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.

Exploring the Realm of AI: Exploratory Analytics, Prophetic AI, and Creative AI

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Artificial intelligence (AI) has taken center stage in the world’s embrace of cutting-edge technology. While generative AI (genAI) has recently gained significant attention with the introduction of ChatGPT in November 2022, it is crucial to comprehend the broader spectrum of AI that has evolved over the years.

To embark on this journey, let’s first dive into the realm of traditional analytics. This category, historically known as analytical business intelligence (BI), has been widely utilized for over four decades. As technology progressed, analytical BI transformed into what we now know as analytics. Traditional analytics primarily focuses on historical data, unraveling valuable insights from the past.

Moving forward, we encounter predictive AI, a forward-looking category distinct from traditional analytics. Predictive AI employs historical data to identify patterns and predict future outcomes with precision. This form of AI is widely adopted by model-driven businesses and is often considered the workhorse of the AI landscape.

The latest addition to the AI repertoire is generative AI. As the name suggests, genAI can analyze various forms of content—text, images, audio, and video—to generate new content tailored to user specifications. By leveraging existing data, generative AI has the power to produce reports, diagnoses, and other forms of novel content.

Despite its growing popularity, generative AI still lags behind predictive AI in terms of its range of applications. Thomas Robinson, COO at Domino, reveals that generative AI currently accounts for only 15% of use cases and models in organizations. In many instances, predictive and generative AI work in synergy. For example, generative AI can analyze radiology images to create reports on preliminary diagnoses or mine stock data to generate reports on potential future growth.

When it comes to developing and deploying AI solutions, it is unnecessary to treat each AI category as a separate entity. Instead of creating independent systems, organizations can opt for a unified platform. While genAI may require additional processing power and improved networking, especially in extensive deployments, the overall processes for governance and testing can remain similar to those used for predictive AI.

An excellent example of a trusted solution for managing AI tools, data, training, and deployment is Domino’s Enterprise AI platform. This platform has gained the trust of one out of every five Fortune 100 companies. By centralizing management under one roof, this platform enables organizations to achieve holistic development, deployment, and management of AI projects, encompassing both predictive and generative AI.

For those interested in exploring the responsible use of genAI and effectively managing associated risks, Domino offers a free whitepaper. This resource provides invaluable insights and guidance for genAI projects, empowering organizations to unlock genAI’s potential while mitigating potential challenges.

FAQ:

Q: What is generative AI (genAI)?
A: Generative AI is an innovative form of artificial intelligence that analyzes various types of content, such as text, images, audio, and video, to create new content according to user specifications.

Q: What are the different categories of AI?
A: AI can be categorized into three main types. Traditional analytics examines historical data to unveil insights from the past. Predictive AI uses historical data to identify patterns and make accurate predictions for the future. Generative AI generates fresh content by analyzing existing data.

Q: How widely is generative AI used?
A: Generative AI currently has a narrower range of applications 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 AI category separately, organizations can utilize a unified platform that facilitates the management of both predictive and generative AI. Domino’s Enterprise AI platform offers an example of such a trusted solution, bringing together 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 invaluable insights and guidance for genAI projects. This resource helps organizations harness the potential of generative AI while effectively mitigating associated risks.

Definitions:

– Artificial intelligence (AI): Technology that enables machines to mimic human intelligence.
– Generative AI (genAI): A category of AI that uses various forms of content to generate new content based on user specifications.
– Predictive AI: A form of AI that utilizes historical data to identify patterns and make accurate forecasts.
– Traditional analytics: Analytical business intelligence (BI) that focuses on historical data analysis.
– Business intelligence (BI): The process of gathering, analyzing, and visualizing data to gain insights into business operations and make informed decisions.
– Unified platform: A comprehensive platform that integrates various AI functionalities and resources for streamlined management.
– MLOps: Short for “Machine Learning Operations,” it refers to the management and deployment of machine learning models.

Sources:
[URL:domino.com]