OpenAI o1: Finally an AI Model That You Can Reason With

OpenAI has launched o1-preview, the first in a series of advanced AI models designed to tackle complex problems in science, coding, and math. This new model family, named OpenAI o1, represents a significant leap in AI capabilities, particularly in areas requiring deep reasoning and problem-solving skills.

Here’s what you need to know about OpenAI o1:

  • Enhanced thinking process: Trained to spend more time reasoning before responding
  • Improved performance: Outperforms GPT-4 in challenging tasks, including International Mathematics Olympiad problems
  • Advanced safety measures: New training approach leverages reasoning capabilities for better adherence to safety guidelines

This is only a preview right now, but I think it is important sign of the direction that AI models are going in. We need to learn more about reasoning and how to interact with these new models, just like we did when we first experienced ChatGPT.

Think Before You Speak?

Imagine if you just thought a little bit before you spoke. I know that would have helped me a few times in my life. o1 introduces the thinking process embedded into the model. This is where the AI is trained to spend more time reasoning before responding.

  1. Improved problem-solving: By taking more time to think, the AI can explore multiple approaches and strategies to solve complex problems. This is particularly crucial for tasks in fields like science, mathematics, and coding, where the solution may not be immediately apparent.
  2. Higher accuracy: Spending more time on reasoning allows the AI to double-check its work, consider potential errors, and refine its answers. This can lead to more accurate and reliable outputs, especially for complex queries.
  3. Mimicking human cognition: This approach more closely resembles how humans tackle difficult problems. We often need time to ponder, analyze, and work through challenging questions step-by-step. By emulating this process, the AI can produce more thoughtful and nuanced responses.
  4. Handling multi-step problems: Many real-world problems require breaking down into smaller steps and solving each part sequentially. The extended reasoning time allows the AI to manage these multi-step problems more effectively.
  5. Reduced impulsivity: Quick responses can sometimes lead to oversimplified or incorrect answers. By taking more time to think, the AI is less likely to jump to conclusions or provide hasty, poorly-considered responses.
  6. Improved explanation capability: With more thorough reasoning, the AI can potentially provide better explanations of its thought process, making its responses more transparent and understandable to users.
  7. Tackling novel situations: Extra reasoning time can help the AI better handle unfamiliar or unprecedented scenarios by allowing it to draw connections between known concepts and apply them to new situations.

I am particularly interested in how to handle novel situations with o1. I experimented with o1 to find new game themes and reason about new things, and it felt like working with someone who really knows what is going on in a particular domain. However, the interactions reinforced that I have to learn a new way to prompt.

Reasoning Models Need New Ways of Prompting

When you’re using these new reasoning models, keep it simple! They’re pretty smart, so you don’t need to jump through hoops for good results. Our old tricks don’t apply anymore and even might degrade the output.

Here’s some advice from my first week of using OpenAI o1:

  • Just say what you want. These models are great at figuring out your meaning, so don’t overcomplicate things.
  • Skip the “think step by step” part. The model is already doing the thinking behind the scenes, so you don’t need to tell it how to think.
  • Use markers to break things up. If your input has different parts, use something like quotes or tags to separate them. This helps the model understand what’s what.
  • Don’t overload it with extra info. If you add context from other sources, stick to the essentials. Too much extra stuff can make the model go off on tangents.

Give it a try. Experiment. o1-preview (and its smaller version, o1-mini) are available in ChatGPT Plus and via the API.

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