Learning about AI

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If you want to learn about medical AI, you may come across lots of unfamiliar terms: machine learning? deep learning? natural language processing?

Below are some articles geared toward beginners. They could be a good starting point for readers interested in further exploring the field of medical AI.

Articles for Beginners without a Technical/Engineering Background

  • Clinical Journal of the American Society of Nephrology
  • IEEE Spectrum, “How Deep Learning Works,” Samuel K. Moore, David Schneider, and Eliza Strickland, September 28, 2021
    “Today’s boom in AI is centered around a technique called deep learning, which is powered by artificial neural networks. Here’s a graphical explanation of how these neural networks are structured and trained…”
  • The Economist, “Non-tech businesses are beginning to use artificial intelligence at scale,” Alexandra Suich Bass, March 31, 2018
    “One of AI’s main effects will be a dramatic drop in the cost of making predictions, says Ajay Agrawal of the University of Toronto and co-author of a new book, Prediction Machines. Just as electricity made lighting much more affordable—a given level of lighting now costs around 400 times less than it did in 1800—so AI will make forecasting more affordable, reliable and widely available.”
  • Harvard Business Review, “How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist,” Kathryn Hume, October 20, 2017
    “Having an intuition for how machine learning algorithms work—even in the most general sense—is becoming an important business skill. Machine learning scientists can’t work in a vacuum; business stakeholders should help them identify problems worth solving…”
  • MIT Technology Review, “The Seven Deadly Sins of AI Predictions,” Rodney Brooks, October 6, 2017
    “Mistaken predictions lead to fears of things that are not going to happen, whether it’s the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us. We need to push back on these mistakes. But why are people making them? I see seven common reasons.”
  • Wired, “The Myth of Superhuman AI,” Kevin Kelly, April 25, 2017
    “I’ve heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. … Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence.”

Additional Resources for Beginners

Resources for Engineers with a Technical/Engineering Background