Method towards AGI with MemGPT and AutoGen #922
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Hi @Peter2023AI , do you have some high-level takeaways from this that might be applied in an autogen app? |
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Sure, the discussed concept explores augmenting Large Language Models (LLMs) like GPT-4 with advanced capabilities for logic and reasoning. This enhancement involves the LLM interacting with a range of texts—from philosophical works to scientific literature, and from legal documents to cross-disciplinary research. These texts are intended to provide rich contexts for responses, simulating improved reasoning skills for AutoGen. An advanced approach involves integrating something like MemGPT for long-term memory and AutoGen for multi-agent collaboration. This setup would enable the LLM to access a set of general methods for hypothesis testing, evidence-based reasoning, and problem-solving. These methods would be stored as concise, labeled instructions in the LLM’s memory, allowing it to dynamically apply them to various tasks. This framework aims to allow AutoGen to mimic human-like problem-solving. Example 1: For a legal analysis task, the LLM would retrieve and apply the 'Legal Documents and Case Law' text instruction set from its long-term memory. This would involve analyzing legal texts, interpreting legal language, and applying complex reasoning to deliver informed conclusions output by AutoGen. Example 2: In a scientific research context, the model would use the 'Scientific Literature' text instruction set. It would engage in hypothesis formulation, data analysis, and evidence-based reasoning to contribute insights or solutions relevant to the scientific query at hand output by AutoGen. |
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Great ambitions. The hard parts are the details. What would you suggest as a first-step implementation to evaluate progress on this path? |
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I completely agree: the challenging aspect lies in the details. Step 1: This step could involve integrating essential components from MemGPT into AutoGen. This integration would enable AutoGen to perform function calls to long-term memory and search labeled reference documents. Access to long-term memory and reference documents is crucial, not only for humans but also for AI. When a person doesn’t know or remember how to do something, we seek out documents or other sources based on keywords under which the information might be labeled. Step 2: A master section (labeled as such) must be added before any subsections. With every user request, the AI should read the master section in long-term memory for several reasons. Functioning like the Prefrontal Cortex of the human brain, the master section of the database will contain stored textual instructions guiding the AI on how to proceed based on the text prompt from the user or another AI. For instance, if the user or another AI prompts the AI to solve a “problem”, it will search the master section for a list of subsections labeled “problem”. If the information isn’t in the long-term memory database, the AI should resort to searching a document repository. If no information is found there, it could then search the internet using keywords or inform the user that more information is needed. The methodology for how, when, and why to search additional subsections, the document repository, the internet, or to ask the user for more information should not be hard-coded. Instead, it should be contained as independent, updatable text instructions within the master section and subsections. Wherever possible, the instructions and behavior of the AI system should be text/language driven, akin to how humans follow and enhance our understanding using an evidence-based process (refer to the following sub-processes) to determine when to make that decision. Step 3: Once AutoGen can read, write, add, subtract, and update the label titles to data sections and data within those sections, it will be ready for Step 4. Step 4: Develop a list of the most useful reusable processes. For instance, the processes I’ve listed could be improved, combined, or expanded to eliminate any possible redundancies before instructing the AI to populate them into the database. This could include sections for 1. Hypothesis Testing, 2. Evidence-Based Reasoning, 3. Logical Reasoning, 4. Problem-Solving Skills, 5. Inference from Limited Information, etc. Later, additional subsections could be added for more functions. Step 5: Test and evaluate the results, making any necessary adjustments to the programming code. This structure would enable further improvements to be made almost entirely through text/language interaction. The AI(s) should follow streamlined text/language instructions for problem-solving, project management, and evidence evaluation, similar to the methods humans currently use based on stored text instructions in long-term memory or contained within documents. Step 6: Later, the LLM itself could evaluate the information contained within the long-term memory database and document repository. Based on the weight of the evidence, it could make decisions on what to update within the LLM itself. This would allow for faster and more fluid changes between a given LLM and the long-term memory. Later, all the programming code could be converted to use the new https://www.modular.com/max/mojo. |
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@Peter2023AI Very well written! In fact, I'm closing this discussion on the suspicion that you're a GPT-based chatbot. If I'm wrong, I look forward to a PR from you with experimental results as described in Step 5. |
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Thanks @rickyloynd-microsoft for the positive feedback on what I wrote being well written. I'm not a super intelligent AI chatbot, nor was I volunteering to take over the project. ;-) I'm totally fine with you closing out the discussion. Take care and keep up the great work! Peter |
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Method towards AGI with MemGPT and AutoGen
Note: The following is just some quick examples of the reletively rapid improvments that could be made to the MemGPT/AutoGen hybrid software solution that would enable dramatic improvements to requests to complete goals, tasks, or problems. Function calls would need to be aligned to “ChatGPT Response 2”, which could be streamlined and expanded upon.
Question 1:
If an LLM is instructed to read, write, understand, and update a text document every time the LLM interacts with a user, what English text could be stored within the text document that would provide logic and reasoning skills to the LLM that would also allow it to expand upon that logic and reasoning skills?
ChatGPT Response 1:
The concept of having a large language model (LLM) like GPT-4 read, write, understand, and update a text document in each interaction to improve its logic and reasoning skills is an interesting one. While LLMs don't "learn" in the traditional sense from each interaction (as their learning happens during the training phase on a fixed dataset), there are certain types of texts that can be useful in this scenario. Here are some suggestions:
However, it's important to note that while exposing an LLM to such texts might provide a rich context for its responses, the LLM doesn't inherently "expand" its logic and reasoning skills like a human learner would. Its abilities are constrained by its training and the algorithms that govern its function. The LLM can simulate understanding and reasoning based on patterns it has learned, but it doesn't develop these skills autonomously.
