A realistic high definition image depicting the evolution of AI. The image starts with traditional analytics represented by flowcharts, graphs, and binary code. It then transitions into predictive AI indicated by machine learning models, neural networks, and data analysis. The final part of the evolution is Generative AI which is symbolized by creative models generating paintings, music, and poetry.

Expanding the World of AI: Traditional Analytics, Predictive AI, and Generative AI

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As the world continues to embrace the potential of artificial intelligence (AI), the focus has shifted towards generative AI (genAI) since the release of ChatGPT in November 2022. This transformative technology has caught the attention of enterprise CEOs and boards of directors, with 84% of CIOs expecting to utilize genAI to support new business models in 2024, as revealed in a PwC report.

GenAI, however, is just one flavor of AI. To fully understand its significance, it is essential to recognize the different categories of AI throughout history.

The first category is traditional analytics, which has been widely used for the past four decades under the name of analytical business intelligence (BI). Over time, as technology advanced, the name shifted to analytics. This type of AI looks backward, utilizing data from the past to unveil valuable insights.

The second category is predictive AI. Unlike traditional analytics, predictive AI is forward-looking. It analyzes historical data to identify predictive patterns and employs current data to provide accurate forecasts for the future. Predictive AI plays a crucial role in model-driven businesses and is still considered the workhorse.

The third and most recent category is generative AI. GenAI analyzes various types of content, including text, images, audio, and video, to generate new content based on user specifications. It has the potential to generate reports, diagnoses, and other forms of new content by using existing data.

While generative AI is gaining momentum, it remains limited in terms of its use cases compared to predictive AI. Thomas Robinson, COO at Domino, reveals that generative AI accounts for only 15% of use cases and models in organizations. In many instances, predictive and generative AI are used synergistically, such as analyzing radiology images to create reports on preliminary diagnoses or mining stock data to generate reports on potential future increases.

When it comes to the development and deployment of complete AI, organizations do not need to treat each type of AI as a separate entity. Rather than creating new stacks from scratch, a unified platform can be employed. While genAI may require additional power and enhanced networking, especially in large-scale deployments, the overall processes for governance and testing can be similar to those used for predictive AI.

Domino’s Enterprise AI platform offers a trusted solution for managing AI tools, data, training, and deployment, trusted by one out of five Fortune 100 companies. This platform allows AI and MLOps teams to manage complete AI, including both predictive and generative AI, from a single control center. By centralizing management under one platform, organizations can achieve holistic development, deployment, and management of AI projects.

To learn more about responsible genAI and how to harness its potential while managing the associated risks, Domino provides a free whitepaper offering insights and guidance for genAI projects.

FAQ:

Q: What is generative AI (genAI)?
A: Generative AI is a category of artificial intelligence that analyzes various types of content, such as text, images, audio, and video, to generate new content based on user specifications.

Q: What are the different categories of AI?
A: There are three main categories of AI. The first is traditional analytics, which looks backward and utilizes data from the past to uncover insights. The second is predictive AI, which analyzes historical data to identify patterns and make accurate forecasts for the future. The third is generative AI, which generates new content by analyzing existing data.

Q: How widely is generative AI used?
A: Generative AI is still limited in terms of its use cases compared to predictive AI. It accounts for only 15% of use cases and models in organizations.

Q: How can organizations develop and deploy complete AI?
A: Rather than treating each type of AI as a separate entity, organizations can use a unified platform that allows for the management of both predictive and generative AI. Domino’s Enterprise AI platform is an example of a trusted solution that centralizes the management of AI tools, data, training, and deployment.

Q: How can organizations manage the risks associated with generative AI?
A: Domino provides a free whitepaper that offers insights and guidance for genAI projects, helping organizations harness its potential while managing the associated risks.

Definitions:

– Artificial intelligence (AI): Τεχνητή νοημοσύνη
– Generative AI (genAI): Δημιουργική τεχνητή νοημοσύνη
– Predictive AI: Προβλεπτική τεχνητή νοημοσύνη
– Traditional analytics: Παραδοσιακή αναλυτική
– Business intelligence (BI): Αναλυτική επιχειρησιακή νοημοσύνη
– Unified platform: Ενοποιημένη πλατφόρμα
– MLOps: Διαχείριση επιλεγμένων μοντέλων μηχανικής μάθησης

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