AI is no longer just a concept of science fiction; it is now a reality shaping our lives and the world around us. From early attempts to imitate human reasoning to more sophisticated machine learning processes, it has emerged as one of the most widely applied technological advancements in our time, finding practical applications in almost all industries including banking, healthcare, education, entertainment, gaming, and even art.
In this blog, we will explore the various stages of AI development to understand its evolution over the years and its potential for the future.
Stage 1: Rule-Based Systems (1950s-1980s)
Rule-based systems, the first stage of AI development, included formulating a set of guidelines that an AI system might utilise to make judgements. This strategy was founded on the notion that if a human expert could describe their decision-making process in a particular domain, a computer programme could do the same.
The Dendral project, which got its start in the 1960s, was one of the first instances of rule-based systems. Dendral was a programme created to use mass spectrometry data to infer the structure of unknown organic compounds. Dendral was successful in properly identifying the structure of unidentified compounds by codifying the scientists’ expertise in a set of principles.
Another example of rule-based systems is the MYCIN system, developed in the 1970s, which was designed to diagnose bacterial infections. Approximately 69% of infections could be correctly identified by MYCIN, which was regarded as quite impressive at the time.
Stage 2: Machine Learning (1980s-2010s)
The second stage of AI development was machine learning, which involves developing algorithms that can learn from data. In this method, rules are learned by the computer programme from the data rather than being encoded.
One of the earliest examples of machine learning is the backpropagation algorithm, which was first proposed in the 1980s. Backpropagation is a technique used to train neural networks, which are a type of machine learning algorithm. Neural networks have been utilised for a range of applications, including image identification and natural language processing, since they have the capacity to learn complicated patterns from data.
The IBM Watson system, which became well-known for its performance on the television quiz programme Jeopardy! in 2011, is another illustration of machine learning. With the aid of its extensive knowledge base and analysis of natural language cues, Watson was able to outwit two human champions.
Stage 3: Deep Learning (2010s-present)
The third and current stage of AI development is deep learning, which is a subset of machine learning that uses neural networks with many layers. Deep learning has led to significant advances in AI, particularly in areas such as image and speech recognition.
One of the most famous examples of deep learning is AlphaGo, developed by Google’s DeepMind. AlphaGo is a program that plays the board game Go and was able to defeat the world champion in 2016. AlphaGo used deep learning techniques to analyze millions of past games and develop its own strategies.
Another example of deep learning is GPT-3 (Generative Pre-trained Transformer 3), developed by OpenAI. GPT-3 is a language model that can generate human-like text and is able to perform a variety of natural language processing tasks, including language translation, question answering, and text summarization.
The future of AI is bright, and we’re excited to see where this technology will take us next.