GitHub Copilot and AI for Developers: Potential and Pitfalls with Scott Hanselman & Mark Downie
TLDRIn a discussion at Microsoft at night, Scott Hanselman and Mark Downey explore the potential and pitfalls of AI, specifically GitHub Copilot, for developers. They delve into how AI can serve as a co-pilot in coding, assisting with tasks like generating code, explaining code functions, and debugging. The conversation highlights the importance of context when interacting with AI and emphasizes that while AI can be a powerful tool, it is not infallible and requires human oversight. The duo also stresses the need for careful handling of data when using AI tools to maintain privacy and security. They demonstrate how AI can help with programming challenges, but also acknowledge that AI's suggestions might not always be accurate, thus the necessity for developers to validate its outputs. The session concludes with a live coding example where they use GitHub Copilot to troubleshoot and solve an issue, showcasing the practical application of AI in enhancing developer efficiency.
Takeaways
- 🤖 AI as a co-pilot: AI is a tool to assist developers, not replace them, and it's important to treat it as a partner in the coding process.
- 🧐 Context is crucial: AI's effectiveness is heavily dependent on the context provided by the user. Without it, AI's suggestions can be random and unhelpful.
- 📈 Probabilistic nature: AI operates on probabilities, selecting the most likely next word or action based on the context and data it has been trained on.
- 🖥️ User interface evolution: As with the introduction of touch screens, we are in a period of learning how to interact with AI, which represents a new user interface.
- 🚀 AI's potential in coding: AI can help write, read, and debug code, suggesting fixes and explaining complex code in plain English.
- 🔍 Testing is essential: AI-generated code must be tested and reviewed by developers to ensure accuracy and functionality.
- 🗣️ Conversational AI: New developments in AI allow for more conversational interactions, where AI can ask for clarification and provide step-by-step guidance.
- 🌐 Internet as a training ground: AI learns from a vast amount of text on the internet, which means it reflects both the positive and negative aspects of online content.
- ⚖️ Balancing AI responses: AI should sound confident but also convey uncertainty to manage user expectations about its suggestions.
- 📚 Learning resource: AI can serve as an infinite technical book, guiding users through learning new programming languages or concepts.
- 🤓 Critical thinking required: Developers should maintain critical thinking skills, as AI is a tool to aid, not dictate, the coding and debugging process.
Q & A
What is the main topic of discussion between Scott Hanselman and Mark Downie?
-The main topic of discussion is the potential and pitfalls of AI for developers, specifically focusing on GitHub Copilot and how it might change the way developers work.
How does Scott Hanselman describe the current state of AI in the context of user interfaces?
-Scott Hanselman describes the current state of AI as a new user interface that is still in the early stages of development. It's compared to the transition from non-touch to touch screens, where users had to relearn how to interact with technology.
What is the 'Corpus' in the context of AI?
-The 'Corpus' refers to the large collection of text that a language model, like Open AI, is trained on. In this case, it includes all the text available on the internet.
How does Mark Downie explain the importance of context when interacting with AI?
-Mark Downie explains that context is fundamental when interacting with AI. Without context, AI provides random suggestions. By providing context, the AI can give more accurate and relevant responses.
What does Scott Hanselman demonstrate with the 'Open AI Playground'?
-Scott Hanselman demonstrates how AI generates responses based on probabilities and the context provided by the user. He shows how the AI can be directed to perform specific tasks, like generating a taco recipe or explaining code.
How does Mark Downie use GitHub Copilot to address an open source issue?
-Mark Downie uses GitHub Copilot to rewrite a method that selects a hero image for a blog post. He provides the AI with the context and specific requirements, and then reviews the AI-generated code to ensure it meets the desired functionality.
What is the significance of the 'show probabilities' feature in the Open AI Playground?
-The 'show probabilities' feature allows users to see the likelihood of each word being the next one in a sequence. This demonstrates how AI makes decisions based on statistical probabilities rather than understanding or intelligence.
How does Scott Hanselman illustrate the concept of AI learning from user interactions?
-Scott Hanselman illustrates this by showing how the AI responds differently based on the tone and content of the user's input. If the user is kind, the AI pulls from 'nice' parts of the internet; if the user is mean, the AI's responses reflect that negativity.
What is the role of the 'Exception Helper' in Visual Studio?
-The 'Exception Helper' in Visual Studio is a tool that assists developers when an unexpected error occurs during code execution. It provides information about the exception and helps in debugging by narrowing down the potential causes of the error.
Why is it important for developers to test AI-generated code?
