AI Reasoning Unveiled: Introducing the Power of OpenAI's o1 Reasoning

September 23, 2024

Leadership in the Loop: Edition 17

Amir Hartman | Managing Director, Dasteel Consulting | Director AI Strategy Research Experience Alliance Fidere.ai, Praxis AI

Venkataraman Lakshminarayanan | Chief Revenue Office & President Cron AI |

Introduction to AI Reasoning and OpenAI o1

You’ve probably heard about the latest addition to the AI arsenal, AI reasoning. OpenAI's new o1 series, known internally as "Strawberry," is designed to solve complex problems by mimicking human-like reasoning processes. In challenging benchmarks across various scientific fields, the o1 models performed at levels comparable to PhD students! Since the release of o1, there have been numerous articles explaining the basics of how AI reasoning works. In this article, we will only briefly summarize the basics, highlight capabilities and constraints, and then move on to explore how we can apply AI reasoning to business situations.

AI Reasoning: a brief overview

The o1 models use advanced techniques, including reinforcement learning, to enhance their problem-solving abilities. Reinforcement learning is a method where algorithms are trained to refine their thinking process, try different strategies, and recognize mistakes. Multi-step problems can now be tackled by AI, much like a human would work through a complex issue. However, it's important to note that while the model shows its reasoning process, it's not necessarily showing the exact steps it followed internally. The displayed reasoning is a human-readable explanation of the model's thought process, designed to be understandable and useful to users. The actual internal processes of the AI are likely more complex and not directly observable. OpenAI has not released detailed technical information about the exact mechanisms used to generate these explanations. As with many aspects of advanced AI models, the full details of how this reasoning is produced and displayed are not publicly available.

Here are the aspects of "AI reasoning:"

1. Thought Process Display: In the OpenAI o1-preview model, users can view the AI’s reasoning via a drop-down menu above the response, showing the steps it took to reach its conclusion.

2. Refined Thinking: OpenAI trained these models to think more thoroughly before responding, much like how a person tackles complex tasks, refining strategies and recognizing mistakes.

3. Step-by-Step Problem Solving: The o1 models break down complex problems methodically, especially in areas like coding, providing step-by-step solutions and pseudocode.

4. Reasoning Explanation: These models can explain their thought processes in detail, making them useful for education or solving complex problems in science, math, and coding.

Capabilities and Limitations To provide a clear understanding of what the o1 preview model can and cannot do, we've compiled a table of its capabilities and limitations:

1. Thought Process Display: In the OpenAI o1-preview model, users can view the AI’s reasoning via a drop-down menu above the response, showing the steps it took to reach its conclusion.
2. Refined Thinking: OpenAI trained these models to think more thoroughly before responding, much like how a person tackles complex tasks, refining strategies and recognizing mistakes.
3. Step-by-Step Problem Solving: The o1 models break down complex problems methodically, especially in areas like coding, providing step-by-step solutions and pseudocode.
4. Reasoning Explanation: These models can explain their thought processes in detail, making them useful for education or solving complex problems in science, math, and coding.

Capabilities and Limitations

To provide a clear understanding of what the o1 preview model can and cannot do, we've compiled a table of its capabilities and limitations:

Challenges and Considerations

• The o1-preview model is more expensive than previous versions, though a budget-friendly mini version is available at 80% lower cost.
• Its deliberate reasoning process can lead to slower response times, which may be a drawback in time-sensitive applications.
• The model has limitations, such as the inability to upload files or browse the web, which could affect certain use cases.
• Ethical considerations, including biases and job displacement, should also be addressed, along with ensuring data privacy when handling sensitive information.
• Integrating these advanced models into existing systems may require substantial technical expertise and resources.

Applying AI Reasoning for Everyday Business

Here are a few examples of how we might apply AI reasoning:

• Marketing: In-depth data analysis and strategic planning, optimizing processes and improving decision-making.
• Healthcare: Aid in diagnostics and treatment planning by analyzing complex medical data and research.
• Legal: Document analysis, case preparation, and predicting case outcomes based on historical data.
• Finance: Risk assessment, fraud detection, and market trend analysis.
• Manufacturing: Optimize supply chains, predict maintenance needs, and improve quality control processes.
• Education: Personalize learning experiences and develop advanced educational content.

Five Step Formula for Utilizing AI Reasoning in Your Business

As always, we recommend a structured approach:

1. Identify Complex Challenges: Determine areas within your business that require advanced problem-solving, e.g. strategic planning, operational optimization, or product development.
2. Select Appropriate Tools: There will be more models in the near future, but for now, choose between the o1-preview or o1-mini. The o1-preview is suitable for tasks requiring extensive computation, while the o1-mini is ideal for coding and smaller applications.
3. Integrate AI into Workflows: Incorporate these AI models into existing workflows to enhance decision-making processes. We have written extensively about the importance of this earlier in our series (Article 2: It’s all about the workflow!)
4. Monitor Performance: Be prepared to adjust strategies based on feedback and results, by regularly evaluating the performance of AI models.

5. Ensure Compliance and Safety: Implement safety measures and guidelines to mitigate risks associated with AI deployment.

What to Expect with AI Reasoning in the Future

We see shifts in the job market as AI takes on more complex reasoning tasks, which may create a demand for roles involving AI oversight and ethical considerations. Breakthroughs in complex fields such as scientific research, drug discovery, and climate modeling may now be achievable much quicker. Given AI's ability to perform at PhD levels in various disciplines, we should expect substantial changes in higher education and professional training. AI reasoning has the potential to increase collaboration between different fields of study, which could lead to innovative solutions for complex problems that have long challenged us. What we are seeing right now is that the o1 family is really good at “architecting the approach to the problem”, and in certain domains solving the problem. We believe we’ll see it and other reasoning engines play a significant role in mixture of agents (MOA). The integration of ChatGPT o1-preview into a mixture of agents approach should improve problem-solving capabilities by leveraging its advanced reasoning skills to architect complex tasks and verify outputs from specialized agents.

As we wrap up this edition, we'd like to emphasize that the journey into AI reasoning is just beginning. We look forward to continuing this conversation and exploring the exciting developments that lie ahead in the world of AI. We encourage our readers to explore the possibilities that AI reasoning presents for their specific industries and use cases. Tell us how you are using AI reasoning. What's working well and not working.

#AI #GenAI #AIReasoning