Researchers from New Zealand have restored the earliest known recording of computer-generated music, which was created nearly 65 years ago on a gigantic computer devised by the famous Alan Turing. Researchers from the University of Canterbury in Christchurch restored the recording created in 1951 using a BBC outside-broadcasting unit and a portable acetate disk. The recording, which begins with the United Kingdom's "God Save the Queen," also includes the nursery rhyme "Baa Baa Black Sheep" and the Glenn Miller hit "In The Mood." "Today all that remains of the recording session is a 12-inch single-sided acetate disc, cut by the BBC's technician while the computer played. The computer itself was scrapped long ago, so the archived recording is our only window on that historic soundscape," the researchers said in a statement Monday. The recording captured on the acetate disk was originally played on a massive computer, which occupied most of the ground floor of Turing's Computing Machine Laboratory.

In a world of ubiquitous smart-phone cameras, drones, and Google Street View cars, there's probably never been a more important time to start protecting the identities of people unwittingly captured in photos and videos. But while websites like YouTube have started offering tools to obscure faces and other objects appearing in digital media, researchers have found that those protections can be defeated at an alarming rate thanks to recent advances in artificial intelligence. In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets--in some cases at a more consistent rate than humans. "We argue that humans may no longer be the'gold standard' for extracting information from visual data," the researchers write. "Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image."

He is most famous for cracking the Enigma code, in a move that is said to have shortened WWII by two years and saved up to 22 million additional lives. But Alan Turing also pioneered the use of computers for making music. Now, 65 years after the first computer-generated music was recorded, researchers have restored the aural artefact, which paved the way for everything from synthesizers to modern electronica. When Professor Jack Copeland (right) and composer Jason Long (left) examined the 12-inch (30.5 cm) acetate disc containing the music, they found the audio was distorted. The recording was made 65 years ago by a BBC outside-broadcast unit at the Computing Machine Laboratory in Manchester, northern England.

Yes, you read that right: an actual artificial intelligence created the advertisement for a movie about terrifying AI. To create the film, the company used experimental Watson APIs and machine learning techniques to comb through hundreds of movie trailers for horror and thrillers. "Let's send Watson to film school," as John Smith, an IBM fellow who helped work on the project, explained. The team behind Watson helped the AI learn how movie trailers work, and then analyzed every scene in the human-made movie to pick the best ones for the trailer. The AI was able to detect which of the movie scenes were cheerful and uplifting, versus which ones were sad or scary.

In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. The challenge for this video is to generate a happy/upbeat song using the RBM Script. You guys are the reason I do this. If you enjoy my videos, I'd appreciate your support on Patreon:) https://www.patreon.com/user?u

BERLIN stands for Behavioural Event Reconstruction Linguistic Interface for Narratives. I introduced BERLIN a few blogs ago - in my "final blog." Theoretically after one's final blog, no further blogs are forthcoming. However, I am now posting bonus blogs reflecting aspects of the same closing subject. Today, I will be elaborating on BERLIN's syntax and how its searches are facilitated. As a general rule, the objective of BERLIN is to convert human-friendly narrative into computer-friendly code. It isn't unusual for computer code to be expressed in a human-like language for the purpose of executing a program. A computer program has rigid parameters. BERLIN on the other hand is designed to support "expression." Rather than the code adapting to the needs of a computer program, it is adapted to the requirements of expression. BERLIN remains shaped by a type of program of sorts.

Hi! I am back again with the transcript of a new-- episode of Capgemini's "Data and The Hunch" podcast series. I am a Principal Analyst for VINT, the Sogeti Trend Lab, and work on anything Analytics related to the Connected Service Experience. My Twitter handle is @BLO2M – B-L-O-Numeric2-M – so if you like, feel free to follow me. Today, Fenny has joined me in the studio. Hi Fen, how can I help you?

Accessing data for investment decisions on environmental, social and governance (ESG) factors can be challenging. Will artificial intelligence (AI) come to the rescue? Responsible investing covers a multitude of issues and risks -- from regulatory, social and cultural issues through to product liabilities, political risk and intellectual property matters to name just a few. Register now -- ESG webinar 28 Sep 2016: Can FinTech and AI solve the ESG data puzzle? The data generated are often unstructured but still need to be processed in real-time to be effective.

A recurring motif put our divided-mind anti-hero Elliot Alderson (Rami Malek) -- and by extension, us, his "hello, friend" buddy -- in the backseat of a car and took us for wild rides by mad men and toady tools. This was the premise of the madly meta "Mr. Robot" sitcom, when Mr. Robot (Christian Slater), Elliot's rogue alter-ego and frequent altered state, seized the wheel of his consciousness and took him on a great escape. This was the cliffhanger of last week's trippy and tricky outing, with Elliot riding shotgun in a cab with a dark passenger he thought was dead, Tyrell Wellick (Martin Wallstrom). He freaked and demanded his immediate release, a hair-pulling panic that might have spoken for us, too. Such was the thrill, frustration, and meaning of season 2, a saga of identity crisis run amok in a high anxiety culture on the blink and on the brink. All of its characters became unreliable narrators of themselves, and their distortions and their dimness became threatening to everyone as they executed reckless campaigns for change. Each of them had ideas for how to make the world great again.

Sony's Computer Science Laboratory in Paris has been working on the research and development of pioneering music technologies since 1997, and a blog post from one of the lab's teams this week unveiled an advancement that could reverberate throughout the music world. The team's Flow Machines project successfully created two entire pop songs composed by artificial intelligence, after learning musical styles from a massive database. After "exploiting unique combinations of style transfer, optimization and interaction techniques, it can compose in any style," the post reads. The project's success aligns with the team's goals, which, according to the lab's site, has the aim to "abstract'style' from concrete corpora (text, music, etc.), and turn it into a malleable substance that acts as a texture." Though the team has been successful in the past with constraint-based spatialization -- intelligent music scheduling using metadata and award-winning systems (MusicSpace, PathBuilder, Virtuoso, etc.) -- the work it showed off this week might take home the prize for being one of the most impressive.