Top Gun was released 30 years ago and it looks as if the Maverick of tomorrow will be made of microchips. Developed by a University of Cincinnati (US) doctoral candidate, an Artificial Intelligence (AI) called ALPHA has consistently beaten other AIs and a retired United States Air Force Colonel in a high-fidelity, air-combat simulator using what's known as a genetic-fuzzy system that relies on off-the-shelf PC processors to do what was thought to be the reserve of supercomputers. Unmanned Combat Aerial Vehicles (UCAVs) have made great strides in recent years, going from items of speculation to the decks of aircraft carriers. But however well they've done in taking off, landing, and carrying out assigned aerial missions, there's still been a big gap between what a human pilot can do and what a combat drone can hope to achieve. Until recently, experienced humans have found it easy to beat UCAVs in simulations after learning their tricks and weaknesses.

A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information and the selected treatment. The goal is to find the treatments that lead to the best patient responses on average. Each experiment is expensive, forcing us to learn the most from each experiment. We adopt a Bayesian approach both to incorporate possible prior information and to update our treatment regime continuously as information accrues, with the potential to allow smaller yet more informative trials and for patients to receive better treatment. By formulating the problem as contextual bandits, we introduce a knowledge gradient policy to guide the treatment assignment by maximizing the expected value of information, for which an approximation method is used to overcome computational challenges. We provide a detailed study on how to make sequential medical decisions under uncertainty to reduce health care costs on a real world knee replacement dataset. We use clustering and LASSO to deal with the intrinsic sparsity in health datasets. We show experimentally that even though the problem is sparse, through careful selection of physicians (versus picking them at random), we can significantly improve the success rates.

This post aims to introduce Gaussian Mixture Models (from now on referred to as GMMs) and explain what they can be used for. To do that I'll be creating some synthetic data and training a GMM on that using rusty-machine. This post is fairly heavy on theory but I promise there is some code. Before jumping into GMMs let's define a more general Mixture Model. A Mixture Model is a probabilistic model used to represent subclasses within a whole population.

Civil unrest events (protests, strikes, and “occupy” events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs.

A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA -- a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment. In its earliest iterations, ALPHA consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.

Aptly name artificial intelligence, ALPHA recently beat a veteran aerial combat expert in a high-fidelity combat simulator. News of an AI beating a highly skilled combat pilot has caused ripples, not only across the artificial intelligence industry, but also the entire tech, social media community. This, a landmark achievement in what's known as genetic-fuzzy systems, was the brainchild of a collaboration between AI development firm - Psibernetix, U.S. Air Force, and a team of scientists from University of Cincinnati. Gene Lee, who is a retired U.S. Air Force Colonel with oodles of experience as an instructor as well as an Air Battle Manager, lost to AI ALPHA, after sparring in what was an action-packed air combat simulation. Lee described ALPHA as, "the most aggressive, responsive, dynamic and credible AI" he has ever seen.

ALPHA, whose destiny was once just to help train fighter pilots in simulated exercises, has proved to be so good that the Air Force Research Laboratory (AFRL) is investigating whether the bot could provide the AI blueprint for combat drones in the future, potentially flying missions alongside human pilots. "ALPHA is an incredibly difficult opponent to face," its developer Nick Earnest, CEO of Psibernetix told Digital Trends. "Even flying against other pilots when ALPHA has severe handicaps to a number of its systems -- including speed, turning, missile capability and sensors -- it is able to win," Earnest said. "There is additional work to be done to both increase ALPHA's capabilities and improve its model fidelity -- but these results represent a significant breakthrough," said Earnest, University of Cincinnati graduate said. ALPHA was created using a Genetic Fuzzy Tree (GFT) system that uses genetic algorithms to train independent and interconnected systems.