Predictive analytics / machine learning / artificial intelligence is a hot topic – what's it about? Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers.

Starting with this article, I will write a series of articles on deep learning covering the popular Deep Learning libraries and their hands-on implementation. Fast forward to 2012, a deep neural network architecture won the ImageNet challenge, a prestigious challenge to recognise objects from natural scenes. Nodes in the graph represents mathematical operations, while graph edges represent multi-dimensional data arrays (aka tensors) communicated between them. Here we solve our deep learning practice problem – Identify the Digits.

At last year's Spark Summit conference, Patrick Wendell, a software engineer at Databricks Inc. and a contributor to the Apache Spark open source project, said the technology's data processing capabilities are impressive but its real power lies in the Spark library components that sit on top of the core engine. "The future of Spark is the libraries," he said. "That's what the community has invested in and where the innovation is coming from." Sure enough, this month's Spark Summit 2015 event prominently featured case studies in which users explained how they're putting the libraries to work in Spark analytics applications. The Spark platform comes with four distinct libraries -- Spark SQL, Spark Streaming, a graph processing library called GraphX and a machine learning one known as MLlib -- that include pre-built algorithms and programming capabilities designed to streamline data preparation, exploration and analysis tasks. The libraries enable users to automate certain tasks and eliminate some of the coding that typically would be required.

I know that in the day and age of Deep Learning this seems to be a really odd answer. So let's start with a bit of background: In 1995–1998 I was using neural networks, 1998–2002 I was working mostly with tree based methods and from 2002 on, logistic regression (and linear models in general including quantile regression, Poisson regression, etc.) ended up to slowly make its way into my heart. In 2003 I published a paper in Machine Learning showing the results on comparing tree based methods against logistic regression on 35 (at the time large) datasets. The short answer (if you want to skip the 30 pages) - if the signal to noise ratio is high, trees tend to win. But, if you have very noisy problems and the best model has an AUC 0.8 - logistic beats the trees almost always.

For all of the good that machine learning and artificial intelligence promise, what happens if either machine learning or AI becomes the next hot marketing buzzword in tech and the business sector? Picture AI falling helplessly into the morass that sucked in "big data," "smart" anything, and "Internet of" you name it. That rumble that you would feel might be Alan Turing fidgeting in his grave. I can hear some marketing newbie now, "Well, our product is made of plastic. And, we think it's really intelligent. For more than 60 years hundreds of very bright and accomplished computer scientists, from Turing to today's doctoral students, have researched and debated what AI is, and what it isn't. At what point is a computer actually thinking? Then, we have machine learning as a subset of or precurser to AI. Feed a neural network with enough examples -- such as text and images -- and it advances to the point where it can translate English into another language, recognize faces of people, or identify the most successful treatments for diseases. I suppose AI is destined to be cast into Buzzword Hades once everyone from that marketing newbie to the CEO desperate for something innovative hear more about the real-world successes of AI and machine learning. Memos and meetings will be punctuated with shouts of, "We need to be doing that." We already are seeing examples of niche applications utilizing techniques such as image recognition in anti-terrorism initiatives and pattern recognition in cybersecurity. Applications in the commercial space seem to be ready to pop up in the public view. A Forbes article cites three industry sectors -- healthcare, finance, and insurance -- as prime candidates for AI and machine learning applications. The article notes, "Sequencing of individual genomes and then comparing them to a vast database will allow doctors -- and/or AI bots -- to predict the probability that you will contract a particular disease and the best ways to treat those diseases when they appear.

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

Brad Grossman (@bradgro) is founder and CEO of Zeitguide, a cultural think tank. As artificial intelligence gets embedded into day-to-day activities -- predicting what we need from virtual assistants, teachers, even doctors -- is the technology neutrally scrubbing out gender biases, or encoding them permanently on our future? The companies developing AI, like most of Silicon Valley, have a predominantly male workforce of engineers and developers. As Melinda Gates noted during this year's Code Conference, "When I graduated 34% of undergraduates in computer science were women… we're now down to 17%." There is real risk that such gender imbalance is invisibly shaping machine learning algorithms and artificial intelligence applications.

For more than a decade, engineers have been eyeing the finish line in the race to shrink the size of components in integrated circuits. They knew that the laws of physics had set a 5-nanometer threshold on the size of transistor gates among conventional semiconductors, about one-quarter the size of high-end 20-nanometer-gate transistors now on the market. A research team led by faculty scientist Ali Javey at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) has done just that by creating a transistor with a working 1-nanometer gate. Boeing CEO vows to beat Musk to Mars Boeing Co. once helped the U.S. beat the Soviet Union in the race to the moon. Now the company intends to go toe-to-toe with newcomers such as billionaire Elon Musk in the next era of space exploration and commerce.

Computing and robotics is changing the face of medicine at a faster rate than ever before, and is going to affect the way we treat patients says a medical expert. Michael Gillam, who is heading to the SingularityU summit in Christchurch next month, is a physician, medical informatics expert and IT health specialist. He is also one of four directors that built and sold the patient information software Amalga which became one of Microsoft's flagship products. According to Gillam, as computing power continues to increase and the cost of testing and research decreases, health providers will be able to tailor treatment to patients. "When I started studying there were a few known types of blood cancers and by 2005, there were over 80 different types so we've come a long way," Gillam said.

No technology trend today comes with more hype than artificial intelligence. Yet AI researchers often joke that most people don't quite see how it's changing the world--because as soon as something like Siri or Nest weaves itself into our lives, people forget it's "artificial." That sort of quiet takeover of AI has already begun in an industry we humans tend to hold pretty sacred--news. Last year, Associated Press announced that the majority of its earnings reports will eventually be written with AI-enabled software. While the company argues it will free up reporters to do more analytical work, the question it raises is unsettling: Should we just leave it to the machines to interpret what they see in piles of Big Data and write up the results?