This post is from guest contributor Steve Lattanzio from MetaMetrics. While we do not tend to cover college rankings at e-Literate, we do care about transparency in usage of data as well as understanding opportunities where technology and data might inform students, faculty, administrators and the general educational community. The following post is an interesting exploration in the usage of the full set of College Scorecard data in a way that is understandable and usable. Ranking colleges has become a bit of a national pastime. There are many organizations that publish "overall" rankings for our institutions of higher education (such as Forbes, Niche, Times Higher Education, and US News & World Report), each with their own methodologies. We don't typically get the complete and precise picture of how these rankings are constructed.

Microsoft and Chinese retailer Alibaba independently announced that they had made software that matched or outperformed humans on a reading-comprehension test devised at Stanford. Microsoft called it a "major milestone." Media coverage amplified the claims, with Newsweek estimating "millions of jobs at risk." Those jobs seem safe for a while. Closer examination of the tech giants' claims suggests their software hasn't yet drawn level with humans, even within the narrow confines of the test used.

At the top of Mount Olympus, Hephaestus – the Greek god of fire and a blacksmith – built a palace for his fellow deities. Inside, he crafted robotic golden statues to serve them. Greek mythology tells us that a fascination with the robotic is as old as western civilization. In the intervening centuries, so advanced have AI, robotics and digital technologies become that sometimes it's easy to think we're being overtaken by innovation. Yet to hide in fear of a jobless apocalypse and a grim vision of rule by robots is to focus on only one side of a complex argument. Flip this negativity on its head and we see that, far from dominating life, technology could be used to help define and complement the human in all of us.

Non-Salesforce app developers may be missing out on a hidden gem in the AI world. When developers think of using the cloud for AI, they might think of IBM Watson, Microsoft Azure Cognitive Services, Google Cloud, or Amazon AI. When they hear of Salesforce Einstein, they might automatically assume it's limited to the Salesforce developer specialization. Any app, whether Salesforce-related or not, can leverage the sophisticated AI cloud technologies that Salesforce has acquired. They've entered the AI market with Salesforce Einstein, their own orchestration of AI cloud services.

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

Dubai based startup, Black Cube Solutions, a frontier tech company, is poised to disrupt the learning and education sector by focusing on skill development and career progression through machine learning and artificial intelligence. Black Cube Solutions has recently unveiled the world's first complete platform for skill development for university students, graduates and working professionals powered by artificial intelligence and machine learning. It's the only platform that brings together all the stakeholders of the skill development ecosystem including universities, corporates, training providers and executive education providers. Peer Mohaideen Sait, Founder and CEO, Training Calendar said, "Skill development is the most pressing challenge for governments and corporates across the world. That is why we came up with the idea of Training Calendar. Our product is aligned to the UAE's national strategy on innovation and is focused on skill development."

It was presented by Cognizant's Center For The Future of Work and Duke Corporate Education. I had no idea how much it would impact my future life decisions! Watch this video (15 mins) to experience what I experienced. In an age of automation, algorithms and AI, many begin to seek the answer to the question, "What do I do when machines do everything?" It was very interesting to hear different speakers' points of view and witness how they occasionally disagreed with one another on stage.

Early in November, a Japanese delegation visited the Indian Institute of Technology, Guwahati campus. The team of five were looking for students with artificial intelligence and deep learning skills from India's premier technology schools for companies in Japan. They had visited IIT-Bombay before flying to the east. In an hour-long meeting, the visitors listed their requirements before K Mohanty, head of career development at IIT-Guwahati and the convenor of the All IITs Placement Committee. "They are willing to come to the placements only if we can provide them with people trained with AI skills. On confirmation, they will hire 15-20 people next year from Guwahati and an equal number from IIT-Bombay," he told FactorDaily.

A large number of E-Commerce and tech companies rely on real time training and predictions for their products. Google predicts real time click-through rates for their ads. This is used as an input to their auction mechanism, apart from a bid from the advertiser to decide which ads to show to the user. Stackoverflow uses real time predictions to automatically tag a question with the correct programming language so that they reach the right asker. An election management team might want to predict real time sentiment using Twitter to assess the impact of their campaign.

Some courts in the U.S., particularly in states from California to New Jersey, use crime-predicting algorithms to determine if a defendant is likely to commit another crime in the future. While the software helps judges decide who gets bail, who goes to jail and who can walk away free, it appears the technology isn't very reliable and opens doors to a more unfair justice system. Dartmouth College researchers Julia Dressel and Hany Farid tackled the issue with the so-called risk assessment algorithms in a paper published in Science Advances. The study examined one popular risk-assessment algorithm, called Compas, and pointed out how the software's recidivism predictions are no different from the answers random people give to online surveys. Farid, who teaches computer science at Dartmouth, and Dressel, who majored in computer science and gender studies at the same school, used Amazon Mechanical Turk in the study.