OpenAI Fine-Tuning Update, Acceleration Debate, and Bundling AI Services

**Erik Torenberg** (0:00)
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**Nathan Labenz** (0:46)
Somebody, friend of a friend reached out to me with a legal question. I'm like, I'm really not qualified to answer this.
But I am qualified to put it into chat, Gpt.

**Erik Torenberg** (0:57)
If they were one company, they could have avoided this whole mess.
And I spoke to Emil Michael, the Chief Business Officer at Uber. And he was like, yeah, I tried to get us to acquire them to merge because they just didn't make sense. But Travis said, we got to kill them. And Lyft was like, we hate Uber. So businesses just are irrational at the end of the day. And the fact that they're funded by VCs, and they all have takeover the world visions, it's challenging for people to be in a spot of like, hey, you play this niche, or hey, you collaborate with this competitor.

**Nathan Labenz** (1:30)
Hello, and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Erik Torenberg.
Let's kick it off with a GPT 3.5 Turbo Fine Tuning update.

**Erik Torenberg** (2:00)
Let's do it.

**Nathan Labenz** (2:01)
It's been just a couple of weeks since the last episode, where we covered it in the immediate wake of the release, and it is interesting. I had had, as I mentioned then, explore fine tuning Lama 2 on my to-do list for a little while, and then said, yeah, I'd probably scratch that off and do the 3.5 fine tuning instead.
They do make it super easy.
I probably, at this point in time, wouldn't have still gotten around to doing the Lama 2 fine tuning, not because it wouldn't have been interesting, but just, okay, even if you do it and you get it to work, then you still have the inference problems that we talked about, where it's like, okay, how am I going to host this, and what kind of load can it handle, and do I auto scale up or down? Who's got a good solution for that? There are solutions coming, but they're not mature. In contrast, OpenAI just makes it so easy. If you've used the previous version of their fine tuning, it's very similar. This time around, they have both the prompt and completion format, which is the old format, and now the primary one is the chat format. You get to set up your system message, who you want the AI to be, what its job is, and then from there, it's a back and forth between user and assistant. You can set up a couple of examples of the task that you want it to do.
Next thing you know, you're off to the races fine tuning. What has been really interesting, though, is using Gpt 4 to create the data set for 3.5 fine tuning. We came to that in a couple of steps, but again, I'm doing all this for Waymark, right? So my goal here is we already have a product in market. It works really relative to anything in the not too distant past, like amazingly well. You can just tell it what kind of video you want. It will make you that video. Next thing you know, you're watching that video complete with a script, images from your business loaded in to complement the narrative and even a voiceover layered on top of that. And that all happens in 20 to 30 seconds. So pretty cool, but obviously, you know, there is still room for improvement in all of these things. Our scripts are often not quite as good as we'd like them to be. And sometimes we see like wonky images chosen where we're like, oh, God, you know, we really rather wish you had to pick something else. And sometimes the voiceover, you know, it's usually well written and like apt, but sometimes it doesn't quite sync up just right with the, you know, the timing of the scenes.