> Ironically, Tao's post convinces me that AI, though amazing, isn't really the solution. Better UX and data quality is.
Umm, and I want a pony? The world doesn't come in perfectly structured data formats. I was pretty amazed when I pasted in some of my medical lab test results and ChatGPT was accurately able to parse everything out and summarize my data. It worked extremely well.
> The world doesn't come in perfectly structured data formats.
In Tao's case, the data was organized already, simply not given to him.
In any case, Tao, per the post, had to manually calculate to confirm that GPT-4 was correct in any case (he implies as much in a comment asking if he checked for correctness).
It doesn't matter what the data format was originally in - what mattered is that Tao didn't have access to that format.
I'm sure the company that did my medical labs also has my data in a structured format somewhere. So what, should I call them up and demand they give me my data in a spreadsheet? I can go down that useless path, or I can paste my data on ChatGPT and get results in 15 seconds.
> I'm sure the company that did my medical labs also has my data in a structured format somewhere. So what, should I call them up and demand they give me my data in a spreadsheet? I can go down that useless path, or I can paste my data on ChatGPT and get results in 15 seconds.
Yes, go ahead and send OpenAI all of your HIPAA data.
It's my data. The entire point of HIPAA is that I own the data and I can send it to whomever I want if I decide to. I get value out of it, others may not choose to do it, that's their right. But I'm pretty sure sharing my CBC results is not going to be the death of me.
Checking a table for correctness and filling up a table are not the same thing.
I also check for correctness when I manually count entries. It's an extremely error prone task.
How exactly could you check for the correctness without effectively finding out the answer? I mean this discrepancy is fundamentally the problem of P = NP or not.
NP is the class of problems for which it's hard to find the answer but easy to verify. The fact such a class even exists should hint to you that finding the answer and checking correctness can be two different things.
the point of my comment is that whether P = NP to begin with is still to be determined. in the case of ChatGPT, it's easy for it to respond, but how difficult is it for you to verify that it was correct, or not? how does that compare with the difficulty in doing the task to begin with?
Consider the time it would take to do manual entry for each of those examples compared with the time it takes to verify that the generated content is correct.
"Are these the correct state and zip codes?" is much faster than typing it by hand. You just ask yourself "is MA the correct state code for Massachusetts? Yep, next" rather than "Massachusetts that's... (lookup) MA, type MA; next that one is MI, already a state code ..." and down the list.
I would be willing to content that GPT will do the list faster and with better accuracy than a human doing the same work (that would also need to be checked for correctness).
>I was pretty amazed when I pasted in some of my medical lab test results and ChatGPT was accurately able to parse everything out and summarize my data
I think an important question is how much faith we give in the answer (especially with medical data!). There are lots of examples of great uses but also a number of examples of hallucinations and just plain bad summaries. When the stakes are high, the conviction needs to be couched in the risk of it being wrong. We need to be cognizant of the automation-trust factor that sometimes makes us place unwarranted trust in these systems.
Umm, and I want a pony? The world doesn't come in perfectly structured data formats. I was pretty amazed when I pasted in some of my medical lab test results and ChatGPT was accurately able to parse everything out and summarize my data. It worked extremely well.