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Challenge Areas

Discussion of technical challenges faced by ELNA and proposed solutions.
The development of ELNA has encountered several technical challenges, and we've been actively working on solutions to overcome them. Here, we discuss these challenges and our proposed solutions:

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Developing AI within Canister Web Assembly Environment

One of the foremost challenges we've tackled is developing AI technologies optimized for the canister's web assembly (Wasm) environment. This environment poses unique constraints and requires specialized solutions to make AI thrive within it.
Solution: We have adopted a canister-centric approach to AI development, ensuring that our models, algorithms, and workflows are tailored to operate efficiently within the canister's Wasm environment. This approach involves optimizing code execution, memory management, and data handling for this specific context.

Instruction and Memory Limitations

The instruction and memory limitations of canisters can become restrictive, especially when dealing with the inference of larger AI models. Balancing the need for advanced AI capabilities with these limitations is a key challenge.
Solution: We are actively researching and implementing techniques to maximize the utilization of available instructions and memory while maintaining optimal performance. This includes exploring model quantization and distillation methods to minimize resource consumption.

Balancing Speed, Scale, Privacy, and Transparency

ELNA is committed to providing AI services that balance speed, scale, privacy, and transparency. Achieving this balance in a decentralized infrastructure poses its own set of challenges, particularly when handling user data and AI processes.
Solution: Our approach involves continuous refinement of algorithms and workflows to ensure that ELNA's AI services are fast, scalable, privacy-conscious, and transparent. We place a strong emphasis on secure and privacy-preserving AI computations.
Moving forward, we are optimistic about the advancements taking place within the Dfinity ecosystem to address these challenges. Some notable improvements include:
  • Latest WebAssembly Stable Memory: The incorporation of the latest wasm-native stable memory provides a solid foundation for AI development within the canister environment. This enhancement allows for more efficient memory management and execution.
  • Advanced Quantization and Distillation Techniques: Emerging techniques in quantization and distillation are poised to minimize the size and instructions required for fine-tuning and inferencing, without compromising performance. These advancements will enable ELNA to work more efficiently within canisters.
  • Enhanced Data Architectures: Ongoing efforts in data architecture and design are aimed at optimizing vector embedding and data handling. This will lead to more streamlined and efficient data processing within the canister environment.
  • Continual Evolution of LLM Models: The ever-evolving landscape of larger language models (LLMs) will continue to provide improved AI capabilities for ELNA. Staying at the forefront of LLM development allows ELNA to offer state-of-the-art AI services to its users.
These advancements within the Dfinity ecosystem align well with ELNA's mission to democratize AI while addressing the technical challenges associated with decentralized AI development.