Collective Learning With Generative AI

**Shervin Khodabandeh** (0:03)
AI is quite literally everywhere. Sam and I come on here every other week to talk with smart people about how they use it at work and in their personal lives. In the last year, we've heard a lot of predictions and watch-outs, and we'd like to spend some time reflecting on these. What's changed? What stayed the same? What concerns are still top of mind? We were fortunate to speak with Microsoft's Eric Boyd right as ChadGPT 3.5 hit the scene and everyone suddenly became aware of the potential of LLMs.

**Eric Boyd** (0:36)
It's one of the things I've pushed my team on a bit is I want us to think about scenarios and products where the portion of people using AI-powered features is 100 percent, right? You can think of something like, yeah, in this team's call, we have transcription and so we could turn transcriptions on, but not everybody uses that. You could go your whole day never using transcription. What are the products that you absolutely can't avoid AI? Because it's just intrinsic to the product. Search is, of course, like that. You can't avoid using AI in search. When you are talking to your phone to compose a text message with speech, you're using AI 100 percent of the time you do that. So increasingly, as we see these scenarios, there's so many things that are just not possible, right? If I'm going to now start with my three bullet points and have that expanded into a paragraph for me, like, well, you can't do that without AI. So every time you're using that functionality, you're using AI. It's pretty ubiquitous, but that's something that there are really a lot more scenarios coming on that. I mean, there's this whole field of AI-powered applications that are really about to start to blossom, where the application just doesn't exist without the AI that powers it. It's really kind of crazy just how quickly this field is moving. The speech quality that we deliver through our speech API literally improves every month. We measure and we've got data to back that up. Our vision models have just exploded in quality recently, and we've seen lots of crazy things. And then let's not even get started on language, right? The large language models are just incredibly powerful these days, and so just a massive explosion going on there. Someone was making a joke the other day that they said, we're not too far from the days where a CEO is going to have four bullet points and ask an AI to create a two-page memo for his staff, and the staff is going to use an AI to reduce this two-page memo to four bullet points, and then go and read them. I thought that was very funny, but that type of process, though, right? I've used AI already to say, you know, I've got some tough email I need to write. I want to make sure I'm getting the tone right. Here's how I wrote it. Can you make this more polite? Or can you give me a suggestion on how I should change? You know, just being able to get valuable input and feedback on that, it's kind of amazing. That's really empowering and so very central then to the work that you're going to try and do as a result of that. We have a lot of scenarios. We've got AI embedded into Dynamics, which is now Microsoft Dynamics. It's a CRM and advertising tool. And one of the things we're using it for is to help people create advertising copy. They can create advertising copy that gets generated for them. A similar use case, we worked with CarMax. And CarMax has every single car on the planet. And they want to have a unique page describing every single car on the planet. And so whenever I talk about CarMax, I say, well, my first car was a 1986 Ford Tempo. The 1986 Ford Tempo was a piece of garbage. It was a piece of garbage in 1986, and it's absolutely one now. But that was my first car. So CarMax wants to have a page describing the 1986 Ford Tempo. And they have user reviews about it, but they want a page that describes it that'll do really well for search engine optimization. And so they used GPT-3 to go and summarize reviews that they've got on each make, each model, each year, and then generate a page for it. And it would have taken them years, literally years. They did the math on how you could sort of go and do that. But now they have this really high quality, valuable content that's directing people to their site as a result of that.