Expecting The Unexpected From AI - Liwaiwai

When we think of artificial intelligence (AI) going rogue, prime examples from the movies include HAL 9000 from 2001: Space Odyssey and Skynet from The Terminator, which were mainframe computers that reacted to the real-world problems in unexpected ways. From industrial manufacturing to autonomous vehicles, machine learning models are becoming increasingly embedded in our lives. Researchers are thus exploring pre-emptive ways to avoid harm from unexpected AI decisions when machine learning models are deployed to act in the real-world--an area of machine learning known as reinforcement learning (RL). "While deep RL has indeed been very successful in achieving state-of-the-art performance in curated academic environments, it has yet to be thoroughly tested in the presence of real-world complexities," said Abhishek Gupta, a Scientist at the Singapore Institute of Manufacturing Technology (SIMTech) and one of the study's senior authors. The work, which was principally conducted by Nanyang Technological University (NTU) graduate student Xinghua Qu and jointly overseen by Gupta and NTU professor Yew-Soon Ong, focused on the performance of vision-based AI, which is likely to be critical for the safe use of AI in applications such as autonomous vehicles.