Representative vs. Random Samples: Key Differences Explained

Learn how representative samples and random samples differ and why combining them minimizes sampling bias for accurate data.
EDN

Purpose-built AI inference architecture: Reengineering compute design

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...
The Motley Fool

What Is AI Inference?

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...
NextBigFuture

OpenAI Strawberry LLM Reasoning Needs More Compute and Energy for Inference

Jim Fan is one of Nvidia’s senior AI researchers. The shift could be about many orders of magnitude more compute and energy needed for inference that can handle the improved reasoning in the OpenAI ...
VentureBeat

How Snowflake's open-source text-to-SQL and Arctic inference models solve enterprise AI's two biggest deployment headaches

Snowflake has thousands of enterprise customers who use the company's data and AI technologies. Though many issues with generative AI are solved, there is still lots of room for improvement. Two such ...
Investopedia

Systematic Sampling: What Is It, and How Is It Used in Research?

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...