May 22, 2019
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Teaching AI how to feel FEAR could make autonomous cars better drivers
Artificial intelligence has become exceedingly advanced in recent years, so much so that the prospect of self-driving cars on city roads is no longer a far-off concept. But despite their current capabilities, there's one thing humans have on our side that AI inherently doesn't have – fear. Physiological responses driven by fear help humans make critical decisions and stay on our toes, especially when it comes to situations like driving.
In a new study, Microsoft researchers build on this idea to improve the decision-making skills of self-driving cars, in effort to develop 'visceral machines' that will learn faster and make fewer mistakes. The team detailed their findings in a paper presented at the 2019 International Conference on Learning Representations (ICLR).
To teach AI to 'feel' fear, the researchers used pulse sensors to track peoples' arousal while using a driving simulator. These signals were then fed to the algorithm to learn which situations caused a person's pulse to spike.
Authors Daniel McDuff and Ashish Kapoor explain in the paper's abstract: 'As people learn to navigate the world, autonomic nervous system (e.g., "fight or flight") responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.). Physiological changes are correlated with these biological preparations to protect one-self from danger.'
According to the researchers, teaching the algorithm when a person might feel more anxious in a given situation could serve as a guide to help machines avoid risks.
'Our hypothesis is that such reward functions can circumvent the challenges associated with sparse and skewed rewards in reinforcement learning settings and can help improve sample efficiency,' the team explains.
The researchers put the autonomous software through a simulated maze filled with walls and ramps to see how they performed with fear instilled in them. And, compared to an AI that was trained based only on wall proximity, the system that had learned fear was much less likely to crash.
The researchers wrote: 'A major advantage of training a reward on a signal correlated with the sympathetic nervous system responses is that the rewards are non-sparse - the negative reward starts to show up much before the car collides. This leads to efficiency in training and with proper design can lead to policies that are also aligned with the desired mission.'
But, there are caveats. The researchers note: 'While emotions are important for decision-making, they can also detrimentally effect decisions in certain contexts. Future work will consider how to balance intrinsic and extrinsic rewards and include extensions to representations that include multiple intrinsic drives (such as hunger, fear and pain).'
Extracted from: www.dailymail.co.uk
