Carnegie Team Uses Random Forests on 406 Samples to Detect 3.3‑Billion‑Year‑Old Life

An interdisciplinary Carnegie research team combined pyrolysis‑GC‑MS with supervised random‑forest machine learning on 406 modern and ancient samples, achieving up to 100% accuracy in distinguishing biogenic from abiotic organic matter and successfully identifying molecular biosignatures dating back 3.3 billion years.

PNASRandom Forestancient life

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Carnegie Team Uses Random Forests on 406 Samples to Detect 3.3‑Billion‑Year‑Old Life

Tencent Cloud Developer

Dec 13, 2018 · Artificial Intelligence

Everything you need to know about AutoML and Neural Architecture Search

AutoML and Neural Architecture Search automate deep‑learning model design by using controller networks to explore and evaluate candidate architectures, with efficient variants like PNAS and ENAS reducing cost, while platforms such as Google Cloud AutoML and open‑source AutoKeras make these techniques accessible, promising broader, democratized AI breakthroughs.

AutoMLDeep LearningENAS

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Everything you need to know about AutoML and Neural Architecture Search

Tencent Cloud Developer

Dec 11, 2018 · Artificial Intelligence

Everything You Need to Know About AutoML and Neural Architecture Search

AutoML and Neural Architecture Search automate deep‑learning model design by sampling and training network blocks, using reinforcement‑learning or efficient weight‑sharing strategies such as PNAS and ENAS, enabling high‑accuracy architectures in days on a single GPU, with services like Google Cloud AutoML and open‑source tools like AutoKeras, while future research aims to expand search spaces beyond hand‑crafted blocks.

AutoMLDeep LearningENAS

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