If you're running a business in 2023, there's no escaping AI. It's everywhere, promising to automate everything from customer service chatbots to supply chain management.
But here's the thing: most of these so-called 'AI-powered' platforms aren't actually powered by AI at all. They're powered by generic algorithms that anyone with a decent programming degree could write.
And that's where we come in. Our content serves startups, agencies, and growth-stage companies who want to outgrow generic AI. We provide a unique perspective on owning and controlling your AI by sharing stories of rulebreakers and boundary-pushers who are pushing the limits with purpose-trained intelligence systems.
Let's start with the basics. When we talk about 'AI', we're really talking about two things: algorithms and data. Algorithms are the rules that tell your AI how to think, while data is the raw material that your AI learns from. A purpose-trained intelligence system is one where both these elements have been tailored specifically to your needs.
This might sound like a lot of work, but it's not as hard as you might think. Take data, for example. Most businesses already have reams of data sitting around in various spreadsheets and databases. All you need to do is collect it all together in one place and start feeding it into your AI system. This will give your AI a much richer understanding of your business than any generic algorithm ever could.
And then there are algorithms. These are the rules that tell your AI how to think. Most 'AI-powered' platforms use off-the-shelf algorithms that anyone with a decent programming degree could write. But why limit yourself to what everyone else is using? At our best, we push the limits of what's possible with purpose-trained intelligence systems. We create our own rules, tailored specifically to our needs.
Take OpenAI's GPT-3 language model as an example. It's a powerful tool that can generate human-like text on demand. But it's not perfect. In fact, it's downright dangerous when used without proper oversight. That's because its rules were designed for one purpose only: to generate text. They don't take into account things like copyright law or editorial standards. So when you use GPT-3 to write your next blog post, you might get some great content—but you could also end up in court if you accidentally infringe on someone else's intellectual property rights.
But here's the thing: we don't have to accept these limitations. We can create our own rules. We can train our AI systems to take into account things like copyright law and editorial standards from the ground up. That's what it means to own and control your AI. And that's what sets us apart from everyone else in this space.
So if you want to outgrow generic AI, you need to start thinking differently about AI. You need to stop seeing it as a magic wand that can solve all your problems with the wave of a hand. Instead, you need to see it as a powerful tool that requires careful planning and execution if you want it to work for you.
And that's where we come in. Our content serves startups, agencies, and growth-stage companies who want to outgrow generic AI.
