AI was a term first coined at Dartmouth College in 1956. Cognitive scientist Marvin Minsky was optimistic about the technology’s future. Let’s start with Simple Definition of AI.
John McCarthy is one of the “founding fathers” of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon.
What is Artificial Intelligence ?
Artificial intelligence is the ability of machines to perform certain tasks, which need the intelligence showcased by humans and animals.
AI or Artificial Intelligence is a terms where Intelligence (Brain) is created Artificially (or made by Human/Machine) or in Simple word we can say when a Machine Think or act or answer like a Human is Called Artificial Intelligence. Example there is Such task which need Intelligence to full fill it like Face recognition, Voice recognition, Answer a Questions, Create a Design or Translation of Languages and many other job done by Machine.
Human Play Chess Game with AI
Applications of Artificial Intelligence
There is Many Applications Which used AI in there Technology to solve clients problems or give suggestion for Example Web Search Engine (Like Google, Bing Etc. ), Below Provide a some Application list which used AI in there Technology to Provide services to there Client
- Web Search Engine (Google, Microsoft Bing, Duck Duck go etc.)
- Decision Making Software (Chat GPT)
- Recommendation System (used by Netflix, Amazon, YouTube Etc.)
- AI Designer (Microsoft Designer, Canva etc. )
- Human Speech Recognition (Amazon Alexa, Google Assistance, Siri etc.)
- Self Driving Car
- Games Strategic Decision (Like Chess Game) etc.
AI Effect
“The AI effect” is that line of thinking, the tendency to redefine AI to mean: “AI is anything that has not been done yet.” This is the common public misperception, that as soon as AI successfully solves a problem, that solution method is no longer within the domain of AI.
The various subfields of AI research are organized around specific objectives and tools. Reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects are among the traditional objectives of AI research. One of the field’s long-term objectives is general intelligence, or the capacity to solve any problem. AI researchers have adapted and integrated a wide range of problem-solving techniques to solve these issues, such as search and mathematical optimization, formal logic, artificial neural networks, and approaches based on statistics, probability, and economics. Computer science, psychology, linguistics, philosophy, and numerous other fields are also used in AI.
History of Artificial Intelligence
AI was a term first coined at Dartmouth College in 1956. Cognitive scientist Marvin Minsky was optimistic about the technology’s future. Government funding for the field decreased from 1974 to 1980, a time known as the “AI winter,” during which several individuals criticized field progress.
Since its inception as an academic field in 1956, artificial intelligence (AI) research has tried and failed with numerous approaches, including simulating the brain, modeling human problem-solving, formal logic, large knowledge databases, and imitating animal behavior. In the years since, AI research has also experienced several waves of optimism, followed by disappointment and funding cuts (the “AI winter”).
However, in the 1980s, when the British government began funding the technology once more out of concern for Japanese competition, the enthusiasm was rekindled. Deep Blue by IBM was the first computer to defeat a Russian Grandmaster in 1997, making history.
AI Winter
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.”
Researchers in the 1960s and 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this to be the goal of their field. Both the British and American governments halted exploratory AI research in 1974 in response to Sir James Lighthill’s criticism and ongoing pressure from the US Congress to fund more productive projects. The subsequent few years would later be referred to as an “AI winter,” a time when it was challenging to obtain funding for AI projects. [Source: Wikipedia]
Goals of Artificial Intelligence
Companies that want to extract value from data by automating and optimizing processes or producing actionable insights require artificial intelligence systems. Companies are able to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more thanks to artificial intelligence systems that are powered by machine learning. These systems enable companies to leverage large amounts of readily available data to uncover insights and patterns that would be impossible for any one individual to identify.
Reasoning or Problem-solving Ability in AI
We know How Human Solve the problem step by step, Just like Human Researchers Develops the Algorithm for Machine, So that Machine can Easily Solve Problem Step by step like Human.
