Generative Artificial Intelligence has gained significant attention recently, transforming how AI creates new content rather than just analyzing it. Unlike traditional AI, which focuses on identifying images, generative AI like "ChatGPT" produces content based on user input. Since its launch in November 2022, ChatGPT became the fastest web service to reach 100 million registered users by January 2023, generating immense global interest. This technology supports diverse applications, from answering questions and drafting documents to composing music and generating code. While it holds great promise, it also raises concerns about copyright, job displacement, and its potential expansion into robotics and heavy industry.

Generative AI is revolutionizing content creation through two key processes: learning and inference. In the learning phase, a model is built by analyzing large datasets to capture their features, which can take months for high accuracy. In contrast, the inference phase quickly generates responses to user inputs using these pre-trained models, typically within seconds, which is critical since users tend to shy away from slow services. Despite its potential, generative AI faces challenges, particularly in generating inaccurate content—a phenomenon known as "hallucination," where the AI produces plausible yet false information. One effective strategy to combat this issue is to enhance the data volume and computational parameters during the learning phase. This approach has reportedly improved the accuracy of generative AI models, such as OpenAI's ChatGPT, highlighting the ongoing evolution of this promising technology.

Long-term reliability is crucial for AI hardware, particularly during the intensive learning phase when processors run at full capacity for extended periods. These processors are engineered for durability to minimize failure risk. As processing power increases, thermal management becomes more critical; thus, components in proximity must also endure high temperatures and maintain reliability over time. To address these demands, Panasonic has created conductive capacitor products designed for prolonged use in high-heat environments, ensuring dependable performance even in elevated temperatures around the processor. 
A comparison of ESR and capacitance changes in durability tests (105°C for 2000 hours) among similar capacitors reveals that, despite sharing similar specifications, Panasonic's SP-Cap™ capacitors exhibit minimal characteristic alteration, demonstrating exceptional reliability and voltage stabilization after long-term use.