How AIOZ W3AI Mitigates the Token-Based Limitations of Generative AI
Tokenization has played a crucial role in the successful development and implementation of Generative AI over the years.
However, it has some limitations that cause problems in the real-world applications of Generative AI, preventing them from attaining their full potential.
In this article, we analyze the use of tokens by Generative AI, highlight some of its limitations, and state how AIOZ W3AI will mitigate them when released.
Let's dive in:
Generative AI is powered by Large Language Models (LLMs) that process text in a completely different way compared to humans, who process text in its raw format.
Most LLMs are built on transformers, a neural network architecture that transforms or changes an input sequence into an output sequence.
Due to the way transformers deduce associations between text, they can't take in or churn out text in its raw format.
For this reason, transformers can only work with text that has been broken down into smaller units known as "Tokens", in a process known as Tokenization.
A token in the context of Generative AI is defined as the smallest unit of text that carries meaning for an LLM.
Depending on the model, a token can be:
- A sentence like "This girl is dramatic"
- A word from the sentence like "dramatic"
- Different syllables in the sentence i.e. "Dra", "ma", "tic"
- Individual letters in the word i.e. "D", "r", "a", "m", "a", "t", "i", "c"
To prepare tokens for use by LLMs, every token is converted to a numerical format by assigning them unique numerical IDs using "embeddings".
This helps tokens serve as the bridge between raw human language and the numerical format that LLMs can understand.
Tokens also provide flexibility in the use of Generative AI since they can represent varying sizes of text.
For instance, LLMs that can process tokens at the sentence level are better suited for certain applications, while those that can process at the word level are more suitable for other applications.
While these benefits might make tokens seem perfect for LLMs, tokens currently have some limitations that are holding Generative AI back from attaining its full potential.
Let's highlight some of these limitations below:
1.) Token Limits: LLMs have a fixed number of tokens they can process at a once known as their "context window".
This limitation greatly affects the length and complexity of text many LLMs can handle and also increases computational costs.
2.) Token Ambiguity: Due to the complexity of some texts, certain words and sentences can be broken down into tokens that are not clear-cut.
For instance, the same word in different letter cases (e.g. "amazed" and "AMAZED") will be broken down into different tokens by a tokenizer, potentially causing inconsistencies in a model's output.
3.) Language Variance: The clear-cut differences between the syntax of many languages mean that each language has its own unique tokenization needs.
Since many tokenizers are created majorly with the English Language in mind, it can take twice as long for an LLM to process texts from other languages like Arabic or Chinese.
These limitations are currently preventing many LLMs from processing a wide variety of texts with a high level of accuracy.
While it would require a radical change in the design of LLMs to eliminate these limitations entirely, a change in the type of infrastructure used for Generative AI computation might help to mitigate these issues.
Let's see how AIOZ W3AI can spearhead this change:
AIOZ Web3 AI (W3AI) is an AI-as-a-service platform powered by 180,000+ AIOZ DePIN nodes for decentralized AI computation/dataset storage and a Web3-incentivized collaborative AI marketplace.
It presents a superior alternative to the infrastructure of centralized cloud service providers which is currently utilized for Generative AI computation by most AI applications.
The shortcomings caused by the underlying design of centralized cloud service infrastructure have exacerbated some of the token-based limitations of LLMs.
AIOZ W3AI can address these problems by providing the following benefits to LLMs:
1.) Edge AI Computing: AIOZ W3AI's approach to edge AI computing allows for distributed processing power, enhancing the scalability and efficiency of AI applications. This can contribute to better handling of complex and ambiguous tokens by LLMs running on W3AI's infrastructure.
2.) Comprehensive AI Ecosystem: The extensive library of AI models and datasets on AIOZ W3AI, combined with its collaborative tools, can foster innovation and improvements in Generative AI, potentially leading to solutions that overcome token-based constraints.
3.) Decentralized Storage: AIOZ W3AI leverages the decentralized storage infrastructure of the AIOZ Network to manage larger datasets and models more efficiently, which can help to reduce many issues caused by token limits.
These benefits will go a long way in empowering LLMs to mitigate the current limitations of tokenization and handle real-world problems better - thereby positioning AIOZ W3AI as the solution that will catapult the field of Generative AI to new heights.
If you would like to learn more about AIOZ W3AI ahead of its release, you can download and read its whitepaper in the link below:
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