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Explained: Generative Model

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Celal Kavuklu
Celal Kavuklu
Celal Kavuklu is the Director, Customer Advisory, Middle East & Africa at SAS. With 20 years of experience in the IT industry, he a seasoned leader and expert in AI, business strategy, and decisioning solutions.

SAS unlocks the power of Generative AI! Learn how Generative Models can revolutionize various industries, from marketing to customer service.

What is the Generative Model?

A Generative Model is a machine learning model that learns to generate new data samples similar to those in the training dataset. Unlike discriminative models that focus on classifying data into predefined categories, Generative Models generate new data points that share characteristics similar to those of the original dataset. They are widely used in various applications, such as image generation, text generation, and anomaly detection. 

SAS—a data and AI solutions pioneer—recently unveiled a game-changing approach for organizations to tackle business challenges head-on. With lightweight, industry-specific AI models for individual licenses, SAS equips organizations with readily deployable AI technology to produce real-world use cases with unparalleled efficiency.

What are the types of Generative Models?

Generative Models employ various techniques tailored to different data types and modeling objectives. To name a few, Generative Adversarial Networks (GANs) and Autoregressive Models are notable types. GANs compete against one another to improve the generation of realistic data samples, whereas Autoregressive Models predict the probability distribution of each data point conditioned on previous points. 

SAS is moving beyond traditional AI implementations to deliver industry-proven deterministic AI models. Engineered for quick integration, SAS’ industry-specific models span use cases such as fraud detection, supply chain optimization, and more.  

What are the real-world use cases of Generative Models?

There are many real-world use cases of Generative Models spanning different industries and impacts. One of the most pertinent cases is how productivity can be greatly increased. For instance, SAS Viya was adopted by a global goods manufacturer, employing its GenAI capabilities to optimize warehouse space, allocate inbound shipments, and compare “what-if” scenarios based on product demand. Therefore, time is saved by using in-depth analytics, with a conversational assistant equipping technical and business users with the ability to make fast and accurate real-world decisions. 

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Another real-world example is the creation of Next Best Action prompts for customer service organizations. By using customer feedback from multiple channels, organizations can use GenAI and Text Analytics to understand the sentiment and draft a response to a customer based on best practices much faster than before. This directly impacts the volume of complaints handled, the average response time, and the cost of handling individual complaints. This is where GenAI technology meets the real-time decision-making on SAS Viya to solve industry business challenges.

Finally, synthetic data generation can increase the accuracy of models by increasing the data available for training. For example, in banking, synthetic data generation can be used to increase data for loan applications, increasing the accuracy of models, in some cases, by more than 50%.

The SAS Viya platform offers orchestration between different LLM providers and cataloging prompts, accelerates the AI lifecycle with Viya Copilot, mitigates data quality and scarcity with Synthetic Data Generation, and helps enable organizations with GenAI capabilities on top of a fully governed platform with trustworthy AI approach.

What are the benefits of Generative Models?

GenAI functionality, especially within SAS Viya, brings transformative advantages across various industries, categorized into five impactful areas: real-time customer interactions, personal productivity, summarizing and explaining complex data, synthetic data generation, and marketing content personalization. 

GenAI powers chatbots to efficiently handle inquiries in real-time customer interactions, providing immediate and accurate responses. It enhances personal productivity by automating tasks such as drafting email responses and creating code descriptions. GenAI summarizes and explains complex data, turning intricate datasets into clear, actionable insights. 

Synthetic data generation ensures responsible data usage, addresses privacy concerns, and enables robust model training. It is particularly critical in addressing certain industry problems like fraud and financial crime detection, risk management, and optimization by ensuring relevant data to detect fraud or stress testing scenarios, eventually solidifying the accuracy of the AI models. 

GenAI personalizes content by analyzing consumer behavior and preferences in marketing. It generates tailored email campaigns and product recommendations that boost engagement and conversion rates. This suite of capabilities makes GenAI a powerful tool for driving innovation and efficiency across all sectors.

What are the limitations of Generative Models?

Governance is GenAI’s main limitation today. The complex nature of these AI models makes it difficult to convert data into digestible information and manage the outputs, mitigating risks like hallucinations, bias, and security. 

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As new rules and regulations continue to be established globally, the ability to share such information with regulatory bodies is crucial. Fortunately, services are being created to ensure customers use data safely and productively. In particular, SAS is launching our AI Governance Advisory, a value-added service for current customers alongside platform capabilities such as the Model Card, which gives a quick and digestible snapshot of every model the organization has built.

Importantly, combining these approaches will increase internal and external trust from better accountability in data usage; internal trust of stakeholders will increase the usage of analytics, and external trust from customers will increase the positive perception and value they see in the organization.

Other than governance, the challenges of adopting GenAI technology span different organization personas. For example, the head of data and analytics would create new business models with GenAI, scale existing investments, and reshape the workforce with the GenAI potential. In contrast, for the head of IT, it is more about IP infringement, integration with existing business flows, and oversight of GenAI initiative costs. Users of GenAI also face challenges with the explainability and accuracy of GenAI models.

SAS addresses these challenges with verticalized industry solutions, models, and trustworthy AI capabilities provided over one platform, SAS Viya. This platform supports end-to-end AI and decisioning lifecycles with full auditability and performance.

How to use Generative Models for data science

Generative Models are another string to the bow of Data Scientists. They alone will not solve problems. Instead, they unlock the capability to facilitate better existing or create new use cases alongside other capabilities as part of the analytics ecosystem. Implementing business rules alongside machine learning models and using GenAI will have a real-world impact. 

Also Read: Web Scraping for AI Training: Can it Comply with GDPR?

Generative Models are often used in data science to create synthetic data, expand datasets, and enhance model performance overall. They can be utilized for various tasks, such as data augmentation and generating realistic samples for training. Incorporating Generative Models into data science workflows enhances data diversity and enables more effective decision-making. 

SAS is prioritizing industry-driven solutions, like Viya Copilot. Viya Copilot enhances productivity for data scientists with a personal assistant to accelerate tasks. A diverse set of tools is also offered: code generation, data cleaning, and data exploration. This tool minimizes the human effort demanded for such tasks, especially in data science. Similarly, SAS Data Maker is our solution designed to address data privacy and scarcity challenges by generating high-quality synthetic tabular data without compromising sensitive information, enabling organizations to address data privacy.

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