Machine learning applications in editing and post-production projects are coming to the fore, and the BBC recently made use of the technology in its first experimental BBC 4 documentary, 'Made by Machine - When AI Met the Archive'. Vast archives of footage, images, marketing materials and post production work can be used for machine learning applications, and in turn drive creative automation. The technology also offers solutions to internal organisational problems. With the cloud and machine learning algorithms, businesses can begin to structure and automate processes for all of their content, saving hours of manual review and data entry. Video intelligence can identify key speech and individual faces.

ObEN Inc., a Pasadena-based artificial intelligence (AI) company, signed new entertainment partnerships and launched new celebrity-based Personal Artificial Intelligence avatars as part of its continued expansion into China's market. ObEN announced its official Chinese name roughly translating to "Celebrity Crew," in a nod to its growing presence in the country's entertainment industry, according to a Jan. 22 press statement. The new name also celebrates the Lunar New Year, when the company plans to release several new celebrity avatars created for the Chinese market. In December, ObEN collaborated with the Chinese music group SNH48 on the world's first human-PAI song and music video. Using its proprietary full-stack AI technology, ObEN created avatars for six of the group's most popular members.

Nowadays many misconceptions are there related to the words machine learning, deep learning and artificial intelligence(AI), most of the people think all these things are same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same. Machine Learning: Before talking about machine learning lets talk about another concept that is called data mining. Data mining is a technique of examining a large pre-existing database and extracting new information from that database, it's easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique. Here's is a basic definition of machine learning – "Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision" Now a days many of big companies use machine learning to give there users a better experience, some of the examples are, Amazon using machine learning to give better product choice recommendations to there costumers based on their preferences, Netflix uses machine learning to give better suggestions to their users of the Tv series or movie or shows that they would like to watch. Deep Learning: Deep learning is actually a subset of machine learning.

Science may never tell us what lies round the next corner, but researchers have come up with the nearest thing: a computer program that turns a normal digital camera into a periscope. In a demonstration of "computational periscopy" a US team at Boston University showed they could see details of objects hidden from view by analysing shadows they cast on a nearby wall. Vivek Goyal, an electrical engineer at the university, said that while the work had clear implications for surveillance he hoped it would lead to robots that could navigate better and boost the safety of driverless cars. He said: "I'm not especially excited by surveillance, I don't want to be doing creepy things, but being able to see that there's a child on the other side of a parked car, or see a little bit around the corner of an intersection could have a significant impact on safety." The problem of how to see round corners has occupied modern researchers for at least a decade.

One of the big misconceptions is that AI is a future technology, akin to humanoid robots that appear in television shows and movies. But AI is not a speculative technology--it already has many real-world applications, and ordinary people rely on it in one form or another every day. In fact, iPhones, Amazon's Alexa, Twitter and Facebook feeds, Google's search engine, and Netflix movie queues--to name just a few examples--all rely on AI. That said, there is a vast gap between the complexity of AI processing needed for the complex geospatial functions performed by self-driving cars versus, say, the basic AI algorithms used for more routine tasks like filtering spam emails. Technologist Melvin Kranzberg famously stated, "Technology is neither good nor bad; nor is it neutral."

I can't find it, but there was an interesting midi sound library that I would've thought for sure someone would've used to make a melody maker AI before. Is this type of thing possible, if anyone has done it I'd really love it if you could link me to some code.

Abstract--Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for humanrobot interaction. I. INTRODUCTION Preference learning [1] seeks to learn a predictive model of human preferences from their observed behavior.

Martin Ford is a futurist and the author of two books: The New York Times Bestselling Rise of the Robots: Technology and the Threat of a Jobless Future (winner of the 2015 Financial Times/McKinsey Business Book of the Year Award and translated into more than 20 languages) and The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, as well as the founder of a Silicon Valley-based software development firm. His TED Talk on the impact of AI and robotics on the economy and society, given on the main stage at the 2017 TED Conference, has been viewed more than 2 million times. Martin is also the consulting artificial intelligence expert for the new "Rise of the Robots Index" from Societe Generale, underlying the Lyxor Robotics & AI ETF, which is focused specifically on investing in companies that will be significant participants in the AI and robotics revolution. He holds a computer engineering degree from the University of Michigan, Ann Arbor and a graduate business degree from the University of California, Los Angeles. He has written about future technology and its implications for publications including The New York Times, Fortune, Forbes, The Atlantic, The Washington Post, Harvard Business Review, The Guardian, and The Financial Times.

Vivek Goyal isn't a professional photographer, but he and his colleagues have developed an intriguing party trick: they can capture the image of an object completely out of sight. They demonstrated the trick in a windowless room on the Boston University campus, where Goyal works as an electrical engineering professor. In the room, a flat-screen monitor displayed a series of crude drawings created by Goyal's graduate student, Charles Saunders. Among them were several masterpieces: A mushroom that resembles Toad from Mario Kart, a Simpsons-yellow dude wearing a sideways red baseball cap, the red letters "BU" for school pride. These are the images that Goyal and his team wanted to capture while pointing the camera lens in a completely different direction.

In the movie "Terminator 2: Judgment Day," the titular robot says, "My CPU is a neural net processor, a learning computer." The neural network of which that famous robot speaks is actually a real kind of machine learning method. A neural network is a way to depict a complex mathematical formula, organized into layers. This formula can be trained to do things like recognize images for self-driving cars. For example, watch several seconds of a neural network performing object recognition.