Whitepaper - Integrating Generative AI and Design Thinking for Custom LLM use cases. Thesio by Alnotrea.

Summary

This whitepaper presents our strategy and process for training and customising large language models (LLMs) using a blend of generative AI and design thinking principles. Our platform aims to foster collaboration among researchers, developers, and businesses by offering a space for creating and fine-tuning AI applications. The ultimate goal is to enable our members to customise, train, and learn how to develop large language models, agents, and use cases. Our primary path-former LLM, Thesio by ALNOTREA, is based on design thinking and learning by doing and is currently in development with our multi-platform integration before it is available for public use.

We encourage members and stakeholders to create novel AI models and paths to developing and training LLMs. We seek to share resources and industry-specific applications with our members, encouraging collective knowledge-building, investment, and new market opportunities.

Our platform fosters the development of diverse AI solutions that are user-friendly and ethical approaches that can impact academic and commercial settings. These solutions should meet the specific needs and requirements of different industries. In the approach, developers can control and modify input training, and users can control and modify input training to achieve desired productivity and cost and integrate agents' use case advancements.

Introduction

Large language models are increasingly shaping industries, providing practical tools for creating, automating, and analysing. However, as sectors require adaptability and learning about AI potentials, this may also initiate new specialised models for users to pursue. Domain-specific AI solutions are increasingly important to bridge effective explainable paths in development and training models.

Our platform closes this gap by providing a collaborative environment for AI learning & development. By combining the power of generative AI and design thinking, we empower users to work together, experiment, and personalise LLMs. We focus on practical applications, ethical AI use and customised model development.

This platform is designed to reduce barriers by providing a customisation of a user-friendly, entry-level, no-code environment model. Also, educators can easily customise code learning, encouraging collaborative development and fostering innovation in a rapidly changing landscape. As advancements continue and new approaches and platforms emerge, our goal is to lead with fresh insights and tools to interpret the potential of AI developments.

Problem Statement

While many AI platforms allow developers to use pre-trained models, most fail to enable deeper collaboration, customisation, and domain-specific training. The high computational cost and complexity of fine-tuning large models often become barriers to entry-level organisations/individuals seeking to create their AI solutions.

Our AI development ethics approach is human-centric, fact-based and scientific.

Large Language Models (LLMs) in machine learning have been developed based on understanding neuroscience - how the brain functions and processes information. Machine learning imitates neural networks using mathematical calculations and statistical analysis of large volumes of data in real-time. The primary benefit of AI is its ability to process large amounts of data quickly, generating information outputs almost instantly based on prompts and settings.

In contrast, the relationship between the conscious and unconscious functions of the human brain, the concept of neuroplasticity, and the influence of design thinking can help us understand and create more sound and ethical paths in AI development.

Design thinking terminologies - problem-solving, approaches, and effects highlight that critical thinking involves the conscious mind representing awareness and deliberate, focused thought, enabling us to reason and make multiple choices and decisions in countless areas of life and our work. Meanwhile, the unconscious mind perceives things differently, operating beneath awareness and responsible for creativity, intuition, and the rapid processing of complex information. While the conscious mind manages more logical decisions, the subconscious mind is more responsible for identifying hidden patterns and connections between thoughts.

Engaging in learning, studying, researching, and developing activities is known for improved neuroplasticity. These activities help broaden our knowledge base and enhance critical thinking skills, leading to heightened creativity and increased neural activity.

Moreover, design thinking is most effective and practical; for example, our work in the past week relies on the power of the unconscious mind. We unconsciously connect past experiences and patterns with new sensory moments or general familiarity, especially when reflecting on problems or solutions. Learning by doing, for example, develops increased intuition for curiosity and creative thinking, unconsciously combining intuition with conscious reasoning.

Furthermore, the LLM augmentation concept - describes a world of new possibilities and discoveries. Training LLM agents likewise involves data augmentation that can spike curated data sets from past outputs to form new paths, adapt (new) reasoning, and curate data.

