what is artificial intelligence with examples:LEARN NOW AND SHINE NOW

what is artificial intelligence with examples

what is artificial intelligence with examples-Here we will talk about what is artificial intelligence with examples. It is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.

AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). 

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.

HISTORY: what is artificial intelligence with examples

what is artificial intelligence with examples-Artificial beings with intelligence appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel Čapek’s R.U.R. These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction.

This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis.This, along with concurrent discoveries in neurobiology, information theory and cybernetics, led researchers to consider the possibility of building an electronic brain. The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

EARLY 1950’S

what is artificial intelligence with examples-By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky.

Closely associated with this approach was the “heuristic search” approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons.

James Manyika and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of Descarte, Boole, Gottlob Frege, Bertrand Russell, and others.

Connectionist approaches based on cybernetics or artificial neural networks were pushed to the background but have gained new prominence in recent decades.

EARLY 1960’S

Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”.Marvin Minsky agreed, writing, “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”.They had failed to recognize the difficulty of some of the remaining tasks.

Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”, a period when obtaining funding for AI projects was difficult.

EARLY 1980’S

what is artificial intelligence with examples-In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.

GOALS: what is artificial intelligence with examples

The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.


what is artificial intelligence with examples-Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.

KNOWLEDGE REPRESENTATION:what is artificial intelligence with examples


Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts.

A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). A truly intelligent program would also need access to commonsense knowledge; the set of facts that an average person knows. The semantics of an ontology is typically represented in description logic, such as the Web Ontology Language.



AI research has developed tools to represent specific domains, such as objects, properties, categories, and relations between objects; situations, events, states, and, time; causes and effects; knowledge about knowledge (what we know about what other people know);.default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); as well as other domains.

Among the most difficult problems in AI are: the breadth of commonsense knowledge (the number of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as “facts” or “statements” that they could express verbally).



Machine learning (ML), a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience.

Unsupervised learning finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs.

Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. 

In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.Transfer learning is when the knowledge gained from one problem is applied to a new problem.(NOW…)


what is artificial intelligence with examples-Natural language processing (NLP) allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include information retrieval, question answering and machine translation.

Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic and the breadth of commonsense knowledge. Modern statistical techniques include co-occurrence frequencies (how often one word appears near another), “Keyword spotting” (searching for a particular word to retrieve information), transformer-based deep learning (which finds patterns in text), and others. 

They have achieved acceptable accuracy at the page or paragraph level, and, by 2019, could generate coherent text.

APPLICATIONS:what is artificial intelligence with examples

what is artificial intelligence with examples-AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

In the 2010s, AI applications were at the heart of the most commercially successful areas of computing, and have become a ubiquitous feature of daily life. AI is used in search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa).

Autonomous vehicles (including drones and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple’s Face ID or Microsoft’s DeepFace), image labeling (used by Facebook, Apple’s iPhoto and TikTok) and spam filtering.what is artificial intelligence with examples


what is artificial intelligence with examples-Smart traffic lights have been developed at Carnegie Mellon since 2009. Professor Stephen Smith has started a company since then Surtrac that has installed smart traffic control systems in 22 cities. It costs about $20,000 per intersection to install. Drive time has been reduced by 25% and traffic jam waiting time has been reduced by 40% at the intersections it has been installed.(FURTHER…)


FUTURE: what is artificial intelligence with examples


what is artificial intelligence with examples-A superintelligence, hyperintelligence, or superhuman intelligence, is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent.

If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. Its intelligence would increase exponentially in an intelligence explosion and could dramatically surpass humans.

Science fiction writer Vernor Vinge named this scenario the “singularity”. Because it is difficult or impossible to know the limits of intelligence or the capabilities of superintelligent machines, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.what is artificial intelligence with examples


RISKS:what is artificial intelligence with examples


what is artificial intelligence with examples-In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that “we’re in uncharted territory” with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. 

Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at “high risk” of potential automation, while an OECD report classifies only 9% of U.S. jobs as “high risk”.

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that “the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution” is “worth taking seriously”. 

Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.what is artificial intelligence with examples


what is artificial intelligence with examples-AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, face recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes aid in producing misinformation; advanced AI can make centralized decision making more competitive with liberal and decentralized systems such as markets.

Terrorists, criminals and rogue states may use other forms of weaponized AI such as advanced digital warfare and lethal autonomous weapons. By 2015, over fifty countries were reported to be researching battlefield robots.what is artificial intelligence with examples

what is artificial intelligence with examples

  1. Google Maps and Ride-Hailing Applications
  2. Face Detection and recognition
  3. Text Editors and Autocorrect
  4. Chatbots
  5. E-Payments
  6. Search and Recommendation algorithms
  7. Digital Assistant
  8. Social media
  9. Healthcare
  10. Gaming
  11. Online Ads-Network
  12. Banking and Finance
  13. Smart Home devices
  14. Security and Surveillance
  15. Smart Keyboard App
  16. Smart Speaker
  17. E-Commerce
  18. Smart Email Apps
  19. Music and Media Streaming Service
  20. Space Exploration

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