In part two of this series, we explored the impact of AI in the studio, with assisted mixing tools from iZotope, right up to full-on machine learning DAWs that can transfer the style of one producer to another project, among many other things. In part three, we'll look at how AI has infiltrated the DJ booth, as well as how hyper-personalised generative music apps could lead to an even-more-siloed listening experience across streaming platforms. It's fair to say contemporary pop music follows a certain formula. Those sometimes predictable patterns make it easier for AI to spot trends and more accurately recreate music. For dance music, those patterns are even clearer, generally following a four-, eight- and sixteen-bar arrangement mould, thanks in part to the modern DAW.

In part one of our AI Futures series, we discussed the looming threats and opportunities around'deepfakes' or style transfers using AI. We spoke to Holly Herndon, a Berlin-based artist who's been deep in the trenches with AI for many years. We also explored how deepfakes are ushering in a Sampling 2.0 era, and explore how the mistakes of the past have a chance to be rectified for the future. It's worth reading part one before you continue. For producers and songwriters, the idea of an autonomous collaborator who makes suggestions for your music or arrangement, helps you write lyrics or simply does the job for you, is generally met with unease.

To facilitate this process, we leverage here machine learning (ML) algorithms and automated synthesis technology to predict PNA synthesis efficiency and …

"By using machine learning to process the data, we felt we could revisit the canonical view of a cell," Weigel says.

Artificial intelligence (AI) isn't always an option, even for small credit unions, the panel agreed.

From remote surgery to rehabilitation robotics and wearable sensors, 5G will be a real driver of innovation in healthcare, helping improve staff efficiency while improving patient care. For example, in Milan, the 5G connected ambulance is allowing paramedics to be continuously connected to the emergency management centre and with hospital doctors, providing a way to share patient details and symptoms before they even reach the hospital. As the "5G capital of Europe", Milan has been at the forefront of innovation in this area and recently acted as the backdrop for our 5G Healthcare Vodafone Conference & Experience Day – an event dedicated to the future of Health and Wellness. Attended by the industry's leading figures, we discussed the key role that new technologies are playing and shared some of our recent breakthroughs. During the event, a remote surgery operation was carried out for the first time in Italy on our live 5G network in collaboration with the Italian Institute of Technology (IIT) and the IRCSS Hospital San Raffaele.

Have you come across a robojournalist? Robojournalism is an awesome way to produce reports on repetitive events available to people and at the same time letting human journalists focus on work that requires research and human insight. One example of such system is Quakebot by LA Times. However, I found it surprisingly hard to find examples where usage of robojournalism is clearly stated. In robojournalism, or automated journalism, some data is transformed into news reports written in some human language.

Due to their convenience and high accuracy, face recognition systems are widely employed in governmental and personal security applications to automatically recognise individuals. Despite recent advances, face recognition systems have shown to be particularly vulnerable to identity attacks (i.e., digital manipulations and attack presentations). Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation. In this context, most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time. To tackle this problem, we introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images (i.e., reference and probe) and then combined for identity attack detection. The experimental evaluation conducted over several databases shows a high generalisation capability of the proposed method for detecting unknown attacks in both the digital and physical domains.

Transformer language models are state-of-the-art in a multitude of NLP tasks. Despite these successes, their opaqueness remains problematic. Recent methods aiming to provide interpretability and explainability to black-box models primarily focus on post-hoc explanations of (sometimes spurious) input-output correlations. Instead, we emphasize using prototype networks directly incorporated into the model architecture and hence explain the reasoning process behind the network's decisions. Moreover, while our architecture performs on par with several language models, it enables one to learn from user interactions. This not only offers a better understanding of language models but uses human capabilities to incorporate knowledge outside of the rigid range of purely data-driven approaches.

Probabilistic programming provides numerous advantages Ideally speaking, a good recommendations system should be over other techniques, including but not able to identify and remove malicious users before they can limited to providing a disentangled representation disrupt the ranking system by a significant margin. However, of how malicious users acted under a structured to eliminate the risk of false positives a resilient ranking model, as well as allowing for the quantification system can use as much data as possible. So we have to of damage caused by malicious users. We show adjust the tradeoff between false positives and the damage a experiments in malicious user identification using set of malicious users can cause to a ranking system.