IBM's artificial intelligence technology, Watson, amazed the world five years ago when it won "Jeopardy." After that, Watson went on to help improve industries like education and transportation. Now Watson is discovering new insights for cancer patients, as IBM has enlisted 20 top cancer institutes to teach Watson.

Much research in this field has led to new advances and improvements in this space, leading to the new research in the areas of machine intelligence, machine learning and deep learning. But what do these words and terms mean? How are they related to each other? This is the overarching term which encompasses all of these research areas. It is usually defined as the science of making computers complete tasks which usually require human intelligence, like learning, decision making and problem solving.

In a very short time, Machine Learning has gone from a theoretical study to (almost) mainstream commercial reality. Much of this growth in machine learning and artificial intelligence can be attributed to the Internet of Things which is creating mega-myriads of data that must be processed in a variety of ways. While the IoT DevCon provides practical training for developing IoT applications such as medical devices, robots, drones, point-of-sale equipment, and industrial automation, the Machine Learning DevCon will explore the hardware and software challenges of building up and debugging these complex machine learning and artificial intelligence systems. The 2017 Machine Learning Developers Conference is directed by Markus Levy, Conference Chairman.

"Unicorn," for example, a rare private company valued at more than 1 billion, is a description that outwore its welcome once there were so many of them. In other words, I need convincing that the latest hot topic in Silicon Valley is worth the effort to understand. Reading Roger Parloff's insightful feature in the current issue of Fortune, "Why Deep Learning Is Suddenly Changing Your Life," I became convinced about the catch-all phrase artificial intelligence and its subset fields of study, including machine learning and deep learning, also knowns as deep neural nets. There isn't space here to do justice to this complex topic. But one quote from Parloff's masterful story might convince you to take the time to go deeper.

Recently, GoPro unveiled an entirely new product lineup. Not just the new Hero5 Black ( 399) and Hero5 Session ( 299), but also the Karma drone, a surprise handheld gimbal -- known as the Karma Grip -- and a brand new cloud service called GoPro Plus. While it's going to be another week or so before we can get our hands on the Karma drone, I had a chance to spend some time with the new flagship Hero5 Black and its sidekick, the Hero5 Session. Both come with some exciting, long awaited new features, which I'll lay out in detail below. With many of the upgrades addressing common pain points, it's clear that this year, GoPro was mainly focused on polishing the user experience. The Hero5 Session is much improved compared to its predecessor, adding 4K and 2.7k video modes, as well as various options for frame rates and field-of-view controls. Unfortunately, the battery life isn't great, and with no way to swap in a fresh one (the battery is built in), you'll need to be prudent on long excursions.

Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.

Google's machine learning subsidiary DeepMind has kicked off a new research partnership with the radiotherapy department at the University College London Hospitals NHS Foundation Trust, a provider organization that specializes in cancer treatment. DeepMind and clinicians in UCLH's radiotherapy team are exploring whether machine learning methods can reduce the amount of time it takes to plan radiotherapy treatment for cancers of the head and neck. To that end, 1 in 75 men and 1 in 150 women will be diagnosed with oral cancer during their lifetime, and oral cavity cancer has risen by 92 percent since the 1970s, DeepMind said. Head and neck cancer in general affects more than 11,000 patients in the U.K. alone each year, the firm added. "Advances in treatment such as radiotherapy have improved survival rates, but because of the high number of delicate structures concentrated in this area of the body, clinicians have to plan treatment extremely carefully to ensure none of the vital nerves or organs are damaged," DeepMind said.

Human (or any other animal for that matter) brain computational power is limited by two basic evolution requirements: survival and procreation. Our "hardware" (physiology) and "software" (hard-coded nature psychology) only had to evolve to allow us to perform a set of basic actions - identify Friend or Foe, obtain food, find our place in the social tribe hierarchy, ultimately find a mate and multiply. Anything beyond this point, or not directly leading to this point can be considered redundant, when viewed from the evolution perspective. To accomplish these "life" goals, our brains evolved to a certain physical limit (100 billion neurons per average brain, on average 7000 synaptic connections per neuron). Obviously, evolving beyond this limit was not beneficiary for survival and procreation in the African savannas.

Data scientists, developers, and researchers are using machine learning to gain insights previously out of reach. Programs that learn from experience are helping them discover how the human genome works, understand consumer behavior to a degree never before possible, and build systems for purchase recommendations, image recognition, and fraud prevention, among other uses. Now you can scale your machine learning and deep learning applications quickly – and gain insights more efficiently – with your existing hardware infrastructure. Popular open frameworks newly optimized for Intel, together with our advanced math libraries, make Intel Architecture-based platforms a smart choice for these projects.

Artificial intelligence's (AI) entrance into any profession often features an odd mixture of ominous predictions from affected workers and executives extolling the virtues of'innovation,' 'change,' and whatever other flashy, vague adjectives apply to the situation. Yet between the hype and horror lies a more moderate, less sexy truth that proceeds on a slower trajectory than vocal pro-and-opponents would have you believe. The future has a way of creeping up on us, and the same can be said (unsurprisingly) for its movement into the legal industry. For while many were debating how much AI could really help legal tasks, let alone just what exactly AI actually is, technologists, vendors and lawyers with foresight were eagerly moving forward. And before we knew it, the adoption of AI-infused legal technology began its transition from the exception to the norm.