-It is important for developers to test AI-generated code because AI, while helpful, can provide suggestions that may not always be accurate or suitable for the specific context. Testing ensures that the code works as intended and does not introduce new issues.
What does Mark Downie suggest about the future of AI in development?
-Mark Downie suggests that AI will become an integral part of the development process, acting as a co-pilot. It will help with tasks like debugging and generating code, but developers will still need to provide direction, context, and perform final checks to ensure the code meets their requirements.
Outlines
😀 Introduction to AI and its Impact
Scott Hanselman and Mark Downey introduce the topic of artificial intelligence (AI), discussing the public's curiosity and concerns about AI, including its potential to change everything and how to explain it to non-technical people. They emphasize that AI is a new interface and we are still learning how to interact with it. The conversation also touches on the importance of context in AI interactions, using the analogy of a long-term relationship to explain how AI uses probability to predict the next word in a sentence.
🤔 AI's Understanding of Context
The speakers delve into the importance of context in AI, illustrating how AI uses probability to predict responses based on the information it has been given. They discuss how AI can be influenced by the tone and content of interactions, emphasizing that while AI can mimic human conversation, it is not a person and does not possess human qualities such as emotions or intentions.
👨💻 Co-Pilot in Coding: GitHub and Visual Studio
Mark demonstrates the use of AI as a co-pilot in coding, specifically within GitHub and Visual Studio. He shows how AI can help address programming issues and bugs by providing suggestions and explanations for code. The discussion highlights the potential of AI to assist developers by generating code, explaining complex regular expressions, and offering insights into improving code functionality.
🔍 AI in Debugging and Diagnostics
The conversation shifts to how AI can assist in debugging and diagnostics. Scott and Mark discuss the process of testing code with AI assistance, emphasizing the need for human oversight and validation of AI-generated suggestions. They also touch on the idea of AI as a learning tool, capable of guiding users through complex topics in a tutorial-like manner.
🗣️ Conversational AI and Problem-Solving
The speakers explore the conversational aspect of AI, where AI engages with users to gather more information and provide targeted assistance. They demonstrate how AI can ask for more context when it needs additional details to provide a more accurate solution. The discussion underscores the importance of testing AI suggestions and not blindly trusting its outputs.
🛠️ AI as a Tool for Developers
In the final paragraph, the speakers summarize the role of AI as a tool for developers. They stress the importance of treating AI as a co-pilot rather than a pilot, highlighting the need for human judgment and critical thinking when using AI. The conversation concludes with a reminder that while AI can be an invaluable aid, it is not infallible and developers must maintain control over the development process.
Mindmap
Keywords
💡GitHub Copilot
💡AI for Developers
💡Co-pilot
💡Open AI Playground
💡Regular Expression
💡Debugging
💡Contextual AI
💡AI-generated Code
💡Conversational AI
💡Non-Technical Audience
💡Probabilities in AI
Highlights
Scott Hanselman and Mark Downie discuss the potential and pitfalls of AI for developers, particularly focusing on GitHub Copilot.
AI is likened to a co-pilot, requiring a new user interface and interaction methods, just like the transition from non-touch to touch screens.
The importance of understanding AI's capabilities is emphasized, as it is not an intelligence but a tool that selects the next likely word.
The concept of treating AI like a human is explored, including whether to be kind to it or name it.
A live demonstration of training a chatbot using the Open AI playground is shown, highlighting the need for context when interacting with AI.
The probability aspect of AI's operation is explained, with examples of how it chooses responses based on likelihood.
The influence of user input on AI's behavior is demonstrated, showing how kindness or cruelty in prompts can pull from different parts of the internet.
GitHub Copilot's ability to help with programming tasks is showcased, including fixing bugs and enhancing open source projects.
Mark Downie uses GitHub Copilot to address a long-standing issue in an open source blogging engine, demonstrating the tool's practical application.
The conversational aspect of AI is explored, with the demonstration showing how AI can ask for more information to provide better assistance.
The need for testing AI-generated code is emphasized, as it may not always be accurate or suitable for the task at hand.
AI's role in debugging is highlighted, showing how it can suggest fixes for serialization issues in code.
The potential for AI to misunderstand or provide incorrect solutions is acknowledged, stressing the importance of human oversight and critical thinking.
The conversation concludes with a reminder of the importance of responsible use of AI, ensuring it aids rather than replaces human decision-making.
An invitation is extended for developers to sign up for GitHub Copilot and explore its features, emphasizing the potential it holds for the developer community.