The article also discusses the future of AI, suggesting that soon businesses will be able to harness data from public AI models while still protecting their sensitive information through enterprise AI. This will mimic and leverage public AI models while ensuring sensitive corporate data does not get out to the public. To build a private AI environment, businesses need to focus on data architecture, connections, and sustainability. The article concludes by reminding readers that implementing AI is a marathon, not a sprint, and encourages continuous learning and assessment of options.
Key takeaways:
- AI can be categorized into two types: Public AI, which is open and accessible to anyone with an internet connection, and Private AI, which is designed for data privacy and security and is built for a specific audience.
- Private AI is often used in industries where data sensitivity is crucial, such as healthcare, finance, and defense, but its use is expanding across various sectors to improve business processes and ensure data safety.
- Enterprise AI is a promising development that aims to leverage the benefits of public AI models while ensuring the protection of sensitive corporate data.
- Building a private AI infrastructure involves focusing on data architecture, ensuring fast and secure connections between data, apps, and AI algorithms, and considering sustainability due to AI's high power consumption.