Generative AI (GenAI) is dominating headlines, celebrated as a groundbreaking technology with vast potential. Beyond the initial (and warranted) excitement, however, a more balanced perspective reveals the substantial – often overlooked – value of another AI technology: traditional machine learning (ML). While generative AI garners attention for its creative capabilities, machine learning remains an unsung hero, utilizing […]
A Deep Dive into Data Strategy: Accelerating AI Adoption for Businesses
In today’s digital age, vast amounts of business data are gathered from different sources. Even when organizations strategically invest in analytics tools, they still face challenges in the form of data silos, unstructured data management, and failure of business-driven insights from tools. To combat these challenges, more businesses are revamping with data strategy to seamlessly […]
From Complexity to Clarity: GenAI Decodes Clinical Trial Data
As AI matures and becomes more integrated into clinical trial operations, generative AI (GenAI) technology is ushering in a new era for clinical research and drug development, redefining the clinical trial landscape and offering a quantum leap in efficiency and precision. This innovative technology streamlines every phase, transforming traditionally time-consuming processes into swift, automated workflows. […]
Generative AI and Data: Using Knowledge Graphs and RAG Together
Generative AI has huge potential, but it also faces problems. If generative AI creates information that is not factually accurate in response to a user request – resulting in so-called hallucinations – it can have a big impact on users. Relying on large language model (LLM) training data on its own is not enough to prevent […]
How AI Will Fuel the Future of Observability
We’re seeing a lot of convergence in the market between observability vendors and companies positioned as artificial intelligence (AI) companies. It’s a natural marriage, since AI has the potential to significantly improve what observability does. The question is how to make the best use of AI to support observability in discovering an organization’s unknowns, providing […]
Output Monitoring to Overcome LLM Vulnerabilities
The vulnerabilities in large language models (LLMs), such as their ability to create hallucinations, their inherent biases, and their susceptibility to corruption, have led to hesitation among businesses in deploying AI applications for business advantage. This concern is compounded further by the lag in legislative regulations to address these risks. Despite these challenges, businesses are […]
Fundamentals of Descriptive Analytics
In descriptive analytics, data aggregation, and data mining techniques are used to collect and review the historical data of a business to gauge the past performance. The most common example of descriptive analytics is the reports that a user gets from Google Analytics tools. A web server’s summarized performance reports may help the user analyze […]
RAG (Retrieval Augmented Generation) Architecture for Data Quality Assessment
A large language model (LLM) is a type of artificial intelligence (AI) solution that can recognize and generate new content or text from existing content. It is estimated that by 2025, 50% of digital work will be automated through these LLM models. At their core, LLMs are trained on large amounts of content and data, and the architecture […]
How AI Liberates BI Data from Dashboards
Business intelligence (BI) has historically been a platform for data and business analysts who build dashboards for executives to make decisions. But the generative AI (GenAI) explosion over the past 18 months has changed the game. People everywhere have tasted the power of ChatGPT and now want a natural language interface to every application. But […]
Ask a Data Ethicist: When Is It OK to Use “Fake” Data?
It’s easier than ever to use AI-generated images or text and to create synthetic data for use in research. A recent high-profile story got me thinking more about this question: When is it OK to use “fake” data? Before we dive into this, I’m putting “fake” in quotes because I’m taking a wide perspective on […]