Exploring Generative AI Responsibly with SAS

There are several key techniques and models behind generative AI that data scientists should understand to use these tools effectively. Some of them are:

1. Generative Adversarial Networks (GANs): GANs are a type of deep learning model that consists of two neural networks, a generator and a discriminator, that work together in a competitive manner. The generator creates new content, while the discriminator evaluates the content’s quality and authenticity.

2. Variational Autoencoders (VAEs): VAEs are another type of deep learning model used for data compression and can be applied in areas like anomaly detection and image denoising.

3. Transformer models: Transformer models have been a game-changer for natural language processing, powering state-of-the-art text generation, translation, and summarization systems.

4. Reinforcement learning: Reinforcement learning involves training algorithms through trial and error, allowing them to learn from their mistakes and improve their performance over time. This approach has found numerous applications in generative AI across various industries.

Understanding these models and techniques can help data scientists make better use of generative AI tools by knowing when they are and are not useful, enabling smarter and more effective use of the methods.

The benefits of integrating generative AI into the analytics lifecycle mentioned in the news content include:

1. Streamlining the foundational data stages of ELT (Extract, Load, Transform): Predictive algorithms optimize data extraction, intelligently organize data during loading, and transform data with automated schema recognition and normalization techniques.

2. Accelerating data preparation through enrichment and data quality: AI algorithms predict and fill in missing values, identify and integrate external data sources to enrich the data set, while advanced pattern recognition and anomaly detection ensure data accuracy and consistency.

3. Enhancing analysis of data, such as geospatial and autoML: Mapping and spatial analysis through AI-generated models enable accurate interpretation of geographical data, while automated selection, tuning, and validation of machine learning models increase the efficiency and accuracy of predictive analytics.

4. Elevating the final stage of analytics, reporting: Custom, generative AI-powered applications provide interactive data visualizations and analytics tailored to specific business needs. Meanwhile, natural language generation transforms data into narrative reports—data stories—that make insights accessible to a broader audience.

These benefits contribute to improved revenue performance, cost management, and risk mitigation for businesses.