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If you thought training AI models was hard, try building enterprise apps with them

Feb 23, 2025 - theregister.com
The article discusses the challenges and advancements in integrating large language models (LLMs) into practical applications. Despite methods like fine-tuning and retrieval augmented generation (RAG) being well-understood, their practical implementation is complex. Aleph Alpha CEO Jonas Andrulis highlights that fine-tuning is not always effective for teaching new information, while RAG requires well-documented processes, which are often lacking. Aleph Alpha aims to address these challenges by developing frameworks and tools, such as their tokenizer-free "T-Free" training architecture, which reduces training costs and carbon footprint. Their Pharia Catch tool helps identify and resolve discrepancies in documented knowledge, enhancing model effectiveness.

Aleph Alpha is positioning itself as a European leader in sovereign AI, focusing on building frameworks for enterprises and governments to develop their own AI strategies. The company collaborates with hardware partners like AMD, Graphcore, and Cerebras to support diverse hardware requirements. Andrulis emphasizes that Aleph Alpha will not become a cloud provider, aiming instead to offer flexible solutions without vendor lock-in. Looking forward, the company anticipates increased complexity in AI applications as the industry shifts towards agentic AI systems capable of complex problem-solving beyond simple chatbot functionalities.

Key takeaways:

  • Fine tuning and retrieval augmented generation (RAG) are methods to expand AI models, but combining them is often necessary for meaningful results.
  • Aleph Alpha focuses on creating frameworks to facilitate the adoption of AI technologies, such as their tokenizer-free "T-Free" training architecture.
  • Aleph Alpha collaborates with various hardware partners to support a wide range of hardware for Sovereign AI applications.
  • The complexity of AI applications is expected to increase as the industry shifts towards agentic AI systems capable of complex problem solving.
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