Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) The paper describes a technique for how to improve the training of an algorithm's ability to recognize images using computer-generated images rather than real-world images. In machine learning research, using synthetic images (like those from a video game) to train neural networks can be more efficient than using real-world images.

There have been some alarming reports recently about the possibility of artificial intelligence leaving half of the world potentially unemployed. Recent research shows that within 30 years, robots will be in a position to perform almost all jobs that are held by humans right now. A recent detailed study from the Martin Oxford School has speculated that approximately 47 percent of U.S. jobs are at risk of automation. As there may be some truth in this report, it is not likely that half of the world's jobs will be taken by machines in 30 years. Additionally, some of the jobs facing the risk of automation might not be automated because of technical, societal and economical reasons.

In February, I took a job designing the personality of a chatbot called Kai. I ghostwrite the lines it says, and I have thought, while testing it, that talking to myself has rarely been so unpredictable. Kai, which was conceived by my employer, Kasisto, to help customers with online banking, works over text message, Slack, and especially Facebook Messenger, where more than thirty-four thousand other chatbots have joined it since April, when Facebook opened the platform to developers. Many of these bots possess no personality. The ones created by CNN and the Wall Street Journal, for instance, greet first-time users with "we," as if the whole newsroom were on the other side of the screen, and run keyword searches rather than engaging in conversation.

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two postdoctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an associate professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data.

In the advertising and marketing industry, event marketing has proved to be the fastest growing approach. Event marketers have adopted various new methods of marketing today, and video or live streaming is perhaps the trend which organizations are embracing willingly and promptly. Sharat Saran, Co-Founder and Chief Executive Officer at ON24, analyzes the shape of things to come, "Following from its rise in 2016, next year we'll start to see marketers in both the B2B and B2C worlds really leverage live video for their initiatives – particularly those types of solutions that can provide tangible insights and data on the back end. Video had long been a black hole for data – where you don't know how long someone watched and what they engaged in – but the latest technology shifts make it a gold mine of stats. Savvy marketers will make the move to glean invaluable intelligence from this channel, and helping transform how they engage with consumers."

There's a new era of social self-expression and mobile entertainment happening, enabled by always-on cameras, real-time computer vision, social media, and other tech. Several of these phenomena -- from emoji to stickers to filters to cultural memes -- emerged in Asia or were popularized in messaging apps there before they made their way into global products. Well, livestreaming is currently exploding in China. And while it's been around in the U.S. for years in various forms, it hasn't really taken off at mainstream scale… yet. So what can we learn as livestreaming evolves in China (and perhaps here too)?

Creating a new mobile app used to begin with a simple question: iOS or Android? And if you believe the hype about chatbots (intelligent helpers that users can summon via instant message), the decision is about to get much more complicated. Tech companies like Facebook, Microsoft, and Slack are all opening up bot platforms within their messaging services, letting users read the news, shop online, hail an Uber, and get customer service within a messaging window. The idea is to allow for quick interactions that don't require installing a full-blown app. But even as these companies build up their chatbot armies, we're probably not headed for another bitter ecosystem war, like the one that led to the dominance of iOS and Android.

Thanks to the wonders of AI, it's possible to ask a robot to help you figure out the best way to deal with a difficult situation. One of the greatest hits as far as Christmas gift-giving was a gizmo called an Echo Dot I gave my nephew, Nathan. It was a hit despite the obligatory annoying learning curve that comes when any Southerner tries to talk to an artificial intelligence device ("Alexer, G-darnit, stop giving me the temperature in Celsius, I want it in American!") I had arrived in Chapel Hill weary from a verbal battle with Siri who, based on the convoluted traffic pattern she recommended, is a huge Duke fan. Artificial intelligence is a big buzzword for 2017.

This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser, with nothing but Javascript. The state of the art on this dataset is about 90% accuracy and human performance is at about 94% (not perfect as the dataset can be a bit ambiguous). I used this python script to parse the original files (python version) into batches of images that can be easily loaded into page DOM with img tags. This dataset is more difficult and it takes longer to train a network. Data augmentation includes random flipping and random image shifts by up to 2px horizontally and verically.

The next BriefingsDirect technology innovation thought leadership discussion explores how rapid advances in artificial intelligence (AI) and machine learning are poised to reshape procurement -- like a fast-forwarding to a once-fanciful vision of the future. Whereas George Jetson of the 1960s cartoon portrayed a world of household robots, flying cars, and push-button corporate jobs -- the 2017 procurement landscape has its own impressive retinue of decision bots, automated processes, and data-driven insights. We won't need to wait long for this vision of futuristic business to arrive. As we enter 2017, applied intelligence derived from entirely new data analysis benefits has redefined productivity and provided business leaders with unprecedented tools for managing procurement, supply chains, and continuity risks. To learn more about the future of predictive -- and even proactive -- procurement technologies, please welcome Chris Haydon, Chief Strategy Officer at SAP Ariba. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions. Gardner: It seems like only yesterday that we were content to gain a common view of the customer or develop an end-to-end bead on a single business process.