iPads, MakerEd and More in Education

Engineers from the University of Cambridge have developed a vegetable-picking robot that can autonomously identify and harvest iceberg lettuce, one of the more manually demanding crops for human pickers. Vegebot was trained using a machine-learning algorithm that helped it distinguish healthy lettuces ready for harvest, in a variety of different weather conditions.

Machine learning is all around us. We all use machine learning systems every day - such as spam filters, recommendation engines, language translation services, chatbots and digital assistants, search engines, and fraud detection systems.

It will soon be normal for machine learning systems to drive our cars, and help doctors to diagnose and treat our illnesses.

It's important that kids are aware of how our world works. The best way to understand the capabilities and implications is to be able to build with this technology for themselves.

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.

Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.

Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.

Google really wants you to learn how to develop AI. It's offering the same course it gives to company engineers free to everyone now.

""Machine learning approaches are even better than we are at seeing patterns,' says wildfire expert."

"This morning I want to talk to you about machine learning. In particular, I want to talk with you about machine learning in the context of education and how it could be introduced in the classroom."

These projects are downloadable step-by-step guides, with explanations and colour screenshots for students to follow.

Each project is a stand-alone activity, written to last for a single lesson, and will guide children to create a game or interactive project that demonstrates a real-world use of artificial intelligence and machine learning.

"Update: This article is part of a series. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7 and Part 8!


Have you noticed that Facebook has developed an uncanny ability to recognize your friends in your photographs? In the old days, Facebook used to make you to tag your friends in photos by clicking on them and typing in their name. Now as soon as you upload a photo, Facebook tags everyone for you like magic:"