#sxt $SXT
What if AI gives you a perfectly calculated answer based on completely wrong information? 🤔
I've been thinking about this problem as AI agents become more involved in financial analysis and business decisions.
Imagine a company allowing an AI agent to analyze thousands of transactions. The agent processes everything, generates a report, and recommends an important decision. But how can the company know whether those calculations were actually performed correctly?
Simply trusting the final answer doesn't seem enough to me.
This is one reason why I find Space and Time ($SXT ) interesting.
Its Proof of SQL technology aims to make database calculations verifiable through cryptographic proofs, without requiring companies to expose their private data. That's an interesting approach because businesses could check whether specific computations were performed correctly while keeping their sensitive information protected.
But there is another side to this story that I don't think we should ignore.
What if the original data was incorrect?
Even if the calculations are 100% accurate, bad input can still produce misleading results. And verifying a calculation doesn't automatically mean the AI's final interpretation is correct either.
So I think the future of trustworthy AI needs more than intelligence. It needs reliable data, verifiable calculations and proper validation of the decisions made from those results.
Maybe this is where projects like $SXT could find an important role.
I'm not jumping to conclusions yet, but I do think this problem deserves more attention.
Would you trust an AI's financial decision if you could verify its calculations but not the accuracy of its original data? 🤔
@SpaceandTime
What if AI gives you a perfectly calculated answer based on completely wrong information? 🤔
I've been thinking about this problem as AI agents become more involved in financial analysis and business decisions.
Imagine a company allowing an AI agent to analyze thousands of transactions. The agent processes everything, generates a report, and recommends an important decision. But how can the company know whether those calculations were actually performed correctly?
Simply trusting the final answer doesn't seem enough to me.
This is one reason why I find Space and Time ($SXT ) interesting.
Its Proof of SQL technology aims to make database calculations verifiable through cryptographic proofs, without requiring companies to expose their private data. That's an interesting approach because businesses could check whether specific computations were performed correctly while keeping their sensitive information protected.
But there is another side to this story that I don't think we should ignore.
What if the original data was incorrect?
Even if the calculations are 100% accurate, bad input can still produce misleading results. And verifying a calculation doesn't automatically mean the AI's final interpretation is correct either.
So I think the future of trustworthy AI needs more than intelligence. It needs reliable data, verifiable calculations and proper validation of the decisions made from those results.
Maybe this is where projects like $SXT could find an important role.
I'm not jumping to conclusions yet, but I do think this problem deserves more attention.
Would you trust an AI's financial decision if you could verify its calculations but not the accuracy of its original data? 🤔
@SpaceandTime