15.10 Achievements of deep learning

  • DL achieved nothing short of a revolution in the (ML) field
  • Remarkable results: Perceptual problems (seeing, hearing) that have long been elusive for machines
    • Near-human-level image classification; Near-human-level speech recognition; Near-human-level handwriting transcription; Improved machine translation; Improved text-to-speech conversion; Digital assistants such as Google Now and Amazon Alexa; Near-human-level autonomous driving; Improved ad targeting, as used by Google, Baidu, and Bing; Improved search results on the web; Ability to answer natural-language questions; Superhuman Go playing
  • Still exploring full extent of what DL can do
    • Applications outside of machine perception/natural-language understanding, such as formal reasoning -> may assist humans in science, software development and more
  • ML and causal thinking
    • “The key [Pearl] […] argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. Once this kind of causal framework is in place, it becomes possible for machines to ask counterfactual questions — to inquire how the causal relationships would change given some kind of intervention — which Pearl views as the cornerstone of scientific thought.”