What is Artificial Intelligence (AI)? Types, Importance, Examples, Applications, Its Future and History

Artificial Intelligence (AI)

Artificial intelligence simulates how the human mind makes decisions and solves problems using computers and other technology.

What is Artificial Intelligence?

Over the past few decades, various definitions of artificial intelligence (AI) have appeared. In this 2004 publication, John McCarthy provides the following definition: "Making intelligent devices, particularly clever computer programs is a scientific and engineering activity. AI should not be restricted to methods that can be seen physiologically, despite the fact that it is related to the related task of using computers to understand human intellect.

The discussion over artificial intelligence, however, started with Alan Turing's 1950 essay "Computing Machinery and Intelligence" decades before this concept. Can computers think? questions Turing, who is frequently referred to as the "father of computer science" in this essay. Then he suggests a test that has come to be known as the "Turing Test," in which a human interrogator would try to tell the difference between a text response that was produced by a computer and one that was written by a human. Although this test has been under a lot of scrutinies since it was published, it still plays a significant role in the development of AI.

Artificial Intelligence: 

A Modern Approach by Stuart Russell and Peter Norvig is one of the top AI textbooks. They explore four alternative objectives or definitions of AI in the book, which distinguish computer systems in the following ways:

Human perspective:

• systems with human-like thinking
• Systems that behave like people

Ideal Approach:

• systems capable of logical thought
• systems that function logically
• Systems that behave like humans would come under Alan Turing's notion of computers.

Artificial intelligence, in its most basic form, is a topic that combines computer science with substantial datasets to facilitate problem-solving. Successful early use of AI, expert systems sought to mimic human decision-making. In the beginning, it took a lot of time to collect and organize human knowledge.

Machine learning and deep learning, which are often referenced in combination with artificial intelligence, are subfields of AI today. These fields are made up of AI algorithms that often classify or predict things based on incoming data. Some expert systems now have higher quality and are simpler to develop because of machine learning.

The technology that powers search engines, product recommendations, and speech recognition systems today is artificial intelligence (AI).

As is the case with any new technology, there is a lot of hype around AI development. Product innovations like self-driving cars and personal assistants follow "a typical progression of innovation, from overenthusiasm through a period of disaffection to an eventual understanding of the innovation's relevance and role in a market or domain," according to Gartner's hype cycle. As Lex Fridman points out in his 2019 MIT lecture, we are at the peak of inflated expectations and heading down the abyss of disillusionment.

Artificial Intelligence Types: Weak Vs Strong

Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is AI that has been programmed to complete particular tasks. The majority of the AI that exists today is weak AI. This form of AI is anything but weak; it supports some potent applications, including Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous cars. "Narrow" could be a better word for it.

Artificial General Intelligence (AGI) and Artificial Super Intelligence make up strong AI (ASI). A computer with general intelligence, also known as artificial general intelligence (AGI) or general AI, would theoretically be capable of learning, problem-solving, and future planning and possess an intelligence comparable to that of humans. Superintelligence, commonly referred to as artificial superintelligence (ASI), would be more intelligent and capable than the human brain. Although there are currently no real-world applications of strong AI, it is still totally theoretical, and experts in the field are studying its potential. The finest instances of ASI in the interim could come from science fiction, like HAL, the misbehaving computer aid in 2001: A Space Odyssey.

Machine Learning Vs Deep Learning

Given that deep learning and machine learning are sometimes used interchangeably, it's critical to know the differences between the two. Deep learning is a branch of machine learning as well as artificial intelligence, as was already mentioned.

Deep learning and machine learning differ in how each algorithm learns. While "deep" machine learning can employ supervised learning, also known as labeled datasets, to direct its algorithm, it is not necessary. Deep learning can absorb unstructured data in its raw form and can automatically recognize the group of traits that distinguishes various sorts of data from one another (such as text and photos). As a result, less human contact is required, and larger data quantities may be handled. In the same MIT lecture as above, cited Lex Fridman notes that "scalable machine learning" is a useful approach to think of deep learning. Machine learning that is "non-deep" or conventional is more dependent on human input. Human experts choose a set of features in order to understand the differences between various data inputs, which frequently need more structured data to learn.

Neural networks are used in deep learning, as well as in certain machine learning. An input-output neural network with more than three layers is referred to as "deep" in a deep learning method. In general, this is shown in the picture below:

One of the biggest advances in AI in recent years has been deep learning, which has lessened the manual labor required to create AI systems. Big data and cloud architectures helped make deep learning viable by enabling access to enormous amounts of data and processing capacity for training AI systems.

