Artificial Intelligence (AI) has become a buzzword in recent times. You must have heard people talking about AI-driven tools or might be using one in your everyday life yourself. Currently, AI tools, such as ChatGPT or Siri, have become part and parcel of our day routine. However, have you ever wondered what AI is exactly?
In a layperson’s language, AI or Artificial Intelligence can be defined as a branch of computer science. It includes digital computers or computer-controlled robots that perform tasks typically associated with human cognitive functions.
AI tools or devices can do a lot of things, such as interpreting speech, playing games, identifying patterns, and more. Interested to know more? Read ahead!
Artificial Intelligence – A Definition
Artificial intelligence is defined as a “branch of computer science dealing with the simulation of intelligent behaviour in computers” by the Merriam Webster dictionary. So, in idealistic terms, AI is emblematic of a computerized machine with level-level intelligence, loaded with an array of cognitive abilities specifically programmed to perform various tasks. The particular applications of AI include expert systems, natural language processing, speech recognition and machine vision.
When we move beyond the technical definition, the meaning of Artificial Intelligence is represented in its name. Basically, it is about designing machines and tools that can emulate human intelligence or intellect, such as the ability to reason, discover the meaning of things, generalize, create patterns, learn from past experience, and so forth. But, one important thing to remember is that despite monumental developments, no AI program has been able to match full human flexibility and cognition. And, will AI transcend or be at par with human intelligence in the future? That is up for debate.
History of Artificial Intelligence
Of course, there is no specific time or date when ‘AI’ actually became a tangible phenomenon. However, it is safe to say that the earliest substantial work in the AI field was done by Alan Turin in the mid-20th century. Turing was a British logician and computer expert who described an abstract computing machine consisting of infinite memory and a scanner that oscillates back and forth through the memory, reading what it finds and writing further symbols.
The description is now known as the Universal Turing Machine and all modern computers are essentially universal Turing machines.
Thereafter, in 1855, John McCarthy organized a workshop at Dartmouth on ‘artificial intelligence,’ which is the first recorded use of the term and its entry into mainstream usage. However, it was during the 1970s and 1980s that Artificial Intelligence grew as a phenomenon and extensive research was conducted. From programming languages that we use even today to books and films that explored the idea of robots, AI became a massive idea very quickly during the 1970s and 1980s. For instance, in the 1970s in Japan, the first anthropomorphic robot was built by an engineering student. Likewise, governmental funding for AI research bolstered everywhere in the 1980s. The period is also marked by innovations, such as the first driverless car created by Ernst Dickma in 1986 and the creation of an autonomous drawing program called AARON.
However, things took a drastic turn in the late 1980s and early 1990s consumer, public and private interest in AI vehemently declined. This lends to a crunch in research funding and lesser breakthroughs. But, from the 1990s, AI research gained traction and many unique innovations were made. For instance, it was during this phase that AI was introduced into the everyday lives of people with developments, such as the first commercially-available speech recognition.
Fast forward to the present times, the world of AI has witnessed a massive rise in common usage with tools like virtual assistance, search engines, etc. Moreover, concepts such as Machine Learning (ML), Deep Learning and Big Data – all of which are AI ancillaries have become mainstream.
Now that we have touched upon the history of AI, it is time to understand how Artificial Intelligence works.
How AI (Artificial Intelligence) Work?
Artificial Intelligence is all about creating a computer system that can emulate human behavior and intellect in a way that solving complex problems can be done with human-like thinking processes. The working of AI is not a unidirectional phenomenon.
To begin with, artificial intelligence works by merging massive sets of information using clever processing techniques and complete several tasks quickly and effectively in a short period of time. To work properly, AI has to leverage two main tools – deep learning and machine learning.
However, AI also needs a bedrock of specialized hardware and software for writing and training the machine learning algorithms. Although we cannot correlate a single programming language with AI, Python, R, Java, C++ and Julia have characteristics that are popular with AI developers.
So, in generic terms, AI works by consuming large amounts of ‘labeled training data,’ analyzing the data for patterns and leveraging the patterns to make future predictions and estimations. To properly understand how AI works, it is essential to understand the main ancillaries – Machine Learning (ML) and Deep Learning.
Simply put, machine learning is particular application of Artificial Intеlligеncе that enables computer systems to understand historical data and show results based on experience and display patterns and make predictions. So, ML is an algorithm that is fed data by a computer and then uses statistical techniques to ‘learn’ how to get better at a task, without even being specifically programmed for that task.
On the other hand, deep learning (DL) is a variant of machine learning that utilises artificial neural systems for processing data &, hence, find results. In other words, deep learning uses artificial neural system that emulate biological neural networks in the brain to processing data, establish connections between data and arrive at inferences premised on positive or negative reinforcement.
Apart from ML and DL, Artificial Intelligence also needs robotics, cognitive computing skills, language processing and computer vision to enable computers to imitate and mimic the way the human brain works while performing complicated tasks.
Wrapping It Up
So, there we have it, a crisp overview of what artificial intelligence is and how it works.