Artificial intelligence, or AI, seems to be the most buzzed-about topic in technology lately. It's helping display the right ads within your social network, recommend the next product to buy from your favorite online retailer, and--soon--direct your autonomous car around town. In other words, every Fortune 500 executive should be thinking about how best to use AI within his or her own company. Baidu chief scientist Andrew Ng, speaking at a Fortune Brainstorm Tech dinner at The Bellagio in Las Vegas, believes CEOs should go further. "You need a chief AI officer," Ng told Fortune assistant managing editor Adam Lashinsky. "If you have a lot of data and you want to create value from that data one of the things you might consider is building up an AI team."

Barbed wire surrounds the site of a cancelled Ford auto manufacturing plant, one day after the U.S. auto company announced the project was called off, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017. The perception in this region was largely that President-elect Donald Trump, who had promised for months to bring manufacturing jobs back to the U.S. while at the same time disparaging Mexicans, had made good before even settling into the White House. Barbed wire surrounds the site of a cancelled Ford auto manufacturing plant, one day after the U.S. auto company announced the project was called off, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017. The perception in this region was largely that President-elect Donald Trump, who had promised for months to bring manufacturing jobs back to the U.S. while at the same time disparaging Mexicans, had made good before even settling into the White House. Alfredo Martinez, left, a 22-year-old robot technician at General Motors, and Angel Rodriguez, 19, who had hoped to find work at the now-cancelled Ford plant, get their hair cut at the barbershop of Omar Rojas, right, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017.

Nissan is the latest in a now long line of automobile companies to go public with plans -- sketchy as they may be -- for autonomous cars. The Japanese company will begin testing driverless cars in Japan and put them into commercial operation by 2020, according to Carlos Ghosn, who spoke at the Consumer Electronics Show in Las Vegas on Thursday. He is chairman and chief executive of Nissan, chairman and CEO of Renault and chairman of Mitsubushi. The only substantial difference between Nissan's announced plans and those of Ford, FCA, General Motors, Audi, Mercedes-Benz and several other companies is a plan to put "humans in the loop" based on technology developed at NASA. The director of the Nissan Research Center in Silicon Valley, Maarten Sierhuis, said human "mobility managers" will intervene if an autonomous car encounters a situation it can't handle, like having to cross a double yellow line when a lane is blocked.

A California lawmaker doesn't want operators of self-driving vehicles sneaking their cars on the road without permission. Democratic Assemblyman Phil Ting of San Francisco Thursday proposed fines as high as $25,000 per vehicle per day for any company testing self-driving vehicles on public roads without first obtaining a two-year, $150 permit from the Department of Motor Vehicles. "I applaud our innovation economy and all the companies developing autonomous vehicle technology, but no community should face what we did in San Francisco," Ting said in a press release. "The pursuit of innovation does not include a license to put innocent lives at risk." The measure is a response to Uber's decision in December to put 16 autonomous Volvos on the street in San Francisco without a permit.

On Thursday, Nissan CEO Carlos Ghosn confirmed that a new generation Leaf electric car is in the works along with several key developments in autonomous driving technology. Ghosn made these announcements during his keynote speech at CES 2017. First, Ghosn confirmed that a second generation Leaf electric car is indeed on the way and will arrive in "the near future." However, few details regarding to next gen Leaf was revealed. Which means they haven't told us how much it will cost or how much range it will have.

IBM researchers revealed their five big predictions for innovations that will change our lives in the next five years. Dubbed IBM 5 in 5, the predictions were culled from more than 3,000 researchers across 12 labs on six continents. These predictions focus on the future of artificial intelligence and mental health, AI-based superhero vision, macroscopes that capture the Earth's complexity, medical labs on a chip, and smart sensors that will detect environmental pollution. Innovations in these areas could enable us to dramatically improve farming, enhance energy efficiency, spot harmful pollution before it's too late, and prevent premature physical and mental health decline. "The scientific community has a wonderful tradition of creating instruments to help us see the world in entirely new ways.

If 2016 seemed politically tumultuous, 2017 promises to be equally tumultuous on the technology front. The pace of change is accelerating at a dizzying rate, with profound implications for the way we we work, play and communicate. So what are the big technology trends to watch out for in 2017? Cybersecurity will undoubtedly be the dominant theme of 2017, as all tech innovations could be undermined by data thefts, fraud and cyber propaganda. Forget Kim Kardashian, it's hacking that could break the internet - and much more besides.

Dr Chaand Nagpaul, chairman of the British Medical Association's GP committee, said the system could add to pressures on hospitals, rather than reduce them. "Owing to the lack of input from a trained professional, this simplistic system could, like NHS 111, result in more people being sent to overstretched GP or A&E services who don't actually need treatment - or conversely serious conditions being missed," he said. Peter Walsh, chief executive of safety campaign group Action Against Medical Accidents, questioned who would be liable if computer error caused patient harm or death. "The NHS 111 algorthym has already proved prone to error - that risk could be even greater under a system like this," he said, calling for robust evaluation of the six-month scheme. Katherine Murphy, from the Patients Association, said: "The stakes here are very high, I would be concerned about the risks to patient safety; this needs to be very carefully evaluated because of the risks of misdiagnosis."

Abstract--Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer . The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we address the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes in the feature subspaces in which the class structure is apparent. We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD), a general adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We apply the OCELAD framework to an ensemble of online learners. Specifically, we create a retro-initialized composite objective mirror descent (COMID) ensemble (RICE) consisting of a set of parallel COMID learners with different learning rates, and demonstrate parameter-free RICE-OCELAD metric learning on both synthetic data and a highly nonstationary Twitter dataset. We show significant performance improvements and increased robustness to nonstationary effects relative to previously proposed batch and online distance metric learning algorithms. He effectiveness of many machine learning and data mining algorithms depends on an appropriate measure of pairwise distance between data points that accurately reflects the learning task, e.g., prediction, clustering or classification. The kNN classifier, K-means clustering, and the Laplacian-SVM semi-supervised classifier are examples of such distance-based machine learning algorithms. In settings where there is clean, appropriately-scaled spherical Gaussian data, standard Euclidean distance can be utilized. However, when the data is heavy tailed, multimodal, or contaminated by outliers, observation noise, or irrelevant or replicated features, use of Euclidean inter-point distance can be problematic, leading to bias or loss of discriminative power.

The United Nations is a vast organization with a diverse set of technology needs. The organization is an umbrella under which numerous sub-organizations--UNHCR and UNICEF, for example--operate somewhat autonomously. The UN is charged with providing global humanitarian relief, resolving multinational disputes, and tracking global crime. Tech innovation--particularly big data--aids the UN's mission by making operations more efficient and providing critical operational insight. The UN generates and tracks information on a global scale but has trouble managing large piles of data and is staffed with policy makers who are not inherently tech-savvy.