That joke is at least as old as Deep Blue's 1997 victory over then world chess champion Garry Kasparov, but even with the great strides made in the field of artificial intelligence over that time, we're still not much closer to having to worry about computers' feelings. Computers can analyze the sentiments we express in social media, and project expressions on the face of robots to make us believe they are happy or angry, but no one seriously believes, yet, that they "have" feelings, that they can experience them. Other areas of AI, on the other hand, have seen some impressive advances in both hardware and software in just the last 12 months. Deep Blue was a world-class chess opponent -- and also one that didn't gloat when it won, or go off in a huff if it lost. Until this year, though, computers were no match for a human at another board game, Go.

The term'Artificial Intelligence' was originally coined in the 1950s by the computer scientist John McCarthy. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Today, the hype around artificial intelligence (AI) is ramping up, especially as big tech companies like Apple, Amazon, Google, Facebook, IBM and Microsoft attempt to commercialize its use. Digital Ad Agencies are also starting to figure out how they can leverage Artificial Intelligence techniques to make their clients' marketing and advertising efforts more effective. So far in 2016, Artificial Intelligence technology has grabbed headlines as the focus of Apple's first acquisition--in the form of Emotient Inc--and Facebook CEO Mark Zuckerberg has resolved to build an AI assistant to run his home and help him at work. Google has also gone down in the history books after its DeepMind team developed an AI program capable of defeating human world champions of complex Chinese board game Go. It's an achievement reminiscent of IBM's milestone moment when its cognitive system IBM's Watson thrashed human contestants in the U.S. game show Jeopardy in 2011. Watson was custom-built to process natural language and reason its way through information.

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one--learning how to get a GPU server online suitable for deep learning--and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

One of my favorite small projects, EmailFiler, was motivated by a school assignment for Georgia Tech's Intro to Machine Learning class. Basically, the assignment was to pick some datasets, throw a bunch of supervised learning algorithms at them, and analyze the results. But here's the thing: we could make our own datasets if we so chose. And so choose I did - to export my gmail data and explore the feasibility of machine-learned email categorization. See, I learned long ago that it's often best to keep emails around in case there is randomly some need to refer back to them in the future.

Learning to learn by gradient descent by gradient descent

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening. Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses.

Google's AlphaGo beats Lee Sedol at the game of Go In 2016, major automakers like Tesla and Ford announced timelines for releasing fully-autonomous vehicles. DeepMind's AlphaGo, Google's AI system, beat the world champ Lee Sedol at one of the most complex board games in history. And other major advancements in AI have had big implications in healthcare, with some systems proving more effective in detecting cancer than human doctors. Want to learn what other cool things AI did in 2016? Here are TechRepublic's top picks.

TensorFlow includes the implementation of the RNN network that is used to train the translation model for English/French tuple. We will use it to train our chatbot. Probably, one might ask: "why the hell we are looking on the translation model if we are writing the chatbot?". But this might be confusing only at the beginning. Think for a moment what is translation?

The key to this new tool is that N2D2 doesn't just compare different hardware on the basis of recognition accuracy. It can compare hardware in terms of "processing time, hardware cost, and energy consumption." This is critical, said Duranton, because different applications for deep learning will likely require different parameters in various hardware implementations. The N2D2 offers benchmarking on a variety of commercial off-the-shelf hardware -- including multi/many-core CPUs, GPUs and FPGA. Barriers to edge computing As a research organization, CEA has been studying how best to bring deep neural networks to edge computing.

IMAGE: This is the Architecture of the Adversarial Autoencoder (AAE). The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.