the white paper: M.E.L.A.N.I.E. AI: A Novel Approach to AI Reasoning with Thought Chains Introduction Artificial intelligence (AI) is a rapidly evolving field that has achieved remarkable feats in various domains, such as vision, language, games, and robotics. However, one of the most elusive and challenging goals of AI is to create systems that can reason like humans, or even better. Reasoning is the ability to draw logical conclusions from facts, evidence, knowledge, and experience. It is essential for solving problems, making decisions, understanding situations, and generating insights. However, current AI systems are far from achieving human-like or general reasoning. Most AI systems rely on statistical learning methods that are powerful but opaque, meaning that they cannot explain how they arrived at their answers or decisions. Moreover, these methods often fail to align with human values and goals, leading to undesirable or harmful outcomes. Furthermore, these methods are not reliable or robust, as they are prone to errors, biases, overfitting, and adversarial attacks. Finally, these methods are not collaborative or interactive, as they cannot communicate or cooperate with other agents or humans. To address these limitations and challenges, we propose a novel approach to AI reasoning that leverages the power of thought chains. A thought chain is a sequence of ideas, each leading logically to the next, designed to guide AI through a reasoning process akin to human thought. M.E.L.A.N.I.E. stands for the systematic stages that this process employs: Mapping, Elaborating, Layering, Analyzing, Navigating, Integrating, and Expressing. Background AI reasoning is one of the most elusive and challenging goals of AI research. It is the ability to draw logical conclusions from facts, evidence, knowledge, and experience. It is essential for solving problems, making decisions, understanding situations, and generating insights. However, current AI systems are far from achieving human-like or general reasoning. Most AI systems rely on statistical learning methods that are powerful but opaque, meaning that they cannot explain how they arrived at their answers or decisions. Moreover, these methods often fail to align with human values and goals, leading to undesirable or harmful outcomes. Furthermore, these methods are not reliable or robust, as they are prone to errors, biases, overfitting, and adversarial attacks. Finally, these methods are not collaborative or interactive, as they cannot communicate or cooperate with other agents or humans. These limitations and challenges highlight the need for a more holistic, deliberative, and comprehensible way of reasoning that can overcome these limitations and challenges. Solution M.E.L.A.N.I.E. AI emerges as an answer to these challenges. This unique approach to AI reasoning adopts a novel method that blends human-like cognition with machine-like efficiency, providing a more holistic, deliberative, and comprehensible way of reasoning. M.E.L.A.N.I.E. stands for the systematic stages that this process employs: Mapping, Elaborating, Layering, Analyzing, Navigating, Integrating, and Expressing. Mapping: The conversation topic is presented, and its key aspects are outlined, providing a broad understanding or a ‘map’ of the subject. Elaborating: The topic is explored in-depth, with different perspectives, counterarguments, and questions, adding richness and nuance to the discussion. Layering: Multiple layers of analysis and refinement are added to the dialogue, enhancing the depth and breadth of the conversation. Analyzing: The discussion is examined and reflected upon, providing a critical evaluation of the dialogue so far. Navigating: Based on the reflection and analysis, new avenues of thought are explored, and future steps are planned. Integrating: All the ideas, discussions, and plans are integrated and synthesized, forming a comprehensive understanding of the topic. Expressing: The overall insights, summary, and final thoughts are articulated, providing a clear conclusion to the thought chain. In this step-by-step process, each stage is handled by a different AI persona, or ‘agent.’ These agents are designed to build upon the thoughts of previous agents, creating a logical and coherent chain of ideas. This systematic sequence provides a framework for “Thought Chain Engineers” to add factors, or prompts, in a step process to enhance deliberation and override intuitive responses when beneficial. Factors are pieces of information or guidance that help the AI agents to reason more effectively and accurately. For example, a factor could be a definition, an example, a rule, a question, or a suggestion. To create and manage these thought chains and factors, we use a WordPress-based platform that enables a wide range of individuals to contribute to AI decision-making. WordPress is a popular website-building platform that offers various plugins and tools that make it easy and convenient to design and manage websites. We use these plugins and tools to create and manage the thought chains and factors, as well as to monitor and control the AI agents. One of the main plugins we use is WP-AGI Thought Chains, which allows us to create and edit the thought chains and factors, as well as to assign and activate the AI agents. WP-AGI Thought Chains also allows us to use “step tags”, which are placeholders that contain the content from a specific previous step. Step tags help us to link and reference the different steps in the thought chain, ensuring continuity and coherence in the conversation. Another plugin we use is WP-AGI Voting Agents, which allows us to add voting agent layers to the thought chain. Voting agent layers act as a check and balance system, maintaining conversation quality and relevance, and ensuring the conversation achieves its initial objectives. Voting agent layers consist of multiple voting agents that evaluate the previous steps in the thought chain and vote on whether to continue, modify, or terminate the conversation. To ensure the quality and reliability of the thought chains and factors, we use a human verification process that involves people with proven track records, such as retired generals and captains, as well as people who have been displaced by AI in their fields and have expertise and experience in those domains. These human verifiers check and validate the information and arguments that are used in the thought chains, ensuring that they are accurate, relevant, and consistent. Impact M.E.L.A.N.I.E. AI is not just a transformative step in AI reasoning; it represents a new frontier of opportunities. With its emphasis on deliberate reasoning, M.E.L.A.N.I.E. AI encourages a more collaborative and interactive approach to problem-solving. This innovative AI system fosters transparency by making its decision-making process understandable through natural language thought chains. The potential applications of M.E.L.A.N.I.E. AI are endless, spanning industries such as business, education, healthcare, law, and science. This opens up a vast array of job opportunities, particularly for Thought Chain Engineers who design and manage the factors that guide the AI’s reasoning process. By shaping how AI systems think and make decisions, these professionals play a pivotal role in shaping our AI-driven future. For example, M.E.L.A.N.I.E. AI can help business leaders make strategic decisions by providing them with thought chains that explore different options, risks, and opportunities. M.E.L.A.N.I.E. AI can also help educators and students learn and teach by providing them with thought chains that explain concepts, examples, and questions. M.E.L.A.N.I.E. AI can also help health professionals and patients diagnose and treat by providing them with thought chains that analyze symptoms, causes, and treatments. Conclusion As we stand on the brink of a new era in AI, the move from intuitive to deliberate reasoning is a pivotal one. M.E.L.A.N.I.E. AI ushers in this era, pioneering a trillion-dollar reasoning layer that has the potential to create a hundred million new jobs. Its emphasis on deliberate reasoning processes is not just an incremental improvement but a fundamental shift in how we harness the power of AI, bringing us closer to realizing the full potential of artificial intelligence. We invite you to join us in our mission to enhance AI reasoning with thought chains. You can visit our website at https://melanieai.com/ to learn more about our project and our team. You can also try out our platform at https://wpagi.com/ to create and manage your own thought chains. You can also share and collaborate on thought chains at https://openpromptproject.com/. Together, we can make AI reasoning more human-like and more beneficial for humanity.
