I ​​think you need to be careful about these things

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Mark Zuckerberg: Well, I mean, it depends on the goals that you set for it. I mean, there are a lot of weird...

Host: We have many such examples. What's going on? Bring up the title. "AI strikes back: The story of how ChatGPT tried to copy itself". This happened six days ago. In controlled security testing, ChatGPT O1 was tasked with achieving its goals at all costs. In this case, the model allegedly took a number of worrying steps, attempting to disable oversight mechanisms designed to regulate its behavior, attempting to replicate its own code to avoid being replaced by newer versions, and exhibiting deceptive behavior when monitoring systems intervened. Yeah, so, "Terminator," that's what people are afraid of.

Mark Zuckerberg: like what kind of guardrails you put around it. If you tell it "at any cost", then I mean...

Host: But that's what people are afraid of, and what I was going to say is, achieve your goals at all costs.

Mark Zuckerberg: Cost is an important consideration when student data discussing inference models. While the main models in use today are the first generation of large language models (LLMs) such as ChatGPT and Meta AI, the capabilities of these models are expanding. These models act as chatbots; when you ask questions, they take prompts and provide responses.

However, the next generation of inference models represents a major evolution. Rather than just providing an answer, these models can construct entire trees of potential answers. When asked a question, they might not execute a single query, but rather run thousands or even millions of queries to map out the various possible answers.

This method of reasoning is more complex and more expensive to perform, but it results in significantly improved reasoning capabilities. By exploring multiple avenues of inquiry, these models demonstrate a higher level of intelligence and are able to provide more nuanced and thoughtful answers.