Learn AI with Spreadsheets: A Low-Code Approach to Understanding LLMs in Excel, (from page 20230331.)
External link
Keywords
- AI
- low-code
- LLM
- Excel
- GPT-2
- tokenization
- word embeddings
- machine learning
- generative AI
Themes
- artificial intelligence
- low-code learning
- spreadsheets
- large language models
- online learning
Other
- Category: technology
- Type: blog post
Summary
Spreadsheets-are-all-you-need is a low-code educational tool that teaches users about Large Language Models (LLMs) using Excel. It implements the forward pass of GPT-2, making complex AI concepts accessible to individuals such as technical executives, marketers, developers, and AI ethicists. This project, showcased at the AI Engineer World’s Fair, includes a series of lessons covering the architecture of GPT-2, tokenization, and word embeddings. The Excel implementation allows users to interact with LLM concepts without needing extensive programming skills. Although it has limitations, such as a small context length and lack of training capabilities, it serves as a practical introduction to understanding advanced AI technologies.
Signals
| name |
description |
change |
10-year |
driving-force |
relevancy |
| Low-code AI education |
A low-code platform for learning AI concepts through Excel spreadsheets. |
Shifting from complex coding to accessible, low-code tools for AI education. |
In 10 years, AI education may become mainstream, accessible to non-programmers through user-friendly tools. |
The need for democratizing AI knowledge among diverse professionals drives this change. |
4 |
| Integration of AI in everyday tools |
Using familiar tools like Excel to teach complex AI concepts. |
Moving from specialized programming environments to common software for AI learning. |
By 2033, AI literacy may be embedded in everyday software used by professionals. |
The push for AI literacy across various industries is motivating this integration. |
5 |
| Simplification of AI concepts |
Breaking down complex AI topics into simpler, relatable formats. |
Transitioning from abstract AI theories to practical applications in familiar formats. |
In a decade, AI concepts may be simplified for all levels of professionals, enhancing understanding. |
The demand for accessible learning resources drives the simplification of AI education. |
4 |
| Hands-on learning with AI |
Utilizing hands-on practices in learning AI through spreadsheets. |
Shifting from passive learning to active, hands-on engagement with AI concepts. |
Future educational models may heavily rely on interactive, hands-on learning methods for AI. |
The effectiveness of experiential learning in understanding complex topics drives this trend. |
4 |
| AI policy and ethics education |
Targeting AI policy makers and ethicists for AI education. |
From general tech education to specialized AI policy and ethics training. |
In 10 years, AI education may increasingly focus on ethical implications and policy frameworks. |
The growing need for ethical oversight in AI development motivates this focused education. |
3 |
Concerns
| name |
description |
relevancy |
| Misunderstanding AI Capabilities |
Users may overestimate the AI’s capabilities based on simplified demonstrations, leading to unrealistic expectations and potential misuse. |
4 |
| Accessibility vs Security |
Making AI learnable through low-code tools may expose sensitive data or lead to security vulnerabilities if not managed properly. |
4 |
| Limitations of Low-Code Systems |
While low-code platforms enhance accessibility, they may also limit understanding of complex AI mechanisms, leading to superficial knowledge. |
3 |
| Dependency on Software |
Heavy reliance on Excel for understanding AI could hinder exploration of more sophisticated tools and frameworks. |
3 |
| Data Privacy Concerns |
The implementation of LLMs in accessible formats raises concerns over the handling and protection of data used in training and demonstrations. |
5 |
| Lack of Critical AI Engagement |
Simplified learning methods could deter critical engagement and ethical considerations in AI development and application. |
4 |
| Potential for Variable User Experiences |
Users may experience inconsistent results due to software limitations or bugs, impacting the learning experience and understanding of AI. |
3 |
| Limited Contextual Understanding |
The model’s restricted context length may lead to misunderstandings about the functional capabilities of AI language models. |
4 |
Behaviors
| name |
description |
relevancy |
| Low-Code AI Education |
Using low-code platforms like Excel to teach complex AI concepts, making AI accessible to non-technical users. |
5 |
| Hands-On Learning with Familiar Tools |
Implementing AI principles through common tools like spreadsheets for practical understanding and engagement. |
4 |
| Community Engagement in Learning |
Encouraging sharing and collaboration among learners through video demonstrations and community meetups. |
4 |
| Incremental Knowledge Building |
Providing structured lessons that gradually introduce complex AI topics, catering to different levels of expertise. |
5 |
| Real-Time Feedback and Iteration |
Utilizing platforms like GitHub for community-driven improvements and real-time feedback on projects. |
4 |
| Integration of AI in Various Professions |
Targeting professionals from diverse fields to adapt AI knowledge for their specific roles and responsibilities. |
5 |
Technologies
| description |
relevancy |
src |
| A low-code approach to understanding AI and LLMs using Excel spreadsheets. |
4 |
53afe9489328cc5d19f2b21c52791d85 |
| Implementation of LLM functionality such as GPT-2 entirely within Excel for educational purposes. |
5 |
53afe9489328cc5d19f2b21c52791d85 |
| Methods like Byte Pair Encoding for processing language data in LLMs, demonstrated through spreadsheet simulations. |
4 |
53afe9489328cc5d19f2b21c52791d85 |
| Concepts of word embeddings used in LLMs, showcased through practical spreadsheet examples. |
4 |
53afe9489328cc5d19f2b21c52791d85 |
| Using Excel to create simulations of AI processes, making AI concepts accessible to a broader audience. |
4 |
53afe9489328cc5d19f2b21c52791d85 |
Issues
| name |
description |
relevancy |
| Low-code AI Education |
The rise of low-code platforms to simplify AI learning, making it accessible to non-technical users. |
5 |
| Spreadsheet-based AI Tools |
Implementing AI concepts within familiar spreadsheet software, bridging the gap between technical and non-technical audiences. |
4 |
| Understanding LLM Architecture |
The need for accessible resources to understand the complexities of LLMs and their underlying architecture. |
5 |
| AI Policy and Ethics Education |
The importance of educating AI policymakers and ethicists on AI technologies and their implications. |
4 |
| Tokenization Challenges in AI |
Discussion on the limitations and alternatives to tokenization methods in language models. |
3 |
| Generative AI Accessibility |
The challenge of making generative AI tools like ChatGPT accessible to a broader audience. |
4 |