What Is Artificial Intelligence and how does it change the world?
Ten years ago, if you had mentioned the term “artificial intelligence” in a boardroom, there’s a good chance you would have been laughed at. But Today it is one of the hottest buzzwords in business and industry.
Artificial Intelligence (AI) technology is a crucial lynchpin of much of the digital transformation taking place today as organisations position themselves to capitalize on the ever-growing amount of data being generated and collected.
So how has this change come about? Well partly it is due to the Big Data revolution itself. The glut of data has led to intensified research into ways it can be processed, analysed and acted upon. Machines being far better suited to humans than this work, the focus was on training machines to do this in a “smart” way as far as possible.
This increased the interest in research in the field – in academia, industry and among the open source community. And it has led to breakthroughs and advances that are showing their potential to generate tremendous change – from healthcare to self-driving cars to predicting the outcome of legal cases.
What is Artificial Intelligence?
The concept of what defines AI has changed over time; but at the core there has always been the idea of building machines which are capable of thinking like humans.
After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change. If we want to build machines to help us to this more efficiently, then it makes sense to use ourselves as a blueprint!
Artificial Intelligence, then, can be thought of as simulating the capacity for abstract, creative, deductive thought – and particularly the ability to learn – using the digital, binary logic of computers.
Research and development work in Artificial Intelligence is split between two branches. One is labelled “applied AI” which uses these principles of simulating human thought to carry out one specific task. And the other is known as “generalised AI” – which seeks to develop machine intelligences that can turn their hands to any task, much like a person.
Research into applied, specialised AI is already providing breakthroughs in fields of study from quantum physics, where it is used to model and predict the behaviour of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.
Btw, Genomic data refers to the genome and DNA data of an organism. They are used in bioinformatics for collecting, storing and processing the genomes of living things. Genomic data generally require a large amount of storage and purpose-built software to analyze.
In industry, it is employed in the financial world for uses ranging from fraud detection to improving customer service – by predicting what services customers will need. In manufacturing it is used to manage workforces and production processes as well as for predicting faults before they occur, thus enabling predictive maintenance.
In the consumer world more and more of the technology we are adopting into our everyday lives is becoming powered by AI – from smartphone assistants like Apple’s Siri and Google’s Google Assistant, to self-driving and autonomous cars, which many are predicting will outnumber manually driven cars within our lifetimes.
Generalised AI is a bit further off – to carry out a complete simulation of the human brain would require both a more complete understanding of the organ than we currently have, and more computing power than is commonly available to researchers. But that may not be the case for long, given the speed at which computer technology is evolving. A new generation of computer chip technology known as neuromorphic processors are being designed to more efficiently run brain-simulator code.
What are the key developments in AI?
All of these advances have been made possible due to the focus on imitating human thought processes. The field of research which has been most fruitful in recent years is what has become known as “machine learning”. In fact, it has become so integral to contemporary AI that the terms “artificial intelligence” and “machine learning” are sometimes used interchangeably.
However, this is an imprecise use of language, and the best way to think of it is that machine learning represents the current state-of-the-art in the wider field of AI. The foundation of machine learning is that rather than have to be taught to do everything step by step, machines, if they can be programmed to think like us, can learn to work by observing, classifying and learning from its mistakes, just like we do.
The application of neuroscience to IT system architecture has led to the development of artificial neural networks– and although work in this field has evolved over the last half century, it is only recently that computers with adequate power have been available to make the task a day-to-day reality for anyone except those with access to the most expensive, specialised tools.
Perhaps the single biggest enabling factor has been the explosion of data, which has been unleashed since mainstream society merged itself with the digital world. This availability of data – from things we share on social media to machine data generated by connected industrial machinery – means computers now have a universe of information available to them, to help them learn more efficiently and make better decisions.
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