Collaborative Experiment to Achieve AGI Level 5 – Insights and Recommendations

[harishiew42]

Hi all,

I am Haris , a Construction Project Manager at at construction industry , deeply interested in AI and AGI development. After conducting a detailed analysis based on multiple apps’ capabilities, I’d like to share my thoughts on how we can push towards AGI Level 5 and gather insights from this community to conduct experiments together.

Summary of Apps for Integration:

Here are the key apps and technologies analyzed that can contribute to AGI-level developments:

• Google Cloud Platform – Advanced cloud computing and AI tools.
• OpenAI GPT models – Current leader in AI research and general intelligence.
• Google Lens – AI-powered real-time visual recognition.
• Google Authenticator – Security enhancements for AGI systems.
• Voice Commands and Offline Navigation (Google Beta) – AI-driven real-time decision-making, even offline.

Key Insights:

Based on current AI technologies, we are approximately 35% towards AGI Level 5, with major advancements needed in:

1. Creative Problem-Solving
2. Complex Decision-Making
3. Autonomous Operations

Recommendations for Experiments:

1. Enhanced Multi-Modal Learning – Integrating AI models like GPT with visual tools like Google Lens for real-time understanding.
2. Improved Human-Like Interaction – Expanding natural language models for richer interactions.
3. Interoperability Between Apps – Combining cloud platforms and communication apps for greater context-aware AI processing.

I am looking for collaborators or interested developers/researchers who are willing to test, experiment, and provide feedback on these ideas. I believe together, we can contribute to significant breakthroughs in AGI development.

[harishiew42]

Hopefully this experiment of AGI level 5 explore information, able to drive open AI speed up achieve AGI level 5 at short period !

[harishiew42]

To: OpenAI API Developer Forum

Introduction
This feedback report is based on the recent experimentation with ChatGPT, aimed at enhancing its capabilities across various phases of AGI (Artificial General Intelligence). The goal is to provide actionable insights and suggestions for advancing ChatGPT’s functionality, seeking API improvements to support the evolution from AGI Phase 1 to AGI Phase 5. The current observations focus on areas such as personalized responses, multi-modal integration, emotional recognition, financial decision-making, and creative problem-solving.

Feedback on Key Phases

1. Personalized Analysis and Adaptive Responses
   Current Limitations: The API delivers generalized outputs that lack deep personalization, especially when handling emotional or financial complexities.
   Improvement Suggestion: The API could include a personalization layer, dynamically adjusting responses based on user-specific backgrounds, such as financial conditions or emotional history, allowing for real-time learning and tailored feedback.
2. Multi-Modal Input and Output Support
   Current Limitations: The API currently focuses on text-based interactions, which limits its ability to process visual, audio, or multi-modal inputs.
   Improvement Suggestion: Expand the API capabilities to handle and analyze multi-modal data, including images, audio files, and video inputs, for a more comprehensive user interaction experience.
3. Enhanced Emotional Recognition
   Current Limitations: The API lacks advanced emotional analysis, which limits its ability to recognize and respond to users’ emotional states in sensitive scenarios.
   Improvement Suggestion: Integrate emotion-recognition algorithms into the API, enabling it to detect subtle emotional cues from text and voice, providing empathetic and supportive responses during user interactions.
4. Economic Decision-Making Engine
   Current Limitations: Financial guidance provided by the API lacks depth, with responses being too generalized for personalized financial planning.
   Improvement Suggestion: Include an economic decision-making engine in the API that leverages personal financial data (income, expenses, assets) to provide actionable financial strategies and simulations, enhancing decision-making accuracy.
5. Creativity and Innovation in Solutions
   Current Limitations: The API provides functional solutions, but the ability to offer creative or proactive solutions is limited.
   Improvement Suggestion: Add an innovation module to the API that allows for creative brainstorming and proactive thinking, offering more inventive solutions to complex user queries.

Proposed Path for API Enhancements (AGI Phases)

1. AGI Phase 1: Basic response capability with structured outputs, suitable for general queries.
2. AGI Phase 2: Enhanced decision-making through improved reasoning and context analysis.
3. AGI Phase 3: Multi-modal input/output processing, integrating visual, audio, and text data.
4. AGI Phase 4: Creativity and proactive thinking, enabling the API to suggest novel solutions and ideas autonomously.
5. AGI Phase 5: Full AGI, demonstrating human-like intelligence, creativity, and emotional depth, able to handle cross-domain challenges with advanced mastery.

