The EBU News Report 2019 examines what the fourth major wave of digital transformation means for journalism. Artificial intelligence has potential for unlocking new opportunities – but there are greater challenges for journalism too. The report addresses the issues and includes practical case studies, checklists and tool-kits.

AI and machine learning algorithms are quite skilled at generating works of art -- and highly realistic images of apartments, people, and pets to boot. But relatively few have been tuned to singing synthesis, or the task of cloning the voices of musicians. Researchers from Amazon and Cambridge put their collective minds to the challenge in a recent paper, in which they propose an AI system that requires "considerably" less modeling than previous work of features like vibratos and note durations. It taps a Google-designed algorithm -- WaveNet -- to synthesize the mel-spectrograms, or the representations of the power spectrum of sounds, which another model produces using a combination of speech and signing data. The system comprises three parts, the first of which is a frontend that takes a musical score as input and produces note embeddings (i.e., numerical representations of notes) to be send to an encoder.

In this day and age, everyone has heard about Artificial Intelligence (AI). It's proven to be very controversial, even though it shouldn't have been. The general populace still doesn't understand what it truly represents. In their minds, they may even equate it to Skynet from the Terminator films. But let's avoid jumping to extreme conclusions.

Samuel Holmes Doten of Plymouth, Mass., was born June 5, 1812, so after the Civil War ended in 1865, he would joke that he "served in the infantry in the war of that date." William Kendall Crossfield, a Peterborough, N.H. native, was having a rest during the battle of Fredericksburg when he was shot in the neck while turning over. The blanket he had pulled up to his chin miraculously cushioned the bullet, but he passed out from the shock of the blow. Vermonter Almeron C. Inman was recommended for the Medal of Honor of Feb. 9, 1887, "for intelligent coolness and bravery" in two 1864 engagements. After going missing for three months in 1895, he was found dead, thought to have killed himself.

Like electricity or the internet, artificial intelligence (AI) is considered a general purpose technology with the potential to transform productivity, accelerate economic growth and improve wellbeing across the whole of society. It has started, and will continue to, drastically transform the way we work and live. At least, this is what the report'Towards Our Intelligent Future' published by New Zealand AI Forum earlier this year affirms. The report represents over nine months of collaborative work on parallel streams exploring AI adoption, policy and strategy in New Zealand and around the world. It highlights the value of AI for achieving New Zealand's wellbeing, sustainability and economic goals.

As the decade wraps up, many of the most evident shifts in technology have already taken place in the AI, DL, and ML landscape. Billions in venture financing have been raised to chase AI opportunities, and media outlets now have dedicated beat reporters focused on the market. Though the space has come a long way, it's also clear that there's still a long way to go. While reflecting on the past year, I started thinking about what 2020 may bring. I wanted to share some predictions on what will shape the industry landscape and the work I do at Determined AI.

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.

Descartes Labs uses artificial intelligence to detect wildfires, and they can correctly spot one in a record-breaking time of nine minutes.