What is an algorithm?
Nebulous and often closely guarded, we unpick a term that unsurprisingly causes lots of confusion
A simple explanation for an algorithm is that it is a set of rules for a computer, but they've never really been known for simplicity. As technology has advanced, the algorithm has become more and more complex, powering software and many things around us. Today, they've become the complicated law that powers and controls seemingly everything in the IT world.
It hasn't always been the case though; algorithms are a form of maths that has been around long before the internet and computers as specifications for performing calculations, data processing and automated reasoning.
They are rules used to automate the treatment of a piece of data. If 'a' happens, then do 'b'. This is the logic that most systems run on, for example, if a user calms to be 18 they can access certain websites, if not, the website will be told to print "sorry, you must be 18 to enter".
This basic example can be found in many more complicated and intricate guises around the world and in many of our most used services. Facebook and Google create and deploy countless algorithms for news searches and monitoring. Driverless cars navigate via algorithmic instructions and even our mobile phones have them running programs in the background.
Algorithms are everywhere
Algorithms are all around us. When you type a search into Google, you're making use of Google's web ranking algorithm. When you ask Spotify to play music you might like based upon your previously listened to tracks, you're using an algorithm. Even when looking at Google Now to see what information it has for you today, based on your calendar and general routine, that's powered by an algorithm.
The important thing to consider is that how certain algorithms work is usually a very well-kept secret, with few companies giving away how they were developed. Their running is hugely complex too, taking hundreds, if not thousands of different factors and pieces of information into account.
Facebook, Twitter, LinkedIn and Instagram all use algorithms to decide which posts to show to who and probably the most recently popularised is Facebook's, which prioritises personal connections above branded content. It analyses what you interact with most and shows more of those to make sure you're only seeing the content you're interested in.
Algorithms fit into the same category as machine learning applying relevant information to a circumstance. They were first used in image recognition technology, training computers to recognise faces or objects in a picture.
But now algorithms have become even more sophisticated, analysing data left, right and centre. They're used to predict the weather, work out whether more policing is needed in certain areas if there's a spike in crime, for translating languages and even working out what you need to add to your shopping list based on when you bought specific items last.
Humans and algorithms aren't mutually exclusive
Despite the efficiency gains that algorithms provide, they're not too great at conversing with people just yet. In fact, the drive to digitise many parts of the customer service industry led to frustration, as disgruntled customers want nothing more than to speak to a human being. Until characteristics like empathy and compassion can be artificially replicated successfully, even the most advanced algorithms remain unattractive alternatives to human-to-human conversations.
Even if algorithms are deployed correctly, machine learning-based algorithms, by definition, need to get some things wrong in order to evolve. Facebook's news algorithm tailors content to your personal tastes, which has raised concerns that users are becoming increasingly isolated from divergent opinions.
Equally worrying was the recent discovery that Facebook's algorithm failed to spot thousands of Russian-sponsored adverts during the US Presidential election, a blunder that prompted the company to roll back its reach and redeploy a team of humans to check for quality.
The ultimate law enforcement agency guide to going mobile
Best practices for implementing a mobile device programFree download
The business value of Red Hat OpenShift
Platform cost savings, ROI, and the challenges and opportunities of Red Hat OpenShiftFree download
Managing security and risk across the IT supply chain: A practical approach
Best practices for IT supply chain securityFree download
Digital remote monitoring and dispatch services’ impact on edge computing and data centres
Seven trends redefining remote monitoring and field service dispatch service requirementsFree download