How Emotional AI is Coming of Age?
Artificial Intelligence (AI) has been constantly redefining the way we live, work, and enjoy. Right from workplaces to the healthcare and entertainment industry, AI is widely used to increase productivity and deliver better and more optimized services. After proving the early naysayers wrong by improving the areas of productivity the AI is fast proving its mettle when it comes to human emotions.
It is not an easy task to understand human emotions- though it is the major factor that differentiates us from insentient beings and helps us claim our superiority over other beings. A majority of issues- right from domestic conflicts to wars, happen due to misinterpretation of emotions. So, it is natural to doubt the authenticity of terms like “Emotional AI”, i.e., humans teaching the machines to understand and interpret emotions- something they haven’t understood fully, yet.
How is emotional AI trained?
AI is generally trained by mimicking how we perform certain activities. So, breaking down the human process of interpreting emotions may be of some help.
Facial expressions, voice inflection, and body language are three ways in which we humans interpret the emotions of others.
Expressions through body, face, or voice, if obvious and well-expressed are easier to interpret- like a person sporting a huge smile or someone with knotted eyebrows or gritting teeth. However, the more subtle these expressions become, the tougher it becomes to interpret them- and we humans are adept at hiding our emotions.
How does emotional AI work?
Just like chatbots, emotional AI also employs large datasets to interpret human emotions. So the wider and more diverse the data, the better AI will be to interpret emotions. Let us understand the different steps involved in the process:
Data collections
To interpret human emotions more precisely, AI is trained on a heavy set of data that is rich in volume as well as variety.
- Text data: One element of the training is LLM or interpreting large volumes of text but there are other elements too that the AI goes through to learn how a more accurate interpretation of emotions. >>
- Facial expressions: Recording phone videos or video calls through Whatsapp/Skype is the primary source of understanding facial expressions.
- Physiological data: Our body functions also react to our emotions. So recording body temperature and heart beating rate during interaction sessions is the key source of understanding physiological data.
Based on the relevance the different sectors use the data that aligns with their needs. For instance, a healthcare practitioner would like to include physiological data as a vital part but such data doesn’t add any value for a customer care center that mainly relies on text, voice, and body language/expressions of customers.
Emotional analysis
Once the data is gathered the next thing is to analyze it properly. Here are some ways in which data is analyzed to infer emotions:
- Text analysis: For interpreting the written text NLP for sentiment analysis is employed for spotting phrases, keywords, and flow of the language associated with specific states of emotions.
- Voice analysis: In this, the NL observes specific aspects of voice that indicate the emotional state of the speaker, like volume, pitch, speed, and tone.
- Facial expression analysis: Deep learning techniques and computer vision magnify the meaning of basic facial expressions to infer sadness, anger, happiness, surprise, etc. Not everyone prefers to express their facial expressions freely though. So, it can also recognize micro or subtle expressions.
- Physiological analysis: With the help of specialized sensors the AI can also be trained to read the physiological data like heartbeat, and biological temperature.
Response management
After understanding the emotions the AI needs to react in a specific manner because the best way to tackle human emotions is to respond to them in a proper way that aligns with your purpose. For instance, based on the interpretation the AI can alert the customer service executive that their caller is getting frustrated. Learning this early on enables the executive to avoid clashes or altercations by strategically dealing with the customer.>>
Different applications of emotional AI
As emotions play a huge role in determining the way we interact with and respond to others, emotional AI has strong roles to play in diverse areas. We have compiled a list of a few key areas where emotional AI can deliver (or has started delivering)outstanding output:
- Advertising: Advertisements can meet their purpose only when they can ignite specific emotions. So, marketing agencies carefully study the data of volunteers on how specific content affects their emotional state. Accordingly, they optimize the content to prompt the desired emotions to meet the purpose of the ad.
- Call centers: By integrating emotional AI into their technical systems, the call centers can help agents better recognize their customers’ emotional state and design further conversation accordingly to make them happy or at least avoid frustrating them further.
- Automotive industry: The research on the role of emotional AI in the automotive industry has produced some encouraging outcomes proving that emotional AI may have a vital role to play in ensuring the safety of drivers, passengers, and vehicles. However, it still has to go a long way to determine its accuracy and the remedial actions based on the specific emotional state of the driver (angry, stressed, exhausted, worried, etc.
- Healthcare: While physical health can easily be studied, understanding mental health is very difficult as one needs to understand the relation between diverse attributes. With the help of physiological datasets, AI makes things less difficult for doctors during the diagnosis and treatment of mental disorders.
- Education: During the learning process emotions play a vital role. With the help of emotional AI the course creators can now design a dynamic course that can understand the emotions of the learner and can change accordingly to keep students engaged and support optimum learning.
Ethical and safety concerns
While the research is still on, there is a wide section of society that is concerned with ethical and safety issues. For instance, service providers use emotional AI to manipulate the audience/customers, or worse still, scammers use AI to scam people. Lack of accuracy is another concern especially when we talk about critical areas like defense and healthcare.
For now, we don’t have an accurate answer for such questions but then, the same is more or less true in the present scenario too. We infer that doctors generally have high ethical standards, but can we surely say that each doctor has such morals? So, this element of uncertainty will always remain, but with time, we hope to minimize this level to the safest possible standards.
Summing up
Emotional AI is a fast-developing field in the area of artificial intelligence. The technology can simultaneously work on different aspects like voice, facial expressions, body language, and other stats to interpret emotions and respond accordingly or optimize the content. While the initial findings are quite encouraging and research studies ignite hope, the questions on ethics, privacy, and safety are something we need to address before embarking further on the journey.
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