When asked to pick a random buddy from our Facebook friends list, we may struggle since our nomination is likely to be biased toward individuals with whom we interact more frequently. Computer assistance in such a case would make things easier: we can just store our friends' names in an array. Whenever a query comes, the computer generates a random array index and returns the name stored in the corresponding location. The question now is whether, for any given data structure problem, we can build a data structure that generates "fair" output while maintaining query efficiency. In the context of data structure query-answering, fairness can be defined as follows: we either return all valid answers or just return a uniformly random one.

The first version of Vymo only solved a basic need. Salespeople around the world found it tedious to report sales data. Without sales data, managers and leaders couldn't forecast accurately or help their teams achieve targets, which impacted their topline directly. So, we built a mobile-first solution to detect all sales activities and then, based on sales data, our solution did what a manager would do. This is quite contrary to the general perception of artificial intelligence (AI) solving -- or exacerbating, depending on whose views you subscribe to on the matter -- all of the world's most complex problems.

Four months ago I attempted to synthesize Wikipedia's crazy list of cognitive biases, and after banging my head against the wall for weeks, came up with this Cognitive Bias Cheat Sheet which John Manoogian III,beautifully organized into the above poster. Since then, I've started working on a book proposal (get on the email list!) around these topics, and wanted to start by creating an actual cheat sheet that doesn't take so long to read. There are four qualities of the universe that limit our own intelligence and the intelligence of every other person, collective, organism, machine, alien, or imaginable god. All 200-ish of our known biases are attempts to work around these conundrums! The first conundrum is that there's too much information in the universe for any individual within the universe to process.

While the deep-learning technology behind many of today's AI advances seems new to most, it has been around for decades--simply needing the data and power available today to fuel it. Artificial-intelligence technology has begun to hit its stride, springing from research labs into real business and consumer applications. Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to find out. An edited version of the experts' remarks follows the video. Li Deng, chief AI officer, Citadel: There are a few factors that really propelled AI to this current state--what many people call "the third wave." The first wave died because people were probably too naive.

Four months ago I attempted to synthesize Wikipedia's crazy list of cognitive biases, and after banging my head against the wall for weeks, came up with this Cognitive Bias Cheat Sheet which John Manoogian III, beautifully organized into the above poster. Since then, I've started working on a book proposal ( get on the email list!) around these topics, and wanted to start by creating an actual cheat sheet that doesn't take so long to read. There are four qualities of the universe that limit our own intelligence and the intelligence of every other person, collective, organism, machine, alien, or imaginable god. All 200-ish of our known biases are attempts to work around these conundrums! The first conundrum is that there's too much information in the universe for any individual within the universe to process.

The criterion commonly used in directed acyclic graphs (dags) for testing graphical independence is the well-known d-separation criterion. It allows us to build graphical representations of dependency models (usually probabilistic dependency models) in the form of belief networks, which make easy interpretation and management of independence relationships possible, without reference to numerical parameters (conditional probabilities). In this paper, we study the following combinatorial problem: finding the minimum d-separating set for two nodes in a dag. This set would represent the minimum information (in the sense of minimum number of variables) necessary to prevent these two nodes from influencing each other. The solution to this basic problem and some of its extensions can be useful in several ways, as we shall see later. Our solution is based on a two-step process: first, we reduce the original problem to the simpler one of finding a minimum separating set in an undirected graph, and second, we develop an algorithm for solving it.