The Reboot [HTC #68]
Letter from the Editor
Dear Readers,
Welcome back to Hold the Code! After a brief hiatus, we’re thrilled to be reviving this newsletter where we explore the latest in AI technology and ethics.
During our time away, the tech landscape has continued to evolve rapidly. From advancements in artificial intelligence to breakthroughs in human-computer interaction, there's so much to catch up on!
We genuinely appreciate your patience and support during our break. As we embark on this new chapter, we invite you to join us in shaping the future of Hold the Code. Your feedback, ideas, and contributions are more valuable than ever, so please don’t hesitate to reach out with your thoughts.
Thanks for sticking with us and happy reading!
Warm regards,
Anika Jaitley
Editor-in-Chief
Hold the Code
Writing Prompts 101: Get the Most out of Generative AI
Written By: Kaitlyn Chen
With the latest AI models capable of everything from expert guidance and language translation to philosophical debate and image generation, the difference between working hard and working smart often comes down to simply knowing how to ask the right questions. As language-based statistical machines, AI models largely base their responses on the way a question or command is phrased. Whether you're aiming for relevancy, accuracy, or creativity, here are a few practices to keep in mind as you craft your next AI prompts.
1. Provide Context: Human communication is filled with nuance, and one way to ensure AI can reflect that in its response is by providing the context behind your question. Always be sure to specify the tone and audience of your preferred response and explain the situation or any background information. It can also be helpful to direct the AI to act as a specific person or role.
Examples:
“Explain DNA replication to a 10-year-old, using simple analogies and a friendly tone.”
“Write a polite and professional email to my professor asking for an extension on this week’s homework, explaining that I got the flu and need two extra days.”
“Act as a tour-guide and tell me the best spots to visit in Chicago this summer.”
2. Be Specific: To ensure the most useful results, provide as many details as possible about what you’re looking for. This could be specific times and locations, possible constraints and goals, and desired output formats. Take advantage of AI’s ability to provide responses in the form of bullet points, numbered lists, charts, diagrams, or anything else you may need.
Examples:
“Write me a 60-second elevator pitch about my meditation app startup.”
“Give me ten multiple-choice questions about the French Revolution, focusing on key events and figures.”
“Create a table comparing San Francisco and New York across five categories.”
3. Give Feedback & Build On Responses: One of the greatest strengths of AI is its ability to iterate responses based on human feedback. Don’t be afraid to tell the AI which parts of the output you liked and which parts you didn’t like, or point out any mistakes you notice. Tailor the response to your liking by providing additional requirements, or even prompting the AI to ask you questions.
Examples:
“That information is incorrect. The correct answer is…”
“Adjust the tone to be more humorous and kid-friendly.”
“Ask me questions to help improve my resume for a marketing internship.”
4. Be Critical: While AI development has come a long way, it is still prone to making mistakes, generating misinformation, and reproducing biases from training data. Despite how convincing and knowledgeable a model might sound, always ask yourself if an answer makes sense and whether it is reliable and representative. It’s also good practice to explicitly remind the model to “be accurate” and supply a list of references when necessary.
The History of AI
Written By: Ashley Wei
As artificial intelligence is increasingly integrating into everyday life, recognizing its history helps to see how past developments are shaping today’s technologies.
Humans have been considering the idea of AI, as in the idea of a technology that is able to adapt and think for itself, since ancient times. “Automatons” in ancient times were mechanical machines that would move without human intervention — Archytas of Tarentum’s wooden pigeon utilized steam to lift the bird. Then, throughout the early 1900s, various art forms questioned whether machines could interact as humans. Karel Čapek’s science fiction play Rossum’s Universal Robots coined the term “robots.” A Logical Calculus of the Ideas Immanent in Nervous Activity, published by Warren McColloch and Walter Pitts, proposed a mathematical model of artificial neurons in the brain, which laid the groundwork for modern implementation of neural networks.
Alan Turing created the first major leap towards realizing AI in the 1950s with his work, Computer Machinery and Intelligence. Famous for breaking Germany’s code “Enigma” that helped end World War II, Turing established a foundational question of “Can machines think?” The Turing Test, then known as the “imitation game,” examines whether a machine can exhibit behaviors that are identical to an actual person. His initial thought experiment expanded to change how we think about the direction of future technologies.
