Wouldn't it be neat to have a brain that could read and understand all that information? There's this startup, Iris AI, that works to fix this problem by building an artificial one. This Singularity University backed team that wants to build an AI capable of highlighting new trends and interconnections of discoveries. This could help researchers in startups, corporations and research institutes to implement what is already out there. I first came across them via TED where they had processed my 2011 (gosh a lot has happened since) TED talk into their awesome system.

Less than 100 years from now, robots will be friendly, useful participants in our homes and workplaces, predicts UBC mechanical engineering professor and robotics expert Elizabeth Croft. We will be living in a world of Wall-Es and Rosies, walking-and-talking avatars, smart driverless cars and automated medical assistants. But much work remains before robots will truly be integrated into our daily lives. In this short Q&A, Croft lays out the rules for engagement between humans and robots and explains why it's crucial to get this aspect right. What role will robots play in our lives in the future?

Language is a system or method of communication as defined by the Oxford Dictionary. Humans acquire skills based on cognition, patterns and connections, while machines learn through programming and algorithms. After years of technological advancement and progress in Artificial Intelligence (AI), language acquisition and understanding is still the number one thing that sets humans apart from machines. An insight into human language acquisition versus machine AI will provide a clear picture of language learning for humans and machines. Advancement of AI is based on understanding different attributes of humans and replicating it on machines.

Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning, it's more true to say their role is to prepare students for life-long careers. But while that was a relatively simple task in the past, it's now much more difficult.

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. "But one reason is that these MOOCs do not provide any teaching assistants. So you can sign up for a course, say in mathematics, or computer science, or web design, or whatever. But you cannot ask anyone a question like'So how do I download this material?'

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.