Every day we see a new news report on how AI is changing the future of every part of society,

Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmonics. We study whether the introduction of physically inspired Gaussian process (GP) priors into audio content analysis models improves the extraction of patterns required for AMT. Audio signals are described as a linear combination of sources. Each source is decomposed into the product of an amplitude-envelope, and a quasi-periodic component process. We introduce the Mat\'ern spectral mixture (MSM) kernel for describing frequency content of singles notes. We consider two different regression approaches. In the sigmoid model every pitch-activation is independently non-linear transformed. In the softmax model several activation GPs are jointly non-linearly transformed. This introduce cross-correlation between activations. We use variational Bayes for approximate inference. We empirically evaluate how these models work in practice transcribing polyphonic music. We demonstrate that rather than encourage dependency between activations, what is relevant for improving pitch detection is to learnt priors that fit the frequency content of the sound events to detect.

In this paper, we present a novel method for co-clustering, an unsupervised learning approach that aims at discovering homogeneous groups of data instances and features by grouping them simultaneously. The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix. This matrix is further factorized to obtain the induced row and columns partitions using multiscale representations approach. To justify our method theoretically, we show how the solution of the regularized optimal transport can be seen from the vari-ational inference perspective thus motivating its use for co-clustering. The algorithm derived for the proposed method and its kernelized version based on the notion of Gromov-Wasserstein distance are fast, accurate and can determine automatically the number of both row and column clusters. These features are vividly demonstrated through extensive experimental evaluations.

Spotify has made its fourth acquisition of the year after it announced that AI startup Niland has joined its ranks. Paris-based Niland offered an API-based product focused on providing more accurate search and recommendation options for music. Spotify said the French company will join its R&D team which is based in New York to help hone its personalization and recommendation features for users. "Niland has changed the game for how AI technology can optimize music search and recommendation capabilities and shares Spotify's passion for surfacing the right content to the right user at the right time," Spotify said in a statement. "We will keep working on new ways to better understand music to craft better innovative listening and discovery experiences," Niland's founding team wrote on its website.

In the same way that R2D2, the faithful droid from the Star Wars movie franchise, travels the far reaches of the galaxy righting wrongs, the software algorithms that guide Robotic Process Automation (RPA) travel the constellations of data collected by companies, resolving anomalies. Agile Finance Revealed: The New Operating Model for Modern Finance, a recent study by Oracle and the American Institute of CPAs (AICPA), predicts that RPA and the related technologies of machine learning and adaptive intelligence will become increasingly important in finance automation as they help finance professionals free up their time for more strategic pursuits. Oracle Vice President Loren Mahon explains that because so many tasks in finance are repetitive in nature, they are prone to human error. Mahon, who works in the CFO's office and is an expert in large-scale transformation using new technologies, predicts that savvy finance teams will embrace automation as a way to "move away from these repetitive tasks and spend more time looking at insights and identifying risk and fraud." That ability to pivot is a core characteristic of "agile" financial leaders--finance experts who embrace new digital technologies, are responsive to change, and offer insight and strategic guidance to the companies they serve, according to the AICPA/Oracle report.

It's been quite an interesting journey for Factmata since we started in January and we're now about to launch a tool that puts factual context in the hands of the people. This will happen around the UK general election, and marks the completion of our Google Digital News Initiative (DNI) project. For 5 months, we've been working around the clock with a distributed team of NLP researchers, PhDs and scientists from around the world to build this, and now finishing off the final touches. As we prepare for launch, we wanted to tell the world about what's next and where we want to take Factmata in the future. Given our team's work in automated fact-checking in previous research, we are uniquely placed to build AI to solve the problem of online misinformation.

Music streaming service Spotify on Wednesday disclosed it has acquired the team and technology behind Niland, a French start-up with a service for delivering music recommendations. The move signals that Spotify wants to incorporate more artificial intelligence (AI) into its system as it fights off competition from alternatives like Apple Music. Niland is not well-known in the field of AI. But for years its CEO, Damien Tardieu, has done research on ways to extract meaningful information from raw music content in order to form connections with other music. This approach differs from collaborative filtering, one of the techniques that Spotify and others use.

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Japan is one of the advanced countries in the AI and robot industries. At the Ise-Shima G-7 summit meeting in Japan in May, world leaders looked amused and fascinated when they watched a robot performing at the International Media Center. At the same time, because of the technological advances and advent of robots, the leaders are concerned that many people in their countries will lose jobs and cry out for assistance from governments. This kind of downside to innovations has been well recorded in the history of modernization. Innovations bring fundamental changes to many traditional business procedures, jobs and professions. But the impact AI will generate is said to be on a far larger scale than previous innovations.

Designs for the much-requested ginger emoji have been revealed. Jeremy Burge, the founder of Emojipedia, posted a series of sample images on Twitter, showing off what the final designs could look like. He included pictures of famous redheads Jessica Chastain and Ed Sheeran alongside the emoji. 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.