GPU technology has been around for decades, but only recently has it gained traction among enterprises. It was traditionally used to enhance computer graphics, as the name suggests. But as deep learning and artificial intelligence have grown in prominence, the need for fast, parallel computation to train models has increased. "A couple years ago, we wouldn't be looking at special hardware for this," said Adrian Bowles, founder of analyst firm STORM Insights Inc. in Boston. "But with [deep learning], you have a lot of parallel activities going on, and GPU-based tools are going to give you more cores."

Many believe Artificial intelligence is a new concept. But in reality it is something that businesses have been trying to implement in key areas. The role of artificial intelligence is to organize operations and improve automation. Some of the biggest international tech giants such as Google, Apple and Microsoft have made announcements related to artificial intelligence this year. In September, a number of companies, including Amazon, Facebook, Google, IBM and Microsoft, also launched a Partnership on Artificial Intelligence to Benefit People and Society to address opportunities with AI technology .

This ended up being more of a blog than a comment lengthwise. I really believe that the intense discussion of'data scientist' vs. 'statistician' is one that's driven by quite a number of factors. And I think it is inappropriate, presumptuous at best to say that there is a'death' of any one particular discipline in this area. The quantitative and computational arena, both in the theoretical and applied arena is evolving, but nevertheless much is grounded in the disciplines of statistical practitioners. Here are some simple armchair thoughts about how and why I believe that'data scientist' and'statistician' are at their core nearly synonymous.

Google's Embedding Projector web applications will give developers a way to visualize how well their machine learning systems are interpreting data.

Chatbots are programs that mimic conversation with people using artificial intelligence. With recent advances in AI these have become much more accurate, especially when focused on a specific domain. Combine these advances with the consumer trend towards messaging -- people are now spending more time in messaging apps than in social media -- and it seems we might be about to enter the age of the chatbot. We decided to take a closer look at the top emerging chatbot start-ups in text chat, across a range of industries, and pick out the most promising. You can find out more about the 25 companies below, or discover more chatbot startups on our search engine.

Last March, Google's computers roundly beat the world-class Go champion Lee Sedol, marking a milestone in artificial intelligence. The winning computer program, created by researchers at Google DeepMind in London, used an artificial neural network that took advantage of what's known as deep learning, a strategy by which neural networks involving many layers of processing are configured in an automated fashion to solve the problem at hand. Unknown to the public at the time was that Google had an ace up its sleeve. You see, the computers Google used to defeat Sedol contained special-purpose hardware--a computer card Google calls its Tensor Processing Unit. Norm Jouppi, a hardware engineer at Google, announced the existence of the Tensor Processing Unit two months after the Go match, explaining in a blog post that Google had been outfitting its data centers with these new accelerator cards for more than a year.

It's been a year of change in the enterprise software market. SaaS providers are fighting to compete with one another, machine learning is becoming a reality for businesses at a larger scale, and containers are growing in popularity. Here are some of the top trends from 2016 that we'll likely still be talking about next year. As more and more companies adopt software-as-a-service products like Office 365, Slack, and Box, there is increasing pressure to collaborate for companies that compete with each another. After all, nobody wants to be stuck using a service that doesn't work with the other critical systems they have.

The next year will see robots begin to join forces to collaborate in unprecedented new ways, experts have predicted. British academics have predicted the rise of a global system called "the internet of robots" which will let machines interact and communicate on an international scale. Although robots are currently too primitive to pose any major threat to humanity, the development of new forms of communication marks the beginning of a world where machines begin to teach each other how to perform tasks and share their knowledge across "cloud" computer systems which can be accessed from anywhere on Earth. Tom Garner, a research fellow at Portsmouth University's School of Creative Technologies, said: "These systems allow robots that have been optimized for different tasks to work on specific problems individually, but to pass solutions between each other." "The robots use the cloud to share the data, enabling it to be analyzed by any other robot or intelligence system also connected to the same network."

Back in 2008, Yair Lavi founded Tonara, an interactive app that "listens to" musicians and assists them as they play. He has transported the lessons learned there to his latest venture, 3DSignals, where the co-founder and head of algorithms uses ultrasonic sensors and deep-learning software to detect anomalies in machine sounds. "Industrial music" you might say. Smart Industry: How do you define "deep learning"? Yair: Deep learning is a method of artificial intelligence used to detect patterns in data, either independently or based on some type of training.

Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises' data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data. The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing.