Machine Learning: Unsupervised, Supervised, and Reinforcement Approaches

Artificial intelligence employs unsupervised, supervised, and reinforcement learning to self-teach and perform various tasks, raising the need for ethical considerations in its development.

### Understanding the Basics and Future of Machine Learning
* 🧠 Artificial intelligence is self-taught and often operates without the designers' exact knowledge of its processes, relying on a **simple set of instructions** to create **unique rules and strategies**.
* 🎓 Machine learning encompasses three basic types: **unsupervised learning**, **supervised learning**, and **reinforcement learning**, each suited for different tasks and requiring **varying levels of human intervention**.
* 🔍 In unsupervised learning, machines analyze data to identify **general similarities and patterns** without human guidance.
* 📊 Supervised learning involves creating algorithms based on labeled data, with doctors and computer scientists actively involved in the process of **refining and updating the program**.
* 🔄 Reinforcement learning uses an iterative approach to gather feedback and create **personalized treatment plans** based on **individual responses**.
* By combining these techniques in complex AI systems, researchers can develop programs that can **supervise and teach each other**.
* 🧠 The most promising models mimic the brain's neural networks, using **millions of connections** to perform tasks like **image recognition** and **language translation**.
* 🔒 As AI's decisions increasingly impact various aspects of life, efforts are underway to make machine learning **more transparent and ethical**.

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.

How does artificial intelligence learn? - Briana Brownell

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TED-Ed

Artificial intelligence operates in mysterious ways, often without the full understanding of its creators.

Machine learning relies on unsupervised, supervised, and reinforcement learning methods to teach AI.

Unsupervised learning is used to identify general patterns and similarities in data, while supervised learning involves creating specific algorithms based on labeled data.

In contrast, reinforcement learning requires active human intervention to make and verify decisions based on the AI's outputs.

The Role of Different Learning Approaches in AI

Supervised learning is used to adjust program parameters and improve accuracy, while reinforcement learning is employed to design treatment plans that can be constantly updated based on patient feedback.

By using these techniques together, researchers can build complex AI systems where programs supervise and teach each other.

The most promising models for creating machine-learning systems are those that mimic the relationship between neurons in the brain, but the transparency of self-directed algorithms remains a challenge for computer scientists.

Enigmatic decisions are significantly affecting work, health, and safety.

Machines need to be taught to investigate, negotiate, and communicate ethically, as well as to teach each other to operate ethically.

This is crucial as machine learning continues to advance.