Wednesday, April 10, 2024
HomeAi in art and designDiscover How a Microsoft ML Researcher Utilizes ChatGPT for Real-World Applications

Discover How a Microsoft ML Researcher Utilizes ChatGPT for Real-World Applications







Conversation with a Principal Researcher at <a href='https://ainewsera.com/microsoft-unlikely-to-get-a-seat-on-openai-board-experts-say-satya-nadellas-company-wont-sit-passively/ai-companies/' title='Microsoft unlikely to get a seat on OpenAI board, experts say Satya Nadella’s company won’t ‘sit passively’' >Microsoft</a>

All right so in today’s video I’ll be talking to a principal researcher at Microsoft

I thought this conversation was quite interesting because we bring such different viewpoints to it. As you know, I always come from the perspective of a power user, a tech enthusiastic consumer that obsesses over finding what’s possible with these tools. She comes from the perspective of a machine learning researcher that works for Microsoft and actually publishes research regularly. As you know, I usually talk about using chat GPT in the web interface and how to find use cases for your everyday life. She’s particularly interested in how to use it for code generation and debugging, so this is a very interesting conversation.

Prompt Engineering Basics

At the core of all interactions with AI models like GPT-3 lies prompt engineering. I like to break prompts down into two parts – instructions and context. Instructions are the tasks you are trying to solve, while context narrows down the possibilities and guides the AI based on the information provided. By defining the context, you unlock the full potential of the AI assistant.

Advanced Techniques: Zero-Shot and Multi-Shot Prompting

Zero-shot and multi-shot prompting are advanced techniques that can enhance the interactions with AI models for tasks like code generation. By providing specific examples and structuring your prompts, you can guide the AI to generate code that fits your needs. Additionally, techniques like Chain of Thought reasoning can help the model understand the logic behind the code snippets it produces.

Custom Instructions and Debugging

Using custom instructions and providing detailed information to the AI assistant can improve the quality of the generated code. Customizing the prompts based on your preferences and providing examples can lead to more accurate results. When it comes to debugging, using follow-up questions and isolating the code can help the model focus on specific issues and provide relevant solutions.

Recommendations for Developers

For developers using AI models for code generation and debugging, it is essential to keep the code compartmentalized and modularized. By isolating functions and asking targeted questions, you can efficiently debug the code and improve the AI’s understanding of the problem. Remember to trust but verify the output and always verify the libraries and functions mentioned in the generated code.

Conclusion

The conversation between the tech enthusiast and the machine learning researcher offers valuable insights into the world of AI-powered code generation. By understanding the basics of prompt engineering, utilizing advanced techniques like multi-shot prompting, and providing custom instructions, developers can enhance their interactions with AI models. Debugging code generated by AI requires a structured approach and targeted questioning to identify and fix errors efficiently.


Leah Sirama
Leah Sirama
Leah Sirama, a lifelong enthusiast of Artificial Intelligence, has been exploring technology and the digital realm since childhood. Known for his creative thinking, he's dedicated to improving AI experiences for all, making him a respected figure in the field. His passion, curiosity, and creativity drive advancements in the AI world.
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15 COMMENTS

  1. 🎯 Key Takeaways for quick navigation:

    01:39 💡 Prompt engineering consists of instructions (task to solve) and context (environment, constraints), crucial for effective communication with AI models like ChatGPT.

    04:24 🔍 Use cases for AI models like ChatGPT vary from enhancing productivity to exploring new problem-solving approaches and browsing documentation efficiently.

    06:43 🔄 Iterative conversation with AI models allows refining prompts over multiple attempts, leveraging social skills and intuition to craft effective inputs.

    07:10 🛠 Advanced techniques like zero-shot and multi-shot prompting can enhance code generation, though optimizing examples for such prompts remains an open research challenge.

    09:29 🤔 Combining Chain of Thought reasoning with prompting aids AI models in understanding dynamic programming problems, emphasizing the importance of context in guiding solution generation.

    13:33 🛠 Custom instructions and preferences can be provided to AI models in advance, shaping their responses to fit user needs, enhancing productivity, and streamlining research workflows.

    15:53 🔄 Lengthy conversations with AI models, particularly when exploring new topics or libraries, facilitate comparison, experimentation, and informed decision-making, albeit requiring verification of generated content and occasional follow-up queries for clarification.

    18:48 🧠 AI models provide knowledge based on the input prompt, making follow-up questions crucial for surfacing relevant information and insights.

    19:30 🚀 Ability to debug code with AI models like GPT-4 is a significant advancement, enabling developers to quickly identify and resolve errors by feeding error messages or code snippets for analysis.

    20:26 💻 Utilizing conversational agents for debugging offers concise insights compared to web search, emphasizing the importance of modularizing code for effective debugging and error isolation.

    21:48 🔍 When using AI models for debugging, providing self-contained code snippets or functions helps focus the model's analysis on specific issues, improving both developer efficiency and model accuracy.

    22:30 📝 Tip: Keep code compartmentalized and tackle debugging one problem at a time, avoiding the common pitfall of pasting entire code files, which can confuse AI models and hinder effective debugging.

  2. ZAP….We call it ISRMD, one of a kind, got to love it. No waste of words, to the point x2, Crisp clean, professionally modest and makes you sit up straight. Guest speakers in this arena are generally the non-plus type, why wait for what they're trying to say… hehe… when you got Igor, no wait. ISRMD❤ i.e. IGOR SPEED RACER MASTER DELIVERY.

  3. Feedback:
    – let your guest speak! Every video is already you, do do "try to drive the conversation", because you ended up drowning it. If we wanted to hear from you, we already have a channel worth of your talking points, but it would have been much more interesting to have 200% more from your guest's output, by reducing your own context window 😉

  4. Excellent talk.

    Just sharing as a friend… You are better solo presenter my friend than interviewing someone. For interviewing, I feel you need to just prepare the foundation and let the guest build on that. After guest complete the answer, you may add a few points in a couple of sentences and link to your next point. You have your own channel and video opportunity to explain what you want to.

    Just my personal opinion. I hope you will take it in right spirit. I have experience of offline interviews. Hence wished to share my honest opinion with you. Sorry if I have hurt you.

  5. Excellent. Please feel encouraged to post more conversations like this. Please keep the "information/text tiles" on screen for a couple of more seconds, so we can read them. OR/AND, please include them in the video description. The definitions and text explanations are equally helpful. Thank you.

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