, although that may be about to change. Here at DT, Matt Smith has been putting AMD's new Ryzen 7 1800X chip through its paces, and he's here to report that the CPU has what it takes to compete against Intel, and at a price that puts it in reach of most people who like to build their own computer systems. The Ryzen 7 packs eight cores against the 10 in Intel's top Core i7 model, and while it isn't quite as fast as Intel's fastest chip, it's still pretty damn fast and at a price far below Intel's premium offerings. Is it a viable non-Intel option for computer builders and OEMs to drop into their machines?

In the high stakes world of professional poker, calculating the odds can go a long way to help win the hand. And a new bot developed by a team of computer scientists could give even James Bond a run for his money. The DeepStack system uses Artificial Intelligence to reduce an exponentially complex number of calculations to a more manageable size - then decides on its play in a matter of seconds. A combination of cool composure, a strong hand and more than a dash of luck allowed James Bond to walk off with a massive £93 million ($115 million) pot in Casino Royale. DeepStack played 3,000 hands each against eleven professional players.

While supervised neural nets trained on huge datasets can achieve impressive performances in tasks such as computer vision and speech recognition, they are often criticized because their internal representations are lacking in interpretability. In order to address some of these concerns, work by scientist Charlie Tang proposes models which add domain-specific knowledge in the form of structured latent variables to standard deep learning methods, leading to good results in one-shot face recognition under illumination variations.

The telescope offers one of the most seductive suggestions a technological object can carry: the idea that humans might pick up a thing, peer into it, and finally solve the riddle of the heavens as a result. Unraveling that mystery requires its own kind of refraction in perspective, collapsing the distance between near and far as a way to understand our planet and its place in the universe. This is why early astronomers didn't just gaze up each night to produce detailed sketches of celestial bodies. They also tracked the movement of those bodies across the sky over time. They developed an understanding of Earth's movement as a result.

The future of bots is sitting in thousands of documents folders, waiting to be born. At least, that's the premise of Albert, a bot and bot-creation tool from NoHold, which released a pro version on Monday. The premise behind Albert is straightforward: upload a document, and then ask the Albert-generated bot to answer questions with information based on that document. Albert is a product of the modern era of chatbots, but Albert's origins are, by tech standards, positively ancient: the key work dates back to a patent filed in 1999. "Do you know what a bucaneve is?" asks Diego Ventura, the CEO and Founder of NoHold.

A fresh Texas Hold'em-playing AI terror has emerged barely a month after a supercomputer-powered bot claimed victory over four professional poker players. But instead of relying on a supercomputer's hardware, the DeepStack AI has shown how it too can decisively defeat human poker pros while running on a GPU chip equivalent to those found in gaming laptops. The success of any poker-playing computer algorithm in heads-up, no-limit Texas Hold'em is no small feat. This version of two-player poker with unrestricted bet sizes has 10160 possible plays at different stages of the game--more than the number of atoms in the entire universe. But the Canadian and Czech reseachers who developed the new DeepStack algorithm leveraged deep learning technology to create the computer equivalent of intuition and reduce the possible future plays that needed to be calculated at any point in the game to just 107.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value.

Building upon his success in getting a humanoid robot to perform an action he was aiming for, Kuniyoshi has now turned his attention to how fetuses develop intelligence to explore the fundamental principles of intelligence of our species. For his studies, Kuniyoshi has modeled a virtual fetus consisting of approximately 400 muscles and a skeleton, gestating in an environment resembling a womb filled with amniotic fluid to run computer simulations. This fetus does not possess "innate behaviors," namely a pre-existing process that spawns specific movements. However, when vibrations sent through neural signals from the spinal cord to random muscles reached the body's other muscles through the skeleton or feedback of pressure from the amniotic fluid and uterine wall, the muscles and corresponding spinal circuits began coordinating their movements, leading to the emergence of actions resembling those of an actual fetus in the womb (movie 4). Recently, Kuniyoshi used a computer model of a fetus in the 32nd week of gestation with cerebral neural circuits, inside the womb of a woman, to observe how the cerebrum receives sensory information and the neural circuits learn about the body through touch and somatic sensation. Furthermore, he compared a fetus that was raised inside the womb with one raised outside it, and found that learning occurring inside the womb led to more enhanced development of neural circuits than that occurring outside it.

The application of AI to education has been the subject of academic research for more than 30 years, with the aim of making "computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit"3. The evidence from existing AI systems that assess learning as well as provide tutoring is positive with respect to their assessment accuracy4. AI is a powerful tool to open up the'black box of learning', by providing a deep, fine-grained understanding of when and how learning actually happens. In order to open this black box of learning, AI assessment systems need information about: (1) the curriculum, subject area and learning activities that each student is completing; (2) the details of the steps each student takes as they complete these activities; and (3) what counts as success within each of these activities and within each of the steps towards the completion of each activity. AI techniques, such as computer modelling and machine learning, are applied to this information and the AI assessment system forms an evaluation of the student's knowledge of the subject area being studied.