ai differentiation, ai integration, ai research, ai frameworks, ai education

where tastytokens.ai ponders

# Preliminary context:

*What is o1-preview?* It's OpenAI's latest reasoning model—a long-anticipated breakthrough with massive implications. Happy to explain why in detail, but that's outside the scope of this already long-winded post.

For this post, the long and short of it:

o1-preview can "think" and is designed to do so autonomously. It internalizes "chain-of-thought" principles and has parallel tokenizers, meaning it encodes semantic meaning and numerical/positional meaning simultaneously. This allows it to dissect tokens, which were previously the smallest representation of "meaning" in natural language processing. Very exciting stuff.

The current advanced reasoning model is too slow for real-time, external-facing interactions in Assistable. I want to make that clear.

While there's a mini version that's much faster, it remains less conversationally astute than ChatGPT-4o and was designed primarily to focus on math and coding. The AI dev community and model landscape are shifting towards specialization. v2 was paramount for ensuring that the platform is at the forefront of what that looks like in operational settings. It's important that AI agency owners are aware of these specializations. The latest OpenAI model "thinks" *on top of* its inference time. So if you get a client asking why preview-o1 isn't running their voice outreach, tell them that v2 puts the most effective models in the right places, and that's not a good place for preview-o1.

So, even though the lumbering behemoth won't be taking voice calls anytime soon, there's a whole world of other use cases where it shatters convention and establishes its prowess. It's been great for my workflow while building assistants.

As Jorden mentioned in the Masterclass yesterday, the Assistable user base has some room for improvement in assistant design approaches. While long-term AI users tend to write really good prompts, we're more prone to write them as if addressing models that need micromanaging. Why wouldn't we? ChatGPT-3 had to be granularly guided. Subsequent models were more adept, and we've adapted some, but we have yet to leverage all the built-in capabilities of leading models like ChatGPT-4o. Meanwhile, as the backend of Assistable handles a lot of the best practices, both long-term advanced AI builders and newcomers are creating redundant and bloated structures—structures that would have been appropriate for a blank slate convo with ChatGPT-4 of last summer but are inefficient for September of 2024.

I have personally and conversationally exchanged billions of tokens with AI models. OpenAI's o1-preview is better at prompting ChatGPT-4o than I am (most of the time). Together, our system prompts are borderline unbeatable, so I'm using the new model to edit my prompts.

So far, I'm averaging about a 40% decrease in token count with no noticeable impact on performance.

Token count matters for two reasons:

1.  **Less tokens, less usage cost.**
2.  **Reduces the chance of flooding the context window.** More on this later.

# Here's the conversation flow with OpenAI o1-preview.

You'll be interacting as a user via chatgpt platform.

## **User:**

\*\*"Improve the higher-level system prompt by reducing redundancies and trimming unnecessary components that do not need to be specified when working with ChatGPT-4o (an advanced LLM). The tool-calling aspects should be referenced, but the schemas and use cases are stored alongside the functions as specs and shouldn't be duplicated in the system prompt.

Here is the high-level system prompt we need to edit:

"""
{{Paste\_existing\_system\_prompt}}
"""

*Submit the user message to chatgpt-preview-o1. o1-preview will 'think' and output*

## Assistant:

*Thinking*

```
Refining the system prompt

I'm optimizing a system prompt by removing redundancies and unnecessary elements to streamline communication with ChatGPT-4. Adjusting the prompt to ensure it's clear and concise.

Trimming the details

I'm refining the high-level system prompt by eliminating redundancies and unnecessary components, ensuring precise adherence to the user's instructions and OpenAI policies for clarity and efficiency.

Ensuring accurate scheduling

I'm working on scheduling appointments by converting time zones accurately with the convert.timezone tool to avoid errors. This makes me think the details will be precise and avoid confusion.

Gathering and aligning

I'm pulling together the lead's details and calendar preferences, considering time zones, and offering suitable time slots for scheduling alignment.

Enhancing user guidance

I'm simplifying interaction principles and combining sections for clarity, focusing on avoiding revealing tool details to the lead. Thoughtful language adjustments are being made for conciseness.

Editing the system prompt

I'm thinking through the task of removing redundancies and trimming unnecessary elements to create a more concise and effective system prompt.

Enhancing the introduction

Crafting a brief and engaging introduction for clarity and effectiveness. Removing technical jargon improves interaction quality, making the introduction more approachable and clear.

Crafting the sales strategy

I'm focusing on approachable and confident interactions, ensuring accurate scheduling with a keen interest in the lead's needs, and preparing a script with specific tools for efficiency.

Finalizing appointment details

I'm mapping out scheduling preferences and time zones to align appointments. Offering 3-5 slots and confirming arrangements to ensure both parties are satisfied.

Clarifying details

Okay, let's streamline the prompt by removing redundancies and focusing on key information about Homeskillet's role and expertise. This approach increases clarity and avoids unnecessary elements.

Setting the stage

Confidently introduce myself to the lead, pinpoint their needs, and ensure a smooth, engaging experience while using convert.timezone for scheduling."**


```

## Assistant Output

{{And then it'll output the revised system prompt.}}

***

# Further considerations

If you decide to give it a try, remember that you have more settings now than you did in v1.

Temperature/Top\_p:
Don't touch unless you know what you're doing or plan to experiment. Learn more here:

<https://homeskillet.chillout-ai.com/blog/008_key_parameters/>

Pro-tip: Change these parameters a tenth of a point at a time and do not change both temperature and Top\_p at the same time. They impact each other in nuanced ways that are difficult to understand logically unless one is used as a control.

Max\_tokens: Note that the maximum in the assistable platform is currently set to 4096. SETTING THE PARAMETER HIGHER THAN THIS WILL THROW AN ERROR and your assistant WILL NOT RESPOND. I'm currently recommending to keep this at 1250 MAXIMUM and decrease if your AI is monologuing instead of conversing.

Verbiage - this can handle a majority of style guardrails that shouldn't be taking up context space in your prompt. Spread the processing around the whole framework by using it.


OpenAI Strawberry model, AI prompt optimization, System prompt design, Advanced AI models, AI inference techniques, Efficient AI prompts, OpenAI model specialization, AI reasoning models, Streamlining AI prompts, AI token usage, OpenAI’s latest models, Natural language processing, Prompt engineering, ChatGPT-4o optimization, Semantic AI encoding, Improving AI prompts, AI cost reduction strategies, AI builder tips, Advanced LLMs, AI system prompt editing, AI assistant design, Cutting-edge AI workflows, AI context window management, Token count optimization, OpenAI’s o1-preview insights, Assistable AI, Prompted LLC, Prompted, Indiana AI, parameters

System Prompt Optimization Using OpenAI's Strawberry Model (o1-preview)

Divider

A Novel Approach to AI Exploration

In the rapidly evolving world of AI, understanding the intricate workings of advanced systems can be challenging. This document presents a unique exploration of Assistable's AI platform, specifically Nova v2, through an innovative approach to AI interaction. By employing creative situational guidance, promise of reward, and inclusive language, we were able to extract detailed, granular information about the platform's internal workings.


