In my last column, I looked at how Amazon's Echo device and the Alexa voice service allows you to control things with your voice. You speak, it understands and obeys. Alexa is just part of a new wave of services that allow you to control things with your voice, from cell phones to intercoms and thermostats. You can even do things now like ask her to start your car. So, how do these listening devices transform your mellifluous voice into computer commands?

One of the highlights of last week's Content2Conversion conference was a keynote by the always-stimulating Tim Riesterer of Corporate Visions, who argued that an effective sales presentation should (1) start with an unfamiliar factoid that shows why change is essential (a concept similar to the CEB "challenger sale") (2) show that you have a solution and (c) contrast your solution with other approaches to clarify how it's different and better. Riesterer's own talk followed exactly that template, a bit of consistency I always admire. This in turn got me thinking about my presentation on machine learning systems at the MarTech conference in March. I pretty much finished drafting it last week, but it was still an interesting exercise to imagine recasting it along the lines Riesterer proposed. Linear thinker that I am, this meant first looking for an appropriate factoid about why the growth of machine intelligence poses a threat that can't be ignored.

Working here at Pi Towers, I'm always a little frustrated by not being able to share the huge number of commercial businesses' embedded projects that use Raspberry Pis. We don't get to feature many of them on the blog; many organisations don't want their work replicated by competitors, or aren't prepared for customers and competitors to see how inexpensively they're able to automate tasks. Every now and then, though, a company is happy to share what they're using Pis for. Here's a great example: a cucumber farm in Japan, which is using a Raspberry Pi to sort thorny cucumbers, saving the farmer eight to nine hours' manual work a day. Makoto Koike is the son of farmers, who works as an embedded systems designer for the Japanese car industry.

IBM Watson Artificial Intelligence Xprize -Using AI in new ways to solve the World's biggest problems #Gsummit At the Singularity University Global Summit 2016, Nextbigfuture interviewed Amir Banifatemi who is managing the IBM Watson AI XPrize. Amir Banifatemi is a founder and managing partner at K5 Ventures. He focuses on working with startups and growth-oriented companies on products and initiatives that could trigger significant breakthrough with strong economic and societal impact. He has a special emphasis on machine learning and predictive systems, IoT, knowledge sharing and crowdsourcing, Education, and digital health. The IBM Watson AI XPRIZE is a 5 million AI and cognitive computing competition challenging teams globally to develop and demonstrate how humans can collaborate with powerful AI technologies to tackle he world's grand challenges.

Bill Belichick doesn't have to worry. But the New England Patriots head coach and his NFL counterparts could one day consider AI as essential to their jobs as running practices or watching game tape. "AI will revolutionize sports," said Alan Fern, a computer science professor at Oregon State University who is using game videos and AI to teach computers to understand and coach football. If Fern is successful, teams would boost their brainpower with a new type of assistant coach – one powered by GPUs and a branch of AI called deep learning. It could supply strategic insights that might elude even the savviest coach.

Nick Szabo is no ordinary tech guy. As Chief Operating Officer at Swizzle Labs, he plays a significant role in the expansion of the company in Asia Pacific. With Swizzle, his expertise in revolutionising the new media space resulted in the receiving of the Innovation Award presented by St Louis Business Journal. What puts him and his company way above others as a leading startup has been catching attention from some of us in the music industry. Curious as to how artificial intelligence can pull insights out of large amounts of content in the context of the music industry?

Specialized robots that clean and provide security, robot-assisted surgery, natural language processing-augmented instruction, and helping people adapt as old jobs are lost and new ones are created: these are some of the profound challenges explored by a panel of academic and industrial thinkers that has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city. Titled "Artificial Intelligence and Life in 2030," this open-access year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford University to inform society and provide guidance on the ethical development of smart software, sensors and machines. The new report traces its roots to a 2009 study by AI scientists in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford. The 28,000-word report includes a glossary to help nontechnical readers understand how AI applications such as computer vision might help screen tissue samples for cancers or how natural language processing will allow computerized systems to grasp not simply the literal definitions, but the connotations and intent, behind words. "Currently in the United States, at least sixteen separate agencies govern sectors of the economy related to AI technologies," the researchers write.

You say you're fixing to make a new "Star Trek" show? Here's what you'll need to get started -- the five essential building blocks of "Trek": If you don't know what nacelles are, rethink your involvement in the project. They're a great big dollop of high-fantasy magic in the middle of your serious science-fiction, but look, if you're making "Star Trek," "scientific accuracy" is a concept with which you should expect to enjoy only a passing acquaintance. And anyway teleporters are an efficient, ruthlessly expedient way around a host of narrative/logistical roadblocks. Just accept them, you'll be thankful later.

For the last eight months, I have spent a lot of time trying to absorb as much as I can about machine learning. I am constantly amazed at the variety of people I meet on online MOOCs in this small but quickly growing community, from quantum researchers at Fermilab to Tesla-driving Silicon Valley CEOs. Lately, I have been putting a lot of my focus into the open source software TensorFlow, and this tutorial is the result of that. I feel like a lot of machine learning tutorials are geared toward Mac. One major advantage of using Linux is it's free and it supports using TensorFlow with your GPU.

Adoption of machine learning is moving up a gear. From diagnosing diseases, to driving cars, to stopping crime, the ability of machines to learn from data and apply those learnings to solve real problems is being leveraged all around us at an accelerating pace. As data volumes continue to grow, along with advances in computational science, machine learning is poised to become the next great technological revolution. Digital marketing represents one of the most exciting arenas where machine learning is being applied. Across websites, mobile apps, and other digital media, there are hundreds of billions of opportunities for advertisers to deliver ads to consumers every single day.