In today's factories and warehouses, it's not uncommon to see robots whizzing about, shuttling items or tools from one station to another. For the most part, robots navigate pretty easily across open layouts. But they have a much harder time winding through narrow spaces to carry out tasks such as reaching for a product at the back of a cluttered shelf, or snaking around a car's engine parts to unscrew an oil cap. Now MIT engineers have developed a robot designed to extend a chain-like appendage flexible enough to twist and turn in any necessary configuration, yet rigid enough to support heavy loads or apply torque to assemble parts in tight spaces. When the task is complete, the robot can retract the appendage and extend it again, at a different length and shape, to suit the next task.

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery. University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical'map' to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications. A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks.

In a recent pilot study, researchers from the National University of Singapore (NUS) have shown that a powerful artificial intelligence (AI) platform known as CURATE.AI could potentially be used to customise training regimens for individuals to personalise learning and improve cognitive performance. Using performance data from a given person, CURATE.AI creates an individualised profile that enables cognitive training to be tailored to the individual's learning habits and competencies so as to enhance training effectiveness. Such dynamic AI-guided personalisation overcomes the current limited improvement produced by using traditional training methods which often involve repetitive behavioural exercises. The results of the study provide evidence that the CURATE.AI platform has the potential to enhance learning, and paves the way for promising applications for personalised digital therapy, including the prevention of cognitive decline. The research, led by Professor Dean Ho and Assistant Professor Christopher L. Asplund from the N.1 Institute for Health (N.1) of NUS, which was formerly the Singapore Institute for Neurotechnology (SINAPSE), was published in the journal Advanced Therapeutics on 22 May 2019.