- Science is in the midst of a data crisis. Last year, there were more than 1.2 million new papers published in the biomedical sciences alone, bringing the total number of peer-reviewed biomedical papers to over 26 million. However, the average scientist reads only about 250 papers a year. Meanwhile, the quality of the scientiﬁc literature has been in decline. Some recent studies found that the majority of biomedical papers were irreproducible1.
- The twin challenges of too much quantity and too little quality are rooted in the ﬁnite neurological capacity of the human mind. Scientists are deriving hypotheses from a smaller and smaller fraction of our collective knowledge and consequently, more and more, asking the wrong questions, or asking ones that have already been answered. Also, human creativity seems to depend increasingly on the stochasticity of previous experiences – particular life events that allow a researcher to notice something others do not. Although chance has always been a factor in scientiﬁc discovery, it is currently playing a much larger role than it should.
- One promising strategy to overcome the current crisis is to integrate machines and artiﬁcial intelligence in the scientiﬁc process. Machines have greater memory and higher computational capacity than the human brain. Automation of the scientiﬁc process could greatly increase the rate of discovery. It could even begin another scientiﬁc revolution. That huge possibility hinges on an equally huge question: can scientiﬁc discovery really be automated?
- I believe it can, using an approach that we have known about for centuries. The answer to this question can be found in the work of Sir Francis Bacon, the 17th-century English philosopher and a key progenitor of modern science.
- Human minds simply cannot reconstruct highly complex natural phenomena eﬃciently enough in the age of big data. A modern Baconian method that incorporates reductionist ideas through data-mining, but then analyses this information through inductive computational models, could transform our understanding of the natural world.
- Such an approach would enable us to generate novel hypotheses that have higher chances of turning out to be true, to test those hypotheses, and to ﬁll gaps in our knowledge. It would also provide a much-needed reminder of what science is supposed to be: truth-seeking, antiauthoritarian, and limitlessly free.
Footnote 1: We are referred to "Colquhoun (David) - The problem with p-values".
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