Predictive AI and Generative AI are two branches of machine learning that serve distinct purposes. These technologies harness machine learning algorithms and deep learning to achieve specific goals.
Both tools, applied with care, help drive efficiency, accuracy, and innovation — from healthcare diagnostics to financial analysis to forecasting revenue, predicting weather, to stock market trends. While both involve pattern recognition, their focus on content creation versus forecasting sets them apart.
Predictive AI uses machine learning to extrapolate the future. Generative AI uses machine learning to create content. Both serve different purposes.
Predictive AI
Predictive AI uses historical data and statistical algorithms to analyze patterns and make predictions about future events or trends. This technology aims to help companies and individuals make informed decisions by forecasting likely outcomes. Approach involves using structured data with continuous numeric variables.
The field of statistics is used to make predictions about the future; predictive AI makes statistical analysis fast and accurate. While there are no guarantees that the predictions will be correct, predictive AI will help businesses prepare for the future and personalize experiences for their customers. Predictive AI looks at hundreds or thousands of factors to identify patterns to show events that recur in the future.
For example, various industries use predictive models to forecast customer behavior, stock market trends, or product demand.
Generative AI
Generative AI focuses on producing new content, such as images, videos, music, or text. Its goal is to create novel outputs that mimic human-like patterns. Generative AI, like GPT-3, for example, creates unstructured, creative content such as natural language transcriptions, music, artistic patterns and images, short videos learned from vast datasets.
Generative AI is similar to predictive AI because it uses statistical analysis to “predict” which words and concepts belong together. But the goals for generative and predictive AI are different, the machine learning models they use are different, and the use cases are different.
Professor Lynda Gratton, from the London Business School, conducted a study of over 250 executives from organizations in Australia, Eurpoe, Japan, and the United States. Generative AI is a top priority for CEOs.
Implications
In an era where AI is shaping industries and transforming how we work and interact, comprehending the distinctions and applications of generative and predictive AI is vital.
Both predictive and generative have unique contributions and challenges, and staying informed about their capabilities empowers us to harness their benefits while navigating ethical considerations.
Three challenges keep getting raised.
- Employment and jobs. Both predictive and generative AI tools are good at producing consistent outputs for repetitive jobs or tasks. Call centers in developing countries employ large numbers of individuals, train them to answer from basic to intermediate troubleshooting questions. Questions like “When will my internet service be restored?” to “Can you explain to me why my credit card has an unrecognized amount of $25 two days ago?” Labor arbitrage is moving from developing countries to artificial intelligence models.
- Data privacy and security. AI tools require volumes of datasets for the models to learn. Data are collected from both public and private sources to feed the models. However, not most data sources require explicit approvals of the owners. Internet data availability doesn’t imply universal usability.
- Copyrights and authorship protections. In December 2023, the New York Times against OpenAI and Microsoft over the use of copyright. The lawsuit claims unauthorized use of published work (by the Times) to train AI models that eventually jockey with the news publication. The lawsuit says “the defendants should be held responsible for billions of dollars in statutory and actual damages related to the unlawful copying and use of The Times’s uniquely valuable works.”
Both these technologies — whether creating captivating content or predicting market trends — will shape the future.
Writer : Unmesh Tambwekar
— Bhuwan Chettri
Editor, CodeToDeploy
CodeToDeploy Is a Tech-Focused Publication Helping Students, Professionals, And Creators Stay Ahead with AI, Coding, Cloud, Digital Tools, And Career Growth Insights.