Al Alcorn knew he was being wooed. Nolan Bushnell, the tall, brash, young engineer from Alcorn's work-study days at Ampex, had shown up at Alcorn's Sunnyvale office. Bushnell was driving a new blue station wagon. "It's a company car," he said with feigned nonchalance. He offered to drive Alcorn, recently hired as an associate engineer at Ampex, to see the "game on a TV screen" that Bushnell and Ted Dabney had developed at their new startup company. The two men drove to an office in Mountain View, near the highway. The space was large, about 10,000 square feet, and looked like a cross between an electronics lab and an assembly warehouse. Oscilloscopes and lab benches filled one area. Half-built cabinets and screen with wires protruding from them sat in another. Bushnell walked with Alcorn to a sinuous, six-foot-tall fiberglass cabinet with a screen at eye level. Bushnell was proud of what he called its "spacey-looking" shape.

Researchers have unveiled a Blade Runner-style AI that can enhance pixelated images. Dubbed EnhanceNet, the system relies on neural networks to boost the image quality, creating high-resolution images from a low-resolution input. Stunning results shared in the study reveal how the new method can create sharper, more detailed results than traditional approaches, leading to photos that are nearly indistinguishable from the real thing. The system uses adversarial training, which pits the networks against each other in competing goals so they ultimately learn together. 'By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios,' the researchers explain in the study.

This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can be because the studied phenomenon does not follow a stochastic sampling scheme, samples are high-dimensional and subsequent multiple-testing corrections render results overly conservative, sample sizes are too low for asymptotic results (as e.g. the central limit theorem) to kick in, or one cannot allow for the small probability of failure inherent to stochastic approaches. This paper instead designs a method based on insights from machine learning and online learning theory: this detection algorithm - named Online FAult Detection (FADO) - comes with theoretical guarantees of its detection capabilities. A version of the margin is found to regulate the detection performance of FADO. A precise expression is derived for bounding the performance, and experimental results are presented assessing the influence of involved quantities. A case study of scene detection is used to illustrate the approach. The technology is closely related to the linear perceptron rule, inherits its computational attractiveness and flexibility towards various extensions.

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

I really liked your previous project, and to be honest I enjoyed that more than the current project. In the previous project, your agents learned how to play a game from scratch by evolving a minimal neural network. Combining that approach with an algorithm to generate random tracks, or play against itself in the experiment, it may even learn to generalize to some extent to previously unseen tracks. Here, I see you are training a predictive coding model to imitate a recorded dataset of actual human play, which is better than Mar I/O from a technical standpoint in the sense that you are learning from pixels, but conceptually I am still more interested in the self-exploration idea. It might be cool though to train your LSTM to imitate the NEAT-evolved agents from Mar I/O, then you can claim the entire system learned to play on its own!

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Training a model with the MNIST dataset is often considered the "Hello world" of machine learning. That's been done many times over, but unfortunately, just because a model does well on MNIST, doesn't necessarily mean it is predictive of high performance on other datasets, especially since most image data we have today are considerable more complex than handwritten digits. Zalando decided it was time to make MNIST fashionable again, and recently released a dataset called fashion-mnist. It's the exact same format as'regular' MNIST except the data is in the form of pictures of various clothing types, shoes, and bags. It is still across 10 categories and the images are still 28 by 28 pixels.

Smart home speakers have quickly become the hot gadget people didn't know they wanted. They can answer your movie trivia questions, call a cab, turn your heating on and do your shopping for you. They're gaining new features every day, but are more than just a utility product. These speakers are a ripe platform for all kinds of screen-free entertainment, and I'm not just talking about streaming a Spotify playlist. Earplay is a popular Alexa skill that tells interactive stories, for example, and never one to be late to a fledgling medium, the BBC has taken note.

I don't know if this has ever happened to you, but trust me, if you rely on your phone to help you navigate, it just might someday. My wife and I were en route from Raleigh to St. Louis, having enjoyed crossing the Smokies and the Cumberlands and just coming up on Paducah, Kentucky. Having been routed around a massive traffic jam in Knoxville by my phone, I was much pleased with it, and so listened carefully when it described a similar problem ahead in Paducah. The result: Following my phone's directions, I pulled off the interstate and began taking a series of roads through Paducah, just as I had done in Knoxville. My phone kept sending me down smaller and smaller roads, and soon we were out in the country, presumably bypassing the massive interstate jam ahead.

Getting to the steamy stuff – you know what we're talking about – just got a little easier. Audible recently launched a feature for some romance audiobooks that lets listeners skip to "the good part." The Amazon company launched the Audible Romance Package, which allows users to "binge to your heart's content" thousands of audiobooks included. Because parsing through the masses can be daunting, there are a few ways to narrow the search, including recommendations from the "Take Me to the Good Part" tool. It offers listeners the option to jump to certain places in select audiobooks based on 10 categories.