We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.

Although machine learning (ML) and artificial intelligence (AI) have been around for decades, most of the recent advances in these fields have been enabled by two trends: larger publicly available research data sets and the availability of more powerful computers -- specifically ones powered by GPUs. Most of the major advances in these areas move forward in lockstep with our computational ability, as faster hardware and software allow us to explore deeper and more complex systems. At Facebook, we've made great progress thus far with off-the-shelf infrastructure components and design. We've developed software that can read stories, answer questions about scenes, play games and even learn unspecified tasks through observing some examples. But we realized that truly tackling these problems at scale would require us to design our own systems.

Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult? The answer is a definite yes!

Machine learning algorithms are worked into a variety of popular products on the market today and used by the biggest technology companies, such as Microsoft, Amazon, Google, Oracle, and IBM, to name a few. But while machine learning algorithms are fairly routine and practical, there is the primary subject from which it branches: artificial intelligence (AI). After decades of popular films and books, what has been achieved from AI? First, the benefits of AI research have yielded various applications, from Apple's Siri to IBM's prototype diagnostic app, Watson. "AI has attracted more than 17 billion in investments since 2009. Last year alone more than 2 billion was invested in 322 companies with AI-like technology" (Kelly).

Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R--a cross-platform, zero-cost statistical programming environment--there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of "Unsupervised techniques."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. You do not need to know any statistics or linear algebra to understand linear regression. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. Linear Regression for Machine Learning Photo by Nicolas Raymond, some rights reserved.

Machine learning is an artificial intelligence branch that has been well applied and recognized as an effective tool to handle a wide range of real situations. In the last few years, we have witnessed the explosion of Big Data, which has enables researchers to store data for analysis in an unprecedented way. This explosion in data available for analysis is as evident in healthcare as anywhere else. In particular, this special issue is focused on the areas of bioinformatics and biomedical engineering. These are two of the fastest developing research fields in the last few decades, since the biological data used to provide information is rapidly generated, and it is mandatory to be able to extract information and knowledge from them, as technological innovation in these fields is to be probably one of the most important developments in the next coming years.

Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning. This post was written for developers interested in applied machine learning, specifically predictive modeling. You do not need to have a background in linear algebra or statistics.

A pioneer in Artificial Intelligence, Adam Cheyer has spent most of his life living by what he calls "Verbally Stated Goals" -- that is, continuously striving to do and achieve more each year. As a child, he dreamed of becoming a magician and, in many ways, he did just that: in 2008, as inventor, computer scientist, engineer, and entrepreneur, he co-created the world's first intelligent personal assistant, Siri, with Dag Kittlaus and Tom Gruber. Siri, Inc. was a technology company borne out of SRI International, a nonprofit research unit, to create a highly clever and personable virtual assistant for smartphone consumers. By 2010, the company had been acquired by Apple Inc., and the Siri app was incorporated into Apple's iPhone 4S handsets. Cheyer became Director of Engineering for the iPhone/iOS team at Apple, where he remained for two years before leaving to spend more time with his family and to pursue personal endeavours. Cheyer is also a founding member of Change.org, a social network for positive social change, and is co-founder of Genetic Finance, which applies advanced artificial intelligence to solve problems within a wide range of industries, including financial trading, insurance, computer networking, and electronics design. Newnham: Take me back to your childhood. What first excited you about technology? Adam Cheyer: As a child, I was allowed to watch an hour of TV a week, and in that time, I got my fill of commercials selling me on the latest toys.