Can computers understand an individual human's personality (and then, presumably, use that understanding to better target marketing messages)? It turns out that's no longer even a question: if you haven't yet played with CrystalKnows, be prepared for some weirdly accurate insights into yourself and those you know well, based on public Internet information. And, yes, Crystal advises you how to interact with others based on those insights, going so far as to suggest changes to your emails to better fit the style of the recipient. If there's a gap between this and letting a computer just manage the whole relationship without any human involvement, it's almost too small to worry about. So the real challenge isn't having the computer analyze your data; it's having enough data for the computer to analyze.

Sean Gallup / GettyDon't worry, the machines are your friend. It used to be the case that you had to program a computer so that it knew how to do things. Now computers can learn from experience. The breakthrough is called "machine learning." It's unimaginably important for understanding where technology is going, and where society is going with it.

Elliptic, a blockchain intelligence firm that uses machine learning to root out fraudulent blockchain transactions, has raised 5 million from private equity investors. The funding round was led by Paladin Capital Group and included participation from Santander InnoVentures and others. The company uses graph analysis and machine learning to identify illicit activity in blockchain transactions, and provides a variety of compliance and securities services related to distributed ledger technology. Interactive gaming company Pivit used distributed ledger technology provided by Digital Asset Holdings to issue a portion of its latest 5 million funding round. Elliptic's technology was used to ensure that the digitally issued securities always remain under the control of Pivit and its investors.

There is huge potential for deep machine learning to become a valuable asset in the intelligence gathering space, according to Pentagon Deputy Secretary Robert Work -- it could ultimately allow U.S. forces to get an edge in the fight against the Islamic State of Iraq and Syria (ISIS, ISIL, IS) by providing greater insights into their networks and practices. The evaluative capabilities and intelligence gathering promise of deep machine learning, Work said, has already shown great potential through the use of publicly available materials on social media, which paint a clearer picture of the events surrounding the downing of Malaysian passenger airliner MH17. Growing tensions between Russia and China were also discussed as a point of concern around the potential for machines to be given lethal authority and how the U.S. might respond in such a case. "There are two things that really keep me up at night about this competition; the first is adversaries who will give machines lethal authority and how will we respond to that," he said.

There is huge potential for deep machine learning to become a valuable asset in the intelligence gathering space, according to Pentagon Deputy Secretary Robert Work -- it could ultimately allow U.S. forces to get an edge in the fight against the Islamic State of Iraq and Syria (ISIS, ISIL, IS) by providing greater insights into their networks and practices. Work made the statement during a roughly hour-long talk called Securing Tomorrow, held March 30 by the Washington Post, where he addressed some of the threat concerns facing the United States and the strategy the Department of Defense is deploying to overcome them. Moderated by Post columnist David Ignatius, the discussion also focused on how the behemoth agency is approaching new technologies and the perceived threats being seen from top international competitors, like Russia and China. "Without question, we are absolutely certain, that the use of deep learning machines is going to allow us to have a better understanding of ISIS as a network and a better understanding of how we can target it precisely and lead to its defeat." The evaluative capabilities and intelligence gathering promise of deep machine learning, Work said, has already shown great potential through the use of publicly available materials on social media, which paint a clearer picture of the events surrounding the downing of Malaysian passenger airliner MH17.

This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. Everything in life depends on time and therefore, represents a sequence. To perform machine learning with sequential data (text, speech, video, etc.) we could use a regular neural network and feed it the entire sequence, but the input size of our data would be fixed, which is quite limiting. Other problems with this approach occur if important events in a sequence lie just outside of the input window. What we need is (1) a network to which we can feed sequences of arbitrary length one element of the sequence per time step (for example a video is just a sequence of images; we feed the network one image at a time); and (2) a network which has some kind of memory to remember important events which happened many time steps in the past.

By now we all know what Random Forests is. We know about the great off-the-self performance, ease of tuning and parallelization, as well as it's importance measures. It's easy for engineers implementing RF to forget about it's underpinnings. Unlike some of it's more modern and advanced contemporaries, descision trees are easy to interpret.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some 8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.

I recently had the opportunity to learn more about Inbenta, a provider of Natural Language Search technology for intelligent assistant and web self-service technologies. I spoke with global marketing director Julie Casson and Kelly Foster, linguist, to gain insight into a company I didn't know much about. Inbenta originated in Barcelona, and now has offices in the United States, France, Singapore, Brazil and the Netherlands. Casson and Foster are located at the office in Sunnyvale, California. Prior to our conversation, I knew that Inbenta offers intelligent assistant technology and an extremely innovative 3D avatar, called Victoria.

Artificial Intelligence (AI) is all the rage in the popular press this week. Even if you are an Alien who just landed on Earth from a planet far away, it is impossible to miss the headlines that AlphaGo--the AI program developed by Google--beat the world champion of the game Go Lee Sedol 4-1. Why is there such excitement about this AI program beating the human champion? What is in fact AI or Artificial Intelligence? What does this all mean to our businesses or each one of us? To be even more melodramatic – what does this mean for humanity?