AI Traits: The Key to Better Interactions
Can we define AI 'traits' to improve human-AI interaction?
While experimenting with the latest LLM model in Hugging Face's chat with Llama 3, I stumbled upon an unexpected outcome - I accidentally made the AI "angry"!
This experience led me to reflect on the importance of AI personality in our work. I was inspired by an article by Emily Campbell, which delved into the concept of AI personality and its potential impact on our interactions with AI agents.
What is AI personality?
When we talk to someone, we ask questions and clarify their thoughts. Sometimes, we even discover new things together. But AI systems, designed to do tasks efficiently, often don't understand human behavior, emotions, and preferences. This can lead to:
Unfriendly interactions that frustrate users
Limited personalization that doesn't meet individual needs
Miscommunication that leads to failed interactions
Lack of trust in AI systems
Defining AI personality is complex because there's no single definition of human personality. Also, AI models can inherit human biases from the data they're trained on, making it hard to ensure fair AI behavior.
While I'm not trying to solve the technical Large Language Model (LLM) problem, I think this UX framework by Shape of AI could help people refine their prompts and get more predictable results from AI models.
Refining AI Interactions
Imagine refining your prompts to get more predictable results from AI models. By incorporating key features into AI, we can make AI more efficient and effective. This means creating a more streamlined and intuitive way for users to interact with AI models.
Three Key Parts
To achieve this, we need three important features:
Filters / Parameters: Let users choose what they want to see in the results, so they get what they need.
Model Management: Let users pick which AI model to use, so they can choose the best one for the job.
Personal Voice: Make sure the AI's answers sound like they're coming from the user, so it feels more like a conversation.
Filters and Parameters
Filters and parameters allow users to fine-tune their requests and get more accurate results. By adding specific instructions to a prompt, users can include or exclude certain features, while filters work behind the scenes to refine the output.
Examples:
Commercial settings: parameters can be used to assign metadata to work, enabling tailored licensing agreements and specific commercial terms.
Academic contexts: parameters help control variables, leading to more predictable and reliable results.
Educational settings: parameters can demystify AI models for non-technical users, providing a hands-on understanding of how bias is embedded in datasets.
Pros:
Gives users more control and agency over their results
Enables more accurate and reliable outcomes
Facilitates tailored solutions for specific industries and use cases
Helps non-technical users understand AI models and bias in datasets
Cons:
May require technical expertise to use effectively
Can be time-consuming to set up and refine
May not be suitable for all types of AI models or use cases
Model Management
Allowing users to choose which AI model to use for their prompts can be beneficial in various ways. Users may want to switch models due to differences in accuracy, updated references, cost, aesthetics, or security concerns.
Examples:
Newer models may be more prone to hallucinations and errors, but contain updated references, making them suitable for low-risk research.
Older models can be used to hone a prompt before applying it to updated data.
Image generators may prefer different models for their aesthetics, similar to choosing a specific album on vinyl for its vibe.
Researchers may want to compare results across models.
Pros:
Gives users more control and flexibility in their workflow
Allows users to learn and adapt to different models, becoming more advanced users of the tool
Enables co-ownership of the model through feedback and prompt results, improving the model overall
Creates commercial opportunities for model blending and testing
Cons:
May lead to poor customer experiences and missed expectations if limitations are not clear
Requires model providers to remain accountable for the results, even if users can switch models
May lead to regulatory issues, with entire models being restricted from use in certain geopolitical areas due to their policies.
Personal Voice
To use AI tools professionally, users need confidence that the output will match their voice, tone, and preferences consistently. This means having the same tone of voice, including the same parameters or inputs into prompts, and storing keywords and templates for future use.
Examples:
Defining a brand or individual's voice to ensure consistency across teams and platforms
Using voice and tone controls to understand how different personas might respond to a prompt in research
Storing terms or information to draw from in the future, such as brand information or product offerings
Pros:
Gives users fuller control over their output
Makes AI more useful in commercial settings
Benefits small-business operators and teams by saving time and increasing productivity
Has non-commercial use cases, such as language learning and rehabilitation
Cons:
Raises ethical concerns about replacing human writers with robots
May lead to the Uncanny Valley effect, where the output sounds non-human enough to feel off
Requires careful consideration of the use cases and limitations of the technology
TLDR
AI personality matters! By incorporating filters, model management, and personal voice, we can create more human-like AI interactions that are efficient, effective, and trustworthy.
Latest News
Stripe for GitHub Copilot and New Developer Tools
Michelle, the lead API designer at Stripe, introduces new developer tools to improve the developer experience. She explains how Stripe's API design principles, such as gradual reveal, reducing dead ends, and creating pits of success, help developers maintain flow state. The new tools include:
Stripe for GitHub Copilot: A VS Code extension that uses generative AI to help developers build and debug their Stripe integration.
Workbench: A new home for developers to build, monitor, and debug their integration, with features like Inspector, API Explorer, and shell.
Sandboxes: A next-generation test mode that allows developers to create isolated environments for testing and development.
Event Destinations: A feature that sends Stripe events directly to AWS, allowing developers to easily consume events in popular AWS tools.
Why it matters:
These new tools aim to improve the developer experience, reduce frustration, and increase productivity. By providing a more streamlined and intuitive integration process, Stripe hopes to help developers build better products and maintain a flow state.
Hello!
I'm Kevin Wang, a product manager by day and a passionate builder of tulsk.io, a Gen-AI Tool, in my free time. I'm on a mission to simplify complex concepts in web3 and AI, so they're easier to grasp and apply to your own learning and growth.
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Good point !