In many settings people must give numerical scores to entities from a small discrete set. For instance, rating physical attractiveness from 1--5 on dating sites, or papers from 1--10 for conference reviewing. We study the problem of understanding when using a different number of options is optimal. For concreteness we assume the true underlying scores are integers from 1--100. We consider the case when scores are uniform random and Gaussian. We study when using 2, 3, 4, 5, and 10 options is optimal in these models. One may expect that using more options would always improve performance in this model, but we show that this is not necessarily the case, and that using fewer choices---even just two---can surprisingly be optimal in certain situations. While in theory for this setting it would be optimal to use all 100 options, in practice this is prohibitive, and it is preferable to utilize a smaller number of options due to humans' limited computational resources. Our results suggest that using a smaller number of options than is typical could be optimal in certain situations. This would have many potential applications, as settings requiring entities to be ranked by humans are ubiquitous.

It is easy to confuse the current geopolitical situation with that of the 1980s. The United States and Russia each accuse the other of interfering in domestic affairs. Russia has annexed territory over U.S. objections, raising concerns about military conflict. As during the Cold War after World War II, nations are developing and building weapons based on advanced technology. During the Cold War, the weapon of choice was nuclear missiles; today it's software, whether its used for attacking computer systems or targets in the real world. Russian rhetoric about the importance of artificial intelligence is picking up – and with good reason: As artificial intelligence software develops, it will be able to make decisions based on more data, and more quickly, than humans can handle.

A leaked White House memo that calls for the government to build and control a "5G" next-generation wireless data service drew immediate backlash Monday from industry groups, regulators, and even some within President Trump's own administration. Although the proposal is reportedly dated, its disclosure sparked a debate over how much weight it carries. Both Democrats and Republicans decried the idea of the government stepping so forcefully into an area that has largely been in the hands of private actors. "Any federal effort to construct a nationalized 5G network would be a costly and counterproductive distraction from the policies we need to help the United States win the 5G future," said Ajit Pai, the Republican chairman of the Federal Communications Commission, the nation's top telecom regulator. Pai's criticism was echoed by the rest of the bipartisan commission. The memo, which was first reported by Axios and attributed to Trump national security officials, argues that Chinese companies have come to dominate the market for the chips and antennas necessary for sustaining a 5G wireless network, and that poses security risks for a U.S. 5G network.

"We want to 10x the speed and impact of security teams' work by making it much easier, faster and more cost-effective for them to capture and analyze security signals that have previously been too difficult and expensive to find," he said in a blog post. Chronicle plans to leverage Alphabet's technology to help companies run analyses faster and store larger amounts of data to recognize patterns. Chronicle illustrates Alphabet's mission to make a variety of smaller bets outside of Google that could become its next big business. Other units include DeepMind, an artificial-intelligence firm; Calico, a life-extension lab; and two investment firms. Its last new unit, the self-driving-car firm Waymo, launched in December 2016. So far, none of the new units have become significant businesses.

When you've got Egyptian heritage and live in the West, something funny happens when you meet another Egyptian. We get giddy, we smile a lot, we act as if we've known each other forever. After some coffee and a quick tour of his California bungalow, the graphic artist known as Ganzeer hands me a stack of some pages from The Solar Grid. The black-and-white pages are crinkled and dried after being soaked in ink. Each panel looks like it may have taken hours.

Preface: Lately we hear too many news about Artificial Intelligence (AI). Google, IBM, Apple, Microsoft etc. years ago announced "mobile" support. Today, mobile is obvious, and to differentiate, they claim to use "AI". The word "AI" for most people can be only the sci-fi movies AI, since we get too many AI movies too: Her, Ex Machina, … and even "Alien: Covenant" is about a rogue AI, the aliens are secondary. Companies went mobile for real: their services run in cell phones. But, we don't see sci-fi "AI" in any service like Alexa, Cortana, Siri etc.

If you've been waiting to hear more on Samsung's next Galaxy flagship, we've got you covered. As well as everything else that happened over the weekend, naturally. Intelligent Scan would work day or night. Samsung has hinted that the Galaxy S9 might include more advanced face recognition, but we're now getting clues of what's involved. Deep inside the Galaxy Note 8's Oreo beta software, there's a hidden Intelligent Scan feature that uses both camera-based face detection and the iris scanner for "better accuracy and security" and improved results in "low or very bright" lighting.

Workers in Mansfield, Sunderland and Wakefield are at the highest risk of having their jobs taken by machines, according to a report warning that automation stands to further widen the north-south divide. Outside of the south of England, one in four jobs are at risk of being replaced by advances in technology – much higher than the 18% average for wealthier locations closer to London. Struggling towns and cities in the north and the Midlands are most exposed. A total of 3.6m UK jobs could be replaced by machines. The Centre for Cities thinktank says almost one-third of the jobs in the Nottinghamshire town of Mansfield, which is home to the Sports Direct warehouse, are involved in lines of work under threat as robots begin to replace humans in the years up to 2030.

How would you protect the US against Chinese cyberattacks? Would you push for stricter security standards, or new encryption technology? The Trump administration's national security team has another idea: a government-controlled 5G network. Axios has obtained documents showing that the team is pushing for a centralized, secure 5G network within 3 years. This would create a secure communications avenue for self-driving cars, AI, VR and other budding technologies.

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.