Artificial intelligence

Smart machines can do many things.

They can learn new tasks. They can solve hard puzzles. Some machines even help cars drive.

These machines use special tools to learn. They can look at things to see them. They can also listen to sounds. Some can even talk to you. They can make art or write stories. This makes our lives easier every day. Smart machines are very helpful tools.

Artificial intelligence, or AI, is a way to make machines smart.

These machines can do tasks that usually need human brains. They can learn, solve problems, and make choices. AI helps us in many ways every day. It powers web searches and helps movies be suggested to you. Some AI can even drive cars or talk to you.

Scientists study many ways to make AI work. One way is called machine learning. This is when programs get better at tasks by themselves.

In supervised learning, the computer is given the right answers to learn from. In unsupervised learning, the computer finds patterns on its own. There is also deep learning. This uses artificial neural networks, which are parts that work like a brain.

Researchers also work on natural language processing. This helps computers read, write, and talk in human languages. Some new tools can even make art or write stories. Scientists want to make AI that can do almost any task a human can do. We call this artificial general intelligence.

People are also studying how to keep AI safe and fair.

Artificial intelligence, or AI, is a way to make computers perform smart tasks.

These tasks usually need a human brain to finish them. This includes things like learning, solving puzzles, and making decisions. AI research uses math and computer science to build these tools. The goal is to help machines understand their world. They can then take actions to reach a specific goal. This technology is already all around us in our daily lives.

One main way AI works is through machine learning.

This is the study of programs that improve by themselves. In supervised learning, the computer uses data that has the right answers. In unsupervised learning, the computer looks for patterns on its own. There is also a type called deep learning. This uses artificial neural networks to process information. These networks are inspired by how biological brains work.

Researchers have worked on AI for a long time. The field started as a way of studying in 1956. Since then, it has gone through many ups and downs. Sometimes people were very excited about new progress. Other times, funding was lost during periods called AI winters. Interest grew a lot after the year 2012. This happened because new computer parts helped neural networks run faster. Then, a new design called the transformer architecture arrived in 2017.

Today, AI can do many impressive things.

It powers web searches and suggests movies on Netflix. Some AI can even drive cars or play games like chess. In the 2020s, generative AI became very popular. This allows machines to create new art or text. Some companies like Google DeepMind and Meta want to reach a new goal. They want to create artificial general intelligence, or AGI. This would be an AI that can do almost any task a human can.

Because AI is so powerful, people have many questions about it.

Scientists study AI safety to prevent unintended harms. There are also many discussions about ethics and rules. People want to know how AI will affect our world in the long run. Some worry about the risks that very smart machines might bring. It is important to make sure these tools are used in a good way. This helps everyone stay safe as the technology grows.

Artificial intelligence, or AI, is the capability of computational systems to perform complex tasks.

These tasks are typically associated with human intelligence. Examples include learning, reasoning, problem-solving, perception, and decision-making. AI is a massive field of research. It combines engineering, mathematics, and computer science. Researchers develop software that allows machines to perceive their environment. These machines use learning to take actions that maximize their chances of achieving specific goals. AI is no longer just a theoretical concept. It is a fundamental part of modern technology.

To understand how AI works, we must look at its different subproblems. One major area is reasoning and problem-solving. Early researchers built algorithms to imitate step-by-step human reasoning. These were used for puzzles and logical deductions. By the late 1980s, scientists developed ways to handle uncertain information. They used concepts from probability and economics to solve these problems. Another core area is knowledge representation. This involves creating a knowledge base, which is a body of information a program can use.

Researchers use an ontology to represent knowledge. An ontology is a set of concepts and relationships within a specific domain. This allows AI to answer questions and make deductions about the real world.

Another vital component is planning and decision-making. In this context, an "agent" is any entity that perceives and acts in the world. A rational agent has specific goals or preferences. In automated planning, the agent works toward a specific goal. In automated decision-making, the agent manages preferences. It assigns a number, called "utility," to different situations. The agent calculates the expected utility for every possible action. It then chooses the action with the highest expected utility. In the real world, things are often not deterministic. This means the agent cannot be certain about the outcome of an action. It must make probabilistic guesses and then reassess the situation.

Machine learning is perhaps the most famous way to achieve intelligence.

It is the study of programs that improve their performance automatically. There are three main types of machine learning. Supervised learning uses training data that is labeled with the correct answers. This includes classification and regression. Unsupervised learning analyzes data to find patterns without any guidance. Reinforcement learning involves an agent that is rewarded for good responses and punished for bad ones. Finally, there is deep learning. This uses artificial neural networks to process inputs. These networks are biologically inspired by the human brain.

Natural language processing, or NLP, is another specialized field. It allows programs to read, write, and communicate in human languages. NLP includes tasks like speech recognition and machine translation. Early work in NLP struggled with word-sense disambiguation. This was due to the difficulty of teaching machines common sense. Modern NLP uses deep learning and the transformer architecture. This architecture uses an attention mechanism to process language. In 2019, generative pre-trained transformer (GPT) models began to produce coherent text. By 2023, these models achieved human-level scores on tests like the SAT and the bar exam.

The history of AI is a series of cycles. It was founded as an academic discipline in 1956. The field has experienced many periods of great optimism. However, it also faced periods of disappointment and lost funding. These low points are known as "AI winters." Interest and funding increased significantly after 2012. This was because graphics processing units helped accelerate neural networks. Deep learning began to outperform older techniques during this time. The arrival of the transformer architecture in 2017 accelerated this growth even further. In the 2020s, we entered an AI boom driven by generative AI.

Today, AI is used in many high-profile ways. You see it in advanced web search engines like Google Search. Recommendation systems on YouTube, Amazon, and Netflix use AI. Virtual assistants like Siri and Alexa are common examples. Autonomous vehicles, such as Waymo, use AI to navigate. Some companies, like OpenAI, Google DeepMind, and Meta, seek to create artificial general intelligence (AGI). AGI would be an AI capable of completing virtually any cognitive task as well as a human.

Because of this power, researchers also focus on AI safety. They discuss ethics, unintended harms, and potential existential risks. This has led to global discussions about AI regulation.