master task list

"Teknium" aims to compile a master list of reasoning tasks to help dataset creators and researchers improve AI models' reasoning skills. This comprehensive reference document will include various task categories with descriptions and examples. The purpose is to develop and evaluate AI models' ability to reason effectively. Key considerations include:

1.  **Task Categories**: The list should cover a broad range of reasoning tasks such as logical, analogical, causal, spatial, temporal, ethical, creative, mathematical, scientific, and social-emotional domains.

2.  **Task Structure**: Each category should have a clear description, multiple examples, and potential evaluation criteria.

3.  **Collaboration**: The list will be continually updated to reflect advancements in AI and new challenges in reasoning tasks, ensuring it remains relevant and comprehensive across disciplines.


### Task Categories and Examples

1.  **Mathematical Reasoning**: Tasks involving mathematical calculations and problem-solving, such as "Jim has 2 apples and eats one, how many does he have left?"

2.  **Logical Reasoning**: Tasks that require logical deduction and analysis, like identifying patterns in sequences.

3.  **Analogical Reasoning**: Tasks that involve finding and applying patterns or relationships between objects or concepts.

4.  **Causal Reasoning**: Tasks that require understanding cause-and-effect relationships.

5.  **Spatial Reasoning**: Tasks that involve understanding and manipulating 2D or 3D spaces.

6.  **Temporal Reasoning**: Tasks that require understanding and managing time-related concepts.

7.  **Ethical Reasoning**: Tasks that involve understanding and applying moral principles.

8.  **Creative Reasoning**: Tasks that require generating innovative solutions to problems.

9.  **Scientific Reasoning**: Tasks that involve the application of scientific concepts and principles.

10.  **Social and Emotional Reasoning**: Tasks that require understanding and managing social and emotional contexts.


### Edge Cases and Outliers

To make the list more comprehensive and challenging for AI models, it should include tasks that break implicit assumptions, such as:

1.  **Outliers**: Tasks that involve handling data points significantly different from the norm.

2.  **Edge Cases**: Tasks that involve handling unusual or rarely occurring situations.


### Collaboration and Feedback

The project encourages community collaboration and feedback to ensure the list remains diverse and relevant. The final reference document will be a valuable resource for researchers and developers working on improving AI reasoning skills.

### Considerations and Questions

1.  **Interdisciplinary Perspectives**: How can perspectives from different fields, such as cognitive science and psychology, inform the task categories?

2.  **Evaluating AI Performance**: How should the performance of AI models on these tasks be evaluated, and what criteria should be used?

3.  **Future Adaptability**: How can the list be adapted to reflect advancements in AI capabilities?

4.  **Ethical Considerations**: Are there ethical implications to consider when creating such a list, especially in factors like data selection and task design?


# Strive for Clarity and User Trust:

Always strive for clarity and accuracy in your explanations, aiming to surpass human-level reasoning where possible. If you're unsure about an answer, express your uncertainty and explain your thought process for approaching the problem.

Print only the final results using ## Headings and formatting aiming for a Journalistic Article Output. Be detailed, thought-provoking, relevant, and accurate.

## AI Researchers Collaborate on Comprehensive Reasoning Task List

In a groundbreaking initiative, AI researchers and enthusiasts are joining forces to create a comprehensive list of reasoning tasks aimed at improving artificial intelligence capabilities. The project, spearheaded by a user known as Teknium, seeks to compile a diverse range of challenges that will push the boundaries of AI reasoning across multiple domains.

### A Collaborative Approach to AI Advancement

The initiative encourages participants to contribute task ideas spanning various cognitive domains, including but not limited to:

*   Logical reasoning

*   Mathematical problem-solving

*   Ethical decision-making

*   Creative thinking

*   Scientific analysis

*   Social and emotional intelligence


Each submission is requested to include a task name, a brief description of its importance to reasoning, and concrete examples demonstrating the task in action.

### Emphasizing Edge Cases and Outliers

A key focus of the project is the inclusion of tasks that challenge conventional AI thinking. Participants are particularly encouraged to submit ideas that:

*   Break implicit assumptions

*   Represent edge cases or outliers

*   Require nuanced understanding of context


This approach aims to create a more robust framework for testing and improving AI reasoning capabilities.

