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I hate how people perceive unequal growth as a bad thing even when everyone benefits on an absolute scale. In particular, it's a completely ridiculous standard

I hate how people perceive unequal growth as a bad thing even when everyone benefits on an absolute scale. In particular, it's a completely ridiculous standard to apply to technology. The fact that Google can train a 12 billion parameter model does not make my 2 million parameter model any less effective. And when you're concerned with actually doing useful things to create value, that's all that matters. I will not go into how completely detached from reality the rest of the comment is except to say there are approximately zero people who cannot learn ML because they don't have access to a GPU.

Abishek_Muthian 7y ago
> there are approximately zero people who cannot learn ML because they don't have access to a GPU That sounds like an entitled, myopic view. There are reputed, fully meritorious govt. run Engineering colleges in India, where tuition fee is ~100 USD/Year. Many of the students studying there are in poverty, govt. provides free laptop albeit obviously cheap one. Almost everyone studying there are placed in top companies around the world. In my previous startup I've conducted ~80 ML/DS interviews, fresh graduates from above colleges perform very well in DSA & other CS aspects; but perform poor with ML when compared to those from expensive private institutions. When I enquired them, it came down to lack of proper access to ML hardware. Their college labs are not equipped to provide ML training at scale, their access to Internet is limited to make use of Google Colaboratory or similar services. Yes, they could run a CPU bound training for days, but it isn't practical and many don't have consistent power supply. Economic disadvantage in education is real, more pronounced when it comes to ML in my experience.