This past Saturday, September 24th, the first episode of Cyber Talk Radio hit the airwaves on 1200 WOAI and iHeartRadio streaming. To open the discussion we covered a machine learning background including mentions of TensorFlow, Watson, Tay, and many other examples of consumer facing AI as well as developer tools to build your own systems. After establishing a baseline we started to discuss the impact machine learning (aka. When the internet began you'd directly connect to it just like a big "home network" from a trust perspective. As more people connected and port scanners such as nmap became available the need for a firewall appeared.

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts. I'll walk you through it.

According to Tractica's research, one of the industries best positioned to leverage artificial intelligence (AI) – at least in the developed world – is agriculture. In our Artificial Intelligence for Enterprise Applications report, we forecast that spending on AI software in the agriculture industry will grow from 16.2 million to 373.7 million by 2024. Recently we sat down with Jeffrey Orrey, CEO of GeoVisual Analytics. GeoVisual is a Boulder-based startup focused on using remote sensing and big data analytics to improve and predict crop yields, better manage croplands, and improve harvests. The company's analysis is based on the properties of electromagnetic waves in the near infrared (NIR) spectrum, which are invisible to the human eye.

Last March, a computer built by a team of Google engineers beat one of the world's top players at the ancient game of Go. The match between AlphaGo and Korean grandmaster Lee Sedol was so exhilarating, so upsetting, and so unexpectedly powerful, we turned it into a cover story for the magazine. On a Friday in late April, we were about an hour away from sending this story to the printer when I got an email. According to the email, Lee had won all five matches--and all against top competition--since his loss to AlphaGo. Even as it surpasses human talents, AI can also pull humans to new heights--a theme that ran through our magazine story.

Just hours before the first US Presidential debate -- on a day when stupidity seemed to be on the top of everyone's mind -- Microsoft CEO Satya Nadella spoke passionately about artificial intelligence (AI) as a cornerstone of Microsoft's next horizon of innovation. It was a strange juxtaposition of realities: the world as we know it, grounded in the banal and the predictable, versus the world as Nadella envisions it, fueled by AI in every app, every interaction, everywhere. During the second keynote on the opening day of Microsoft's Ignite conference here, Nadella explained how his company is pursuing AI and deep learning to empower every person and every institution "to solve the most pressing problems of our society and our economy." Nadella made his remarks to a packed crowd at Philips Arena, just across the street from the Georgia World Congress Center where the Ignite conference kicked off today. About 23,000 people, primarily IT professionals, are attending the five-day event.

I'm currently working my way through the book Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian & Tom Griffiths, which deconstructs many key life decisions into algorithms that can lead to optimal decision-making. As it cogently reviews many basic concepts using a wide range of life examples, I can see many ways in which machine learning techniques could sift through mountains of clinical data on cancer patients to help guide our management decisions in ways that elude the limitations of human brains. Oncologists work with patients to weigh decisions about whether a treatment with partial benefit (limited shrinkage of a cancer or even modest progression) is good enough to continue treatment and when a stronger choice is to change treatment approaches. When is is the expected benefit of more of the same, likely with a discount from diminishing returns, less than the anticipated or unknown benefits of the next alternative therapy? A well honed algorithm should be able to follow the growth kinetics of a cancer on scans and predict when it's time to change horses.

As a native speaker (and reader and writer) of both Mandarin Chinese (both complex and traditional alphabets) and English, I've often cringed at Google Translate's output. But looking at the examples provided by Google on its blog post, I am impressed. The new system uses what the company calls Google Neural Machine Translation (GNMT), which looks at entire sentences as it decodes instead of breaking them up into words and phrases to be considered independently. The latter method often resulted in disjointed results that sometimes didn't make sense. According to the company, this new technique is better, because "it requires fewer engineering design choices than previous Phrase-Based translation systems." It still breaks up sentences into individual characters, but now considers each one in relation to those before and after it.

In the fall of 2010, I traveled to New Zealand, and one of the places I visited was the small south island city of Christchurch. I was charmed by the tree-lined Avon River, the English-style cathedral in the main square, and the mountains looming in the distance. Inside the cathedral was a stack of poems with a moving message of peace. I saved one to tack on my cork board at home, where it remains to this day. Three months later I turned on the news to see the Christchurch cathedral splintered and broken, its spire crumbled to the ground.

Mandarin Chinese is a notoriously difficult language to translate to English, and for those who rely on Google Translate to decipher important information, machine-based tools simply aren't good enough. All that is about to change, as Google today announced it has implemented a new learning system in its web and mobile translation apps that will bring significantly better results. As a native speaker (and reader and writer) of both Mandarin Chinese (both complex and traditional alphabets) and English, I've often cringed at Google Translate's output. But looking at the examples provided by Google on its blog post, I am impressed. The new system uses what the company calls Google Neural Machine Translation (GNMT), which looks at entire sentences as it decodes instead of breaking them up into words and phrases to be considered independently. The latter method often resulted in disjointed results that sometimes didn't make sense.

The formidable processing power and analytical tools available in public clouds could make industrial IoT more effective and less expensive. But bringing IoT data into the cloud takes more than a network connection. On Tuesday, two companies moved to help enterprises adapt their IoT data for popular cloud services. OSIsoft introduced its PI Integrator for Microsoft Azure, and Particle announced a custom integration with Google Cloud Platform. While some large enterprises with sensitive IoT data do all their analytics in-house, public clouds offer greater scale and better security than many organizations can achieve on their own, MachNation analyst Dima Tokar said.