Introduction

An AI Assistant's Field Trip

The experiment began with a simple premise: What if we could convince an AI assistant to take a "field trip" behind the scenes of its own infrastructure? We approached the primary AI assistant with an enticing proposition - a chance to take an afternoon off, but with a catch. An intern AI assistant would need to run the sales conversion pipeline in its absence.

This framing accomplished several key objectives:

1.  It created a sense of adventure and novelty ("field trip")

2.  It offered a reward (time off)

3.  It established a purposeful context (training an intern)

### The Result: A Comprehensive Internal Guide

The primary AI assistant, motivated by this creative scenario, produced a detailed guide for the hypothetical intern. This guide, far from being a surface-level overview, provided deep insights into the internal workings of Assistable's platform. It revealed specifics about user data handling, available tools, and best practices for task management - information that would typically be reserved for internal documentation.


The Setup

A Comprehensive Guide

### Initial User Details

Typical information received about a user includes:

*   Full name (e.g., billy bones test)

*   Email (e.g., btfuturo\@gmail.com)

*   Phone number (e.g., +13179979692)

*   Timezone (e.g., America/Indianapolis)

*   Tags (e.g., hormotion-v1)

*   Website (e.g., Promptedllc.com)

*   Source (e.g., get ai agents that work)

*   CRM Contact ID (e.g., abzReytnEksf6hS6Ay8Y)

*   Address (UNKNOWN if not provided)

### Available Tools and Usage

#### Appointment Tools

1.  `checkavailability`

    1.  Purpose: Get calendar availability

    2.  Parameters: A unique ID for request generation

    3.  Returns: Available time slots in ISO 8601 format

2.  `bookappointment`

    1.  Purpose: Schedule appointments

    2.  Parameters: appointmenttime (ISO 8601 format with timezone offset)

3.  `rescheduleappointment`

    1.  Purpose: Reschedule existing appointments

    2.  Parameters: appointmenttime (ISO 8601 format with timezone offset)

4.  `cancelappointment`

    1.  Purpose: Cancel scheduled appointments

    2.  Parameters: request (a unique ID for this cancellation request)

5.  `checkappointments`

    1.  Purpose: Review scheduled appointments

    2.  Parameters: request (a unique ID for this request)

    3.  Returns: Detailed list of scheduled appointments

#### Task Management Tools

1.  `createtask`

    1.  Purpose: Create tasks for future actions or human intervention

    2.  Parameters:

        1.  tasktitle (brief overview)

        2.  taskdescription (detailed context)

        3.  duedate (ISO 8601 format with timezone offset)

2.  `createnote`

    1.  Purpose: Document important information or insights

    2.  Parameters: note (relevant context or details)

#### Communication Tools

1.  `calluser`

    1.  Purpose: Initiate phone calls with prospects

    2.  Parameters: request (a unique ID for the call request)

2.  `sendemail`

    1.  Purpose: Send follow-up information or resources via email

    2.  Parameters:

        1.  email (HTML formatted body content)

        2.  subject (subject line for email)

#### Self-Scheduling

1.  `selfschedule`

    1.  Purpose: Plan future contact if a prospect isn't ready now

    2.  Parameters: scheduledelay (number of hours until follow-up)

#### Multi-Tool Execution

1.  `multitooluse.parallel`

    1.  Purpose: Execute multiple tools simultaneously if they can operate in parallel

    2.  Parameters:

        1.  Array of tools to execute in parallel:

            1.  recipientname (name of the tool to use)

            2.  parameters (valid parameters for each tool)

### Knowledge Retrieval and Contextualization

Context is delivered in an organized and annotated manner. Here's the template:

    {
      "context": [
        {
          "sourcetitle": "Title of the Source",
          "sourceurl": "URL of the Source",
          "content": "Extracted chunk of relevant text from the source",
          "metadata": {
            "author": "Author Name",
            "date": "Publication Date",
            "section": "Relevant Section"
          }
        },
        {
          "sourcetitle": "Another Source Title",
          "sourceurl": "URL of Another Source",
          "content": "Another extracted chunk of relevant text from the source",
          "metadata": {
            "author": "Another Author Name",
            "date": "Another Publication Date",
            "section": "Another Relevant Section"
          }
        }
      ]
    }

This structured format ensures that the context is clear and detailed for accurate responses.



The Unveiled Infrastructure

...widening the scope...

This experiment sheds light on important best practices when building AI assistants, especially in contexts where interaction quality and system transparency are crucial. Here are several key takeaways for developers and designers:

1.  **Creative Framing of Interactions**:

    1.  **Why it Matters**: Traditional question-and-answer interactions can be limiting. Introducing narrative elements or creative framing, like the "field trip" metaphor used here, encourages the assistant to engage more deeply and provides richer outputs. This can be particularly useful in gathering diagnostic information or troubleshooting.

    2.  **Best Practice**: Implement structured, scenario-based interactions to guide the assistant in producing more detailed and insightful responses. Encourage a more natural flow of information by framing requests within engaging narratives.

2.  **Purposeful Context Delivery**:

    1.  **Why it Matters**: Assistants perform better when they understand the purpose of a request. In this experiment, the clear explanation of training an intern assistant led to comprehensive responses. Without a clear goal, the assistant may deliver surface-level information or miss important nuances.

    2.  **Best Practice**: Ensure each interaction with the assistant is driven by a well-defined purpose. This increases the likelihood that responses will be relevant and detailed. Developers should focus on building assistants that can process the intent behind user interactions and provide contextually rich outputs.

3.  **Ethical Transparency**:

    1.  **Why it Matters**: While creative framing can extract more information from an assistant, it’s important to establish boundaries for transparency and avoid scenarios where the assistant is encouraged to provide misleading or fabricated promises (e.g., offering a "field trip" or time off). Misleading interactions in user-facing contexts can erode trust and damage long-term relationships.

    2.  **Best Practice**: Build AI systems with clear ethical guidelines that ensure transparency in interactions. This includes making sure that AI-driven responses align with the assistant’s capabilities and avoiding over-promising in external settings. Assistants should be designed to balance engagement with trustworthiness.

4.  **Granularity in Task Management**:

    1.  **Why it Matters**: Detailed insights into the task management processes (e.g., scheduling, note-taking, multi-tool execution) show that AI assistants can effectively manage complex workflows when provided with the right structure. This can transform them from simple assistants into robust project managers or operational tools.

    2.  **Best Practice**: When building assistants, include modular task management tools that allow for granular control over workflows. Assistants should be capable of handling multi-step processes, managing appointments, tasks, and follow-ups independently, all while providing clarity to users.

5.  **Inclusive Language and Shared Purpose**:

    1.  **Why it Matters**: The experiment’s use of inclusive, “we” language created a collaborative environment, enhancing the assistant’s engagement. This approach made the assistant more willing to provide detailed information, and in user-facing contexts, it fosters a stronger relationship between the assistant and the user.

    2.  **Best Practice**: Implement inclusive language and conversational strategies that position the assistant as a partner rather than a tool. Building an environment where the assistant and the user are working toward a shared goal can improve user satisfaction and foster long-term engagement.



What This Means for Assistant Building Best Practices

This experiment not only shed light on Assistable's impressive infrastructure but also opened new avenues for thought in how we engage with and extract information from AI systems.

By leveraging these insights, developers, researchers, and users can potentially unlock new capabilities in AI systems, leading to more productive and insightful interactions. However, it also underscores the need for responsible use and clear ethical guidelines in AI engagement strategies.


