Trust in AI Credit: blog.affectiva.com
To trust we need to engage in truth. Technologically, truth means nothing. The imitation and original are the same. With a mindset of obedience, accepting the visions of AI-experts and their products, many seem to have lost interest in questioning the value of truth. To them truth is like money: its value depends on the currency’s liquidity and the issuer’s credibility. Money is useless when it is not liquid and provided by a government authorized institution. Likewise, truth is a mere abstraction of general accepted values defined by society. It engages the evolution of technology that grows beyond ethics and purpose. Billions of people connected to the internet have already accepted this process as Emotional AI is gradually and subconsciously entering our daily lives.
According to Wikipedia Emotions are mental states brought on by neurophysiological changes, associated with thoughts, feelings, behavioral responses and a degree of pleasure and displeasure. There is no scientific consensus on a specific definition of emotions. They are often intertwined
with mood, temperament, personality or creativity. Research on emotion has increased over the past two decades, with many fields contributing including psychology, medicine, history and computer science. Theorizing about the evolutionary origin and possible purpose of emotion dates back to the research of Charles Darwin. Today research activities are focused on the neuroscience of emotion, using tools such as PET or fMRI scans to study related processes in the brain. From a mechanistic perspective, emotions can be defined as positive or negative experiences that are associated with a particular pattern of physiological activity. Emotions are complex, involving multiple different components such as subjective experiences, cognitive processes, psychophysiological changes and individual behavior. A similar multi-componential description of emotion is found in sociology. Peggy Thoits from the Indiana University in Bloomington described emotions as involving physiological components, cultural or emotional labels such as anger or surprise, expressive body actions and the appraisal of situations and contexts. Cognitive processes like reasoning and decision-making are often regarded as separate from emotional processes, differentiating between ‘thinking’ and ‘feeling’. However, not all theories of emotion regard this separation as valid. Today most research about emotions focuses on emotion dynamics in our daily life, predominantly observing the intensity of specific emotions and their variability and instability. Whether and how emotions augment or blunt each other over time and the differences in these dynamics between individuals defines another activity of emotion research.
Can AI understand Emotions?
When John McCarthy and Marvin Minsky founded Artificial Intelligence (AI) in 1956, they were amazed how a machine could perform incredibly difficult puzzles quicker than humans. However, the real challenge is to teach a machine what emotions are and how to replicate them. Although some of us are more perceptive than others, a majority can easily interpret the emotions and feelings of those around us. This base level intelligence which we are partly born with and have learned during childhood, tells us how to behave in scenarios that relate to emotional behaviour. Emotion AI, also known as Affective Computing, dates back to 1995 and refers to the branch of AI which aims to process, understand, and even replicate human emotions. The technology applied with Emotion AI aims to improve natural communication between man and machine to create an AI that communicates in a more authentic way. If AI can gain emotional intelligence, maybe it can also replicate those emotions. With ‘Multimodal Emotion AI’, analysing facial expression, speech and body language, one can gain a complete insight into an individual’s mood. As a result, so-called sentiment analysis or opinion mining as a sub field of Natural Language Processing (NLP) is the process of algorithmically identifying and categorizing opinions expressed in text to determine the user’s attitude towards emotions. There is much debate within this field if a simulation of emotion demonstrates true understanding or is still artificial. Emotions are inherently difficult to read and there is often a disconnect between what people say they feel and what they actually feel. A machine may never get to this level of understanding. Moreover, how we interpret each other’s emotions is full of bias and opinion. To overcome this barrier, Emotion AI might help us to better understand human behavior and its relationship to intelligence, adding a new layer to human communication.
Why Emotion AI is important for Organizations and Developers
As artificial intelligence learns to interpret and respond to human emotions, corporate leaders should begin contemplating how Emotion AI could play a critical role in their enterprises and marketing activities. Despite its name, Emotion AI does not refer to problems typically used in the application of AI- technology. It is used as a discipline of AI, which by means of training and machine learning, is able to analyse, understand and perhaps even replicate human emotions. Emotion AI has many possible applications, particularly in customer service. The technology can analyse comparable speech patterns to offer insights in real-time, including recommendations on how to handle a particular customer call. It is already used to detect insurance fraud, with AI using voice analysis to help detect whether someone calling to file a claim is lying. Emotion AI can also use speech analysis to ensure an agent’s planned customer call is successful. For example, was a customer happy and satisfied by the call’s end, or did their voice indicate frustration or anger? As Emotion AI evolves, organizations are already using sophisticated AI simulators in the training of future customer service agents. These AI-powered simulators change and adapt responses based on their input, assessing their levels of empathy and ability to help customers. Emotion AI is a technology in progress. Programmers must continually review and train data models to ensure ongoing accuracy and reliability. Part of that training must include domain-specific industry data. Otherwise, organizations are at risk of getting inaccurate interpretations and skewed results, negatively impacting any insights used to make business decisions. From a critical point-of-view, as AI has the ability to understand human emotions, it could be used in ways that compromise user privacy by monitoring and analysing emotional reactions infringing on consumer privacy rights.
Emerging Trends and Applications of Emotion AI
Emotion AI has made enormous advances in recent years, affecting a variety of industries with its disruptive powers. This branch of AI is based on the idea that emotions play a critical part in human communication and decision-making, making it a vital component for improving human-computer interaction. The following provides a summary of some emerging trends:
Advanced Facial Recognition: One of the most important trends in Emotion AI is the development of advanced facial recognition technology. These systems can analyse facial expressions to identify emotions with a high level of accuracy and are used in various industries, from customer service to mental health monitoring.
Emotionally Intelligent Robotics: Organizations engaged in robotics have started to employ Emotion AI to build machines that can understand and respond to human emotions. This is particularly valuable in healthcare, where robots can assist patients to provide emotional support.
In addition to these trends, the following provides a summary of some emerging applications:
Healthcare: Emotion AI is being used in the healthcare industry to monitor and support patients’ emotional well-being, especially in cases of mental health issues. Virtual therapists and emotional support robots are becoming increasingly common.
Education: Emotion AI can help teachers and students alike. It can gauge students’ emotional states and adapt the learning experience, making education more personalized.
Entertainment: The gaming industry is using Emotion AI to create games that respond to the player’s emotions. This can lead to more immersive and emotionally engaging experiences.
Security and Surveillance: Emotion AI has applications in security and surveillance. It can detect unusual emotional patterns in website activities, helping to enhance organizational security.
The future of Emotion AI looks promising. Emerging trends and a wide range of applications with the potential to transform the way we interact with technology and each other indicate a significant potential for improving business processes. As this field continues to develop, we can expect more emotionally intelligent systems that understand and respond to our feelings, leading to more personalized and empathetic interactions in various aspects of our lives. However ethical and privacy concerns need to be considered to make our technology-driven world a more emotionally connected and supportive one.