If you think artificial intelligence was huge in 2016, just wait. While you can't discount the achievements made by AI in the past year -- autonomous driving, AlphaGo's victories, the world's saddest assistant -- 2017 stands to best each of them. And we've started with January off with a bang (and an AI that's now $800,000 richer, sorta). All of this pales in comparison to what's to come. As amazing as current AI seems, it's a compounding effect that uses previous advancements to build upon the last until AI ultimately starts to learn from its mistakes and get smarter than every. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets.

DAVOS: Open markets and global trade have been blamed for job losses over the last decade, but global CEOs say the real culprits are increasingly machines. And while business leaders gathered at the annual World Economic Forum (WEF) in Davos relish the productivity gains technology can bring, they warned this week that the collateral damage to jobs needs to be addressed more seriously. From taxi drivers to healthcare professionals, technologies such as robotics, driverless cars, artificial intelligence and 3D printing mean more and more types of jobs are at risk. Adidas, for example, aims to use 3D printing in the manufacture of some running shoes. "Jobs will be lost, jobs will evolve and this revolution is going to be ageless, it's going to be classless and it's going to affect everyone," said Meg Whitman, chief executive of Hewlett Packard Enterprise.

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.

Are you considering getting into the field of machine learning and artificial intelligence? For a limited time, StackSocial is offering up a massive discount on four classes that are designed to help you understand the fundamentals of one of the most interesting topics in the world. First off, you'll get a course titled "Artificial intelligence and machine learning training" that delivers 17 hours of content over 91 lessons. This will provide a good starting point, and it'd usually sell for about 300 bucks on its own. Next, you'll have two hours and 10 lessons of "Introduction to machine learning" to help you get up to speed on what machine learning is really capable of handling.

In the simplest terms, cognitive computing simulates human thought through artificial intelligence, Caserta explains. Until recently, cognitive was limited to a small subset of industries, i.e. medical advancements and call center automation. However, self-teaching robots and chatbots have become regular news topics. This year, these technologies will become an integral part of enterprise data analytics by influencing and enhancing the customer experience. In 2016, IBM and Google did huge marketing pushes for their Watson and Brain offerings respectively.

Apple will take a significant step toward disclosing more of its artificial intelligence research this week by becoming a member of a non-profit AI research consortium founded by five of the tech industry's biggest players. Last September, Amazon, Facebook, Google, Microsoft, and IBM publicly announced The Partnership on AI, an organisation established "to study and formulate best practices, to advance the public's understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society". As one of the biggest researchers in AI, Apple's name was conspicuously absent, but that looks set to change in the coming days, following a Bloomberg report on Thursday that Cupertino is ready to add its name to The Partnership's list of corporate heavyweights. According to its website, the Partnership on AI intends to conduct research, organize discussions, share insights, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning. Apple's imminent membership is just the latest indication that the company is prepared to reveal more of its work in areas of artificial intelligence.

A team from the Stanford Artificial Intelligence Laboratory set up a machine-learning AI to teach itself to diagnose cancer by looking at skin lesions just as a doctor would during an exam. The team tested the algorithm against 21 board-certified dermatologists, and it performed just as well as its human counterparts. "We realized it was feasible, not just to do something well, but as well as a human dermatologist," Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory, said in a statement. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

I have the opportunity to review a lot of different technology products and I tend to put them into one of three categories. First, there are those products that really impress me and make me thankful for living in the Digital Age. The second category is made up of some useful and fun tech products that aren't very original, but enjoyable. The third category includes tech products that are poorly made, unoriginal or serve no purpose. Recently, I determined I should create a fourth category after a friend asked an intriguing question. He knows I'm a techie and always testing the latest, greatest tech products and gadgets.

Created by Bjørn Karmann at CIID, Objectifier empowers people to train objects in their daily environment to respond to their unique behaviours. It gives an experience of training an artificial intelligence; a shift from a passive consumer to an active, playful director of domestic technology. Interacting with Objectifier is much like training a dog – you teach it only what you want it to care about. Just like a dog, it sees and understands its environment. Using computer vision and a neural network, Objectifier allows for complex behaviours to be associated with your command.