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Jamie will pull up the relevant information

Jamie will pull Host: What, you don’t know? It happened recently.  We were talking about this the other day, and it’s

so shocking. When it realized it was going to be phased liechtenstein email list 150000 contact leads out and they were going to put out a new

version and it was going to be shut down, it actually tried to copy its own code and tried to proactively rewrite the code.

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 practical skills of h5 marketing 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: I ​​think you need to be careful about these things, 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 mobile lead what I was going to say is, achieve your goals at all costs.

Mark Zuckerberg: Cost is an important consideration when 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 Jamie will pull.

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