What Happens When We Train AI on AI-Generated Data?

In this contributed article, Ranjeeta Bhattacharya, senior data scientist within the AI Hub wing of BNY Mellon, points out that In the world of AI and LLMs, finding appropriate training data is the core requirement for building generative solutions. As the capabilities of Generative AI models like Chat GPT, DALL-E continues to grow, there is an increasing temptation to use their AI-generated outputs as training data for new AI systems. However, recent research has shown the dangerous effects of doing this, leading to a phenomenon called “model collapse.”

Where Artificial Intelligence Is Making a Difference in Healthcare

In this contributed article, Rajesh Viswanathan, Chief Technology Officer for Inovalon, discusses how for the past year, AI was at the center of conversations throughout healthcare. While the potential for AI to revolutionize healthcare is clear, from care delivery to enhancing operational efficiencies and accelerating research, many organizations are still figuring out where to begin.

Heard on the Street – 4/18/2024

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Vero AI Evaluates 10 Leading Generative AI Models Using Its Comprehensive VIOLET Framework to Gauge Responsible AI 

Vero AI, an analytical engine and scoreboard that helps enterprises fully harness the potential of advanced technology including artificial intelligence while minimizing risk, announced the findings of its inaugural “Generating Responsibility: Assessing AI Using AI” report.

Avoid these 7 Common Business-related Mistakes On Data Projects

This article is excerpted from the book, “Winning with Data Science: A Handbook for Business Leaders,” by Howard Friedman and Akshay Swaminathan with permission from the publisher, Columbia Business School Publishing. The article covers how to avoid 7 common business-related mistakes on data projects that all stem from failures in planning, preparation and communication.

Why the Modern Data Stack is Broken and How to Fix It

In this contributed article, Stavros Papadopoulos, Founder and CEO, TileDB, discusses how we are quickly reaching a threshold where the vulnerabilities of the modern data stack are starting to outweigh its advantages. Here’s what we need to do next.

Why Accelerating Data Engineering Across Public Clouds and Private Data Centers is a Game Changer

In this contributed article, Rob Gibbon, Product Manager at Canonical, suggests that data engineers typically know what they need to get done. The problem is that their environment doesn’t always make it easy. If you’re working on premise, it can be hard to get data-intensive solutions off the ground quickly. However, cloud solutions come with lock-in and unpredictable pricing. The game-changer in this scenario is a hybrid solution that will allow you to accelerate data engineering.

Video Highlights: Deep Reinforcement Learning for Maximizing Profits — with Prof. Barrett Thomas

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Dr. Barrett Thomas, an esteemed Research Professor in at the University of Iowa’s College of Business, to delve deep into Markov decision processes and how they relate to Deep Reinforcement Learning.

The Power of Data Visualization: Techniques and Best Practices

In this contributed article, freelance writer Ainsley Lawrence discusses how data visualization is a powerful tool that can help viewers quickly analyze and assess the status or results of an analysis. Good visualization can make even the largest and most complex datasets relatively straightforward to interpret.

Data Insights are Illuminating the Future of the Power Sector

In this contributed article, David Thomason, Industry Principal – Power Generation at AVEVA, believes that the power sector has more data than ever on nearly every process in its value chain. Now, new technologies are helping make sense of all those details to provide competitive advantages – and it’s not a moment too soon.