The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3]. On the darker side, you do not have to be paranoid to be paranoid about the potential technologies that the super-secret computers of the US National Security Agency (NSA) have on their digital drawing-boards.

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. One reason that understanding language is so difficult for computers and AI systems is that words often have meanings based on context and even the appearance of the letters and words. In the images that accompany this story, several artists demonstrate the use of a variety of visual clues to convey meanings far beyond the actual letters.

We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.

Jeremy P. Howard, @JeremyPHoward, is a leading Machine Learning and Deep learning researcher and entrepreneur. His current startup is fast.ai Previously, he was CEO and founder of Enlitic, Kaggle President, and #1 ranked Kaggle competitor. Jeremy initiatives attracts a lot of attention in the industry, so I was very interested to learn from him about his latest project, a first Deep Learning for coders MOOC at course.fast.ai. The course is totally free and includes no advertising - Jeremy created it purely as a service to the community.

K-12 educators are deep in the midst of rethinking the design of the classroom and the responsibilities of the teacher within it. The internet brought a rush of new educational resources and technologies, like high-quality, free instructional videos from Khan Academy (which has 2.9 million YouTube subscribers) and crowdsourced lesson plans from nonprofit websites like ReadWriteThink and Teacher.org. Deciding on the best way to integrate these resources into schools has become another factor in the perpetual discussion about how to improve American public school education. As these new technologies made their way into schools, the phrase "blended learning" was coined to describe education environments where the traditional teacher-led classroom is augmented by digital media and online resources. As schools reconfigure their classrooms around blended learning, the role of the teacher is transitioning from "the sage on the stage" to the "guide on the side."

What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience. The primary purpose of this blog post is to develop an understanding of how Convolutional Neural Networks work on images. If you are new to neural networks in general, I would recommend reading this short tutorial on Multi Layer Perceptrons to get an idea about how they work, before proceeding. Multi Layer Perceptrons are referred to as "Fully Connected Layers" in this post.

Imagine the holy grail: getting the right message to the right customer at exactly the right time -- every time. And what if you could deliver hyper-relevant cross-channel customer experiences that amplify loyalty and result in increased Average Order Value, reduced churn, and increased conversions faster? As hyper-scalable programmatic technology shifts from the adtech space and into the martech world, organizations are, for the first time, able to leverage predictive scoring on an incredible scale built upon a real-time view of every customer. Leading companies have already implemented user-centric strategies that place an emphasis on marrying systems of record, systems of intelligence, and systems of action to create what is now being called Programmatic CRM. Join master marketers in our latest VB Live executive event, where you'll learn how to turn martech innovation into user-centric, budget-stretching, personalized marketing that works.

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.

Learning to learn by gradient descent by gradient descent

Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on January 19th, January 20th, & January 21st 2017 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for businesses to effectively tailor their services by effective use of AI. Proper use of Artificial Intelligence can be a major competitive advantage for any business considering vast amount of data being generated.