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OpenAI's announcement of Strawberry has turned the spotlight on a fundamental limitation of language models, their inability to analyze individual letters within words. This weakness reveals profound aspects about how they work. The counting problem. When ChatGPT is asked to count the R's in the word strawberry, the model often errs. This error does not result from a lack of intelligence but from the way the language models analyze the text. To understand why, one needs to know the concept of tokenization. The worlds seen through tokens language models do not see words as sequences of letters, but as tokens, units of meaning converted into numbers. It is like reading a book where each word is replaced by a numerical code. The word school books, for example, is divided into two separate tokens school and books. This explains why the model struggles to count the O's in this word correctly. It does not in fact see it as a word, an illuminating example. Imagine learning a language where the word school is always represented by the number four quantui. If someone asked you how many O's there are in Four Twi, we could not answer correctly without ever having seen the word written in full. Language models are in a similar situation. They process meanings through numbers without access to the literal composition of words. The challenge of compound words, the problem becomes even worse with compound words. Timekeeper is broken into separate tokens, making it difficult for the model to determine the exact position of the letters and this fragmentation affects not only letter counting, but also understanding the internal structure of words. The solution to the strawberry problem, perhaps. This future OpenAI model, Strawberry, should overcome this limitation by introducing an innovative approach to text processing. Instead of relying only on traditional tokenization, the model should be able to analyze words at the level of individual letters, allowing for more precise counting and analysis operations. Future implications. The importance of this problem goes beyond simple letter counting. This granular analysis capability could significantly improve the linguistic understanding of AI models, enabling them to tackle problems that require detailed character-level text analysis. The planned integration of this technology will be a major advance in the direction of language models that are more capable of reasoning about the fundamental details of language, not just statistical patterns. Welcome to Electe's newsletter, English. This newsletter explores the fascinating world of how companies are using AI to change the way they work. It shares interesting stories and discoveries about artificial intelligence in business, like how companies are using AI to make smarter decisions, what new AI tools are emerging, and how these changes affect our everyday lives. You don't need to be a tech expert to enjoy it. It's written for anyone curious about how AI is shaping the future of business and work. Whether you're interested in learning about the latest AI breakthroughs, understanding how companies are becoming more innovative, or just want to stay informed about tech trends, this newsletter breaks it all down in an engaging, easy to understand way. It's like having a friendly guide who keeps you in the loop about the most interesting developments in business technology without getting too technical or complicated. Subscribe to get full access to the newsletter and publication archives.