This dish is probably familiar to everyone, and it's one of my favorite dishes to eat—blanched lettuce!

It's been a while since I last checked, and the vegetables outside my door have grown so quickly. They look so refreshing, and tonight I can enjoy hotpot again. I don't know why, but every time I eat vegetables grown at home, they always seem to taste a bit more fragrant🤣.

Recently, I've also realized that I rely quite a bit on AI. Whether it's searching for information or images, I use it for everything. I'm not worried about that, but when it comes to using AI for trading references, I prefer it to come up with some work plans, so I feel more at ease.

It's not that I think it does a bad job; I just don't understand how it reaches its conclusions—it's a complete mystery! I know nothing about it!

What if it makes a mistake and I lose money because of incorrect trading advice? Or if there are issues with medical-related suggestions, who can I turn to for an explanation then?

Is it a problem with the model itself, or is there an issue with the nodes running it? Especially in fields like finance and healthcare, if something really goes wrong, a simple "AI made this judgment at the time" is useless. People want clear explanations and verifiable sources.

I later found out that my friends are paying attention to @inference_labs, which is specifically solving this problem, so I decided to start paying serious attention to it.

I took a quick look at this project. Instead of chasing after the hype of "large models, high parameters," I focused on the core issue: how to make AI's conclusions transparent and verifiable every time, so people can see the source and trace it back. If something goes wrong, they can identify who should be responsible.

This approach may seem simple or even a bit "silly," but for ordinary people like us, it's actually the most reassuring part—next time, I'll talk more about how it specifically addresses these issues!
#InferenceLabs #AI #可验证AI #Web3