The break room is stocked with free sparkling water. The worktables are a lacquered, honey-blonde wood. And in the center of the gleaming workspace, under the scalding white ceiling of a two-story atrium, stood the solution to the southern border problem. Its four mechanical legs spread over the polished cement. A metal mast rose in bolted sections and at the top, level with the second floor's glass-walled offices, were two boxes.

The Artificial Intelligence industry should create a global community of hackers and "threat modellers" dedicated to stress-testing the harm potential of new AI products in order to earn the trust of governments and the public before it's too late. This is one of the recommendations made by an international team of risk and machine-learning experts, led by researchers at the University of Cambridge's Centre for the Study of Existential Risk (CSER), who have authored a new "call to action" published in the journal Science. They say that companies building intelligent technologies should harness techniques such as "red team" hacking, audit trails and "bias bounties" – paying out rewards for revealing ethical flaws – to prove their integrity before releasing AI for use on the wider public. Otherwise, the industry faces a "crisis of trust" in the systems that increasingly underpin our society, as public concern continues to mount over everything from driverless cars and autonomous drones to secret social media algorithms that spread misinformation and provoke political turmoil. The novelty and "black box" nature of AI systems, and ferocious competition in the race to the marketplace, has hindered development and adoption of auditing or third party analysis, according to lead author Dr Shahar Avin of CSER.

Jan Swafford reviews Beethoven X—a new completion of the composer's unfinished Tenth Symphony, rendered by AI.

Social bots - machines that interact as social partners for us humans - are increasingly encountered in everyday life. So far, they mainly appear as software robots in social media, where they like, retweet, but also text and comment. Therefore, they have natural language capabilities and can even communicate synchronously with users as chatbots. However, what happens when they leave the cyber world and suddenly appear face to face with us? How does this affect us as interaction partners and how will our interactions with the machines develop?

Have you ever spent hours searching for a suitable dataset for your data science project? It can get pretty daunting, right? Whether you are a student or a professional looking for high-quality datasets for machine learning or data analysis projects--we've got you covered! In today's article, we will share with you a comprehensive list of 65 open machine learning datasets that you can access for free. We will regularly update this list, so feel free to suggest datasets you are using and we will make sure to add them. "Where can I get free datasets for machine learning?" Here's the list of the best open dataset finders that you can use to browse through a wide variety of niche-specific datasets for your data science projects.

LighthousePE Makes Automated Mobile Proximity Engagement More Accessible with New Machine Learning Capabilities. The latest release of the mobile …

For once, the dour director-general of the BBC was effusive with his praise. For you, Herr von Ribbentrop, I would gladly fly the swastika from the top of Bush House, John Reith promised the departing German Ambassador in 1938, at a gala BBC event. A year later, after appeasement collapsed in shame as the tanks rolled into Prague, the BBC's first boss was still praising Hitler's "magnificent efficiency". Reith's biographer daughter later confirmed her father had revered the Fuhrer. Regarding jazz music as "hot" and a "filthy product of modernity", the Scot ensured it was banned from the airwaves.

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.

The aim of this work is to investigate the impact of crossmodal self-supervised pre-training for speech reconstruction (video-to-audio) by leveraging the natural co-occurrence of audio and visual streams in videos. We propose LipSound2 which consists of an encoder-decoder architecture and location-aware attention mechanism to map face image sequences to mel-scale spectrograms directly without requiring any human annotations. The proposed LipSound2 model is firstly pre-trained on $\sim$2400h multi-lingual (e.g. English and German) audio-visual data (VoxCeleb2). To verify the generalizability of the proposed method, we then fine-tune the pre-trained model on domain-specific datasets (GRID, TCD-TIMIT) for English speech reconstruction and achieve a significant improvement on speech quality and intelligibility compared to previous approaches in speaker-dependent and -independent settings. In addition to English, we conduct Chinese speech reconstruction on the CMLR dataset to verify the impact on transferability. Lastly, we train the cascaded lip reading (video-to-text) system by fine-tuning the generated audios on a pre-trained speech recognition system and achieve state-of-the-art performance on both English and Chinese benchmark datasets.