3 ms·

That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicin

That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicine, ...

auggierose 3y ago
On their own, yes. But if you have an application where you can check the correctness of what they come up with, you are golden. Which is often the case in the hard sciences. It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)
- RandomLensman 3y ago
  Unclear to me. The economics might not be so great as you might need (i) expensive people, (ii) there could be a lot to check for correctness, and (iii) checking could involve expensive things beyond people. Net productivity might not go up much then. For some industries where I understand the cost stacks with lower and higher skilled workers, I'd say it only takes out the "cheap" part and thereby not taking out a large chunk of costs (more like 10% cost out prior to paying for the AI). That is still a lot of cost reduction, but something that also will potentially be relatively quickly be "arbitraged away", i.e., will bleed into lower prices.
  - Philpax 3y ago
    My interpretation of the parent post is not that LLMs' output should be checked by humans, or that they are used in domains where physical verification is expensive; no, what they're suggesting is using a secondary non-stochastic AI system/verification solution to check the LLM's results and act as a source of truth. An example that exists today would be the combination of ChatGPT and Wolfram [1], in which ChatGPT can provide the method and Wolfram can provide the execution. This approach can be used with other systems for other domains, and we've only just started scratching the surface. [1] https://www.wolfram.com/wolfram-plugin-chatgpt/ https://www.wolfram.com/wolfram-plugin-chatgpt/
    - auggierose 3y ago
      Yes, your interpretation is correct. I think the killer app here is mathematical proof. You often need intuition and creativity to come up with a proof, and I expect AI to become really good at that. Checking the proof then is completely reliable, and can be done by machine as well. Once we have AI's running around with the creativity of artists, and the precision of logicians, ... Well, time to read some Iain M. Banks novels.
- __loam 3y ago
  > But if you have an application where you can check the correctness of what they come up with, you are golden. You're glossing over a shocking about of information here. The problems we'd like to use AI for are hard to find correct answers for. If we knew how to do this, we wouldn't need the AI.

That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicine, ...

auggierose 3y ago

On their own, yes. But if you have an application where you can check the correctness of what they come up with, you are golden. Which is often the case in the hard sciences. It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)

RandomLensman 3y ago
Unclear to me. The economics might not be so great as you might need (i) expensive people, (ii) there could be a lot to check for correctness, and (iii) checking could involve expensive things beyond people. Net productivity might not go up much then. For some industries where I understand the cost stacks with lower and higher skilled workers, I'd say it only takes out the "cheap" part and thereby not taking out a large chunk of costs (more like 10% cost out prior to paying for the AI). That is still a lot of cost reduction, but something that also will potentially be relatively quickly be "arbitraged away", i.e., will bleed into lower prices.
- Philpax 3y ago
  My interpretation of the parent post is not that LLMs' output should be checked by humans, or that they are used in domains where physical verification is expensive; no, what they're suggesting is using a secondary non-stochastic AI system/verification solution to check the LLM's results and act as a source of truth. An example that exists today would be the combination of ChatGPT and Wolfram [1], in which ChatGPT can provide the method and Wolfram can provide the execution. This approach can be used with other systems for other domains, and we've only just started scratching the surface. [1] https://www.wolfram.com/wolfram-plugin-chatgpt/ https://www.wolfram.com/wolfram-plugin-chatgpt/
  - auggierose 3y ago
    Yes, your interpretation is correct. I think the killer app here is mathematical proof. You often need intuition and creativity to come up with a proof, and I expect AI to become really good at that. Checking the proof then is completely reliable, and can be done by machine as well. Once we have AI's running around with the creativity of artists, and the precision of logicians, ... Well, time to read some Iain M. Banks novels.
__loam 3y ago
> But if you have an application where you can check the correctness of what they come up with, you are golden. You're glossing over a shocking about of information here. The problems we'd like to use AI for are hard to find correct answers for. If we knew how to do this, we wouldn't need the AI.