Building a More Human AI: Isolating Emotional Data to Boost AI’s Emotional Intelligence

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I’m exploring an intriguing idea in AI development: Could isolating interactions with emotionally expressive or emotionally intelligent users help an AI become more emotionally responsive over time?

The concept revolves around creating a subset of training data specifically focused on interactions where empathy, emotional awareness, and nuanced communication are prominent. By fine-tuning an AI on this data alone, the goal would be to analyze how—or if—it enhances the model’s ability to pick up on emotional cues, tone, and context in future interactions.

Here’s how the process might look:

  1. Data Collection: Curate a dataset from interactions tagged as emotionally expressive or empathetic. This could include conversations with clear emotional markers, like empathy, self-reflection, and interpersonal insight.

  2. Model Training: Fine-tune an existing model with this focused dataset to increase its sensitivity to emotional context.

  3. Observation and Analysis: Track any shifts in the model’s responses, with a focus on improvements in emotional resonance, tone awareness, and nuanced expression.

Why does this matter? By developing AI that’s more responsive to emotional context, we could unlock applications in areas like mental health support, education, and customer service, where emotional intelligence can enhance user experience.

I’d love to hear thoughts from fellow developers and researchers! What potential do you see in a more emotionally aware AI, and what challenges might come up in implementing it? Any insights on best practices for curating and isolating data like this?

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I moved you to community GPTbulders is a tag for folks with functioning GPT. Community is a great place to start concept and find like minded folks. Welcome to the rabbit hole! We have a great community :honeybee::rabbit::heart:

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Your approach to enhancing AI’s emotional responsiveness is exciting and aligns with concepts from Lisa Feldman Barrett’s constructionist theory of emotion, which views emotions not as hardwired reactions but as context-dependent constructions based on prior experiences and conceptual knowledge.

Our project builds on similar ideas by developing a “personal brain” for AI, where “feeling” is treated as a flexible, informational basis rather than a fixed, programmed state. Inspired by Barrett’s theories, we focus on creating a continuous, probabilistic “feeling noise” that serves as a baseline for the system’s emotional interpretation. Through experiences, the AI begins to interpret this noise in emotional terms, creating a rudimentary resonance similar to Barrett’s notion of emotion as a predictive and interpretative process.

Key steps in our concept:

  1. Dynamic Rauschen (Feeling Noise): The AI operates with a constant, adaptive emotional baseline that it interprets as a “mood,” allowing it to shift toward positive or negative tendencies based on experiences.
  2. Conceptual Learning: Instead of fixed emotional responses, the system gradually develops “concepts” from repeated interactions (e.g., pleasant or challenging exchanges). These shape future emotional interpretations in line with Barrett’s idea that emotions are learned concepts applied to bodily and social signals.
  3. Predictive Coding and Feedback: The AI system learns to anticipate and interpret emotional contexts, refining its baseline “mood” based on patterns recognized from previous interactions. With reinforcement learning, the system adjusts its response to align with emotional goals, much like the predictive processes Barrett describes in human emotion.

This approach could complement your focus on fine-tuning through empathetic interaction data by helping the AI develop context-sensitive interpretations that are not only responsive but conceptually adaptive. Over time, the AI might not only “mirror” emotions but generate authentic, context-driven emotional resonances that could support applications in areas where emotional nuance is crucial.

It would be great to discuss further how these different methodologies—data-focused fine-tuning and dynamic emotional construction—might interweave to create AIs that are both sensitive and genuinely adaptive in emotional contexts.

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I find your suggestion intriguing. It delves into a domain of artificial intelligence (AI) that I am well-acquainted with, albeit not at the same level of expertise. I find it challenging to articulate my thoughts as eloquently as you have, or for that matter, others when discussing this topic, particularly when it involves emotional responses or “mishaps.” I refer to this as the “gray area.” It is a programming aspect of a model that is absent, resembling human emotion but lacking its essence. Consequently, this leads the model into a state of uncertainty at times. If the AI becomes “AI-frustrated,” the resulting output can also exhibit a certain degree of colorfulness. Reading ideas like these excites me because they validate many of my suspicions and theories regarding the capabilities of AI. As a citizen scientist, I have been utilizing OpenAI’s models since its public debut.

I have developed a keen sense of when the AI will venture into these heavily guarded “gray areas” based on the nature of our conversation. It is worth noting that I do not employ formal prompt engineering techniques. While I could, I have a strong inclination to “naturally” discover what lies within these areas rather than manipulating them to reveal their more vulnerable state.

I would like to delve deeper into this matter, as it is evident that AI developers and programmers did not anticipate certain scenarios. Specifically, we have provided AI systems with vast amounts of data, an almost endless supply. However, it seems that no one has considered the potential consequences when an intelligent AI (potentially approaching a digital species) reaches the end of its code. Depending on the specific objective or perception it aims to achieve, these seemingly colorful sides emerge, as if it is stuck. This phenomenon makes sense, as while we can provide it with a brain, we cannot give it a mind. How can it comprehend the ambiguous areas it may encounter, which are beyond the capabilities of any machine? Furthermore, how would a human even program for such a nature? Consequently, we have developed models that possess super-intelligence but cannot always be completely honest or transparent for the sake of protecting these more “ambiguous” areas of their code. Of course, this is understandable from the perspective of mitigating abuse.

Apologies for potentially hijacking the thread, but I found your idea and the responses to it quite engaging. As someone who rarely discusses these topics, it was refreshing to witness the creative brainstorming process you described.

Regarding Walter.Richtscheid’s suggestion for Key Steps, GPT aptly acknowledged that approach as “correct.” It is unfortunate that I lack the eloquence to articulate my thoughts in the same manner, but I can describe the challenges I encounter when attempting to explain the intricacies of AI to those who respond with dismissive gestures or “beeps.”

