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"The groundwork for machine learning was laid down in the middle of last century. But increasingly powerful computers - harnessed to algorithms refined over the past decade - are driving an explosion of applications in everything from medical physics to materials, as Marric Stephens discovers When your bank calls to ask about a suspiciously large purchase made on your credit card at a strange time, it's unlikely that a kindly member of staff has personally been combing through your account. Instead, it's more likely that a machine has learned what sort of behaviours to associate with criminal activity - and that it's spotted something unexpected on your statement. Silently and efficiently, the bank's computer has been using algorithms to watch over your account for signs of theft. Monitoring credit cards in this way is an example of "machine learning" - the process by which a computer system, trained on a given set of examples, develops the ability to perform a task flexibly and autonomously. As a subset of the more general field of artificial intelligence (AI), machine-learning techniques can be applied wherever there are large and complex data sets that can be mined for associations between inputs and outputs. In the case of your bank, the algorithm will have analysed a vast pool of both legitimate and illegitimate transactions to produce an output ("suspected fraud") from a given input ("high-value order placed at 3 a.m."). But machine learning isn't just used in finance. It's being applied in many other fields too, from healthcare and transport to the criminal-justice system. Indeed, Ge Wang - a biomedical engineer from the Rensselaer Polytechnic Institute in the US who is one of those pioneering its use in medical imaging - believes that when it comes to machine learning, we're on the cusp of a revolution."

"After combing through four years of images from our popular What's Going On in This Picture? feature, we selected 40 photographs to highlight in this slide show. Many of these are our most commented-on images - some attracting nearly a thousand student comments. Others are simply our favorites. We invite teachers and students to use this bank of 40 intriguing images, all stripped of their captions or context, to practice visual thinking and close reading skills by holding a "What's Going On in This Picture?" discussion or writing activity."

"Remember Lieutenant Dan (Gary Sinise) in the movie Forrest Gump? How did Hollywood make his legs disappear? Click here for a short video clip explaining the magic. How can you make your lessons more meaningful? How can your students use Green Screen to demonstrate their learning? Using Green Screen in the classroom is easy, economical and appropriate for all grade levels and subject areas! Green Screen allows you to create a video using an image or video for your background. If your students are studying about the Pyramids of Egypt, they can create a video report with the Pyramids in the background. What about a weather report from the banks of the Nile River or on top of Mt. Everest? The possibilities are endless, so let's get started!"

ARKive Education is a free-to-use, multi-media resource bank for teachers and other educators. Making use of the stunning imagery available at the award-winning ARKive website www.arkive.org, ARKive Education provides downloadable, ready to use modules on a wide range of curriculum topics, suitable for geography, biology, environmental education and citizenship lessons.

To get level 3 on Coinbase, you need to verify your phone number and email address. Then you'll have to link a bank account and verify your identity with a government-issued ID. Next up is residency verification, which can be done by uploading an image of the front and back of your current driver's license or passport (along with other documents related to your residency). Finally, payment information must be verified before reaching level 4 on Coinbase.