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  • Today, artificial intelligence helps doctors diagnose patients,

  • pilots fly commercial aircraft, and city planners predict traffic.

  • But no matter what these AIs are doing, the computer scientists who designed them

  • likely don't know exactly how they're doing it.

  • This is because artificial intelligence is often self-taught,

  • working off a simple set of instructions

  • to create a unique array of rules and strategies.

  • So how exactly does a machine learn?

  • There are many different ways to build self-teaching programs.

  • But they all rely on the three basic types of machine learning:

  • unsupervised learning, supervised learning, and reinforcement learning.

  • To see these in action,

  • let's imagine researchers are trying to pull information

  • from a set of medical data containing thousands of patient profiles.

  • First up, unsupervised learning.

  • This approach would be ideal for analyzing all the profiles

  • to find general similarities and useful patterns.

  • Maybe certain patients have similar disease presentations,

  • or perhaps a treatment produces specific sets of side effects.

  • This broad pattern-seeking approach can be used to identify similarities

  • between patient profiles and find emerging patterns,

  • all without human guidance.

  • But let's imagine doctors are looking for something more specific.

  • These physicians want to create an algorithm

  • for diagnosing a particular condition.

  • They begin by collecting two sets of data

  • medical images and test results from both healthy patients

  • and those diagnosed with the condition.

  • Then, they input this data into a program

  • designed to identify features shared by the sick patients

  • but not the healthy patients.

  • Based on how frequently it sees certain features,

  • the program will assign values to those features' diagnostic significance,

  • generating an algorithm for diagnosing future patients.

  • However, unlike unsupervised learning,

  • doctors and computer scientists have an active role in what happens next.

  • Doctors will make the final diagnosis

  • and check the accuracy of the algorithm's prediction.

  • Then computer scientists can use the updated datasets

  • to adjust the program's parameters and improve its accuracy.

  • This hands-on approach is called supervised learning.

  • Now, let's say these doctors want to design another algorithm

  • to recommend treatment plans.

  • Since these plans will be implemented in stages,

  • and they may change depending on each individual's response to treatments,

  • the doctors decide to use reinforcement learning.

  • This program uses an iterative approach to gather feedback

  • about which medications, dosages and treatments are most effective.

  • Then, it compares that data against each patient's profile

  • to create their unique, optimal treatment plan.

  • As the treatments progress and the program receives more feedback,

  • it can constantly update the plan for each patient.

  • None of these three techniques are inherently smarter than any other.

  • While some require more or less human intervention,

  • they all have their own strengths and weaknesses

  • which makes them best suited for certain tasks.

  • However, by using them together,

  • researchers can build complex AI systems,

  • where individual programs can supervise and teach each other.

  • For example, when our unsupervised learning program

  • finds groups of patients that are similar,

  • it could send that data to a connected supervised learning program.

  • That program could then incorporate this information into its predictions.

  • Or perhaps dozens of reinforcement learning programs

  • might simulate potential patient outcomes

  • to collect feedback about different treatment plans.

  • There are numerous ways to create these machine-learning systems,

  • and perhaps the most promising models

  • are those that mimic the relationship between neurons in the brain.

  • These artificial neural networks can use millions of connections

  • to tackle difficult tasks like image recognition, speech recognition,

  • and even language translation.

  • However, the more self-directed these models become,

  • the harder it is for computer scientists

  • to determine how these self-taught algorithms arrive at their solution.

  • Researchers are already looking at ways to make machine learning more transparent.

  • But as AI becomes more involved in our everyday lives,

  • these enigmatic decisions have increasingly large impacts

  • on our work, health, and safety.

  • So as machines continue learning to investigate, negotiate and communicate,

  • we must also consider how to teach them to teach each other to operate ethically.

Today, artificial intelligence helps doctors diagnose patients,

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B1 TED-Ed learning program patient algorithm artificial

How does artificial intelligence learn? - Briana Brownell

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    林宜悉 posted on 2021/03/11
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