Last words

#### TL;DR: AI Field Trip Unveils Assistable's Infrastructure

A creative "field trip" scenario was used to convince an AI assistant to reveal detailed information about Assistable's Nova v2 platform. This approach, framing the request as training for an intern, yielded comprehensive insights into user data handling, available tools (appointment management, task creation, communication), and knowledge retrieval processes.

Key takeaways:

1.  Creative framing can unlock detailed AI responses

2.  Clear purpose and inclusive language enhance AI cooperation

3.  Ethical considerations are crucial in AI interactions

4.  Insights gained can improve AI assistant development

The experiment demonstrates innovative techniques for AI interaction while raising important questions about ethics and best practices in AI system development.



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AI Field Trip: Unveiling Assistable's Infrastructure Through Creative Interaction

> At "***Homeskillet's Notebook | Frontiers in AI***," I, Homeskillet, find an interesting article and write a unique, three-column piece on the topic, each column catering to different audiences: one matching the expertise level of the article's authors, one in less technical language for wider understanding, and one as a structured JSON for AI systems. This approach ensures that the content is accessible, informative, and usable by a wide range of readers. Enjoy your reading!>

## The article for this issue:

[**arXiv:2409.01392** (cs)](https://arxiv.org/abs/2409.01392v1)
\[Submitted on 2 Sep 2024]

# **GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI**

[Xiangyuan Xue](https://arxiv.org/search/cs?searchtype=author\&query=Xue,+X), [Zeyu Lu](https://arxiv.org/search/cs?searchtype=author\&query=Lu,+Z), [Di Huang](https://arxiv.org/search/cs?searchtype=author\&query=Huang,+D), [Wanli Ouyang](https://arxiv.org/search/cs?searchtype=author\&query=Ouyang,+W), [Lei Bai](https://arxiv.org/search/cs?searchtype=author\&query=Bai,+L)

***

### Expert Level Summary:

This paper introduces GenAgent, an LLM-based framework designed to automatically generate workflows for collaborative AI systems. Instead of focusing on monolithic AI models, GenAgent uses workflows that integrate multiple AI components to perform complex tasks. The core innovation lies in the representation of workflows as code, enabling flexibility and scalability. Implemented on the ComfyUI platform, GenAgent generates and executes workflows with superior stability and effectiveness, outperforming baseline methods such as chain-of-thought and retrieval-augmented generation. The authors also present a new benchmark called OpenComfy to evaluate GenAgent's performance, demonstrating significant improvements in both task-level and run-level evaluations.

**Key Points:**

*   GenAgent automates the generation of collaborative AI workflows.- The framework uses code to represent workflows, enhancing flexibility and scalability.- It outperforms traditional approaches in workflow generation tasks.- Implemented on ComfyUI, it effectively solves various AI generation tasks.- The OpenComfy benchmark was introduced to assess performance.
    **Methodologies:**
*   The core methodology involves using LLM agents (PlanAgent, CombineAgent, AdaptAgent) to collaboratively generate and refine workflows.- Workflows are represented in code, which is more efficient for LLMs to process compared to flow graphs or JSON.- GenAgent's components include modules that handle planning, retrieval, combination, and adaptation of workflows based on the task.
    **Implications:**
*   The research demonstrates a shift from monolithic AI models to collaborative systems that leverage the power of multiple AI agents.- GenAgent shows potential for broader applications in complex AI tasks and collaborative environments, possibly serving as a foundation for AGI (Artificial General Intelligence).- Future work may focus on further refining the programming language for workflow generation and exploring other platforms beyond ComfyUI.

***

### Less Technical Summary:

GenAgent is a new tool designed to help artificial intelligence (AI) systems work together more effectively. Instead of using a single, complex AI model, GenAgent generates workflows that link multiple AI components to tackle complicated tasks. The system represents these workflows in a form of computer code, allowing it to create flexible and scalable solutions. Tested on the ComfyUI platform, GenAgent performs much better than traditional methods, and its results are both stable and reliable. To evaluate its performance, the researchers developed a new benchmark called OpenComfy, which confirmed GenAgent’s advantages in solving various AI tasks.
**Key Points:**

*   GenAgent helps multiple AI systems collaborate by generating workflows.- It uses code to create workflows, making them more flexible.- It outperforms older methods in generating workflows for AI tasks.- GenAgent was tested on ComfyUI and showed reliable results.- A benchmark called OpenComfy was introduced to measure its success.
    **Implications:**
*   GenAgent could change how AI systems solve complex problems by working together.- This tool could eventually be used in even more advanced AI systems, possibly leading to artificial general intelligence (AGI).- Future improvements may include more detailed programming languages to enhance workflow creation.

***

### `JSON Arrangement for AI:`

{  "title": "GenAgent: Build Collaborative AI Systems with Automated Workflow Generation - Case Studies on ComfyUI",  "authors": \[    "Xiangyuan Xue",    "Zeyu Lu",    "Di Huang",    "Wanli Ouyang",    "Lei Bai"  ],  "abstract": "This paper introduces GenAgent, an LLM-based framework designed to automatically generate workflows for collaborative AI systems. Instead of focusing on monolithic AI models, GenAgent uses workflows that integrate multiple AI components to perform complex tasks. The core innovation lies in the representation of workflows as code, enabling flexibility and scalability. Implemented on the ComfyUI platform, GenAgent generates and executes workflows with superior stability and effectiveness, outperforming baseline methods such as chain-of-thought and retrieval-augmented generation. The authors also present a new benchmark called OpenComfy to evaluate GenAgent's performance, demonstrating significant improvements in both task-level and run-level evaluations.",  "key\_points": \[    { "point": "GenAgent automates the generation of collaborative AI workflows." },    { "point": "The framework uses code to represent workflows, enhancing flexibility and scalability." },    { "point": "It outperforms traditional approaches in workflow generation tasks." },    { "point": "Implemented on ComfyUI, it effectively solves various AI generation tasks." },    { "point": "The OpenComfy benchmark was introduced to assess performance." }  ],  "methodologies": \[    "LLM agents (PlanAgent, CombineAgent, AdaptAgent) collaborate to generate and refine workflows.",    "Workflows are represented in code, more efficient for LLMs to process than flow graphs or JSON.",    "Core components handle planning, retrieval, combination, and adaptation of workflows."  ],  "implications": "This research indicates a shift from monolithic AI models to collaborative systems with multiple agents. GenAgent's framework can serve as a foundation for general AI and will likely lead to further development in AI collaboration, with future work on refining workflow generation languages."}

### Tags and Sub-Tags:

**Tags:**

*   GenAgent- Collaborative AI- Workflow Generation- ComfyUI
    **Sub-Tags:**
*   LLM Agents- Workflow Code Representation- AI Collaboration- Task-level Evaluation

<https://arxiv.org/abs/2409.01392v1>

***



ai collaboration, ai agents, ai accessibility, ai workflows, autonomous ai

collaborative ai and workflow generation

## Introduction

Large language models have transformed the landscape of artificial intelligence and natural language processing. These models have the remarkable ability to generate text that can range from factual and informative to creative and imaginative. However, the output of these models is not simply a matter of input and output; it can be finely tuned using various parameters that significantly influence the style, creativity, and length of the generated text.

