A few months ago, my company, CrowdFlower, ran a machine learning competition on Kaggle. It perfectly highlighted the biggest opportunity (and challenge) with machine learning: What do you do with an 80% accurate algorithm? We uploaded data collected on our platform and Kaggle sent it out to over 1,000 data scientists, who competed to see who could build the best search model. The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model.

When HP Labs research scientist Lei Liu was a child in XianYang, China, he read a newspaper article detailing how HP originated in a garage in Palo Alto. "That inspired me," he recalls. "Silicon Valley was clearly somewhere where you could have a dream, incubate it, and see it come true." Today, Lei is living that dream as a member of HP's Print and 3D Lab. After studying for his B.S. and M.S. in computer science at the Beijing University of Posts and Telecommunications, he moved to Michigan State University where he received his Ph.D. in Computer Science and Engineering, focusing on data mining and machine learning.

Sometimes the simplest methods are best... Random sampling is easy to understand, hard to screw up, and unlikely to introduce bias into your process. Building a training pipeline using a random sample (without replacement) of your dataset is a good way to work faster. Once you have a pipeline you're satisfied with, you can then run it again over your entire dataset to estimate the gain in performance from using the entire dataset. If your training pipeline is robust, your results should not change too much, and although your performance might rise, it will tend to do so very slowly as you add more data. The basic intuition here is that the strongest signals in your data will show up even with relatively small samples of the data, almost by definition (if they didn't, they wouldn't be strong!).

During the holiday season, warehouses across the country used different kinds of robots to fill retail orders and they made--and continue to make--warehouses more efficient than ever before. In fact, if robots were more broadly implemented it's estimated retailers could reduce their fulfillment costs by 450 million to 900 million in North America, according to research from Janney Capital Markets. Today there is a growing group of startup companies developing robots for use in manufacturing, e-commerce and logistics operations. A decade ago, robots working in a warehouse -- or, for that matter, writing newspaper stories, caring for the elderly or providing comfort to hospitalized children -- was the stuff of science fiction. But today, the world is on the cusp of a rapid economic transformation via robotics and artificial intelligence.

Just a few months ago, the social network thought that its AI experts were on the cusp of a breakthrough, making a computer that could play Go faster than any previous machine. Then Google came along and blew them out of the water, revealing first that it had built a Go computer capable of defeating a professional human player, and then going on to beat Lee Sedol, the greatest player of the last decade, 4-1 over the course of a week. Facebook has already tried to spoil Google's thunder once, with Mark Zuckerberg releasing a coincidentally timed statement on the company's Go progress just one day before Google announced its victory over the European champion Fan Hui (and one day after Google had already revealed to the press that the victory had occurred). Zuckerberg himself has been more conciliatory this time round, posting after a message of congratulations after AlphaGo's third victory in a row: "Congrats to the Google DeepMind team on this historic milestone in AI research – a third straight victory over Go grandmaster Lee Sedol. We live in exciting times."

The capabilities of Google's artificial intelligence are staggering. The phantoms within Google Photos can organize your pictures, the wizards inside Google Docs let you type and edit using spoken commands, and the brilliant pixies powering AlphaGo can easily beat a master of a 2,500-year-old game more complex than chess. Google's new Smart Reply feature uses artificial neural networks to come up with appropriate responses to email messages. Whether you're using Google's sleek email manager in the browser or via mobile app, Inbox can now answer your emails for you. The machine replies aren't sent automatically, which is good.

Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet. Volkswagen's Audi, Daimler's Mercedes-Benz, BMW and car industry suppliers Bosch and Continental are all working on technologies for autonomous or semi-autonomous cars.

Shedding drivers would save Uber a lot of money. Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet.

When we imagine a future where humans and robots coexist, it doesn't take long for us to arrive at a conclusion where the human race tragically ends. A robot takeover usually occurs, followed by the inevitable enslavement of all humankind. But when it comes to the future and what will actually unfold, Palmer Luckey, founder of Oculus VR (which Facebook now owns) and inventor of the Oculus Rift virtual reality headset, isn't sweating it. "The reason I'm not creeped out is pretty simple," said Luckey, who sat down with Apple co-founder Steve Wozniak and Re/code journalist Kara Swisher on Saturday at the Silicon Valley Comic Con in San Jose, California. "A lot of people look to science-fiction for representations of technology. It can also be flawed."

Small and medium sized businesses are being targeted by IBM and MasterCard as they look to bring big data analytics insights to better understand their markets and consumers. A partnership has been formed by the two companies that sees MasterCard integrate IBM Watson Analytics into its platform, along with its own anonymised transaction data that is gathered through the payment company's Local Market Intelligence. This combination will bring artificial intelligence to its payments platform. The aim is to be able to offer SMBs insights on revenue, market share, customer demographics and competitors in a particular location and across multiple locations. The problem being tackled is that smaller merchants often don't have the resources to maximise data insights.