Shawn Wang: belief

Transcript context

…Yeah, I think one of the things that is a helpful bit of context when thinking about HCI and AI in particular is, historically, HCI and AI have been sort of competing disciplines. Competing very specifically in the sense that they often fought for the same sources of funding and the same kinds of people and attention throughout the history of computer science. HCI and AI both used to come from the same or very aligned, similar, parallel motivations of, we have computers. How do we make computers work better with humans? And one way to do it was to make the machine smarter. Another way to do it was to design better interfaces. And through the AI booms and busts, when the AI boom was happening, HCI would get less funding. And when AIs had winters, HCI would get a lot more attention because it was sort of the alternative solution. And now that we have this sort of renewed attention on how to build better interfaces for AI, I think it's interesting that it's kind of a scene now. There are podcasts like this where I get to talk about interfaces and AI. But it's definitely not a fully-fledged field. My favorite definition of sort of what distinguishes the two apart comes from Andy Matuszak, where he, I'm going to butcher the quote, but he said something to the effect of, a field has at their disposal a powerful set of established tools and methods and standards and a shared set of core questions they want to answer. And so if you look at machine learning, which is obviously a really dominant established field, if you want to answer, if you want to evaluate a model, if you want to answer, if you want to solve a particular task or build a model that solves a particular task, there are powerful methods that we have, like gradient descent and specific benchmarks, for building solutions and then re-evaluating how to do the solutions. Or if you have an even more expensive problem, there are surely attempts that have been made before and then attempts that people are making now for how to attack that problem and frameworks to think about these things. In AI and UX, I think, we're very early in the evolution of that space and that community, and there's a lot of people excited, a lot of people building, but we have yet to come up with a set of best practices and tools and methods and frameworks for thinking about these things. And those will surely arise, and as they do, I think we'll see the evolution of the field. In prompt engineering and using language models in products at large, I think that community is a little farther along. It's still very fast moving because it's really young, but there are established prompting techniques like React and distillation of larger instruction following models. And these techniques, I think, are the beginnings of best practices and powerful tools at the disposal of this language model using field. Yeah, and mostly it's just following Riley Goodside. It's how I learn about prompting techniques. Right, right. Yeah, pioneers. But yeah, I am actually interested in this. We've recently kind of rebranded the podcast or the newsletter somewhat in towards being for this term AI engineer, which I kind of view as somewhere between machine learning researcher and software engineer, some kind of in-between mix. And I think creating the media, creating meetups, creating a de facto conference for it, creating job titles, and then I think that core set of questions that everyone wants to get better at, I think that is essentially how this starts. Yeah, yeah. Pretty excited of. Creating a space for the people that are interested to come together, I think, is a really, really key important part of it. I'm always, whenever I come back to it, I'm always amazed by how if you look at the sort of golden era of theoretical physics in the early 20th century, or the golden era of early personal computing, there are maybe like two dozen people that have contributed all of the significant ideas to that field. They all kind of know each other. I always found that really fascinating. And I think the causal relationship actually goes the other way. It's not that all those people happen to know each other. It's that because there was that core set of people that always, that were very close to each other and shared ideas often, and they were co-located, that that field is able to blossom. And so I think creating that space is really critical.…

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