This is an interesting study, following a line of studies in the visual system trying to link the representation of stimuli in different layers of artificial neural networks to the representation in different stages of biological neural processing. The authors claim (and I do not dispute this claim) That this is the first such study in the auditory system, making this study novel and potentially impactful. My main concern is the dimensionality of the comparisons, and in particular the searchlight approach. The images in Figure 1 are indeed compelling, but they have the potential to be misleading. First, us humans tend to look for patterns in images, so it is important to provide more objective summaries.

The blocks keep coming and the game itself gets reinterpreted, twisted and remade for new generations. Now, a 13-year-old boy has become the first person to'beat' the NES version of Tetris, 34 years after it was first released. Yes, 'beat' goes in quotes because there's no way to complete the game. Instead, he played such a flawless game that he forced a kill screen, from an overflow error. While he's the first person to do this, but not the first time it's been achieved: An AI program called StackRabbit forced a kill screen with the NES Tetris back in 2021.

Last week, at an early showing of The Super Mario Bros. Movie, the lights dimmed and the Nintendo logo glowed out over the audience. From the back, a toddler whose parents were probably their age when Mario first burst onto the scene shouted out "Nintendo!" It was a moment that cut through any debates that people might have been having about the film's quality. Ultimately, The Super Mario Bros. Movie is for the children. And the children turned out in force.

Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with facial expressions, emotions, and semantics, in addition to the known correlation with audio, text, and speaker identity. Based on this observation, we propose a baseline model, Cascaded Motion Network (CaMN), which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition.

Would you buy a 43-inch TV that works in vertical mode? Why didn't you buy Anki's cute toy robots? When are you going to try that meatless Burger King Whopper? Cozmo and Vector couldn't save it.Anki is closing the doors on its toy-robot business Anki, the startup responsible for adorable robotics, is closing its doors and will terminate nearly 200 employees Wednesday. Recode reported CEO Boris Sofman broke the news to staff Monday.

The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. A broad view of the discovery process is given, from data acquisition through data visualization to inductive model building and pattern discovery, and it turns out that AI plays an important role in all stages of such an ambitious enterprise. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.