Ever since computers were clunky, whirring machines that took up entire floors, humans have marvelled at their potential, envisioning all the ways they could help or even be like us. Tapping into our own dark nature, science fiction tends to reach what creepily feels like the natural conclusion of obscenely smart machines with human dispositions; our demise. There's no robot apocalypse on the horizon, but the revolution is well under way. It's been here, in some form, since the '60s, and it's poised to lead the city, and world, in to the future. On April 1, 1964, U of A built Canada's first Department of Computing Science around five academics, a small support staff and the LGP-30, an 800-pound, deep freeze-shaped digital computer.

Digital technologies have already become an internal part of our life. They change the way we are looking for information, how we communicate with each other, even how we behave. This transformation applies to many areas, including education. The main objective of this article is to identify prospective impact of artificial technologies to the study process and to predict possible changes in educational landscape. In presented literature review we considered four categories: customized educational content, innovative teaching methods, technology enhanced assessment, communication between student and lecturer.

Python image recognition sounds exciting, right? However, it can also seem a bit intimidating. There's no need to be scared! This tutorial will teach you Python basics and how to use TensorFlow. Take this chance to discover how to code in Python and learn TensorFlow linear regression then apply these principles to automated Python image recognition. Through this course, you'll master Python image recognition software and learn with hands-on examples.

The pace of technology disruption has reached a crescendo in 2018. Society, in general, and industry and government, in particular, are spiritedly embracing digital technologies. Technologies such as Artificial Intelligence, Internet of Things, Augmented Reality, Virtual Reality, Machine Learning, Big Data Analytics, Robotic Process Automation, and 3D Printing are increasingly becoming mainstream, cutting across industries. For instance, blockchain was originally thought to be an application in financial services and retail. It has now found its way into agriculture, healthcare, governance, logistics, etc. These technologies are providing numerous new opportunities for large enterprises as much as they are aiding the start-ups in developing new products and service-lines, improving efficiency, productivity, and competence levels, giving thrust to the economic growth of the country while simultaneously bringing about social equality.

For Chinese guests at Marriott International hotels, the check-in process will soon get easier. The hotel giant announced last summer that it's developing facial recognition systems that will allow guests to check in at a kiosk in less than a minute via a quick scan of their facial features. Half a world away, fearful of what such technological advances will mean to their future job security, thousands of Marriott workers across the United States voted this fall to authorize their union to strike. In addition to calls for higher wages and better workplace safety, they pushed for procedures to protect them from the looming impact of technological advancement. "You are not going to stop technology. The question is whether workers will be partners in its deployment or bystanders that get run over by it," the union's president told The New York Times.

As a quick data point, today, on 353rd day of year 2018, I have come across the question from someone "I want to become a'Data Scientist, can you help me with a plan?" 48th time so far. If Google search trends tell a story, the frequency of this question coming from within the close and extended working groups also means that'Becoming a Data Scientist' excites a lot of IT practitioners. Here is my attempt to answer the question for the new entrants and those who are willing to cross/up-skill themselves and eventually become data scientists. This is a great quote from all time great Usain Bolt about Dreams and Goals. If you dream to be a data scientist, as a first step, you need to quickly translate that Dream into a SMART goal and remember that the goals do not come without a price. The price is time, effort, sacrifice and sweat.

It seems the more I read, the more confused I get - models, algorithms, surrogates; my head is spinning. Assume the dataset is in perfect condition - pure as the driven snow, no correlated features, no null in sight, nothing; and it has "enough" observations. To simplify, let's say we are looking at binary classification. Let's also say that we want to try four different algorithms: for example - logistic regression, naive Bayes, gradient boosted tree and multilayer perceptron. And, finally, let's assume that (since all this is for educational purposes), we have no issues with time, efficiency, computing power, computing budget and whatnot; we don't care if this is an overkill or if we're going after a fly with an elephant gun: we want to throw everything, including the kitchen sink, at the problem so we can extract every last ounce of performance when it's time to make predictions on totally unseen data.

As fears of artificial intelligence replacing human workers grows, some companies are focusing on the people who make the brands work. Heather Brunner of WP Engine and Barbara Humpton of Siemens USA emphasized the need for changing education, investment in current employees, and rethinking prerequisites that were once considered sacrosanct. Brunner, for instance, noted that WP Engine has removed its college degree requirement and is partnering with more community colleges, workforce development agencies, and coding programs in universities to train its talent. Both women noted that AI isn't going away, but that doesn't necessarily mean humans won't be a critical factor anymore. "The key question people keep asking us is, 'Are we transforming humans out of the equation?' And the answer is'no, we're elevating the role of the human. We're finding out what is truly humanly possible'," Humpton said Tuesday at Fortune's Most Powerful Women International Summit in Montreal.

Photos of an artificial intelligence textbook for Chinese preschoolers have gone viral. Artificial Intelligence Experiment Materials is a 33-volume textbook series aimed at Chinese students from kindergarten to high school that was published this July by Henan People's Publishing House. AI researchers from Google, the Institute of Automation of the Chinese Academy of Sciences, and key Chinese universities collaborated on the textbooks, which pertain to an AI education initiative launched this July by the China Education Technology Association Smart Learning Committee and UNESCO. The aim is to democratize AI education in 100 Chinese schools, introduce pre-teens to the basics, strengthen teenagers' capability for using intelligent and applied technologies, and help train hundreds of new AI teachers. Also included in the initiative is a cloud-based AI e-learning platform that students can access via PC or WeChat.

On Wednesday, January 25, Andrew Ng -- former Baidu Chief Scientist, Coursera co-founder, and Stanford Adjunct Professor -- gave a talk at the Stanford MSx Future Forum. During the talk, Professor Ng shared his opinion on AI. He mainly discussed how artificial intelligence (AI) is transforming industry and business. About a century go, we started to electrify the world through the electrical revolution. By replacing steam powered machines with those using electricity, we transformed transportation, manufacturing, agriculture, healthcare and so on.