There's a lot of interest in various ML communities on more efficient training and inference. Both vision and NLP have had a growing focus on these problems in recent years.
I think you make a good observation that much of ML progress is driven by tinkering with existing models, though instead of describing it as more "alchemy than science" it's probably more accurate to say it's very experimental right now. Being very experimental is neither unscientific nor unusual in the development of knowledge. James Watt worked as an instrument maker (not a theoretician) when he invented the Watt steam engine in 1776 [1], and at the time the idea of heat as Phlogiston [2] was still more prevalent than anything that looks like modern thermodynamics. Theory and practice naturally take turns outpacing each other, which is part of why we need both.
I'd also caution against the belief that experimental work doesn't require "particularly demanding thought". There are many things one can tweak in current ML models (the search space is exponential) and, as you point out, the experiments are expensive. Having a solid understanding of the system, great intuition, and good heuristics is necessary to reliably make progress.
For those who are interested in the theory of deep learning, the community has recently made great strides on developing a mathematical understanding of neural networks. The research is still very cutting edge, but the following PDF helps introduce the topic [3].
It's a good point and the study does some investigation of the question in Section 7 [1]. They find the trend seems to generalize across multiple speaker identities. Personal experiences appear more effective than facts at fostering respect for a wide range of different speakers.
I think this example misleads one's intuition for the following reason: in the proposed scenario, you'd only see the coin come up heads 10 times in a row about 1 in 1024 times you ran the experiment. While your conclusion would likely be incorrect, you almost never run into that scenario.
For example, if you conducted a study every week for 20 years, you'd both be extremely prolific and expect to have drawn about one wrong conclusion.
The example is a case of an absurd premise (i.e., a fair coin comes up heads 10 times in a row) leading to an absurd conclusion (that the coin is biased). Of course, this is exactly the guarantee the hypothesis test provides: under robust assumptions, you'll draw the wrong conclusion only rarely.
I think you make a good observation that much of ML progress is driven by tinkering with existing models, though instead of describing it as more "alchemy than science" it's probably more accurate to say it's very experimental right now. Being very experimental is neither unscientific nor unusual in the development of knowledge. James Watt worked as an instrument maker (not a theoretician) when he invented the Watt steam engine in 1776 [1], and at the time the idea of heat as Phlogiston [2] was still more prevalent than anything that looks like modern thermodynamics. Theory and practice naturally take turns outpacing each other, which is part of why we need both.
I'd also caution against the belief that experimental work doesn't require "particularly demanding thought". There are many things one can tweak in current ML models (the search space is exponential) and, as you point out, the experiments are expensive. Having a solid understanding of the system, great intuition, and good heuristics is necessary to reliably make progress.
For those who are interested in the theory of deep learning, the community has recently made great strides on developing a mathematical understanding of neural networks. The research is still very cutting edge, but the following PDF helps introduce the topic [3].
[1]: https://en.wikipedia.org/wiki/James_Watt
[2]: https://en.wikipedia.org/wiki/Phlogiston_theory
[3]: https://www.cs.princeton.edu/courses/archive/fall19/cos597B/...