We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.

Google is now offering an Introductory course on Artificial Intelligence(AI) and Machine Learning(ML) for free on its new Learn With AI site. Google hopes the site will be a hub of information for AI and ML. The site is intended to be a resource for everyone from beginners to advanced researchers. Google claims the site "will be a place where one can learn about core ML concepts, develop and hone your Machine learning skills, and apply ML to real-world problems" Google understands people are not familiar with these concepts, including those in fields like app development where AI and ML actually matter. The site contains a free crash course that was initially designed for Google employees.

It's 9 am on a February morning and the mercury is just inching past 20 degrees Celsius in Bengaluru. The workday is already two hours old in the metropolis's densely laid-out eastern suburb of Marathahalli. A student batch of both unemployed and working software professionals at Robotek Minds, a tech training institute, has just finished its data science class. Data science is the new buzzword in the tech industry and the code jocks in the Marathahalli class have a singular focus: a job or a leg-up at one of the shiny information technology campuses dotting the city and housing the world's leading tech corporations. Which, they hope, will be a passport to a comfortable salary that will grow in long strides in the years ahead as the use of data in the world economy explodes.

Does Statistics and ML walk together like partners? Is Machine learning just a polished and shined version of statistics? There are many question such like this. At-least in my mind even today I get these questions why I struggle with these two in different areas of my mind. Is Machine learning a computerised or glorified version of statistics as a matter of fact "NO" (In my personal opinion). To my understanding they both complement each other and work like partners.

In a paper published recently in the Proceedings of the National Academy of Sciences (PNAS), the researchers describe a technique called "iterative Random Forests," which they say could have a transformative effect on any area of science or engineering with complex systems, including biology, precision medicine, materials science, environmental science, and manufacturing, to name a few. "Take a human cell, for example. There are 10170 possible molecular interactions in a single cell. That creates considerable computing challenges in searching for relationships," said Ben Brown, head of Berkeley Lab's Molecular Ecosystems Biology Department. "Our method enables the identification of interactions of high order at the same computational cost as main effects -- even when those interactions are local with weak marginal effects."

I'm Max, and I work on applied AI here at SC5. As a consultancy, SC5 is expected to provide our clients with services that are not only well-designed and functional, but also capable of scaling and withstanding production load. An application isn't much good unless it works in the real world. Machine learning is, in many ways, a completely different beast than "traditional" software engineering. Machine learning solutions also need to be deployed to production to be of any use, and with that comes a special set of considerations.

Although the term "Chatbot" is still a buzz word to many people, this revolutionary technology has already been considered "the next big thing" due to massive benefits it can bring to companies across various industries. In this article, we will discuss several chatbot use cases to solidify our claim that businesses should embrace chatbots. According to a research conducted by Chatbots Journal, a leading chatbot community, E-Commerce will benefit the most from chatbots compared to other industries. Successful commerce relies much on B2C (Business-to-Consumer) interaction. Chatbots enrich the relationship between online shops and customers with "conversational commerce", making B2C interaction quicker and friendlier than just a mere business transaction.

On 1st March Google introduced Learn with Google AI Website – a set of educational resources that aims at bringing machine learning skills and makes artificial intelligence accessible to all. Google says the new course has been developed by Machine Learning (ML) experts at the company and is free and available to all users. These educational resources will encourage everyone from beginners to researchers to learn about machine learning concepts by providing a huge range of free, in-depth content, develop skills in the area, and apply AI concept to the real-world problems. Previously, the Machine Learning Crash Course(MLCC) was an internal course at Google, designed to train the Google's employee with a practical introduction to Machine Learning concepts and Artificial Intelligence(AI), as Google continues its moves to be an "AI First" company. Around 18,000 Google employees have already signed up for the course.

Google on Thursday introduced "Learn with Google AI" -- a set of educational resources developed by Machine Learning (ML) experts at the company, for people to learn about concepts, develop skills and apply Artificial Intelligence (AI) to real-world problems. "Learn with Google AI" comes with existing content as well as the new Machine Learning Crash Course (MLCC). "We believe it's important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement. "This is for everyone -- from deep ML experts looking for advanced developer tutorials and materials, to curious people who are ready to try to learn what ML is in the first place," Kemp added.

I remember the first time I was introduced to the concept of synesthesia. I was in seventh grade, sitting in the dark, watching an educational video about the neuroscience of the phenomenon in lieu of our typical life science coursework. A British woman with lexical-gustatory synesthesia appeared on screen to describe the way every name she'd ever spoken had a different taste. Many of the particulars of the documentary have faded in the decade since I last saw it, but I still recall the woman saying "the name Catherine tastes like chocolate cake." For years, I have wished (perhaps unfairly) that I had synesthesia, a rare neurological condition where senses enterwine.