Mind Readings: What I’m Paying Attention To In AI

In today's episode, let's talk about what I'm paying attention to in artificial intelligence these days. What has uh got my attention and where I'm spending my time. The big uh the big tech models like uh GPT 4 and ChatGPT as the interface to it, etc. These are certainly very capable models. They are very good services.

They are uh in GPT-4's case, sort of a jack of all trades, master of none. But what we're seeing is increasing restrictions on what these models are capable of doing. Um this can be because of feedback that they've received, this can be because of decisions being made by these companies, but it reduces their utility. I'll give you an example. The other day I was working on a set of slides, and I wanted to include an image of a person holding uh a tape measure and not being able to correctly measure a window.

And I used the word limp in the description when I was using it with um the DOLI system for uh image generation, and it kept saying this prompt violates our our content policy. I'm like, a person holding a limp tape measure is not exactly, shall we say, lascivious and uh and exciting imagery as a tape measure, but for whatever reason the software decided that that particular keyword was not okay. When I removed it, it was able to generate the image. And I'm like, okay, I mean that that's a bit of a stretch. Um between that and the ownership of these tools, I've really started paying much more attention to open source projects.

And what I mean by ownership is this we are in a situation now where as consumers, we've been conditioned to not own things, right? If you have Spotify, you don't own any of that music. The moment you stop paying your bill, you no longer have access to that. Whereas, say if you bought a CD back in the 90s, right, uh from a musician, you bought the CD and you have this CD and you had this disc that uh you held on to, and it was yours for as long as you wanted it, you owned it. And when we look around at the spaces in things like software and content, yeah, we own very, very little.

We don't own much. You if you have a favorite show on Netflix, if Netflix decides, nope, we don't want to show that anymore, delete, you have no control over it. You don't own it. And this is happening with things, for example, like chat GPT. You don't own that.

You have no control over it, it doesn't run locally on your computer. And as a result, if OpenAI changes its mind about what content is prohibited, and that happens to include content that you're working with, that becomes a problem. Open source software, software that is open to the community, that is typically free, uh, is it solves that problem because you download the software, sometimes you compile it yourself, um you run it on your computer and it's there. It's a local piece of software on your computer that doesn't go away, that doesn't ask for more money, that doesn't uh stop working at the whim of uh a large corporation, and it's it is yours, right? It that code is forever.

When we think about AI tools, you know, you've seen the posts on LinkedIn or Twitter with people announcing, you know, the latest uh group of tools. And yeah, there's like 200 new companies being started every day in the AI space. The list is endless. Most of these companies are not going to be around in six months or a year. And if you got used to doing business with them and then they're just not there anymore, that's a problem, right?

You you've become reliant on something and you have to find a solution. If you're using open source software, it runs on your software, on your hardware, on your on your laptop, maybe on your servers at work. And if the the project comes to an end, that software still keeps running, right? It keeps running until you know your development environment or your production environment is no longer able to run it. That could be years from now.

So that's why I'm paying so much attention to open source in AI, because A, it's where all the innovation is, and B, you own it. It's yours. It's on your laptop, it's on your server, it's on your desktop, and you can do with it what you want. Typically, there's uh fewer rules or restrictions, and there's no wait lists for new features. You just run the software.

And you know, granted, you have to have, in some cases, a pretty beefy computer to do it, but it's yours. So the the top five pieces of software that I'm looking at lately, um, one is called llama.cpp. This is a I would call it the probably the most powerful basic large language engine that can use almost any of the the major open source large language models. This is a command line tool. So it is definitely not user-friendly, but it's extremely powerful.

It's very fast and it's very efficient. The tool that I would recommend everyone take a look at is one called GPT4All. So if you go to GPT4AL, GPT, the number four, ALL dot IO. You can download a desktop application and then choose which models you want to use. This is very low tech, right?

There's there's no technical skill required, just install the application. And it functions like what you're used to with things like Chat GPT, but it's running on your computer. Now, depending on the speed of your computer and the age and stuff, it may not run real fast, but it does run. And you can use sensitive information with it, right? When you use Chat GPT, if you were to paste in, you know, some credit card records or something and ask it to do pattern detection, you're giving sensitive information to a third party, which is a big no-no in things like finance and health and law.

When you're running a piece of software like GPT for all locally on your computer, you don't even need the internet for that. No data is leaving your computer, no data is um going to a third party, you would you're just using it locally. So that is a project that I recommend everyone take a look at sooner or later. A couple other projects that again, these are more require technical skills. Meta's music gen is fascinating.

This is open source music generation, and it's pretty good. It's pretty good. Now it's an initial model, and there are definitely some limitations with it, but it gives you a sense of okay, this is where this is going. This is pretty cool. And that it can, with a sufficient enough prompt generate a decent sounding piece of music.

And of course, being AI generated, you don't have to worry about things like copyrights because uh nothing a machine generated can be copyrighted. And so you don't have to worry about uh taking uh dealing with royalties and all that stuff. Uh another project I think is interesting is automatic, automatic one one one one is the name of it. Um this is a web interface to stable diffusion, the image generation model. Again, this is something that either runs in you can run it in a cloud environment like Google Colab, or if you've got a beefy enough laptop, you can run it right on your machine and generate images.

So if you've seen stable diffusion imagery, the AI generated art, you can run this locally, and there are some fine tunes of the stable diffusion library that you can use to generate specific kinds of styles. Um you can also add in additional tools that will clean up the inevitable, doesn't quite get the faces right on people, but it's a pretty cool tool. And the fifth one that I've been paying a lot of attention to is Tortoise TTS. So Tortoise is a relatively slow uh voice generator, so it can synthesize voices, uh, which again is very useful if you want natural sounding computers to be able to speak. Now, there are paid services like 11 labs that offer basically this technology, but in a in a SaaS-based environment.

But again, you don't own that. Um, and they have a lot of understandably a lot of restrictions on what you can and can't do with it. If you want to train Tortoise on your own voice and have you, you know, reading out I don't know, some fan fiction you wrote, and that would violate, you know, say 11 labs content policy, but it's it's your stuff and you want to have your voice doing it, these tools will let you do it. So that's what's captured my attention lately in the AI world. Yes, there are a lot of cool companies and a lot of cool things happening, but I'm paying a lot of attention to open source because of its durability.

If I'm going to offer a service or strategic advice to a client, uh I want to know that that recommendation is going to endure longer than six months, right? Um especially if the client's going to be building something on that architecture. I want to make sure that it has a solid foundation. And open source software is one of those foundations. So that's the show for now.

Thanks for tuning in. I'll talk to you next time. If you like this video, go ahead and hit that subscribe button.