This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in hundreds of instances of several E-T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure.

When did electricity take over from steam in the UK? When did football replace cricket as the most popular sport? And what year did women start to become more frequently mentioned in the press? Specifically, a new paper by a team artificial intelligence researchers at the University of Bristol that used AIto analyse the news from 100 different British regional newspapers over the past 150 years. The team of academics, led by professor Nello Cristianini, collaborated closely with the company findmypast, which is digitising historical newspapers from the British Library as part of their British Newspaper Archive project.

What major advances do you foresee in artificial intelligence in 2017? Despite the impressive progress that machine learning made in 2016, AI systems remain specialists: they cannot add new skills to their repertoires without erasing what they already know. This is the problem of catastrophic forgetting. An AI trained to recognise faces in photographs, for example, would not apply well to another visual task, such as recognising street signs. Each system would need to be trained for its own limited task.

Software can now predict when people are most likely to try new things. Scientists analyzed purchasing data from over 280,000 shoppers and found that the more people purchase a product, the more likely they are to stick with it. However, it also found those who have recently switched brands are twice as likely to try a new one. Scientists analyzed purchasing data from over 280,000 shoppers and found that the more people purchase a product, the more likely they are to continue to do so. Researchers at the University of London designed an AI to predict when people are most likely to try new things - such as switching from one product to another.

While there are some sectors of the tech-driven economy that thrive on rapid adoption on new innovations, other areas become rooted in traditional approaches due to regulatory and other constraints. Despite great advances toward precision medicine goals, the healthcare industry, like other important segments of the economy, is tied by several specific bounds that make it slower to adapt to potentially higher performing tools and techniques. Although deep learning is nothing new, its application set is expanding. There is promise for the more mature variants of traditional deep learning (convolutional and recurrent neural networks are the prime example) to morph into domain-specific tools to bolster healthcare capabilities in new ways. Of course, this is not without a set of challenges, which we will get to in a moment.

Poker Pro Dong Kim shown here in the first Brains vs. AI contest in 2015. Four of the world's best professional poker players will compete against artificial intelligence developed by Carnegie Mellon University in an epic rematch to determine whether a computer can beat humans playing one of the world's toughest poker games. Artificial Intelligence: Upping the Ante," beginning Jan. 11 at Rivers Casino, poker pros will play a collective 120,000 hands of Heads-Up No-Limit Texas Hold'em over 20 days against a CMU computer program called Libratus. The pros -- Jason Les, Dong Kim, Daniel McAulay and Jimmy Chou -- are vying for shares of a $200,000 prize purse. The ultimate goal for CMU computer scientists, as it was in the first Brains Vs. AI contest at Rivers Casino in 2015, is to set a new benchmark for artificial intelligence. "Since the earliest days of AI research, beating top human players has been a powerful measure of progress in the field," said Tuomas Sandholm, professor of computer science. "That was achieved with chess in 1997, with Jeopardy! in 2009 and with the board game Go just last year.

Every year at CES, some of the world's biggest tech companies try to one-up each other. TVs get thinner and brighter. Home appliances get chattier and robots get friendlier. But this year, instead of standing out for their memorable devices, a lot of companies showed up with a shared identity: the voice of Alexa. Within a span of just two years, Amazon's cloud-based voice service has spread far beyond the Echo speaker where it first debuted.

Many of the self-driving demonstrations at CES involved systems required months or even years of training. NVIDIA and Audi decided to see what they could do in four days. The automaker and chip company gave the AI installed on an Audi Q7 images of what it should perceive as a road including, white lines, orange cones, rows of rocks and a dirt road and that's it. It didn't program a path or even add any additional sensors beyond the single forward-facing camera. While the results were impressive, it's important to point out that this is only a very small part of an autonomous driving system. But it does show how powerful AIs have become and how quickly they can make sense of the real world.

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge – by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis.

HONG KONG: McDonald's Corp has agreed to sell the bulk of its China and Hong Kong business to state-backed conglomerate CITIC Ltd and Carlyle Group LP for up to $2.1 billion (Dh7.71 billion), seeking to expand rapidly without using much of its own capital. Zurich The Swiss National Bank expects a 2016 full-year profit of 24 billion francs (Dh86.68 billion; $23.6 billion), enabling it to shell out money to the federal government and municipalities. Foreign-currency holdings contributed more than 19 billion francs, and valuation gains on its gold holdings added 3.9 billion francs, the central bank said on Monday, citing an initial estimate. Last year's result is set to be the second-best in the Read More ... Tags: Corporate Enterprises Finance Sectors Banks Profits Financial institutions German automaker Volkswagen saw sales jump 16 percent in December for its namesake brand, propelled by a big increase in China, Volkswagen's biggest market. Global sales reported Monday rose to 567,900 from 487,700 despite the damage to the company's reputation from its scandal over cars rigged to cheat on diesel emissions tests.