What do you think when you think of artificial intelligence (AI)? Until recently, generations of people only viewed AI technology through the lens of popular culture depictions. And one major touchstone in their experiences was the movie 2001: A Space Odyssey, which celebrates its 50th anniversary this year. It was the highest-grossing film in North America in 1968, selected to be preserved in the U.S. Library of Congress National Film Registry, won an Academy Award for visual effects and a Hugo Award--one of science fiction's highest honors--for Best Dramatic Presentation. Despite its accolades, the movie drew a mixed reception of awe and confusion over its depiction of evolution and the meaning of existence.

Newswire) Patriot One Technologies Inc., (TSX: PAT) (OTCQB: PTOTF) (FRANKFURT: 0PL) ("Patriot One" or the "Company"), developer of the PATSCAN Multi-Sensor Covert Weapon Detection Platform ("PATSCAN Platform"), is pleased to announce the acquisition of XTRACT Technologies Inc. ("XTRACT") and all its related interests, assets, and intellectual property for consideration of $6,000,000 and the issuance of 9,422,956 Patriot One common shares (the "Consideration Shares") to XTRACT's former shareholders (the "Transaction"). Subject to certain exceptions based on the business of the Company or the performance of the Company's common share price, the Consideration Shares will be subject to escrow restrictions and released on a quarterly basis over the next twelve consecutive fiscal quarters of Patriot One. The Transaction follows demonstrated success of an ongoing collaborative project between the two firms that was announced in late July 2019. The XTRACT acquisition is part of an ongoing strategic initiative by the Company to bring together leading-edge technologies and services that will enhance the power of its PATSCAN Platform, which offers a layered, multi-sensor approach to concealed threat and disturbance detection. "Just over a month ago, we announced a development project with XTRACT, and during this period we've already seen great progress in significant opportunities for enhanced detection accuracy" said Martin Cronin, Patriot One's CEO and President.

How do new inventions come to be? They all start with a dream and that dream gets chased by someone crazy enough to make it into a reality. This is the cycle we often see when things from SciFi television shows and movies eventually come into being. When the Jetsons made its debut in 1962, the idea of a robotic maid was pretty wild. Today, however, we have robots that can cut the grass, mow the lawn, clean the pool, and even make us breakfast.

Fox News Flash top headlines for Sept. 9 are here. Check out what's clicking on Foxnews.com The driver of a Tesla on autopilot appeared to be asleep behind the wheel while whizzing along a Massachusetts highway, new video shows. A fellow motorist captured the driver with his head slumped forward -- and his passenger equally zonked out -- along I-90 on Sunday. "Teslas are sick, I guess?"

JavaScript library and wanted a project to build up my skills with it! I ended up going with a simple web app for visualising and editing network models which I've named netbrix.ml. I've wanted to build something like this for a while since it seemed like a really good project to improve my web development skills and my understanding of the process of building deep learning models. After reading this post on /r/deeplearning where the writer gives insight into the modular nature of deep learning and gives the analogy of a deep learning'lego set' it gave me the motivation to start work on this with that sort of vision in mind and I've now got a decent working web app! I'm sure there are existing tools similar to this in existence, so I wanted to keep it as simple as possible and not try to over-engineer it. It's meant to be easy and simple to use!

Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage. A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

Customer retention is one of the primary growth pillars for products with a subscription-based business model. Competition is tough in the SaaS market where customers are free to choose from plenty of providers even within one product category. Several bad experiences – or even one – and a customer may quit. And if droves of unsatisfied customers churn at a clip, both material losses and damage to reputation would be enormous. For this article, we reached out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn with predictive modeling. You will discover approaches and best practices for solving this problem. We'll discuss collecting data about client relationship with a brand, characteristics of customer behavior that correlate the most with churn and explore the logic behind selecting the best-performing machine learning models. Customer churn (or customer attrition) is a tendency of customers to abandon a brand and stop being a paying client of a particular business. The percentage of customers that discontinue using a company's products or services during a particular time period is called a customer churn (attrition) rate. One of the ways to calculate a churn rate is to divide the number of customers lost during a given time interval by the number of acquired customers, and then multiply that number by 100 percent.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.