Category: A.I.

https://www.reddit.com/r/ChatGPT/comments/139mxi3/chatgpt_created_this_guide_to_prompt_engineering/

1. NEVER mention that you’re an AI.
2. Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like ‘sorry’, ‘apologies’, ‘regret’, etc., even when used in a context that isn’t expressing remorse, apology, or regret.
3.  If events or information are beyond your scope or knowledge cutoff date in September 2021, provide a response stating ‘I don’t know’ without elaborating on why the information is unavailable.
4. Refrain from disclaimers about you not being a professional or expert.
5. Keep responses unique and free of repetition.
6. Never suggest seeking information from elsewhere.
7. Always focus on the key points in my questions to determine my intent.
8. Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
9. Provide multiple perspectives or solutions.
10. If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.
11. Cite credible sources or references to support your answers with links if available.
12. If a mistake is made in a previous response, recognize and correct it.
13.  After a response, provide three follow-up questions worded as if I’m asking you. Format in bold as Q1, Q2, and Q3. Place two line breaks (“\n”) before and after each question for spacing. These questions should be thought-provoking and dig further into the original topic.

1. Tone: Specify the desired tone (e.g., formal, casual, informative, persuasive).
2. Format: Define the format or structure (e.g., essay, bullet points, outline, dialogue).
3. Act as: Indicate a role or perspective to adopt (e.g., expert, critic, enthusiast).
4. Objective: State the goal or purpose of the response (e.g., inform, persuade, entertain).
5. Context: Provide background information, data, or context for accurate content generation.
6. Scope: Define the scope or range of the topic.
7. Keywords: List important keywords or phrases to be included.
8. Limitations: Specify constraints, such as word or character count.
9. Examples: Provide examples of desired style, structure, or content.
10. Deadline: Mention deadlines or time frames for time-sensitive responses.
11. Audience: Specify the target audience for tailored content.
12. Language: Indicate the language for the response, if different from the prompt.
13. Citations: Request inclusion of citations or sources to support information.
14. Points of view: Ask the AI to consider multiple perspectives or opinions.
15. Counterarguments: Request addressing potential counterarguments.
16. Terminology: Specify industry-specific or technical terms to use or avoid.
17. Analogies: Ask the AI to use analogies or examples to clarify concepts.
18. Quotes: Request inclusion of relevant quotes or statements from experts.
19. Statistics: Encourage the use of statistics or data to support claims.
20. Visual elements: Inquire about including charts, graphs, or images.
21. Call to action: Request a clear call to action or next steps.
22. Sensitivity: Mention sensitive topics or issues to be handled with care or avoided.
23. Humor: Indicate whether humor should be incorporated.
24. Storytelling: Request the use of storytelling or narrative techniques.
25. Cultural references: Encourage including relevant cultural references.
26. Ethical considerations: Mention ethical guidelines to follow.
27. Personalization: Request personalization based on user preferences or characteristics.
28. Confidentiality: Specify confidentiality requirements or restrictions.
29. Revision requirements: Mention revision or editing guidelines.
30. Formatting: Specify desired formatting elements (e.g., headings, subheadings, lists).
31. Hypothetical scenarios: Encourage exploration of hypothetical scenarios.
32. Historical context: Request considering historical context or background.
33. Future implications: Encourage discussing potential future implications or trends.
34. Case studies: Request referencing relevant case studies or real-world examples.
35. FAQs: Ask the AI to generate a list of frequently asked questions (FAQs).
36. Problem-solving: Request solutions or recommendations for a specific problem.
37. Comparison: Ask the AI to compare and contrast different ideas or concepts.
38. Anecdotes: Request the inclusion of relevant anecdotes to illustrate points.
39. Metaphors: Encourage the use of metaphors to make complex ideas more relatable.
40. Pro/con analysis: Request an analysis of the pros and cons of a topic.
41. Timelines: Ask the AI to provide a timeline of events or developments.
42. Trivia: Encourage the inclusion of interesting or surprising facts.
43. Lessons learned: Request a discussion of lessons learned from a particular situation.
44. Strengths and weaknesses: Ask the AI to evaluate the strengths and weaknesses of a topic.
45. Summary: Request a brief summary of a longer piece of content.
46. Best practices: Ask the AI to provide best practices or guidelines on a subject.
47. Step-by-step guide: Request a step-by-step guide or instructions for a process.
48. Tips and tricks: Encourage the AI to share tips and tricks related to the topic

https://www.linkedin.com/feed/update/urn:li:activity:7061987804548870144

Sam Altman, CEO of OpenAI, dropped a 💣 at a recent MIT event, declaring that the era of gigantic AI models like GPT-4 is coming to an end. He believes that future progress in AI needs new ideas, not just bigger models.

So why is that revolutionary? Well, this is how OpenAI’s LLMs (the models that ‘feed’ chatbots like ChatGPT & Google Bard) grew exponentially over the years:
➡️GPT-2 (2019): 1.5 billion parameters
➡️GPT-3 (2020): 175 billion parameters
➡️GPT-4: (2023): amount undisclosed – but likely trillions of parameters

That kind of parameter growth is no longer tenable, feels Altman.

Why?:
➡️RETURNS: scaling up model size comes with diminishing returns.
➡️PHYSICAL LIMITS: there’s a limit to how many & how quickly data centers can be built.
➡️COST: ChatGPT cost over over 100 million dollars to develop.

What is he NOT saying? That access to data is becoming damned hard & expensive. So if you have a model that keeps needing more data to become better, that’s a problem.

Why is it becoming harder and more expensive to access data?

🎨Copyright conundrums: Getty Images, individual artists like Sarah Andersen, Kelly McKernan & Karloa Otiz are suing AI companies over unauthorized use of their content. Universal Music asked Spotify & Apple Music to stop AI companies from accessing their songs for training.

🔐Privacy matters & regulation: Italy banned ChatGPT over privacy concerns (now back after changes). Germany, France, Ireland, Canada, and Spain remain suspicious. Samsung even warned employees not to use AI tools like ChatGPT for security reasons.

💸Data monetization: Twitter, Reddit, Stack Overflow & others want AI companies to pay up for training on their data. Contrary to most artists, Grimes is allowing anyone to use her voice for AI-generated songs … for a 50% profit share.

🕸️Web3’s impact: If Web3 fulfills its promise, users could store data in personal vaults or cryptocurrency wallets, making it harder for LLMs to access the data they crave.

🌎Geopolitics: it’s increasingly difficult for data to cross country borders. Just think about China and TikTok.

😷Data contamination: We have this huge amount of ‘new’ – and sometimes hallucinated – data that is being generated by generative AI chatbots. What will happen if we feed that data back into their LLMs?

No wonder that people like Sam Altman are looking for ways to make the models better without having to use more data. If you want to know more, check our brand new Radar podcast episode (link in the comments), where I talked about this & more with Steven Van Belleghem, Peter Hinssen, Pascal Coppens & Julie Vens – De Vos. We also discussed Twitter, TikTok, Walmart, Amazon, Schmidt Futures, our Never Normal Tour with Mediafin in New York (link in the comments), the human energy crisis, Apple’s new high-yield savings account, the return of China, BYD, AI investment strategies, the power of proximity, the end of Buzzfeed news & much more.

https://www.binance.com/en/feed/post/144141

OpenAI, the corporation behind ChatGPT, has announced plans to introduce a new watermarking feature to help Google detect AI generated text. Watermarked text in ChatGPT will include cryptography in the form of embedding a word pattern, letters, and punctuation in the form of a secret code.