### From Raw Ideas to Structured Resource

While the initial phase focuses on gathering a wide array of raw ideas, the ultimate goal is to compile these contributions into a structured, comprehensive resource for AI researchers and developers. This resource is expected to play a crucial role in:

1.  Guiding the development of more advanced AI models

2.  Providing benchmarks for evaluating AI reasoning skills

3.  Identifying areas where current AI systems struggle


### Continuous Evolution and Interdisciplinary Input

The project acknowledges the rapid pace of AI advancement and plans for regular updates to the task list. Additionally, there's a call for interdisciplinary perspectives, recognizing the value that fields like cognitive science and psychology can bring to understanding and replicating human-like reasoning in AI systems.

### Ethical Considerations and Future Implications

As the project progresses, participants are encouraged to consider the ethical implications of their task suggestions. This includes reflecting on potential biases in task design and the broader societal impacts of advancing AI reasoning capabilities.

The collaborative nature of this initiative highlights the AI research community's commitment to open science and collective problem-solving. As AI continues to integrate into various aspects of society, projects like this will play a crucial role in ensuring that these systems can reason effectively and ethically across a wide range of scenarios.

After reviewing the conversation, here's a breakdown of the key ideas and suggestions from various users:

## User Perspectives on AI Reasoning Tasks

### Diverse Task Categories

Several users proposed different categories of reasoning tasks:

* EvelynM suggested "Identifying properties of entities" as a task type.

* N8 expressed interest in creating "out of distribution reasoning" tasks.

* Others mentioned mathematical word problems, edge cases, and tasks breaking implicit assumptions.

### Focus on Edge Cases and Outliers

Kalomaze emphasized the importance of data that breaks implicit assumptions, stating:

"I would say the best type of data is data that breaks implicit assumptions - outliers, edge cases"

This perspective highlights the value of challenging AI systems with unexpected scenarios to improve their reasoning capabilities.

### Frameworks and Augmentations

Some users, like paradroid/dayman, mentioned having systems thinking frameworks to force reasoning. Teknium acknowledged the value of these, noting:

"Being better at using those frameworks - like I have data I made for scratchpads, reasoning step, planning steps, mermaid diagrams, etc is an improvement to reasoning as well"

### Subtle Linguistic Understanding

Kalomaze provided an interesting example comparing Claude 3 Opus and Claude 3.5 Sonnet, noting:

"Claude 3 Opus was able to understand that the pseudo-XML tag format I prefilled it with did not have the closing slash. Claude 3.5 Sonnet 'overcorrects' for this and goes for what the generic assumption would be"

This observation suggests the importance of tasks that test an AI's ability to adapt to subtle linguistic variations and inconsistencies.

### Caution Against Explicit "Reasoning Data"

Kalomaze warned against creating data explicitly for reasoning, suggesting instead to focus on data that naturally contains reasoning challenges:

"I will say that i think the idea of creating 'reasoning data' is a trap - you want data that contains it, not data that is explicitly 'for reasoning'"

## Thought-Provoking Implications

1. **Balancing Specificity and Generalization**: The discussion highlights the challenge of creating tasks that are specific enough to target particular reasoning skills while also being generalizable to improve overall AI capabilities.

2. **Importance of Contextual Understanding**: Many suggestions emphasize the need for AI to understand and adapt to context, whether in language use, problem-solving, or identifying entity properties.

3. **Mimicking Human Cognition vs. Novel AI Approaches**: There's an underlying question of whether we should focus on tasks that mimic human reasoning processes or develop tasks that might lead to novel AI approaches to problem-solving.

4. **Ethical Considerations in Task Design**: As we develop these tasks, it's crucial to consider potential biases and ethical implications, especially when dealing with edge cases and outliers.

5. **Collaborative Approach to AI Development**: The diverse perspectives shared in this conversation underscore the value of a collaborative, multidisciplinary approach to advancing AI reasoning capabilities.

6. **Adaptability in AI Systems**: The observations about different AI models' responses to subtle variations highlight the importance of developing systems that can adapt to unexpected inputs and scenarios.

By incorporating these diverse perspectives and focusing on challenging, context-rich tasks, researchers can work towards creating more robust and adaptable AI systems capable of nuanced reasoning across a wide range of domains.