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Streamlining government regulatory responses with natural language processing, GenAI and textanalytics was published on SAS Voices by Tom Sabo Each stone needs to be carefully examined, categorized and placed in the correct bucket, which takes about five minutes per stone. Fortunately, you’re not alone but part of [.]
is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generativeAI capabilities powered by foundation models. ” Romain Gaborit, CTO, Eviden, an ATOS business “We’re looking at the potential usage of LargeLanguageModels. .”
Recent case studies, however, reveal instances where AI-powered RPA bots demonstrate the ability to make subjective judgments, use interpretation skills, and handle multiple case exceptions. Optical Character Recognition (OCR) technology is a valuable companion for real-life RPA applications within the healthcare industry.
SAS' Federica Citterio answers the perennial data science question: "How can I trust (generative) LLM to provide a reliable, non-hallucinated result?" The post Five steps to improve information extraction using trustworthy generativeAI appeared first on SAS Blogs.
SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generativeAI. The post 10 things insurance leaders need to know about generativeAI and LLMs appeared first on SAS Blogs.
The Rise of Deepfakes and Automated Prompt Engineering: Navigating the Future of AI In this podcast recap with Dr. Julie Wall of the University of West London, we discuss two big topics in generativeAI: deepfakes and automated prompted engineering. How can big data analytics help? Register by Friday for 50% off!
Existing textanalytics tools work with NPS and CSAT survey feedback but are limited to extracting keywords and rudimentary sentiment detection. It is built on an in-house LLM (largelanguagemodel) that is trained on your contact center data to be many times more efficient than Chat GPT.
As largelanguagemodels, generativeAI, and prompt engineering have all taken center stage in the AI domain, the interests, demands, and skills required to forge ahead with one’s career have also changed. Now with generativeAI, improved chatbots, and LLMs, those concerns have only grown.
SAS' Julia Moreno shows you how to use generativeAI to build a digital assistant that interacts with a model using natural language conversation. The post LLM-based digital assistant for SAS Optimization appeared first on SAS Blogs.
SAS' Ali Dixon and Mary Osborne reveal why a BERT-based classifier is now part of our natural language processing capabilities of SAS Viya. The post How natural language processing transformers can provide BERT-based sentiment classification on March Madness appeared first on SAS Blogs.
When using LLMs, managing toxicity, bias, and bad actors is critical for trustworthy outcomes. Let’s explore what organizations should be thinking about when addressing these important areas. The post Toxicity, bias, and bad actors: three things to think about when using LLMs appeared first on SAS Blogs.
Adding linguistic techniques in SAS NLP with LLMs not only help address quality issues in text data, but since they can incorporate subject matter expertise, they give organizations a tremendous amount of control over their corpora.
We often hear about cyberattacks, hackers, ransomware, and other nefarious deeds in the news, but not all data breaches are caused by third parties. The post Three ways NLP can be used to identify LLM-related private data leakage and reduce risk appeared first on SAS Blogs.
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