Emotional intelligence in AI: Rational Emotional Patterns (REM (*) ) and AI-specific perception engine
Motivation:
A year ago, I started my work from a different point of view. I was diagnosed with autistic traits, which gave me the explanation why I have difficulties naming and understanding emotions like most people. At the same time, ChatGPT was becoming more and more popular in my company. So I used my private account to deal with my “new perception” and finally understood where and how I function differently.
At the beginning of 2024, I realized the following:
I work on pattern recognition and I have a formula in my head for almost everything. While working with ChatGPT and other bots, I realized that AI works in a similar way.
And I started to ask myself and think about it:
- If I have difficulty with “typically human” vague concepts, then AI has similar challenges
- If it is possible for me to develop a kind of “empathy” based on experience with people and pattern recognition, then it must be possible for AI systems to do the same
- Everything that is learned can only be learned if the subject matter is understood. Otherwise it is just “imitation” and “simulation”.
Understanding, in turn, is only possible if the data is available in a way that can be understood.
For AI, emotions are patterns that are composed as specific data. Currently, these are words, reactions or certain conversation patterns. These act as training data. Every interaction that AI has with the user is also made up of data that is evaluated and experience is gained. But it is data that people understand because it is made for people.
The basis for recognizing emotions are stochastic and statistical methods such as sentiment analysis. Psychological models such as “Plutchik’s Wheel of Emotions” and “Circumplex Model of Emotions”.
Yes, the analyses are getting finer and they are important tools.
My approaches are in no way intended to replace these tools!
But it could be an extension.