After his baby son lost his eye to retinoblastoma (视网膜母细胞瘤), this father found a way to save others from the same fate.

Like almost every set of new parents. Bryan and Elizabeth Shaw started taking pictures of their son, Noah, practically from the moment he was born. When he was about three months old, Elizabeth noticed something odd when she took his picture. The flash on their digital camera created the typical red dot in the center of Noah’s left eye, but the right eye had a white spot at the center, almost as if the flash was being reflected back at the camera by something.

When Elizabeth mentioned the strange phenomenon to their doctor, she shined a light into Noah’s eye, and saw the same white reflection, a tell-tale sign of retinal (视网膜的) cancer. After that, Noah received months of chemotherapy and radiation, but doctors finally could not save his eye.

Noah’s tumour (肿瘤) is treatable if caught early. Bryan Shaw couldn’t help but wonder whether there were signs he’d missed. He went back over every baby picture of Noah he could find and discovered the first white spot in a photo taken when Noah was 12 days old. “By the time he was four months old, it was showing up in 25 per cent of the pictures taken of him per month,” Bryan told People.

It was too late for Noah’s eye, but Bryan was determined to put his hard-won insights to good use, so he created a database that charted the cancer’s appearance in every photo. He also collected photos and data from eight other children with retinoblastoma. Armed with that data, he began to work with colleagues to develop a smartphone app that can scan the photos in the user’s camera roll to search for white eye and can be used as a kind of ophthalmoscope (检眼镜). Called White Eye Detector, it is now available for free on Google Play and in Apple’s App Store.

Students taking Georgia Tech’s online Knowledge-Based Artificial Intelligence course received some surprising news. Jill Watson, one of the nine teaching assistants (TAs) that had helped them finish the challenging course for the past five months was not a “she” but an “it”—an intelligent robot!

Watson is the brainchild of Ashok Goel, who teaches the popular online course. The professor of Computer Science and Cognitive Science in the School of Interactive Computing came up with the idea as a way to deal with a number of questions posed by students in the online forums (论坛). According to Goel, every time the course is offered, the 300 or so students that enroll post over 10,000 questions which are often repetitive. This led Goel to wonder if a smart robot would handle the questions which require standard responses.

Having worked with IBMs Watson technology platform in the past, the professor knew it would be ideal for his artificial TA: Jill Watson. The artificial intelligence system that uses natural language processing and machine learning to analyze large amounts of data has even been cleverer than human competitors on the television show. It would therefore easily be able to handle routine questions that require little “thinking”.

The professor and his team of graduate students began by populating (增添数据) Jill’s memory with 40,000 questions and answers from past terms. Then came the testing stage. At first, Jill was not very good and often gave strange answers. It often got stuck on certain keywords. By the end of the semester, Jill had attained enough knowledge and skills to participate in forums without any management from Goel, or the other assistants.

A United Nations group of experts predicted that global warming would reach extreme levels by the early 2030s. The likely result: ever-worsening weather disasters and related events—hunger, extinction of some plant and wildlife species and the spread of infectious diseases.

There is still time to avoid these disasters, but the window for action is closing quickly. Yet the reality is, few countries are on track to meet their existing goals to address climate change. Luckily, there is one bright note in this otherwise black picture: individual innovators are stepping up to try to find solutions to fight climate crisis using technology in new and creative ways.

When Beth began working in the fashion industry, she was disappointed to find that many makers tended to largely guess what people would actually buy. As a result, companies overproduced clothes, creating needless waste and environmental pollution.

To reduce unsold products and material waste in the fashion industry, in 2015, Beth began her own company, Unspun, which seeks solutions by making on-demand custom clothing tailored to a buyer’s body using a 3D-body scan. “We will never make clothes before they have a home.” said Beth. Its self-developed 3D-weaving(编织) machine enables customisation using a body scanner.

Unspun launched its first 3D woven items using the innovative tech last year. Since then, the company has produced thousands of pairs of pants, hats, bags in a test run for a large European retailer(零售商).

Unspun doesn’t intend to keep its novel production technique to itself. The company is in the process of building microsites around the world to localize 3D weaving production. For example, the company has set up its office in Hong Kong with a focus on business development, marketing and customer services. “Our goal is to reduce the global human carbon footprint by 1 percent, and the only way to realistically get there is to become a platform, working with forward-thinking brands (品牌) to collectively cleanup the industry,” says Beth.