Anthropic, an artificial intelligence company founded by exiles from OpenAI, has introduced the first AI model that can produce either conventional output or a controllable amount of reasoning needed to solve more grueling problems.

Anthropic says the new hybrid model, called Claude 3.7, will make it easier for users and developers to tackle problems that require a mix of instinctive output and step-by-step cogitation. The user has a lot of control over the behavior—how long it thinks, and can trade reasoning and intelligence with time and budget, says Michael Gerstenhaber, product lead, AI platform at Anthropic.

Claude 3.7 also features a new scratchpad that reveals the model’s reasoning process. A similar feature proved popular with the Chinese AI model DeepSeek. It can help a user understand how a model is working over a problem in order to modify or refine prompts.

Dianne Penn, product lead of research at Anthropic, says the scratchpad is even more helpful when combined with the ability to ratchet a model’s reasoning up and down. If, for example, the model struggles to break down a problem correctly, a user can ask it to spend more time working on it.

Frontier AI companies are increasingly focused on getting the models to reason over problems as a way to increase their capabilities and broaden their usefulness. OpenAI, the company that kicked off the current AI boom with ChatGPT, was the first to offer a reasoning AI model, called o1, in September 2024.

OpenAI has since introduced a more powerful version called o3, while rival Google has released a similar offering for its model Gemini, called Flash Thinking. In both cases, users have to switch between models to access the reasoning abilities—a key difference compared to Claude 3.7.

The prospect of consciousness in artificial systems is closely tied to the viability of functionalism about consciousness. Even if consciousness arises from the abstract functional relationships between the parts of a system, it does not follow that any digital system that implements the right functional organization would be conscious. Functionalism requires constraints on what it takes to properly implement an organization. Existing proposals for constraints on implementation relate to the integrity of the parts and states of the realizers of roles in a functional organization. This paper presents and motivates three novel integrity constraints on proper implementation not satisfied by current neural network models.

A team of Carnegie Mellon University researchers set out to see how accurately large language models (LLMs) can match the style of text written by humans. Their findings were recently published in the Proceedings of the National Academy of Sciences.

“We humans, we adapt how we write and how we speak to the situation. Sometimes we’re formal or informal, or there are different styles for different contexts,” said Alex Reinhart, lead author and associate teaching professor in the Department of Statistics & Data Science.

“What we learned is that LLMs, like ChatGPT and Llama, write a certain way, and they don’t necessarily adapt to the writing style. The context and their style are actually very distinctive from how humans normally write or speak in different contexts. Nobody has measured or quantified this in the way we were able to do.”