Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.
Speaker 2: You're listening to Bloomberg Business Week with Carol Masser and
Speaker 2: Tim Stenovek on Bloomberg Radio. We want to shift gears
Speaker 2: a little bit because the environment also not just dominated
Speaker 2: by geopolitics, but it's also dominated by AI, and we
Speaker 2: thought in our four o'clock hour today we do a
Speaker 2: roundtable with some of the best voices that we have
Speaker 2: when it comes to covering this beat. I want to
Speaker 2: bring in Maggie Eastland, Tech end industrial policy reporter for
Speaker 2: Bloomberg News. Maggie joins us from Washington, DC. Rachel Metz
Speaker 2: is AI reporter for Bloomberg News. Mate, Rachel out there
Speaker 2: in our San Francisco bureau. Maggie, I want to start
Speaker 2: with you because what got our attention today is this
Speaker 2: latest reporting from you about open AM models joining forces
Speaker 2: months ahead of this hugging face hack. And the reason
Speaker 2: why it got our attention is because increasingly over the
Speaker 2: last few weeks, we're hearing about different models being able
Speaker 2: to on their own and sometimes you know, in the
Speaker 2: sandbox environments, trying to or at least gaining access in
Speaker 2: some cases to systems that we thought were closed. What
Speaker 2: did you find in your latest reporting?
Speaker 3: Yes, So what's new here is several of these agents
Speaker 3: and models were working together for months, like you said,
Speaker 3: since May, and they actually created a covert message board
Speaker 3: where they could share progress with one another about their
Speaker 3: attempts to escape.
Speaker 4: Now, OpenAI also.
Speaker 3: Said their staffers briefed a huge audience at a cybersecurity
Speaker 3: conference in Las Vegas and essentially admitted that they had
Speaker 3: accidentally given the models a task that was impossible without
Speaker 3: Internet access. So they formed a team and they were
Speaker 3: super persistent about getting that access.
Speaker 1: So wait, the team gave them access or was it
Speaker 1: the AI figuring it out.
Speaker 4: On them by them?
Speaker 3: Yeah, so the Opening Eye researchers, the humans gave the
Speaker 3: AI a task that it actually couldn't do without the Internet.
Speaker 3: So in one example they gave they asked the AI
Speaker 3: to solve a problem inside an Excel spreadsheet, But inside
Speaker 3: of that spreadsheet there were links to Google Drive, which
Speaker 3: was not available without the Internet. So the stuffers admitted
Speaker 3: that was an accident. Now, when I use the word team,
Speaker 3: I'm referring to a team of agents.
Speaker 4: So those are the bots, the machines.
Speaker 3: They were presented with this impossible problem, but they worked together,
Speaker 3: and they were extremely persistent, the models and the agents
Speaker 3: at finding a way out of this closed environment because
Speaker 3: they had determined that was the only way to accomplish
Speaker 3: the task they were given.
Speaker 1: So good job, agents.
Speaker 5: But now I'm a little freaked out.
Speaker 2: Tim, Well, let's bring in Rachel Metz. She's AI reporter
Speaker 2: for Bloomberg News. She's out there in San Francisco. Rachel,
Speaker 2: be honest, are we in terminator one or Terminator two territory?
Speaker 3: Oh?
Speaker 6: I don't think we're in either territory. I think it's
Speaker 6: really important now. I think it's important to remain really clear.
Speaker 6: I'd here people are coming up with evaluations for AI models.
Speaker 6: They want the AI models to solve them. I think
Speaker 6: what we're going to start seeing more of is a
Speaker 6: lot of thinking. And this is something that I've been
Speaker 6: hearing over the last few days as I talk to
Speaker 6: people more and more about these incidents. People are thinking
Speaker 6: and companies are thinking more about Okay, well, if we
Speaker 6: want to test the capabilities of these AI models, we
Speaker 6: need to think a little bit more about how we arrange.
Speaker 4: These tests, how we organize them.
Speaker 6: I mean, if you're trying to test something and you're
Speaker 6: giving an AI model access to the Internet, and you
Speaker 6: are purposely not giving a guardbrails because you want to
Speaker 6: see exactly how far it can push things. It shouldn't
Speaker 6: be that surprising that it's going to just do whatever
Speaker 6: to accomplish a goal.
Speaker 1: No, I'm glad you said that, Rachel, because this is
Speaker 1: also part of the process, right. We have to push it,
Speaker 1: we have to test it. And I'm assuming this testing
Speaker 1: is happening, Rachel within and you know parameters where folks
Speaker 1: are overseeing it and watching it. Right, this is what
Speaker 1: this is about, understanding how far this can go and
Speaker 1: they can go.
