MINDLER was founded in July, 2015 by Prateek Bhargava along with his mentor and career coach, Prikshit Dhanda. The organisation is based in Punjabi Bagh in New Delhi. MINDLER is a technology-enabled eco-system for career planning, development and mentoring for school students (class VIII-XII). The startup blends artificial intelligence and machine learning with strategic human interventions to help students and parents choose the best-suited career path. MINDLER's distinctive feature comes in the form of a 5 step assessment process - world's most advanced multi dimensional career assessment battery, algorithm driven semi-automated career planner & tracker and course correction mechanism.

Plus, how one journalist is handling the challenges of reporting on the drone war in northwest Pakistan. The world's largest social media network is also one of the biggest news platforms - so allegations of a bias towards liberal news issues has triggered a lot of scrutiny, both from outside and from within. This week we unpick how Facebook delivers the news to you and why it matters. Many journalists and writers have been tracking the Facebook story and its implications. For this report, we have spoken to: Zeynep Tufekci, assistant professor at the School of Information and Library Science, University of North Carolina; Callum Borchers, media and politics reporter at The Washington Post; Will Oremus, technology reporter at Slate.com; and Kelly McBridge, media ethicist, The Poynter Institute.

This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

An historic event unfolded in March 2016. The victory of the program AlphaGo over professional gamer Lee Sedol in the Google DeepMind Challenge demonstrated how far artificial intelligence (AI) has come: "Go's simple rules and elaborate possibilities have made it one of the most sought-after milestones in the field of AI research," writes Sam Byford of The Verge. The idea of computers learning autonomously has been around for decades. Why has machine learning gained so much ground in recent years? Increased computing power has made machine learning possible, at last.

Andrew Ng is chief scientist of Baidu, chairman and cofounder of Coursera, and a computer science faculty member at Stanford. His AI work focuses on deep learning, which develops learning algorithms by building large-scale simulations of the brain. In 2011, he founded and led the Google Brain project, which built the largest deep-learning (neural network) systems at the time, leading to the celebrated "Google cat" result. His team's technology has also had a huge impact across numerous Google applications, including speech recognition, maps, and more. Ng currently leads Baidu Research in developing the next generation of deep-learning algorithms.

Anthony Goldbloom is cofounder and CEO of Kaggle, a platform for machine-learning competitions. Almost 500,000 of the world's top data scientists compete on Kaggle to solve important problems for industry, government, and academia. Kaggle has catalyzed breakthroughs in areas ranging from automated essay grading to automated disease diagnosis from medical images. Before cofounding Kaggle in 2010, Anthony was an econometrician at the Australian treasury. In 2013 MIT Technology Review named him one of 35 top innovators under the age of 35.

Ten years ago, Justin Kan and Emmett Shear had just sold their app company, Kiko, and were itching for another venture. They had a concept -- livestream video -- but no idea how to build it. So they sent an email to the MIT engineering listserv, requesting a "hardware hacker" for an unspecified project. Kyle Vogt, a young student fascinated with robotics, replied. They met over coffee where Kan and Shear pitched their idea before flying out to San Francisco.

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.