In the late 00's some clever academics rebranded a subset of neural network techniques to'Deep Learning', which just means a stack of different nets on top of one another, forming a sort of computationally-brilliant lasagne. When I say'machine learning' in this blogpost, I'm referring to some kind of neural network technique.) Robotics has just started to get into neural networks. This has already sped up development. This year, Google demonstrated a system that teaches robotic arms to learn how to pick up objects of any size and shape.

Imagine a future in which a personal travel assistant looks over your schedule, checks traffic for you, tells you when to leave for an appointment, ferrets out parking spaces and alerts people of your arrival time -- all while learning your driving patterns to get more helpful over time. That assistant will be BMW Connected, whose first version made its debut Thursday at Build 2016, Microsoft's developer conference in San Francisco. "Mobility needs are unique and personal and a very important aspect of our everyday life," says Thom Brenner, BMW Group vice president of Digital Life. "We are really focused on how we can integrate our offerings smoothly and seamlessly into the digital life of our customers." Powered by the Open Mobility Cloud, which is based on Microsoft Azure, and available for iOS, BMW Connected is part of the automaker's vision for the "future of mobility," which includes digital services, automated driving and assistance, and interiors designed for digital seamlessness.

We recently ran some fresh benchmarks for Spark v1.1 and v1.2 and Graphlab clusters, and included some updated numbers from other recent published benchmarks. RCV1-v2 (Reuters news data, LYR2004 distribution) benchmarks are for OAA (One Against All) classification, since RCV1-v2 has 103 independent topic labels. RCV1-v2 is a small dataset (0.5 GB). BIDMach was run on a single Amazon g2.xlarge instance, while Spark was run on a cluster of m3.2xlarge high-memory instances. The other systems were run on an 8-code Intel E-2660 system.

A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence. This was the home of Watson, the electronic genius that conquered Jeopardy! in 2011. The original Watson is still here--it's about the size of a bedroom, with 10 upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines' backs. It is surprisingly warm inside, as if the cluster were alive. Today's Watson is very different. It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred "instances" of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand.

Legal is one of the industries faced with a huge potential impact through the advent of AI. AIBusiness.org met with Counselytics Founder Jason Gabbard in New York City, one of the most exciting start-ups in this space. Counselytics client Gavin Solotar, Partner of Wachtell, Lipton, Rosen & Katz stated that'Counselytics is the first truly significant innovation I've seen in the legal field since word processing'. We call ourselves a cognitive augmentation company rather than an artificial intelligence company. The notion with cognitive augmentation is that while we may not have true thinking machines (ie, true AI), we do have really powerful and sophisticated technologies that people can utilize to perform their jobs more intelligently and more efficiently.

For those of you who don't know who or what Amelia is, she is IPsoft's cognitive agent or, in other words, an Artificial Intelligence agent that can converse with people and act as an electronic call center agent. She can do what I would say is at least 30 percent or more of the work currently performed in today's call centers. When I met Amelia, she read a Wikipedia article and had a conversation about it with us. She effectively operated similar to an eighth-grader's ability to synthesize what was in that article and answer questions. She went on to show how she could converse with us to open bank accounts, help us file insurance claims, or sell us a homeowner's or car insurance policy.

In recent years however, information technology has entered the field of international politics gaming and made some major contributions. The most successful international politics games at present are concerned with the personalities of political adversaries. An interesting international politics game could define personality in several variables including concepts like aggressiveness, nastiness, and paranoia. Computer programs would allow then the international politics gamer to vary personalities by ascribing a different numerical value to each of these variables. In this way, an infinite number of different "artificial" personalities can be created by means of using concepts like heuristics, algorithms, fuzzy logic, statistics, neural networks, and virtual reality.

Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult? The answer is a definite yes!

Salesforce just agreed to buy a small startup called PredictionIO, a software maker that helps developers build predictive applications. The announcement was made through PredictionIO's blog post. The exact terms of the deal were not disclosed. PredictionIO's website says it's a maker of open source software for machine learning apps. That means anyone could use or modify the designs of its software to build an app that does a lot of data analytics and predictive work.

Of all the fast and powerful computers in the world, our brain remains by far the most impressive. Now an interdisciplinary team of scientists, led by Baylor College of Medicine, aims to reveal the computational building blocks of our brain and use them to create smarter learning machines. To enable this ambitious project, the U.S. government's Intelligence Advanced Research Projects Activity(IARPA) has awarded a 21 million contract to an interdisciplinary team of neuroscientists, computer scientists, physicists and mathematicians, led by principal investigator Dr. Andreas Tolias, associate professor of neuroscience at Baylor. The research team includes scientists from Baylor, the California Institute of Technology, Columbia University, Cornell University, Rice University, the University of Toronto and the University of Tuebingen. The program supporting this research is known as Machine Intelligence from Cortical Networks (MICrONS) and was envisioned and organized by Jacob Vogelstein, a neuromorphic engineer and program manager with IARPA.