Why Machine Learning Models Should be Smaller in Size?

Recently, analysts at NVIDIA declared MegatronLM, a huge transformer model with 8.3 billion parameters (multiple times bigger than BERT) that accomplished cutting-edge performance on a variety of language tasks.

There are numerous instances of monstrous models being trained to accomplish somewhat higher precision on different benchmarks. In spite of being 24X bigger than BERT, MegatronLM is just 34% better at its language modeling task. As a coincidental trial to exhibit the performance of new hardware, there isn’t a lot of damage here. However, in the long-term, this pattern will cause a couple of issues.

As more artificial intelligence

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