How AGI-lite happens

GPT-4o, OpenAI's latest generative model, as a foundation for developing an advanced AI system: This system aims to seamlessly integrate multimodal sensing, real-time interaction, and sophisticated automation capabilities, this starts to eek us into the realm of AGI. To me, AGI is an and artificially created, generally intelligent software system,

Basically: everything our brains can think about and process.

Setup

The envisioned architecture for this AI system is practical. At its core, an iPhone 15 Pro with a high-resolution camera captures screen content in real-time, utilizing computer vision techniques for image processing and object detection. Leveraging GPT-4o's extremely enhanced text comprehension capabilities, the system meticulously analyzes and interprets this visual content, employing natural language processing for contextual understanding. The next step involves converting these instructions into vocal commands using ChatGPT-4o's build-in voice processing and dictation technology, which is delivered through a high-quality microphone, utilizing speech recognition for accurate recognition, MacOS Diction can do this incredibly well today. This quality vocal output by ChatGPT-4o model is essential, as it allows MacOS to input commands into any interface, executed in a consistent, robotic cadence at any speed. This setup not only simplifies the interaction process but also enhances the system's efficiency and accuracy due to the integration of ML/DL (machine and deep learning) components.

To further enhance this AI system, several features can be incorporated:

  • Multimodal Sensing and Interaction: By integrating diverse sensory inputs such as cameras, microphones, and lidar sensors, along with various interaction methodologies like voice dictation, gesture recognition, and haptic feedback, the system can more comprehensively perceive and engage with different environments, utilizing sensor fusion and multimodal processing techniques.
  • Orchestration and Automation: The AI system could be empowered to orchestrate and execute complex tasks using multiple modalities, including voice commands, API interactions, and robotic manipulators, enabling the system to perform a wide range of functions seamlessly and autonomously, utilizing workflow management and automation tools.
  • Video-Based Stable Diffusion: Implementing video-based stable diffusion technologies, like to those pioneered by Pika Labs, can significantly improve the system's ability to analyze and interpret visual data, utilizing computer vision and machine learning techniques for video analysis and understanding.
  • Knowledge Graph and Memory Systems: Establishing an extensive knowledge graph, coupled with a sophisticated memory system, would support the retention of information, situational adaptation, and continuous learning from past experiences, utilizing graph databases and cognitive architectures for knowledge representation and retrieval.
  • Self-Improvement and Reflection: Incorporating mechanisms for self-assessment and optimization would enable the AI system to identify performance deficiencies, modify its operational parameters, and create new instructions to enhance its functionality, utilizing meta-learning and self-modifying code techniques for self-improvement.

Baking a Cake: A Test Case To illustrate the potential of this advanced AI system in the wild, let's consider an experiment: baking a cake without any prior knowledge of baking, cooking, or oven operations. The system begins by analyzing a youtube, the system will watch the youtube video and use video to text and video-based stable diffusion, allowing it to grasp the sequence of actions and ingredient interactions. Next, it utilizes multimodal sensing and interaction to recognize and manipulate ingredients, utensils, and appliances. The AI then generates vocal commands to guide the preparation process, leveraging GPT-4o's video feed understanding capabilities to interpret recipe instructions.

As the cake mixture takes shape, the system employs orchestration and automation to expertly combine ingredients and adjust cooking parameters. Moreover, the system uses stable diffusion video analysis to predict future events in the baking process, such as anticipating the opening of the oven door and understanding the visual cues associated with it. This enables a robot arm to duplicate the action, accurately opening the oven door and ensuring the cake is properly baked. Throughout the process, the knowledge graph and memory systems continuously learn and adapt, refining the baking technique.

By predicting and replicating these steps, the system demonstrates its ability to learn and execute complex tasks without prior knowledge, paving the way for autonomous cooking and beyond. Extrapolating this idea further, the system could potentially:

  • Predict and replicate various cooking actions, such as stirring, flipping, or seasoning
  • Anticipate and respond to different cooking scenarios, like adjusting heat or handling ingredients
  • Learn to recognize and create specific cooking techniques, such as braising or roasting
  • Develop an understanding of cooking principles, enabling it to improvise and create new recipes

Deploying a Kubernetes Cluster: A Test Case To showcase the potential of this advanced AI system for work, let's consider another complex task: deploying a Kubernetes cluster without any prior knowledge of Linux, DevOps, or software engineering. Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerized applications. Deploying a Kubernetes cluster typically requires significant technical expertise, involving tasks such as setting up virtual machines, configuring networks, and managing containers. However, with the help of the AI system, even someone with no technical background can accomplish this task. The system begins by querying the LLM, which is essentially a vast knowledge base, for information on Kubernetes and the steps involved in deploying a cluster.

GPT-4o then processes this information and generates a sequence of natural language instructions that are easy to understand, even for non-technical users. Using its vision capabilities, the AI system interprets the terminal window, which is a text-based interface for interacting with the operating system, identifying prompts, commands, and outputs. It then converts the generated instructions into voice commands using the speech module, which are subsequently transcribed by the dictation software and executed in the terminal. As the deployment progresses, GPT-4o continuously monitors the terminal window, analyzing the output and adjusting its instructions based on the feedback received. If errors occur, the AI system can consult the LLM for troubleshooting guidance and generate corrective actions. Throughout the process, the AI system learns and adapts, refining its understanding of Kubernetes and the deployment process. By autonomously executing this complex task without prior knowledge, the AI system demonstrates its ability to learn and apply new skills in real-time, opening up possibilities for streamlining various aspects of software engineering and DevOps, making these technical tasks more accessible to a broader audience.

The advanced integration of these capabilities could potentially lead to AGI-like functionalities, such as:

  • Cross-domain learning and adaptation
  • Human-like understanding and interaction
  • Complex and creative task management
  • Perpetual self-refinement and improvement

This level of sophistication would represent a significant leap forward in AI development, bringing us closer to achieving true AGI, whatever the flying f that means :)

It seems likely an MVP of this will come to market in Q4 2024.

with love!

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Never written by AI, often proofed by it.