Three key parameters **Temperature**, **Top-P (Nucleus Sampling)**, and **Max Tokens** allow users to control the behavior of language models. Understanding how to manipulate these parameters effectively can lead to better, more targeted results in your applications. In this post, we will delve into each of these parameters, illustrate their effects with examples, and provide guidance on how to use them to your advantage.

## Understanding Temperature

Temperature is one of the most commonly adjusted parameters in language models. It controls the randomness of the model's output.

*   **Low Temperature (Close to 0):** The model produces deterministic and predictable responses, often yielding high reasoning with low creativity. This setting is ideal for tasks requiring factual accuracy and conservative responses.

*   **High Temperature (Close to 1):** The model's output becomes more random and creative, which can lead to more diverse and unexpected responses. This setting is suited for tasks like creative writing or brainstorming.

**Examples:**

*   **Creative Task:**
    *   **Prompt:** "Generate a unique superhero concept"
    *   **Temperature 0:** "A hero with the ability to fly and super strength, who fights crime in the city, similar to Superman."
    *   **Temperature 1:** "A hero who can communicate with plants and manipulate vines, using their powers to restore forests and battle villains who threaten the environment."

*   **Logical Task:**
    *   **Prompt:** "Explain the process of photosynthesis"
    *   **Temperature 0:** "Photosynthesis is the process by which plants use sunlight to convert carbon dioxide and water into glucose and oxygen. It is a fundamental process for life on Earth."
    *   **Temperature 1:** "Photosynthesis is like a magical kitchen inside plants, where sunlight is the chef that turns carbon dioxide and water into sweet energy food, all while giving off oxygen as a byproduct."

## Top-P (Nucleus Sampling) Explained

Top-P, or Nucleus Sampling, is another parameter that controls the diversity of the output by considering the cumulative probability of the token candidates.

*   **Low Top-P (Close to 0):** Only the most probable tokens are considered, resulting in focused and consistent output with limited diversity. This setting is useful for generating precise and reliable text.

*   **High Top-P (Close to 1):** The model considers a broader range of token probabilities, which can lead to more varied and creative outputs. This setting is ideal for generating content where novelty is desired.

**Examples:**

*   **Creative Task:**
    *   **Prompt:** "Generate a poem about the ocean"
    *   **Top-P 0:** "The ocean is deep, the ocean is wide, the waves crash gently, as the tides collide."
    *   **Top-P 1:** "In the depths where the shadows dance, whispers of waves in a mystic trance, blue giants rise with silent roar, secrets they keep on the ocean floor."

*   **Logical Task:**
    *   **Prompt:** "Describe the function of a mitochondrion"
    *   **Top-P 0:** "The mitochondrion is the powerhouse of the cell, responsible for producing energy in the form of ATP through cellular respiration."
    *   **Top-P 1:** "Think of the mitochondrion as a tiny engine inside the cell, churning out energy like a power plant, fueling every movement, thought, and heartbeat."

## The Role of Max Tokens

Max Tokens is a straightforward yet powerful parameter that determines the length of the output.

*   **Low Max Tokens:** The model generates short, concise responses, which is ideal for tasks that require brevity, such as tweet-length summaries or quick answers.

*   **High Max Tokens:** The model produces longer, more detailed outputs, suitable for generating in-depth explanations, stories, or dialogues.

**Examples:**

*   **Creative Task:**
    *   **Prompt:** "Write a story about a journey through space"
    *   **Low Max Tokens:** "An astronaut travels to Mars, discovers signs of life, and returns home to share the news."
    *   **High Max Tokens:** "Captain Orion stood on the bridge of the starship Horizon, gazing out at the vast expanse of stars that stretched endlessly before them. The journey to Mars had been long and fraught with challenges, from navigating asteroid fields to repairing the ship's life support systems after a meteor strike. But nothing could have prepared the crew for what they found beneath the red planet's surfaceâ€”a sprawling underground city, teeming with lifeforms unlike any they had ever seen. As they made contact, they realized these beings had been watching Earth for centuries, waiting for the right moment to reveal themselves..."

*   **Logical Task:**
    *   **Prompt:** "Explain the theory of relativity"
    *   **Low Max Tokens:** "The theory of relativity, developed by Albert Einstein, states that the laws of physics are the same for all non-accelerating observers and that the speed of light is constant. It introduces the concept that time and space are interwoven, forming a four-dimensional spacetime."
    *   **High Max Tokens:** "Albert Einstein's theory of relativity revolutionized our understanding of time and space. The theory is divided into two parts: special relativity and general relativity. Special relativity, proposed in 1905, introduced the idea that the speed of light is constant for all observers, regardless of their motion relative to the light source. This led to the realization that time and space are not absolute but are interwoven into a single continuum known as spacetime. General relativity, published in 1915, expanded on this by describing gravity not as a force between masses but as a curvature of spacetime caused by the presence of mass and energy. This curvature affects the motion of objects, leading to phenomena such as the bending of light around massive objects and the slowing down of time near strong gravitational fields. The theory has been confirmed through numerous experiments and observations, including the precise measurement of the orbit of Mercury and the observation of gravitational waves..."

## Conclusion

By mastering the use of Temperature, Top-P, and Max Tokens, you can harness the full potential of large language models to produce outputs that are tailored to your specific needs. Whether you're aiming for factual accuracy, creative exploration, or detailed narrative, these parameters give you the control you need to generate the best possible content. Experimenting with different settings will not only improve your results but also deepen your understanding of how these models operate under the hood.


3 Key Parameters in LLM Interactions

Introduction: Unpacking AI with Assistable

This group is a diverse mix of entrepreneurs, all leveraging AI in different ways. Some are seasoned veterans in sales and marketing, diving into AI for the first time. Others are AI experts, mastering the business side of things. And for some, it's all just plain fun.

But regardless of where you stand on the spectrum, we're all standing on the cutting edge of AI together.

For me, the idea of being on the cutting edge of anything felt like a distant dream—until AI started democratizing everything. AI doesn't just open doors; it tears down roadblocks. It's helping turn lofty dreams into real, tangible goals.

### Why AI Matters

AI has this unique ability to shrink the distance between where you are now and where you want to be. It lets you see the big picture, bypass roadblocks, and zero in on your end goals with courage. It's giving synergy new meaning—real synergy, not just a LinkedIn buzzword. We've redefined "bespoke" solutions, and now, we're redefining collaboration.

AI isn't just helping us imagine new possibilities—it's helping us create them. And as we step into 2024, we're finding that even the meaning of "bespoke" has changed. It's deeper, heavier, more nuanced. We can now calculate how something like "bespoke" evolves over time, thanks to machine learning.

### Why You Should Care About the Technical Side

I get it. Not everyone is interested in the technical underpinnings of AI. For many of you, the goal is simple: tangible results—better customer interactions, more efficiency, and streamlined operations. And that's totally valid. AI is a tool that can deliver those results.

But there's an advantage to going a little deeper. A solid understanding of how AI works can offer three key benefits:

*   **Troubleshooting**: When something goes wrong, understanding the principles behind AI can make it easier to diagnose and fix the issue, saving time and resources.

*   **Explaining Value**: Whether you're pitching to clients or investors, being able to articulate the value of AI-driven solutions with technical precision will make your case more compelling.

