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How does O1 break down problems into steps? While prior models memorized content, O1 adds a layer where it thinks about how to arrive at the right sequence of steps to do more complex tasks and get the right answer.

44,219 次观看 • 1 年前 •via X (Twitter)

10 条评论

Garry Tan 的头像
Garry Tan1 年前

Why OpenAI's o1 Is A Huge Deal | YC Decoded

Romain Lacombe 的头像
Romain Lacombe1 年前

Just a note Garry: it's not a layer, CoT is all happening in-context.

SLOMP 的头像
SLOMP1 年前

keeping yc founders on the bleeding edge

protomemetic 的头像
protomemetic1 年前

Appreciate this video. Just last night I asked o1 what it meant by "thinking" and it told me to stop asking.

rudy@FURE.Cab 🌐 - e/acc 的头像
[email protected] 🌐 - e/acc1 年前

Sim to First Principal thinking 🤔

djcows 的头像
djcows1 年前

i see pizza math problem solving, i press like

Steven Musielski 的头像
Steven Musielski1 年前

This is incredible Garry. WOW.

Dennis Hackethal 的头像
Dennis Hackethal1 年前

@ycombinator No model ever ‘memorized’ or ‘thought’ about anything. Stop using misleadingly humanizing verbiage and spreading this false advertising

DWA 🌊 🚀 的头像
DWA 🌊 🚀1 年前

Keep hyping guys…

Jon_D 的头像
Jon_D1 年前

I wonder what the next thinking process upgrade will be! Definitely a fascinating breakthrough. What do you think is next?

相关视频

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,401 次观看 • 1 年前