Using Smaller ML Models To Train Large Language Models
A new research project by MIT has developed a way to train large language models on smaller machine learning
Topic: Machine Learning
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
How AI/ML Can Help Banks Bridge the Human-Digital Divide
When applied correctly, AI/ML and other technologies can take banking back to its roots to solve challenges and help identify new business
Digital Twins and the Next Generation Simulation Engine
By running simulations of different scenarios, a digital twin offers an accurate reflection of the current state versus the future state and can provide the …
Researchers Use Machine Learning To Forecast Carbon Capture Emissions
Researchers from Heriot-Watt University and EPFL have developed a machine learning algorithm to more accurately forecast amine
McKinsey Acquires MLOps Platform Iguazio
The goal of the acquisition is to help the company dramatically accelerate and scale its client's AI
Enabling Transformation Change With HPC, Simulation, AI, and Data Analytics
We’re at the confluence of the use of three industry technologies – high-performance computing (HPC), simulation, and data analytics – that will change …
MIT Researchers Create Explanation Taxonomy to ML Models
For non-technical staff, the lack of understanding and obfuscation by some developers is one of the lead causes of under-utilization of machine learning …
Machine Learning With Heart
Advances in both computational modeling and machine learning have shown the promise of a clear paradigm shift in personalized medicine. As such, a new research …
Recommender Systems: Why the Future is Real-Time Machine Learning
The future of recommender systems is real-time machine learning. Here's everything you need to know about what that means for enterprise applications.
What Is MLOps and How Will It Change the Data Center?
MLOps can be used to improve time to market and ensure ML models meet organizational, compliance, and end-user
