History of AI: Timeline, Advancement & Development

Artificial Intelligence is a young domain of 60 years, which includes a set of techniques, theories, sciences, and algorithms to emulate the intelligence of a human being. Artificial intelligence plays a very significant role in our lives. The revolution of industries has made a lot of developments in business with the implementation of artificial intelligence. In this blog, we will discuss an outlook on the history of artificial intelligence.

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What is Artificial Intelligence?

Artificial intelligence is defined as the ability of a machine to perform tasks and activities that are usually performed by humans. Artificial intelligence organizes and gathers a vast amount of data to make useful insights. It is also known as machine intelligence. It is a domain of computer science.

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Introduction to the Timeline

Apart from mathematics and computer science, the contribution from economics, psychology, convictions, and bioengineering is remarkable throughout the history of artificial intelligence. The exact timeline when it all began was the year of 1950. John McCarthy introduced the word Artificial intelligence. John, along with Allen Newell, Alan Turing, Marvin Minsky, and Herbert A Simon, are founding fathers of artificial intelligence. They contributed significantly to the history of artificial intelligence.

Advancement and Development of AI: A Brief History of Artificial Intelligence

  • The most crucial push in the advancement of AI was during the second world war. Marvin Minsky introduced the 1st neutral computer in the year 1950. In the same year, Alan Turing also developed the Turing test.
  • The timeline of 1952 plays a vital role throughout the history of artificial intelligence. Problem-solving development was designed to emulate the problem-solving approach of humans. Arthur Samuel initiated a self-learning project in 1952. 
  • In 1954, the IBM organization experimented with automatic machine translations.
  • In 1956, Herbert Simon and Allen Newell developed the 1st reasoning project of logic theorists.  
  • In 1959, Nathaniel Rochester, in the IBM organization, developed a program to prove geometric theorems. Arthur Samuel developed the phrase machine learning. Marvin Minsky and John McCarthy also proposed the MIT AI project. 
  • McCarthy initiated the artificial intelligence lab in 1963 at Stanford. 
  • From 1966 to 1973, there was a lack of outputs that impacted the growth of artificial intelligence. The complexity of computational algorithms restricts the advancement of artificial intelligence in this timeline. The researchers at Stanford introduced DENDRAL from the mass spectrometer information. 

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  • After that timeline, the researchers focused on AI-specific applications. 
  • In 1972, the PROLOG language was developed. 
  • The wave of computer systems developed in the timeline of 1974. Gradually with time, they become more affordable and can store more data. The best part of the history of artificial intelligence is that it achieved NLP (Natural Language Processing) in this timeline. 
  • The timeline of 1980 was the year of artificial intelligence. The research of AI pushes forward with the growth of tools and funds. This timeline initiated a new era of AI throughout the history of artificial intelligence. The first commercial system, known as Digital Equipment Corporation, was developed in 1980. 
  • The fifth-generation computer project was launched in 1982 by the Japan Ministry of International Trade and Industry.
  • The US government developed a strategic computing project in 1983.
  • The timeline of the 2000s is the landmark of artificial intelligence. 
  • In 2005, self-driving won the grand challenge of DARPA.
  • In 2008, Google made advancements in speech recognition. 
  • In 2014, Google made self-driving cars. 

Also Read: Artificial Intelligence Salary in India

Artificial Intelligence is Everywhere

Artificial intelligence is evolving day by day. Today AI is everywhere. AI research is continuously increasing. Artificial intelligence has now become commonplace in every phase of life. Now, organizations and workplaces are becoming more efficient and intelligent as humans and machines are starting to operate together. Artificial intelligence use cases have been productive in various industries such as banking, manufacturing, technology, entertainment, weather predictions, marketing, health diagnosis, and many more. upGrad offers multiple other blogs on such topics. You can visit their website to further read about such topics.

Present Nature of AI

When we look at the history of AI from a modern lens, the advancement that has taken place post-2000s has been in leaps and bounds. Today, AI has emerged to be an integral part of our day-to-day lives, from voice aids to AI chatbots. The research is still on to develop AI that promises further ease to your lives. 

Narrow AI

The AI we interact with today is called Narrow AI. The term refers to the fact that this AI can only execute a narrow range of specific tasks. They have a predefined set of information that they use to carry out a single task and nothing beyond that. They can only tell the weather or answer a question based on their limited exposure to information. 

Narrow AI is also called weak AI because its intelligence and the range of cognitive abilities are not the same as humans. They do what they are designed to do using big data, deep learning, machine learning, data science, natural language processing and other elements that give AI the ability to process data and produce results.

However, narrow AI has still made our lives much easier, considering the brief history of AI and how far the technology has come. They help process data faster than humans and simplify day-to-day mundane tasks. 

Latest Research

Currently, most efforts by organizations are to advance Narrow AI by helping them achieve a wider range of tasks, promote transfer learning and increase its range of cognitive abilities. Breakthroughs are taking place regularly, including the development of Alpha Go, GPT-4, and Gato. 

AlphaGo is the first program to defeat a world champion in the game of Go. GPT-4 is a deep-learning language model debated to be an example of early AGI. Gato is a deep neural network, a transformer like GPT-3, developed by DeepMind.

What Does the Future Hold?

The history of artificial intelligence in short, has shown us the rate of development when it comes to AI is significant. Today, companies like Google, IBM, OpenAI, DeepMind, Anthropic, and more are actively researching how to develop AGI or Artificial General Intelligence. 

AGI is a ‘strong AI’ with the intelligence to match humans, who will have more cognitive abilities and a better range of tasks. The timeline for the innovation of such a creation is still unclear, but there have been speculations that it will be achieved by 2050 or so. 

However, the immediate future will see companies adopting AI to their business models for increased profits; as AGI develops, the workforce will see a significant change, including increased automation of tasks, and, as can be seen now, there will be a massive rise of jobs related to AI.

The Verdict

The journey in the history of artificial intelligence is tremendous. Starting with education, healthcare, electricity, e-commerce, and technology, everything is automated today. AI technology performs several tasks that require human reasoning and thinking. Cognitive psychology is one of the main tools for all the advancement areas in cognitive sciences. Artificial intelligence technologies have helped to achieve effectiveness and efficiency. Now, the machines can perform the smartest of humans.

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What are the limitations of using artificial intelligence?

The availability of high quality data is one of the most significant obstacles to AI implementation. Data is frequently inconsistent and of low quality, posing hurdles for firms seeking to generate value from AI on a large scale. To react to the changing corporate environment, software programs must be updated on a regular basis. The entire artificial intelligence system is quite costly, and many industries are unable to afford it. Machines cannot be expected to function in a creative manner. Human intervention is required in that case.

How are machine learning and artificial intelligence related to each other?

Machine learning is a subset of artificial intelligence. It's essentially one method of putting artificial intelligence into practice. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate the way people learn. ML is a subset of AI that solves problems that require human intelligence by learning from data and making predictions.

Do I need to be a pro at coding to learn artificial intelligence?

Experts in machine learning or artificial intelligence are required to have a strong understanding of coding, but the focus is on Ml models and algorithms, the ability to use various libraries such as NumPy, Pandas, and SciPy, and skill in developing distributed systems using Hadoop, among other things. To do well in artificial intelligence, you need to have a fundamental understanding of many programming languages, although it is not required to be an expert in them.

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