As more video providers finding audiences directly through apps and the web -- and away from pay-TV-based packages -- we're seeing the emergence of more analytics to measure how those videos are delivered, and who is watching them. Conviva, a company that has developed a set machine-learning-based algorithms to do just that, today announced that it has raised $40 million from strategic, new and existing investors to continue building out its platform and business. Investors include Australia's sovereign wealth fund Future Fund, NEA, Foundation Capital, and Time Warner Investments. The company is not disclosing its valuation, but a source close to the company confirms that it is around $300 million. Conviva has raised $121 million to date. If you've had your eye on the streaming video industry for a while, you'll know that Conviva is not exactly a spring chicken.

In this blog post, the third one of our Topic Models series, we are showcasing how you can use BigML Topic Models to improve your model performance. We are using movie reviews extracted from the IMBD database to predict if a given review has a positive or a negative sentiment. Notice that in this post we will not dive into all the configuration options that BigML offers for Topic Models, for that we recommend that you read our previous post. The dataset contains 50,000 highly polarized movie reviews labeled with their sentiment class: positive or negative. This dataset was built by Stanford researchers for their paper from 2011, which achieves an accuracy of 88.89%.

Sphero is moving beyond cute, connected rolling balls fast. Less than a month after introducing a tie-in for the animated movie franchise Cars, the company's now taking the wraps off of its first non-robotic product. And it's another toy built in partnership with Disney. Meet Sphero's Spider-Man, a replica of the beloved Marvel superhero than listens and responds to your voice commands. Unlike with its versions of BB-8 and Ultimate Lightning McQueen though, this Spider-Man doesn't depend on an app to be fully functional. Right behind the character's light-up eyes, there's an Android device, and all you have to do to turn the "premium rubber" doll into a talking and listening companion is click the spider on his chest.

The city of Chicago is using an algorithm to predict whihch individuals are likely to be the victim or perpetrator of a crime. It sounds like the premise behind the TV show Person of Interest. Chicago, a city that has seen its crime rate surge, is using this algorithm in an attempt to help get crime under control. That's a good thing if it can help reduce crime. But there are ethical concerns about using data in this way. Do we want to predict the likelihood of someone committing a crime, like in the film Minority Report, and incarcerate them before the crime even happens?

Shimon--a four-armed marimba playing robot--has been around for years, but its developers at Georgia Tech have recently taken this futuristic musical machine to the next level. Using deep learning, the robot can now study large datasets from well-known musicians, and then produce and perform its own original compositions. Shimon was originally developed by Gil Weinberg, director of Georgia Tech's Center for Music Technology. Under its original programming, the robot was capable of improvising music as it played alongside human performers, using an "interestingness" algorithm to make sure it wasn't just copying its bandmates. But now, thanks to the efforts of Ph.D. student Mason Bretan, Shimon has become an accomplished composer, capable of autonomously generating the melodic and harmonic structure of a song.

I recently just got into Machine Learning and decided to make my own Neural Net class (and also convolution neural net class, but that isn't quite done yet) in Java just to experiment with ML and see what I could do. For me, it was a fun opportunity to learn more about Neural nets and try to figure out how they work. As I was reading ML articles, it always felt like they glossed over how to do batch processing and it felt like even fewer articles described how to calculate the gradient in a batch learning model in a way that was understandable for someone who hasn't studied this area before. Since it seemed like there weren't a ton of concrete examples from my searches, I thought maybe I'd post this here in case anyone else was curious. I know I still have stuff to do on my project, but I'd appreciate any thoughts / constructive criticism on what could be done better / or ideas on what to do next!

Shimon, a four-armed, marimba playing robot, is writing and playing its own music using deep learning. This is the first of its two songs. A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. Researchers fed the robot nearly 5,000 complete songs -- from Beethoven to the Beatles to Lady Gaga to Miles Davis -- and more than 2 million motifs, riffs and licks of music.

Bob Friday is co-founder and CTO of Mist Systems. Artificial intelligence (AI) is taking the world by storm, with innovative use cases being applied across all industry segments. We are decades away from replacing a doctor with an AI robot, as seen in the movies, but AI is helping experts across all industries diagnose and solve problems faster, enabling consumers like myself to do amazing things, like find songs with a voice command. Most people focus on the results of AI. For those of us who like to look under the hood, there are four foundational elements to understand: categorization, classification, machine learning, and collaborative filtering.

In Door No. 1, a new virtual reality comedy show coming to Hulu, the audience becomes the director, choosing not only where to look, but also selecting certain actions and propelling the story forward in unique ways. You're at your 10-year high school reunion and there are plenty of characters to interact with, including a janitor who wants to smoke you out and a faded former best friend. Viewers pick people to hang out with just by directing their sight toward the desired action, no gamepads required. Regardless of the input method, Door No. 1 is essentially a live-action choose-your-own-adventure game, a comparison that crystalizes as the creative team at RYOT explains their approach to development. Hulu's Noah Heller, and RYOT's Nora Kirkpatrick and Molly Swenson joined us on the Engadget stage to talk about the differences -- and similarities -- between film and video games in VR.

Artificial intelligence has proved itself incredibly capable of analysing images, now its getting rhythm in the form of a four-armed, marimba-playing robot. The robot, named Shimon, was given a vast amount of musical data: more than 5,000 complete songs, two million motifs, riffs and short passages of music by researchers at Georgia Institute of Technology. It was then asked to compose and perform its own music. It's been in development for some years, but this is the first time it has composed its own music. Once it had been fed the data it was able to use deep learning techniques to create two 30 second pieces of original music.