The word “algorithm”, meaning a precise series of operations carried out to solve a problem, derives from the Latin translation of the name of the medieval Persian mathematician al-Khwārizmī.
Many of us first encounter algorithms at school, where we are taught simple ones to solve mathematical problems such as long division or calculating the area of complex shapes. Although, in the digital age, the word algorithm has become closely associated with computers, for centuries they were tools for humans to use; a way to codify and communicate solutions to difficult tasks, so that others could—often laboriously—work through the steps and get the required result.
When computers were invented, their particular strengths and weaknesses made them a perfect fit for algorithms. What computers lack in intelligence and creativity, they make up for in their ability to quickly and precisely follow mathematical instructions, without getting confused, bored, or fatigued. In automated management and decision-making systems, computers can augment and replace human control, targeting tasks where humans perform poorly, such as those that require very fast reactions, unwavering concentration, difficult mathematics, perfect reproducibility, and exact audit trails.
Just as they first allowed humans to communicate reliable ways of solving problems between themselves, algorithms, expressed in the form of computer code, now allow humans to communicate solutions with computers. They represent a type of crystallised intelligence, created—often at great cost in terms of time and research—in one place and time, then used and reused in many others. This in turn allows humans to concentrate on further improving and refining these algorithms, tailoring them to take better advantage of computers' strengths. The reciprocal benefits of this arrangement multiples the power of both human creativity and computer calculation.
We interact with algorithms every day, often without realising it. Of course, they are at the heart of every computer system we interact with: desktop PCs, mobile phones, ATMs, etc. But they also work behind the scenes to manage traffic on our roads, and regulate our public transportation systems. They monitor and control the infrastructure that supplies electricity, water and gas to our homes and workplaces. The labyrinthine supply chains that move food and other goods around the world to end up on shop shelves are controlled by algorithms. Even the temperature of our surroundings is often controlled by the algorithms within an air-conditioning system.
The growth in complexity of modern life does not look set to abate. Rather, the internet has turbocharged the process. And while the “Internet of Things” has seen both hype and subsequent backlash, its vision of a world where every object and device is connected to the network seems inevitable. This will present new opportunities, but also new challenges. Traditional algorithms and approaches to managing complexity will struggle to cope with billions of interconnected devices and almost infinite volumes of data. The rapid growth in research and interest around big data and machine intelligence is a response to this problem.
In essence, the aim of artificial intelligence, and ancillary fields such as machine learning, big data and predictive analytics, is to develop a new generation of algorithms. Whereas traditional algorithms have crystallised a solution to a circumscribed problem, these new algorithms attempt to codify, at least to some extent, the processes of creativity and learning themselves, which have so-far remained the remit of humans. The fundamental conjecture that underlies AI research is that these processes are themselves algorithmic in nature, and it is only a matter of uncovering what these algorithms are.
To what extent AI researchers succeed will determine the future course of our world, perhaps more than any modern politician, philosopher or nation state can hope to do. We all live enmeshed in a web of algorithms that far surpasses the ability of any one person to understand. Creating algorithms that themselves understand algorithms may be the only way to deal with this ever growing complexity.
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