It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago. Today we're going to take a look at 3 industries on the cusp of disruption through the adoption of AI and NLP technologies;

Adam Coates, director of Baidu Research's Silicon Valley AI Lab, isn't worried about artificial intelligence taking over the world. "I will tell you why I am not actually afraid of AI right now," he said at the InformationWeek Elite 100 Conference in the Four Seasons Hotel in Las Vegas on Tuesday, May 3. "I don't go around seeing every Camero on the street thinking that's a Transformer. I know what's under the hood. It's self-evident the technology is just not capable of doing something like that today." Not everyone in the audience was entirely convinced there's no need for fear.

Deep learning works by providing AI systems with huge amounts of data so it can learn to identify patterns and recognize important features, trends, and "decisions" to be made in processes such as playing a game or rendering a diagnosis. Deep learning has already made huge strides in detecting financial transaction fraud, making voice recognition systems like Siri work, and recognizing faces and patterns in photographs.

It won't be long before connected cars serve two masters: owners and manufacturers. And one software maker thinks it can build car brains smart enough to work for them all. If you drive an Entune-equipped Toyota (most of them) or a Lexus with Enform, you're already using connected-car software from UIEvolution (recently renamed Xevo). Besides getting a new name, Xevo is also about to acquire Surround.io, More: Connected cars: will you need an in-car ad blocker?

The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, bagging, and boosting. So how do ensemble methods work and why are they superior to individual models? They average out biases: If you average a bunch of democratic-leaning polls and republican-leaning polls together, you will get an average something that isn't leaning either way. They reduce the variance: The aggregate opinion of a bunch of models is less noisy than the single opinion of one of the models. In finance, this is called diversification -- a mixed portfolio of many stocks will be much less variable than just one of the stocks alone.

While we think of them as the latest thing in tech, conversational interfaces have been around for quite some time. From Cleverbot and Smarter Child to labyrinthine phone trees ("say REPRESENTATIVE"), we have been trying for years to build technology that mimics how we interact with humans. Recent advances have positioned these tools for substantial growth and brought them back to the foreground of the conversation on the future of technology. Conversational interfaces for both speech and text have risen to prominence thanks to virtual assistants or "chatbots," such as Apple's Siri and Amazon's Alexa. Also, text-based chatbots, or messaging platforms such as Slack and Facebook Messenger, have seen a huge spike in utilization.

Robots have long been a manufacturing mainstay--tasked with doing the heavy lifting on the assembly line or taking over tedious repetitive motions. Up until recently, however, they've been limited to basic activities, typically on the automotive factory floor. But advancements in the form of small, light-weight collaborative robots, cloud analytics and artificial intelligence are ushering in a new era of robot applications and collaboration that will support a wide variety of industries. Industry observers say these new robot systems will transform the way products are made, as well as the way people work. "The next step is changing the way we work globally," said Esben Østergaard, CTO of collaborative robot company Universal Robots during an interview at IMTS 2016.

At the annual Gartner Symposium/ITxpo in Orlando, Florida this week, over 8000 technology leaders gathered to get analyst predictions on where we are heading. Artificial intelligence, machine learning, and smart things were key analytics themes for a savvy digital future. The overall top three themes this year were intelligent, digital, and mesh. For a fantastic free overview, check out the following openly published article, "Gartner's Top 10 Strategic Technology Trends for 2017" and linked resources. Last week I shared an article and webinar regarding the Fourth Industrial Revolution aka Digital Transformation.

The whole technology landscape is changing and is ushering in new ways to put it to use. The way things have been run in the past 20 years no longer holds water and we need to change to adapt and be relevant. At the Bengaluru ITE.BIZ 2016, Bhaskar Ghosh, Group Chief Executive, Accenture Technology Services, shed light on what the next wave in the IT sector would be. "We went through several waves - IT services, e-commerce, cloud, artificial intelligence (AI) and quantum computing. New tech is maturing faster and the time to learn and execute in the market is short. We need to learn to master the speed. Every business is a software business. Even manufacturing is driven by software," he explained.

Our 12 days of Christmas predictions has hit lucky seven, and just in time for the 13th of December too! Of course this means 13 shopping days until Christmas Day, but for those who have made their preparations and are not yet tired of Wham and Shakin' Stevens on the airwaves, our countdown continues with a look at one of the key trends of 2016 which is expected to continue into the new year. The concept of machine learning, automation and artificial intelligence (AI) have got a lot of attention over the past couple of years, and it is predicted that this will continue into 2017. Sian John, Chief Strategist of EMEA at Symantec, predicted that AI and machine learning will require sophisticated Big Data capabilities as in 2017, machine learning and AI will only continue to grow. "With this growth comes new, powerful insights for businesses to tap, and an increased collaboration between humans and machines," she said.