Science and computer programming have been part of Nell Watson’s world since her formative years, influenced by her father who was a rocket science engineer. The co-founder of two companies Quantacorp and EthicsNet, Nell has been deeply involved in the realm of Artificial Intelligence for nearly a decade.
“Robotics can be defined as an embodied form of cognition which can interact within a 3D environment. These robots can come in many different forms and one of these is autonomous vehicles, which are like a robot that you can climb inside.”
“There are multiple potential levels in autonomous driving, ranging from cruise control, to full autonomy. We are already at a point where vehicles can navigate urban environments in most situations, but rural situations are more difficult, as they have less road markings. Fine-tuning these types of nuances will be difficult, as will be socialising the behaviour of autonomous vehicles.”
“Quantacorp, the machine vision company which I co-founded in 2011, is really involved in the visual recognition side of AI” explains Nell. “It can turn two photos taken by smartphone into a 3 D model, principally used for body measurements for clothing. This is really providing a solution that enables mass customisation of products. This system was developed over a long period. It involved a lot of hand coding at the beginning and then, as technologies developed, we started to use far more sophisticated mechanisms for coding.” “I founded this venture as I had anticipated that smartphones would soon be able to deploy artificial intelligence. The company is doing very well now and I have just taken on a great new CEO to run the company. This means that I am able to devote more of my time to EthnicsNet, which is a non-profit company which focuses on teaching machines pro-social behaviour.”
DEFINING AI AND ROBOTICS “In very broad terms, Artificial Intelligence can be described as any system that mimics how a biological organism might function. Traditionally, in the past, it was hand-coded, like a vast decision tree of ‘yes’ or ‘no’ decisions. Technologies have now advanced to the point where we have developed ways of teaching machines about the world, by introducing them to sets of experiences. We now see deep learning which involves sophisticated algorithms, with multiple layers of understanding. This deep learning enables machines to create more nuanced decisions. It could be compared, in a way, to how a chef creates recipes – by varying ingredients, proportions, cooking times, temperatures and other parameters.”
“More than just rules of the road to be obeyed, there are ways of behaving that may or may not be perceived as acceptable social behaviour – so it’s important for machines to know how to be grateful and friendly on the roads too. When you think about it, an autonomous vehicle doesn’t just have its own life experiences, it has the experiences of other autonomous vehicles being pooled together. These shared experiences are what are so important and valuable.”
TEACHING ETHICS TO MACHINES “Over the last five years there has been much more interest in machine ethics. This is a domain of expertise that is emerging around the concept of ‘socialising’ machines and teaching them to understand human values. That is why I co-founded EthicsNet. It’s all about safeguarding a dataset to teach machines to act in a way that is kind and friendly. Even very intelligent brains need sets of virtual experiences to better understand the world and the things that human beings are likely to find acceptable. With this, we are heading towards a more intuitive form of artificial intelligence.” “If machines are to increasingly act as ambassadors in the world, it’s important that they understand the things that people value. At the moment we are seeing machine intelligent systems which are
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