Google Arts & Culture has teamed up with the Centre Pompidou, a cultural complex in Paris, to pay tribute to Vassily Kandinsky with a virtual exhibition of the artist's works and other documents. You can view some of Kandinsky's pieces in an augmented reality gallery. At the heart of the exhibit is a machine learning experiment that tries to replicate synesthesia, a condition the abstract art pioneer had. In a nutshell, synesthesia turns information that stimulates one of your senses into a multi-sensory experience. For some people (including Kandinsky, Billie Eilish and Pharrell Williams), the condition deepens the association between colors and sounds or moods.

Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its introduction. In contrast, related tasks in the music audio domain remained, until recently, largely untackled. While several style conversion methods tailored to musical signals have been proposed, most lack the 'one-shot' capability of classical image style transfer algorithms. On the other hand, the results of existing one-shot audio style transfer methods on musical inputs are not as compelling. In this work, we are specifically interested in the problem of one-shot timbre transfer. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. We evaluate the method using a set of objective metrics and show that it is able to outperform selected baselines.

Online-dating company Match Group Inc. has reached an agreement to acquire South Korean social-media company Hyperconnect for $1.73 billion, broadening its services beyond connecting people in their love lives. The cash-and-stock deal, announced Tuesday, marks Match Group's largest acquisition to date. Hyperconnect, based in Seoul, has developed two video apps that focus on helping people interact one-on-one and with new communities. Hyperconnect's first app, Azar, offers live video and audio chat and can instantly translate voice and text for users that speak different languages. Hyperconnect's other app, Hakuna Live, is a social live-streaming app that provides group video and audio broadcasts.

Last spring, I started boiling two eggs for breakfast every morning--one for me, and one for the crows. A mated pair patrolled the rooftops around my Berlin apartment building; I'd begun luring them to my balcony with peanuts and other snacks. They loved not only eggs but also mealworms, cat food, cashews, chicken hearts, stale bread, cheese, and chunks of lamb fat; they barely touched liver, walnuts, vegetables, and dried fruit. In Germany, we were under a COVID-19 lockdown. But the birds were free.

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning. A chapter on datasets as benchmarks examines their histories and scientific bases. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences. Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.

Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified "Hallmarks" of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.

This paper is preoccupied with the following question: given a (possibly opaque) learning system, how can we understand whether its behaviour adheres to governance constraints? The answer can be quite simple: we just need to "ask" the system about it. We propose to construct an investigator agent to query a learning agent -- the suspect agent -- to investigate its adherence to a given ethical policy in the context of an information-seeking dialogue, modeled in formal argumentation settings. This formal dialogue framework is the main contribution of this paper. Through it, we break down compliance checking mechanisms into three modular components, each of which can be tailored to various needs in a vast amount of ways: an investigator agent, a suspect agent, and an acceptance protocol determining whether the responses of the suspect agent comply with the policy. This acceptance protocol presents a fundamentally different approach to aggregation: rather than using quantitative methods to deal with the non-determinism of a learning system, we leverage the use of argumentation semantics to investigate the notion of properties holding consistently. Overall, we argue that the introduced formal dialogue framework opens many avenues both in the area of compliance checking and in the analysis of properties of opaque systems.

Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in general, freezing the trained feature extractor (the lower layers) and retraining the classifier (the upper layers) on the same dataset leads to worse performance. In this paper, for the first time, we show that the frozen classifier is transferable within the same dataset. We develop a novel top-down training method which can be viewed as an algorithm for searching for high-quality classifiers. We tested this method on automatic speech recognition (ASR) tasks and language modelling tasks. The proposed method consistently improves recurrent neural network ASR models on Wall Street Journal, self-attention ASR models on Switchboard, and AWD-LSTM language models on WikiText-2.

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes.