Isla Fulford, Researcher Openaihave a intuition In-depth research Even before it is released, it will be hit.
Fulford helped build AI Agents that independently explore the network decide their own links, what to read and what to organize into in-depth reports. Openai first provides in-depth research internally; Fulford says whenever it falls, she is overwhelmed by the questions her colleagues are eager to come back. “My number makes us very excited,” Fulford said.
Since residency to the public on February 2, in-depth research has also proven to be a hit with many users outside the company.
Stripe CEO Patrick Collison Posted on X A few days after the product is released. “It’s really great.
“In-depth research is an AI product, which really gained a part of DC’s policy-making community to start feeling AGI,” he said. Write Dean Ball, a researcher at George Mason University, specializes in AI policy.
As part of the in-depth study Chatgpt Pro Plan, priced at $200 per month. It requires queries such as “write me a report about the Massachusetts health insurance industry,” or “tell me a report about the wires Ministry of Government Efficiencyand then propose a plan, search for relevant websites, comb their content, and explore what links and what information should be investigated further after dozens of minutes of exploration.
Many of the tools currently using it as an AI proxy are essentially chatbots without the need for too high chatbots to connect to simple programs. In-depth research on the model itself goes through a kind of artificial reasoning before making plans and moving forward. The model provides detailed information on this reasoning after the side window study.
“Sometimes, it’s like, ‘I need to go backtrack, which doesn’t seem so hopeful’, said Josh Tobin, another OpenAi researcher who was involved in building an in-depth study. “It’s really cool to read some of these trajectories just to understand the idea of the model.” ”
Openai clearly sees in-depth research as a tool that can work more office work. “This is something we can expand on,” Tobin said, adding that agents can be trained to do specific white-collar work. For example, an agent with access to internal company data can quickly prepare a report or presentation. The longer goal, Tobin said, is to “build an agent that is not only good at building reports through search networks, but is good at many other types of tasks.”
Since the in-depth study has been trained to analyze and summarize personal texts, Tobin said his team was surprised to see many people using it to generate code. “It’s an interesting thread,” he said. “We’re not completely sure what to do.”
