An overview of Artificial Intelligence
Artificial intelligence (AI) has become ubiquitous; from product and movie recommendations on Amazon and Netflix to friend suggestions on Facebook or autofill in search on Google, AI has permeated our daily lives. But what exactly is AI, and how do we design for it?
AI typically refers to human-imitative intelligence; the simulation of human thought processes, such as learning or problem solving in a computerized model. AI systems are often used to recognize patterns, make predictions, and provide insights from large amounts of data.
Some common use cases of AI include:
Extracting information from pictures
Natural Language Processing
Pulling insights and patterns out of written text
Autonomously moving through spaces based on sensory input
Looking for patterns in data
AI can be used to augment or automate workflows. Automation can refer to the full or partial replacement of a function previously carried out by a human operator, and as such, can be thought of as a continuum of levels, from the lowest level of fully manual performance to the highest level of full automation:
Levels of Automation of Decision and Action Selection
The computer decides everything, acts autonomously, ignoring the human
The computer informs the human only if it, the computer, decides to
The computer informs the human only if asked
The computer executes automatically, then necessarily informs the human
The computer allows the human a restricted time to veto before automatic execution
The computer executes a suggestion if the human approves
The computer suggests one alternative
The computer narrows the selection down to a few
The computer offers a complete set of decision/action alternatives
The computer offers no assistance: human must take all decisions and actions
It is important to understand (1) whether to automate or augment a task, and (2) if automating, how much to automate in order to design successful human-AI interactions.
How AI Works
There are various ways to implement AI, but at a high level, there are two approaches:
The rules-based approach uses algorithms, a process or set of instructions that a computer uses for calculations or problem-solving.
The examples-based approach uses data to create models. These models are the result of training an AI on data to find patterns.
Examples-based approaches are promising in areas wherein specifying a sequence of rigid rules is difficult, such as diagnosing a disease or recommending a video for someone to watch. This way of problem solving is largely made possible by machine learning.
Machine learning (ML) is a subset of AI that uses an examples-based approach to get an AI to accomplish tasks without being given specific instructions, which is especially beneficial when data has several different variables.
ML uses different ways to teach a machine how to learn:
Supervised learning requires labeled data. In other words, it utilizes data grouped into samples that have been tagged with one or more labels. Supervised learning typically uses classification when we want to predict a thing or regression when we want to predict a number. It is effective when there is a clear output in mind (e.g., “is this plant safe or poisonous”).
Unsupervised learning finds commonalities and patterns in the input data on its own, without using labeled data. It typically uses clustering, which is the grouping of data by some set of characteristics or features. It is effective when there is an unclear output in mind.
Reinforcement learning rewards positive behavior and punishes negative behavior, which over time, can help the AI determine the optimal behavior for a particular environment or situation. It is effective when the AI is not provided with historical data and instead learns from the environment, collecting and learning from data real-time (e.g., self-driving cars).
AI-Assisted Decision Making
Decision support/augmentation is one of the most common AI applications. A closer look reveals the wide range of possible applications of AI-assisted decision making, many of which involve high-stakes scenarios and a need for explainability.
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