Uses Of Artificial Intelligence

AI systems have a wide range of practical applications nowadays. Some of the most typical instances are shown below:

Speech Recognition:

Speech recognition, also known as automated speech recognition (ASR), computer speech recognition, or speech-to-text, is a skill that converts spoken language into written language using natural language processing (NLP). Speech recognition is a common feature in mobile devices that allows voice search (like Siri) and messaging accessibility.

Customer Service: 

Online chatbots are taking the place of human customer service representatives throughout the customer journey, which has altered how we see customer involvement on websites and social media platforms. Chatbots give tailored advice, cross-sell items, and provide sizing recommendations for consumers. They respond to frequently asked questions (FAQs) concerning subjects like shipping. Virtual agents on e-commerce websites, chatbots for Slack and Facebook Messenger, and duties often carried out by virtual assistants and voice assistants are a few examples.

Computer Vision:

Computer vision is an AI technique that allows machines to extract useful information from digital photos, movies, and other visual inputs before acting appropriately. Computer vision, which uses convolutional neural networks, is used for self-driving automobiles in the automotive sector, radiological imaging in healthcare, and photo tagging on social media.

Recommendation Engines: 

By using historical data on consumer behavior, AI algorithms may help identify data trends that can be applied to create more successful cross-selling tactics. Online businesses employ this strategy to present clients with pertinent product recommendations throughout the checkout process.

Automated Stock Trading: 

High-frequency trading platforms powered by AI execute hundreds or even millions of deals per day without the need for human interaction, helping to optimize stock portfolios.

Fraud Detection:

Banks and other financial institutions may use machine learning to spot fraudulent transactions. Using supervised learning, a model can be trained using information about transactions that have been identified as fraudulent. Anomaly detection can be used to find transactions that stand out and need further investigation.

Key Dates And Figures In Artificial Intelligence History

Following are some significant occasions and turning points in the development of artificial intelligence since the invention of electronic computing:

1950:

1950 sees the release of Computing Machinery and Intelligence by Alan Turing. The Turing Test is proposed in the article to assess if a computer can exhibit the same intelligence (or the outcomes of the same intelligence) as a human. Turing is famed for his role in deciphering the Nazis' Enigma code during World War II. Since then, many have argued over the Turing Test's usefulness.

At the first-ever AI conference held at Dartmouth College in 1956, John McCarthy first uses the phrase "artificial intelligence." (McCarthy later created the Lisp language.) Allen Newell, J.C. Shaw, and Herbert Simon develop the Logic Theorist later that year, which is the first functioning AI software ever.

1967:

Frank Rosenblatt creates the Mark 1 Perceptron in 1967, the first machine based on a neural network that "learned" by making mistakes. Perceptrons, written by Marvin Minsky and Seymour Papert, is published just a year later. It quickly establishes itself as a classic work on neural networks while also serving as, at least temporarily, a counterargument to further neural network research.

1973: 

The theorem-proving method known as resolution serves as the foundation for the PROLOG programming language. PROLOG grows in popularity in the AI world and gives researchers the ability to compile and logically query knowledge.

1980: 

Neural networks, which train themselves via a backpropagation technique, are increasingly employed in AI applications.

1997:

1997 saw IBM's Deep Blue defeat former world chess champion Garry Kasparov (and rematch).

2011: 

Ken Jennings and Brad Rutter were defeated by IBM Watson on Jeopardy!

2015: 

Baidu's Minwa supercomputer classifies and identifies pictures more accurately than the average person using a specific type of deep neural network called a convolutional neural network.

2016: 

Lee Sodol, the reigning world champion Go player, is defeated by DeepMind's AlphaGo computer program in a five-game match. Given the enormous number of possible movements as the game develops (more than 14.5 trillion after just four plays! ), the win is noteworthy. 

In 2014, Google reportedly paid $400 million to acquire DeepMind.

The Future Of AI

While Artificial General Intelligence is still a ways off, more and more companies will use AI in the near future to address certain problems. By 2025, 50% of organizations will have platforms to operationalize AI, up from 10% in 2020, according to Gartner.

An upcoming AI technique is knowledge graphs. They may be used to drive upsell tactics, recommendation engines, and tailored treatment by encapsulating correlations between different kinds of information. Applications for natural language processing (NLP) are also anticipated to become more sophisticated, allowing for more natural interactions between humans and machines.