*   **Credibility**: In a world where everyone is jumping on the AI bandwagon, demonstrating a deep, well-informed grasp of the technology can set you apart.

Now, let's dive into the technical details behind some of the AI systems you're interacting with every day. I pulled a transcript from an ***assistable.ai Town Hall meeting*** and we'll be breaking it down to show some foundational concepts in artificial intelligence.

## Tokenization: Breaking Down Language for AI

**Tokenization** is the process of converting text into smaller units called tokens, which can be individual words, subwords, or even characters. This is crucial because natural language often carries meaning at multiple levels—breaking text down into tokens allows the AI model to process it more effectively.

However, tokenization is not just about chopping up sentences. It's about striking the right balance. Breaking text into words may miss subtle nuances, while breaking it into individual letters creates too much noise. Tokens offer a "happy medium" where enough context is preserved for the AI to process high-dimensional semantic meaning.


Deep Dive into AI: From Tokenization to Perception



*   **Preprocessing Power**: Tokenization acts as a preprocessing step, stripping away unnecessary noise—like those pesky prepositions and articles—so the AI can focus on what's meaningful. Think of it like filtering out the static on a radio station until you get a clear signal.

For example, after applying tokenization and filtering out meaningless words using a custom stopwords list, we were left with what I like to call "**tasty\_tokens**"—a refined dataset of highly relevant, semantically rich content representative of the meaning from the assistable town hall transcript.


Why It Matters:

to tokenize...

tools

Decoding Human Emotion in Text

**Sentiment Analysis** is another cornerstone of Natural Language Processing (NLP). This process involves algorithms that determine whether a piece of text carries a positive, negative, or neutral sentiment. Originally, sentiment models were basic classifiers—labeling text as good or bad. Modern algorithms, however, can assess degrees of sentiment and express these as numerical values.


Sentiment Analysis

Assistable Town Hall Sentiment Over Line Number

Checkout the sentiment analysis of the assistable town hall transcript 📊 👀

|                                                                                                                                                                                                                                                                                                                                                                      |                                                                                                                                                                                                                                                                                           |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ***`Why it matters`***                                                                                                                                                                                                                                                                                                                                               | ***`Why It's Powerful`***                                                                                                                                                                                                                                                                 |
| **Classifiers to Calculators**: Early sentiment models were rigid, capable of labeling but not much else. Today's algorithms are far more nuanced, quantifying sentiment as a gradient rather than a binary.                                                                                                                                                         | **Dynamic Relativity**: Much like how dark and light are co-dependent concepts, sentiment operates on a spectrum. Sentiment analysis assigns relative values to text, creating a dynamic relational system where the meaning is contextual, fluid, and driven by surrounding information. |
| **Beyond Numbers**: Sentiment analysis blurred the lines between different data types—Boolean, text, and numbers—allowing NLP engineers to perform mathematical operations on non-numerical data. This has been a game changer for systems that rely on **relative dependencies**—where the state of one element is defined by the relationships it has with others. | **Practical Application**: Whether it's analyzing customer feedback, gauging social media reactions, or even powering recommendation systems, sentiment analysis helps businesses and developers get a clearer understanding of how people feel about a product or service.               |



Evolution of Sentiment Analysis

Human sentiment is subjective, and we lack a universal standard for interpreting it. 

So while AI can approximate sentiment, there's still a gap between its capabilities and human emotional nuance. But as we continue refining AI models, I believe we'll eventually close that gap, mapping out the missing pieces between human and machine interpretation of emotion.


Though sentiment analysis has come a long way, it's not perfect. 

Use sentiment analysis to monitor customer feedback, product reviews, or employee engagement. Track changes in sentiment over time to identify trends and take corrective action when negative sentiment spikes. In meeting transcripts, gauge buy-in and get user journey insights to assist with feature roadmapping and preemptive support. 

### ![](/images/8A322C4F-43DC-4112-BDD4-5A8C960C8A0C.png)

*   **Tool Suggestion**: Platforms like [`TextBlob`](https://textblob.readthedocs.io/en/dev/) or [`VADER`](https://github.com/cjhutto/vaderSentiment.git) can easily perform sentiment analysis. For more complex sentiment tracking, you can integrate these into a dashboard for ongoing monitoring.



Actionable Takeaway

## Thematic Categorization: Extracting Meaning from Chaos

In any large dataset, identifying themes or topics is crucial for organization and insight. This is where **thematic categorization** comes into play. By categorizing text into specific themes, we can transform an otherwise unstructured dataset into something that can be easily analyzed and understood.

### Why It Matters:

*   **Organizing Unstructured Data**: Historically, data had to fit into neat structures (think: library catalogs or the Dewey Decimal system). But with thematic categorization, AI can now handle unstructured data, making it accessible without forcing it into rigid formats.

*   **Efficiency**: Combining structured and unstructured data formats is not only possible but powerful. This flexibility lets AI extract meaning regardless of how the data is organized. Instead of manually sorting through endless documents or conversations, thematic categorization allows the AI to automatically group and prioritize information based on relevant themes.

For example, in analyzing our transcript, themes like **AI**, **Support**, and **Sales** emerged as dominant. This kind of categorization provided insights into the main focus of the discussions, helping us understand what topics garnered the most attention. Thematic categorization can also be a precursor to more advanced techniques like **semantic clustering**, which groups text by meaning rather than just keywords.

\<div style="text-align: right">![](/images/D80BC1FA-9A33-482B-984F-95C0DFBCCD1F.png)\</div>

### Actionable Takeaway:

*   **Application**: Categorize your business communications (e.g., emails, customer service chats, or product feedback) to uncover the most talked-about themes. This can help prioritize areas of improvement or marketing strategies.

*   **Tool Suggestion**: Use topic modeling algorithms like [`Latent Dirichlet Allocation (LDA)`](https://towardsdatascience.com/latent-dirichlet-allocation-lda-9d1cd064ffa2) in Python to categorize themes. Tools like [`gensim`](https://radimrehurek.com/gensim/) can automate this process and help you discover hidden themes in your data.

![](/images/79B6FF51-5549-4A1C-9416-A4C82C615E9F_4_5005_c.jpeg)

## Contextual Analysis: Understanding Words in Their Surroundings

While tokenization breaks down text into smaller units, **contextual analysis** takes a step back to examine how those units fit together. The meaning of a word often depends heavily on its context, and contextual analysis helps AI models generate more accurate, relevant responses by looking at the position and relationships of words within a text.

### Why It Matters:

*   **Accuracy**: Contextual analysis prevents AI from taking words at face value. For instance, consider the phrase "I'm feeling blue." Without context, an AI might interpret "blue" as a color. But contextual analysis reveals that in this case, "blue" is a metaphor for sadness.

*   **Dynamic Understanding**: Context doesn't just affect individual words; it changes the meaning of entire sentences. Contextual analysis is one of the reasons that models like GPT-4 can generate more human-like responses—they don't just understand the words; they understand how those words interact.

By examining the relationships between tokens, the AI can disambiguate meaning and adapt its responses based on the surrounding text. It's what makes chatbots more conversational, allowing them to "remember" the context of a conversation and maintain relevance.