Due to their “combinatorial explosion,” many of these algorithms were found to be insufficient for solving large reasoning problems: As the problems got bigger, they got slower by an exponential amount.[53] Even humans rarely use the step-by-step deduction that early AI research could model. The majority of their issues are resolved through quick, intuitive judgments.
Knowledge Engineering by AI
Knowledge Engineering or Knowledge Representation is Provide a Ability to Machine to Answer a Question or Make a Decision or Discus on about real world Facts.
Learning (Machine Learning ) by AI
Machine Learning is a concept where Machine learn from Human or From Resource to just Increase there Ability to Solve Particular task like a human learn for Solving Problems of real world.
Generally there are two Type of Learning
- Unsupervised Learning
- Supervised Learning
- Unsupervised learning identifies patterns in an input stream.
- There are two main types of supervised learning, each of which requires a human to label the input data first: classification and regression using numbers. The program will learn to classify new inputs after seeing a number of examples from various categories. Classification is used to determine what category something belongs in. The process of trying to come up with a function that both describes the relationship between the inputs and the outputs and predicts how the outputs should change as the inputs change is known as regression. Regression learners and classifiers both function as “function approximators” attempting to learn an unknown (possibly implicit) function. A spam classifier, for instance, can be thought of as learning a function that maps an email’s text to one of two categories: “spam” or “not spam.”
Natural Language Processing (NLP) By AI
Natural language processing, also known as NLP, enables machines to comprehend and read human speech. Natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts, would be possible with a sufficient natural language processing system. Information retrieval, question answering, and machine translation are all straightforward uses of NLP.
There Are many Other goals of AI for Example
- Perception
- Social Intelligence
- General Intelligence
- Search Optimization
- Logic Building
- Artificial Neural Network
- Deep Learning
Facts About Artificial Intelligence
- AI-enabled gadgets are all over. Today, AI technology is present in some form or another in nearly 77% of devices.
- Since 2000, the number of AI startups has increased 14 times. Additionally, we would wager that more of them will appear annually.
- Leaders in business have faith in AI’s capacity to spur growth. 84% of C-level executives believe that in order to achieve growth goals, they must adopt and use AI.
- The AI market is expanding globally. By 2025, it will be worth 190.61 billion dollars, growing at a rate of 36.62 percent per year.
- Artificial Intelligence will boost the world’s GDP by 14% by 2030, adding 15.7 trillion dollars.
- In the future, there will be more AI assistants than people. By 2024, there will be 8.4 billion AI-powered digital voice assistants worldwide, which is more than the entire global population.
Myths About Artificial Intelligence
There are some popular Myths about AI which we must Need to know
- AI will Take Over All Jobs
- AI will Control the world
- AI robots Will rule Humans
- AI will Develop its Own Copy Without Human Interption
- AI will Kill All the Human
- AI will Eventually work like Human Brain
- Only Big Companies can use AI
- AI puts our Data at Risk and Many Others
Future Impact of Artificial Intelligence
Almost All Area will Impact by AI in upcoming area and these impact defiantly positive by which our technology is Far better than Present we Have, for example
- Healthcare
- Security & Defense
- Education
- Banking & Finance
- Autonomous vehicles
- Manufacturing
- Entertainment
- Workplace
Summary
The term “artificial intelligence” refers to a collection of various technologies that work together to give machines the ability to perceive, comprehend, act, and learn at human-like levels.
AI development does not happen in One Day it take Decades to Developed Artificial Intelligence which we see today, journey of AI will start from era of 1950s to Present and Still on Development, So There many more about to Come from AI to Easier Our Life
Ultimate Goal of AI is to easier life experience for human and For Growth and Advancement of Human, Some Famous people have many different Thoughts on AI but all we Know AI is Our Present and Also Our Future.
The term “artificial intelligence” refers to a collection of various technologies that work together to give machines the ability to perceive, comprehend, act, and learn at human-like levels.
– Larry Page
