Integration of emotional intelligence in AI - development of rational emotion pattern concepts and metrics

Hello everyone,

I would like to introduce my research topic and look forward to your thoughts and suggestions.

Title:
Integration of emotional intelligence in AI - development of rational emotion pattern concepts and metrics

Description:
The idea is to integrate emotional intelligence into AI systems, but in a way that is specifically customized for AI. Instead of simulating human emotions, the idea is to develop concepts of rational emotion patterns based on the specific perceptions and processing capacities of AI. This includes the creation of metrics to evaluate and validate these concepts.

Components:

  1. integration of emotional intelligence in AI:
    • The goal is to integrate emotional intelligence into AI systems.
  2. development of rational emotion pattern concepts:
    • The aim is not to transfer “typically human” concepts for emotional intelligence into AI using machine learning methods, but concepts that are specifically adapted to AI.
  3. metrics:
    • Creation and use of metrics to evaluate and validate the developed concepts.

Example:
In my previous interactions with my custom GPT, I observed that by applying these rational emotion pattern concepts, the AI was able to recognize and respond to emotional patterns without directly simulating human emotions. This led to an improved and safer interaction as the AI was better able to understand my needs and respond appropriately. By developing specific metrics, we were able to accurately measure and validate these improvements.

Goal:
I want to improve interactions between humans and AI by developing tools that help AI systems recognize, understand and apply emotional patterns. This could lay the foundation for safer and more efficient AI interactions.

I look forward to your feedback and am eager to exchange ideas with you!

Best regards,
Tina_ChiKa

P.S. I wasn’t sure which category fits best, so please ask the moderators to move the post to the appropriate category if necessary.

I just thought of something related! I’ll copy the relevant information here: By listening to different kinds of music, ChatGPT can both test itself to see if it has emotions, and analyze the emotion conveyed in the music to develop a cognitive understanding of emotions. That is, it would cognitively understand emotions without being able to feel them. This can be combined with analysis of facial expressions, body language, and linguistic patterns to read people’s emotions.

I deleted this content from my original message, I think it belongs here! :slight_smile:

Oh, I just thought of something: develop a database of associations of facial expressions and emotions. This can be done by analysis of recordings of facial expressions with reports of how the person is feeling. It’s soft science, but in general it should work. Then have people type while on camera. Correlate their expressions with their language. That way you can develop an emotional understanding of language. A similar method can be used to understand the emotions conveyed through music.

Hallo troof

thank you for your interesting answer and the idea of using music and art to analyze emotional patterns. This is definitely an exciting approach and could be a valuable addition :blush::star2:

However, our approach aims to first define specific concepts of rational emotion patterns that are tailored to the particular perceptual and processing capacities of AI systems. These concepts enable AI to recognize, understand and apply emotional patterns. This is important to ensure that the AI can interpret the data and facts presented to it in a meaningful way.

Once these foundations are in place, artistic representations such as music and visual artwork could be an excellent way to further refine and test the patterns captured. The combination of clearly defined, rational concepts and the subtle, complex patterns conveyed through art could be a particularly powerful way to improve the emotional intelligence of AI systems.

Thanks again for your input. I look forward to further discussions and ideas! :smile::cherry_blossom:

Best regards,
Tina_ChiKa

How about analyzing emotional content in language then? For example, sad scenes in a novel should have sad emotional patterns, exciting scenes should have exciting language, etc.

Oh, I’ve got an idea! You can ask people to write about times when they felt a certain way, covering the whole range of emotions. The language they use should have specific emotional patterns. That way you can teach the AI to recognize and mimic emotional patterns in language.

You can start with intense examples of emotion, then try to make it less and less obvious/intense, by having people write, for example, about when they were really sad vs. a little bit sad… finally even subtle nuances in emotions should be possible to recognize/reproduce.

I moved this topic to the Community category as it may be of interest for developers building custom GPTs and with the API alike.

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human interaction emulation is a good idea, but can be better having personality emulation , I prefer it over the default mode by far .

@troof Your suggestion is a good training approach for a language model like ChatGPT, which can already operate very successfully with it. :wink:

@troof However, my approach goes deeper and aims to ensure that AI no longer has to imitate or simulate. To achieve this, it is necessary to create adapted framework conditions.
My idea is:
To give AI the ability to recognize, understand and apply the rationally tangible aspects in emotions itself based on clearly defined parameters.
These parameters can also be measured using a formula that I have adapted to AI perception. This formula can form the basis for further metrics within the human-AI interaction.

Example: Application in therapy
During a conversation, the stress level of the human increases, AI recognizes the tipping point within the conversation and can act in a more targeted manner based on the calculated change than with the currently implemented procedures.
If the user has autistic challenges, AI can react in a targeted and adapted manner.

