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We review the classic clinical versus statistical prediction debate as well as related modern work on humans versus. algorithms. Despite the successes of statistical prediction over clinical prediction, there is still widespread resistance to algorithms. We discuss recent attempts to understand that resistance. Current research focuses on when people use algorithmic predictions, how people perceive algorithms, and how algorithms can be made more appealing. We also examine attempts to boost human forecasting accuracy, either by spotting talent, cultivating talent via training, or developing algorithms that aggregate individual forecasts. We hypothesize that hybrid models with both human and algorithmic predictions may encounter less resistance than algorithms alone, especially when the algorithm is "humanized" (with anthropomorphic features) and the human is "algorithmized" (by reducing nose, decreasing bias and increasing signal).