Our approach to designing machine learning agents involves understanding patterns in inputs and outputs and processing new input. Like neuroplasticity and how we strengthen neural connections, LLMs can learn from spiked memory importance and the output connection loop and respond/predict the next likely input token reasoning. The design thinking approach targets token selection using a pre-trained dataset of operational factors. Additionally, GenAI is pre-trained with user-project or portfolio curated data, then applies design thinking methodology - response purpose/input, goal/output and machine learning question queue MLQQ output solutions for user input for fine-tuning output. Our framework model aims to enhance user experience and develop educational materials (use-cases) code for multimodal agent development. ?

Our Agent Development Model Design Thinking approach.

Path-former prototyping

The Generative AI Path-former gathers data training and changes in input parameters and loop sequences unit-group-colour-code-measure-train. This system is beneficial for training language models, generating new data points, and fine-tuning specific agent use case models. Thesio by (LLM) employs three input loop stages and 2x ten external conversions – (X) input ascend, (Z) input descend integrations with (Y) internal neural network.

LOOP 1 - Pre-training - learning-by-doing (user-experience) modelling

1. user input prompt (purpose).

2. choosing standard or custom input parameters and sequences' colour codes (user) measure.

3. output path-former -- train-to-training prompts (machine learning) user-curated-domain data.

LOOP 2 - Scenario Simulations and fine-tuning.

1.????? Users change/add new datasets or new prompts (standard or custom) direct Path-formers training with design thinking (goal) training.

2.????? Memory banking output importance.

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Loop 3 output - language model theories training under different conditions and preparations,

1. Narrow reasoning output (solution) formats.

2. Machine learning question queue MLQQ and user input for fine-tuning output.

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Integrating agents into larger language models.

For development in collaboration with our members and stakeholders.

Advanced developer tools and APIs - Supervision lead Agent Cluster

Path-former gateway.

Agent model training for multimodal agents. ?

With the training of (user/company) curated data sets and use case agents, further integrations and training are applied to advance on a larger cluster language model. This architecture is integrated with the design thinking architecture Loop 1,2&3 purpose, goal, and solution and pre-set Agents (6 operational factors) multimodal agents and supervised cluster Lead-agent “use case” - mainframe bridge for integration with layers:

• Mainframe bridge: Lead agent for multimodal pre-set five agents with other "use-case model" agent integration layers.

Path-former gateway 6.0 - Multimodal Supervision Agent Cluster - Spike Loop of 6 operational factors and two-unit-group spike measure train.

Agent 1-2 Group - 6.1

Agent 2-3 Group - 6.2

Agent 3-4 Group - 6.3

Agent 4-5 Group - 6.4

Agent 5-6 Group - 6.5

Agent 6-1 Group - 6.6

The example below for pre-set Agents: Our thesio use case

Preparations from Path-former-prototyping (design thinking) and curated data for path-former-gateway for integration with other agents.

Unit 1: Industry – Use case: How to develop LLM agent integrations.

Problem Definition and Empathy

Users begin by clearly defining the problem they aim to solve through collaboration, employing the empathy phase of design thinking. This could include challenges like improving customer service automation, developing a specialised healthcare model or creating industry-specific AI assistants.

Unit 2: Security - Data Collection and Preparation

Participants can upload domain-specific datasets to fine-tune existing LLMs. Our platform supports various formats, including structured, unstructured, and curated data and preprocessing tools to clean, prepare, and curate the data for training.

Unit 3: New best practice approach - Model Customisation

Users are given access to a suite of tools for model fine-tuning, including the ability to:

- Adjust model architecture

- Apply transfer learning techniques

- Implement ethical AI principles such as compliance records and bias detection and removal

- Test model performance under different conditions

Unit 4: Compliance - Collaborative Iteration

Collaboration features allow multiple users to refine the model iteratively. Users can add comments, suggest improvements, and track real-time changes. Feedback loops and testing environments help ensure that models evolve in response to team input and external testing.

Unit 5: Human-centric approach - Thesio-thesis documentation and Knowledge Sharing

Upon developing a model, users can generate a thesis use case before development and thesio model report on development documenting the process, challenges, solutions, and ethical considerations involved. This feature promotes academic collaboration and practical knowledge sharing across industries and compliances.