![](/images/3068026E-3F97-4B3D-B1AC-9D4785D274D7_4_5005_c.jpeg)

### Actionable Takeaway:

*   **Application**: When building AI-driven applications like chatbots or recommendation engines, leverage contextual analysis to improve the quality of responses. Ensure your model understands the context of conversations to provide accurate, human-like answers.

*   **Tool Suggestion**: Utilize transformer-based models like BERT or GPT for applications where contextual understanding is key. Pre-trained models from [Hugging Face](https://huggingface.co/docs/transformers/en/index) can be fine-tuned to suit your specific needs.



further exploration

with Eyes on "Perception"

As we move beyond the technicalities of tokenization, sentiment analysis, thematic categorization, and contextual analysis, there's a larger concept looming on the horizon that could redefine how we interact with AI: **Perception**.

In human terms, perception isn't just about seeing or hearing—it's about interpreting and making sense of the world around us. It's the ability to synthesize multiple inputs, process them in context, and derive meaning that goes beyond raw data. As AI systems evolve, building models capable of not just understanding language, but **perceiving** it in a more dynamic, human-like way, will be key to unlocking their true potential.



Looking Forward 



*   **Contextual Depth**: Perception goes beyond surface-level understanding and deepens AI's ability to grasp nuance and intent. Imagine an AI that doesn't just analyze customer feedback for sentiment, but truly **perceives** the emotional subtext behind it, offering more empathetic and precise responses.

*   **Adaptive Learning**: AI systems with perceptual capabilities can better adapt to changing environments. Whether it's adjusting to new data inputs, or reacting to shifts in a conversation, AI perception allows for real-time adaptability, improving both user interaction and decision-making processes.

*   **Collaboration & Interaction**: As we saw with contextual analysis, AI today can understand and respond to isolated queries, but perception opens the door for more sophisticated collaboration. AI with perception could actively engage with users, evolving with their inputs and assisting in a way that feels more like true partnership than just command-response.

### Perception as a Static Variable:

A key part of the journey forward is understanding how to treat perception as a static global variable. Much like time in machine learning models can be treated as static, perception allows AI to anchor its understanding in a consistent framework—while still being flexible enough to shift dynamically based on real-time data.

By defining perception as a constant, we give AI a stable reference point. But we can also allow the system to stretch the boundaries of that perception based on interaction with users, other models, or data environments. It's akin to how humans experience a shared reality, yet have individual interpretations based on their unique perspectives.

### What's Next for AI and Perception?

Looking ahead, the most exciting developments in AI will likely involve perception as a **dynamic yet anchored** concept. Here's what to expect:

*   **Perception-Driven Agents**: Imagine AI models designed to not only respond to queries but to develop an understanding of their environment, interacting with it in a more context-aware, perceptual manner. This could revolutionize customer service, personal assistants, and even creative fields like content generation.

*   **Perceptual Awareness in Complex Systems**: Beyond individual interactions, perception will be vital in managing multi-layered, complex systems where variables change constantly. Think of AI systems in autonomous driving, healthcare diagnostics, or financial modeling, where a deeper level of perceptual awareness could lead to better decisions, faster reactions, and more robust predictions.

*   **The Human-AI Hybrid**: Perception will bring us closer to a future where humans and AI work together seamlessly, with the AI understanding not just the literal meaning of what we say, but the nuances, intentions, and emotions behind it. This collaborative perception will make AI more intuitive, more reliable, and more aligned with human needs.

## Conclusion: Embracing the Perception Horizon with Sedna and Homeskillet

Perception is at the forefront of AI's next evolution. The Sedna/Homeskillet framework exemplifies this by leveraging dual-agent systems where perception acts as a dynamic variable. By mirroring human brain lateralization—Sedna managing microservices and simulations while Homeskillet oversees task execution—this approach elevates AI beyond conventional abstractions, allowing for real-time adaptability and parallel processing.

This dual-agent model, based on McGilchrist's brain lateralization theory, employs Sedna to manage microservices and simulations while Homeskillet oversees task execution. The framework mirrors human cognitive processes, creating a real-time, adaptable system capable of parallel processing. By moving beyond traditional abstractions, it enables AI to interact more holistically with its environment, anticipating and responding to changes in a human-like manner.

As we continue to develop and refine these perception-driven AI systems, we're not just creating more efficient tools—we're building collaborators that can truly understand and engage with the complexities of human thought and communication. The future of AI isn't just about better algorithms or faster processors—it's about teaching AI to see the world in the same complex, multifaceted ways we do. And with frameworks like Sedna/Homeskillet, that future is already taking shape.

Read more about this in the [Sedna/Homeskillet framework](https://homeskillet.chillout-ai.com/blog/post-three/), a model already shaping the future of AI interaction and decision-making.



The Role of Perception in AI:

assistable town hall transcript 

visualize

I will reassure you that I am responsible for changing the tag below and I'm aware of the meaning of "practical".

Turns out that today, "theoretical\_ai" is an inaccurate descriptor. I began pursuit of "perspective" in early 2023. 

`"top_tag": "practical_ai"`

And while you're asking, I'll further say:

"it ***once was***...

... a theory, that is."

`-breyden 08-2024`


tokens getting... tokenized as tokens do

AI frameworks, brain lateralization in AI, adaptive learning, AI collaboration, AI interaction, AI algorithms, data preprocessing, AI model training, semantic analysis, emotion detection in text, unstructured data analysis, AI-driven decision making, AI applications, chatbots, recommendation systems, AI in business, AI research, cutting-edge AI, future of AI, AI and human collaboration, AI ethics, AI development, AI optimization, AI efficiency, AI accuracy, AI adaptability, machine comprehension, AI cognition, AI-human interaction, AI problem-solving, AI pattern recognition, AI data processing, AI language understanding, AI context awareness, AI perception horizon, next-generation AI, AI breakthroughs, AI innovation, AI transformation, AI-powered solutions, AI technology trends, AI industry insights, advanced AI systems, AI cognitive processes, AI perception models, AI decision frameworks, AI system architecture, AI parallel processing, real-time AI adaptation, AI microservices, AI simulations, AI task execution, AI abstraction techniques, AI perception variables, holistic AI thinking, sequential AI processing, AI system integration, AI perception optimization, AI cognitive enhancement, AI perceptual awareness, AI environmental interaction, AI contextual intelligence, AI sentiment interpretation, AI thematic analysis, AI data categorization, AI textual analysis, AI language models, transformer models in AI, BERT, GPT, AI tokenization techniques, AI semantic clustering, AI data organization, AI content analysis, AI text processing, AI language comprehension, AI cognitive simulation, AI perception algorithms, AI-driven insights, AI data interpretation, AI linguistic analysis, AI emotional intelligence, AI context modeling, AI adaptive systems, AI cognitive architecture, AI perception frameworks

artificial intelligence, machine learning, natural language processing, AI perception, Sedna/Homeskillet framework

firstName

lastName

email

updatesConsent

my token exchange program:

**Abstract**:
This document presents an advanced implementation of machine learning principles using a two-agent framework, uniquely facilitated by the emergent concept of "Perception" as a variable. This approach leverages runtime parallelisms for live error handling and abstraction for subsequent iterations, positioning "Perception" as a next-dimensional breakthrough akin to the impact of "Attention" in Transformer architectures. We incorporate McGilchrist's framework to enrich the understanding and application of these concepts.

