The REAL Potential of Generative AI

**Ali Rowghani** (0:00)
You've heard of large language models like ChatGPT. ChatGPT.

**Raza Habib** (0:04)
ChatGPT.

**Ali Rowghani** (0:05)
ChatGPT. It can answer questions, write stories, and even engage in conversation. But if you want to build a business that uses this technology, you'll need to ask yourself an important question.

**Raza Habib** (0:15)
How do I take this raw model and this raw intelligence and actually customize this to my use case? How do I make it really good for my users so that it's differentiated and better than what's out there?

**Ali Rowghani** (0:25)
This is Raza Habib. His company, Humanloop, enables large language models to have even greater superpowers.

**Raza Habib** (0:32)
We can help you build differentiated applications and products on top of these models. The range of use cases is like, now feels to be more limited by imagination than it is limited by technology.

**Ali Rowghani** (0:42)
You can replicate your exact writing style, customize tone, fact check answers, and train the model on your company's unique data.

**Raza Habib** (0:50)
We really hope that this is a platform on top of which, you know, the next million developers can build LLM applications.

**Ali Rowghani** (0:56)
In our conversation, we explore the secrets to building an app that stands out.

**Raza Habib** (1:00)
What made it so good that a million users signed up in five days was a fine-tuning exercise.

**Ali Rowghani** (1:05)
The impact of generative AI on developers.

**Raza Habib** (1:08)
Their, you know, finding a significant fraction of their code is being written by a large language model.

**Ali Rowghani** (1:14)
And what the future of large language models might bring to society as a whole.

**Raza Habib** (1:19)
It's an ethical minefield. There are going to be societal consequences on the path to AGI.
Potential benefits are huge as well, but we do need to tread very carefully.

**Ali Rowghani** (1:30)
Let's start like basics and high level. Like what is a large language model? And why is it that they've suddenly sort of made a splash? I assume they've been around a lot longer than the past year or two.

**Raza Habib** (1:41)
Yeah, so language models themselves are a really old concept and old technology. And really all it is is a statistical model of words in English language. You take a big bunch of texts and you try to predict what is the word that will come next given a few previous words. So the cat sat on the mat is the most likely word. And then you have a distribution over all the other words in your vocabulary.
As you scale the language models, both in terms of the number of parameters they have, but also in the size of the data set that they're trained on, it turns out that they continue to get better and better at this prediction task. Eventually, you have to start doing things like having world knowledge.
Early on, the language model is learning letter frequencies and word frequencies. And that's fairly straightforward.
And that's kind of what we're used to from predictive text in our phones. But if the language model is going to be able to finish the sentence, today the President of the United States acts, it has to have learned who the President of the United States is. If it's going to finish a sentence that's a maths problem, it has to be able to solve that maths problem. And so where we are today is that, you know, I think starting from GPT-1 and 2, but then GPT-3 was really the one that I think everyone said, okay, something is very, very different here. We now have these models of language that they're just models of the words, right? They don't know anything about the outside world. There's loads of debates about whether they actually understand language, but they are able to do this task extremely well. And the only way to do that is to have gotten better at, you know, some form of reasoning and some form of knowledge.

**Ali Rowghani** (3:05)
What are some of the challenges of using a pre-trained model like ChatGPT?

**Raza Habib** (3:09)
So one of the big ones is that they have a tendency to confidently bullshit or hallucinate stuff. I think Nat Friedman described it as alternating between spooky and kooky.
Sometimes it's so good that you cannot believe the large language model was able to do that. And then just occasionally, it's horrendously wrong. And that's just to do with how the model is originally trained. They're trained to do next word prediction, and so they don't necessarily know that they shouldn't be dishonest.

**Ali Rowghani** (3:33)
Yeah, sometimes they get it wrong.

**Raza Habib** (3:34)