Artificial Emotional Intelligence

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What is Artificial Emotional Intelligence?

Artificial Emotional Intelligence (AEI) is a specialized AI field that integrates various technologies, including natural language processing, machine learning, computer vision, and affective computing to create algorithms and systems that can perceive, interpret, and simulate human emotions. The goal is to enable machines to interact more naturally and intuitively with humans by incorporating emotional cues into their responses.

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According to popular belief, machines lack the capacity to comprehend emotions; however, the emergence of artificial emotional intelligence has cast doubt upon the validity of this claim. Artificial emotional intelligence refers to the software application’s ability to discern and interpret human emotions. By observing human behavior, such as watching a game and exhibiting various emotions and moods, software applications can process and interpret these emotional states. This process relies on the analysis of facial expressions and heart rates. Notably, the software is capable of comprehending fluctuations in heart rates by assessing changes in facial coloration, removing the need for physical attachment of any devices to the human body.

Artificial emotional intelligence has many use cases. For example, businesses could not only sell targeted products and services to customers but also save a lot on advertising expenses. Companies spend a lot on understanding customer behavior and preferences and artificial emotional intelligence can supplement these studies.

How does it work?

The software with artificial emotional intelligence must first be trained to interpret human emotions correctly before deployment. Artificial emotional intelligence is based on machine learning, artificial intelligence, and huge volumes of data on human behavior such as gestures, tones, facial expressions, and tone of voice. The software uses AI which again relies on multiple sources such as computer vision, cameras, sensors, real-world data on human emotions and behavior, speech algorithm, and deep learning techniques to gather data. The software continuously learns about human behavior through these data points and, over time, learns to accurately interpret human emotions. For example, the force of keystrokes can indicate a state of mind such as aggression, anger, excitement, or deep involvement. It’s the job of artificial emotional intelligence to accurately identify the cause a human being was applying a certain degree of force on the keys in the keyboard. It’s a hugely complex task and the risks of errors are extremely high, especially in the early stages of development. However, the accuracy will increase over time.

Here are some real-world examples of how artificial emotional intelligence works.

Affectiva

Affectiva is an artificial emotional intelligence software company that has been helping many reputed brands drive their marketing campaigns. The name of its product is Affdex for Market Research. It captures, processes, and interprets the emotional responses of the viewers to the digital advertisements based on their facial expressions such as smiles, smirks, and frowns. It gauges the emotional state of the viewer while viewing digital content by measuring heart rates. It gauges the heart rates through a webcam that monitors the change in the colors in the face of a viewer.

Many reputed companies such as Kellogg’s and Mars and CBS have been using Affdex for Market Research to optimize their marketing campaigns and get better results. Affectiva maintains an extensive database of human emotions that it uses to measure the likelihood of various prospective actions such as the likelihood of purchase, annoyance, and joy. It provides various norms that enable companies to quantify their marketing campaigns with parameters such as an increase in sales, recalling a brand or product, purchase intent, and the possibility of sharing an advertising campaign by a customer.

Realeyes

Realeyes is another artificial emotional intelligence software company that uses computer vision, artificial intelligence, webcams, and machine learning to interpret the emotional state and responses of the viewers of digital content. First, the customers of Realeyes select the geography where it wants to launch its products, for example, Northern Europe. Second, Realeyes selects a targeted group of 300 viewers from the same region and they view the promotional content from the customers at a time of their choosing. Third, as they view it, Realeyes captures their emotional states. Fourth, Realeyes captures the data, processes it, and provides the results in a dashboard for the customers. The dashboard contains a detailed report on how the target group perceived the promotional digital content. The report also contains detailed recommendations on how customers of Realeyes should leverage the findings of the test to better optimize their marketing campaigns. The products of Realeyes are used by brands like Coca-Cola, Hershey’s, and Mars, ad agencies like MarketCast, Ipsos, and Publicis, and media companies such as Teads, Oath, and Turner. According to reliable reports, the market potential of the technology is huge.

Criticism

Neuroscientists point out that artificial emotional intelligence software applications are poorly equipped to accurately gauge human emotions, at least for now. One argument is that artificial emotional intelligence software applications are not suited to understanding the cultural differences that manifest in facial expressions.

Conclusion

The field of Artificial Emotional Intelligence (AEI) holds significant potential for revolutionizing marketing campaigns and enhancing human-machine interactions. By leveraging technologies such as natural language processing, machine learning, computer vision, and affective computing, AEI aims to enable machines to perceive, interpret, and simulate human emotions, thereby creating more natural and intuitive interactions.

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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…