What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“? Thought about large numerical and mathematics models for a few seconds.Confirming Additional BreakthroughsOK, I’m confirming if LNMs/LMMs need more than Transformer models to match LLM performance, and noting the user’s comprehensive response. Yes. While the Transformer architecture provided […]
The availability and structure of mathematical training data, combined with the unique characteristics of mathematics itself, suggest that training a Large Numerical Model (LNM) is feasible and may require less data than training a general-purpose LLM. Here’s a detailed look: Availability of Mathematical Training Data Structure of Mathematics and Data Efficiency Mathematics’ highly structured nature
Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents
Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents
Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents
The successful application of machine learning to understand the behavior of complex real-world systems from healthcare to climate requires robust methods for processing time series data. This type of data is made up of streams of values that change over time, and can represent topics as varied as a patient’s ECG signal in the ICU
