Today, users can listen to music and discover new artists, songs or albums on a variety of music streaming platforms, including Spotify, Apple Music, Amazon Music Unlimited and more. Many developers have been trying to create tools that could improve these services, such as music recommendation systems that suggest new songs or playlists to users based on their preferences and on music they listened to in the past. Researchers at Seoul National University recently created an interactive data visualization tool that could enhance both existing and emerging music streaming services. This tool, called Music Circles, can represent songs as unique vectors and then calculate similarities between different vectors to group similar songs into clusters. "As music lovers with different tastes, we came together for a project that would find novel ways of visually representing and grouping abstract music data," Seokgi Kim, Jihye Park, Kihong Seong, Namwoo Cho, Junho Min and Hwajung Hong, the researchers who carried out the study, told TechXplore via email.

I scrapped all their webpages categorized under "stories". AllSides is a brilliant initiative that takes a news event and collects articles written on it by a left leaning, right leaning and center leaning media outlet. They write a summary on this event and briefly mention what is being emphasized on by each of the three outlets. An example of this can be viewed here. They publish pre-established metrics for the contemporary political bias of all major media outlets.

Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to discrete and sequential data has been limited. In this work, we present a technique for training diffusion models on sequential data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps. We apply this technique to modeling symbolic music and show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings.

U.S. banking regulators announced on Monday they were soliciting public input on the growing use of artificial intelligence by financial institutions. In a joint statement, the regulators said they wanted feedback on the use of the technology by banks to police fraud, underwrite loans and for other purposes, and what perks and challenges it presents.

Thousands of historical images from across Ireland are being brought to life in color for the first time, thanks to a new AI-led photo project. Combining digital technology with painstaking historical research, professors John Breslin and Sarah-Anne Buckley at the National University of Ireland, Galway, have been able to turn photos, originally shot in black in white, into rich color images. It includes portraits of key figures like Oscar Wilde and poet W.B. Yeats, as well as defining moments in history, like the Titanic setting sail from the Belfast shipyard where it was constructed. Yet, some of the most compelling photos depict everyday scenes -- people herding pigs, spinning wool or packed onto the back of horse-drawn carts. And while poverty is evident in pictures of barefoot villagers crowding around for a photo, or of Dublin's working-class tenement buildings, there are also well-to-do family shots and depictions of upper-class pastimes like fox hunting.

The future will involve artificial intelligence, but (in case you weren't already cautious) we need to be very careful what data we feed into these systems. A team led by computer scientists from MIT examined ten of the most-cited datasets used to test machine learning systems and found that around 3.4 percent of the data was inaccurate or mislabeled. Some have been cited over 100,000 times in machine learning research. Some of the errors are bigger than others, but include mistaking Bruce Springsteen for an orchestra and mislabelling a baby as a nipple. These errors could have huge ramifications for machine learning systems unless addressed. It may still be the early days of artificial intelligence, so we might want to check its homework.

With the continuous growth within the AI & Machine Learning fields, the question that often arises is: "which changes will people see in the working …

Back in the 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created one of the first chatbots, which he named ELIZA. Weizenbaum modeled its conversational style after Carl Rogers, a psychotherapist who was known for parroting patients' responses back at them. His hypothesis was that while chatbots could emulate human conversation superficially, they could not fully capture the nuances of a genuine discussion between humans. To Weizenbaum's complete surprise, ELIZA did end up fooling many people into believing they were having a therapeutic breakthrough with a real live therapist. While modern customer service bots aren't likely to help customers dig deep into their psychological issues, the technology behind computers processing human language is getting more advanced day by day.

Palmaz Vineyards in Napa Valley harnesses Big Data to produce the perfect bottle, and some Australian winemakers are using machine learning to …

The report found that 46% of respondents have built their digital twins with at least 50% input from artificial intelligence or machine learning – and it's …