Products featured in this Mail Best article are independently selected by our shopping writers. If you make a purchase using links on this page, MailOnline may earn an affiliate commission. Smart home devices were a hit over Black Friday and Cyber Monday, but if you missed out, we have some good news. Right now, you can buy two Amazon Echo Dot (3rd Gen) smart speakers for £29.99 - so you can treat yourself and a friend this Christmas. The same sell-out deal that was seen during Amazon's Black Friday sale, for a limited time, shoppers can score two Alexa devices for £29.99 using the promo code 2ECHODOT at checkout.

There has been tremendous progress in Artificial Intelligence (AI) for music, in particular for musical composition and access to large databases for commercialisation through the Internet. We are interested in further advancing this field, focusing on composition. In contrast to current black-box AI methods, we are championing an interpretable compositional outlook on generative music systems. In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars. Quantum computing is a nascent technology, which is very likely to impact the music industry in time to come. Thus, we are pioneering a Quantum Natural Language Processing (QNLP) approach to develop a new generation of intelligent musical systems. This work follows from previous experimental implementations of DisCoCat linguistic models on quantum hardware. In this chapter, we present Quanthoven, the first proof-of-concept ever built, which (a) demonstrates that it is possible to program a quantum computer to learn to classify music that conveys different meanings and (b) illustrates how such a capability might be leveraged to develop a system to compose meaningful pieces of music. After a discussion about our current understanding of music as a communication medium and its relationship to natural language, the chapter focuses on the techniques developed to (a) encode musical compositions as quantum circuits, and (b) design a quantum classifier. The chapter ends with demonstrations of compositions created with the system.

Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may be over-represented in some detected communities and under-represented in others. This paper defines a novel $\ell_1$-regularized pseudo-likelihood approach for fair graphical model selection. In particular, we assume there is some community or clustering structure in the true underlying graph, and we seek to learn a sparse undirected graph and its communities from the data such that demographic groups are fairly represented within the communities. Our optimization approach uses the demographic parity definition of fairness, but the framework is easily extended to other definitions of fairness. We establish statistical consistency of the proposed method for both a Gaussian graphical model and an Ising model for, respectively, continuous and binary data, proving that our method can recover the graphs and their fair communities with high probability.

Famous social media influencer Gary Vayernurchuk says, "The future belongs to Voice." Look at all AI (artificial intelligence) driven assistants around us, from Alexa to Google Assistant, there is inherent convenience in just saying it out loud and having voice based conversations with your'virtual' assistant rather than writing commands or selecting a drop down menu. We all know that healthcare needs to be digitised in order to reach the next level of patient care. Technologies like AI and Blockchain need to be integrated into existing healthcare systems in order to make them more efficient. But these technologies can only work if all our processes are digitised first.

The smart home has never been more accessible, with more affordable entry points than ever. From security cameras to smart light bulbs, pretty much any internet-connected device that you can control via an app is enough to get you started on your smart home journey. But before you dive in, we advise taking a few preliminary steps. For one, don't try to outfit your entire smart home in one go. Not only can this be quite expensive, we think it's generally best to buy just one or two items first to see if you like them. From there, you can figure out if you want to buy more devices, making sure that they're compatible with one another.

Hit play on the player above to hear the podcast and follow along with the transcript below.This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text. Welcome back to Talking Tech. If you used Amazon's Alexa, I will only be saying this name once by the way, because I don't want to trigger anyone who has an Echo speaker, but if you've used Amazon's digital voice assistant, obviously it's got many different ways it can help you play a song, set a reminder, set an alarm. Now Amazon's digital assistant has a new role, helping seniors and caregivers.

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model.

This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and Environmental Harms. The first area concerns the perpetuation of stereotypes, unfair discrimination, exclusionary norms, toxic language, and lower performance by social group for LMs. The second focuses on risks from private data leaks or LMs correctly inferring sensitive information. The third addresses risks arising from poor, false or misleading information including in sensitive domains, and knock-on risks such as the erosion of trust in shared information. The fourth considers risks from actors who try to use LMs to cause harm. The fifth focuses on risks specific to LLMs used to underpin conversational agents that interact with human users, including unsafe use, manipulation or deception. The sixth discusses the risk of environmental harm, job automation, and other challenges that may have a disparate effect on different social groups or communities. In total, we review 21 risks in-depth. We discuss the points of origin of different risks and point to potential mitigation approaches. Lastly, we discuss organisational responsibilities in implementing mitigations, and the role of collaboration and participation. We highlight directions for further research, particularly on expanding the toolkit for assessing and evaluating the outlined risks in LMs.

Apple Music's recently announced Voice Plan will launch alongside iOS 15.2, according to the patch notes the company shared for the update's release candidate. When Apple first announced the more affordable tier at its fall Mac event in October, the company said it would become available "later this fall" in 17 countries, including the US, UK and Canada. Apple also confirmed Apple Music Voice Plan will launch with iOS 15.2 pic.twitter.com/6uHeaTdr41 The plan will offer access to Apple Music's entire song catalog for $5 per month, provided you're willing to rely on Siri for control. You can play specific tracks and playlists, as well as complete albums on your Apple devices.

A curated list of applied machine learning and data science notebooks and libraries across different industries.