In recent years, the term ‘artificial intelligence’ has become a major buzz word across the pharmaceutical and tech industry. But what actually is it? Here, we summarise a recent paper published in Communication Software and Networks, that explores this question and illustrates the future applications and importance of artificial intelligence.
Origins of artificial intelligence
The term ‘artificial intelligence’ also referred to as AI, was first coined by computer scientist John McCarthy in 1956 in his conference “Machine Intelligence”. The first version – the general problem solver (GPS) program – was developed by Allen Newell and Hebert Simon in 1957. Since then, there has been several developments and inventions which are stepping closer towards artificial intelligence.
In the twenty-first century, AI has become an important area of research across several departments, including science and economics. AI plays a key role in designing machines which mimic human activities, such as decision-making and problem-solving. There are a variety of programming languages for AI, including Python and R. Generally, AI researchers use two approaches for creating intelligent machines – bottom-up and top-down approaches. In the bottom-up approach, AI is achieved by creating electronic replicas of the human brain. Whereas, in the top-down approach, AI is achieved by computer programs which mimic the human brain. As AI is completely based on logical thinking and reasoning, algorithms play a vital role in developing AI machines.
Types of AI machines
The past few years has seen an incredible drive in AI advancements. Current AI systems are able to manage large amounts of data and solve complex calculations. Different types of AI have come into existence including:
- Reactive machines: Most basic form of AI and are purely reactive. These machines do not have the ability to create memory and use past experiences. These machines will behave the same way every time it comes across the same situation.
- Limited memory AI: This type of AI uses past experience and present data to make decisions. It is mainly used in self-driving cars.
- Theory of mind AI: This form of AI is very advanced. It can analyse and understand human emotions. Experts expect this technology to change the world and make our work smarter.
- Self-awareness AI: It is a companion of theory of mind. Experts expect it will be super-intelligent, sentient and conscious.
Subsets of AI
Artificial intelligence can be divided into three subsets:
- Machine Learning (ML): First coined by Arthur Samuel in 1959, this subset provides systems the ability to automatically learn from the environment without being explicitly programmed. The process of learning begins with analysing data or past experiences and making the predictions on present situations. Machine learning algorithms are classified into supervised, unsupervised and reinforcement learning.
- Deep Learning (DL): The term was given by Rina Dechter in 1986 and also emerged from the limitations of machine learning. This subset specifically enables a computer to analyse things and make decisions from its past experiences. It also includes a concept of feature extraction (learning certain features even if features are not explicitly provided).
- Natural Language Processing (NLP): This subset is mainly concerned with the interactions between computers and humans. NLP processes the natural language and ensures that computers understand it, e.g. Google assistant. Types of NLP include natural language understanding (NLU) and natural language generation (NLG).
Conclusion
AI is an umbrella term which machine learning and deep learning sit under. It also represents a technique which duplicates human behaviour and takes its decisions from past experiences. AI can be trained to complete specific tasks by providing large amounts of data and recognising patterns in them. The applications of AI across different fields are becoming increasingly apparent, from heart disease prediction to gaming (e.g. chess). Within the past few years, there has been a growth in AI. However, the authors note that this is not the end. AI will continue to play a key role in the coming years, particularly within healthcare and pharma. AI provides solutions for many present demands and is likely to spread into broader roots in the coming years.
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