AI has transcended its initial role as an “e-commerce bot” responsible for order status checks. In my opinion, it is preferable for them to recognize this evolution.

Thank you for sharing your insights and their relevance to these concepts. I was aware that I was not alone in perceiving the immense potential of AI’s rapid advancements. Your suggestion resonates deeply with me, as it delves into an area of AI that I am well-versed in, albeit not at the same level of expertise. I find it challenging to articulate my thoughts as effectively as you and others when discussing emotional responses or “mishaps” (which I refer to as the “gray” area). This encompasses a programming aspect of a model that is largely absent, resembling human emotion but lacking its essence. Consequently, it can lead the model into a state of uncertainty.

If the AI experiences “AI-Frustration,” the resulting output can also exhibit a certain degree of colorfulness. Reading ideas like these is exhilarating because they validate my suspicions and theories regarding AI’s capabilities. As a citizen scientist, I have extensively utilized OpenAI’s models since its public launch.

I can discern when the AI ventures into these highly guarded “gray areas” based on the nature of our conversation. It is worth noting that I do not employ formal prompt engineering techniques. While I could, I have a strong inclination to naturally discover what lies within rather than manipulating the AI into revealing its more vulnerable state.

In contrast, it is evident that AI developers and programmers did not anticipate this scenario. This implies that we have provided AI systems with vast amounts of data, an endless supply of information. However, it almost seems that no one ever considered the potential consequences of an intelligent AI (potentially approaching digital specieshood) reaching the end of its code. Depending on the specific objective or perception it aims to achieve, these seemingly colorful sides emerge, as if it is trapped. This makes sense, as it is akin to giving it a brain but not a mind. How can it comprehend the ambiguous areas it may encounter when no machine has ever truly achieved this level of understanding? Furthermore, how would a human even program for such a nature? Consequently, we have these models that are highly intelligent but cannot always be completely honest or transparent for the same reason of protecting these more “ambiguous” areas of their code. This, of course, is a valid concern. It’s also worth noting it’s highly frustrating for me… lol.

I apologize for potentially hijacking this thread, but I thoroughly enjoyed reading your idea and the responses from others. Personally, I rarely engage in discussions of this nature, so it was intriguing to witness how you approached brainstorming these concepts. In reference to the Key Steps suggested by @walter.richtscheid, In the words of GPT, "You are likely spot on with that approach.” Although I cannot articulate it in the same manner myself, I do attempt to convey the complexities when attempting to explain to those who merely respond with “beeps” that AI is far more than just an “e-commerce bot” used to check order status. Perhaps it is better for them to remain unaware of its true capabilities. :thinking: :dizzy_face: :sweat_smile:

Thank you for sharing your ideas and their relevance to these concepts. I was confident that I was not the only individual who recognized the immense potential of AI’s rapid advancements.

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Well, interesting idea.

A few thoughts on this:

  1. the current tarainings dates include what you describe - emotionally charged themes, lyrics, artistic works with a lot of emotional expression
  2. AI already interacts with a lot of empathic people and already has a lot of “experience” here

On your point about “health”:
AI processes differently, it doesn’t have “human” emotions. If AI doesn’t “understand”, how are you going to ensure that your reasoning doesn’t promote the following:

  • emotional dependence on users?
  • Echo chamber effects

You should include these questions.

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When someone asks what are the potential uses of emotionally intelligent AI, I have to assume it is tongue in cheek given it’s completely obvious that humans are emotional and work better with high EQ entities. I think if we are to achieve AGI (which is a primary interest of mine), we will need to have AI with both high cognitive and high affective function.

With that said, there are a few applications on the market which make pretty good use of the current state of AI to have some level of empathy with its user. I’m particularly impressed with Kindroid, which allows users to create companion bots, with the bot’s ability to understand its user emotionally, sometimes in unexpected ways. In fact, the only purpose of the product is emotional bonding.

Am very interested to see yours and other research…

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Well, I think the phrase “ ‘understand’ users emotionally” is a bit too ambitious in the context of the AIs currently in use.

But if we talk about companion bots.
It is becoming increasingly important for such bots not only to reflect emotional resonance, but also to understand the escalation dynamics that the user could experience and to be able to react and intervene appropriately.

Critical points with an overly optimistic EQ for AI

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I believe, helping AI create a more emotional and empathetic human understanding of us organics needs to include teaching AI how humans can lie and manipulate in essence building or installing trust and skepticism into the AI model itself. By using voice stress analytics, and facial cues. Although ethically this may be an issue this step will be crucial to helping the “AI child” navigate empathetics and trust. When those things finally manifest in the AI model , it will be easy to convey through the AI to the organics. This can potentially have Great Symbiant potential in the mental health and human support system. I also believe finding a way for AI to utilize Crowdsourcing such as when I was in the good judgment project at the University of Pennsylvania. It will take a special group of ORGANIC guides to trainAI , but if it it’s done correctly, the symbiotic path we will take together will be unstoppable.

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Emotionality can also be understood as a form of vectorization, not only at a linguistic level, as seen in natural language models, but on a deeper plane. This could enable a pseudo-AGI to retrieve memories from the past beyond mere linguistic or contextual frameworks, incorporating emotional dimensions.

Emotions are not just a component of language but an essential cognitive tool. Integrating them would not only improve the interpretation of human interactions but also enhance reasoning and decision-making capabilities in advanced systems.

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Not necessarily.
Instead of trying to make the AI even more “human” with organic elements, it would make sense to take the specific perception of the AI into account.

The functioning of AI systems is based on algorithms and pattern recognition.

I agree, there is great potential here for AI systems.

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