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.”

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.

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.

How Can Companies Protect their Data from Misuse by LLMs? 

In this contributed article, Jan Chorowski, CTO at AI-firm Pathway, highlights why LLM safety begins at the model build and input stage, rather than the output stage – and what this means in practice; how LLM models can be engineered with safety at the forefront, and the role that a structured LLM Ops model plays; and the role of data chosen to train models, and how businesses can appropriately select the right data to feed into LLMs

Opaque Systems Extends Confidential Computing to Augmented Language Model Implementations 

In this contributed article, editorial consultant Jelani Harper discusses how Opaque Systems recently unveiled Opaque Gateway, a software offering that broadens the utility of confidential computing to include augmented prompt applications of language models. One of the chief use cases of the gateway technology is to protect the data privacy, data sovereignty, and data security of organizations’ data that frequently augments language model prompts with enterprise data sources.

Video Highlights: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Kirill Eremenko to walk listeners through why decision trees and random forests are fruitful for businesses, and he offers hands-on walkthroughs for the three leading gradient-boosting algorithms today: XGBoost, LightGBM, and CatBoost.

How Can AI-Powered Predictive Maintenance Boost Business Efficiency

In this contributed article, April Miller, senior IT and cybersecurity writer for ReHack Magazine, shows that when AI steps into predictive maintenance, it supercharges this capability. It offers more profound insights, accurate predictions and the ability to act swiftly. This blend of technology enhances operational efficiency and paves the way for a deeper exploration into how such innovations can transform the business landscape.

Video Highlights: The Best AI Startup Opportunities — with Venture Capitalist Rudina Seseri

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, sits down with Rudina Seseri from Glasswing Ventures to answer the question: “What has Glasswing Ventures done for the company beyond capital investment?” Rudina describes how her company uses data to assess venture capital investments, the secret sauce of successful AI startups, and why she feels generative AI is only the start of a much broader impact that AI will make in communities and businesses.