According to Kuhn the development of a science is not uniform but has alternating ‘normal’ and ‘revolutionary’ (or ‘extraordinary’) phases. The revolutionary phases are not merely periods of accelerated progress, but differ qualitatively from normal science. Normal science does resemble the standard cumulative picture of scientific progress, on the surface at least. Kuhn describes normal science as ‘puzzle-solving’ (1962/1970a, 35–42). While this term suggests that normal science is not dramatic, its main purpose is to convey the idea that like someone doing a crossword puzzle or a chess problem or a jigsaw, the puzzle-solver expects to have a reasonable chance of solving the puzzle, that his doing so will depend mainly on his own ability, and that the puzzle itself and its methods of solution will have a high degree of familiarity. A puzzle-solver is not entering completely uncharted territory… Revolutionary science, however, is not cumulative in that, according to Kuhn, scientific revolutions involve a revision to existing scientific belief or practice (1962/1970a, 92). Not all the achievements of the preceding period of normal science are preserved in a revolution, and indeed a later period of science may find itself without an explanation for a phenomenon that in an earlier period was held to be successfully explained...
Kuhn’s view is that during normal science scientists neither test nor seek to confirm the guiding theories of their disciplinary matrix. Nor do they regard anomalous results as falsifying those theories. (It is only speculative puzzle-solutions that can be falsified in a Popperian fashion during normal science (1970b, 19).) Rather, anomalies are ignored or explained away if at all possible. It is only the accumulation of particularly troublesome anomalies that poses a serious problem for the existing disciplinary matrix. A particularly troublesome anomaly is one that undermines the practice of normal science. For example, an anomaly might reveal inadequacies in some commonly used piece of equipment, perhaps by casting doubt on the underlying theory. If much of normal science relies upon this piece of equipment, normal science will find it difficult to continue with confidence until this anomaly is addressed. A widespread failure in such confidence Kuhn calls a ‘crisis’
Under this view, perhaps a certain set of interpretability techniques might emerge under a paradigm that makes certain assumptions (eg, that ML kernals are “mostly” linear, that systems are “mostly” stateless, that exotic hacks of the underlying hardware aren’t in play, etc). If a series of anomalies were to accumulate that couldn’t be explained within this matrix, you might expect to see a new paradigm needed.
The Standford Phil Encylopedia gives:
Under this view, perhaps a certain set of interpretability techniques might emerge under a paradigm that makes certain assumptions (eg, that ML kernals are “mostly” linear, that systems are “mostly” stateless, that exotic hacks of the underlying hardware aren’t in play, etc). If a series of anomalies were to accumulate that couldn’t be explained within this matrix, you might expect to see a new paradigm needed.