The evolution of artificial intelligence is a complex yet fascinating journey originating from 1940s. It all started from the idea of some genius and curious minds to create something that could imitate the working system of human brain. Human brains are inarguably the most complex and incomprehensible creation in the world. Hence, to create such a thing that could imitate such a complex system was a daring dream. But this dream of visionaries bore results and the most alluring journey of development started in 1940s. Let’s look into the stages of evolution that Artificial intelligence had to go through to reach at the point where it is now.
The Origins (1940s - 1950s)
The origin of AI can be traced back in the early 40s of 20th century when Alan Turing and John Von Neumann laid the theatrical foundation for Artificial intelligence. It was Turing who first proposed that computer could possess the artificial intelligence if it could imitate human responses under specific conditions. Later in 1950s, he founded a test called Turing’s test to determine whether or not a machine is capable of thinking like a human brain. The test required three terminals. One terminal was operated by computer and the other two terminals operated by humans. During test, one of the human would function as questioner, while the second human and the computer would work as respondents. At the end of the test, the questioner would have to identify which respondent was human and which was computer. If the questioner would make the correct determination in half of the test or less, the computer would be considered to have artificial intelligence. The term Artificial intelligence was coined in 1950s and it was the time when the field started to get recognition.
Early Symbolic AI (1950s - 1960s)
During this era, the AI focused mainly on the logical and rules based simulation of human brain rather than using numerical data. This process is undertaken by reasoning engines that use algorithms to manipulate symbols. Symbolic AI is being used in various fields. For example, Siri and other digital assistants and autonomous cars use symbolic AI in them to recognize the signs and symbols. Logic theorist and general problem solver were the significant milestones in this era. Logic theorist is a computer program that was written in 1956. This program was engineered to perform the automated reasoning. It was aimed at enabling computer to mimic the human brain.This was also termed as “the first artificial intelligence program”. Similarly, the general problem solver theory was the attempt to enable computers to solve problem like human brains by imitating the functioning of a human brain. The methodology for testing the theory involved developing a computer simulation and then comparing the simulation with human behaviour.
AI Winter (1970s - 1980s)
After reaching to the point of symbolic AI, the evolving AI suffered a setback within the era of 70s and 80s of 20th century. Because of the lack of interest and investment in the AI from stakeholders, progress slowed down in this time frame. The term “AI winter” was coined to describe the reduced interest and slow pace of AI. This was the first winter in the evolution of AI. In the earlier decade, there were a lot of innovations and new theories were introduced in the industry which created hype and higher expectations from the data scientists. But the limited computational powers in those times became hurdle in the pace of advancement. As soon as the pace of development became slow, people started loosing interest in it by thinking that the AI is mere a fiction and is not achievable. It resulted in lack of funds and resources that added to the woes of evolving AI even more. But even in this time, with the fact that the pace slowed down but AI still somehow managed to survive the winter of the evolution journey.
Expert Systems (1980s)
After passing through the AI winter stage of the evolution, interest in AI again picked up with the development of AI powered expert systems. This was the rebirth of AI as private entities and venture capital firms and corporations began to invest in the field as well. The acceptance of AI increased because the computers now were more powerful and widely available then they were in early 70s. Therefore, the adoption of AI flourished. Expert system is a software that uses knowledge to solve the problems that would usually require human beings. Expert systems are being used in all walks of life now but it has most significant application in the medical field. Some examples of expert systems are MYCIN, DENDRAL, R1/XCON, PXDES, CaDet. All these softwares use the artificial based system expert system that use the knowledge from its memory to identify the diseases and can also prescribe medicines to the patients. Hence, expert systems were truly some successful forms of artificial intelligence.
Machine Learning Renaissance (1980s - Early 2000s)
This was the time when the learning of AI shifted from symbolic AI to Machine learning. At this point of time, researchers were able to develop the algorithms that could learn from data such as neural networks, decision trees and genetic algorithms. This lead to the new dimension of pattern recognition and natural language processing. At this stage, advancement in machine learning, availability of more data, and increased computational powers lead to a resurgence in AI. Techniques like support vector machines and deep learning started gaining prominence. Support vector machines are supervised learning models with associate learning algorithms that analyze data. SVMs can be used for variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection and anomaly detection. SVMs can manage high dimensional data and non linear relationships.
Deep Learning Revolution (2010s - present)
Deep learning is the subset of Artificial intelligence. It is, by far, the most advanced stage of Artificial intelligence. It has made the most significant breakthroughs in the field. Deep learning is essentially a neural network with three or more layers. These neural network try to simulate the behaviour of human brain. The layers in the neural network helps in making predictions, refine data and optimize for accuracy. Deep learning also uses other processes such as backpropagation to calculate errors in predictions and then adjust the errors by moving backwards. At this stage of the evolution, Artificial intelligence with the help of deep learning is not only able to learn from data but is also capable of making predictions of the upcoming events or expected behaviours. This is possible because of the large datasets and information the machines are able to keep. The huge amount of data make machines to learn from the patterns and predict the events correctly with the help of those patterns.
Application of advanced AI in industries in the present time
At the present moment, Artificial intelligence is at it’s best so far. It has taken all the industries by storm. The most interesting thing about AI is that it has not remained limited to few industries, rather with it’s unique and extraordinary attributes, it has engulfed all the industries in the world. The usage and application of AI in all the industries has become so much inevitable that it is impossible to think of the future without AI. The artificial intelligence is now being used in all the industries including healthcare, finance, automotives, customer care, art and what not. But the most significant impact it has made on the area is business. Businesses of any scale use artificial intelligence in one or the other way. It has become impossible for businesses to grow in such competitive world without AI. This is so because AI brings tremendous benefits for businesses including saving time, increasing productivity and making a lot of routine work automated. Keeping in mind this dire need of businesses to have AI based automated systems to save them time for more productive tasks, Rev9Solutions is also helping businesses to develop the customer care chatbots that meet their specific business needs. It’s the high time for businesses to automate their routine work and make best use of their time with some more creative and productive actions.
Conclusion:
Artificial intelligence has come a long way in the past seven decades but still the journey of evolution of Artificial intelligence is far from over. We have witnessed how the artificial intelligence has become such a powerful asset in the present times by going through all the winter periods and then getting resurgence all over again. The story of Artificial intelligence is the real example of turning a dream into reality. It is fair to say that after going through its continuous evolution process, AI has become strong, learned and intelligent enough that In the present times, it has become an indispensable part of all the industries around the world. Due to its astounding abilities of deep learning and making predictions, It has become inevitable for the industries to incorporate the artificial intelligence into their working systems.