Fox News Flash top entertainment and celebrity headlines are here. Will Smith is facing accusations of using artificial intelligence to create a crowd in a video shared online. Smith, 56, posted a YouTube clip allegedly featuring scenes from a tour performance, but eagle-eyed fans were quick to point out purported inaccuracies in the video. The "Gettin' Jiggy Wit It" singer appeared to be singing to a packed room while on tour, only for distorted images to materialize in the crowd. Will Smith faced backlash for alleged AI use in a video shared online.

Hey - I'm the head of engineering at Elementl, the company that builds Dagster. This post is my take on a crash-course introduction to Dagster. And if you want to support the Dagster Open Source project, be sure to star our Github repo. Dagster is a data orchestrator. Think of Dagster as a framework for building data pipelines, similar to how Django is a framework for building web apps.

Can artificial intelligence ever boost productivity of firms and industries the way the PC and ... [ ] networking did in the '80s and '90s? A big pastime of economists in the 1980s and 1990s was trying to gauge how much corporate and industrial productivity would benefit from the then-novel phenomena of personal computers, workgroup servers, and computer networking. At first it was hard to see, but in time, economists did indeed find evidence that information technology contributed to boosting economic productivity. It's too soon to expect to see data showing a similar boom from artificial intelligence, today's big IT revolution. The technology is just becoming industrialized, and many companies have yet to even try to use things such as machine learning in any significant way.

Can artificial intelligence ever boost productivity of firms and industries the way the PC and networking did in the '80s and '90s? A big pastime of economists in the 1980s and 1990s was trying to gauge how much corporate and industrial productivity would benefit from the then-novel phenomena of personal computers, workgroup servers, and computer networking. At first it was hard to see, but in time, economists did indeed find evidence that information technology contributed to boosting economic productivity. It's too soon to expect to see data showing a similar boom from artificial intelligence, today's big IT revolution. The technology is just becoming industrialized, and many companies have yet to even try to use things such as machine learning in any significant way.

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose Helix, a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. Helix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized -- trading off the cost of materialization for potential future benefits -- to speed up future iterations. Additionally, Helix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of Helix programming interface and the speed and ease of iterative development using Helix. In our evaluations, Helix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.

I just came back home from the MVP Summit in Redmond, USA, and I was welcomed by a snow-covered Gothenburg and a close friend that cheerfully told me he had purchased a house. As much as I love the MVP Summit, and always have so much fun, it's always nice to come home to your loved ones. I'll write a separate post on the summit, and I'll make sure to post the many pictures I took as well. But for now, I'd like to share a solution to peculiar problem that I came across while in Bellevue this week. We use Azure Log Analytics at work, and we push our log entries to Azure and other metrics by using Application Insight.

What's the next $300 billion industry to be disrupted by technology? For background, engineered wood is the technical name for any wood product (like particle board) that is created by bonding wood chips into different shapes using an adhesive. It's much cheaper than using a solid piece of wood, and can be used to make anything from an Ikea desk to kitchen countertops. Materialize.X, launching today at TechCrunch Disrupt SF 2017, has two new products that it thinks will revolutionize the $300 billion a year engineered wood market. A lot of engineered wood is created using an adhesive called urea-formaldehyde, which has recently been labeled by the FDA as a toxic carcinogen.

Prolific science fiction author Arthur C. Clarke wrote that "Any sufficiently advanced technology is indistinguishable from magic." As technology advances, what once was deemed "fantasy" begins to materialize in the world around us. Movies inspire us to have fun with the idea that any sufficiently capable magic is indistinguishable from technology – on-screen magic makes things like teleportation, omnipotent medicine and the creation of objects out of thin air all seem real. In many ways we already live in a future predicted by yesterday's science fiction, with interactive touch devices connected to a vast world of information. These universal machines can support experiences of ever-greater richness and complexity.

The purpose of a game is to deliver a captivating interactive experience to its users. If we delve games that have made a huge success out of themselves we'll find just one common trait- Design. People can call it'Fancy Technology', but we all know it's the fascination of technology that attracts users the most. Then comes the graphics part, where smoothness and asymmetry concoct an effective broth. With the entrance of technologies like Virtual Reality, Augmented Reality, the user experience has drastically shot up and so the competition.

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation.