Artificial intelligence

Introduction

Artificial intelligence is the study of intelligent behavior. It spans many disciplines, including computer science, neuroscience and psychology. Artificial intelligence was first defined in 1956 by American computer scientist John McCarthy who wrote "every program one writes must be either an algorithm or a machine."

Definition

Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages. The first AI program was created at Dartmouth College in 1956 by John McCarthy who called it "the Dartmouth" after his alma mater.

Development of the field

Artificial intelligence has been around for decades, but it wasn't until recently that people started to think about how computers could be used to do things that people couldn't do. For example, if you had an idea for a new product or service, you might want to test it out by creating several different versions of the same thing—you could see what would work best in different environments and with different customers! But this means having lots of different versions of things: each version needs its own hardware and software; each version costs money; each version isn't exactly the same as any other version...and so on (and so on).

It turns out that there are some problems which aren't solvable by pure computation alone: these kinds of problems require both computation (the thinking part) AND knowledge (the remembering part). This means that even though computers have gotten really good at solving certain types of mathematical problems over time—like those related specifically toward decision-making—they haven't quite reached their full potential yet because there's still so much more knowledge we could use from nature itself!

The scope of AI is disputed

Artificial intelligence (AI) is a broad field, but it's not all encompassing. AI is a subset of computer science and machine learning, which are themselves subsets of data science. A few examples of other types of AI include human-computer interaction (HCI), natural language processing (NLP), emotion recognition, moral reasoning and planning algorithms.

Applications for AI

AI has been used to help with speech recognition, robotics and self-driving cars. It's also being used in medical research and computer security. And it can be used in finance and education too!

AI is a powerful tool that can be applied across many industries, including health care, law enforcement, retail sales or even engineering design.

History

Artificial intelligence (AI) research started in the 1950s, when computer scientists and mathematicians began to study how computers could be programmed to perform tasks that people could not do. In order to get these machines to learn on their own, they needed a way for the computer's memory and processing power to grow over time. This led them down a path where they could teach computers about different subjects by giving them input data about those subjects—and then allowing the system itself to figure out what answers it should give based on its own experiences with those inputs.

While this may sound like an exciting breakthrough at first glance, many early AI projects were designed with military applications in mind: they wanted soldiers' brains wired up so they'd better understand what was going on around them during battle situations!

Characteristics of AI systems

Artificial intelligence systems are designed to solve problems and make decisions. They're also designed to learn from their mistakes, improve over time, work in a variety of situations and be autonomous.

AI systems can be as complex as you want them to be: they can understand natural language (like English or Chinese), remember information about objects or people's preferences based on what they've seen before, recognize images through voice commands or facial expressions...the list goes on!

Components of an intelligent agent

An intelligent agent is an autonomous computer program that can learn, reason, and act autonomously. It must also be able to sense its environment and communicate with other agents.

An intelligent agent's behavior is determined by its knowledge of the world and how it interacts with it. The ability to learn means that an agent can modify its behavior based on past experiences, which allows it to improve over time as new information becomes available or as old information is forgotten due to lack of use (i.e., forgetting). This type of learning is called supervised learning because you tell your robot what things are so that it knows what actions should be taken when faced with them (such as "go left if there's a wall"). Unsupervised learning does not have this restriction; instead you give no instructions about what actions should be taken next time around—for example: if I'm walking down the street at night without knowing where I am going then there isn't much benefit from giving my robot any instructions ahead of time because everything could change between now and when we reach our destination!

Natural language processing

Natural language processing is the process of understanding and analyzing natural language. Natural languages are spoken, written, or otherwise communicated by humans.

The first step in natural language processing is text analysis. Text analysis involves breaking down text into individual words and phrases so that they can be understood by computers. This allows us to search for specific terms within a document or extract information from it (such as who wrote or spoke it).

Natural language understanding is the next step after text analysis; it describes how computer programs can understand what people mean when they speak or write sentences in their native languages (such as English). Natural-language processing systems often use machine learning techniques such as deep learning neural networks to analyze human speech patterns more accurately than humans do themselves!

Learning and adaptation

Learning and adaptation are two of the most important concepts in AI. Learning algorithms allow an AI system to change its behavior based on experience, while adaptive systems learn how to adapt to new situations under their control.

Neural networks: An artificial neural network is a network of simple processors that use a set of simple rules to solve problems by taking into account multiple variables at once. They're used everywhere from computers and smartphones (like Siri) to self-driving cars (Google's TensorFlow). Neural networks are very complex; they have hundreds or thousands of processing steps per second! But they're also very powerful because they can perform certain tasks very quickly—fast enough for humans who need them right now (like your phone) but slow enough that humans haven't yet figured out how best use them yet either.* Machine learning: Machine learning algorithms use large amounts of data collected from real world examples along with some known patterns within said data set in order find new solutions using techniques like clustering algorithms which identify similar patterns between different instances in order predict future outcomes based upon past experiences.* Deep learning*: Deep learning uses more advanced methods than traditional machine learning methods such as supervised versus unsupervised learning which allows developers access more accurate results without having access simply identifying features like age gender income level education etc..

Robotics and automation

Robotics and automation are two of the most important things to know about AI. In fact, you’ll see examples of both throughout this article.

Robotics are machines that accomplish tasks by using artificial intelligence (AI). A robot can be anything from a vacuum cleaner to an autonomous car, but they all have one thing in common: they move around without human input or supervision. They’re also known as “automation tools” because they automate certain processes at work—like making coffee in the morning! Nowadays robots do more than just make coffee; some even have their own personalities!

Robots have been used for decades now with great success by manufacturers like Ford Motor Company who use them for assembly line production lines where workers can focus on specific tasks while still getting paid well enough so they don't complain too much about being replaced by machines yet again during this economy crisis...

Computer vision

In other words, it's the process of creating and analyzing representations of objects in digital form. Computer vision can be applied to any kind of scene, whether it's something as simple as determining how many people are in an image or as complex as identifying objects based on their appearance (such as cars and faces).

Computer vision has been used for many applications over time: medical imaging, driver assistance systems like adaptive cruise control, robotics—the list goes on! It's also part of artificial intelligence (AI) research because we use computers to analyze images and then make decisions based on those analyses; this process is called machine learning.

Expert system

An expert system is a computer program that emulates the decision making process of a human expert. It does this by using the rules and if-then statements in its programming language to solve problems that are too complex for a human to solve.

Expert systems have been used in many fields, including military planning, business management and even medical diagnosis. The earliest examples were based on logic and mathematics rather than natural language processing (NLP) or machine learning techniques such as deep neural networks (DNNs).

Social impact of AI research

AI has the potential to help solve many social problems. For example, it can be used to improve healthcare by better diagnosing and treating patients. It can also be utilized in education, agriculture and transportation—all of which are struggling areas with a high need for resources.

Other fields that stand to benefit from AI research include energy efficiency (where humans often make mistakes), cybersecurity (where hackers are increasingly able to gain access) and even environmental monitoring (which is useful when knowing how much pollution is being produced).

Example systems and applications

Self-driving cars.
Automated trading.
Machine translation.
Speech recognition, synthesis and understanding (including automated language processing).

In addition to these applications in the field of computer science and engineering, there are also many applications in other fields such as robotics (automated reasoning), expert systems (automated knowledge discovery), machine vision or machine learning related to natural language processing or speech synthesis/understanding