CARY – SAS, a pacesetter in data analytics going way back to its founding in 1976 by billionaires Jim Goodnight and John Sall, is now embracing artificial intelligence as a smarter way to maximize data use. In an exclusive interview with WRAL TechWire, AI thought leader Reggie Townsend, vice president of the SAS Data Ethics Practice, talked about the “dawn of a new age” in AI with tools such as ChatGPT driving more powerful utilization. In part two, Townsend talks about what SAS is doing – and offering – to clients as part of a “digital nervous system:”
- What has the SAS experience been with current AI offerings – is there growing demand, and if so, in what areas, and by how much?
Thousands of organizations around the globe rely on AI and advanced analytics from SAS to better detect fraud and manage risk, optimize factory operations and supply chains, and improve customer loyalty.
Additionally, AI is no longer just the purview of data scientists. The democratization of analytics has put the power of AI in the hands of business users, as well, which opens up possibilities in new areas.
Fortunately, as a cloud-native analytics and AI company, SAS is well-positioned. The SAS Viya platform brings fast, comprehensive AI capabilities to the market. In such a dynamic market, we have to stay flexible. SAS customers use Viya the way they want to – on premises or in the cloud they choose. And they can tap into the power of industry-specific solutions we’ve built on Viya to further tailor their analytics journey to their specific needs.
As more organizations commit to responsible AI we expect the uptake of Viya to accelerate. SAS Viya includes trustworthy AI capabilities like bias detection, explainability, decision auditability and model monitoring, governance and accountability. Since bias can take many forms throughout the AI process, those capabilities help organizations identify potential bias risks during data management and modelling, increasing confidence in an organization’s responsible AI efforts.
Demand for the SAS Viya platform is higher than ever. We’ve experienced double-digit growth in our cloud business, and SAS Viya is chosen as the enterprise “digital nervous system” by organizations around the world. While we have added many new Viya customers and migrated many more from previous SAS versions to Viya, we are not sharing specific numbers at this time.
- How is SAS responding to developments such as chatGPT and other natural language models – is AI being used to write user manuals, other work at SAS or at SAS client sites?
ChatGPT and Google’s Bard, have captured the public interest. It’s great that more people are being introduced and becoming familiar with generative AI.
SAS has offered text analytics and natural language processing (NLP), which supports generative AI, for many years, helping to turn text data into useful information for better data searches and even for chatbots. As ChatGPT has shown, these technologies are valuable in achieving our goal of analytics for everyone, everywhere. They are great examples of analytics for the people – you 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 having discussions within SAS about the best ways to incorporate NLP and generative AI into our internal processes and customer offerings.
We must recognize that the data used to train ChatGPT still comes from humans. The results of generative AI, at their core, are a reflection of us, humans. There’s still an inherent risk that these models can be informed by inaccurate data, misinformation or biases. Consumers must continue to apply critical thinking whenever interacting with conversational AI and avoid automation bias — the belief that a technical system is more likely to be accurate and true than a human.
Generative AI’s moment in the sun is exciting, but with any form of AI, we must consider the risks while marveling at the potential.
- How is AI/machine learning improving the processing and analysis of data?
AI technologies – machine learning, deep learning, computer vision, natural language processing – are finding success in different industries and across different parts of an organization’s business. Organizations with large amounts of data – which today is most organizations – are using AI in new and different ways.
Machine learning and deep learning are two areas that are getting the broadest use with the most promising results. Machine learning can detect patterns in the data and make predictions without being told what to look for. Deep learning does the same but gets better results with bigger and more complex data (such as video or images). As these capabilities are being applied to traditional approaches of segmenting, forecasting, customer service, and other areas, organizations are finding they get better results than without these AI technologies.
For example, manufacturers are having success using computer vision to identify quality issues and reducing waste. Another example is retailers that use machine learning techniques to improve forecasts and save on inventory and product waste costs. Banks are having success using conversational AI and natural language processing to improve marketing and sales. Retailers are using machine learning to improve forecasting.