There is no doubt that 2016 was a breakthrough year for some of the technologies we have been watching. AI, VR, AR, Chatbots, self-driving cars all took significant leaps forward in terms of their practical applications and adoption, taking many by surprise. It is definitely true to say that the robots are no longer coming- they are here, and they are taking jobs. Rates of innovation and adoption will not slow down in 2017, so we've pulled together the key emerging technology trends to watch and plan for. One thing is for certain in 2017, whether you work in strategy, risk management, operations, start-ups, R&D or marketing, you need to be abreast of the potential of disruptive digital technologies which are no longer purely the realm of the CIO or CTO.

Guest blog post, authored by Brandon Rohrer, Senior Data Scientist at Microsoft. Machine learning (ML) is the motor that drives data science. Each ML method (also called an algorithm) takes in data, turns it over, and spits out an answer. ML algorithms do the part of data science that is the trickiest to explain and the most fun to work with. That's where the mathematical magic happens.

A new energy demand response start-up is preparing to launch within weeks which will use machine learning and artificial intelligence to manage a portfolio of storage assets and provide real-time energy reserves to the grid. Upside Energy's Virtual Energy Store aims to relieve stress on the grid by using predictive algorithms to manage a number of distributed storage resources to reduce reliance on the spinning reserve capacity provided by traditional power stations. The energy start-up's cloud service currently coordinates batteries and other devices at around 40 sites but has the potential to manage thousands more across a broad portfolio of technologies, including small batteries within uninterruptible power supplies (UPS), electric vehicles and solar PV. The company signed a firm frequency response (FFR) bridging contract with the National Grid in November and is currently qualifying its assets before officially launching its commercial service by early March. Upside will begin by providing initial service to large batteries at Sheffield and Manchester Universities before seeking to take advantage of National Grid's decision to lower the minimum entry point for its main frequency response tendering market from 10MW to 1MW from April.

Though artificial intelligence experts may cringe at the portrayals of humanlike AI in science fiction, some researchers are nudging us closer to those visions. "I think it's useful that your user interface not only understand your emotions, your personality, your tone, your motivations, but that it also have a set of emotions, personality, motivations," says Rob High, the CTO of IBM Watson. "I think that makes it more natural for us." Last month, High's company unveiled Project Intu, an experimental platform that allows developers the ability to build internet of things devices using its artificial intelligence services, like Conversation, Language and Visual Recognition. Someday, the system promises to let programmers create a staple character of sci-fi: the gregarious, hyper-connected AI like J.A.R.V.I.S. of Iron Man, KITT of Knight Rider, or Star Wars' C3PO.

Panasonic will invest more than $256 million in a New York production facility of Elon Musk's Tesla Motors to make photovoltaic cells and modules, deepening a partnership of the two companies. SAN FRANCISCO -- Tesla Motors CEO Elon Musk famously called Apple a "Tesla graveyard" where his failed employees go to toil. That was a nifty bit of Musk-esque verbal sparring in what is a growing talent war between the tech titans. But it seems he's now robbing the graveyard. In a blog post Tuesday, Tesla announced that it was hiring 11-year Apple veteran Chris Lattner, an engineer who was primarily responsible for creating Swift, the programming language for building apps on Apple platforms.

Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that BP is close to optimal for all regimes considered and improves upon competing state-of-the-art algorithms.

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. Ensemble machine learning methods such as random forests offer powerful alternatives.

Human-robot relationships are a running theme in pop culture, from the cylons of Battlestar Galactica to Spike Jonze's film Her and last year's hit show Westworld. But that kind of scenario might not be science fiction much longer. Romance between humans and machines is already nearing the realm of the possible. This year, the California company Abyss Creations plans to start selling a new generation of high-tech sex robots -- dolls that can actually speak and respond to touch. And according to artificial intelligence expert Dr. David Levy, in a few generations, we won't just be having sex with robots, we'll be marrying them.

It was actually about when technology goes wrong: In many ways, 2016 was the year of the exploding batteries. A little over a year ago, hoverboards topped many holiday wish lists. By December 2015, they were being recalled by the thousands. According to Popular Science, "…cheaply made hoverboards have exploded and caught fire, forcing Amazon to stop selling specific models and Overstock to discontinue all sales." Rolling into 2016, major computer companies recalled batteries for fire hazards, baby monitors were pulled from shelves and major airlines diverted flights for emergency landings when tablets caught fire onboard.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. More on Network World: IBM says soon you won't need passwords; mind reading will be routine; the so-called digital divide will cease to exist and junk mail will become important Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.