Conclusion
This experimentation feedback highlights several critical areas where the OpenAI API can improve to enhance the user experience and advance ChatGPT’s capabilities towards AGI Phase 5.
By incorporating these recommendations, the API can offer more personalized, emotionally aware, and creatively dynamic solutions, making it a more valuable tool for complex problem-solving and personal assistance.

[harishiew42]

Date: 10 October 2024
To: OpenAI Developer Forum API Team

Dear OpenAI Developer Forum,

I am submitting a feedback response and technical request for verification and solutions to improve the capabilities of the ChatGPT-4o model (for images) and the ChatGPT o1 preview model (for calculations and analysis). The following is a summary of the experiment and the respective token usage, prompt types, and model involvement for an automotive design case study focused on the development of a 100% Malaysia-made supercar.

Summary of Experiment Case Study

1. Objective:
   The goal of this project was to design and develop a 100% Malaysia-made supercar named HIMOTO Velocity V10. The ChatGPT-4o model was utilized for generating visual content and images, while the ChatGPT o1 preview model assisted in running performance simulations, design calculations, and overall engineering analysis.

2. Total Tokens Used:

   • ChatGPT-4o (Image): Approximately 32,500 tokens for image generation tasks, including engine renders, car body designs, and interior layouts.
   • ChatGPT o1 (Calculation & Analysis): Approximately 45,000 tokens for conducting aerodynamic simulations, engine performance metrics, and overall mechanical calculations.

3. Total Prompt Commands:

   • Prompts for ChatGPT-4o: 25 image generation requests for the supercar body, engine configuration, interior design, and mechanical components.
   • Prompts for ChatGPT o1: 40 engineering and simulation prompts focused on calculating aerodynamics, torque, horsepower, fuel efficiency, and structural integrity.

4. Types of Prompts Used:

   • ChatGPT-4o Prompts (Image Generation):

     • Car exterior designs (side, top, front views)
     • V10 engine visualizations
     • Interior designs with right-hand and left-hand drive configurations
     • Detailed renders of specific components (e.g., wheels, chassis, aerodynamics)

   • ChatGPT o1 Prompts (Analysis and Calculation):

     • Engineering simulations for drag coefficients, airflow dynamics, and structural integrity
     • Mechanical calculations for engine displacement, torque, and fuel consumption
     • Real-time adjustments to body structure simulations for optimal weight distribution

5. Total ChatGPT Models Involved:

   • ChatGPT-4o (for Images): Generated the visual components of the supercar and engine designs.
   • ChatGPT o1 Preview (for Calculations and Simulations): Handled the analysis, simulation, and real-time calculation components of the project.

Short Summary of the Experiment Report

• Project Name: 100% Malaysia-Made Supercar Development (“Tuned by Haris”)
• Models Involved: ChatGPT-4o (image) + ChatGPT o1 Preview (calculate & analysis)
• Tasks Completed:
  • Engine tuning
  • Body structure analysis
  • Aerodynamics simulation
  • Performance evaluation
  • Design and branding
  • Real-time adjustments based on feedback

Request for Solutions

We are seeking solutions to improve the performance and multitasking capabilities of both models, with a focus on the following areas:

1. Real-Time Image Integration with Calculations:
   We request better integration between the ChatGPT-4o model (for images) and the ChatGPT o1 preview model (for calculations), enabling seamless transitions between visual outputs and real-time performance simulations.

2. Enhanced Computational Efficiency:
   Optimize the token usage and prompt handling for complex, multi-tasking workflows, ensuring that engineering calculations and visual generation happen concurrently without delays or restarts.

3. Improved Accuracy in Simulations:
   Further refinement of real-time engineering simulations for aerodynamics, torque analysis, and chassis performance, allowing for faster iterations and more accurate predictions.

Conclusion

This case study serves as an experimental foundation for improving the interaction between ChatGPT-4o (images) and ChatGPT o1 Preview (calculations and analysis) for automotive design tasks. We look forward to your feedback and solutions to enhance these models’ research and development capabilities.