The Future of Generative AI: Unlocking Enterprise Potential
The recent partnership between Fujitsu and Cohere Inc. marks a significant milestone in the development of large language models (LLMs) for enterprise use. This strategic collaboration will focus on creating innovative Japanese LLMs for private cloud usage, enabling businesses to leverage industry-leading language capabilities and deliver improved experiences for customers and employees.
“We are very pleased to strengthen our generative AI for enterprises portfolio through this partnership with Cohere.” - Vivek Mahajan, Corporate Vice President, CTO, and CPO, Fujitsu Limited
The jointly developed AI technology, Takane, will be offered through Fujitsu Kozuchi, providing a secure environment for enterprise data. This advanced Japanese language model is based on Cohere’s frontier enterprise-grade LLM, Command R+, which features enhanced retrieval-augmented generation (RAG) capabilities to mitigate hallucinations.
Understanding the Capabilities and Limitations of LLMs
While LLMs have sparked debates about artificial intelligence, it’s essential to understand their capabilities and limitations. According to David Chiang, PhD, University of Notre Dame, Assoc. Professor, Department of Computer Science and Engineering, LLMs operate by predicting the next word in a sequence, enabling them to write poetry, solve complex math problems, and play chess at a high level. However, they lack true dialogue understanding and struggle with consistent role-playing in conversations.
Clinical Decision-Making: The Role of LLMs
A recent study by Atropos found that general-purpose LLMs are not fit for use in clinical decision-making. The study compared the performance of leading LLMs, including ChatGPT, Claude, and Gemini, to answer healthcare questions submitted by clinicians. While general-purpose LLMs only provided relevant information 2%-10% of the time, language models built specifically for healthcare performed better, pulling relevant insights 24% of the time.
Atropos’ own LLM, ChatRWD beta, performed the best, pulling relevant data 58% of the time. ChatRWD queries its data and generates an answer to the question based on the 160 million de-identified patient records available to it.
Comparison of LLMs in clinical decision-making
The Future of AI Assessment
As the capabilities of LLMs continue to evolve, critical examination of AI technologies remains crucial. Balancing appreciation for AI abilities with awareness of constraints is key to navigating the evolving landscape of artificial intelligence. By understanding both the impressive capabilities and inherent limitations of language models, we can unlock their full potential and accelerate digital transformation across global markets.
The evolving landscape of artificial intelligence
