The rapid advancement of AI technology has captivated the attention of enterprise CEOs and boards of directors worldwide. According to a recent report by PwC, an overwhelming 84% of CIOs anticipate incorporating generative AI (genAI) into their business models by 2024. While genAI undoubtedly possesses transformative capabilities, it is crucial to recognize that it is just one facet of the AI landscape and may not be the optimal solution for every use case.
The realm of AI has undergone considerable evolution over the years. What once qualified as AI has shifted significantly, with advancements in technology reshaping our understanding of its capabilities. In broad terms, the history of AI can be categorized into three distinct phases.
Traditional Analytics, an approach prevalent over the past four decades, utilized business intelligence (BI) to analyze historical data and derive insights about past occurrences. As technology progressed, the term shifted to analytics to reflect its increasing sophistication.
Predictive AI, on the other hand, employs historical data to identify patterns and generate accurate forecasts about future events. This forward-looking technology enables organizations to make informed decisions based on projected outcomes.
GenAI, the latest addition to the AI landscape, focuses on generating content such as text, images, audio, and video according to user-defined criteria. While genAI accounts for a significant portion of use cases and models, it currently represents only around 15%, as confirmed by Thomas Robinson, COO at Domino.
Interestingly, there are instances where predictive and generative AI collaborate to enhance results. For instance, combining the analysis of radiology images with the generation of preliminary diagnostic reports or utilizing stock data mining to generate reports on potentially profitable investments. This synergy prompts the need for a unified platform that facilitates the development of complete AI.
Fortunately, organizations do not need to treat each type of AI as isolated entities with distinct stacks. The development and deployment of complete AI require a common platform that accommodates both predictive and generative AI. While genAI may necessitate additional computational power and network resources, building an entirely new infrastructure is unnecessary for most organizations unless their genAI deployment is on a massive scale.
Governance and testing processes also do not need a complete overhaul. Granted, there are differences between managing the risks associated with predictive AI and genAI, such as genAI’s susceptibility to “hallucinations.” Nevertheless, the principles of rigorous testing, validation, and continuous monitoring apply to both predictive and generative AI.
To facilitate the seamless management of AI tools, data, training, and deployment, many Fortune 100 companies trust Domino’s Enterprise AI platform. Consolidating predictive and generative AI under a single control center, this platform empowers AI and MLOps teams to drive complete AI development, deployment, and management with ease.
Unlock the potential of your genAI projects while managing associated risks responsibly. Explore Domino’s free whitepaper on responsible genAI to learn how to navigate the rewards and challenges in the world of AI.
FAQ Section:
1. What is generative AI (genAI)?
Generative AI, also known as genAI, is a type of artificial intelligence that focuses on generating content such as text, images, audio, and video based on user-defined criteria. It is the latest addition to the AI landscape.
2. What are the three phases of AI?
The three phases of AI are:
– Traditional Analytics: This approach uses business intelligence (BI) to analyze historical data and derive insights about past occurrences.
– Predictive AI: This type of AI employs historical data to identify patterns and generate accurate forecasts about future events.
– Generative AI: GenAI focuses on generating content based on user-defined criteria.
3. Can predictive and generative AI collaborate?
Yes, predictive and generative AI can collaborate to enhance results. For example, combining the analysis of radiology images with the generation of preliminary diagnostic reports or utilizing stock data mining to generate reports on potentially profitable investments.
4. Is it necessary to have a separate infrastructure for genAI deployment?
For most organizations, building an entirely new infrastructure for genAI deployment is unnecessary unless it is on a massive scale. While genAI may require additional computational power and network resources, a common platform that accommodates both predictive and generative AI is preferable.
5. How can AI tools, data, training, and deployment be managed seamlessly?
Many Fortune 100 companies trust Domino’s Enterprise AI platform to facilitate the seamless management of AI tools, data, training, and deployment. This platform consolidates predictive and generative AI under a single control center, empowering AI and MLOps teams to drive complete AI development, deployment, and management with ease.
Key Terms/Jargon:
– AI: Artificial Intelligence
– genAI: Generative AI
– BI: Business Intelligence
– MLOps: Machine Learning Operations
Suggested Related Links:
– Domino: Official website of Domino, the Enterprise AI platform mentioned in the article.
– Domino’s Whitepapers: Access Domino’s whitepapers, including the free whitepaper on responsible genAI mentioned in the article.