How does artificial intelligence learn? - Briana Brownell
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.