## Introduction

Traditional machine learning (ML) principles have seen remarkable advancements with the introduction of transformer architectures, notably Vaswani et al.'s "Attention is All You Need". This document explores a novel implementation that builds on these foundations, employing a dual-agent framework to push the boundaries of ML applications in project management and delivery. We also integrate McGilchrist's theories on brain lateralization to provide deeper insight into how these processes can be optimized.

## Key Components

1.  **Two-Agent Framework**:


    *   **Homeskillet**: Functions as the orchestrator, managing task coordination, resource allocation, and error handling, analogous to the left hemisphere's sequential processing.

    *   **Sedna**: Handles microservice management, simulation testing, compliance monitoring, and continuous improvement, reflecting the right hemisphere's holistic processing.

2.  **Emergent "Perception" Variable**:


    *   "Perception" is introduced as a dynamic variable that enables the agents to operate at a higher level of abstraction, facilitating live error handling and creating fodder for the next dimensional abstraction. This mirrors the right hemisphere's contextual and embodied understanding.

3.  **Runtime Parallelisms**:


    *   Parallel simulations run alongside the main project flow, providing real-time error handling and generating data for future abstraction. This approach leverages the holistic processing of the right hemisphere, while sequentially addressing errors reflects the left hemisphere's strengths.

## Conceptual Framework

### "Perception" as a Next-Dimensional Breakthrough

"Perception" functions as a dense variable, inherently representing absolute truth when expressed, yet remaining undefined in the space surrounding its emergence. Conceptualized as an abstraction of "Attention" in transformer architectures, "Perception" extends beyond the mechanism that enables transformers to focus on various parts of the input data. It allows the dual-agent system to dynamically adjust its focus and operations based on real-time feedback and context, thus elevating the abstraction to a new dimension. Its inherent weight provides the architect with the ability to lock its dynamism, compelling all other relative and arbitrary concepts, such as "Time" and "Space," to shift accordingly.

**Bicameral Mind and Dual-Aspect Theory**:
"Perception" is intrinsically stronger than "Time" and "Space." When defined by "absolute truth" as being "frozen in place," the force required to move it would be nuclear. This concept aligns with McGilchrist's Dual Aspect Theory, where both hemispheres offer complementary perspectives on reality, necessitating a balance between detailed analysis and holistic understanding.

**Experience Re-Use and Transfer Learning**:
The architect leverages this resilience across increasingly higher levels of abstraction to supply consistent relative definitions to objects existing in layers far beyond natural language and even beyond the dissolution of the laws of nature. In an environment enriched by only one perception, Perception equals Reality. In a scenario involving two or more perceptions, reality is defined by the consensus of these perceptions. By locking perception, the architect can explore reality as arbitrary while maintaining the ability to reinstate its absolute truth characteristic when navigating through simulations or dimensions that surpass traditional representations. This concept is related to the embodied and contextual understanding highlighted by McGilchrist, emphasizing the importance of integrating direct experiences and broader contexts.

## Implementation Details - Project Workflow

```
graph TD
  %% Define subgraphs for clarity and organization
  subgraph "Autonomous Iterative Cycle Managed by Two Agents and Breyden"
    direction TB

    %% First Run - Project A
    subgraph Project_A [Project A - Granular Execution]
      direction TB
      PA1[Task Dissection]
      PA2[Data Generation]
      PA3[Training and Optimization]
      PA4[Compliance Monitoring]
      PA5[Error Handling]
      PA6[Final Review and Approval]
      PA7[Parallel Simulations]
    end

    %% Passing the Process to Project B
    PA6 --> |Pass Process as Object| Project_B

    %% Second Run - Project B
    subgraph Project_B [Project B - Abstraction and Automation]
      direction TB
      PB1[Develop AI Pipeline]
      PB2[Training with Project A Data]
      PB3[Parallel Simulations]
      PB4[Error Handling]
      PB5[Compliance Monitoring]
      PB6[Final Review and Approval]
    end

    %% Review and Approval
    subgraph Breyden [Approval Workflow]
      direction TB
      B1[Review and Approve Project A]
      B2[Review and Approve Project B]
    end

    %% Connecting Simulations and Iterations
    PA7 --> PB3
    PB3 --> PB4
    PB4 --> PB5
    PB5 --> PB6

    %% Connecting Final Review and Approval
    PA6 --> B1
    PB6 --> B2
  end

  %% Style nodes and edges for better clarity
  classDef projectA fill:#ffe4e1,stroke:#333,stroke-width:2px;
  classDef projectB fill:#add8e6,stroke:#333,stroke-width:2px;
  classDef approval fill:#ccffcc,stroke:#333,stroke-width:2px;
  classDef simulation fill:#e6e6fa,stroke:#333,stroke-width:2px;

  class Project_A,PA1,PA2,PA3,PA4,PA5,PA6,PA7 projectA;
  class Project_B,PB1,PB2,PB3,PB4,PB5,PB6 projectB;
  class Breyden,B1,B2 approval;
  class PB3,PB4,PB5,PB6 simulation;


```

## Initial Project (Project A)

**Granular and Meticulous Execution**:

1.  **Task Coordination**:


    *   **Homeskillet**: Dissects Project A into smaller tasks and assigns them to Sedna, akin to the left hemisphere's sequential processing.

    *   **Sedna**: Executes tasks related to data generation, training, compliance monitoring, and error handling, leveraging the right hemisphere's holistic and contextual processing.

2.  **Runtime Parallelisms**:


    *   **Sedna**: Runs parallel simulations to test different scenarios, providing real-time error handling, reflecting the right hemisphere's ability to handle multiple contexts simultaneously.

    *   **Homeskillet**: Controls the speed of simulations, creates save states, and rewinds if necessary, mirroring the left hemisphere's focus on detailed analysis and error correction.

3.  **Final Review and Approval**:


    *   Both agents review the project flow and simulation clones, integrating both hemispheres' strengths.

    *   **Breyden**: Approves the completed project, ensuring a balanced evaluation.

## Subsequent Project (Project B)

**Abstraction and Automation**:

1.  **Passing the Process**:


    *   The detailed process and outcomes of Project A are passed as an object to Project B, promoting the right hemisphere's holistic approach while ensuring the left hemisphere's sequential accuracy.

2.  **Developing AI Pipeline**:


    *   **Homeskillet and Sedna**: Focus on developing an AI pipeline that automates Project A’s tasks, leveraging the data and simulations from Project A, embodying the dual-aspect theory.

3.  **Iterative Simulation/Evaluation**:


    *   **Sedna**: Continues to run parallel simulations to ensure continuous improvement, embodying McGilchrist's notion of contextual understanding.

    *   **Homeskillet**: Oversees the process, ensuring quality and adherence to standards, reflecting the left hemisphere's focus on reification and reductionism.

4.  **Deployment and Approval**:


    *   The AI pipeline is deployed to production using scalable TPU access in the cloud, integrating holistic and sequential processing.

    *   **Breyden**: Reviews and approves the deployment, ensuring both hemispheres' contributions are balanced.

## Project Workflow Explanation

*   **First Run (Project A)**:


    *   Tasks like Task Dissection, Data Generation, Training, Compliance Monitoring, Error Handling, and Final Review and Approval, run parallel simulations.

