At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. We recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2016 and key trends they 2017. "What were the main Artificial Intelligence/Machine Learning related events in 2016 and what key trends do you see in 2017?" Common themes include the triumphs of deep neural networks, reinforcement learning's successes, AlphaGo as exemplar of the power of both of these phenomena in unison, the application of machine learning to the Internet of Things, self-driving vehicles, and automation, among others. We generally asked participants to keep their responses to within 100 words or so, but were amenable to longer answers if the situation warranted.

When it comes to Artificial Intelligence (AI) and Automation, there is no debate that advances in these areas will engender profound changes in our world. Rather, the debate centers on what these changes might look like. There are many who express concern or even outright fear about the impact of AI on our future, and with good reason. A recent report from Forrester predicts that by 2021, intelligent agents and related robots will have eliminated 6% of a net jobs. A widely noted study, "The Future of Employment: How susceptible are jobs to computerisation?", estimates that 47% of all US jobs are at risk. The Luddites were textiles workers who protested automation, eventually attacking and burning factories because, "they feared that unskilled machine operators were robbing them of their livelihood".

There are more bots on the internet than humans. According to figures from Distil Networks, a cybersecurity firm, almost 60 percent of 2014's internet traffic consisted of automated code. Despite the world's growing population of internet users, that figure is undoubtedly higher today. Among the oldest of those bots is ELIZA, who turns 50 this year. ELIZA, who was written at the MIT Artificial Intelligence Laboratory in the mid-1960s by a German-Jewish computer scientist named Joseph Weizenbaum, can perform natural language processing and pattern match users' responses to different scripts.

Clancy declined to say how much Zuckerberg may have paid Crestron for the services, though he said it was probably not a normal rate, since Facebook is a big Crestron customer and they "helped him out." Zuckerberg's engineering did add some powerful new features that Clancy said he expects will be in high demand. For one, Zuckerberg's artificial intelligence went beyond fixed phrases or hitting buttons, since it has the ability to learn. Plus, Facebook's facial recongition technology and ability to text with Facebook Messenger are unique, Clancy said, not to mention the voice by Morgan Freeman. Unlike an off-the-shelf consumer product like Google Home or Apple HomeKit, Crestron manufactures all the devices in its ecosystem and has government-facility level cybersecurity.

Internet of Things (IoT) has brought everything connected through internet. Increase application of smart devices are enabling us to collect big data on a regular basis. The data gathered are becoming more complex and uncertain and therefore artificial intelligence (AI) came into picture. AI can efficiently deal with the difficulties created by big data. Artificial Intelligence is basically the simulation of logical human thinking using computer technology.

Artificial Intelligence, depending on who you speak to, will either be our greatest achievement or our last great achievement as a species. It will either provide complete freedom from laborious tasks or further enslave or exterminate us. AI will either provide a world free of warfare or become the world's greatest war machine turning on its masters. The potential liberation and enhancement to mankind cannot be underestimated, but fears are well founded. Given the latter, let's take a quick look at the concerns about AI.

This article is Part VI in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read the earlier parts – (Part I, Part II, Part III, Part IV and Part V). Continuing on the walkthrough, in this part we build the model that will predict the first booking destination country for each user based on the dataset created in the earlier parts. The first step to building a model is to decide what type of algorithm to use. Below we look at some of the options.

As far as artificial intelligence goes, 2016 has been the year of deep learning. Brain-inspired neural networks have received massive amounts of investment in time, resources and funding -- and, boy, has it ever paid off! In a new piece of research -- carried out by investigators at Rutgers University, the University of North Carolina at Charlotte, Lehigh University, and the Chinese University of Hong Kong -- neural networks have been used to generate high quality images based on nothing more detailed than basic text descriptions. "Generating realistic images from text descriptions has many applications," researcher Han Zhang told Digital Trends. "Previous approaches have difficulty in generating high resolution images, and their synthesized images in many cases lack details and vivid object parts. Our StackGAN for the first time generates 256 x 256 images with photo-realistic details."

Discussion of artificial intelligence has skyrocketed since the end of the last decade, according to a new analysis looking at public perception of the technology. The paper by Stanford computer science PhD Ethan Fast and Eric Horvitz, technical fellow and a managing director at Microsoft Research, looked at more than three million articles published in the New York Times between January 1986 and May 2016. The study is under review to become a conference paper at the Thirty-First AAAI Conference on Artificial Intelligence. According to the authors, no other collection of text aimed at a general audience extends so far into the past, making it a good proxy for public opinion. They found that from 1985 to 2009 AI was discussed in somewhere between 5 and 10 out of every 10,000 articles.

We've heard that it is prohibitively expensive for startups and academics to train machine learning models, and this is due to the rental or purchase costs of hardware. The results from one recent Google paper were estimated to cost $13k to emulate. That's just to reproduce the final model, not to emulate the whole experimentation and hyperparameter optimisation caboodle. Equally, there are intelligence tasks (training, inference, or prediction) that would ideally happen on the cellphone or remote sensor but are too compute constrained locally, so currently rely on uploading data to the cloud for processing. Machine intelligence is the future of computing, so what needs to happen at a hardware level to make it faster and more energy- and cost-efficient? We talked to Simon Knowles, CTO of Graphcore, about hardware acceleration of machine intelligence.