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The brief history of artificial intelligence: The world has changed fast what might be next?

But the road to the modern world’s AI, big data, and deep learning has been a long one. Let’s take a tour down the history of AI and the historical avenues to find out how AI evolved into what it is today. During the 1980s and 1990s, the field of Artificial Intelligence (AI) witnessed a remarkable resurgence of interest in neural networks and connectionism. This period marked a shift away from rule-based expert systems towards more biologically inspired approaches to machine learning. Let’s delve into the resurgence of interest in neural networks, the role of backpropagation, and its connection to the development of deep learning.

Joseph Weizenbaum creates ELIZA, an early NLP (natural language processing) computer program capable of engaging in conversations with humans. Remarkable results in computer vision, natural language processing, and speech recognition tasks evolved from these advancements. The availability of large-scale data sets and the advancements in computing power led to the emergence of deep learning.

History of AI: from Alan Turing to John McCarthy, the first definition of Artificial Intelligence

The jobs that are most vulnerable in the future, according to Dr. Kaku, are the ones that are heavily based on repetitive tasks and jobs that include doing a lot of search. Self-driving cars will likely become widespread, and AI will play a large role in manufacturing, assisting humans with mechanisms like robotic arms. This led to the introduction of the “bottom-up approach,” which has more to do with learning from Mother Nature. In other words, teaching robots as if they were babies, so they can learn on their own, according to Dr. Kaku. The advanced computers that were made using codes at the time were not very effective. An analysis of how artificial intelligence functions is difficult due to its extreme complexity.

It powers applications such as speech recognition, machine translation, sentiment analysis, and virtual assistants like Siri and Alexa. Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. Learning, reasoning, problem-solving, perception, and language comprehension are all examples of cognitive abilities. University of Montreal researchers published “A Neural Probabilistic Language Model,” which suggested a method to model language using feedforward neural networks. The success in May 1997 of Deep Blue (IBM’s expert system) at the chess game against Garry Kasparov fulfilled Herbert Simon’s 1957 prophecy 30 years later but did not support the financing and development of this form of AI. The operation of Deep Blue was based on a systematic brute force algorithm, where all possible moves were evaluated and weighted.

Artificial Intelligence is Everywhere

Large language models such as GPT-4 have also been used in the field of creative writing, with some authors using them to generate new text or as a tool for inspiration. The use of generative AI in art has sparked debate about the nature of creativity and authorship, as well as the ethics of using AI to create art. Some argue that AI-generated art is not truly creative because it lacks the intentionality and emotional resonance of human-made art. Others argue that AI art has its own value and can be used to explore new forms of creativity. Velocity refers to the speed at which the data is generated and needs to be processed. For example, data from social media or IoT devices can be generated in real-time and needs to be processed quickly.

• The reason was simple, the AI technology was limited by the technology of its time.
• Philosophers and inventors at the time may not have known they were early proponents of robotics and computer science, but they laid the groundwork for future AI advancements.
• 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.

• On the other hand, blue collar work, jobs that involve a lot of human interaction and strategic planning positions are roles that robots will take longer to adapt to.

In ancient Greek mythology, the story of Pygmalion and his ivory statue, Galatea, illustrates the concept of bringing inanimate objects to life. Similarly, ancient Chinese and Egyptian cultures had tales of mechanical figures that could perform tasks, suggesting an early fascination with automation. This program was capable of drawing autonomously creating a benchmark in the history of AI technology. Later on in the year 1986, Ernst Dickmann and his team demonstrated the first driverless robot car which could drive up to the speed of 55 mph. Following that Alactrious Inc. launched its first strategy managerial advisory system known as Alacrity.

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

Artificial Intelligence (AI) in simple words refers to the ability of machines or computer systems to perform tasks that typically require human intelligence. It is a field of study and technology that aims to create machines that can learn from experience, adapt to new information, and carry out tasks without explicit programming. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. MuZero is an AI algorithm developed by DeepMind that combines reinforcement learning and deep neural networks. It has achieved remarkable success in playing complex board games like chess, Go, and shogi at a superhuman level.

The pace of technological advancement picked up at the turn of the century. Taking cues from the film and literature at the time, people of science began experimenting with machines and wondering about their capabilities and potential uses. Like much of human history, artificial intelligence first surfaced in Ancient Greece. Before that, Hephaestus made Talos, a mechanical “robot” assigned to protect Crete from invasions. The start of AI’s practical applications came in 250 BC when inventor and mathematician Ctesibius built the world’s first automatic system — a self-regulating water clock. Nonetheless, the legacy of expert systems endures in various applications, showcasing how AI can enhance human expertise and decision-making in specialized domains.

Later on in the year 2016, a humanoid named Sophia is created by Hanson Robotics. Sophia is much better than Kismet considering it had a realistic human appearance. Adding to it, the system had the ability to replicate emotions and the capability to communicate them. A couple of years later in 1973, a British mathematician James Lighhill submitted a report to the British Council. This report focused on the level of advancement that took place in the AI realm. However, in the Stanford AI lab, the first autonomous vehicle got created with the name “Stanford Cart” in the year 1961.

The centuries leading up to the 1950s saw the emergence of several philosophical and logical concepts that served as the foundation for theories intelligence. Ancient Greek philosophers had a major influence on Western culture, with ideas about the essence of consciousness, human thought, and learning. For hundreds of years, these concepts evolved to eventually become more focused on the possibility of machines gaining the capacity to learn and on artificial intelligence, as technology was further integrated into human life. Ever since the Dartmouth Conference of the 1950s, AI has been recognised as a legitimate field of study and the early years of AI research focused on symbolic logic and rule-based systems. This involved manually programming machines to make decisions based on a set of predetermined rules. While these systems were useful in certain applications, they were limited in their ability to learn and adapt to new data.

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His aim was to demonstrate that a machine could simulate human intelligence. Discover how it took off, from Alan Turing’s test, to the advent of ChatGPT. Deep learning represents a major milestone in the history of AI, made possible by the rise of big data. Its ability to automatically learn from vast amounts of information has led to significant advances in a wide range of applications, and it is likely to continue to be a key area of research and development in the years to come. For example, a deep learning network might learn to recognise the shapes of individual letters, then the structure of words, and finally the meaning of sentences. For example, early NLP systems were based on hand-crafted rules, which were limited in their ability to handle the complexity and variability of natural language.

Read more about The History Of AI here.