Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Nick is a former robotics postdoc at Stanford and received his PhD in Control Systems from UC Berkeley. At Dash Robotics, Nick handles team-building and project management. You can find all the interviews here. We'll be posting one per week on Robohub.

Crying the alarm for the death of the sales professional at the hands of machine learning and artificial intelligence (AI) is a bit too dramatic. But, it's safe to say that organizations that transform their sales functions due to machine learning and AI will find themselves rising above the competition. Now that we generate and collect vast amounts of data, we are able to give machines the amount of data that is required for them to learn by using algorithms to interpret the data and predict outcomes. When machines get feedback about whether those predictions were right or wrong, they use that feedback as learning experiences to make their predictive capabilities better in the future. Machine learning is one aspect of artificial intelligence and it is disrupting roles and responsibilities for professionals in every industry. There's a personal side to selling that machines will not (at least for a long time) be able to replace.

I am completely unqualified in matters of raising children, yet this topic has provoked me to post my opinion on the problem. I came across a Guardian article by George Monbiot about raising children in a new academic environment of robots. The article emphasized the need for not making children robot-like, but teaching them to collaborate and use their creativity and curiosity. The post gave me a pause: on the surface, it criticized current state of education, but for me it presented a controversy between technologies and education. I dug a little deeper and saw that technologies, robots and AI are not welcomed in education like I thought.

Earlier this year, the UK parliament rushed through a bill that could see an increase of university student tuition fees to £9250. Controversially, the new legislation also allows universities to increase these fees year on year in line with inflation, if they choose to. At the same time, the 2016-17 academic year saw the number of students dropping out of their degrees increase for the second year running, now up to 6.2% from 6% last year. For any business, the prospect of retaining 6% of annual revenue from churned customers would be a no-brainer. When that 6% could translate into £6 million in tuition fees for places that can't be resold, the stakes are high.

This article was posted by Bob E. Hayes on Customer think. Bob, PhD is Chief Research Officer at Appuri. Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight.

Someday soon, you might be managing, working with or even working for a robot. A core MBA class at Missouri University of Science and Technology prepares students for this distinct possibility, and teaches them how to coexist with their future artificial intelligence colleagues. Dr. Keng Siau introduced artificial intelligence and machine learning into his business curriculum during the spring 2017 semester. The Artificial Intelligence, Robotics, and Information Systems Management course looks at the latest developments in artificial intelligence, machine learning, robotics, automation and advanced information technology, and "their effect on our current ways of life and work as well as on economic/business models," says Siau, professor and chair of the business and information technology department. The course will be offered again in spring 2018.

Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).

Denise Garcia knows that her students sometimes cheat, but the situation she unearthed in February seemed different. A math teacher in West Hartford, Connecticut, Garcia had accidentally included an advanced equation in a problem set for her AP Calculus class. Yet somehow a handful of students in the 15-person class solved it correctly. Those students had also shown their work, defeating the traditional litmus test for sussing out cheating in STEM classrooms. Garcia was perplexed, until she remembered a conversation from a few years earlier.

It was because of the letter K that I found my youn ger sister, but for 14 years, it was also the letter K that kept us apart. I'd been searching for her online under variations of the name Maria Christina Sugatan since we lost touch in 1997, after our mom refused to let me speak to her. She was Maria at school but Chris at home and, later, Chrissy. It became my ritual to search for variations of her name online. Meredith Talusan is a freelance writer focusing on minority issues.

Machine learning has come a long way in the past decade. With many interesting developments, machines are now capable of doing intelligent work without being explicitly programmed. Machine learning is the ability of a machine(computer) to learn without being explicitly programmed. But it has been made possible by artificial intelligence. There has been huge progress in cognitive science and neural networking.