In April, two semiautonomous drones, developed by Saildrone, a marine tech startup based in Alameda, California, in close collaboration with the National Oceanic and Atmospheric Administration in Washington, D.C., are set to return from an 8-month tour of the Pacific Ocean. This the first scientific test for the drones, which are powered only by the wind and sun, in the Pacific Ocean. The voyage is an important step in showing that such drones, carrying 15 different sensors, could help replace an aging and expensive array of buoys that are the main way scientists sniff out signs of climate-disrupting El Niño events. If successful, scientists envision fleets of similar drones spreading across the ocean, inviting thoughts of what it could be like to do oceanography without a ship.

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).

Right before Laura Franco went to look at a three-bedroom apartment for rent in Santa Clara, Calif., in mid-January, she got a surprising text message from the property manager, Zenplace. "They said a robot would meet me at the property. I thought, 'a robot?' " said Ms. Franco, 31, an event planner and bartender. When she arrived at the listing, a text message provided her with a code she used to let herself in. Then a long-necked white robot on wheels, with a screen that looks like a small tablet, rolled up to her.

One-third of its global online sales are now picked up in the store, the company says, but that has created long lines in some cities and waits for attendants to retrieve packages, customers say. To speed up the process, Zara said earlier this year it would roll out a robot-run version of click and collect, automating the service. The collection points in brick-and-mortar stores will allow shoppers who have ordered items online to scan or enter a code, triggering a behind-the-scenes robot to search for the customer's package in a small warehouse, and then deliver it quickly to a drop box. The move comes as Zara faces heightened challenges to maintain its momentum and compete with online-only apparel retailers, such as Zalando and ASOS, that sell a variety of brands. Annual sales at both have grown more than 20% in the past couple of years compared with low double-digit percentage growth at Zara's Spanish parent company Inditex SA ITX 0.46% .

Flippy, a robot hamburger flipper, has been installed by a California burger chain as the answer to employees who can't stand the heat in the kitchen. PASADENA -- The Caliburger chain can't keep burger flippers employed -- they quit too often, it says. So the plan is to try something new: A robot that has been programmed to flip hamburgers all day long. Named Flippy, the $100,000 machine is capable of flipping as many as 2,000 burgers a day. As of Monday, a human at Caliburger's restaurant here is making the burger patties and seasoning them, and then placing them in a tray for the robot.

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 city data already available at the municipal level, but here is where we have started as of January. Now that we have these three critical aspects of municipal operations profiled, we are going to work to profile as many cities as we can.

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).

"It was a little weird," said Ms. Franco, who signed a deal last week for a $3,925-a-month apartment she found through Zenplace. "It was like she was there but she wasn't there." A new crop of companies is introducing technology they say will reshape how property is rented and sold. By using robots to do some of the tasks that people normally handle--such as showing properties, creating floor plans and shooting video of homes--these firms hope to bring a leaner, more-efficient approach to the traditional real-estate business. REX, a brokerage based in Woodland Hills, Calif., places a robot in each seller's home that answers property questions and collects data from people touring the homes.

In December, Bloomberg reported that desperate demand for software engineers who know how to build artificial intelligence systems turned a previously low-key academic event "into a recruiting frenzy more akin to the National Football League's draft day." Meanwhile, the Tencent Research Institute released a report indicating that there are currently only 300,000 AI researchers and practitioners worldwide, but the demand is for millions. Earlier this month, Element AI set out to assess the AI talent pool. Based on LinkedIn postings, conference proceedings, and other data, the firm concluded that there are 22,000 PhD-educated researchers in the entire world who are capable of working in AI research and applications--but only 3,074 candidates currently looking for work. And this week, job search firm Indeed weighed in, reporting that demand for engineers with AI expertise has grown consistently over the past year and a half, and more than doubled over the past three years, however, job seeker interest in these positions has leveled off.

By adding the Altocloud solution to its portfolio, Genesys strengthens its capability in artificial intelligence (AI) and machine learning to help organizations deliver a highly responsive, predictive, and fully-contextual experience throughout all stages of the customer journey – from marketing to sales to service. "The acquisition of Altocloud bolsters our ability to optimize and connect the entire customer journey to ensure the best business outcomes," said Paul Segre, chief executive officer of Genesys. "We are particularly excited by applications, like Altocloud, which give organizations a live look into consumer behavior and their potential as customers. By empowering employees with this depth of actionable insight, organizations are better positioned than ever to convert shoppers into buyers, leads into customers, and consumers into brand advocates." With this acquisition, Genesys increases its capability to engage and intervene in a customer's journey at the right moment to drive a desired action, such as buying a product, registering for an event, or booking a trip.