AI Explained For Everyone

CJ: You can essentially take a model and fine-tune it about specific data about your organization like how your company works, your org chart, maybe specific trade secrets that you don't want to be hosting on some third-party service. And you can do all of that internally with open-weight models. And that's really one of the main benefits and one of the main misconceptions I want to clear up because this is a real benefit to US-based companies. If we can take these open-weight models and modify them so that we can better do our business or whatever that may be and not necessarily have to depend on a large AI company like OpenAI, Anthropic or Google really just do it ourselves. That also breeds innovation. We don't necessarily have to wait for these AI labs to make their own progress in terms of releasing newer and better models. We can take existing open models and then make them work really well for one specific purpose or one specific business case.

CJ: When an AI lab makes their model freely available for people to download and use, we call this open weight because we can actually look inside the model file and see all of those numbers or weights. And this is where we can make a clear distinction between open weight and open source. Now, open source is a term that comes from the world of software. It's how we license open source software. But the main difference here is that we can see the weights, the numbers inside of that model file, but we can't actually see how that model was created or what data was used or what actually encoded all of those weight values inside of the file.

CJ: There was a recent study done by Booz Allen Hamilton trying to determine do these Chinese models, even if we're running them ourselves, let's say we're not talking to them hosted in China, do they have inherent biases? … The result of that study says, in short, yes. On all accounts, our testing revealed two core findings. One, Chinese LLMs produce more vulnerable code when prompted with a US government persona than without, and the vulnerabilities are highly obfuscated. Two, Chinese LLMs inject PRC aligned political bias into both the answers and the code they generate.

CJ: The same risk goes for using models from the big US AI labs, right? ChatGPT, Claude, Gemini. We can't see into those models. We don't know what inherent biases they have. We don't know what data they have or have not been trained on. And we actually see that sometimes they hallucinate or they give us the wrong information. And we're supposed to be responsible enough to not just accept those bad answers and basically correct the model ourselves, even though we're paying for access to that model from some big company. And so it's not something we can just handwave over and say, well, if you're running it locally, that doesn't mean you're not prone to something bad happening.