The first ‘hybrid justification’ of an anthropic world launches the EU model

The difference between an ordinary model and a thought is similar to the two types of thinking described by Nobel-award-winning economist Michael Kahneman To think quickly and slowly: Fast and instinctive system-1 think and more consumer system-2 thinking.

The model type, which is well known as a large language model or LLM, removes an offer immediately to make a large nervous network request. These speeches may be surprisingly smart and suitable, but can answer questions that require step-by-step justification, including simple accounts.

If an LLM is instructed to get acquainted with a plan to follow, it may be forced to imitate thoughtful thought. This trick is not always reliable and the models are usually struggling to solve problems that require extensive, careful planning. Openai uses Google and now anthropic A machine learning method known as reinforcement learning To get the latest models to learn to give you the right thing to do right. This requires additional training information from people in solving special problems.

The Penn says that the clod’s justification regime says using computers, using computers and answering complex legal questions, including additional information. “We are progressing … are technical topics or subjects that are long-thought,” says Penn. “Those from our customers are very interested in placing our models into real workloads.”

The anthropic says that some criteria such as Klod 3.7, SWE-DIC, Openai O1 says O1 O1 O1 O1 is especially good in solving the coding problems that require O1. The company today releases a new tool called Claude code specially designed for Yardimly coding such ai.

“The model is already good during coding,” said Penn. But “Additional thinking would be good for situations that may require a very complex planning, say you are looking at a large code base for a company.”