At a conference at Dartmouth in 1955, the term “artificial intelligence” was officially coined by John McCarthy, which has identified this form of technology ever since.
Along the way in the late 1900s, various inventions led humanity closer and closer to what we deem as modern AI technology. Allen Newell and Herbert Simon created programs to mimic problem-solving via computational methods in 1952. John McCarthy’s programming language Lisp, developed in 1958, is now a crucial tool for AI researchers.
In the late 1900s to early 2000s, large milestones laid the groundwork for AI, releasing the first AI into everyday life. In 1997, artificial intelligence Deep Blue beat Gary Kasparov, the world chess champion, for the first time. Nasa’s Mars rovers navigated without human input in 2003. Then, in 2006, companies like Twitter, Facebook, and Netflix implemented AI for advertising and user experience algorithms. Siri was released in 2011 as the first mainstream virtual assistant, capable of providing responses with human-like interactions given the user’s voice input.
The most recent developments further immersed AI into daily life. In 2016, Hanson Robotics created Sophia, the first robot with a realistic human appearance and a capability of communicating emotions. Facebook’s AI chatbots were unveiled in 2017, learning to converse. However, over time, they created a new language autonomously. In 2020, Open AI started beta testing GPT-3, ultimately leading to the commercialization of AI chatbot technology.
Whatever the future holds, the groundwork for AI was laid a long time ago, but it has exponentially grown over the recent years. It is difficult to predict how AI will turn out.
Sources:
https://www.ibm.com/think/topics/history-of-artificial-intelligence
https://www.tableau.com/data-insights/ai/history
https://www.coursera.org/articles/history-of-ai
Understanding Vibe Coding: The Future (or Not?) of Programming
Written By: Anika Jaitley
What is Vibe Coding?
Vibe coding is all about harnessing the power of artificial intelligence to assist in the coding process. Instead of writing every line of code manually, developers can collaborate with AI tools by providing prompts that guide the AI in generating code to complete the desired task. Vibe coding can enhance productivity, reduce the time spent on repetitive tasks, and make programming accessible to a wider audience.
What are the Benefits of Vibe Coding?
Increased Efficiency: By leveraging AI to generate code snippets or entire functions based on prompts, developers can focus on higher-level design and problem-solving rather than becoming embroiled in syntax details.
Accessibility: Vibe coding makes programming more accessible to individuals who may not have extensive coding knowledge. Even those with minimal experience can start building applications using the user-friendly prompts.
Enhanced Creativity: Collaborating with AI tools may spark new ideas and approaches, allowing developers to explore solutions they might not have considered on their own.
How Does Vibe Coding Work?
Vibe coding relies on several key technologies, including:
Natural Language Processing (NLP): Developers interact with AI tools using natural language, allowing them to specify what they want the code to do without worrying about the precise syntax of their desired code.
Machine Learning Models: These models are trained on vast datasets of code, allowing them to generate contextually relevant code snippets based on the prompts they receive.
What are the Real-World Applications of Vibe Coding?
Code Completion Tools: Tools like GitHub Copilot use AI to suggest code completions based on the current context, saving developers time and effort.
Rapid Prototyping: Startups, for example, can quickly prototype applications by leveraging vibe coding to generate proof-of-concept applications, allowing for faster testing and iteration.
Educational Tools: Platforms that teach coding often incorporate vibe coding principles, enabling learners to build projects by prompting AI for guidance to familiarize themselves with the syntax.
What are the Challenges of Vibe Coding?
While vibe coding has certain benefits, we must also consider its challenges:
Quality Control: AI-generated code may often not be optimized for performance. Developers need to review and revise AI-generated code to ensure it meets quality standards.
Dependency on AI: Vibe coding can lead developers to excessively rely on AI tools which may hinder their foundational programming skills development and problem-solving abilities.
Security and Ethical Concerns: AI-generated code can inadvertently incorporate vulnerabilities or biased algorithms which may be difficult for developers to detect later on.
Limited Understanding: While vibe coding can simplify coding tasks, it may also lead to a developer's lack of understanding of how the actual code works, likely creating challenges in debugging and maintaining applications.
Is Vibe Coding the Future of Programming?
Rather than replacing traditional programming, vibe coding will likely serve as a complementary tool, enhancing the coding process while still requiring human oversight and expertise. The incorporation of vibe coding into programming settings should be balanced with ethical considerations and a commitment to maintaining coding skills and best practices.