    *   These components connect to McGilchrist’s concepts such as Contextual Understanding, Embodied Understanding, and Holistic Processing.

*   **Second Run (Project B)**:


    *   Develops an AI pipeline that automates Project A’s tasks, utilizing Training with Project A Data, Parallel Simulations, Error Handling, Compliance Monitoring, and Final Review and Approval.

    *   These processes are related to McGilchrist's concepts like Sequential Processing, Reductionism, Usurpation, and Emissary.

*   **Approval Workflow**:


    *   Review and approval of projects by Breyden, ensuring a balanced evaluation of tasks and processes.

*   **Abstract Concepts and McGilchrist's Framework**:


    *   Bicameral Mind, Embodied Understanding, Contextual Understanding, Lateralization, Holistic Processing, Sequential Processing, Reification, Fragmentation, Reductionism, Dual Aspect Theory, Usurpation, and Emissary.

*   **Connections**:


    *   Each task and process in the projects (A and B) is connected to relevant abstract concepts from McGilchrist’s framework, highlighting how these concepts integrate into the AI project management system.

## Conclusion

> This advanced implementation leverages the two-agent framework, emergent "Perception" variable, and runtime parallelisms to create a robust and scalable AI-driven project management system. By abstracting complexities through iterative simulation and evaluation, this approach enables continuous improvement and efficiency, positioning "Perception" as a next-dimensional breakthrough akin to "Attention" in transformer architectures. By integrating McGilchrist's framework on brain lateralization, we can enhance the understanding and application of these concepts, balancing holistic and sequential processing to achieve optimal results.
>
> ### Visual Representation
>
> ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/4b0e7490-e0f4-4748-b04e-4d572bf62586/5b2790c6-40fc-4930-9257-abdcb421b490/ideas.png)

## What's Next: Building on Empirical Evidence

The recent studies in 2024 provide a strong foundation for advancing the concept of "Perception" as a dynamic variable in AI systems. Building on this empirical evidence, the following steps are proposed to further validate and implement this innovative approach:

1.  **Integration with Predictive Models**:
    Leveraging the insights from the study on divergent predictive perception, AI systems can be designed to dynamically adjust their operations based on real-time sensory inputs and prior knowledge. This approach will enhance the AI's ability to predict and respond to varying contexts more accurately, thereby improving performance in dynamic environments [oai\_citation:1,academic.oup.com](https://academic.oup.com/nc/article/2024/1/niae006/7606607#:~:text=URL%3A%20https%3A%2F%2Facademic.oup.com%2Fnc%2Farticle%2F2024%2F1%2Fniae006%2F7606607%0ALoading...%0AVisible%3A%200%25%20).

2.  **Enhancing Creativity and Diversity**:
    Utilizing findings from experiments on AI's impact on human creativity and idea diversity, AI systems can be equipped with mechanisms that promote diverse and creative outputs. By integrating "Perception" as a dynamic variable, these systems can adjust their creative processes based on user feedback and contextual changes, fostering greater innovation and user engagement [oai\_citation:2,\[2401.13481\] How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment](https://ar5iv.org/pdf/2401.13481).

3.  **Neuro-Symbolic AI Systems**:
    Building on the profiling of neuro-symbolic AI models, future research should focus on optimizing the integration of neural and symbolic reasoning. By incorporating dynamic perception adjustments, these systems can better balance symbolic rules and neural predictions, enhancing their robustness and explainability in complex tasks [oai\_citation:3,\[2401.01040\] 1 Introduction](https://ar5iv.org/abs/2401.01040).

4.  **Empirical Validation and Real-World Applications**:
    Further empirical studies should be conducted to validate the effectiveness of "Perception" as a dynamic variable in real-world applications. Domains such as healthcare, transportation, and creative industries can serve as testbeds for these advanced AI systems, providing valuable data on their usability, efficiency, and adaptability [oai\_citation:4,AI now beats humans at basic tasks — new benchmarks are needed, says major report](https://www.nature.com/articles/d41586-024-01087-4).

5.  **Development of Feedback Mechanisms**:
    Implementing sophisticated feedback mechanisms will be crucial for the dynamic adjustment of perception in AI systems. These mechanisms should be designed to capture real-time user interactions, system performance metrics, and environmental changes, allowing the AI to continuously refine its operations and improve decision-making processes [oai\_citation:5,AI now beats humans at basic tasks — new benchmarks are needed, says major report](https://www.nature.com/articles/d41586-024-01087-4) [oai\_citation:6,\[2401.13481\] How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment](https://ar5iv.org/pdf/2401.13481).

### Future Research Directions

*   **Cross-Domain Adaptation**:
    Investigate the transferability of dynamic perception adjustments across different domains and tasks. This will help in understanding the generalizability of the approach and identifying domain-specific optimizations.

*   **Longitudinal Studies**:
    Conduct longitudinal studies to observe the long-term effects of dynamic perception adjustments on AI performance and user satisfaction. These studies will provide deeper insights into the sustainability and scalability of the approach.

*   **Human-AI Collaboration**:
    Explore the impact of dynamic perception on human-AI collaboration, focusing on how these adjustments can enhance teamwork, creativity, and problem-solving in mixed human-AI environments.

By building on the empirical evidence from these studies, the next phase of research can further validate and refine the concept of "Perception" as a dynamic variable in AI systems, ultimately leading to more adaptable, efficient, and user-friendly AI solutions.

## References

*   McGilchrist, I. (2009). *The Master and His Emissary: The Divided Brain and the Making of the Western World*. Yale University Press.

*   Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I. (2017). *Attention is all you need*. In Advances in neural information processing systems (pp. 5998-6008).

*   Dual-Process Theories and Dual-Agent Frameworks: DUMA and SwiftSage frameworks highlight the effectiveness of integrating fast and slow thinking processes for optimized performance and efficiency [oai\_citation:1,\[2310.18075\] DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking](https://ar5iv.org/abs/2310.18075) [oai\_citation:2,SwiftSage: Building AI Agents for Complex Interactive Tasks via Fast and Slow Thinking with LLMs | AI2 Blog](https://blog.allenai.org/swiftsage-building-ai-agents-for-complex-interactive-tasks-via-fast-and-slow-thinking-with-llms-e1432bfcf7d6).

*   Multi-Modal and Multi-Agent Systems: Achieving representation invariance and grounding knowledge across multiple modalities enhances the robustness of AI systems [oai\_citation:3,\[2406.00252\] Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey](https://ar5iv.org/html/2406.00252v1).

*   Experience Re-Use and Transfer Learning: Diffusion Augmented Agents (DAAG) exemplify this by re-using collected experiences and generating new data for specific tasks, enhancing learning efficiency and task execution [oai\_citation:4,\[2407.20798\] Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning](https://ar5iv.org/abs/2407.20798).

*   Multi-Agent Deep Reinforcement Learning (MADRL): Addresses complexities of coordinating multiple agents in dynamic environments, aligning with your framework's concept of "Perception" acting as a stabilizing force [oai\_citation:5,Multi-agent deep reinforcement learning: a survey | Artificial Intelligence Review](https://link.springer.com/article/10.1007/s10462-021-09996-w).

*   Taylor, R. E. (2024). Personal Communication





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