How do you get high school students to enjoy studying science? How about by giving them the parts to build a robot and then letting them compete against each other to see who builds the best "bot?"

What college student, past or present, doesn't have an opinion as to which they prefer? The real question, though, was always which allowed the professor to better assess their students' performance. Open-ended surveys (the post-collegiate equivalent of essay exams) can provide detailed information and feedback about a company's performance, products and/or services. The drawback to these types of questions (as opposed to multiple choice) is that someone must take extra time to sort, organize and interpret the data, which can be very time consuming and costly. But now Datanautix – a client of the UCF Business Incubator in Winter Springs – has developed an affordable and reliable means of mining open-ended content for revelations that can quickly transform business decisions.

Research at Purdue University funded through a National Science Foundation grant has led to development of a new kind of toy-building kit that allows children to create robots and control them remotely like a puppeteer. The new kit, called Ziro, was developed in research led by Karthik Ramani, the Donald W. Feddersen Professor of Mechanical Engineering and co-founder and chief scientist of the company ZeroUI, with locations at the Purdue Research Park and in San Jose, California. Ziro is the first commercial application of ZeroUI's gesture-based Natural User Interface technology platform. Sensors in a "smart glove" communicate with wireless motorized modules, enabling users to direct the robotic creations with the lift of a finger or flick of a wrist in real-time. Research funding was provided as part of the NSF grant to both the university as well as through the Small Business Innovation Research program, designed to move innovations from discovery to commercialization. The NSF is nurturing a national innovation ecosystem through development of technologies, products and processes that benefit society.

The third annual RE•WORK Future of Education workshop will take place in London on 20 June as part of London Technology Week, bringing together education practitioners, technologists, edtech startups, investors and policy leaders to discuss, explore and collaborate to discover how rapidly advancing technology will impact education. Topics explored will include: Wearable Technology, Augmented Reality, Artificial Intelligence, Gamification, Internet of Things, Robotics, Human-Computer Interaction and Facial Recognition. Over the past two years 200 attendees have come together to share their insights into technological advancements, as well as discuss key areas such as: What experience do we want students and teachers to have? How can we make these technologies purposeful? What problem are we trying to solve?

PROVO, Utah – Madeline MacDonald says she was an 18-year-old freshman at Brigham Young University when she was sexually assaulted by a man she met on an online dating site. She reported the crime to the school's Title IX office. That same day, she says, BYU's honor code office received a copy of the report, triggering an investigation into whether MacDonald had violated the Mormon school's strict code of behavior, which bans premarital sex and drinking, among other things. Now MacDonald is among many students and others, including a Utah prosecutor, who are questioning BYU's practice of investigating accusers, saying it could discourage women from reporting sexual violence and hinder criminal cases. Some have started an online petition drive calling on the university to give victims immunity from honor code violations committed in the lead-up to a sexual assault.

Many people have gotten jobs in machine learning just by completing that MOOC. There're other similar online courses that help; for example the John Hopkins Data Science specialization. Participating in Kaggle or other online machine learning competitions has also helped people gain experience. Kaggle has a community with online discussions from which you can learn practical skills. Attending local meetups or academic conferences (if you can afford it) and talking to more experienced people will also help.

In this post, I discuss a possible new approach to teaching Programming for Data Science. Programming for Data Science is focussed on the R vs. Python question. Everyone seems to have a view including the venerable Nature journal (Programming – Pick up Python). Here, I argue that we look beyond Python vs. R debate and look to teach R, Python and SQL together. To do this, we need to look at the big picture first (the problem we are solving in Data science) and then see how that problem is broken down and solved by different approaches.

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?

Cluster assessment is a staple of unsupervised machine learning and knowledge science. It is incredibly useful for knowledge mining and significant knowledge because it routinely finds patterns in the knowledge, without the will need for labels, contrary to supervised machine learning. In a true-world setting, you can think about that a robotic or an synthetic intelligence will not normally have access to the exceptional response, or it's possible there isn't an exceptional right response. You'd want that robotic to be able to investigate the world on its own, and study factors just by hunting for patterns. Do you ever ponder how we get the knowledge that we use in our supervised machine learning algorithms?

Fredkin University Professor of Artificial Intelligence and Machine Learning Tom Mitchell has been elected to the American Academy of Arts and Sciences. Tom Mitchell, the Fredkin University Professor of Artificial Intelligence and Machine Learning at Carnegie Mellon University, has been elected to the American Academy of Arts and Sciences (AAAS), joining the world's most accomplished scholars, scientists, writers, artists and civic leaders. Mitchell founded the world's first Machine Learning Department at CMU's School of Computer Science in 2006 and led the department until earlier this year. His research focuses on statistical learning algorithms for understanding natural language text and on understanding how the human brain represents information. His work has been featured in The New York Times and on CBS's "60 Minutes."