From Volume 3 Issue 2 of Connected.
The ability to successfully adopt and utilize artificial intelligence (AI) technology has quickly become a necessity for organizations across the globe. Understanding how AI works, identifying relevant applications within your organization and preparing your infrastructure for seamless integration are crucial steps on the path to unlocking its full potential. By taking the time to understand the inner workings of AI, companies can position themselves for successful AI adoption and unlock new opportunities for growth and innovation.
When it comes to AI, it is important to understand the two types that we see most often: Reactive and Limited Memory.
- Reactive AI, also known as Reflexive AI, is the simplest form of artificial intelligence. It has no memory and only reacts to the current situation based on predefined rules/ algorithms, meaning it cannot analyze past data or predict future events. Reactive AI is typically designed for specific tasks: spam filters, automatic door openers and video game enemies.
- Limited Memory AI, also called Learning AI or Generative AI, is much more complex than Reactive AI. Limited memory AI can analyze and learn from past data, allowing it to improve its performance over time and adapt to new situations. Although it can learn, the “memory” is not like ours. It’s more about statistical analysis and pattern recognition gathered from data, not storing individual experiences. Think: ChatGPT, virtual assistants and self-driving cars.
Organizations that want to leverage the power of AI need to ensure that their foundational infrastructure and data management systems are efficient and effective. AI analyzes large amounts of data to identify trends, patterns and risks that humans might miss, but it needs organized, accurate historical data to do so. It is imperative to have systems in place that can effectively collect, store, clean and manage large data sets. Additionally, companies should have cybersecurity measures to ensure that data is protected. It is important that companies have the necessary hardware, software and network infrastructure to support the integration of AI technology. The more data AI absorbs, the more complex its resource requirements become.
From anticipating supply chain issues to optimizing material quality and performance, AI presents a whole new world of predictive technology that can drive increased efficiency, cost savings and smarter decision-making across the board.