A Chinese fugitive was arrested after an AI-powered facial recognition system alerted authorities to his presence in a crowd of 60,000 people attending a pop concert. Welcome to the age of robot snitches. Wanted for "economic crimes," the 31 year-old man was reportedly surprised when police apprehended him. He'd traveled nearly 100 km (about 60 miles) with his wife and friends to attend the event, a concert headlined by Cantopop star Jacky Cheung, before authorities nabbed him on a tip from a venue camera. Chinese authorities have entirely embraced facial recognition systems and AI-powered surveillance monitoring.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, data scientist on Oracle's Information Management Platform team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that comes with using machine learning in a big data environment.

Imagine you wanted to determine how much solar energy could be generated from adding solar cells to a particular house. This is what Google's Project Sunroof does with Deep Learning. Enter an address and Google uses a Deep Learning framework to estimate how much money you could save in energy costs with solar cells over 20 years (see Figure 1). But let's assume there "might" be an even better way to estimate solar energy savings. For example, you want to use Deep Learning to estimate how much solar energy we could generate with solar panels on the Golden Gate Bridge (that probably wouldn't be a very popular decision in San Francisco).

Imagine you wanted to determine how much solar energy could be generated from adding solar cells to a particular house. This is what Google's Project Sunroof does with Deep Learning. Enter an address and Google uses a Deep Learning framework to estimate how much money you could save in energy costs with solar cells over 20 years (see Figure 1). But let's assume there "might" be an even better way to estimate solar energy savings. For example, you want to use Deep Learning to estimate how much solar energy we could generate with solar panels on the Golden Gate Bridge (that probably wouldn't be a very popular decision in San Francisco).

BEIJING – China has started building the world's largest test site for unmanned ships -- a technology with both civilian and military applications -- off a port in the disputed South China Sea, state media has reported. The test area is being constructed off the southern port city of Zhuhai bordering Macau, China's official Xinhua News Agency said Tuesday. China asserts sovereignty over almost all the South China Sea despite competing partial claims from the Philippines, Vietnam, Malaysia and Brunei, and has reclaimed several islands which it controls to bolster its claims. Unmanned or "autonomous" ship technology, still in its infancy, would allow both civilian and military craft to be remotely controlled. It could revolutionize the shipping industry by creating more cargo space on unmanned ships, which would also save huge sums in labor costs.

Drone delivery is expected to take off big time in the next few years. Chinese online retailer JD.com has already launched drone delivery in four provinces in China, while DHL and Zipline are delivering medicines with drones in rural and hard-to-reach areas. Amazon, Google, and UPS are all working on getting drone delivery service off the ground. There are a lot of issues to think about when it comes to package delivery using drones--safety, privacy, and logistics being some of the main concerns. In a new study, researchers tackle two other important aspects: energy use and greenhouse gas emissions.

We have been working hard to understand the core stack of data services that make our cities work, or not work, depending on where you live. While we have labeled this research "smart cities", we are starting with the basics of open data required for city operations. This is the current data sets available via existing services, which may or may not exist in a machine readable format, via an API, depending on the city you live in. There is a huge amount of data already available at the municipal level, but here is where we have started as of January. Real Time Streaming 311 Incidents In Chicago 511 - Traffic, Travel & Transit Adding 511 Data To Our Existing Transit Data Research Getting Your 511 Traffic Incidents in the San Francisco Bay Area as a Real Time Streaming API 911 - Emergency Events Making 911 Data Real Time Streaming 911 Emergency Data For Baltimore, MD We've targeted these three areas because they make a difference in our lives at the local level, and have huge potential when it comes to making available via web APIs, and in real time using Server-Sent Events (SSE).

Yes, although the real work is happening not on the slopes of Davos, but on a hill overlooking the Golden Gate Bridge. The location, in Presidio National Park, illustrates how the Forum and key member companies have shifted their focus in recent years to the promise and perils of emerging technologies. After all, San Francisco has become the global capital of a tech industry with companies that are as influential as countries. "The tech companies were having an increasing impact on pretty much every foreign policy issue that we were dealing with," says Zvika Krieger, who served as the U.S. State Department's envoy to Silicon Valley in the Obama administration's final year. In early 2017, the WEF recruited Krieger and Silicon Valley entrepreneur Murat Sönmez to establish its first major office outside Switzerland, bearing the ambitious name World Economic Forum Center for the Fourth Industrial Revolution.

The biggest underwater eruption of the last century took place 600 miles (1,000km) off the coast of New Zealand, scientists have found. The discovery was made after an airline passenger saw a strange substance spreading across the Pacific Ocean in 2012. At the time, scientists identified the material as pumice, a volcanic substance which floats. However, it has taken six years for researchers to understand the scale of the eruption with the help of remotely-operated deep search robots. The findings have been described as a'scientific goldmine' and could increase our understanding of how magma rises from the earth's crust to the surface More than 80 per cent of the volcanoes on Earth are located on the sea-floor.

Farmstead, an artificial intelligence (AI)-powered digital "micro-grocer" that sources and delivers fresh food from farm to fridge in one hour, has launched a hub allowing for 30-minute grocery pickup in the San Francisco Bay Area. The new 30-minute Express Pickup service hubs, strategically located in San Francisco and San Mateo, are intended to put the San Francisco-based startup on par with the largest incumbent players in the rapidly expanding online grocery delivery market, many of which don't yet offer online ordering and pickup in the area. Groceries are ready for pickup within 30 minutes of placing an online order, and arriving customers simply tap an "I'm here" button on their phone to signal a Farmstead employee to place a custom-packed order in their car. The new service is Farmstead's latest step toward working to transform the way the grocery industry sources and distributes food from farms to customers. The startup, which first presented its plan to shake up the grocery industry in early October, uses AI technology to calculate and predict returning shoppers' habits and know exactly how much food to order from local sources daily, weekly, seasonally and annually, thus reducing food waste.