I was sharing my latest Algorithmic Rotoscope image on Facebook and a friend asked me what I meant by training a machine learning model. When you get too close to the fire, you lose your words sometimes. It is why I try to step away and write stories about it -- it helps me find my words and learn to use them in new and interesting ways. Thankfully, I have a partner in crime who understands this stuff and knows how to use her words. I use my blog as a reference for my ideas and thoughts, and I didn't want to lose this one. I'm playing with machine learning so that I can better understand what it does and what it doesn't do.

Did you ever think you'd live in a world where the foxiest job title was "Data Scientist," people who build "models" all day? Well, Weird Science has come to pass. This is our new and very exciting reality. Knowledge and experiences are wrapped together for a new set of challenges, from data scientist to developer to marketers. Last year's news that Google was using a new machine learning tool called RankBrain -- used to contribute to its search engine results -- caused a kerfuffle in the SEO world, leaving us wondering just what kind of impact it would have.

That impact will be significant. This year alone at least 1 billion people will be touched in some way by artificial intelligence, which is transforming everything from financial services to transportation, energy, education and retail. In healthcare alone, IBM Watson is engaged in serious efforts to help radiologists identify markers of disease; to help oncologists identify personalized treatments for cancer patients; and to help neuroscientists identify genetic links to diseases like ALS, paving the way for advanced drug discovery.

A group of Silicon Valley roboticists who developed Shakey, a pioneer mobile robot project, gathered last night at the Computer History Museum in Mountain View, Calif., to dedicate the tall, wheeled machine as an IEEE Milestone. Joining the group were other robotics visionaries, IEEE officers and local IEEE section members, and fans of computing history. Shakey, developed at SRI International between 1966 and 1972, was honored as the world's first mobile, intelligent robot. "Stanford Research Institute's Artificial Intelligence Center developed the world's first mobile, intelligent robot, SHAKEY. It could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers."

As the Trump administration struggles to determine the future of a controversial executive order banning immigration from seven majority-Muslim countries, the futures of international students from those countries hang in the balance. Even though the ban was struck down by a federal court and travel has resumed, the students still face uncertainty, especially since President Trump says he may issue a new version of the executive order. Not only are many indefinitely separated from their families, but their professional opportunities are also at risk. For students who have already achieved success in research or entrepreneurship during their time in the United States, the executive order is particularly troubling. One of the seven countries named in the executive order, Iran has long contributed to American intellectual advancement.

Machine Learning has 2 phases. Typically, model building is performed as a batch process and predictions are done realtime. The model building process is a compute intensive process while the prediction happens in a jiffy. Therefore, performance of an algorithm in Python or R doesn't really affect the turn-around time of the user. Python 1, R 1. Production: The real difference between Python and R comes in being production ready.

"Data Scientist is the sexiest job of the 21st century" So, what exactly is data science and why all the hype around data scientists. Frankly speaking, multiple job descriptions and explanations of the same role make it harder for businesses to clearly understand what a data scientist is and does. This complicates the ROI business leaders expect when investing in them. To me, data Science involves mining actionable and sensible insights from multiple data formats by applying mathematics, statistics, machine learning, etc. Data scientists typically analyze data sets, or data depositories that are maintained within an organization and/or they analyze data scraped from publicly available sources.

IBM announced Feb. 15 that it's bringing some of Watson's artificial intelligence to the private cloud with a new cognitive computing platform called Machine Learning. IBM called Machine Learning the "first cognitive platform" to use analytical models to help companies more effectively analyze their operations and make sound, AI-based decisions. Machine Learning, which is designed for both novice and advanced data scientists, will continue to learn as users feed it fresh data. It can also help scientists choose the right algorithms to make decisions and can be put to work in most industries, even those as wide-ranging as retailing or oil exploration. It runs on IBM's z System mainframes, which are widely deployed in enterprise data centers and, according to the company, can process billions of transactions each day.

This week in the Substance ÉTS science review, we highlight articles on two topics that are top concerns for many people: Artificial Intelligence and the environment. Each quarter, The Economist publishes a fairly detailed review of a particular aspect of technology. In the first quarter of 2017, The Economist published seven articles describing the progress and limitations of language technology with, in addition, a glimpse of the future in this field. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in the US, are trying to figure out how MIT students solve a planning problem. They found that the strategies used by the majority of students could be described using a language called "linear temporal logic".

Artificial Intelligence, the term, was coined way back in 1956 by John McCarthy, a Stanford professor. As an idea it had its share of disappointments, battled scepticism and was kept on the backburner for several decades. However, intelligent machines and for them to be considered at par with human intelligence, are but two different propositions. Sci-fi readers would recollect HAL 9000, Arthur C. Clarke's AI–based protagonist in the book 2001: A Space Odyssey, which turns to an antagonist as the plot unfolds, and eventually turns villainous. Will it remain fictional, or is there a strong possibility that we may actually experience this in our lifetime?