Videos have become omnipresent, from streaming our favorite movies and TV shows to participating in video conferences and calls. With the increasing use of smartphones and other capture devices, the quality of videos has risen in importance. However, due to various factors like low light, digital noise, or simply low acquisition quality, the quality of videos captured by these devices is often far from perfect. In these situations, video enhancement techniques come into play, aiming to improve resolution and visual features. Over the years, various video enhancement techniques have been developed until the arrival of complex machine learning algorithms to remove noise and improve image quality.

One of the most common dramas experienced by data scientists is the uncertainty that the available volume of data will suffice for model building. Challenging such insecurity, I've decided to make a Machine Learning algorithm fitted with the lowest possible amount of instances. After many reflections, I've found that working with a binary classification task would be an interesting idea. Since I wished to use the minimum amount of data, the final model needed to be fed with only two instances, each one belonging to a specific category. The data points selection would be performed with K-Means.

Assassin's Creed Infinity won't be a free-to-play online game, Ubisoft CEO Yves Guillemot has confirmed during the developer's latest earnings call. Bloomberg first reported about the project's existence back in July, and the company has shared details about it bit by bit since then. The publication said that AC Infinity, which is the project's name that could change upon release, will be a live online service similar to Grand Theft Auto Online that requires you to own the base game. "This game is going to have a lot of narrative elements in it. It's going to be very innovative game, but it will have what players already have in all the other Assassin's Creed games, all the elements that they love... right from the start. So it's going to be a huge game. But with lots of elements that already exist in the games that we published in the past."

But a new algorithm created by researcher Mauro Martino goes one step further by creating portraits entirely from scratch. Instead of style transfer, AI Portrait Ars uses a machine learning system called a generative adversarial network to conjure up new, Renaissance-like paintings that evoke your likeness. This AI isn't just remixing previous paintings it has seen into something new: it's directly inspired by a picture of you. On the project's website, you can upload a photo of yourself, and the site generates a unique work of art that looks like it was painted by an old master, but with your face. All photos are immediately deleted from the project's servers to protect users' privacy.

From global health to social justice to humanitarian aid, a slew of scientists, technologists, and activists came together this year to create impactful solutions to some of our most pressing problems. SEE ALSO: 9 incredible ways we're using drones for social good In no particular order, here are 30 innovations that made a tangible difference in 2017. This paper device, which only costs 20 cents to make, can help scientists and doctors diagnose diseases like malaria and HIV within minutes -- no electricity required. The Paperfuge, developed by Stanford assistant professor of bioengineering Manu Prakash, is a hand-powered centrifuge that was inspired by a whirligig toy. It can hold blood samples on a disc, and by pulling the strings back and forth, it spins the samples at extremely fast rates to separate blood from plasma, preparing them for disease testing.

Organizations are adaptive systems that continually attempt to push the limits of their own effectiveness to approach perfection. This approach is true of the "mom and pop" store that is threatened by the growth of shopping malls. It is true of the gigantic corporation that is threatened by public regulation and private competition. It is particularly true of organizations that are confronted with complex tasks, the vagaries of uncertainty, and the high and visible costs of irreversible error. The cause of organization ineffectiveness or, indeed, failure is often perceived to be human frailty (Perrow 1984).

We are victims of one common superstitionthe superstition that we understand the changes that are daily taking place in the world because we read about them and know what they are. The anthropological stories and the concept of memes were brought to my attention several years ago by Lynn Conway Much of the vision and some of the material was drawn from a paper that we worked on together but never published. The important distinction between process and product, was made crisp for me by John Seely Brown, who also has encouraged and made possible projects like Trillium, which I watched with interest, and like Colab, in which I participated. Joshua Lederberg kindled my interest in biological issues and a respect for knowledge processes and their partial automation that has not faded Dan Bobrow listened to my ramblings on several runs, agonized over my confusions, helped to get the kinks out of the arguments, and suggested the title for the article Sanjay Mittal and I have spent many hours speculating together on the issues in building community knowledge bases and knowledge servers and in understanding the principles of knowledge competitions Austin Henderson helped me to understand the Trillium story and to report it accurately. Austin and Sanjay hounded me to say, more precisely, what a knowledge medium is Agustin Araya and Mark Miller participated in a Colab session in which we tried to jointly lay out these ideas, and together asked me to make the prescriptions clearer Ed Feigenbaum persuaded me to be more precise in the discussion of the limits of today's expert systems technology Thanks to Agustin Araya, Dan Bobrow, John Seely Brown, Lynn Conway, Bob Engelmore, Ed Feigenbaum, Felix Frayman, Gregg Foster, Austin Henderson, Ken Kahn, Mark Miller, Sanjay Mittal, Julian Orr, Allen Sears, Lucy Suchman, and Paul Wallich for reading early drafts of this paper and for helping to clarify the ideas and improve the article's readability Stephen Cross triggered the writing of this article when he invited me to give the keynote address at the Aerospace Applications of Artificial Intelligence Conference in Dayton, Ohio, in September 1985.

The difficulty of finding information on the World Wide Web by browsing hypertext documents has led to the development and deployment of various search engines and indexing techniques. However, many information-gathering tasks are better handled by finding a referral to a human expert rather than by simply interacting with online information sources. A personal referral allows a user to judge the quality of the information he or she is receiving as well as to potentially obtain information that is deliberately not made public. The process of finding an expert who is both reliable and likely to respond to the user can be viewed as a search through the network of social relationships between individuals as opposed to a search through the network of hypertext documents. Project is to create models of social networks by data mining the web and develop tools that use the models to assist in locating experts and related information search and evaluation tasks.

The project is large and has a number of components that have been documented at length. These components have never been drawn together in one document; thus, this article describes the project and gives a taste of the individual subprojects that have kept the project members so busy for so long. A large number of publications have emerged from the project, so a full bibliography of the work appears for the reader who wants to follow up on any intriguing topics. AAP straddles a number of research areas and, thus, does not fall easily into any one sphere of interest. A certain amount of work has been done on the parallelizing of expert systems, most notably by Gupta (1986).

The fifth Distributed Artificial Intclligencc Workshop tias held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984 It was attended by 20 participants from academic and industrial institutions. As in the past,' this workshop was designed as an informal meeting It included brief research rcport,s from individual groups along with genera1 discussion of questions of common interest. Distributed artificial intelligence (DAI) is concerned with cooperative solution of problems by a decentralized and loosely coupled collection of knowledge sources (KSs), each embodied in a distinct processor node. The KSs cooperate in the sense that no one of them has sufficient information to solve the entire problem; mutual sharing of information is necessary to allow the group as a whole to product an answer. By decentralized we mean that both control and data are logically and often geographically distributed; there is neither global control nor global data storage.