- Published on March 17, 2022
- In AI Features
Why changing computing trends across different ML eras matter
the history of compute in machine learning can be categorised into three eras – the Pre Deep Learning Era, the Deep Learning Era and the Large-Scale Era.
- By Shraddha Goled
Intel co-founder Gordon Moore, in 1965, said that the number of components per integrated circuit would double every year; he predicted that such rate of growth would continue for at least another decade. He extrapolated that computing power would increase dramatically at a relative decrease in cost. Also called the Moore’s Law, this became the golden rule for the electronics industry. That said, Moore’s Law has been debunked multiple times.
In a recent paper titled “Compute Trends Across Three Eras of Machine Learning”, the authors have studied the compute, data, and algorithmic advances that eventually guide the progress of machine learning. They demonstrated that before 2010, the training compute grew according to Moore’s law, doubling every 20 months. However, with t
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Shraddha Goled
I am a technology journalist with AIM. I write stories focused on the AI landscape in India and around the world with a special interest in analysing its long term impact on individuals and societies. Reach out to me at shraddha.goled@analyticsindiamag.com.
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