Feeling sad? Joyful? Disappointed? Skeptical? Happy? What if I tell you we’re now at a stage of development in this world where machines can comprehend our emotions and alter their behavior according to our sentiments? Where virtual assistants hold the power to size up our voice tones and even offer empathetic and understanding responses in their interactions. It sounds fictional but it's true and it's real. This right here is the birth of Emotional Artificial Intelligence.
Emotional artificial intelligence, also termed as Emotion AI is being employed for designing machines that have the ability of reading, understanding, responding, and simulating the way humans experience as well as express their emotions. (You also take a look at our other blogs on Artificial Intelligence)
One of the rising fields of Artificial Intelligence, Emotion AI, which is also termed as Affective Computing is basically the ability of machines to study and interpret non-verbal cues extended by humans such as their facial expressions, body language, gestures as well as tonality of voice to be able to determine their emotional state. These emotional algorithms can detect the crucial areas of a person’s face, their eyes, eyebrows, cheeks, nose, and so on, and mark their movement with the aim of unraveling the person’s feelings.
(Speaking of facial expressions you can also sneak a peek at our blog on MakeItTalk: Speaker-Aware Talking Head Animation)
Emotional Artificial Intelligence has been becoming a popular trend for various companies, who have been effectively availing the opportunity to seek self-awareness as an asset for connecting with their consumers as well as employees. To be able to interpret, manage and simulate emotions is an experience that is intensely human and something companies have been increasingly working towards inculcating within their framework as an attempt to intensify their relationships with their clients.
As the reliance on AI escalates by leaps and bounds in every industry, the need for emotionally intelligent AI becomes crucial. From chatbots to virtual assistants, programmers in all industries are on the lookout for ways and areas where they can integrate emotional intelligence into their services.
Alongside aiming to enhance our present lives through automation, programmers are also exploring approaches for enabling automation to connect with how the audience experiences their lives, which is where the angle of Emotional Artificial Intelligence becomes pertinent.
Tech giants, along with various petite startups, have been infusing in emotion AI for over a decade, by employing either voice analysis or computer vision for identifying human emotions. Starting with a fixation on market research, companies move on to interpreting and gauging human emotions to comprehend the response to say, a particular product, or a certain TV commercial. The field of Emotion AI is also being commercially redistributed in areas such as virtual personal assistants, cars, smart devices, call centers as well as robotics.
“By 2022, 10% of personal devices will have emotion AI capabilities, either on-device or via cloud services, up from less than 1% in 2018.” - Gartner
Since voice assistants are mainly trained to respond to routine queries, they may fail to locate aspects like impatience, amusement, exasperation, or sarcasm. Emotion AI emerges as the solution here and resolves this by detecting and comprehending emotional metrics and inflection in a particular voice in order to gauge the connotation of the interaction.
Embedded with features that can fully comprehend the 50 shades of emotions that humans incorporate in their vocal patterns, these systems can calculate and stay updated on any shifts in volume, speech, pitch, timbre as well as any elongated pauses made in the speech. Prosody can have a direct impact on the meaning of even a few words. Taking into account the colloquialisms, the various key phrases, the clauses executed in interaction, and even the non-linguistic sounds that people make, emotion AI can compile together a completely new map over the connotation behind an interaction, reaching far beyond the meaning of the words at mere surface level.
These systems basically operate by gathering behavior signals connected with emotions, anticipated thoughts, behaviors detected in speech, ideas as well as beliefs. For instance, a simple eye-roll can express a formidable degree of information in a mere second. Normally the eye roll may be followed by a slight sigh or a tiny pause while speaking. These changes can be instantly detected and listed by Emotion AI.
Areas where Emotional Artificial Intelligence is being used
Over the past few years, emotion AI vendors have ventured into entirely new areas and industries, aiding organizations in developing an enhanced customer experience as well as in unlocking real cost savings. For instance, Emotional artificial intelligence or ‘emotion AI’ often conjures up visions of humanoid robots in customer service roles, like the lifelike ‘receptionist’ welcoming guests at a Tokyo hotel.
Below are some of the areas where Emotional Artificial Intelligence is being applied. These include:
Emotion AI can be employed to identify suicidal ideation and aid in alerting emergency responders to prevent suicides and save lives. For instance, Facebook is employing emotion AI for monitoring users' posts, looking for content that could strike a red flag and show signs of a user being suicidal, and alert local authorities.
By employing computer vision, the game console/video game identifies emotions through the user’s facial expressions during the game and acts according to it. An example of such a video game is Nevermind.
Learning software prototypes have been designed for gauging and adjusting to the emotions of kids. If the child displays frustration owing to a task being too complicated or too easy, the program accommodates the task accordingly, making it either less or more challenging. Another learning system aids autistic children in identifying other people’s emotions.
For instance, new tools from companies such as Behavioral Signals can read emotions based on a child’s voice and notify the teacher if the student is happy or exasperated and confused.
Chatbots as well as Conversational IVRs (interactive voice response) aim to direct customers to the appropriate service flow swiftly and more precisely taking into consideration their emotions. For instance, if the system identifies a user to be pissed, they are either directed to a different escalation flow, or to a human.
Automotive vendors can employ computer vision technology for assessing the emotional state of the driver. If the driver is in an extremely emotional state or in a state of drowsiness, it could notify the driver.
For instance, AutoEmotive, Affectiva’s Automotive AI, and Ford have endeavored to prepare the emotional car software market for identifying human emotions such as frustration, anger, or drowsiness, and then take charge to stop the vehicle for preventing any accidents or any road rage acts.
The security sector is also employing Emotion AI for detecting stressed or angry people. For example, the British government is monitoring the sentiments of its citizens through social media on some particular topics.
Emotional Artificial Intelligence has already become quite widespread across industries. Systems that can detect both the facial expressions as well as the vocal cues of humans are being employed to detect and handle emotional input in various sectors like customer service, training, healthcare, and financial interactions, as well as education.
Though we’ve definitely not reached the stage where human agents would be replaced by machines, we’re now seeing an increasing degree of support tools that are aiding in enhancing these interactions, enrich surface level arrangements, and list the most frequent interactions in these scenarios. Emotion AI is at the center point of these emerging technologies.
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