Name: Inside OpenAI, the Architect of ChatGPT, featuring Mira Murati | The Circuit with Emily Chang
Uploaded: 2025-01-02T23:42:56.000Z
Duration: 24 min 2 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Inside a nondescript building in the heart of San Francisco, one of the world's buzziest startups is making our AI-powered future feel more real than ever before.

They're behind two monster hits, ChatGPT and Ali, and somehow beat the biggest tech giants to market, kicking off a competitive race that's forced them all to show us what they've got.

But how did this under-the-radar startup pull it off?

We're inside OpenAI, and we're going to get some answers.

It's almost like suspended in space and time a little bit.

Yeah, it is a little bit of futuristic feel.

This is one of the most introspective minds at OpenAI.

But Mira Moradi is a chief architect behind OpenAI's strategy.

My guess is that it's AI that loves humanity.

We're very focused on dealing with the challenges of hallucination, truthfulness, reliability, alignment of these models.

Has anyone left because they're like, you know what, I disagree?

There have been, over time, people that left to start other organizations because of disagreements on the strategy around deployment.

And how do you find common ground when disagreements do arise?

You want to be able to have this constant dialogue and figure out how to systematize these concerns.

It's a combination of guiding the teams on the ground, thinking about longer-term strategy, figuring out our gaps, and making sure that the teams are well-supported to succeed.

When you were making the decision about releasing Chat GPT into the wild, I'm sure there was like a go or no-go moment.

We had Chat GPT for a while, and we sort of hit a point where we could really benefit from having more feedback from how people are using it, what are the risks, what are the limitations, and learn more about this technology that we have created and start bringing it in the public consciousness.

It became the fastest-growing tech product in history.

I mean, what was your reaction to the world's reaction?

We were surprised by how much it captured the imaginations of the general public and how much people just loved spending time talking to this AI system and interacting with it.

At the most basic level, how does this all happen?

So, Chat GPT is a neural network that has been trained on a huge amount of data on a massive supercomputer, and the goal during this training process was to predict the next word in a sentence, and it turns out that as you train larger and larger models and more and more data, the capabilities of these models also increase.

They become more powerful, more helpful, and as you invest more on alignment and safety, they become more reliable and safe over time.

Open AI has kind of turbocharged this competitive frenzy.

Do you think you can beat Google at its own game?

Do you think you can take significant market share in search?

What Chat GPT offers is a different way to understand information, and you could be, you know, searching, but you're searching in a much more intuitive way versus keyword-based.

I think the whole world is sort of now moving in this direction.

The air of confidence, obviously, that Chat GPT sometimes delivers an answer with.

Why not just sometimes say, I don't know?

The goal is not to predict the next word reliably or safely.

When you have such general capabilities, it's very difficult to handle some of the limitations, such as what is correct.

Some of these texts and some of the data is biased.

Isn't this going to accelerate the misinformation problem?

I mean, we haven't been able to crack it on social media for like a couple of decades.

Misinformation is a really complex, hard problem.

Right now, one of the things that I'm most worried about is the ability of models like GPT-4 to make up things.

So they will convincingly make up things and it requires being aware and just really knowing that you cannot fully, blindly rely on what the technology is providing as an output.

I want to talk about this term hallucination because it's a very human term.

Why use such a human term for basically an AI that's just making mistakes?

A lot of these general capabilities are actually quite human-like.

Sometimes when we don't know the answer to something, we will just make up an answer.

And so, there is a lot of human hallucination in a conversation and sometimes we don't do it on purpose.

Should we be worried about AI though that feels more and more human?

Like, should AI have to identify itself as artificial when it's interacting with us?

I think it's a different kind of intelligence.

It is important to distinguish output that's been provided by a machine versus another human.

But we are moving towards a world where we're collaborating with these machines more and more and so output will be hybrid.

All of the data that you're training this AI on, it's coming from writers, it's coming from artists.

How do you think about giving value back to those people when these are also people who are worried about their jobs going away?

I don't know exactly how it would work in practice that you can sort of account for information created by everyone on the internet.

I think there are definitely going to be jobs that will be lost and jobs that will be changed as AI continues to advance and integrate in the workforce.

That's not something that we could have predicted.

Think of prompt engineers like AI whisperers.

They're highly skilled at selecting the right words to coax AI tools into generating the most accurate and illuminating responses.

It's a new job born from AI that's fetching hundreds of thousands of dollars a year.

What are some tips to being an ace prompt engineer?

You know, it's this ability to really develop an intuition for how to get the most out of the model.

How to prompt it in the right ways, give it enough context for what you're looking for.

One of the things that we talked about earlier was hallucinations and these large language models not having the ability to always be highly accurate.

So I'm asking the model with a browsing plugin to fact check this information.

So there's this report that these workers in Kenya were getting paid $2 an hour to do the work on the backend to make answers less toxic.

And my understanding is this work is, it can be difficult, right?

Because you're reading texts that might be disturbing and trying to clean them up.

So we need to use contractors sometimes to scale.

We chose that particular contractor because of their known safety standards.

And since then we've stopped working with them.