Spotify has bigger plans for the technology behind its new AI DJ feature after seeing positive consumer reaction to the new feature. Launched just ahead of the company's Stream On event in L.A. last week, the AI DJ curates a personalized selection of music combined with spoken commentary delivered in a realistic-sounding, AI-generated voice. But under the hood, the feature leverages the latest in AI technologies and large language models, as well as generative voice -- all of which are layered on top of Spotify's existing investments in personalization and machine learning. These new tools don't necessarily have to be limited to a single feature, Spotify believes, which is why it's now experimenting with other applications of the technology. Though the highlight from Spotify's Stream On event was the mobile app's revamp, which now focuses on TikTok-like discovery feeds for music, podcasts, and audiobooks, the AI DJ is now a prominent part of the streaming service's new experience.

Generative AI is absolutely everywhere right now, so it's no surprise to see Spotify putting it to use in its latest feature, simply called "DJ." It's a new way to immediately start a personalized selection of music playing that combines Spotify's well-known personalization tools that you can find in playlists like Discover Weekly as well as the content that populates your home screen with some AI tricks. I got early access to DJ and have been playing with it for the last day to see how Spotify's latest take on personalized music works, but the feature is available as of today in beta for all premium subscribers in the US and Canada. While Spotify has loads of personalized playlists for users, I've found that the app lacks a simple way to tell it to just play some music you like. On Apple Music, for example, I can ask Siri to play music I like and it'll start a personalized radio station based on music I've played alongside some things it thinks I'll enjoy but haven't played before. It's a reliable way to jump right into my collection.

Selection of appropriate collective variables for enhancing molecular simulations remains an unsolved problem in computational biophysics. In particular, picking initial collective variables (CVs) is particularly challenging in higher dimensions. Which atomic coordinates or transforms there of from a list of thousands should one pick for enhanced sampling runs? How does a modeler even begin to pick starting coordinates for investigation? This remains true even in the case of simple two state systems and only increases in difficulty for multi-state systems. In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by supervised machine learning literature. In particular, we show how the decision functions in supervised machine learning (SML) algorithms can be used as initial CVs for accelerated sampling. Using solvated alanine dipeptide and Chignolin mini-protein as our test cases, we illustrate how the distance to the Support Vector Machines decision hyperplane, the output probability estimates from Logistic Regression, and other classifiers may be used to reversibly sample slow structural transitions. We discuss the utility of other SML algorithms that might be useful for identifying CVs for accelerating molecular simulations.

The UAE on Thursday appointed Omar Bin Sultan as the country's first Minister of State for Artificial Intelligence as part of a cabinet reshuffle. Aged just 27, Sultan's appointment is part of the UAE's ambition to be at the forefront of the global technological revolution which sees it planning to be build homes on the planet Mars by 2117. The position was announced in a tweet by Sheikh Mohammed bin Rashid Al Maktoum, UAE Prime Minister and Vice President and ruler of Dubai who said: "The new Government is a Government for the new Emirati percentage. The move comes just days after Sheikh Mohammed announced the UAE Strategy for Artificial Intelligence (AI), a major part of the UAE Centennial 2070 objectives. The initiative aims to improve government performance and create an innovative and highly-productive environment by means of investing in AI. Other new positions created in the reshuffle include a Minister for Advanced Sciences and another for Food Security, according to a series of tweets, written in Arabic. Sheikh Mohammed said: "The new Government is a Government for the new Emirati percentage.

Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to time-consuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We've designed Osprey to provide scientists with a practical, easy-to-use way of finding optimal model parameters. The software works seamlessly with scikit-learn estimators (Pedregosa et al. 2011) and supports many different search strategies for choosing the next set of parameters with which to evaluate a given model, including gaussian processes (GPy 2012), tree-structured Parzen estimators (Yamins, Tax, and Bergstra 2013), as well as random and grid search. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times.