Exploring OpenAI's new o1 models: A technical insight and their impact on productivity

OpenAI has introduced a new family of models, o1-preview and o1-mini, with the full version of o1 expected soon. In this post, I wanted to share a technical sneak peek of the model and the model’s impact on the world of productivity. The overview should help AI enthusiasts with an interest in productivity. I have linked references for anyone wanting to go deeper on a topic or concept.

Technical snapshot of the model

• o1 models are single individual models and not a system of interconnected models. It marks the beginning of a new family of AI models different from the GPT series.
• ?The o1-mini is particularly effective in coding tasks and is more cost-efficient, whereas o1-preview is a broader model that excels in more complex reasoning tasks across multiple domains, including science, math, and problem-solving; The models are currently trained till October 2023 data.
• o1 models are trained with the algorithm/logic of solving problems; the logic data may be synthetic data and is used by a new reinforcement learning model; the focus is less on training with overall world knowledge, which is the case for the GPT series models.
• o1 model uses chain of thought (CoT) reasoning at the inference time to generate output. This involves breaking complex problems into smaller, manageable parts and systematically solving each one. Additionally, the model may explore multiple potential solutions to various aspects of a problem.
• Effectively, o1 model accuracy increases due to an increase in learning time during the data training steps and chain of thought (CoT) reasoning time during the inference stage. See below the chart shared by OpenAI on their announcement page.

?"Let’s Verify Step by Step" paper (https://arxiv.org/pdf/2305.20050) is a good read for anyone wanting to deep dive on the building block that makes the new model accurate.?

Hypotheses?on how o1 will reshape productivity

STEM Capabilities:

The o1 model excels in STEM fields, which will significantly enhance productivity across scenarios that require complex data analysis, mathematical reasoning, and inference from large datasets. This is particularly beneficial for applications in fields like research, finance, and engineering where higher precision and automation of intricate tasks will boost both speed and quality of work.

Long-Form Coherent Responses and Creativity:

Unlike previous models, o1 can generate coherent and extended responses. The new model is closer to adapting to novelty over reproducing memorized patterns; similar to how humans use first-principles thinking to navigate unfamiliar environments. This sets up the model well for creative tasks such as writing, ideation, or problem-solving and increasing the output quality compared to the older models.

System 2 Thinking and Inference Scaling:

The new paradigm is scaling inference time vs scaling parameters. Great news is that the AI models will start doing system 2 thinking. For now, the flip side is increasing inference time to do System 2 thinking. This is ideal for agentic use cases where users do not mind giving AI the time to finish a task. So, the model can help support more complex, agentic tasks where slower, deliberate reasoning is necessary. This makes o1 ideal for high-value, low-frequency use cases, such as deep strategic planning, complex research workflows, or legal document review, where the model’s slower, thoughtful analysis provides greater utility than instantaneous answers.

Domain-Specific AI and Mobile Use Cases:

With the rise of smaller, efficient AI models like Microsoft's phi3, there is a shift towards local deployment on mobile devices. However, challenges remain in accuracy and domain-specific adaptability. The o1 model’s use of Chain-of-Thought (CoT) training techniques offers a solution, potentially opening mobile applications that previously required server-side processing. This could allow for more effective, on-device AI for tasks like local data analysis or offline content generation, expanding the real-world use cases of LLMs on mobile platforms.

Sharing an example of the real-life effectiveness of the o1 preview model in healthcare

There is so much value that can be unlocked from these LLM models. I am genuinely excited about where things are headed and hope you are too!

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