![]() ![]() We can use it right now to do really, really useful things that would have impossible only last year. They get more and more capable every day and they’re well beyond stochastic parrots at this point even if they still do stupid things sometimes. While many naysayers gleefully point out what these systems can't do that doesn't mean they are not incredibly valuable and fun to use. Maybe it's useless to them because they want it to be perfect but I just used it to save me an hour of crappy and frustrating coding and so did Kyle McDonald. Even better, it documented it.Īgain, it's not hard to imagine a more advanced form of this where you use a "-test" to simulate the output, and other advanced guardrail and editing features, rather than just letting it run and potentially damage your files with an unexpected "rm -rf".ĪI can already do really useful things right now despite naysayers who constantly point out the technology's limitations as if it's useless. Then I remembered I could just ask ChatGPT. I knew I could write the script but that I'd probably spend a half hour digging around forums trying to find the right regex or the right syntax for sed and awk to slice and dice text so I was avoiding it. I was doing a Stable Diffusion fine tuning project and I wanted to create a bunch of text files with the same name as the image files so I could write the labels for each image. I was a sys admin for a decade so I got pretty good with Bash scripts but I'm mostly out of practice these days. We talk to LLMs like ChatGPT as if we're talking to another person.Ī perfect example happened to me just the other day. We've trained our whole lives to talk and ask questions and give verbal commands. ![]() It takes time.īut when it comes to AI, we already know how to interact with it. Photoshop is immensely powerful but it's not something you can pick up and understand immediately. Think about the first time you tried to figure out all the buttons and menus in Photoshop before you could do anything even remotely interesting. That's different because older technology had a massive learning curve and that made adoption much slower. It's because there's almost no learning curve. There's something immensely weird about how quickly AI gets adopted now. The venerable image diffusion model saw a massive community of apps and fine-tuned models spring up around it almost overnight. Stable Diffusion made large scale image generation possible and was the fastest app to ever hit 10,000 stars on Github, rocketing to 33K in only two months. We're right about here at the "takeoff stage":ĬhatGPT roared out of the gate and became the fastest application to reach 100 million users, doing it in just two months. But to get there, let's start with right now. There are some clear trends developing and we can start to use them to figure out where things are going over the next 5 to 10 years. As you might imagine that didn't age well as we do all that and more on the web now.Īs Yoda said, "difficult to predict, always in motion is the future." We might see a clear trajectory if things keep going the way they're going and then a black swan completely changes the arc of what's possible. In 1995, Clifford Stoll confidently opined in Newsweek that the web wouldn't amount to much and that people would never telecommute, buy books, chat, read newspapers or shop online. Predictions can look foolish a decade later. ![]() Or course, before I do I'll say what I always say: Prediction is a tricky business. Mostly people are asking the questions but the TLDR of these articles is nobody really knows yet.īut we can start to make some strong predictions and I'm going to do that right here. ![]() There are even more critical questions too: Will AI be open or closed? Are we rapidly retreating from open source to the closed retro-computing era of Microsoft and the Wintel dynasty? Will it be the foundation model companies? The masters of UI/UX design? The open source AI pioneers? The big tech behemoths like Google and Microsoft and Facebook, with their small army of engineers? Or will it be a bunch of new companies that come out of nowhere to capture market and mindshare fast? Who can build big, powerful defensible businesses around machine learning and fend off a horde of challengers? A lot of people are wondering who's going to make the big money in AI? ![]()
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