How would you feel about getting therapy from a robot? Emotionally intelligent machines may not be as far away as it seems. Over the last few decades, artificial intelligence (AI) have got increasingly good at reading emotional reactions in humans.
But reading is not the same as understanding. If AI cannot experience emotions themselves, can they ever truly understand us? And, if not, is there a risk that we ascribe robots properties they don’t have?
The latest generation of AIs have come about thanks to an increase in data available for computers to learn from, as well as their improved processing power. These machines are increasingly competitive in tasks that have always been perceived as human.
AI can now, among other things, recognise faces, turn face sketches into photos, recognise speech and play Go.
Recently, researchers have developed an AI that is able to tell whether a person is a criminal just by looking at their facial features. The system was evaluated using a database of Chinese ID photos and the results are jaw dropping. The AI mistakenly categorised innocents as criminals in only around 6% of the cases, while it was able to successfully identify around 83% of the criminals. This leads to a staggering overall accuracy of almost 90%.
The system is based on an approach called “deep learning”, which has been successful in perceptive tasks such as face recognition. Here, deep learning combined with a “face rotation model” allows the AI to verify whether two facial photos represent the same individual even if the lighting or angle changes between the photos.
Deep learning builds a “neural network”, loosely modelled on the human brain. This is composed of hundreds of thousands of neurons organised in different layers. Each layer transforms the input, for example a facial image, into a higher level of abstraction, such as a set of edges at certain orientations and locations. This automatically emphasises the features that are most relevant to performing a given task.
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Given the success of deep learning, it is not surprising that artificial neural networks can distinguish criminals from non-criminals – if there really are facial features that can discriminate between them. The research suggests there are three. One is the angle between the tip of the nose and the corners of the mouth, which was on average 19.6% smaller for criminals. The upper lip curvature was also on average 23.4% larger for criminals while the distance between the inner corners of the eyes was on average 5.6% narrower.