CARY SAS, a pioneer in data analytics since its founding in 1976 by billionaires Jim Goodnight and John Sall, is now embracing AI as a smarter way to maximize data usage. In an exclusive interview with WRAL TechWire, AI Thought Leader Reggie Townsend, Vice President of SAS Data Ethics Practice, spoke about the dawn of a new era in AI with tools like ChatGPT driving more powerful usage. In part two, Townsend talks about what SAS is doing and offering to customers as part of a digital nervous system:
- What has been SAS’s experience with current AI offerings, is there growing demand, and if so, in which areas and by how much?
Thousands of organizations around the world rely on artificial intelligence and advanced analytics from SAS to better detect fraud and manage risk, optimize factory operations and supply chains, and improve customer retention.
Furthermore, AI is no longer just the purview of data scientists. The democratization of analytics has put the power of AI into the hands of business users as well, which opens up possibilities in new areas.
Fortunately, as a cloud-native AI and analytics company, SAS is well positioned. The SAS Viya platform brings fast and comprehensive AI capabilities to market. In such a dynamic market, we must remain flexible. SAS customers use Viya the way they like on premises or in the cloud they like. And they can tap into the power of the industry-specific solutions we’ve built on Viya to further tailor their analytics journey to their specific needs.
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As more organizations commit to responsible AI, we expect adoption of Viya to accelerate. SAS Viya includes robust AI capabilities such as bias detection, explainability, decision auditability and pattern tracking, governance, and accountability. Because bias can take many forms during the AI process, these capabilities help organizations identify potential risks of bias during data management and modeling, increasing confidence in an organization’s responsible AI efforts.
Demand for the SAS Viya platform is higher than ever. We have seen double-digit growth in our cloud business and SAS Viya has been chosen as the digital business nervous system by organizations around the world. While we have added many new Viya customers and migrated many more from previous SAS releases to Viya, we do not currently share specific numbers.
- How is SAS responding to developments such as chatGPT and other natural language models, AI being used to write user manuals, other work at SAS or at SAS customer sites?
ChatGPT and Googles Bard, have captured the public interest. It’s great that more people are being introduced to and familiar with Generative AI.
SAS has for many years offered text analytics and natural language processing (NLP), which supports generative AI, helping transform text data into actionable insights for better data searches and even chatbots. As ChatGPT has demonstrated, these technologies are invaluable in achieving our goal of analytics for everyone, everywhere. They are great examples of analytics for people who don’t need to be a data scientist or statistician to benefit from AI, NLP and related technologies.
However, ChatGPT and related technologies are still very new. We are discussing within SAS about the best ways to incorporate NLP and Generative AI into our internal processes and customer offerings.
We must acknowledge that the data used to train ChatGPT still comes from humans. The results of generative AI, in essence, are a reflection of us humans. There is still an inherent risk that these models could be informed by inaccurate data, misinformation or bias. Consumers must continue to apply critical thinking whenever they interact with conversational AI and avoid the automation bias, the belief that a technical system is more likely to be accurate and true than a human.
Generative AI’s moment of glory is exciting, but with any form of AI, we must consider the risks while marveling at the potential.
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- How does Artificial Intelligence/Machine Learning improve data processing and analytics?
Artificial intelligence, machine learning, deep learning, computer vision, and natural language processing technologies are gaining momentum in different industries and different parts of an organization’s business. Organizations with large amounts of data, which are the majority of organizations today, are using AI in new and different ways.
Machine learning and deep learning are two areas that are getting the widest use with the most promising results. Machine learning can detect patterns in data and make predictions without being told what to look for. Deep learning does the same but performs better with larger, more complex data (like video or images). As these capabilities are applied to traditional approaches to segmentation, forecasting, customer service, and other areas, organizations are finding they perform better than without these AI technologies.
For example, manufacturers are having success using computer vision to identify quality issues and reduce waste. Another example is retailers using machine learning techniques to improve forecasting and save on inventory costs and product waste. Banks are having success using conversational AI and natural language processing to improve marketing and sales. Retailers use machine learning to improve predictions.
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