Phoenix, one of Kodi's most popular add-ons, has just closed down. It provided access to TV shows, films and sports channels, and the announcement will undoubtedly surprise and disappoint a huge number of fans. While specific reasons for its closure haven't been detailed, one of Phoenix's developers has hinted that it was because of the legal issues surrounding Kodi. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Submissions should generally be about Artificial Intelligence and its applications. If you think your submission could be of interest to the community, feel free to post it. Try to avoid posting submissions that seem like a self-advertisement. Those usually contain clickbaity titles, speculations or overstatements. If you want to advertise on /r/artificial, the best way is by doing an IAMA.

The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Pix2pix project has unleashed a new tool that analyzes portraits and fills them in with colors and textures using a technique called generative adversarial networks (GANs). During the process, the system determines if its result match the sketch and will keep repeating the generation process until its own passes as'real' – regardless of how nightmarish the results may look. The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Users are presented with an input box and an output box and are prompted to draw a face in input, select process and in seconds, the AI will reveal its version of the sketch.

Deep learning, a computing construct based loosely on the architecture of the human brain, has emerged as one of the most promising enabling technologies in the world of artificial intelligence (AI). Although many of the concepts underlying AI and technological biomimicry of human intelligence are over 50 years old, deep learning's growth today is the result of a rather sudden convergence of three key trends: big, even colossal, data generation; advancements in hardware capabilities; and improvements in algorithms. According to a new report from Tractica, deep learning has been commercialized into more than 100 distinct use cases to date, touching virtually every industry. Tractica forecasts that deep learning software revenue will grow from $655 million in 2016 to $34.9 million worldwide by 2025. "Businesses around the world are beginning to harness deep learning due to its ability to drive efficiencies in the form of speed, accuracy, agility, and access in several key areas," says principal analyst Jessica Groopman.

As a side-project I've been working on a CRF-as-RNN layer for Lasagne, based on this paper. It allows you to add differentiable CRF inference as a layer to any fully-convolutional model that produces classification output whose resolution matches the input. It's particularly useful for semantic segmentation models, but can be used for any dense prediction task. It's still somewhat of a WIP but it does work. I was able to successfully train a network with the CRF layer as a component.

Directed by Oscar Sharp and starring Baywatch icon David Hasselhoff, It's No Game takes us to an alternate reality where, in midst of heated writer's strike in Hollywood, AI script writers have gradually began to replace human ones. Using an advanced nanobot technology, producers have found a way to channel the inner thoughts and mannerism of the AI writers directly to human actors, causing them to act out borderline non-sensical lines put together by various algorithms trained on Shakespeare, Aaron Sorkin and Golden Age Hollywood movies. To crank out the script for the movie, Benjamin's creator Ross Goodwin trained the AI according to six different models, sourcing dialogue lines from classic movie and television titles like Knight Rider and Baywatch. Here's how Ars Technica summed up the process: Put simply, the algorithm learns to create long sentences based on learning rules from a corpus of writing.

I went for tensorflow and installed jupyter for a start and implemented my first models for handwriting recognition. The actual problem is that I have a 15 element input tensor (describing the scheduling scenario) and I want to generate a 4 element output tensor that gives me some parameters for my algorithm. I randomly generated a data set (labled as far as I understand) that contains input and output elements and connects them with a score which should be as low (good) as possible. So if I want to generate a model that gives me as good as possible output so that the algorithm can compute a schedule with the score as good as possible, how can I model a NN that in can be trained by this data later gives me good ouput for any scenario. Output will be used for Java scheduling algorithm.

The success of any machine learning project starts with defining why you're doing it. Side projects: The next way to trip up is to define a machine learning project as a side project. Yet another promising machine learning project was refused because the site data specialist wanted to understand how the artificial intelligence algorithms worked and the supplier did not want to answer his request, for secrecy and competitive reasons. So, while the data scientists and process engineers I mentioned in the first article are required, they don't have the right profiles to drive these types of fundamental issues, the questions about the why.

You might have noticed a comment was made on the first article in this series: I didn't mention the'why' as a KSF in bringing home a machine learning project. And it's true, forgetting the why is a serious problem. As the commenter noted: Unless there's a why and a businessperson involved, all you've got is a handicapped specialty group who have been given tools, but not told what to do with them. So here's my amended list of KSFs for a machine learning project: The why is the business reason for the project. It's rarely as exciting or motivating as the technology involved. But it pays the bills.

Back in high school, when I was being counseled on where to put my energy as I transitioned into college, many adults scoffed at the art degree I wanted. My grades were weighted with APs and I loved school, so my GPA was actually more than a 4.0. Adults argued that I could certainly find something more practical and lucrative with my grades; they treated art as something expendable, a last resort. That's not what art is at all. Now, more than a decade later, I am so glad that I got two degrees in art.