“As an analogy, the genome specifies some details about how the lungs grow—but lung growth depends on environmental regularities such as the existence of oxygen and nitrogen at certain concentrations and pressures in the atmosphere; without those gasses, lungs don’t grow right. Does that mean the lungs ‘learn’ their structure from atmosphere gasses rather than just from the information in the genome? I think that would be a peculiar way to look at it. ”—Geoffrey Miller
I wanted to highlight this quote because I like this analogy and feel like there’s a very valuable insight here. We need to keep in mind as we evaluate pre-trained machine learning models that we are evaluating a thing which is a result of the interaction between a learning algorithm and a dataset. Sometimes the nature of this dataset and how it shapes the result gets overlooked.
“As an analogy, the genome specifies some details about how the lungs grow—but lung growth depends on environmental regularities such as the existence of oxygen and nitrogen at certain concentrations and pressures in the atmosphere; without those gasses, lungs don’t grow right. Does that mean the lungs ‘learn’ their structure from atmosphere gasses rather than just from the information in the genome? I think that would be a peculiar way to look at it. ”—Geoffrey Miller I wanted to highlight this quote because I like this analogy and feel like there’s a very valuable insight here. We need to keep in mind as we evaluate pre-trained machine learning models that we are evaluating a thing which is a result of the interaction between a learning algorithm and a dataset. Sometimes the nature of this dataset and how it shapes the result gets overlooked.