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Tokenization in AI, especially in natural language processing (NLP), is the process of breaking down text into smaller units called "tokens." These tokens are typically words, parts of words, or punctuation marks that the AI can understand and analyze. It’s like chopping up a sentence into bite-sized pieces so the system can process it step-by-step.

How It Works:
1.⁠ ⁠Input Text: You start with a sentence or phrase.
2.⁠ ⁠Splitting: The text is split into tokens based on rules (e.g., spaces, punctuation, or even more advanced methods).
3.⁠ ⁠Output: Each token becomes a unit that the AI can work with think of it as giving the AI a list of ingredients to cook with.

Simple Example:
Let’s take the sentence:
"I love to run!"

Step-by-Step Tokenization:
•⁠ ⁠Basic Word Tokenization: Split the sentence by spaces and punctuation.
Result: ⁠ ["I", "love", "to", "run", "!"] ⁠
Here, each word and the exclamation mark becomes a separate token.

•⁠ ⁠Why It Matters: The AI can now analyze these tokens individually. For instance, it might recognize "love" as a positive word or "run" as an action.

A Slightly More Complex Case:
If we use a method like “subword tokenization“ (common in models like mine or others), it might break things down further:
•⁠ ⁠Input: "Running is fun!"
•⁠ ⁠Result: ⁠ ["Run", "##ing", "is", "fun", "!"] ⁠
Here, "Running" is split into "Run" and "##ing" (a suffix), which helps the AI understand patterns like verb endings across different words.

Why Tokenization Is Key:
•⁠ ⁠It turns messy human language into structured data an AI can process.
•⁠ ⁠It helps with tasks like translation, text generation, or sentiment analysis by giving the AI a clear starting point.

In short, tokenization is like preparing the raw materials before building something simple but essential!
Do you like this post? Share your thoughts in the comments below.
Thank you for your attention! 🇨🇭😊
#AITokenization #SHOMA_AI #NLP

1 year ago (edited) | [YT] | 1

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AI Usage:
How often do you use AI-powered applications or services?

1 year ago | [YT] | 2

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Serena Spada unveils Shoma AI, a groundbreaking app designed to revolutionize the music industry with its cutting-edge capabilities. ‪@hyperhyperbroadcast‬ #ai #shomaai #music #artists

1 year ago | [YT] | 1

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Don't miss Serena Spada's thrilling episode, premiering on February 25, 2025, at 5:30 PM (CET) on YouTube. #ShomaAI #Music

1 year ago | [YT] | 1

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The difference between AI, ML, LLM, and Generative AI.


Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLM), and Generative AI are not just buzzwords—they are transformative technologies reshaping our world.

Artificial Intelligence (AI) is the overarching field that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include problem-solving, learning, reasoning, perception, and language understanding. AI encompasses a wide range of technologies and methods, with the ultimate goal of developing machines that can think and act like humans.

Machine Learning (ML) is a subset of AI that involves training algorithms to learn from and make predictions or decisions based on data. Instead of being explicitly programmed to perform a task, ML algorithms use statistical techniques to improve their performance over time. Common applications of ML include recommendation systems, fraud detection, and image recognition.

Large Language Models (LLM) are a specific type of ML model designed to understand and generate human language. These models are trained on vast amounts of text data, enabling them to perform tasks such as text generation, translation, summarization, and answering questions. LLMs, like GPT-3 and BERT, have advanced natural language processing capabilities and can generate coherent and contextually relevant text.

Generative AI refers to AI systems that can create new content, such as images, music, or text. Unlike traditional AI that focuses on recognizing patterns and making decisions, generative AI uses techniques like Generative Adversarial Networks (GANs) and variational autoencoders (VAEs) to produce original outputs. Generative AI has applications in various fields, including art, entertainment, and product design.

In essence, AI is the dream, ML is the method, LLM are the linguists, and Generative AI is the artist. Together, they are driving the future of technology, making our lives smarter, more efficient, and infinitely more interesting.

1 year ago | [YT] | 1

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Which is your favorite AI tool?

1 year ago | [YT] | 1

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Learn about the top four AI tools for the music industry.🎶 #music #AI

1 year ago | [YT] | 1

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2025 Grammy Awards: Winners Revealed. My congratulations go out to all of these talented Artists 😇. #grammyawards2025 #winners

1 year ago | [YT] | 1

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Each AI model has unique strengths and weaknesses, making them suitable for specific tasks. ChatGPT is the most versatile for general AI assistance, conversation, and creative content generation. DeepSeek excels in research, data analysis, and knowledge-based tasks, prioritizing accuracy. Qwen 2.5 Max is the top choice for multilingual applications, translation, and cross-cultural communication. #ChatGPT #DeepSeek # Qwen25max #shomaai

1 year ago | [YT] | 1

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Are you tired of being disappointed by streaming platforms? Discover my analysis of how much artists and music rights holders actually receive after the streaming royalty breakdown, including labels, distribution, and more. The report covers the highest and lowest paying platforms, revealing that artists and rights holders often receive peanuts.
If you're fed up with this discrepancy, join the SHOMA AI Movement to make the music industry fair for all. #streaming #musicfair #artists #shomaai

1 year ago | [YT] | 1