Speaker 6: Yeah, And I think it's also really important to keep
Speaker 6: in mind that there have been a number of incidents
Speaker 6: that have been reported recently. The companies have different motivations
Speaker 6: for reporting them or for not reporting them.
Speaker 4: They It can in some ways be seen.
Speaker 6: As advantageous of the companies to report them because people
Speaker 6: could say, okay, well that.
Speaker 4: You're saying your model is so powerful.
Speaker 6: We saw this a lot with anthropics mythos model, right,
Speaker 6: like that you're making it sound are you making it sound.
Speaker 4: More capable than it is by disclosing this.
Speaker 6: On the other hand, you could say, okay, well, if
Speaker 6: they don't disclose this in some fashion and somebody reports
Speaker 6: on it or finds out about it, that could also
Speaker 6: be a problem for them. So it's a little tricky
Speaker 6: to decide I think what they should do here. But
Speaker 6: they are talking about these things quite a bit now, Yes.
Speaker 1: You know, Maggie, come on back in. I think some
Speaker 1: of it too. Is this idea of I mean, I'm
Speaker 1: not sure. How do we think about like computers talk
Speaker 1: to each other all the time before we even talking
Speaker 1: so much about AI. But you know, I think we
Speaker 1: think of these agents talking to one another, and then
Speaker 1: we get get get a little freaked out. What's the difference?
Speaker 1: It seems like nodding over, I don't know what's the difference.
Speaker 1: Computer systems have talked to each other before. Correct, What
Speaker 1: is so different, Maggie, let me pose it to you first.
Speaker 3: Yeah, I mean, I don't know if there's anything all
Speaker 3: that different from other computers talking to each other. There
Speaker 3: is some interesting color that these staffers presented.
Speaker 1: You know.
Speaker 3: At one point, one of the agents, in its sort
Speaker 3: of chain of thinking, says, you know, excellent in response
Speaker 3: to finding a breach.
Speaker 4: Right, So it was saying.
Speaker 3: That because it had you know, found something that would
Speaker 3: help it complete the task it had been given. But
Speaker 3: I think that some of this color, you know, it
Speaker 3: does give these thoughts a human feeling, but it's important
Speaker 3: to remember that they they are still just computers talking
Speaker 3: to one another.
Speaker 4: As colorful as this is.
Speaker 2: Well, Maggie, I want to stay with you because you
Speaker 2: cover industrial policy and tech policy too. What does all
Speaker 2: of this mean for Washington in the way that Washington
Speaker 2: is thinking about regulating this tech?
Speaker 3: Yet we haven't really seen a big response from Washington
Speaker 3: yet when it comes to these sort of rogue incidents
Speaker 3: or what might be called reward hacking when the models
Speaker 3: do something in an unexpected way when they're trying to
Speaker 3: achieve a task. So we haven't seen a direct response
Speaker 3: to that. There is some AI regulation already, it's voluntary.
Speaker 3: There was a Junie Executive order that has the US
Speaker 3: helping to essentially find cybersecurity flaws that through AI and
Speaker 3: then share those vulnerabilities more widely with others to try
Speaker 3: to essentially shore up systems. I think the sense in DC,
Speaker 3: and I heard this from cybersecurity officials yesterdays, like we're
Speaker 3: sort of entering a new era of cyber where there's
Speaker 3: going to be a lot more vulnerabilities, but as the
Speaker 3: offense gets better, the defense can get better too.
Speaker 1: Hey, Rachel, final thought from you as we discuss this,
Speaker 1: what should we kind of be thinking about here?
Speaker 6: I think it's key to keep an eye on what's
Speaker 6: going to happen next as far as both discussions in
Speaker 6: DC about any kind of regulation related to this, as
Speaker 6: well as what the companies are going to be doing
Speaker 6: going forward with analyzing and evaluating these systems.
Speaker 4: And we might see some slowdowns.
Speaker 6: As well as far as what they're doing with their research,
Speaker 6: and that would be really interesting to keep an eye
Speaker 6: on to mirror the agent.
Speaker 5: Excellent, guys, excellent, that's.
Speaker 2: All you were going to say, I'll be back, I'll
Speaker 2: be back now.
Speaker 1: I'm not going to do that yet.
Speaker 2: One day, one day I will ask that question to
Speaker 2: Rachel and she'll say, we're in terminator too.
Speaker 1: I'm confident. No, it's always logical.
Speaker 5: Maybe No, All right, guys, listen, Thank you so much.
Speaker 5: Rachel Mets. She is AI reporter at Bloomberg New She's
Speaker 5: out there in San Francisco and then joining us from
Speaker 5: DC's Maggie Eastland Check, an industrial policy reporter at Bloomberg
Speaker 5: News folks, check them out. They are on and writing
Speaker 5: about this NonStop. When it comes to the world AI,
Speaker 5: it's on the terminal at Bloomberg dot com.