SAS has updated its SAS Platform to deliver additional innovation in artificial intelligence (AI), specifically in the areas of machine learning, computer vision, natural language processing (NLP) and other technologies that underpin AI. Building on its recently announced $1 billion investment in AI, SAS is also refining computer-vision software to help organizations use visual data to improve business outcomes.
“Our continuous innovation, clearly exhibited in the SAS Platform and in SAS AI technologies, propels front-line business, executives and data scientists to change the trajectory of their organizations with advanced analytics,” said SAS CEO Jim Goodnight.
With the latest release of the SAS Platform, SAS automates the complex tasks required to build world-class analytical models. Data cleansing, data transformations, selecting best variables, model building and comparing models, model deployment and retraining tasks are automated while using established best practices.
The SAS Platform automatically compares thousands of analytical models to help choose the best for a given business problem. Using natural language generation (NLG), analytical results are displayed in plain language, so users of all backgrounds can easily interpret them and make informed business decisions faster.
This helps democratize analytics, as business users and executives can use AI technology along with data scientists and analytics experts, and understand how the analytics arrived at its results. “Too many companies are caught in AI science-project mode and don’t have the know-how to make the leap to a machine learning model that meaningfully affects business,” said Oliver Schabenberger, SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer. “Thousands of SAS PhDs and data scientists are helping customers with strategies to transform data into intelligence, and our extensive training is helping build skills in organizations. We’re simplifying our technology to help users of all skill levels use powerful AI and ML analysis to innovate. We’re making AI real.”
SAS has simplified computer vision for a wide array of applications. New capabilities like automatic segmentation can, for example, help doctors quickly identify changes in the shape and size of tumors and note their color to better fight disease.
The enhancements to the SAS Platform that make AI more accessible to users of all skill levels include:
- A new project insights area that provides a high-level narrative summary to explain, for non-data scientists, what and how an analysis was performed. SAS is making it easier for business users, data scientists, and IT experts to discuss their models and algorithms. This improved collaboration builds more trust in AI, which leads to greater adoption and more business benefits.
- Enhanced interpretability and explainability of AI models – With NLG capabilities in the SAS platform, users can automatically generate explanations of analytics results in layman’s terms, such as why a transaction was flagged as potentially fraudulent or why specific customers are the best targets for a marketing campaign. These explanations help business analysts and other business users easily understand analytic results, and encourage the real-world use of AI and advanced analytics by a wide variety of users (i.e., not just data scientists).
- Improve decision-making – The SAS Platform works seamlessly with SAS solutions like SAS Intelligent Decisioning to automate and manage decisions across the enterprise.
- Open Application Program Interfaces (APIs) that developers can use to access data and create custom web applications to help business and technical users leverage machine learning, natural language processing and other SAS AI capabilities in an automated way, without users having to understand how to code or use statistics.
- More valuable content embedded in the SAS portfolio – Software like SAS Visual Investigator and the new SAS Mobile Investigator bring the operational and investigative power of SAS Viya, its machine learning capabilities and other AI technologies to users in the field and on the go. Users can access information from their mobile device and input data from text, documents or photos and the system is updated immediately. This real-time data can also be used to update analytical models and run risk assessments based on new information