Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

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How does AI work?

As the hype around AI has accelerated, vendors have been scrambling lớn promote how their products và services use AI. Often what they refer to lớn as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing và training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R & Java, are popular.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, & using these patterns khổng lồ make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to lớn produce lifelike exchanges with people, or an image recognition tool can learn to identify & describe objects in images by reviewing millions of examples.

AI programming focuses on three cognitive skills: learning, reasoning and self-correction.

Learning processes. This aspect of AI programming focuses on acquiring data & creating rules for how lớn turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how lớn complete a specific task.


This article is part of

A guide to artificial intelligence in the enterprise

Which also includes:

Why is artificial intelligence important?

AI is important because it can give enterprises insights into their operations that they may not have been aware of previously và because, in some cases, AI can perform tasks better than humans. Particularly when it comes to lớn repetitive, detail-oriented tasks like analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly và with relatively few errors.

This has helped fuel an explosion in efficiency and opened the door khổng lồ entirely new business opportunities for some larger enterprises. Prior lớn the current wave of AI, it would have been hard to imagine using computer software to connect riders to lớn taxis, but today Uber has become one of the largest companies in the world by doing just that. It utilizes sophisticated machine learning algorithms lớn predict when people are likely to lớn need rides in certain areas, which helps proactively get drivers on the road before they"re needed. As another example, Google has become one of the largest players for a range of online services by using machine learning to lớn understand how people use their services & then improving them. In 2017, the company"s CEO, Sundar Pichai, pronounced that Google would operate as an "AI first" company.

Today"s largest & most successful enterprises have used AI to improve their operations and gain advantage on their competitors.

What are the advantages and disadvantages of artificial intelligence?

Artificial neural networks và deep learning artificial intelligence technologies are quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions more accurately than humanly possible.

While the huge volume of data being created on a daily basis would bury a human researcher, AI applications that use machine learning can take that data and quickly turn it into actionable information. As of this writing, the primary disadvantage of using AI is that it is expensive lớn process the large amounts of data that AI programming requires.

Advantages

Good at detail-oriented jobs; Reduced time for data-heavy tasks; Delivers consistent results; and AI-powered virtual agents are always available.

Disadvantages

Expensive; Requires deep technical expertise; Limited supply of qualified workers khổng lồ build AI tools; Only knows what it"s been shown; and Lack of ability lớn generalize from one task to another.

Strong AI vs. Weak AI

AI can be categorized as either weak or strong.

Weak AI, also known as narrow AI, is an AI system that is designed và trained to lớn complete a specific task. Industrial robots & virtual personal assistants, such as Apple"s Siri, use weak AI.

What are the 4 types of artificial intelligence?

Arend Hintze, an assistant professor of integrative biology & computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to lớn sentient systems, which vị not yet exist. The categories are as follows:

Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able lớn infer human intentions & predict behavior, a necessary skill for AI systems khổng lồ become integral members of human teams. Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
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What are examples of AI technology và how is it used today?

AI is incorporated into a variety of different types of technology. Here are six examples:

Machine learning. This is the science of getting a computer to lớn act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line và text of an e-mail and decides if it"s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis & speech recognition. Robotics. This field of engineering focuses on the design và manufacturing of robots. Robots are often used to lớn perform tasks that are difficult for humans to lớn perform or perform consistently. For example, robots are used in assembly lines for oto production or by NASA khổng lồ move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
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AI is not just one technology.

What are the applications of AI?

Artificial intelligence has made its way into a wide variety of markets. Here are nine examples.

AI in healthcare. The biggest bets are on improving patient outcomes và reducing costs. Companies are applying machine learning khổng lồ make better và faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language và can respond lớn questions asked of it. The system mines patient data and other available data sources to size a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots lớn help patients & healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to lớn predict, fight and understand pandemics such as COVID-19.

AI in business. Machine learning algorithms are being integrated into analytics & customer relationship management (CRM) platforms lớn uncover information on how to lớn better serve customers. Chatbots have been incorporated into websites to provide immediate service khổng lồ customers. Automation of job positions has also become a talking point among academics và IT analysts.

