A considerable amount of research has been devoted to training deep neural networks that can compose piano music. For example, Musenet, developed by OpenAI, has trained large-scale transformer models capable of composing realistic piano pieces that are many minutes in length. The model used by Musenet adopts many of the technologies, such as attention layers, that were originally developed for NLP tasks. See this previous TDS post for more details on applying attention-based models to music generation. Although NLP-based methods are a fantastic fit for machine-based music generation (after all, music is like a language), the transformer model architecture is somewhat involved, and proper data preparation and training can require great care and experience. In particular, I'll focus on fully convolutional neural networks based on dilated convolutions, which require only a handful of lines of code to define, take minimal data preparation, and are easy to train. In 2016, DeepMind researchers introduced the WaveNet model architecture,¹ which yielded state-of-the-art performance in speech synthesis. Their research demonstrated that stacked 1D convolutional layers with exponentially growing dilation rates can process sequences of raw audio waveforms extremely efficiently, leading to generative models that can synthesize convincing audio from a variety of sources, including piano music. In this post, I build upon DeepMind's research, with an explicit focus on generating piano music.

Research Reports Inc. added an innovative statistical data of Artificial Intelligence (AI) Hardware market.

On the photo: image processing based on samples taken by robotic platform in combination with artificial intelligence.

The Automotive Artificial Intelligence market has been segmented as By Component (Hardware, Software, and Service), By Technology (Machine …

An artificial intelligence (AI) technology made by a firm co-founded by billionaire Elon Musk has won praise for its ability to generate coherent stories, novels and even computer code but it remains blind to racism or sexism. GPT-3, as Californian company OpenAI's latest AI language model is known, is capable of completing a dialogue between two people, continuing a series of questions and answers or finishing a Shakespeare-style poem. Start a sentence or text and it completes it for you, basing its response on the gigantic amount of information it has been fed. This could come in useful for customer service, lawyers needing to sum up a legal precedent or for authors in need of inspiration. While the technology is not new and has not yet learnt to reason like a human mind, OpenAI's latest offering has won praise for the way its text resembles human writing.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Microsoft announced new technology to combat disinformation for the 2020 U.S. presidential election, including deepfakes. The technology can detect manipulated content with the goal of assuring people that the content they're viewing is real. It is part of Microsoft's Defending Democracy Program, targeted at keeping campaigns secure and protecting the voting process.

There is nothing more agonizing than starting something on Netflix and realizing 20 minutes in that you aren't interested. In this project, I tried taking matters into my own hands by calculating a probability that I will finish a show given certain show characteristics. Being able to sleep at 2am and roll out of bed at 9am for work hasn't been great for my productivity from an active standpoint. Once upon a time, I actually enjoyed walking 30 minutes to the Caltrain station and back every weekday. The only consolation for my slow descend into complete and utter languidness is how productive I have been pushing through Netflix originals. A combination of having nothing left to watch and deciding to be slightly more productive with my time encouraged me to try my hand at using neural networks to predict what I should watch next.

I've created an RSS Reader that uses machine learning to deconstruct articles marked by the user as liked/disliked, scoring article's elements (words, categories, authors...) and allowing the user to filter out articles irrelevant to their interests. Users can also order articles by score instead of date, showing the most relevant ones at the top of the list. I'd be interested to know whether anyone here would like to try this system once it's online? Perhaps it could help researchers, skimming lots of articles on their subject every day, or even ordinary people, trying to find a car of their dreams, filtering out the noise of online auctions irrelevant to them. I'd prefer avoiding comments about RSS decline, if possible please.

Microsoft has added to the slowly growing pile of technologies aimed at spotting synthetic media (aka deepfakes) with the launch of a tool for analyzing videos and still photos to generate a manipulation score. The tool, called Video Authenticator, provides what Microsoft calls "a percentage chance, or confidence score" that the media has been artificially manipulated. "In the case of a video, it can provide this percentage in real-time on each frame as the video plays," it writes in a blog post announcing the tech. "It works by detecting the blending boundary of the deepfake and subtle fading or greyscale elements that might not be detectable by the human eye." If a piece of online content looks real but'smells' wrong chances are it's a high tech manipulation trying to pass as real -- perhaps with a malicious intent to misinform people.

The Artificial Intelligence in Fashion market in Asia Pacific is expected to grow from US$ 3,718.6