What is Artificial Intelligence?
Learn what artificial intelligence (AI) is, how it works, its types, examples, and benefits.
It’s the capability of a computer system to mimic human-like cognitive functions such as learning and problem-solving.
How does artificial intelligence (AI) work?
Using math and logic, a computer system simulates the reasoning that humans use to learn from new information and make decisions.
An artificially intelligent computer system makes predictions or takes actions based on patterns in existing data and can then learn from its errors to increase its accuracy. A mature AI processes new information extremely quickly and accurately, which makes it useful for complex scenarios such as self-driving cars, image recognition programs, and virtual assistants.
How AI relates to machine learning
Machine learning is considered a subset of AI. Machine learning focuses on training machines to analyze and learn from data the way humans do. Therefore, machine learning is a technique that helps develop AI systems.
How AI relates to cognitive APIs
APIs—application programming interfaces—connect applications to other systems, services, or applications. When you use cognitive APIs, you’re requesting access to a library of domain-specific intelligent models.
How AI relates to data science
AI and data science both involve gathering, analyzing, and collecting large data sets—but they have different goals. AI focuses on how computers can make decisions based on data. Data science, on the other hand, focuses on the use of mathematics, statistics, and machine learning to extract insights from data.
How AI relates to robotics
A robot typically has both a physical form and the software that controls it. Robots that are controlled by AI software move autonomously—they don’t need direct instruction from a human. But not all robots are controlled by AI, and not all AI requires a physical form.
Types of artificial intelligence
Artificial narrow intelligence (Narrow AI)
Artificial narrow intelligence—sometimes called “weak AI”—refers to the ability of a computer system to perform a narrowly defined task better than a human can.
Narrow AI is the highest level of AI development that humanity has reached so far, and every example of AI that you see in the real world falls into this category—including autonomous vehicles and personal digital assistants. That’s because even when it seems like AI is thinking for itself in real time, it’s actually coordinating several narrow processes and making decisions within a pre-determined framework. The AI’s “thinking” doesn’t involve consciousness or emotion.
Artificial general intelligence (General AI)
Artificial general intelligence—sometimes called “strong AI” or “human-level AI”—refers to the ability of a computer system to outperform humans in any intellectual task. It’s the type of AI that you see in movies where robots have conscious thoughts and act on their own motives.
In theory, a computer system that has achieved general AI would be able to solve deeply complex problems, apply judgment in uncertain situations, and incorporate prior knowledge into its current reasoning. It would be capable of creativity and imagination on par with humans and could take on a far wider range of tasks than narrow AI.
Artificial super intelligence (ASI)
A computer system that has achieved artificial super intelligence would have the ability to outperform humans in almost every field, including scientific creativity, general wisdom, and social skills.
Machine learning
Machine learning is a process that computer systems follow to achieve artificial intelligence. It uses algorithms to identify patterns within data, and those patterns are then used to create a data model that can make predictions.
Machine-learning models are trained on subsets of data. When the data that’s used to train the model accurately represents the full dataset that will be analyzed, the algorithm calculates more accurate results. When the machine learning model has been trained well enough to perform its task quickly and accurately enough to be useful and trustworthy, it’s achieved narrow AI.
Deep learning
Deep learning is an advanced type of machine learning that uses networks of algorithms that are inspired by the structure of the brain, known as neural networks. A deep neural network has nested neural nodes, and each question that it answers leads to a set of related questions.
Deep learning typically requires a large data set to train on; training sets for deep learning are sometimes made up of millions of data points. After a deep neural network has been trained on these large data sets, it can handle more ambiguity than a shallow network. That makes it useful for applications like image recognition, where the AI needs to find the edges of a shape before it can identify what’s in the image. Deep learning is also what trains AI that surpasses human skill in complex games like chess.
Examples of artificial intelligence
- Self-driving cars: Use computer vision and deep learning to navigate.
- Bots and digital assistants: Use NLP to understand and respond to users.
- Recommendation engines: Analyze user behavior to suggest products or content.
- Spam filters: Use machine learning to identify and block spam emails.
- Smart home technology: Learns user habits to optimize energy usage.
- Health data analysis: Identifies patterns in medical data to aid diagnosis.
Benefits of artificial intelligence
- Around-the-clock availability: AI systems don't need sleep and can work 24/7.
- Communication at scale: Chatbots can handle thousands of inquiries simultaneously.
- Automation of repetitive tasks: Frees up humans for more creative work.
- Faster, more accurate decisions: AI can process data much faster than humans.
- More relevant recommendations: Personalizes user experiences.