AI in education. AI can automate grading, giving educators more time. It can assess students & adapt lớn their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. And it could change where and how students learn, perhaps even replacing some teachers.

AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data và provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street.


AI in law. The discovery process -- sifting through documents -- in law is often overwhelming for humans. Using AI to help automate the legal industry"s labor-intensive processes is saving time và improving client service. Law firms are using machine learning khổng lồ describe data và predict outcomes, computer vision to classify and extract information from documents và natural language processing khổng lồ interpret requests for information.

AI in manufacturing.

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Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed khổng lồ perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans và take on responsibility for more parts of the job in warehouses, factory floors & other workspaces.

AI in banking. Banks are successfully employing chatbots to make their customers aware of services và offerings and to handle transactions that don"t require human intervention. AI virtual assistants are being used lớn improve & cut the costs of compliance with banking regulations. Banking organizations are also using AI lớn improve their decision-making for loans, và to set credit limits and identify investment opportunities.

AI in transportation. In addition khổng lồ AI"s fundamental role in operating autonomous vehicles, AI technologies are used in transportation khổng lồ manage traffic, predict flight delays, and make ocean shipping safer and more efficient.

Security. AI & machine learning are at the đứng top of the buzzword list security vendors use today to lớn differentiate their offerings. Those terms also represent truly viable technologies. Organizations use machine learning in security information and event management (SIEM) software and related areas khổng lồ detect anomalies and identify suspicious activities that indicate threats. By analyzing data và using xúc tích to identify similarities khổng lồ known malicious code, AI can provide alerts to lớn new & emerging attacks much sooner than human employees & previous công nghệ iterations. The maturing công nghệ is playing a big role in helping organizations fight off cyber attacks.

Augmented intelligence vs. Artificial intelligence

Some industry experts believe the term artificial intelligence is too closely linked khổng lồ popular culture, and this has caused the general public to have improbable expectations about how AI will change the workplace và life in general.

Ethical use of artificial intelligence

While AI tools present a range of new functionality for businesses, the use of artificial intelligence also raises ethical questions because, for better or worse, an AI system will reinforce what it has already learned.

This can be problematic because machine learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human being selects what data is used lớn train an AI program, the potential for machine learning bias is inherent & must be monitored closely.

Anyone looking to use machine learning as part of real-world, in-production systems needs to lớn factor ethics into their AI training processes và strive khổng lồ avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning & generative adversarial network (GAN) applications.

Explainability is a potential stumbling block to lớn using AI in industries that operate under strict regulatory compliance requirements. For example, financial institutions in the United States operate under regulations that require them khổng lồ explain their credit-issuing decisions. When a decision to refuse credit is made by AI programming, however, it can be difficult lớn explain how the decision was arrived at because the AI tools used lớn make such decisions operate by teasing out subtle correlations between thousands of variables. When the decision-making process cannot be explained, the program may be referred lớn as black box AI.

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These components 3d responsible AI use.

Despite potential risks, there are currently few regulations governing the use of AI tools, và where laws vì chưng exist, they typically pertain to AI indirectly. For example, as previously mentioned, United States Fair Lending regulations require financial institutions lớn explain credit decisions khổng lồ potential customers. This limits the extent to which lenders can use deep learning algorithms, which by their nature are opaque & lack explainability.

The European Union"s General Data Protection Regulation (GDPR) puts strict limits on how enterprises can use consumer data, which impedes the training và functionality of many consumer-facing AI applications.

In October 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered.

Crafting laws khổng lồ regulate AI will not be easy, in part because AI comprises a variety of technologies that companies use for different ends, & partly because regulations can come at the cost of AI progress and development. The rapid evolution of AI technologies is another obstacle khổng lồ forming meaningful regulation of AI. Công nghệ breakthroughs & novel applications can make existing laws instantly obsolete. For example, existing laws regulating the privacy of conversations and recorded conversations bởi vì not cover the challenge posed by voice assistants lượt thích Amazon"s Alexa và Apple"s Siri that gather but vị not distribute conversation -- except to lớn the companies" technology teams which use it lớn improve machine learning algorithms. And, of course, the laws that governments vì chưng manage khổng lồ craft lớn regulate AI don"t stop criminals from using the giải pháp công nghệ with malicious intent.

