Last line of the paper is destined to resonate into history: "We’re only one creative algorithm away from valuable near-term applications" Reminds me of the la

Last line of the paper is destined to resonate into history: "We’re only one creative algorithm away from valuable near-term applications" Reminds me of the last line from Watson and Cricks 1953 Structure of DNA paper: "It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material" ;) I bring up genomics specifically because the main profit driver appears to be biotech. Predicting near-instantaneously whether regulatory proteins bind to a target genome site could revolutionize drug discovery. And while full "programmatic" control of transcription factors may be dozens of years away. In the near term, annealing based ML can speed up analysis of genome-wide datasets Unconventional machine learning of genome-wide human cancer data https://arxiv.org/abs/1909.06206 https://arxiv.org/abs/1909.06206