Decoder, developed in collaboration with a games developer, gets users to assume the role of an intelligence officer tasked with breaking up global criminal gangs (users are able to select a character and their backstory).
To meet the objective, users have to identify different combinations of number strings in missions littered with distraction.
Winning each mission means users unlock letters of the next criminal location (the higher the score, the more letters revealed).
A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMindâs AlphaZero conquer chess and Go could tackle an even more complex taskâtraining a robotic knee to help amputees walk smoothly.
Computer algorithms help prosthetics wearers walk within minutes rather than requiring hours of training.
Law firms are under tremendous pressure to innovate to provide better value to their clients, who demand more value for their legal dollars. Providing higher-value services in turn boosts firmsâ competitiveness.
However, much of the day-to-day work of any legal office â whether itâs in-house counsel, a boutique firm or one of the largest legal power houses â is the tedious, repetitive work of reading and preparing answers to complaints. Larger firms may have armies of junior associates do much of this necessary but mundane case-preparation work. At smaller firms, partners and senior associates are often involved in all stages of litigation. Preparing responses is time-consuming. It can take several hours to a full day to complete. Those are hours that both attorneys and firms would prefer to use tackling more strategic legal work.
We asked ourselves, what if, instead of taking hours, those high-volume, repetitive tasks could take a couple of minutes?
A collaboration of researchers from MIT and Microsoft have developed a system that helps identify lapses in artificial intelligence knowledge in autonomous cars and robots. These lapses, referred to as âblind spots,â occur when there are significant differences between training examples and what a human would do in a certain situation â such as a driverless car not detecting the difference between a large white car and an ambulance with its sirens on, and thus not behaving appropriately.
