In addition to queueing up songs, it will provide insightful commentary too.

Music streaming service Spotify is the latest tech company to launch an artificial intelligence (AI)-powered product. Dubbed DJ, the service was launched for users in the U.S. and Canada on Wednesday, a company press release said.

Personalized suggestions are what Spotify is known for. The company has used machine learning to understand music, its users, and their preferences and looked to match them (pun intended) successfully. At a time when companies are keen to use generative AI models in their products, Spotify too has jumped into the fray.

Spotify/ YouTube.

Human beings are capable of processing several sound sources at once, both in terms of musical composition or synthesis and analysis, i.e., source separation. In other words, human brains can separate individual sound sources from a mixture and vice versa, i.e., synthesize several sound sources to form a coherent combination. When it comes to mathematically expressing this knowledge, researchers use the joint probability density of sources. For instance, musical mixtures have a context such that the joint probability density of sources does not factorize into the product of individual sources.

A deep learning model that can synthesize many sources into a coherent mixture and separate the individual sources from a mixture does not exist currently. When it comes to musical composition or generation tasks, models directly learn the distribution over the mixtures, offering accurate modeling of the mixture but losing all knowledge of the individual sources. Models for source separation, in contrast, learn a single model for each source distribution and condition on the mixture at inference time. Thus, all the crucial details regarding the interdependence of the sources are lost. It is difficult to generate mixtures in either scenario.

Taking a step towards building a deep learning model that is capable of performing both source separation and music generation, researchers from the GLADIA Research Lab, University of Rome, have developed Multi-Source Diffusion Model (MSDM). The model is trained using the joint probability density of sources sharing a context, referred to as the prior distribution. The generation task is carried out by sampling using the prior, whereas the separation task is carried out by conditioning the prior distribution on the mixture and then sampling from the resulting posterior distribution. This approach is a significant first step towards universal audio models because it is a first-of-its-kind model that is capable of performing both generation and separation tasks.

FRIDA can create finger paintings based on human inputs, photographs, and music.

“AI is the future!” is a statement of the past now, as AI is no longer the future but our present. After ChatGPT shocked the whole world with its abilities, researchers at Carnegie Mellon University created an AI-powered robot that could create exceptional artwork on physical canvas with the help of simple text prompts, according to a press release.

The FRIDA robot (the Framework and Robotics Initiative for Developing Arts) can create unique paintings using photographs, human inputs, or even music. The final result is somewhat a resemblance to a basic finger painting.

Carnegie Mellon University.

After ChatGPT shocked the whole world with its abilities, researchers at Carnegie Mellon University created an AI-powered robot that could create exceptional artwork on physical canvas with the help of simple text prompts, according to a press release.