Analytics

Algorithms will soon rule our lives so we’d better understand how they work

24th Mar `14, 06:52 AM in Analytics

One of the most interesting announcements in last week’s Budget – well, for me at least, as someone…

BDMS
Guest Contributor
 

One of the most interesting announcements in last week’s Budget – well, for me at least, as someone who has no savings and doesn’t play bingo or drink much – was the new Alan Turing Institute: £220 million of government support will be invested into “big data and algorithm” research.

Today, especially with the processing power of modern computing and enormous “big data” analysis, algorithms increasingly influence the media we read, the products we buy, and with whom we socialise. But most of us have no idea what they are. At its core, an algorithm is just a simple formula which must be followed to calculate the answer to a mathematical problem. (The word “algorithm” itself is derived from the eighth-century Persian mathematician Al-Khwārizmī but the concept goes all the way back to the Greeks.)

Algorithms are vital to the internet because they help to order and arrange vast volumes of data at a scale and speed impossible for a human. Google’s famous PageRank algorithm counts the number of links to a page and assesses their quality to determine how important a website is. The quality and quantity of websites’ links to each other are compared and ordered; the more important websites are displayed first on the Google search page when a search query is entered.

Facebook’s feed prioritisation algorithm calculates which posts a user sees on his or her news feed, how close a user and the creator of a post are, how valuable the content is (with photos determined to be the most worthwhile, and plain text the least so), and how long a post has been up for. Nothing is left to chance: by weighing up these three factors, the algorithm decides what you see – and Facebook updates and improves these regularly. And you’ll all be familiar with Amazon’s algorithms that constantly propose annoyingly accurate recommendations of what books that you might also like to buy, based on what others have bought.

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