AI Writes The History of Artificial Intelligence by Sagar Howal

One example is the General Problem Solver (GPS), which was created by Herbert Simon, J.C. Shaw, and Allen Newell. GPS was an early AI system that could solve problems by searching through a space of possible solutions. This concept was discussed at the conference and became a central idea in the field of AI research. The Turing test remains an important benchmark for measuring the progress of AI research today. Alan Turing, a British mathematician, proposed the idea of a test to determine whether a machine could exhibit intelligent behaviour indistinguishable from a human.

• It was designed to analyze chemical mass spectrometry data and identify organic compounds.
• Starting from 1997, a legendary chess match took place between the IBM-built AI system Deep Blue and the chess grandmaster Gary Kasparov.
• The weights are adjusted during the training process to optimize the performance of the classifier.
• This discovery led to chain rule, an important advancement in the creation of neural networks.

A normal computer program would need human interference in order to fix bugs and improve processes. In particular, talk of human-level general intelligence shouldn’t be taken too seriously. The risk with high expectations for the short term is that, as technology fails to deliver, research investment will

dry up, slowing progress for a long time. Mathematicians, engineers, and visionaries began to explore the concept of creating machines that could simulate human intelligence. The Specific approach, instead, as the name implies, leads to the development of machine learning machines only for specific tasks.

The History of Artificial Intelligence: From Concept to Reality

In the mid 1950s, McCarthy coined the term “Artificial Intelligence”

and defined it as “the science of making intelligent machines”. One of the greatest innovators in the field of machine learning was John McCarthy, widely recognized as the

“Father of Artificial Intelligence”. The computer program ‘Watson’ competes in a U.S. television quiz show in the form of an animated on-screen symbol and wins against the human players. In doing so, Watson proves that it understands natural language and is able to answer difficult questions quickly. Microsoft demonstrates its Kinect system, able to track 20 human features at a rate of 30 times per second. View citation[20]

The development enables people to interact with a computer via movements and gestures.

The Enlightenment era saw the development of philosophical ideas about cognition and computation, laying the groundwork for future AI concepts. There has been a total yearly investment of $5 billion between the time period 2020 and 2022 in the AI startup domain. This is the reason multiple artificial intelligence development companies have emerged in the last decade. The entire history of AI that led us to the advanced society we live in today is finally bearing fruits.

Man vs Machine

The term describes a period of low consumer, public, and private interest in AI which leads to decreased research funding, which, in turn, leads to few breakthroughs. Both private investors and the government lost interest in AI and halted their funding due to high cost versus seemingly low return. The time between when the phrase “artificial intelligence” was created, and the 1980s was a period of both rapid growth and struggle for AI research. From programming languages that are still in use to this day to books and films that explored the idea of robots, AI became a mainstream idea quickly. We now live in the age of “big data,” an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process.

Lovelace’s work laid the foundation for computer programming and is considered the birth of computer science. Starting from 1997, a legendary chess match took place between the IBM-built AI system Deep Blue and the chess grandmaster Gary Kasparov. The match ended with 4 consecutive losses of Gary and the match ended with resign. This event made its mark in the history of AI considering there hasn’t been a system capable of defeating such a decorated figure in its own space.

Also, the support for the advancement of technology from the government also plummeted for a while. Firstly in the year 1987, the market for LISP-based specialized hardware collapsed because of cheaper and more accessible computer systems. Second in the year 1988, a chatbot with the name Jabberwacky was invented by Rollo Carpenter. Despite this, there wasn’t anything notable that happened period. Following this “ELIZA” also known by the name “Chatterbot” was a mock psychologist  introduced in 1966.

• He wanted to test a theory that a machine could prescribe the core principles of intelligence.
• Looking ahead, the future of AI holds the promise of human-level AI (AGI), robust machine learning, and AI’s integration into diverse industries, including healthcare.
• In 1726, Jonathan Swift released Gulliver’s Travels, where a machine called “The Engine” became the earliest known reference to a computer.
• In 2012, AlexNet did something really big, and that was just the start of an exciting time in AI.
• These new algorithms focused primarily on statistical models – as opposed to models like decision trees.

In the context of the history of AI, generative AI can be seen as a major milestone that came after the rise of deep learning. Deep learning is a subset of machine learning that involves using neural networks with multiple layers to analyse and learn from large amounts of data. It has been incredibly successful in tasks such as image and speech recognition, natural language processing, and even playing complex games such as Go. Machines today can learn from experience, adapt to new inputs, and even perform human-like tasks with help from artificial intelligence (AI).

While Von Kempelen’s automaton may have been the most famous, it wasn’t the first. Pierre Jaquet-Droz’s childlike automatons surprised spectators by writing, drawing, and making music. An inventor named Jacques de Vaucanson even invented a “Digesting Duck,” which could eat, drink, and well… you know. A lot of evolution has already happened with Artificial Intelligence in the world, but the news and research do not stop. Universities and companies around the world continue to launch new solutions using Artificial Intelligence to facilitate people’s daily lives and make machines increasingly intelligent. Then, in 2008, tech giants Google and Apple launch their voice recognition features.

Unfortunately, there were several limitations to knowledge-based AI of the 1980s. Expert systems were expensive to maintain, very rigid and inflexible, and not very efficient for large problems. Throughout the ’80s expert systems proved useful but only in a very limited set of use cases. An expert system is composed of a knowledge base (containing a set of facts and rules) and an inference engine (which deduce new facts from known facts). Successful applications of expert systems included MYCIN for diagnosing diseases, XCON for inventory management, and AARON for creating original works of art.

Deep learning, big data and artificial general intelligence: 2011–present

We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution. Training computation is measured in floating point operations, or FLOP for short. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers.

Towards the other end of the timeline, you find AI systems like DALL-E and PaLM, whose abilities to produce photorealistic images and interpret and generate language we have just seen. They are among the AI systems that used the largest amount of training computation to date. Robots equipped with AI algorithms can perform complex tasks in manufacturing, healthcare, logistics, and exploration. They can adapt to changing environments, learn from experience, and collaborate with humans.

The odyssey of Artificial Intelligence (AI) mirrors the ceaseless human endeavor to transcend the customary bounds of capability and knowledge. From ancient civilizations’ musings on artificial beings to the modern-day prowess of machine learning and deep learning, AI has traversed a remarkable journey. In the mid-1960s, Joseph Weizenbaum created ELIZA at the MIT Artificial Intelligence Laboratory.

It is so pervasive, with many different capabilities, that it has left many fearful for the future and uncertain about where the technology is headed. Jobs have already been affected by AI and more will be added to that list in the future. A lot of automated work that humans have done in the past is now being done by AI as well as customer service-related inquiries being answered by robots rather than by humans. There are also different types of AI software being used in tech industries as well as in healthcare.

2012 is important as it pronounced the dominance of deep learning, with AlexNet’s groundbreaking performance in the ImageNet challenge serving as a catalyst. Post this path-breaking event, our narrative will shift to the rapid advancements in Convolutional Neural Networks (CNNs) from 2012 to 2017, emphasizing their contributions to image classification and object detection. While it was a breakthrough in its time, it could only handle linearly separable data.

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