As the world's undisputed leader in Internet search, Google has undeniable expertise in running data centers. After Amazon launched its cloud computing service in 2006, Google put that data center expertise to work by launching a cloud service of its own. Soon it became known as one of the "big three" public cloud vendors, along with AWS and Microsoft. Depending on which analyst report you read, today Google is either the third or the fourth largest public cloud vendor, behind Amazon, Microsoft and (possibly) IBM. While its larger competitors tout their broad portfolios of services, Google has a narrower focus and specializes in meeting the needs of developers. Through its Web search and advertising businesses, the company has become particularly adept at handling big data, and it has transferred those capabilities to its cloud computing business as well, developing a solid reputation for prowess in analytics, artificial intelligence and machine learning.

The really cool thing from my perspective about the Estimators API is that using it is a very easy way to create distributed TensorFlow models. Many of the TensorFlow samples that you see floating around on the internets are not distributed -- they assume that you will be running the code on a single machine. People start with such code and then are immeasurably saddened to learn that the low-level TensorFlow code doesn't actually work on their complete dataset. They then have to do lots of work to add distributed training code around the original sample, and who wants to edit somebody else's code? So, please, please, please, if you see a TensorFlow sample that doesn't use the Estimators API, ignore it.

Technology is gradually taking over workplaces and that is one of the reasons why'human workers' are becoming redundant. As a result of which, time and again we see reports about companies laying off employees. While imagining the repercussions of having automation in other industries is palpable, what does it mean for the technology industry? Adoption of newer technologies is believed to be one of the key reasons for the job cuts and if tech workers are not trained in advanced skills, the future for human workforce appears all the bleaker. India would lose about 69,000 jobs until 2021 due to the adoption of IoT technology, according to a research conducted by a consulting firm.

Those who have gained some experience with machine learning, trying to digest the huge volumes of data that make it meaningful, quickly get to grappling with algorithms needed to optimize how the data is processed. And no matter how brilliant is the AI worker or data scientist trying to tame the data, it's usually a laborious, trial-and-error and often expensive process. That's what Scott Clark observed when he was a student at Cornell University, and later when he worked on the ad targeting team at Yelp. To help himself, he worked on some tools to put a degree of automation into the process of optimizing data used for machine learning. Now a PhD in applied mathematics, Scott is the CEO and co-founder of SigOpt, in the business of helping companies get to machine learning in a more practical way.

Get up-and-running building cutting-edge Edison devices with help from this DIY guide. Programming the Intel Edison: Getting Started with Processing and Python lays out the Edison's powerful features and teaches the basics of Internet-enabled embedded programming. Discover how to set up components, connect your PC or Mac, build Python applications, and use USB, WiFi, and Bluetooth connections. Start-to-finish example projects include a motor controller, home temperature system, robotic car, and wearable hospital alert sensor.

Will artificial intelligence (AI) replace radiologists? During a session on AI and imaging yesterday at the Big Data in Biomedicine conference, panelists preempted this question (which keeps some radiologists up at night) by clarifying how, at least for now, AI isn't a replacement for doctors, but a tool to help them. "The human-machine system always performs better than either alone," said Curt Langlotz, MD, PhD, a professor of radiology and biomedical informatics at Stanford. And while AI is achieving human-level performance, it's not necessarily superseding it -- yet. All panelists spoke about AI's capacity to increase efficiency.

In the not-too-distant future, a visit to the doctor's office might start with a conversation with the doctor's robot. Such robots could access diagnostic and medical data to evaluate the urgency of a patient's needs before they ever speak with a human healthcare worker. At the 2016 World Science Festival, computer scientist Eric Horvitz and cognitive scientist Bertram F. Malle discussed the benefits of such bots, for both individual patients and the healthcare system at large. Watch the full program here: https://youtu.be/oQ-vBOm-9Go Like us on Facebook: https://www.facebook.com/worldscience... Follow us on twitter: https://twitter.com/WorldSciFest

In mythology unicorns are skittish things, and in business they appear little easier to pin down. EP Vantage has compiled a list of private start-up companies in the healthcare arena that are widely considered to be worth more than $1bn; notably it features very few makers of human therapeutics. Instead these unicorns are involved in cutting-edge computational research such as artificial intelligence, sequencing or virtual reality, or are in risky, unproven areas. It is plausible that one of the reasons they have not been bought is because no acquirer knows where to put them (see table below). Those that are in the business of developing human therapeutics are working in the as-yet unproven field of mRNA - Moderna Therapeutics and Curevac.

Google tends to throw lots of ideas at the wall, and then harvest the data from what sticks. Right now the company is feasting on photos and videos being uploaded through its surprisingly popular app Google Photos. The cloud-storage service, salvaged from the husk of the struggling social network Google in 2015, now has 500 million monthly active users adding 1.2 billion photos per day. No one is quite sure what Google plans to do with all of these pictures in the long run, and it's possible the company hasn't even figured that out. But in a landscape fast becoming dominated by artificial intelligence, data -- in this case, your photos -- has become its own reward.

In drone footage captured on May 18 in Monterey, California, a group of orcas is seen carrying out a coordinated attack on a blue whale. Orcas, also known as killer whales, are known to prey on other marine mammals, including dolphins and seals. But even these fearsome predators don't stand much of a chance against a mature blue whale: The largest animal on the planet, an adult blue whale can reach up to a hundred feet long and weigh close to 200 tons. In this instance, the large blue whale flipped on its side, sending up what seemed like a wall of water, and swam away at a speed that far outpaced the orcas, says marine biologist Nancy Black, who captured the event from on board a whale-watching boat. The real reason the orcas likely orchestrated an attack? "They were probably doing it for the heck of it," says Black. "They play with [whales] like cats play with their prey.