Since the emergence of ChatGPT in November 2022, generative AI (genAI) has taken center stage for enterprise CEOs and boards of directors. The potential of this transformative technology has led many organizations to consider its implementation in their business models. However, it is crucial to recognize that genAI is just one aspect of AI and may not be the best solution for every use case.
The concept of AI has evolved over time, and its history can be categorized into three distinct phases.
First, there is traditional analytics, which has been used by organizations for the past four decades. Originally known as business intelligence (BI), analytical tools have grown more sophisticated over time. Analytics primarily focuses on looking back at past data to uncover insights about historical events.
The next phase is predictive AI. This forward-looking technology analyzes past data to identify patterns and uses current data to make accurate predictions about future events. Predictive AI is widely used in model-driven businesses and remains a staple in organizations’ AI strategies.
Lastly, we have generative AI, or genAI. This form of AI examines various types of content such as text, images, audio, and video and generates new content based on user specifications. While genAI has its strengths, it is important to note that it accounts for a smaller percentage of use cases and models compared to predictive AI.
Interestingly, there are already instances where predictive and generative AI work together harmoniously. For example, radiology images can be analyzed using both types of AI to create reports on preliminary diagnoses. Similarly, mining stock data can generate reports on stocks that are likely to increase in the near future. As a result, organizations require a unified platform for comprehensive AI development.
Fortunately, complete AI development and deployment do not require treating each AI type as a separate entity with its own infrastructure. While genAI may require additional power and improved networking for optimal performance, organizations do not have to build an entirely new stack unless they are undertaking massive genAI deployments like Meta or Microsoft.
Moreover, processes for governance and testing can be adapted from predictive AI to manage genAI effectively. Although there are distinctions, such as genAI’s susceptibility to “hallucinations,” the general risk management processes remain similar.
Leading the charge in managing AI tools, data, training, and deployment, Domino’s Enterprise AI platform is trusted by many Fortune 100 companies. This platform allows AI and MLOps teams to oversee complete AI development and deployment from a single control center. By unifying MLOps under one platform, organizations can enable comprehensive AI development, deployment, and management.
Discover how to navigate the opportunities and challenges of genAI projects responsibly with Domino’s insightful whitepaper on responsible genAI.
FAQs:
1. What is genAI?
GenAI refers to generative AI, a form of artificial intelligence that examines different types of content and generates new content based on user specifications.
2. What are the three phases of AI?
The three phases of AI are traditional analytics, predictive AI, and generative AI.
3. What is traditional analytics?
Traditional analytics, also known as business intelligence (BI), focuses on looking back at past data to uncover insights about historical events.
4. What is predictive AI?
Predictive AI uses past data to identify patterns and make accurate predictions about future events.
5. How do predictive and generative AI work together?
There are instances where predictive and generative AI can work together. For example, radiology images can be analyzed using both types of AI to create reports on preliminary diagnoses.
6. Do organizations need separate infrastructure for each AI type?
Organizations do not need to build an entirely new infrastructure for each AI type. While genAI may require additional power and improved networking for optimal performance, a unified platform can be used for comprehensive AI development.
7. Can governance and testing processes for predictive AI be adapted for genAI?
Yes, processes for governance and testing can be adapted from predictive AI to effectively manage genAI, although there may be some distinctions in risk management.
8. What is Domino’s Enterprise AI platform?
Domino’s Enterprise AI platform is a trusted platform used by many Fortune 100 companies for managing AI tools, data, training, and deployment. It allows for overseeing complete AI development and deployment from a single control center.
Definitions:
– GenAI: Generative AI, a form of artificial intelligence that generates new content based on user specifications.
– Traditional analytics: Business intelligence that focuses on looking back at past data to uncover insights about historical events.
– Predictive AI: Forward-looking technology that analyzes past data to identify patterns and make accurate predictions about future events.
– Domino’s Enterprise AI platform: A platform used for managing AI tools, data, training, and deployment, allowing for comprehensive AI development and deployment from a single control center.
Related Links:
DominosDataLab.com – The main domain of Domino’s Enterprise AI platform for more information on their services.