Introduction

A hypothesis was well laid down by the founding fathers of Artificial Intelligence in 1956. The theory postulates that every aspect of learning or any other feature of intelligence can be in principle be so precisely described that a machine can be made to simulate it. As at the time this hypothesis was made, AI was about transitioning into a device having a vast amount of human intelligence, including language, vision, and reasoning. Emotions were not on this checklist. However, as technological innovations progress, consumers would grow to appreciate an artificial intelligence system that can accurately calculate thoughts, moods, and feelings, making our lives more comfortable with more personalized and convenient experiences in tune with our emotions. It might sound creepy, but it isn’t.

On a fundamental level, what distinguishes machines from humans is emotion. However, computers can read emotions and respond accordingly. Before proceeding, it is imperative to understand the concept of Natural Language Understanding (NLU).

Natural Language Understanding
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What is Natural Language Understanding?

Natural Language Understanding is a field of computer science that analyzes what language means, rather than merely what individual words say. This area of research and development relies on foundational elements from Natural Language Processing systems, which map out linguistic features and structures. Natural Language Processing is an already established field operating at the intersect of computer science, artificial intelligence. The ultimate of Natural Language Processing is to read, decipher, understand, and make sense of the human languages by machines.

Natural Language Understanding (NLU) seeks to intuit many of the connotations and implications that are innate in social communications such as the emotion, effort, intent, or goal behind a speaker’s statement. For instance, an Italian artificial intelligence company that specializes in natural language reading and semantics is using its AI tech to extract emotions and sentiment from 63,000 English-language social media posts to create a semantic analysis of people’s feelings during COVID-19.

What is Artificial Emotional Intelligence?

Artificial Emotional Intelligence or Affective Computing is a subset of Artificial Intelligence. Just as the name suggests, it deals with understanding emotions and using them as a competitive advantage across applications. It deals with measuring human emotions, understanding stimuli, and giving back an appropriate response. It is often argued that if we are to live and interact with robots comfortably, these machines should be able to understand and appropriately react to human emotions. Some companies are leading concerning Emotional Intelligence.

For instance, Affectiva is an emotional AI-based company established in 2009. They use the Webcam available to track the user’s emotions and moods. They identify the various twitches and subtle changes in micro-expressions for emotion detection. With appropriate permission, they use the data and recording through the Webcam. This way, Affectiva is then able to help advertisers target more effectively using the emotional quotient of the users. There is also CrowdEmotion, which is based is a London based company that was founded in 2013. The company strives to leverage the technology that uses emotional artificial intelligence. Their emotional engine helps their clients to recognize and understand human emotions.

Can AI understand emotions?

In its annual report, the Artificial Intelligence Now Institute, an interdisciplinary research center studying the societal implications of artificial intelligence, called for a ban on Artificial Emotional Intelligence in some instances. According to the researchers, such technology should not be used in decisions that “impact people’s lives and access to opportunities,” such as hiring decisions because it is not sufficiently accurate and can lead to biased judgments. So, what exactly is the problem here? The thing is Emotions are tricky because they tend to depend on the context. For instance, John might not be frustrated; perhaps, he is just thinking. Facial recognition has come a long way using machine learning, but identifying a person’s emotional state based entirely on looking at the person’s face is missing essential information.

The fact is that emotions are expressed not only through a person’s expression but also where they are and what they are doing then. These contextual causes are difficult to feed into even modern machine learning algorithms. To address this issue, there have been active efforts to augment artificial intelligence techniques to consider the context. Admittedly, the natural reaction to the ethical and privacy concerns entrenched with the use of Emotional Intelligence systems is to call for a ban is specific circumstances. Indeed, using AI for job interview results or criminal sentencing procedures seems dangerous. However, there are useful applications of AI in this regard. For instance, in helping spot warning signs to prevent youth suicide and detecting drunk drivers. It is for situations like this that concerned researchers, regulators, and citizens have generally stopped short of calling for blanket bans on AI-related technologies. The problem doesn’t end here.

Furthermore, Artificial Intelligence, in general, almost always have fairness, accountability, transparency, and ethical flaws inherent in their pattern matching. For instance, one study found that facial recognition algorithms rated faces of black people as angrier than white faces, even when they were smiling. Several research groups are tackling this problem, but it seems clear that the problem can’t be solved.

Conclusion

The fact is that Artificial Intelligence is already a big part of our life. Starbucks uses Artificial Intelligence in its rewards program and its mobile application to keep track of customer’s orders, the time such order was placed, the weather more to customize recommendations. This enriches the overall experience. Amazon revolutionized retail in part by using customer’s previous purchases to make recommendations about other products. These efforts are awe-inspiring. But the thing is they barely touch on the subject on how Artificial Intelligence could be utilized to understand our wants and needs with precision.

For instance, John is at a restaurant and is quite frustrated by the slow customer service. At the table, a small, AI-equipped computer with some sensors detect the frustrated facial expression on John, then pings another employee to come and assist. If the AI system tagged John particularly angry, the restaurant could offer a free treat. From detecting struggling school students in classes to suicidal youths on the streets, to drink drivers on the road, to individuals with suspicious intentions, the possibilities are quite endless.

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