The implications of Sam Altman's testimony plus prompt hacking / prompt engineering tips. EU AI Act, Open Source OpenAI Models and much more.

Links

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AI News this week
Sam Altman’s abridged testimony (Twitter):

Senate sub-comittee hearing in full:

Sam Altman Testimony on TikTok

Genie AI Linkedin

Mentions:

Sparks of AGI Microsoft Paper

OpenAI open source model

EU AI Act

Crazy things this week

https://mealpractice.com/generate - Effortless meal planning with AI-generated recipes

Elon saying OpenAI wouldn’t exist without him.

Discussion Points

• AI generated opening statement by Richard Blumenthal


• Sam Altman (OpenAI), Gary Marcus (Uber, Robust.ai), Christina Montgomery (IBM)


• Senate is aware they don’t understand this enough to legislate on it


• OpenAI is making significant efforts towards safety


• OpenAI advocates for a committee to regulate AI development for large companies


• Election fraud seems to be a major concern for OpenAI


• New, better jobs will be created according to OpenAI

Prompt hacking tips

1. Be succint and specific!

   
   



2. Prime the language model by providing a persona for it.

   
   



3. Asking the model to think through its response step-by-step “Let’s think step-by-step”

   
   



4. Providing clear context: Offer concise background information for guidance. Use Cases: Definition explanations, historical event summaries, concept descriptions.

   
   



5. Specifying the output format: Indicate desired answer structure explicitly. Use Cases: Generating lists, step-by-step instructions, summarizing long texts.

   
   



6. Using explicit instructions: Request specific detail or critical thinking. Use Cases: Debating pros and cons, analyzing biases, evaluating arguments.

   
   



7. Redundancy and rephrasing: Reinforce information by reiterating questions. Use Cases: Clarifying ambiguous topics, extracting specific details, verifying facts.

   
   



8. Temperature and token settings: Adjust randomness and output length. Use Cases: Creative writing, focused summaries, generating multiple response variations.

   
   



9. Iterative refinement: Refine prompts based on previous responses. Use Cases: Troubleshooting, problem-solving, narrowing down complex topics.

   
   



10. Prompt engineering: Craft effective prompts using templates or examples. Use Cases: Analogies, translating complex topics into simple explanations, generating structured responses.

    
    



11. Advanced techniques like asking the language model to think step by step

    
    

Shout out to DeepLearning.AI’s free prompt engineering course: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

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