What I have recently felt through exhibitions and various business negotiations is that the utilization of generative AI is becoming not just a means of improving operational efficiency, but rather a fundamental strategy for achieving significant performance enhancement for companies. However, many companies are caught in a serious dilemma where, while there are directives from management to "actively adopt AI," the information systems department and on-site staff are hesitant due to various concerns. The background to this includes fears of confidential information leaks, the complexity of various business flows that have become difficult for on-site staff to explain, and subscription models that are hard for management to predict, such as pay-per-use based on token quantities, which are structural risks associated with traditional cloud-based AI. Therefore, in this blog, I will outline the "four critical challenges" that companies face when they begin to fully utilize AI, and introduce the advantages of a "completely closed platform" as a strategic option to address these challenges.

With the spread of generative AI, including ChatGPT, AI has begun to be utilized in various scenarios such as text generation, summarization, translation, and programming assistance. On the other hand, in corporate and research and development settings, there is an increasing expectation for more advanced AI applications, not just to answer questions, but to: - Automatically search for necessary information from internal technical documents - Compare multiple pieces of information to determine the next course of action - Operate external tools and business systems - Progress through a series of tasks including research, analysis, code generation, and report creation This system, which infers based on objectives and utilizes necessary information and tools to advance tasks, is referred to as "Agentic AI." Supporting this Agentic AI is NVIDIA's open model family, known as NVIDIA Nemotron. In this article, we will introduce what NVIDIA Nemotron is, how it differs from traditional generative AI, and what applications can be considered in manufacturing and research and development.

The term "generative AI" has become something we hear in our daily lives. Generative AI, including ChatGPT and Gemini, is being utilized in various situations such as writing and summarizing text, information retrieval, image generation, and program creation. In companies, expectations for generative AI are rising for purposes like improving operational efficiency, utilizing information, and creating new services. On the other hand, because generative AI has become more accessible, many people may want to clarify the basics: "What exactly is generative AI?" "What is different from previous AI?" "How can it be specifically utilized in companies?" "What kind of environment is necessary to fully leverage generative AI?" This time, as the use of generative AI in businesses is progressing, we will revisit and organize our understanding of generative AI, its potential, and the computing environment that supports it.

Now, AI is trying to leap from "the screen" into "the real world." Local LLMs, which excel in security and high-speed processing, have now become a powerful practical engine that goes beyond mere dialogue tools, playing a central role in corporate DX and products. Many of you may have seen examples like "life-sized AI avatars for customer service" being introduced in convenience stores. What happens when local LLMs are further integrated into hardware such as communication robots? AI will evolve into "Physical AI," capable of recognizing the real world and physically interacting with it. The key to this paradigm shift lies in cutting-edge technology known as the "VLA (Vision-Language-Action) model." In this context, we have created an experimental prototype to actually experience the "sense of AI connecting with the real world." We would like to introduce you to its operation and potential!