While 2016 saw plenty of shiny new tech on the shelves, it wasn't much of a year for significant breakthroughs. Even the latest iPhone 7 drew groans of boredom. Will things get any better in 2017? Expect the big buzzwords of 2016 – the Internet of Things, voice search, blockchain, artificial intelligence, chatbots, virtual reality and augmented reality – to be less about theory and more about real world products embedding themselves in society. However, with post-Brexit, post-Trump isolationism a possibility and threats to free markets, free movement and data privacy on the horizon, 2017 could see tech battle with the prospect of an un-connected world.

Artificial Intelligence (AI) will evolve from a buzzword to a critical capability that helps drive better outcomes for clients (e.g., advice tailored to their specific and complex needs), increases efficiency for banks, and solves for talent shortfalls in banker advisory skills. AI, already at work in our living rooms (Alexa), phones (Siri), investment portfolios (robo-advising), and back office (chatbots) will be rapidly deployed across banking's front lines over the next 24 months. These will be primarily first- and second-generation interfaces used to stimulate needs-based discussions with clients. Next-generation advisory models being developed by leading banks will leverage AI interfaces that assess a large number of complex data sets including: econometrics, industry trends, peer analyses, foreign and domestic tax rates, bank fees, FX, interest rates, cash-flow cycles/seasonality, liquidity needs, costs of capital, etc. These data sets are mapped against the client's current, short-, medium-, and long-term needs to create highly tailored advice.

Experts at the University of Oslo, Norway have discovered a new way for robots to design, evolve and manufacture themselves, without input from humans, using a form of artificial evolution called "Generative design," and 3D printers – although admittedly the team, for now at least, still has to assemble the final product, robot, when it's printed. Generative design is something we've talked about several times before and it's where artificial intelligence programs – creative machines, if you will – not humans, innovate new products – such as chairs and even Under Armour's new Architech sneakers. The labs latest robot, "Number Four," which is made up of sausage like plastic parts linked together with servo motors, is trying out different gaits, attempting to figure out the best way to move from one end of the floor to the other. And while you might look at this video and think it's weird, or funny remember that this is just the start. Today it's evolving, trying to learn how to move from A to B in the most efficient manner, but tomorrow – well, it could be "evolving" anything, and all at a much faster rate than humans.

Broad adoption of powerful cloud computing has unleashed innovation in artificial intelligence technologies, and 2017 is poised to be the year that AI and machine learning applications make their way into the hands of the general public. For those in IT and – more specifically – the managed services space, tools driven by AI are increasingly popping up in everything from customer service and security, to CRM and remote monitoring and management. Machine learning can have a particular impact for IT tech services firms, where increased efficiency can translate directly into more revenue falling to the bottom line. "There is an absolute revolution occurring in artificial intelligence," John Ball, general manager of Salesforce Einstein, told Bloomberg when that AI product launched in September. Machine learning, which represents one type of artificial intelligence, is joined at the hip with big data.

Artificial Intelligence (AI) is on the tip of everyone's tongue these days as a hot new technology ripping its way through the enterprise. Tech giants who lead the pack in this space, Google, Apple, Amazon, and Facebook will certainly be jumping on the AI bandwagon, but according to brianmadden.com, the technology will be important for IBM and Microsoft as well. More importantly, AI like machine learning, will ultimately have an effect on Enterprise Mobility Management (EMM). According to brianmadden.com, the new technology will mostly cause alterations to "business logic within apps and devices themselves." AI will be able to create new apps and refresh old ones and they'll still be delivered the same way, in desktop, web or mobile form.

In the latest episode of the ArchiTECHt Show podcast, I speak with Baidu chief scientist Andrew Ng. Among prominent AI experts, Ng has a particularly unique and global perspective, perhaps because of his broad experiences. Aside from from heading AI strategy for a massive Chinese internet company, Ng also co-founded Coursera, taught machine learning at Stanford and was an early member of the Google Brain team. Keep reading for highlights from the interview, and scroll to the bottom for links to listen to the podcast pretty much everywhere else you might want to. In the news portion of the show, co-host Barb Darrow and I talk even more about AI--specifically Amazon's open source strategy in that space, and if it can repeat the product-first success it had in the broader cloud computing market.

Deckard's photo-enhancing gear in Blade Runner is still the stuff of fantasy. However, Google might just have a close-enough approximation before long. The Google Brain team has developed a system that uses neural networks to fill in the details on very low-resolution images. One of the networks is a "conditioning" element that maps the lower-res shot to similar higher-res examples to get a basic idea of what the image should look like. The other, the "prior" network, models sharper details to make the final result more plausible.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are looking to learn. But we wanted to develop a more data-driven approach to what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity. The ranking is based on average rank of CRAN (The Comprehensive R Archive Network) downloads and Stack Overflow activity (full ranking here [CSV]).

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. ABI Research finds the government and defense, banking, and technology market sectors to be the primary drivers and adopters of machine learning technologies. User and Entity Behavioral Analytics (UEBA) along with Deep Learning algorithm designs are emerging as the two most prominent technologies in cybersecurity offerings, especially in innovative hot tech startups.

Summary: For those of you traditional data scientist who are interested in AI but still haven't given it a deep dive, here's a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI). We and others have written quite a bit about the various types of data science that make up AI. Still I hear many folks asking about AI as if it were a single entity. AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.