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GPT-2 Integrated into Microsoft Excel: An Unconventional Approach to AI Modeling

TL;DR

  • Developer Ishan Anand integrates GPT-2 into Microsoft Excel, offering a novel perspective on AI modeling.
  • Despite limitations, the Excel-based GPT-2 facilitates understanding next-token prediction and Transformer architecture.
  • Anand’s creation is an educational resource for diverse audiences interested in AI principles and applications.

In a remarkable feat, software developer and self-proclaimed spreadsheet enthusiast Ishan Anand has successfully integrated the GPT-2 language model into Microsoft Excel. This groundbreaking achievement not only demonstrates the versatility of spreadsheets but also provides a unique perspective into how large language models (LLMs) operate, particularly the underlying Transformer architecture responsible for intelligent next-token prediction.

Anand’s pioneering approach

Recognizing the inherent complexity of AI systems, Anand believes that understanding a spreadsheet can unlock the secrets of artificial intelligence. “If you can understand a spreadsheet, then you can understand AI,” he confidently states. The developer’s innovative approach has resulted in a 1.25GB spreadsheet, which he has generously made available on GitHub for anyone to download and explore.

While Anand’s spreadsheet implementation of GPT-2 may not match the cutting-edge capabilities of contemporary LLMs, it offers a valuable glimpse into the groundbreaking GPT-2 model, which garnered significant attention in 2019 for its state-of-the-art performance. It’s important to note that GPT-2 predates the era of conversational AI, with ChatGPT emerging from efforts to prompt GPT-3 conversationally in 2022.

Exploring the transformer architecture

At the core of Anand’s Excel implementation lies the GPT-2 Small model, which boasts 124 million parameters. In contrast, the full version of GPT-2 employed a staggering 1.5 billion parameters, while its successor, GPT-3, raised the bar even higher with up to 175 billion parameters. Despite its relatively modest size, Anand’s implementation showcases the Transformer architecture’s ability to perform smart “next-token prediction,” where the language model intelligently completes an input sequence with the most likely subsequent part.

While the spreadsheet can handle only 10 tokens of input, a minuscule fraction compared to GPT-4 Turbo’s capacity of 128,000 tokens, Anand’s work serves as a valuable educational resource. He believes his “low-code introduction” is ideal for tech executives, marketers, product managers, AI policymakers, ethicists, developers, and scientists seeking to understand the foundations of LLMs better.

A foundation for modern LLMs

Anand asserts that the Transformer architecture employed in his GPT-2 implementation remains “the foundation for OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Bard/Gemini, Meta’s Llama, and many other LLMs.” His multi-sheet work guides users through word tokenization, text positions and weightings, iterative refinement of next-word prediction, and ultimately, selecting the output token – the predicted last word of the sequence.

One of the noteworthy benefits of Anand’s Excel-based implementation is the ability to run the LLM entirely locally on a PC, without relying on cloud services or API calls. However, he cautions against attempting to use this Excel file on Mac or cloud-based spreadsheet applications, as it may lead to crashes and performance issues. Additionally, Anand recommends using the latest version of Excel for optimal performance.

While Anand’s GPT-2 implementation may not match the capabilities of contemporary LLMs, it serves as a remarkable educational tool and a testament to the versatility of spreadsheets. By demystifying the inner workings of language models, Anand’s work empowers individuals from diverse backgrounds to gain a deeper understanding of artificial intelligence and its underlying architectural principles.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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John Palmer

John Palmer is an enthusiastic crypto writer with an interest in Bitcoin, Blockchain, and technical analysis. With a focus on daily market analysis, his research helps traders and investors alike. His particular interest in digital wallets and blockchain aids his audience.

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