I'm sure you've probably heard about the 2018 FIFA Football World Cup in Russia everywhere during the last few months. And, if you are a techy too, I guess you also have realized that Machine Learning and Artificial Intelligence are buzzwords too. So, what better way to get ready for the World Cup than by practicing in a project that combines these two hot topics? In order to do that, we're going to leverage a dataset of the Fifa 2018 video game. My goal is to show you how to create a predictive model that is able to forecast how good a soccer player is based on their game statistics (using Python in a Jupyter Notebook).

Blockchain technology continues to be one of the most exciting emerging technologies in the world today. Individuals, corporations, and even governments are developing means to use this revolutionary tool in advancing their processes. Many experts have come out to say that Blockchain holds the key to the actualization of a number of important technological breakthroughs. Within the haze of excitement and optimism, the core of the discourse surrounding Blockchain tends to center on strengths and weaknesses of the technology. A key figure in the Blockchain and cryptocurrency community is Fabio Zoffi, the President and Executive Chairman of ORS GROUP.

Blockchain technology continues to be one of the most exciting emerging technologies in the world today. Individuals, corporations, and even governments are developing means to use this revolutionary tool in advancing their processes. Many experts have come out to say that Blockchain holds the key to the actualization of a number of important technological breakthroughs. Within the haze of excitement and optimism, the core of the discourse surrounding Blockchain tends to center on strengths and weaknesses of the technology. A key figure in the Blockchain and cryptocurrency community is Fabio Zoffi, the President and Executive Chairman of ORS GROUP.

PARIS (Reuters) – South Korean electronics giant Samsung will make France its third global research and development hub in artificial intelligence after Korea and the United States and hire about one hundred experts in Paris, the French president's office said on Wednesday.

Google, Facebook and others fill their pockets with billions using our data. But artificial intelligence can do much more – for example ending world hunger. During my studies in artificial intelligence I didn't learn that much about how powerful computers are. Most of all, I learned that many things humans do are less complex than we think they are. Here you can find our introductory text on artificial intelligence by the physicist and neuroscientist David Hofmann (German) Artificial Intelligence – even though the term triggers associations such as Do we want that robots take care of us in the future, wonders Dirk Walbrühl here (German) humanoid robots, world supremacy and apocalypse, very often it means »no more« than data analysis. This might sound less exciting, but it is a powerful tool indeed – not only to understand what we click and buy, but also to find answers to questions that might improve the lives of many people.

In humans, the profound biological differences that exist between the sexes mean that a single male is physically capable of having far more children than is a single female. Women carry unborn children for nine months and often nurse them for several years prior to having additional children.1 Men, meanwhile, are able to procreate while investing far less time in the bearing and early rearing of each child. So it is that, as measured by the contribution to the next generation, powerful men have the potential to have a far greater impact than powerful women, and we can see this in genetic data. The great variability among males in the number of offspring produced means that by searching for genomic signatures of past variability in the number of children men have had, we can obtain genetic insights into the degree of social inequality in society as a whole, and not just between males and females. An extraordinary example of this is provided by the inequality in the number of male offspring that seems to have characterized the empire established by Genghis Khan, who ruled lands stretching from China to the Caspian Sea. After his death in 1227, his successors, including several of his sons and grandsons, extended the Mongol Empire even farther--to Korea in the east, to central Europe in the west, and to Tibet in the south.

France's Emmanuel Macron knows his country is not about to develop a local equivalent of Google or Facebook to power the development of artificial intelligence. But in a national AI strategy to be unveiled this week, the French president will argue that France has other assets to fuel innovation: its foreign brain trust, huge troves of state-owned data and links to European research institutes to share and leverage knowledge, according to French officials. Macron's aim is nothing less than to drag his country into the age of AI and erase 30 years of underperformance on innovation. "We missed all the big technological revolutions of recent years, but France has a card to play in the field of artificial intelligence," said an aide to the president who asked not to be named. "Either we seize the chance now, or we watch another wave pass us by."

In this continuation of "Hybrid content-based and collaborative filtering recommendations wi..." I will describe the application of the {ordinal} clm() function to test a new, hybrid content-based, collaborative filtering approach to recommender engines by fitting a class of ordinal logistic (aka ordered logit) models to ratings data from the MovieLens 100K dataset. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. This second part of the study relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and the analyses. But before we proceed to approach the recommendation problem from a viewpoint of discrete choice modeling, let me briefly remind you of the results of the feature engineering phase and explain what happens in OrdinalRecommenders_2.R which prepares the data frames that are passed to clm().

Einride's autonomous T-Pod may look like a giant freezer on wheels, but it's likely to be on the road fairly soon. According to TechCrunch, the company announced today that the first customer deliveries of the self-driving transport vehicle will begin this fall. The T-pod can transport standard cargo pallets and travel up to 124 miles on a single charge. It can drive itself on highways, but a human will take over on main roads via remote. TechCrunch says that the trucks will use the Nvidia Drive AI platform to plan driving paths and intelligently sense the environments.

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.