Affective computing allows companies to expand their usual view of their customers or users by determining their mood from their facial expression, their handwriting or their speech. But how exactly does this technology work?
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Good morning and welcome to Weekly Insights with John Plassard. Feeling happy? How about an advert for a beach holiday to keep your spirits up? Feeling depressed? How about an advert for a drink that could turn things around?
Emotion artificial intelligence, or affective computing, allows such a "feat".
Indeed, an algorithm could determine your mood from your appearance (by training a deep learning algorithm on facial data), your handwriting or your speech and then offer you a product or service accordingly.
This may sound futuristic, but it is an investment theme that is already proving increasingly impressive.
Affective computing is a branch of computer science that relies on bodily data (pulse, body temperature, etc.) and facial expressions to recognise and interpret a person's primary emotions.
Affective computing allows companies to broaden their usual view of their customers or users.
It also enables governments to detect malicious attack or monitor the spread of development of a disease.
The term was coined by Rosalind Picard in 1995 to refer to “"computing that relates to, arises from, or deliberately influences emotion”.
In her book, Picard discussed in detail how she envisaged advances in emotional computing, as well as areas such as possible applications and potential concerns.
She argued that there must be something like emotional reasoning for there to be any form of true artificial intelligence.
Her main idea is that it should be possible to create machines that relate to, arise from or deliberately influence emotions and other affective phenomena.
Programmers need to take affect into account when writing software that interacts with people.
Affective computing captures signals from human users through cameras, microphones, skin sensors or other means, gathering information about facial expression, tone of voice, gestures and other variables that may indicate an emotional state.
By evaluating these data points, the system interprets the user's emotional state.
There are of course many useful applications and cases in affective computing: marketing, customer service, health care, insurance or education.
Overall, the size of the global affective computing market in the post-Covid-19 scenario is expected to grow from $28.6 billion in 2020 to $140.0 billion by 2025, at a CAGR of 37.4% during the forecast period The analysis of human behaviour is becoming increasingly important with the development of artificial intelligence and big data.
However, this growing field should be treated with caution, as it may have implications for privacy.
Have a great week, stay safe and keep on winning.