Cognitive computing & AI

The terms AI & cognitive computing are sometimes used interchangeably, but, generally speaking, the label AI is used in reference to lớn machines that replace human intelligence by simulating how we sense, learn, process and react to lớn information in the environment.

The label cognitive computing is used in reference to products and services that mimic and augment human thought processes.

What is the history of AI?

The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull lớn René Descartes & Thomas Bayes used the tools and súc tích of their times khổng lồ describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation.

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tư vấn for the modern field of AI, 1956 khổng lồ the present.

The late 19th and first half of the 20th centuries brought forth the foundational work that would give rise to the modern computer. In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada Byron, Countess of Lovelace, invented the first thiết kế for a programmable machine.

1940s. Princeton mathematician John Von Neumann conceived the architecture for the stored-program computer -- the idea that a computer"s program & the data it processes can be kept in the computer"s memory. And Warren McCulloch & Walter Pitts laid the foundation for neural networks.

1950s. With the advent of modern computers, scientists could kiểm tra their ideas about machine intelligence. One method for determining whether a computer has intelligence was devised by the British mathematician và World War II code-breaker Alan Turing. The Turing demo focused on a computer"s ability to fool interrogators into believing its responses to lớn their questions were made by a human being.

1956. The modern field of artificial intelligence is widely cited as starting this year during a summer conference at Dartmouth College. Sponsored by the Defense Advanced Research Projects Agency (DARPA), the conference was attended by 10 luminaries in the field, including AI pioneers Marvin Minsky, Oliver Selfridge & John McCarthy, who is credited with coining the term artificial intelligence. Also in attendance were Allen Newell, a computer scientist, & Herbert A. Simon, an economist, political scientist and cognitive psychologist, who presented their groundbreaking ngắn gọn xúc tích Theorist, a computer program capable of proving certain mathematical theorems & referred to as the first AI program.

1950s và 1960s. In the wake of the Dartmouth College conference, leaders in the fledgling field of AI predicted that a man-made intelligence equivalent to the human brain was around the corner, attracting major government and industry support. Indeed, nearly 20 years of well-funded basic research generated significant advances in AI: For example, in the late 1950s, Newell & Simon published the General Problem Solver (GPS) algorithm, which fell short of solving complex problems but laid the foundations for developing more sophisticated cognitive architectures; McCarthy developed Lisp, a language for AI programming that is still used today. In the mid-1960s MIT Professor Joseph Weizenbaum developed ELIZA, an early natural language processing program that laid the foundation for today"s chatbots.

1970s and 1980s. But the achievement of artificial general intelligence proved elusive, not imminent, hampered by limitations in computer processing & memory and by the complexity of the problem. Government & corporations backed away from their support of AI research, leading khổng lồ a fallow period lasting from 1974 lớn 1980 và known as the first "AI Winter." In the 1980s, research on deep learning techniques and industry"s adoption of Edward Feigenbaum"s expert systems sparked a new wave of AI enthusiasm, only lớn be followed by another collapse of government funding and industry support. The second AI winter lasted until the mid-1990s.

1990s through today. Increases in computational power and an explosion of data sparked an AI renaissance in the late 1990s that has continued khổng lồ present times. The latest focus on AI has given rise to lớn breakthroughs in natural language processing, computer vision, robotics, machine learning, deep learning and more. Moreover, AI is becoming ever more tangible, powering cars, diagnosing disease & cementing its role in popular culture. In 1997, IBM"s Deep blue defeated Russian chess grandmaster Garry Kasparov, becoming the first computer program to beat a world chess champion. Fourteen years later, IBM"s Watson captivated the public when it defeated two former champions on the trò chơi Jeopardy!. More recently, the historic defeat of 18-time World Go champion Lee Sedol by Google DeepMind"s AlphaGo stunned the Go community and marked a major milestone in the development of intelligent machines.

AI as a service

Because hardware, software & staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings or providing access khổng lồ artificial intelligence as a service (AIaaS) platforms. AIaaS allows individuals và companies to lớn experiment with AI for various business purposes và sample multiple platforms before making a commitment.