Alternative approaches could solve some of these issues. “Neurosymbolic AI”, for instance, combines statistical learning with structured representations of prior knowledge, often inspired by cognitive science. It appears more capable of common-sense reasoning, and decisions are more readily interpretable by humans.26 While deep learning focuses on finding correlations in data, “Causal AI” seeks to give machines a deeper understanding of cause and effect that could be crucial for general intelligence.27 And “Bayesian AI” approaches use the statistics of probability to help AI better understand the uncertainty of the real world while achieving continuous learning.28
So far, these approaches have not achieved the same success as deep learning. Many researchers also believe that similar capabilities will emerge spontaneously as deep learning models become larger and more sophisticated. But our theoretical understanding of deep learning lags far behind the practice, making it difficult to spot potential roadblocks to progress. It is possible that some combination of these approaches may be necessary if we want to recreate the kind of general intelligence seen in humans.