We listen to music with our ears, but also our eyes, watching with appreciation as the pianist's fingers fly over the keys and the violinist's bow rocks across the ridge of strings. When the ear fails to tell two instruments apart, the eye often pitches in by matching each musician's movements to the beat of each part. A new artificial intelligence tool developed by the MIT-IBM Watson AI Lab leverages the virtual eyes and ears of a computer to separate similar sounds that are tricky even for humans to differentiate. The tool improves on earlier iterations by matching the movements of individual musicians, via their skeletal keypoints, to the tempo of individual parts, allowing listeners to isolate a single flute or violin among multiple flutes or violins. Potential applications for the work range from sound mixing, and turning up the volume of an instrument in a recording, to reducing the confusion that leads people to talk over one another on a video-conference calls.

First of all, the peer-review process is broken. Every fourth NeurIPS submission is put on arXiv. There are DeepMind researchers publicly going after reviewers who are criticizing their ICLR submission. On top of that, papers by well-known institutes that were put on arXiv are accepted at top conferences, despite the reviewers agreeing on rejection. In contrast, vice versa, some papers with a majority of accepts are overruled by the AC.

They often focus almost exclusively on machine learning (ML)--sometimes even using "ML" as a synonym for "AI."

Ethics, responsibility, and fairness are all problems that have started to spring up around machine learning and artificial intelligence, and Anaconda …

Public debate about AI is dominated by Frankenstein Syndrome, the fear that AI will become superhuman and escape human control. Although superintelligence is certainly a possibility, the interest it excites can distract the public from a more imminent concern: the rise of Artificial Stupidity (AS). This article discusses the roots of Frankenstein Syndrome in Mary Shelley's famous novel of 1818. It then provides a philosophical framework for analysing the stupidity of artificial agents, demonstrating that modern intelligent systems can be seen to suffer from 'stupidity of judgement'. Finally it identifies an alternative literary tradition that exposes the perils and benefits of AS. In the writings of Edmund Spenser, Jonathan Swift and E.T.A. Hoffmann, ASs replace, oppress or seduce their human users. More optimistically, Joseph Furphy and Laurence Sterne imagine ASs that can serve human intellect as maps or as pipes. These writers provide a strong counternarrative to the myths that currently drive the AI debate. They identify ways in which even stupid artificial agents can evade human control, for instance by appealing to stereotypes or distancing us from reality. And they underscore the continuing importance of the literary imagination in an increasingly automated society.

The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the computational burden arising from large number of training instances, features and labels, problems in XMC are faced with two statistical challenges, (i) large number of 'tail-labels' -- those which occur very infrequently, and (ii) missing labels as it is virtually impossible to manually assign every relevant label to an instance. In this work, we derive an unbiased estimator for general formulation of loss functions which decompose over labels, and then infer the forms for commonly used loss functions such as hinge- and squared-hinge-loss and binary cross-entropy loss. We show that the derived unbiased estimators, in the form of appropriate weighting factors, can be easily incorporated in state-of-the-art algorithms for extreme classification, thereby scaling to datasets with hundreds of thousand labels. However, empirically, we find a slightly altered version that gives more relative weight to tail labels to perform even better. We suspect is due to the label imbalance in the dataset, which is not explicitly addressed by our theoretically derived estimator. Minimizing the proposed loss functions leads to significant improvement over existing methods (up to 20% in some cases) on benchmark datasets in XMC.

A machine learning system is an automated process that ingests data continuously — or, at least, regularly – and that passes through a problem-solving …

June 30, 2020 – A team from the Lawrence J. Ellison Institute for Transformative Medicine of USC have developed a technique to train a deep learning …

Alexa is the world's most popular smart assistant and the driving force behind Amazon's beloved Echo smart speaker lineup. These voice-controlled, Alexa-enabled smart speakers can be used to manage your smart home, give you the forecasts for the day ahead, and much more. If you're thinking about inviting Alexa into your home via one of Amazon's Echo speakers, you may be wondering which one to buy. We took a look at two of Amazon's most popular smart speakers, the Echo (third-generation) and the Echo Dot (third-generation) to help you decide which of these handy smart speakers is best for you. The Echo Dot (third-generation) is one of the smallest Amazon Echo smart speakers. The most obvious visual difference between the Echo Dot and the Echo is the size.

Mayra Leiva of Reseda, California, knew her eight-year-old son was a little interested in history. But she was surprised when all at once he became a walking encyclopedia, spouting dates and pretending every tire swing was a time machine. "It happened after he saw Night at the Museum," she says. I've had to do a lot of Googling to keep up!" Not many children will tell you that their favorite school subject is history. Memorizing dates and learning long-ago facts that don't seem relevant isn't exactly high on their fun list. Perhaps that's why pop culture--movies, music, television, and even video games and comic books--can be such useful teaching tools. "Teaching through pop culture helps students relate history to their own background and experiences," says Gail Hudson, a fifth-grade teacher and 2020 Nevada Teacher of the Year. "It's tying into something that's already caught their interest." Take the movie version of the Broadway show Hamilton, which releases on Disney July 3.