Timandra Harkness is a regular on BBC Radio 4, writing and presenting BBC Radio 4's FutureProofing series and documentaries such as Data, Data Everywhere, and Personality Politics. Her book Big Data: does size matter? A regular public speaker and chair on scientific and technological topics, she works with the Cheltenham Science Festival, the British Council, the Institute of Ideas, the Wellcome Collection and a Robotics conference in Moscow, among many others. She is a member of the Royal Statistical Society and has 86% of a Mathematics and Statistics degree with the Open University. She hopes to reach 100% in 2017.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

The corporate learning environment has been experiencing a great deal of development over recent years. It shows no signs of stopping as learners become more involved in their own education. Gone are the days when the organization dictated what should be learned and how. Learners are now demanding that they are educated in a way that works for them. The teams usually responsible for corporate learning within companies, human resources, are also undergoing a period of change as they identify areas where they need to come up to speed to deliver the most tangible results for their companies.

Artificial intelligence (AI) has gradually become an integral part of modern life, from Siri and Spotify's personalized features on our phones to automatic fraud alerts from our banks whenever a transaction appears suspicious. Defined simply, a computer with AI is able to respond to its environment by learning on its own--without humans providing specific instructions. A new report from Stanford University in Palo Alto, California, outlines how AI could become more integrated into people's lives by 2030, and recommends how best to regulate it and make sure its benefits are shared equally. Here are five examples--some from this report--of AI technology that could become a part of our lives by 2030. Smart traffic lights using artificial intelligence technology to learn and adapt to traffic patterns in real time could make intersections safer and more efficient.

If you've been to college or university, you'll know the feeling: when your professor drones on for hours on end, but you're hesitant to bring it up out of politeness (or fear of said professor's wrath). You won't have to be quite so shy in Wei Xiaoyong's science classes, though. The Sichuan University educator is using a custom-built facial recognition system to scan students' faces and determine whether or not they're bored. The approach gauges the emotion in your face over time, helping Wei refine his lectures so that he doesn't lose your interest. It's not guaranteed to be completely effective, of course (what if you're particularly stoic?), and it's easy to see students being nervous about the privacy ramifications of scanning faces.

With students from a range of science, technology, engineering, and math (STEM) competitions from across the country looking on, U.S. President Barack Obama delivers remarks after viewing science projects at the White House Science Fair, at the White House, March 23, 2015 in Washington, DC. (Photo: Drew Angerer/Getty Images) It is already a given fact that gender inequality still continue to persist in the field of education. Despite the government's efforts to ensure that all students should have access to high quality education, gender gap remain notable, particularly in STEM (Science, Technology, Engineering And Mathematics)-related and CTE (Career and Technical Education) curricula. Fortunately, artificial intelligence (AI) has been considered as a powerful tool in bridging the gender gap in STEM education. That's why, Stanford has launched a tuition-free AI camp called SAILORS to encourage young girls, as well as "underrepresented minorities" to explore STEM-related fields. Initially launched on the summer of 2015, Stanford Artificial Intelligence Outreach Summer aka SAILORS was created by computer science professor Fei-Fei Li and Postdoc (postdoctoral scholar) Olga Russakovsky.

I recently participated in a weekend-long data science hackathon, titled'The Smart Recruits'. Organized by the amazing folks at Analytics Vidhya, it saw some serious competition. Although my performance can be classified as decent at best (47 out of 379 participants), it was among the more satisfying ones I have participated in on both AV (profile) and Kaggle (profile) over the last few months. Thus, I decided it might be worthwhile to try and share some insights as a data science autodidact. The competition required us to use historical data to create a model to help an organization pick out better recruits. The evaluation metric to be used for judging the predictions was AUC (area under the ROC curve).

Ozobot's Bit impressed us a few years ago with its simply take on programming education: kids just need to draw lines on a piece of paper or mobile device to program the tiny robot. As they get more comfortable, they can start to program on mobile devices and computers. Now Ozobot is taking a major step forward with the 100 Evo, a new robot that has sensors to interact with its environment, lights, a speaker and social capabilities. While Ozobot's previous devices were aimed directly at kids, it's hoping that Evo can break through to high schoolers and even college students, according to founder and CEO Nader Hamda. The new bot has a shot of appealing to older students simply because it can do a lot more than before.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

We can see a few interesting things in this table. In a similar talk by Owen at ODSC Boston 2015 titled "Open Source Tools and Data Science Competitions", he again summarized common parameters he uses: We can see some minor differences that may be relevant. Finally, Abhishek Thakur, in his post titled "Approaching (Almost) Any Machine Learning Problem" provided a similar table listing out key XGBoost parameters and suggestions for tuning. The spreads do cover the general defaults suggested above and more. It is interesting to note that Abhishek does provides some suggestions for tuning the alpha and beta model penalization terms as well as row sampling. You can develop and evaluate XGBoost models in just a few lines of Python code.