Sega's popular video game character Sonic the Hedgehog is famous for running fast, but now he's slowing down to grab a bite to eat with some extra friendly company. To help promote the upcoming "Sonic Forces" video game, which will be released in Japan on Nov. 9, Sega is turning Hooters outlets across Tokyo from the restaurant's trademark orange to blue. Hooters outlets in Shinjuku, Ginza and Akasaka are planning to decorate their stores with Sonic dolls, hand out "Sonic Forces" coasters and offer a fast-food set that includes French fries, a Sonic-blue colored drink and a chili dog -- Sonic's favorite food. For those who aren't up to speed, video-game-and-food collaborations have been around since the days of Atari, with video game characters showing up in children's cereal or Pac-Man seen drinking 7-Up soda. Now, however, they're becoming more interactive than simply slapping a sticker on a product, and several popular mashups have caught gamers' attention this year.

The board game Go is older and more complex than chess. While it's been 20 years since IBM's Deep Blue beat world chess champion Garry Kasparov, computers only started beating Go experts a few years ago. An Oct. 18 report in the science journal Nature tells us that this particular man/machine contest is done. A system built by the DeepMind unit of Alphabet (ticker: GOOGL) beat Go's reigning world champ 100 games to none. The deposed champ, you should know, is a prior version of the same artificial intelligence system, which beat one of humankind's international champions in 2016.

Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some targeted workloads. We propose a novel framework for privacy preservation based on the k-anonymity model that is ideally suited for workloads that require preserving the probability distribution of the quasi-identifier variables in the data. Our framework combines the principles of distribution-preserving quantization and k-member clustering, and we specialize it to two variants that respectively use intra-cluster and Gaussian dithering of cluster centers to achieve distribution preservation. We perform theoretical analysis of the proposed schemes in terms of distribution preservation, and describe their utility in workloads such as covariate shift and transfer learning where such a property is necessary. Using extensive experiments on real-world Medical Expenditure Panel Survey data, we demonstrate the merits of our algorithms over standard k-anonymization for a hallmark health care application where an insurance company wishes to understand the risk in entering a new market. Furthermore, by empirically quantifying the reidentification risk, we also show that the proposed approaches indeed maintain k-anonymity.

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a non-negative risk estimator for PU learning: when getting minimized, it is more robust against overfitting, and thus we are able to use very flexible models (such as deep neural networks) given limited P data. Moreover, we analyze the bias, consistency, and mean-squared-error reduction of the proposed risk estimator, and bound the estimation error of the resulting empirical risk minimizer. Experiments demonstrate that our risk estimator fixes the overfitting problem of its unbiased counterparts.

An elderly woman looks at a robot as she visits the China International High-Tech Expo in Beijing on May 23, 2013. As China's population ages, elderly parents find that they are often unable to rely on their only children to care for them. Only 38% of those over 60 live with their children. With over 185 million people over 60 years of age, even the most dedicated working children of aging parents and grandparents struggle to meet their needs. Many elderly individuals are forced to move into retirement homes that face rising labor costs.

The eye-watering $999 price tag doesn't seem to be keeping fans away as thousands waited in long lines outside of Apple stores around the country to get their hands on the much-anticipated iPhone X. And the high-end device is testing the patience of consumers and investors because the company did not make enough models to meet demand worldwide. So those who do manage to get through the door to buy one this morning will be pleased to know they are sitting on potential gold mines, with some devices already being auctioned off on eBay for up to $18,000. The X is Apple's next generation smartphone that uses facial recognition software for the first time and is on sale today in cities around the world - with queues building at Apple Stores amid rumors of limited stock. And sales had Wall Street booming as shares hit an all-time high on Friday morning as optimistic reviews poured in about how the X would make this quarter's earnings soar.

A possibly lethal exception could be the error that leads a self-driving car's AI to make the wrong decision at the wrong time. That is why researchers developed a bug-hunting method that can systematically expose bad decision-making by the deep learning algorithms deployed in online services and autonomous vehicles. The new DeepXplore method uses at least three neural networks--the basic architecture of deep learning algorithms--to act as "cross-referencing oracles" in checking each other's accuracy. Researchers at Columbia University and Lehigh University designed DeepXplore to solve an optimization problem in which they looked to strike the best balance between two objectives: maximizing the number of neurons activated within neural networks, and triggering as many conflicting decisions as possible among different neural networks. By assuming that the majority of neural networks will generally make the right decision, DeepXplore automatically retrains the neural network that made the lone dissenting decision to follow the example of the majority in a given scenario.

Alphabet boss Eric Schmidt has warned the Chinese are poised to erase a key American advantage -- and says the Trump administration is helping them. 'I'm assuming our [U.S.] lead will continue over the next five years and then that China will catch up extremely quickly,' the Google leader told the Center for New American Security's Paul Scharre at the Artificial Intelligence & Global Security Summit on Wednesday, according to Defense One. Schmidt, who also chairs the Defense Innovation Advisory Board, said the key difference was the importance the Chinese government put on AI - and slammed Donald Trump's administration for falling behind. Schmidt said the key difference was the importance the Chinese government put on AI - as slammed Donald Trump's administration for slashing funds for basic science and research. 'We need to get our act together, as a country…This is the moment when the [U.S.] government collectively, and private industry, needs to say, 'these technologies are important.'

One of the most important things I have learned while reporting on AI is that systems built on machine learning may be able to beat humans, or approximate their results, but they can often fail in ways that humans never would, and with unintended consequences. Consider the tale of a Redditor who asked earlier this week whether the Google Home had an internal temperature sensor. He was feeling a bit chilly at home and asked the assistant "What's the temperature inside?" He figured that the system would figure out the temperature from his Nest thermostat and report that back to him. Instead, the Google Assistant went and fetched the weather report for Side, a resort town in Turkey.

A few years ago I met a robot in a Japanese-style café in Osaka. She wore a traditional kimono and greeted me from where she sat in the corner of the dim room. She took my order and called it out to the barista at the bar: "One tea!" But I knew she wasn't doing it on her own--the robot understood nothing. Somewhere upstairs, I knew, must be the human controlling this hyper-realistic android.