Name: Tech Live 2023: What’s Next for OpenAI?
Uploaded: 2023-10-18T19:34:00Z
Duration: 7 min 39 s
Description: At the WSJ’s Tech Live event, OpenAI’s CEO Sam Altman and CTO Mira Murati sat down with WSJ senior personal tech columnist Joanna Stern to talk about AGI, the capabilities of future GPT models and more. Julie Chang hosts. Photo: Patrick T. Fallon/AFP/Getty Images

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At the WSJ’s Tech Live event, OpenAI’s CEO Sam Altman and CTO Mira Murati sat down with WSJ senior personal tech columnist Joanna Stern to talk about AGI, the capabilities of future GPT models and more. Julie Chang hosts. Photo: Patrick T. Fallon/AFP/Getty Images

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Tech Live 2023: What’s Next for OpenAI?

Welcome to "Tech News Briefing," it's Wednesday, October 18th. I'm Julie Chang for the Wall Street Journal. These days you're probably hearing generative AI this, generative AI that, and naturally that's leading to a lot of questions and concerns. OpenAI is probably the most well-known generative AI company at the moment because of its popular tools, ChatGPT and Dolly, our senior personal tech columnist Joanna Stern, sat down with OpenAI CEO Sam Altman and CTO Mira Murati at this Year's Tech Live, here are highlights from their conversation. By the way, you're going to hear them talk about AGI that stands for Artificial General Intelligence and that means a system in which computers have human level cognitive abilities.

What is AGI, Muira if you could just define it for everybody in the audience?

I will say it's a system that can generalize across many domains that would be equivalent to human work that produce a lot of productivity and economic value. And, but you know, we're talking about one system that can generalize across a lot of digital domains of human work.

And Sam, why is AGI the goal?

The two things that I think will matter most over the next decade or few decades, to improving the human condition the most, giving us sort of just more of what we want, our abundant and inexpensive intelligence. The more powerful, the more general, the smarter, the better. I think that is AGI and then and then abundant and cheap energy. And if we can get these two things done in the world then it's almost like difficult to imagine how much else we could do. We're big believers that you give people better tools and they do things that astonish you. And I think AGI will be the best tool humanity has yet created, with it, we will be able to solve all sorts of problems.

Mira, how's ChatGPT 5 going?

We're not there yet, but it's kind of need to know basis. I'll let you know.

That's such a diplomatic answer. I'm gonna make Mira to all of these. I would've just said, oh yeah, here's what's happening. That's great. Leave it. No, no.

No, no, we're not sending them back.

Who paired these two? Who paired whose idea was this? You're working on it, you're training it.

We're always working on the next thing.

You just do a staring contest. That's what makes us human. All of these steps though, with GPT, is it, or GPT 3, 3.5, 4, are steps towards AGI, with each of them, are you looking for a benchmark? Are you looking for this is what we want to get to?

Yeah, before we had the product, we were sort of looking at academic benchmarks. And OpenAI is known for betting on scaling, throwing a ton of compute and data on this neural networks and seeing how they get better and better at predicting the next token. But it's not that we really care about the prediction of the next token. We care about the tasks in the real world to which this correlates to. And so that's actually what we started seeing once we put out research in the real world and we build out products through the API eventually through ChatGPT as well. And so now we actually have real world examples. We can see how our customers do in specific domains, how it moves the needle for specific businesses. And of course, with GPT4 we saw that it did really well in exams like SAT and LSAT and so on. So it kind of goes to our earlier point that we're continually evolving our definition of what it means for these models to be more capable. But, as we increase the capability vector, what we really look for is reliability and safety. And, as we build the next model, the next set of technologies, we're both betting continuing to bet on scaling. But we're also looking at, this other element of multimodality because we want these models to kind of perceive the world in a similar way to how we do. And, we perceive the world not just in text but images and sounds and so on.

Host: Will GPT5 solve the hallucination problem?

We've made a ton of progress on the hallucination issue with GPT4, but we're not where we need to be. But we are sort of on the right track and it's unknown, it's research. It could be that continuing in this path of reinforcement learning with human feedback, we can get all the way to really reliable outputs. And we're also adding other elements like retrieval and search. So you can, you have the ability to provide more factual answers or to get more factual outputs from the model. So there's a combination of technologies that we're putting together to kind of reduce the hallucination issue.

Sam, I'll ask you about the data, the training data. Obviously there's been maybe some people in this audience who may not be thrilled about some of the data that you guys have used to train some of your models, not too far from here in Hollywood. People have not been thrilled. When you're considering now as you're as you're walking through and touring, going to work towards this, these next models, what are the conversations you're having around the data?

So a few thoughts in different directions here. One, we obviously only want to use data that people are excited about us using. It may be a new way that we think about some of these issues around data ownership and like how economic flows work. But we want to get to something that everybody feels really excited about. But one of the challenges has been people, different kinds of data owners have very different pictures. So we're just experimenting with a lot of things. We're doing partnerships of different shapes and we think that like with any new field, we'll find something that sort of just becomes a new standard. Also, I think as these models get smarter and more capable, we will need less training data. What really will matter in the future is like particularly valuable data. People want, people trust the Wall Street Journal and they want to see content from that. And the Wall Street Journal wants that too. And we find new models to make that work.

And that's it for "Tech News Briefing." Today's show was produced by me, Julie Chang with supervising producer Melanie Roy. We had additional support from Zoe Thomas who will be back tomorrow. Thanks for listening.

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