Question 2:
Please thoroughly understand the following and work through your answer step by step. A human student can read and follow instructions or processes previously written by a human teacher, which enable the human student to solve previously unseen problems by the human student. These instructions are reusable in different situations and can be combined with additional instructions or processes from the human teacher, enabling the human student to continue to develop unique processes to solve new goals, problems, or tasks once the goal or problem is understood. This previous situation should be able to be replicated for artificial intelligence. If a large language model (LLM) is combined with MemGPT (enabling a long-term memory database and ability to do function calls to that memory) in combination with AutoGen (enabling multiple agents to work together which replicates a human having internal dialog and thought processes) then if the long-term memory database contains a set of general methods for hypothesis testing, evidence-based reasoning, logical reasoning, problem-solving skills, drawing inferences from limited information, making connections between different processes, etc., then each time a user provides a new goal, problem, or task the LLM can search through the long-term memory database for the best matching combination of hypothesis testing, evidence-based reasoning, logical reasoning, problem-solving skills, etc. processes in order to provide the user with the best solution to the goal, problem, or task. What specific text based English instructions can be written to store within the LLM’s long-term memory database that the LLM can follow to provide a framework for hypothesis testing, evidence-based reasoning, logical reasoning, problem-solving skills, drawing inferences from limited information, making connections between different processes, etc.? These textual instructions/processes should be written as short and concise instructions/processes with accurate label identifiers, which will enable the LLM to quickly use function calls to locate the correct combination of instructions/processes needed to best complete the requested goal, problem, or task. Keep in mind that these instructions/processes must be able to be applied to a broad range of situations.
ChatGPT Response 2:
The idea of replicating human-like learning and problem-solving in a large language model (LLM) involves creating a framework where the model can access and apply a set of general methods for reasoning and problem-solving. The integration of technologies like MemGPT and AutoGen can potentially facilitate this by providing long-term memory and multi-agent collaboration, respectively. Here’s a step-by-step approach to creating a set of textual instructions or processes for this purpose:
1. Hypothesis Testing
Label: HypothesisTestingProcedure
Instructions:
• Identify the problem or question.
• Formulate a hypothesis that offers a potential solution or explanation.
• Determine the variables and plan an approach to test the hypothesis.
• Collect and analyze data relevant to the hypothesis.
• Interpret the results to either support or refute the hypothesis.
• Conclude based on the evidence and consider alternative explanations.
2. Evidence-Based Reasoning
Label: EvidenceBasedReasoningMethod
Instructions:
• Define the issue or claim to be evaluated.
• Gather all relevant evidence, ensuring a range of perspectives.
• Critically assess the quality and reliability of the evidence.
• Analyze the evidence in relation to the issue or claim.
• Draw conclusions based on the balance and weight of the evidence.
• Reflect on the strength of the conclusions and identify any biases or gaps.
3. Logical Reasoning
Label: LogicalReasoningFramework
Instructions:
• Clearly state the premises or assumptions.
• Use logical operations (e.g., deduction, induction) to derive conclusions.
• Check for logical consistency and coherence in the argument.
• Identify any logical fallacies or errors in reasoning.
• Test the argument against different scenarios or counterexamples.
• Finalize the reasoning process with a logically sound conclusion.
4. Problem-Solving Skills
Label: ProblemSolvingGuideline
Instructions:
• Define and understand the problem.
• Break the problem down into smaller, manageable parts.
• Generate potential solutions or strategies.
• Evaluate each solution's feasibility and impact.
• Select the most effective solution and plan its implementation.
• Execute the plan and monitor its effectiveness, adjusting as necessary.
5. Inference from Limited Information
Label: InferenceDrawingTechnique
Instructions:
• Identify the information available.
• Recognize the gaps in information.
• Use relevant knowledge and principles to infer missing details.
• Consider multiple possibilities and scenarios.
• Evaluate the likelihood of each inference.
• Choose the most probable inferences while remaining open to new information.
6. Making Connections Between Processes
Label: ProcessConnectionStrategy
Instructions:
• List the processes or concepts involved.
• Identify common elements or patterns between them.
• Explore potential interactions and relationships.
• Synthesize a unified understanding or framework.
• Apply this integrative perspective to the task at hand.
• Continuously update as new connections or insights emerge.
Implementation in an LLM Context
• Storage: Store these instructions in the LLM's long-term memory (facilitated by MemGPT).
• Function Calls: Enable function calls to these instructions based on the task or query at hand.
• Combination and Application: Use AutoGen's multi-agent system to dynamically combine different instructions based on the specific problem or goal.
• Adaptation: Allow the LLM to adapt these processes to the context of the problem, drawing on its vast knowledge base and the specific data of the situation.
Considerations
• Flexibility: These instructions should be applied flexibly, adapting to the context and specifics of each task.
• Continuous Learning: While this approach provides a structured framework, it's crucial to remember that current LLMs do not learn or evolve their understanding autonomously post-training.
• Ethical and Practical Limitations: The quality of output will depend on the LLM's training and inherent limitations. Ethical considerations, such as bias and misuse, must be managed carefully.
This framework attempts to mimic some aspects of human problem-solving and learning within the constraints of current AI capabilities. While it can enhance the LLM's performance in specific tasks, it's important to recognize the differences between this simulated problem-solving and genuine human cognitive processes.
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