A swarm intelligence similar to the one that predicted Oscar winners and Kentucky Derby finishers has come to nearly unanimous conclusions about the presidential potential of Hilary Clinton and Donald Trump. From social issues to trustworthiness and ethics, the swarm spoke loud and clear, expressing practically the same sentiment for both candidates -- extreme pessimism. The swarm consisted of 85 Democratic, Republican, or independent American voters who were asked to answer identical questions on Clinton and Trump through the swarm intelligence platform UNU. The speed at which they came to a conclusion helps calculate the percentage of "brainpower" for a particular swarm. Anywhere between 70 and 85 people participated in each round.

The following interview is one of many included in the report. As part of our ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Gurjeet Singh. Singh is CEO and co-founder of Ayasdi, a company that leverages machine intelligence software to automate and accelerate discovery of data insights. Author of numerous patents and publications in top mathematics and computer science journals, Singh has developed key mathematical and machine learning algorithms for topological data analysis. David Beyer: Let's get started by talking about your background and how you got to where you are today.

Even in the early days of bots, people were attempting to communicate with them as if they were human beings, Weizenbaum was attempting to create a bot that would learn from its interactions. IBM set up a competition between two of Jeopardy's most successful contestants and Watson; an intelligent natural language processor that uses machine learning technologies to answer questions. Google, Apple, Microsoft, and Autodesk are just a small sampling of the organizations working hard to build a bot that can interact with people using natural language and learn from those experiences. Watson is the blueprint; Slack, Facebook, Google, and Apple have examples of the interfaces humans will utilize.

Bots are currently all the rage, but they're not a new concept. Bots are only as useful as the services they are integrated with, and their purpose is essentially automation -- that is, creating and executing actions based upon a set of criteria. In order to know where bots are going, though, we need to understand where they've been. While no one knows exactly when bots started, they're widely thought to have gotten off the ground with ELIZA. The bot was built by Joseph Weizenbaum, an MIT professor, in 1964.

Over the past few months, the internet has been buzzing about bots. Microsoft, Facebook and other major players are opening up their A.I. platforms for developers, and these toolkits are an exciting and important step towards democratizing access to A.I. For the enterprise, however, the bot frenzy has accelerated a challenge that executives have been facing for the past couple of years. Many enterprise companies understand that they need an A.I. strategy and that the technology will be deployed throughout their business. Yet the challenge for them is where to actually begin. These companies understand that A.I. is a transformational integration for their business and that it will eventually touch everything from their customer service, their analytics and business intelligence, sales and CRM, and even internal knowledge management and HCM.

"Cogito's behavioral analytics technology will systematically analyze communication patterns within negotiating sessions and provide insight into the cadet's psychological state," Ness, who directs the engineering psychology program at West Point, said in a statement. A company that makes software designed for people who work in call centers might seem like a strange fit for West Point, but Cogito has also partnered with the likes of the Defence Advanced Research Project Agency (DARPA). He also mentioned the call center software the company makes, called Dialog, which he said "actually helps people be more charming on the phone." "Helping cadets advance their negotiation skills is a wonderful use of Cogito's technology," Feast said in a statement about the West Point deal.

A company that sells software that analyzes the human voice and touts the virtues of empathy, rapport and emotional intelligence is joining forces with West Point United States Military Academy in an effort to help cadets become better negotiators. Cogito Corp. is a Boston-based company that makes software that can analyze a person's voice in real-time. That information, the company says, can help customer service representatives show more empathy; the result is phone conversations that are more efficient and personalized, according to Cogito. Col. James Ness of West Point said that this kind of tech will help their students become better negotiators, a key skill for people in the military. "Cogito's behavioral analytics technology will systematically analyze communication patterns within negotiating sessions and provide insight into the cadet's psychological state," Ness, who directs the engineering psychology program at West Point, said in a statement.

Numerous companies in the healthcare space are experimenting with artificial intelligence, but what does the future hold in this sphere? You'll probably know IBM's supercomputer, Watson, from its 2011 appearance on Jeopardy. Up against two of the US quiz show's longest-running and highest-earning contestants, Watson clinched a 1m prize after answering a series of quick-fire general knowledge questions. It wasn't a close call either – at the final score, Watson's total was 31,547 ahead of its rivals' combined. But in the five years since, IBM's supercomputer has been working towards another goal, one far more lucrative than the Jeopardy!

Summary: I describe how the TrueSkill algorithm works using concepts you're already familiar with. TrueSkill is used on Xbox Live to rank and match players and it serves as a great way to understand how statistical machine learning is actually applied today. I've also created an open source project where I implemented TrueSkill three different times in increasing complexity and capability. In addition, I've created a detailed supplemental math paper that works out equations that I gloss over here. Feel free to jump to sections that look interesting and ignore ones that seem boring. Don't worry if this post seems a bit long, there are lots of pictures. It seemed easy enough: I wanted to create a database to track the skill levels of my coworkers in chess and foosball. I already knew that I wasn't very good at foosball and would bring down better players. I was curious if an algorithm could do a better job at creating well-balanced matches. I also wanted to see if I was improving at chess. I knew I needed to have an easy way to collect results from everyone and then use an algorithm that would keep getting better with more data. I was looking for a way to compress all that data and distill it down to some simple knowledge of how skilled people are. Based on some previous things that I had heard about, this seemed like a good fit for "machine learning." Machine learning is a hot area in Computer Science-- but it's intimidating. Like most subjects, there's a lot to learn to be an expert in the field. I didn't need to go very deep; I just needed to understand enough to solve my problem. I found a link to the paper describing the TrueSkill algorithm and I read it several times, but it didn't make sense. It was only 8 pages long, but it seemed beyond my capability to understand.

However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.