AlphaGo handily beat 18-time world Go champion Lee Sedol 4-1, and in doing so taught us several interesting lessons about where AI research is today, and where it is headed. One fascinating thing about AlphaGo is the unusual way it was designed. The software combined deep learning--the hottest AI technique out there today--with a much older, and far less fashionable, approach. Deep learning involves using very large simulated neural networks, and usually it eschews logic or symbol manipulation of the kind pioneered by the likes of Marvin Minksy and John McCarthy. But AlphaGo combines deep learning with something called tree-search, a technique invented by one of Minksy's contemporaries and colleagues, Claude Shannon.

Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity of artificial intelligence, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition, itself. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence." To make this summary article more useful, we've quickly broken down all reports by source, definition / meaning of "artificial intelligence", valuation, and timeline.

CAPE CANAVERAL, Fla. – Fresh supplies shipped out late Tuesday for the International Space Station, where the shelves finally are getting full after a string of failed deliveries. Launching beneath the light of a nearly full moon, the unmanned Atlas V rocket provided late-night sparkle as it headed north with its precious cargo and paralleled the East Coast on its way to orbit. Orbital ATK's Cygnus capsule holds nearly 8,000 pounds of food, equipment and scientific research for NASA, including a commercial-quality 3-D printer anyone can rent and experimental robotic grippers modeled after the thousands of sticky hairs on geckos' feet. There's also a fire experiment that will remain on the Cygnus. Researchers will ignite a large-scale blaze, in a contained box, to see how it spreads in weightlessness.

Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting, collecting, structuring and analyzing data present serious risks. For example, a recent Wall Street Journal article entitled "Tweets Provide New Way to Gauge TV Audiences" provides evidence of a disconnect between mainstream viewers and folks who use Twitter. The chart above shows the disconnect between the most popular and most tweeted shows - the most tweeted show is not a top ten show. While Twitter data can be useful for detecting trends and sentiments for certain areas (e.g., disease surveillance, natural disaster surveillance, product sentiments, financial trading, politics) in limited circumstances using scientific methods, it can also mislead and present a false view of reality.

United Launch Alliance launched an Atlas V rocket from Cape Canaveral that is headed towards the International Space Station. An Atlas V rocket launch carrying a Cygnus spacecraft bound for the International Space Station lifts off from Cape Canaveral Air Force Station in Florida on March 22, 2016. CAPE CANAVERAL -- An unmanned Cygnus supply ship is headed for a Saturday morning rendezvous with the International Space Station after an 11:05 p.m. ET Tuesday blastoff from Cape Canaveral Air Force Station aboard an Atlas V rocket. The 194-foot United Launch Alliance rocket blazed into a night sky brightened by a nearly full moon, carrying the Orbital ATK Cygnus packed with 7,500 pounds of food, equipment and science experiments. "These resupply missions are critical," Kenny Todd, NASA's space station operations integration manager, said before the launch.

CAPE CANAVERAL, FLORIDA – Fresh supplies are due to ship out late Tuesday for the International Space Station, where the shelves finally are getting full after a string of failed deliveries. An unmanned Atlas V rocket is scheduled to blast off at 11:05 p.m. by the light of a nearly full moon. The launch may be visible along the entire East Coast. Orbital ATK's Cygnus capsule holds nearly 8,000 pounds of food, equipment and scientific research for NASA, including a commercial-quality 3-D printer anyone can rent and experimental robotic grippers modeled after the thousands of sticky hairs on geckos' feet. There's also a fire experiment that will remain on the Cygnus.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

A neuro-technology company has received Food and Drug Administration (FDA) clearance for a medical device that could detect concussions in less than 60 seconds on the sidelines of playing fields across the nation. EYE-SYNC, a product of SyncThink, is an integrated head-mounted eye-tracking device that analyzes eye movement impairment through the use of virtual reality. Dr. Jamshid Ghajar, neurosurgeon at Stanford University, president of the Brain Trauma Foundation, and SyncThink founder, told FoxNews.com the product is distinct mainly because it does not claim to diagnose a concussion but rather detects disruption in visual information. "All of the other technologies out there say that they're'diagnosing concussion,' but there's no accepted definition, so how are you diagnosing it?" he said. Data released by the National Football League (NFL) in January revealed the rate of concussions in the 2015 season was up nearly 32 percent compared with data from 2014, while the Centers for Disease Control and Prevention (CDC) reports that each year nearly 500,000 children are treated for a traumatic brain injury, including concussion.

"What is the relationship like between your team and the data scientists?" This is, without a doubt, the question I'm most frequently asked when conducting interviews for data platform engineers. It's a fine question – one that, given the state of engineering jobs in the data space, is essential to ask as part of doing due diligence in evaluating new opportunities. I'm always happy to answer. But I wish I didn't have to, because this a question that is motivated by skepticism and fear. If you read the recruiting propaganda of data science and algorithm development departments in the valley, you might be convinced that the relationship between data scientists and engineers is highly collaborative, organic, and creative. However, it's not a well kept secret that this is seldom the case. Most shops foster a relationship between engineers and scientists that lies somewhere in the spectrum between non-existent1 and highly dysfunctional. Data scientists: the folks who are "better engineers than statisticians and better statisticians than engineers".

Vivek Wadhwa is an academic, entrepreneur, and author who holds appointments at Stanford, Duke, and Singularity University. For the past century, the price and performance of computing has been on an exponential curve. And, as futurist Ray Kurzweil observed, once any technology becomes an information technology, its development follows the same curve, so we are seeing exponential advances in technologies such as sensors, networks, artificial intelligence, and robotics. The convergence of these technologies is making amazing things possible. Yes, with every good there is a bad; wonderful things will become possible, but with them we will also create new problems for mankind. Here are six of the technologies that will make this happen, and the good they will do.