The holidays are over, and it’s time to digest. Although I don’t subscribe to the idea that history or technology moves in jerky one-year increments, it’s still valuable to take stock, look at what’s happened, and decide what was important and what isn’t.

We started the year with many people talking about an “AI winter.” A quick Google search will show that anxiety about an end to AI funding has continued through the year. Funding comes and goes, of course, and with the possibility of a media-driven recession, there’s always the possibility of a funding collapse. In reality, 2022 has been a fantastic year for AI. GPT-3 wasn’t new, of course, but ChatGPT made GPT-3 usable in ways people hadn’t imagined. GitHub CoPilot also wasn’t new in 2022, but in the last year we’ve heard of more and more programmers who are using ChatGPT to write production code. It isn’t just people “kicking the tires.” DALL-E 2, Stable Diffusion, and Midjourney made it possible for people without artistic skills to generate pictures based on verbal descriptions, with results that are often fantastic. And while I haven’t mentioned Google and Facebook, they have demoed equivalents to most of these applications. The existence of those tools will certainly inspire new startups with new applications, and those companies will inevitably attract investment.

Those tools aren’t without their problems, and if we really want to avoid another AI Winter, we’d do well to think about what those problems are. Intellectual property is one issue: GitHub is already being sued because CoPilot’s output can reproduce code that it was trained on, without regard for the code’s initial license. The art generation programs will inevitably face similar challenges: what happens when you tell an AI system to produce a drawing “in the style of” some artist? ChatGPT’s ability to produce plausible text output is spectacular, but its ability to discriminate fact from non-fact is limited (and its ability to create, or “hallucinate,” entire bodies of non-fact is also spectacular). Will we see a Web that’s flooded with “fake news”? We arguably have that already, but tools like ChatGPT can generate content at a scale that we can’t yet imagine.

Related work from others:  Latest from Google AI - Deciphering Clinical Abbreviations with Privacy Protecting ML

At its heart, ChatGPT is really a user interface hack: a chat front end bolted onto an updated version of the GPT-3 language model. “User interface hack” sounds pejorative, but I don’t mean it that way. We now need to start building new applications around these models. UI design is important–and UI design for AI application is a topic that hasn’t been adequately explored.  What can we build with large language and generative art models? How will these models interact with their human users?  Exploring those questions will drive a lot of creativity in the coming years.

Perhaps the biggest surprise was the rise of Mastodon. Mastodon isn’t new, of course; I’ve been looking in from the outside for some time. I’ve never thought it had achieved critical mass, or that it was capable of achieving critical mass. I was proven wrong when Elon Musk’s antics drove thousands of Twitter users to Mastodon (including me). Mastodon is a federated network of communities that are (mostly) pleasant, friendly, and populated by smart people. The influx of Twitter users proved that Mastodon could scale. There were some growing pains, but not as much as I would have expected. I haven’t seen a single “fail whale.”

The growth of Mastodon proved that the federated model worked. It’s important to think about this. Mastodon is a decentralized service based on the ActivityPub protocol. Nobody owns it; nobody controls it, though individuals control specific servers. And there isn’t a blockchain or a token in sight. In the past year, we’ve been treated to a steady diet of noise about Web3, most of which insists that the next step in online interaction must be built on a blockchain, that everything must be owned, everything must be paid for, and that rent collectors will have there hands out taking their cut on each transaction. I won’t go so far as to claim that Mastodon is Web3; but I do think that Web3, however it evolves, will look much more like Mastodon and will be based on protocols like ActivityPub.

Related work from others:  Latest from MIT Tech Review - How to create, release, and share generative AI responsibly

Which does lead us to blockchains and crypto. I’m not going to engage in Schadenfreude here, but I’ve long wondered what can be built with blockchains. At one time, I thought that supply chain management would be the poster child for the Enterprise Blockchain. Unfortunately, IBM and Maersk have abandoned their TradeLens project. NFTs? I have always been skeptical of the connection between NFTs and art. NFTs seemed an awful lot like buying a painting and framing the receipt. They existed purely to show that you could spend cryptocurrency in quantity, and the people who used them that way have gotten what they deserved. But I’m not willing to say that there’s no value here. NFTs may help us to solve the problem of online identity, a problem that we haven’t yet solved on the Web. A number of companies, including Starbucks and Universal Studios, are using NFTs to build customer loyalty programs, theme park experiences, and other applications. At this point, NFTs still look like a technology in search of a problem to solve, but I’m not convinced that the appropriate problem isn’t out there.

Similar Posts