Journal of Parallel and Distributed Computing 104, 19-35.

You've decided that you want to bike from your house by UC Berkeley to the Golden Gate Bridge. To make matters worse, you are new to the Bay Area, and all you have is a good ol' fashion map to guide you. How do you get started? Let's first figure out how to ride a bike. One strategy would be to do a lot of studying and planning.

My ride from the north into San Francisco follows a stunning route, from the foothills of Mt. Slow and steady, I pay close attention to details--the condition of the roads, as well as the quality of the day's news or music that stream into my headphones. Some of the roads are butter-smooth, while others are pothole-ridden. Some of the sounds are meaningful, and even inspirational; others are just noise--buzzword-filled stories or advertisements for products and services I don't want or need. These lengthy rides provide me with ample thinking time.

Microsoft recently launched its new Artificial Intelligence for Earth program. They're using the speed of computers to tackle some of our most pressing environmental issues. "Artificial intelligence is just the idea of trying to teach computers to be able to do similar tasks that humans can naturally do," said Microsoft Chief Environmental Scientist Lucas Joppa. One of their projects is focused on saving the Southern Resident Killer Whales (SRKW) from extinction, using the same premise of facial recognition software. "What we're trying to do is bring that same level of sophistication to animals like orcas. But to be able to track them through photos, we need to be able to teach computers what we see," Joppa said.

But it's not going to happen overnight. While organizations look forward to "big wins" with AI, they also need to start small and look for opportunities to select, apply and monetize AI for practical, everyday use. The following 7 rules are offered as a starting point. That Taxonomy slide was awesome, right? But unless you are selling to data nerds, your customers don't care about your technology.

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.