From depression chatbots to diagnosing eye diseases, developers are coming up with incredible ways of teaching robots to transform the African industries they work in. So says smart technology entrepreneur Jacques Ludik, founder of AI company Cortex Group and of the Machine Intelligence Institute of Africa. According to Ludik, artificial intelligence could be the powerful tool Africa needs to tackle its challenges, suck as shortcomings in health, education or employment. While many people see robots as a potential threat to jobs, Ludik believes AI holds the key to uplifting communities, through education and economic development. "Education is key to bridging the increasing gap of our experiences of the world, and improve and increase access to jobs (and other services) that aren't bound by geography. AI, the very technology that is moving the rest of the world forward so rapidly, has the power to do that."

It's a device that scans a patient's retina to diagnose potential issues in real time. How it works: After the retina is scanned, the images are then analyzed by DeepMind's algorithms, which return a detailed diagnosis and an "urgency score." It all takes roughly 30 seconds. The prototype system can detect a range of diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration. Most notably, it can do this as accurately as top eye specialists, DeepMind claims.

Three institutions working together have applied DeepMind's neural network learning system to the task of discovering and diagnosing eye diseases. Moorfields Eye Hospital has been working with Google's DeepMind Health subsidiary and University College London in the effort, and have documented their progress in a paper published in Nature Medicine. As the researchers note, eye doctors currently use a machine that carries out optical coherence tomography (OCT) on patients to find out if they have an eye disease. While the technique is quite useful and accurate, it requires highly trained doctors to spend time looking at results. The researchers suggest this creates a backlog that sometimes prevents patients from getting the care they need in time to save their vision.

Demis Hassabis, CEO of Google's DeepMind, poses with late physicist Stephen Hawking. Hassabis, Hawking and Tesla CEO Elon Musk all endorsed a set of guidelines last year for ethical AI development that will benefit humanity. AI has been a buzzword in Korean society ever since. As AlphaGo retired from competitive gaming in March 2017, the company has instead been concentrating on tackling "a very wide range of problems" that humans find difficult to resolve. "The majority of the AlphaGo team now devotes their time to new projects with the intention of using these general-purpose algorithms to help solve some of the world's most complex challenges in science and medicine," said Demis Hassabis, co-founder of DeepMind, in an email interview with the JoongAng Ilbo to mark the two-year anniversary of the high-profile match.

In the latest issue of the biology journal Cell, they describe a way of using artificial intelligence and machine learning to recognise macular degeneration and diabetic macular oedema, the two most common causes of irreversible blindness. They say this new technology could be used anywhere in the world and could prove especially important in rural areas, and places like China, India, and Africa. Traditional computer programs work by defining actions to take given certain circumstances - such as typing in some numbers and doing calculations. Type in something it isn't programmed to react to - like a word instead of a number - and it won't know what to do. AI is a part of computer science where machines are designed to have some intelligent behaviour.

Using artificial intelligence and machine learning techniques, scientists have developed a computational tool that may speed up treatment of patients with retinal diseases and pneumonia. For the study, published in the journal Cell, researchers reviewed more than 200,000 eye scans conducted with optical coherence tomography, a non-invasive technology that bounces light off the retina to create two- and three-dimensional representations of tissue. The researchers then employed a technique called transfer learning in which knowledge gained in solving one problem is stored by a computer and applied to different but related problems. They next added occlusion testing in which the computer identifies the areas in each image that are of greatest interest and the basis for its conclusions. "Machine learning is often like a black box where we do not know exactly what is happening," Kang Zhang from the University of California, San Diego in the US.