The article Ki-token: The smallest linguistic unit of artificial intelligence by Maria Gramsch first appeared on Basic Thinking. You always stay up to date with our newsletter.
More and more people used artificial intelligence at work or in everyday life. But how do large AI models work at all? The basis forms AI token, which are the smallest linguistic unit.
Artificial intelligence has developed rapidly in recent years. The number of users continuously increases. In 2024 alone, around 315 million people use at least one AI tool worldwide. The number should Forecasts rise to around 730 million, i.e. more than double.
In order for AI tools to answer questions from people and solve tasks, they must be trained. So-called AI tokens play a crucial role. As the smallest linguistic unity of artificial intelligence, they form the basis for understanding the large voice models.
What are AI token?
Large Language Models (LLMS)-big AI language models-are based on neuronal networks. These are so-called transformer models that build on the attention mechanism. The functionality of LLMS can be divided into four steps:
- Tokenization
- Embedding
- forecast
- Decoding
In the case of tokenization, the first step, a voice model dismantles the entered text into smaller parts, the so-called AI tokens. This procedure can also be compared to human language. Because man usually uses words as a token when communicating.
For AI language models, on the other hand, there are various techniques for tokenization. For example, individual particles, partial words or entire words can act as AI tokens.
Once Einlm has disassembled the language into Ki-token, the second step-the embedding, is also called embedding. The determined tokens are shown on vectors.
Language models then usually assign two semantically similar token to a similar vector. But not only semantics plays a role. Because the position of a token in the sentence can also influence the assignment to a vector.
Spring models make predictions
The prediction is the third step of how LLMS works. It forms the actual core of the voice models, because a AI tool calculates the likelihood of the next token.
Based on this probability calculation, the language model decide which tokens you output in decoding.
Large LLMS are based on various strategies. For example, you can select the so-called TOP-K Sampling or the TOP P SAMPLING TOKEN. These methods determine how many of the most likely tokens should be taken into account. On the other hand, use the Greedy Algorithm always select the most likely token at a current time.
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The article Ki-token: The smallest linguistic unit of artificial intelligence by Maria Gramsch first appeared on Basic Thinking. Follow us too Google News and Flipboard.
As a tech industry expert, I find the concept of Ki-token to be intriguing and potentially revolutionary in the field of artificial intelligence. By breaking down language into its smallest linguistic units, Ki-token has the potential to greatly enhance natural language processing and understanding capabilities of AI systems.
This approach could lead to more accurate and nuanced language processing, allowing AI systems to better understand context, tone, and meaning in human communication. This could have significant implications for a wide range of applications, from chatbots and virtual assistants to language translation and sentiment analysis.
Overall, I believe that Ki-token has the potential to significantly advance the capabilities of artificial intelligence and revolutionize the way we interact with and utilize AI technology in the future. I look forward to seeing how this concept develops and its impact on the tech industry as a whole.
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