Unit 6: Trust transparency factor - Deployment and Feedback Loop

After customisation, the model is ready for deployment across platforms. Feedback from real-world usage can be fed back into the platform, allowing for further fine-tuning and improvement, creating a continuous learning loop.

Supervision router deep learning

Recall memory loop spike/s for input Query docs, Analysing Data and SQL Query loop trace database. ?Examples of colour code layers, memory banking importance, data sampling and token selection.

Path-former gateway 6.0 - Multimodal Supervision Agent Cluster configuration: Unit-group-color-code-measure-train.

Support framework for LLM agent design thinking modelling

1. Emphasise: We start by understanding the unique challenges that our users face—whether they are researchers, developers, or business users. Our platform helps users define their problems and the solutions they are trying to solve using AI.

2. Ideation: At this stage, the users can brainstorm other path-former loops, identify challenges and devise new directions and solutions. This process includes rapid prototyping and iterative development tools.

3. Prototype Testing: Once a prototype is developed, test users give feedback on the model to iteratively improve it in cycles. This points to a recreative input loop to ensure improved training effectively meets user needs.

4. Deployment: Once satisfied with a model tuned and tested, we perform an easy deployment framework for easy application (six operational factors).

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Key Features of Our Platform

1. Collaborative Customization Environment:

?? - Users can work together to tune models on their specific datasets.

?? - Enable real-time collaboration among stakeholders across various dimensions of academia, business, and technology.

2. Customisable Language Models:

?? - Host a host of large language models.

?? - Domain-specific model customisation tools; transfer learning, data augmentation, and bias reduction techniques.

3. Ethical AI Framework:

- Our platform gives you an integrated set of tools that helps to make ethical considerations part of the model customisation process.

?? - Includes features like bias detection, fairness auditing and compliance records, and explainability tools that empower developers and users to create ethical AI models.

4. User-Friendly Interface:

?? - A no-code or low-code environment for users who may not have advanced technical skills. Create a custom pre-trained LLM Thesio by (---) to extract data and information and merge on Larger language models.?

?? - Advanced developer tools and APIs for users seeking more control and customisation.

5. Data Privacy and Security integration tools:

?? - Our platform ensures secure collaboration by implementing state-of-the-art encryption, privacy controls and user data authentication (curated data).

?? - Users can securely upload proprietary or sensitive datasets for model training and customisation.

6. Thesis Creation and Documentation Tools:

?? - Tools to document a model for development, process and create detailed industry thesio model reports or academic theses.

?? - This is aimed at fostering research collaboration and creating a knowledge base that can be referenced by others in the AI community - for learning and development in academia and industry.

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Benefits of Our Platform

-Customization Flexibility: Tailored AI models that fit different industry needs.

-Collaboration-Driven Innovation: Our collaboration settings accelerate the development of creative solutions and foster innovation with multimodal learning capabilities with industry–model–developer–data and information labelling and authentications.???

-Ethical AI Development: Built-in tools to ensure that AI models are ethically sound and meet industry standards.

-Time and Cost Efficiency: Our platform simplifies the complex process of training and fine-tuning LLMs, reducing both time and financial investment.

-Academic and Commercial Impact: By offering thesio reporting and thesis conception services, we bridge the gap between academia and industry, encouraging collaboration that drives theoretical advancements to practical applications of AI.

Conclusion

As AI continues to evolve, the demand for specialised, ethical, and collaborative solutions is multiplying. Our platform combines generative AI with design thinking principles, providing a unique approach to customising large language models. By providing collaboration tools for ethical AI development, we aim to empower businesses, researchers, and developers to learn how to integrate and create effective AI models, addressing various challenges.

We invite collaboration with us, using our platform to customise your design architecture and contribute to a growing body of knowledge. Through cooperation, we can push the boundaries of what WE and AI can achieve, develop solutions, ethical considerations, and ideas that favourably impact society, and create new industry goals.

Contact Information

For more information or to collaborate with us, please contact Anthony Siljeg, Founder of Thesio by Alnotrea.

- Website: https://thesioby.org coming soon

This whitepaper is a living thesio document and will be updated as our platform ThesiO factor evolves and more collaborators join us in shaping the future of AI.