Hello everyone, this is the real human speaking!
Today, we’re going to dive deep into OpenAI’s new model, Deep Research. This model focuses on enhancing the efficiency of knowledge work and automating complex tasks. Let’s explore its features and potential together!
Table of Contents
What is Deep Research?
The Importance of Agents and the Background of Deep Research
Differences Between the O1 Model and Deep Research
Key Features and Outcomes of Deep Research
Various Use Cases
Technical Foundation and Performance Evaluation
The Future of Deep Research and the AGI Roadmap
1. What is Deep Research?
Deep Research is OpenAI’s new model designed to perform multi-step research using the internet. Unlike simple information collection, it is built to discover, synthesize, and infer data. Essentially, it explores and analyzes information to provide users with structured reports—a revolutionary tool for complex research tasks.
2. The Importance of Agents and the Background of Deep Research
OpenAI has been advancing its AGI (Artificial General Intelligence) roadmap for a long time. One key element of this vision is the concept of agents. Mark and the OpenAI team believe that agents will revolutionize knowledge work for both enterprises and individuals. Deep Research is a realization of this concept, focusing on efficient knowledge management and exploration.
Looking back, the O1 model launched last year faced limitations like long processing times and lack of access to external tools. Deep Research was developed to overcome these challenges.
3. Differences Between the O1 Model and Deep Research
O1 Model: While capable of extensive research, it had limitations such as restricted access to external tools.
Deep Research: It directly performs multi-step research on the internet and delivers structured reports, improving on O1’s shortcomings.
4. Key Features and Outcomes of Deep Research
One of the main advantages of Deep Research is its lack of latency constraints. Unlike typical AI models that provide quick responses, this model can take 5 to 30 minutes to complete tasks because it thoroughly analyzes multiple web pages and synthesizes data.
Its outcomes include:
Comprehensive, citation-supported reports
Applications across fields like market research, specific item searches, and presentation slide content generation
5. Various Use Cases
Deep Research is useful for both personal and professional research. Let’s explore some examples:
High-value product purchases: It helps users compare products with reliable data.
Market research: It can analyze industry trends and competitors.
Presentations: It automatically organizes necessary content for slides.
6. Technical Foundation and Performance Evaluation
This model is built on OpenAI’s O3 model and trained through reinforcement learning. As a result, it can efficiently process complex information and produce expert-level outputs. Its performance has been evaluated across various areas, demonstrating high accuracy and practical applications.
Notably, it has performed well even in complex tasks like finding specific TV shows or analyzing market trends.
7. The Future of Deep Research and the AGI Roadmap
Deep Research is not limited to its current capabilities. OpenAI plans to add more features and expand its scope. As a crucial element of the AGI roadmap, its goal is to handle long-term and autonomous tasks. This means users can request comprehensive, well-structured information with a single query.
Conclusion
Deep Research has the potential to transform knowledge-based work. Whether you are making purchasing decisions or conducting corporate research, this model can be a valuable asset. As we look forward to its future advancements, it will be exciting to see how Deep Research positively impacts our lives.
I’m rooting for you all!
