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Somehow I don't think that this is quite how Jerome Powell would respond in an interview: > Interviewer: How do you think a global pandemic would impact the fi

by vitus 5y ago

Somehow I don't think that this is quite how Jerome Powell would respond in an interview: > Interviewer: How do you think a global pandemic would impact the financial system? > Mr. Powell: A pandemic will have impact on the economy. It will have an impact on the financial system. > Interviewer: What would the impact be? > Mr. Powell: If a major pandemic occurred, the economy would be affected, and the financial system would be affected. Yes, GPT-3 can provide a convincing chatbot, but it shouldn't be confused for domain expertise.

robbedpeter 5y ago
This is a bad example. Here's an output from gpt-j-6b by EleutherAI: Prompt( Jerome Powell was interviewed about the impact of a pandemic on the economy. > > Interviewer: What would the impact be? > Mr. Powell: If a major pandemic occurred) Output( it could be very disruptive. > > Interviewer: How disruptive? > Mr. Powell: Well, it could be disruptive in a number of ways. > > Interviewer: How so? > Mr. Powell: Well, one of the first things that would be disrupted would be the supply chain. ) Using prompts well makes a huge difference. If you parse the generated output, classify it, then develop a decision tree that uses further prompts to refine the response, you can get more sophisticated, valuable responses. The output in the parent is comparable to an off-the-cuff interview response. If you emulate a deeper thought process, you can get more meaningful output, and if you use the right prompts, you can access the semantic networks in the model related to your domain of interest.
- notahacker 5y ago
  I think the "bad example" is actually the good one, because it's a reminder that actually you're not getting business advice from someone with Warren Buffet or Jerome Powell's understanding of the economy, you're getting text generated by analysing patterns in other not-necessarily-applicable text. If you start forcing it in very specific directions you start getting text that summarises the commentary in the corpus, but most of that commentary doesn't come from Warren Buffet or Jerome Powell and isn't applicable to the future you're asking it about...

by vitus 5y ago

robbedpeter 5y ago

This is a bad example. Here's an output from gpt-j-6b by EleutherAI: Prompt( Jerome Powell was interviewed about the impact of a pandemic on the economy. > > Interviewer: What would the impact be? > Mr. Powell: If a major pandemic occurred) Output( it could be very disruptive. > > Interviewer: How disruptive? > Mr. Powell: Well, it could be disruptive in a number of ways. > > Interviewer: How so? > Mr. Powell: Well, one of the first things that would be disrupted would be the supply chain. ) Using prompts well makes a huge difference. If you parse the generated output, classify it, then develop a decision tree that uses further prompts to refine the response, you can get more sophisticated, valuable responses. The output in the parent is comparable to an off-the-cuff interview response. If you emulate a deeper thought process, you can get more meaningful output, and if you use the right prompts, you can access the semantic networks in the model related to your domain of interest.

notahacker 5y ago
I think the "bad example" is actually the good one, because it's a reminder that actually you're not getting business advice from someone with Warren Buffet or Jerome Powell's understanding of the economy, you're getting text generated by analysing patterns in other not-necessarily-applicable text. If you start forcing it in very specific directions you start getting text that summarises the commentary in the corpus, but most of that commentary doesn't come from Warren Buffet or Jerome Powell and isn't applicable to the future you're asking it about...