The new Huracán is lighter, faster, and corners like a champ - thanks to technology. Lamborghini CEO Stefano Domenicali, left, and head of research and development Maurizio Reggiani recently visited San Francisco with their Huracan Performante in tow. "This car represents so much of what we are," says Reggiani, who joined CEO Stefano Domenicali for a breakfast interview with USA TODAY Tuesday. "We are looking to the future." The future, these days, seems to be all about self-driving cars designed to completely detach the driver from the transportation experience.

Sometimes it's just a matter of recognizing an opportunity, as well as having the right combination of technical expertise and a willingness to learn. That's the situation Rob May found himself in when he sold his data recovery startup Backupify to Datto in 2014. May said he started to look AI, among other emerging technologies, when he realized he didn't want to stay at Datto for much longer. May began reading research papers on machine learning. At the same time, he recognized the rise of work collaboration platforms like Slack and HipChat, which weren't just providing better ways for workers to communicate but also creating giant repositories of data.

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.

One of America's greatest living artists sits in a former clog factory in San Francisco. Tim Schafer's videogame career has spanned every platform from the Commodore 64 to the current generation of consoles. Along the way, his extraordinary talents as a writer, puzzle maker, and industry rabble-rouser have consistently pushed the entire medium forward. Grim Fandango and Psychonauts, in particular, are unchallenged classics. His best game, Brütal Legend, stands as an important but little known artistic achievement of the early 21st century.

If you follow China's bold ambition to join the great space powers, it will eventually lead you here, to the neglected eastern edge of steamy Hainan island, in a speck of a village that doesn't appear on most maps. Rocket replicas and signs for Wi-Fi welcome visitors past coconut trees and peppers grown from seeds bred in space. Guide maps show what this hamlet of about 50 residents might become, though the blacktop still looks fresh and most of the noise comes from the chicken coop. Local officials envision Haosheng as the start of a thriving tourist destination tied to nearby Wenchang Satellite Launch Center, much in the way Florida's Space Coast draws visitors interested in Cape Canaveral. China's newest spaceport opened for tours last year and just sent the country's first cargo spacecraft into orbit.

The girls were dancing on a neon tank, wearing sequined bikinis lit up by red and green laser light. A strobing fixed-wing aircraft passed overhead like the acid-trip kissing cousin of a Mitsubishi A6M Zero, with more sequined women dangling from it, trapeze-style. Flashing robots had preceded them -- wheeling through the room, pumping their fists at the crowd -- while the audience, seated on tiers of glittery red plastic swivel chairs, waved glow sticks. As the music throbbed, twin walls of video screens threw up bizarre images. The Technicolor dream machine the women were using as a stage displayed, at the end of its barrel, a rainbow-colored star -- just where, on an ordinary tank, the death comes out. But this was no ordinary tank. It was a fixture of the one-hour show that takes place three times a night at Robot Restaurant, a kind of eye-melting Japanese dinner theater, a cabaret show of such migraine-inducing decadence that Las Vegas falls silent before it. On this hot Tokyo night in July 2013, two Americans, Roger Ver and Nicolas Cary, sat in the crowd. As far as Cary could tell, they were the only gaijin in the place. He was drinking a beer, while Ver, as usual, was abstaining. Their unappetizing bento boxes sat untouched: you don't go to Robot Restaurant for the food. In the midst of the cartoonish spectacle -- earlier, a woman wielding an oversized mace had ridden in on a stegosaurus to battle two heavily armored robots -- they had business to discuss.

A suicide prevention group will dispatch a drone to monitor remote areas around Tojinbo in Fukui Prefecture -- whose lonely cliffs remain romanticized in popular imagination as a destination where people go to end their lives -- in the hopes that the technology will enhance efforts to minimize the suicide rate. Retired police officer Yukio Shige, the 73-year-old head of the nonprofit group, said that although there has been a decline in the total number of people leaping off the cliffs in recent years, suicide remains a persistent problem. "This year we have managed to stop five people from committing suicide but five is a very small number; it's only one per month," Shige said, adding that far more still think about killing themselves. "So far we've monitored (Tojinbo) by ourselves but with the use of drones we could reach places that the human eye can't see." The group's 16 members -- made up of retired police officers, academics and company workers -- patrol the cliffs six times a week, from 11 a.m. until sunset.

Think back to the best shopping experience you ever had. Chances are it involved a well-informed and upbeat salesperson who engaged with you, asked about your needs and guided you through the purchase. You left knowing you made the right decision. This kind of conversation-driven sale--high on empathy and knowledge, low on price and haggling--has long been a staple of successful retail loyalty. It focuses on understanding the needs of consumers and delivering an exceptional experience.

Uber is hiring Raquel Urtasun, a prominent artificial intelligence researcher at the University of Toronto, as the ride-hailing company aims to build a lab for driverless car research in the Canadian city, a hotbed for AI talent. Ursasun--an associate professor at the university who specializes in the computer vision software that allows driverless cars to view the world around them--will oversee the new venture. "We hope to draw from the region's impressive talent pool as we grow, helping the dozens of researchers we plan to hire stay connected to the Toronto-Waterloo Corridor," Travis Kalanick, Uber's embattled CEO, wrote in a blog post published this morning. The move resonates on multiple levels, given the ongoing legal attack against Uber's existing computer vision technology by Waymo--the driverless car company that grew out of Google--and the widespread controversy over the Uber's allegedly misogynistic internal culture. Urtasun could help the company forge another much-needed path to the kind of AI that driverless cars will require.

While Tinder users have been analyzing profile pictures of potential dates, a programmer has been stealing them for artificial intelligence experiments focused on facial recognition. Some 40,000 photos were scraped from the dating app, all of users in the San Francisco Bay area, by a member of Kaggle – a platform focused on machine learning that was recently acquired by Google. The data set, named'People of Tinder', consisted of six downloadable zip files and each housed about 10,000 profiles pictures in each. Some 40,000 photos were scrapped from the dating app, all of users in the San Francisco Bay area, by a member of Kaggle. The plan was to use photos in artificial intelligence research focusing on facial recognition.