Emotional AI, or Affective Computing, is a subset of Artificial Intelligence that incorporates human emotion into the decision-making process of machine learning models which rely heavily on statistical reasoning for their outputs. This approach is beneficial for more human-facing AI models like chatbots that need to identify emotion and tone to be more helpful. 

With emotional AI, Large Language Models and other algorithms can become better suited to assisting creative job tasks or generating artistic content that humans can relate to. These features can also be applied to more practical products like cars that can sense a driver’s stress levels and prevent road rage incidents. So, how is emotion coded into AI exactly?

Defining Emotional AI & Affective Computing

Traditionally, Machine Learning models rely on logical operations to perform all of their tasks and can be imagined as an advanced gearbox that pulls different levers based on inputs. In certain contexts, like chatGPT, this gearbox is massive, with a seemingly limitless amount of responses, but emotional AI plays a role in that natural language process.

Emotional AI can be contrasted with traditional AI models in the same way that wisdom and intelligence are not the same. While logic plays a strong role in most decision-making, humans allow their emotions to provide significant influence over their reasoning. The same can be said about Emotional AI which must use context clues taken from facial recognition or voice modulations to recognize a user’s emotional state which can help AI create more accurate outputs. 

Historical Context

Emotional AI can first be traced back to the Turing Test proposed by Alan Turing in the 1950s when he introduced the idea of a machine that can exhibit human-like intelligence. Nearly two decades later, Joseph Weizenbaum created ELIZA, an early AI model that emulated a Rogerian Psychotherapist and displayed the earliest signs of empathy however simulated they were. 

During the 1990s, Dr. Rosalind Picard coined the term Affective Computing following MIT’s publication of her first book under the same name which led to the development of Emotional AI as a branch of study. This led to advancements in emotional technology which quickly became helpful at the turn of the century when Web 2.0 and social media became more common. 

Applications of Emotional AI

Affective computing has many practical purposes in human society and can help us in many ways. Here are a few examples:

• Service: Chatbots and virtual assistants can register emotionally charged behavior to better assist customers who may be frustrated or confused. 

• Healthcare: Affective computing can be used to help monitor mental health patients who may be experiencing unexplainable emotional outbreaks. 

• Education: Personalized learning can help students with learning disabilities or social handicaps that make it difficult for them to learn at a standard pace. 

• Entertainment: Emotional AI can help content creators explore more themes and ideas that improve scripts and mass media content. 

• Automotive: AI models have the ability to track and monitor a car’s movements to determine the stress levels of the driver to reduce collisions. 

How Emotional AI Works

Affective Computing begins with input data created by context clues left behind by a person such as their facial expressions or the tone of their voice. Computer vision and recording instruments can pick up subtitles in these actions to recognize patterns that identify their emotional state. 

With this input data, deep learning models can evaluate their response options. Over time, these options and decisions made by algorithms improve, creating more effective replies to human engagement such as a frustrated customer being met with a calming response. Neural Networks can then filter through these responses, analyzing their success rates to craft more effective replies. 

Challenges and Criticisms

One of the core dilemmas involved with Emotional AI is the vast complexity of human emotion. Within every culture, there are massive social systems in place that influence how we feel and think. Tuning AI models to these intricacies poses a problem, but becomes compounded when we consider how many unique cultures exist around the world. Tuning an AI system to one model will be challenging enough, but creating a model that can understand emotions from all aspects of humanity is a daunting task. 

We must also take into account the negative aspects of culture such as racism and discrimination that cause anger and hatred. While these emotions are necessary to create more comprehensive AI models, they are not worth recreating and could cause obstacles for programmers, especially if violence becomes involved. 

The Future of Emotional AI

Affective computing could lead to some of the most unique technology ever created. AI models can be integrated into many different products, and emotional AI is no different. By placing these algorithms into wearable products such as watches or shoes and other body sensors, we can effectively create new ways of measuring human emotion.

These types of developments could create a revolution in the study of psychology as we’re given more meaningful insights into how the brain operates under stress or alignment. With treatment for mental health problems becoming more accepted, the influence created by emotional AI could be life-changing for many people.