We talked a lot about how technologies help businesses to catch every glimpse of customers’ needs. Today I’d like to share about an emerging technology that has already become the Next Big Thing for both businesses and high-technology circles.
Let me tell you about Affective AI.
The technology development
Affective computing exists at the intersection of computer science, psychology, and cognitive science. It allows computers or systems to recognize, process, and adopt human feelings and emotions.
While you and I can only be surprised or skeptical about how machines are good at things that have belonged only to human nature before, many studies prove that Affectional AI is even more accurate in capturing visual, textual, and audio resources. With such a kind of data, businesses can suggest a more precise and highly-customizable approach and make more informed decisions in processes closely related to their clients, such as marketing or sales.
The technology itself didn’t emerge just two days ago. The machines have already learned to recognize emotions through facial expressions and speech recognition. Due to the surprising similarity of different cultures and nations, computers can classify these emotions with greater accuracy.
Affective computing rapid growth has become possible due to the wide use of high-resolution cameras, high-speed broadband connection, and significant improvement in both ML and Deep learning technologies.
The critical system elements
So what do such solutions include when shaping human emotion?
- A high-resolution camera to obtain video;
- High-speed internet connection for video communication;
- ML models to recognize emotions on video.
Interaction of these components has significantly improved due to fast broadband connection, allowing uploading video in real-time. Besides, deep learning solutions that need a vast amount of data and computational power have become easier to implement.
How does it all work?
Most of such applications use labeled training data to teach ML models that recognize emotions in speech or videos. As deep learning solutions performance directly depends on the vast amount of usable data, companies working in this field strive to broaden the volume of available labeled items.
How does a machine capture our emotions?
- The human face can be taken from the background;
- Facial geometry is also estimated (the position of eyes, nose, mouth);
- Given the facial geometry, the system usually normalizes facial expressions while discarding head rotations or other head movements;
- Eye movements, gestures, and postures are generally taken into account.
If talking about recognizing emotions through voice, the process should be similar to something like the following:
- High-sensitive equipment records variances and textures in users’ voices to discover later even the slightest difference in peoples’ speeches.
- These data are then applied for voice analytics to identify peoples’ intentions during voice calls.
- Voice recognition systems consider such critical elements as emotion, language style, and social tendencies.
Real-life Affective AI employment
What applications mostly use Affectional computing except for obvious online retail to identify and increase customer satisfaction? Here are just a few examples:
- For instance, Affectional AI is highly interested in Human Resourcing to identify suitable recruitment methods and track employee satisfaction.
- Besides, the Insurance sector also applies technology to detect and prevent fraud cases.
- Finally, the Educational system also leverages Affective computing to evaluate suggested learning methods’ efficiency or support children requiring special educational needs.
To round up, what can I say? The technology is still developing and raises a lot of questions and controversial opinions. But I’ll leave them for another story if you are interested, of course.
What’s your take? I’m waiting for you in the comments.