Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
SHARE THIS:

1 month ago
Balancing Training Data and Human Knowledge to Make AI Act More Like a Scientist

 
Originally published in Tech Xplore, March 8, 2024. 

When you teach a child how to solve puzzles, you can either let them figure it out through trial and error, or you can guide them with some basic rules and tips. Similarly, incorporating rules and tips into AI training—such as the laws of physics—could make them more efficient and more reflective of the real world. However, helping the AI assess the value of different rules can be a tricky task.

Researchers report March 8 in the journal Nexus that they have developed a framework for assessing the relative value of rules and data in “informed machine learning models” that incorporate both. They showed that by doing so, they could help the AI incorporate basic laws of the real world and better navigate scientific problems like solving complex mathematical problems and optimizing experimental conditions in chemistry experiments.

“Embedding human knowledge into AI models has the potential to improve their efficiency and ability to make inferences, but the question is how to balance the influence of data and knowledge,” says first author Hao Xu of Peking University. “Our framework can be employed to evaluate different knowledge and rules to enhance the predictive capability of deep learning models.”

Generative AI models like ChatGPT and Sora are purely data-driven—the models are given training data, and they teach themselves via trial and error. However, with only data to work from, these systems have no way to learn physical laws, such as gravity or fluid dynamics, and they also struggle to perform in situations that differ from their training data.

To continue reading this article, click here.

 

2 thoughts on “Balancing Training Data and Human Knowledge to Make AI Act More Like a Scientist

  1. As a student, finding affordable research papers online has been a game-changer for me. With tight budgets and looming deadlines, the option to buy research papers for cheap https://essayhub.com/cheap-research-papers has been a huge relief. It’s reassuring to know that I can access high-quality academic papers without breaking the bank. Plus, the process is straightforward, and I can choose from a variety of topics and subjects. This convenience has saved me time and stress, allowing me to focus on other important aspects of my academic journey.

     
  2. As a student navigating the complexities of academia, I’ve come to understand the significance of constantly expanding my knowledge base. It’s akin to training AI to emulate the scientific method—balancing raw data with human insights. Just as https://www.nursingpaper.com/ offers invaluable resources for nursing students, leveraging external sources enhances problem-solving capabilities. Much like AI, integrating diverse sources of information enables me to tackle academic challenges with a multifaceted approach.

     

Leave a Reply