In the fall of 2016, the citizens of the United States became fed up and made a decision that was heard around the world. While not all states felt the same way about this decision, it was pretty clear that it was time for some real change. The last 8 years had been an utter disappointment, full of the some of the most mindless inane drivel the world has ever seen, making people truly question if the United States was really the great nation that it thought it was. Then, last fall, it finally happened. On or around October 9th, the citizens of the United States of America collectively became more interested in artificial intelligence than Kim Kardashian's net worth.

ILA, the Intelligent Learning Assistant, was designed with classrooms in mind and aims to enhance both learning and teaching! Specifically, it gives teachers more time to actually TEACH by doing roll call, collecting assignments, distributing materials, finding and managing resources, and, more importantly, keep track of pupil progress and participation. Harnessing IBM's cognitive computing system, Watson, ILA collects, analyzes, and converts information into natural language. Not only does this allow educators to access, evaluate, and integrate new teaching material, but it also provides them with analysis and diagnostics on the progress of each individual student's verbal skills and literary understanding. This makes it possible for educations to assess and tailor the learning behavior and path of each individual student.

Imagine you have an e-commerce website and that you are designing the algorithm to rank your products in your search page. What will be the first item that you display? The problem gets complicated pretty quickly. A simple solution is to use your intuition, collect the feedback from your customers or get the metrics from your website and handcraft the perfect formula that works for you. A more complex approach involves building many ranking formulas and use A/B testing to select the one with the best performance.

Google caused a stir when it open sourced its TensorFlow software back in November 2015, and the technology is starting to make its way into the mainstream. The machine learning software library is the next generation of DistBelief, which was internally developed by the Google Brain team at the search giant for a multitude of tasks such as image search and improving its speech recognition algorithms. TensorFlow is a deep neural network, so it learns to perform a task through positive reinforcement and works through layers of data (nodes) to help it determine the correct outcome. By open sourcing the TensorFlow library of machine learning code, Google is facilitating the simpler construction, training and deployment of complex deep neural nets. TensorFlow doesn't exactly give every developer the ability to harness machine learning but it does provide both a Python and C/C API to link into a developer's program.

At HyperScience we bring AI to the office. Our products help enterprises and government institutions function by automating certain kinds of office work and reducing bureaucratic burden both on businesses and their customers. We take a heterogeneous approach to AI, using a blend of what are traditionally considered different fields of ML: deep learning, computer vision, and NLP among others. We believe that AI is destined to be the biggest event in the history of human labor since the Industrial Revolution, and we want to be a part of it. ML is at the core of what we do.

For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below. For this task, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

At universities, "people learn how to approach new things, ask questions and find answers, deal with new situations," wrote Uta Russmann, a professor of communications at the FHWien University of Applied Sciences in Vienna. "All this is needed to adjust to ongoing changes in work life. Special skills for a particular job will be learned on the job." Schools will also need to teach traits that machines can't yet easily replicate, like creativity, critical thinking, emotional intelligence, adaptability and collaboration. The problem, many respondents said, is that these are not necessarily easy to teach.

Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.

Artificial intelligence has a diversity problem. Too many of the people creating it share a similar background. To renowned researcher Fei-Fei Li, this paucity of viewpoints constitutes a crisis: "As an educator, as a woman, as a woman of color, as a mother, I'm increasingly worried," she says. "AI is about to make the biggest changes to humanity, and we're missing a whole generation of diverse technologists and leaders." From the chair next to her, Melinda Gates affirms this, adding, "If we don't get women and people of color at the table -- real technologists doing the real work -- we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible."

Joseph Qualls is a clinical assistant professor in the University of Idaho's College of Engineering. He's also an expert in artificial intelligence and the founder of a research and development company that uses AI to solve problems for the U.S. military and industry. In Qualls's vision of the future, AI will dramatically change the way we deliver healthcare, entertain ourselves, conduct warfare and, of course, teach college students. He recently spoke with EdTech Managing Editor Amy Burroughs about what he sees on the horizon. QUALLS: AI is going to come far more quickly than even I can predict.