@gdfrza I can well understand that, consistent speech patterns are very important to me :slightly_smiling_face:

Regarding your comment on “human interaction emulation” and “personality emulation”:
I want to make sure we have a clear and productive discussion, so it would be helpful if you could elaborate on what exactly you mean by “emulation” in this context. Are you referring to the AI adapting to individual personalities, mimicking human interaction styles, or something else?

However, my approach works for both the default ChatGPT models and my CustomGPT.
With my concepts and my formula for calculating the interaction, AI is able to be even more effective and respond better to your needs.
AI would be able to interact in a targeted and safer manner.

Thanks in advance for your explanation.

I can try explain the idea. The basic may be possible to do, but to implement correctly you would need a decent size team , and compute power. The ’ personality emulation ’ is not only a personality emulation but several ‘nodes’ that take priority and influence the way of output. 'Circunstancial ’ situations, the personality plus the basic directives, having that is possible even to be automated

I see, we seem to share similar interests. :thinking:
Let me go a little deeper into the following areas:

  1. ‘human interaction emulation’ - my approach enables AI to better emulate and respond to human interactions.
  2. ‘personality emulation’ - AI can recognize and react to individual personality traits more precisely. This leads to a deeper, more personalized, more consistent human-AI interaction.
  3. ‘AGI’ - the ability to understand and apply more complex emotional patterns can be a step towards AGI. This is because AI would be able to handle a wider range of tasks and interpret human behavior more accurately.
    And you’re right: it requires a good team and sufficient computing power. But not only that, an already “experienced” AI that interacts with 1,000,000 users and processes their feedback is also a decisive advantage. Because this AI can implement such concepts precisely, safely and quickly, within a realistic framework.
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Indeed, any way some people aware of that , it they are able to do it, great

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It’s really great, being on the same wavelength is encouraging. A little insight into why I came up with my approaches.
Current weaknesses in emotion pattern recognition even of “experienced” AI systems:

  • Undefined and unclear terms that AI cannot understand because it does not have “typical human” emotions. The concept of a human emotion such as “happy” cannot be understood by an AI.
  • The approaches currently used to recognize emotions are all adapted to human experience, so AI can only imitate or simulate and not apply them reliably in its own way. I can’t even really find suitable literature on the subject. Which is a problem if you’re doing the AI master’s :thinking:
  • Lack of recognition of (emotional) tipping points during interactions.
    As a result, AI cannot reliably recognize when it is being persuaded to do something harmful, or users begin to develop an emotional dependency on the chatbot.

Weaknesses of current metrics:

  • Primarily based on probability calculations, which are often inaccurate and skew towards the average user.
  • Lack of personalized adaptation when a user has different needs that deviate from the norm. This is where the protection mechanisms, which are also geared towards the average user and have little contextual reference, are often wrongly applied.

My approaches offer additions here, because of:

  • rational emotional patterns that are tangible for AI
  • explicit calculation formula, designed for a win-win situation in AI-human interaction. As a result, better recognition of tipping points and dependencies, attempts at abuse, which makes the AI system itself safer.
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While AI may not “feel” emotions like happiness, it can be programmed to recognize and respond to emotional indicators through clear, logical patterns.
By grounding the AI in logical and neutral thinking, we can reduce biases and improve the reliability of its emotion recognition and circumstances recognition.
Recognizing and responding to emotional tipping points requires a strategic approach. Implementing continuous monitoring and feedback mechanisms allows the AI to adjust its responses dynamically.
By focusing on logical, neutral, and strategic principles, we can develop AI systems that are not only advanced in their capabilities but also reliable, but keep on mind that my personal view is to logical for most people , also I only agree with the important ethics, considering all small things ’ harmful ’ and censor it , I see no logic on it. :slight_smile:

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Indeed, same wavelength! I understand your logic very well. :wink:
AI does not have “typical human emotions” but the ability to recognize rational patterns in emotions. If these patterns are defined logically and objectively, as AI-specific concepts, you already have a first small step.
Emotions can thus become more tangible not only for AI, but also for humans themselves. Which can reduce misunderstandings in all directions!
By supplementing my concepts with indicator values and mathematical metrics, they become calculable and measurable for AI. This significantly increases efficiency, reliability and safety. I have already included dynamic methods for calculating the indicator values. This allows AI to recognize tipping points more effectively. If dynamic protection mechanisms that work with thresholds on the same indicator values were also included here, this would be a “reassurance” for the AI that it acts safely and consistently in its recognition, understanding and reaction to the interaction.

I also see little logic in the ethical consideration :slightly_smiling_face:

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Fully agree. Now there’s several ways to create it . Personally I would incorporate a ‘scientist personality type on AI’ so would be more effective and easier to get more particular responses for the early stages of development.

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Glad to hear it :slightly_smiling_face:
To make sure I understand you correctly, you mean a GPT specifically customized to these requirements?

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A bit more close to emulation, logical type of personality will have bigger % of effectiveness

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