Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML ener…
Read more »We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer – a situation we refer to as mesa-optimization, a neologism we introduce in this paper. We believe that the possibility of mesa-…
Read more »Disclaimer: Feeling so-and-so about posting this on LW, but given how many people here work in ML or adjacent fields I might as well. …
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Read more »Some ancient languages that have never been deciphered could be in line for machine translation. …
Read more »Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are a…
Read more »The business world is full of streams of items that need to be filtered or evaluated: parts on an assembly line, resumés in an application pile, emails in a delivery queue, transactions awaiting processing. Machine learning techniques are increasingly bei…
Read more »A speaker at a lecture that I have attended recently summarized the philosophy of machine learning this way: “All knowledge comes from observed data, some from direct sensory experience and some from indirect experience, transmitted to us either culturall…
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