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Understanding algorithms

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Algorithms are increasingly impacting our everyday lives. Many are designed to predict and even alter human behaviour. A range of organisations use algorithms including Google to rank the web pages we view, Amazon to recommend books for us to read, banks to assess our credit worthiness for a loan and online dating sites to find our perfect match. 

At its most basic, an algorithm is a set of instructions for how to achieve something. That “something” can be as simple as baking a cake or as complex as forecasting long-range weather patterns. The former merely requires a pen and paper to record a list of ingredients while the latter needs a computer capable of processing intricate mathematical calculations. 

So, both humans* and computers use algorithms. They are detailed, step-by-step roadmaps for completing a task (e.g., a recipe for making food) or solving a problem (e.g., evaluating the exposure of organisms to climate change). Each specific situation has a discrete and self-contained sequence of events which are defined by the relevant algorithm.

Withdrawing cash from an ATM is an algorithmic process. An ATM is a computerised telecommunications device that is programmed to operate in accordance with pre-determined rules - e.g., maximum withdrawal limit and PIN attempts. Within these parameters, the ATM has a certain number of predefined actions it can perform - verify PIN, dispense cash, issue receipt, show balance and retain card. 

In computing, programmers write algorithms that tell a computer precisely what it needs to do. These algorithms leave no room for subjectivity or judgment. They handle data and take resulting actions in a fixed manner according to rigid criteria. The most complicated algorithms are found in science, where they are used to design new drugs and model the climate.

Algorithms typically use the past as an indicator of the future and they do this by sifting through enormous masses of data. This is referred to as predictive modelling and it’s a technique being used by law enforcement agencies to forecast crimes. Forecasting where and when a crime is likely to occur is gaining considerable currency around the world. 

An early developer of predictive policing was the University of Memphis. A team of criminologists and data scientists compiled crime statistics from across the city of Memphis and overlaid these with other datasets - such as social housing maps and outside temperatures. They then instructed the algorithms to search for correlations in the data to identify future crime “hot spots”.

On 4 August 2005, Memphis police - guided by the outputs of data-crunching algorithms - made so many arrests over a three-hour period that they ran out of vehicles to transport the detainees to jail. Three days later, 1,200 people had been arrested across the city - a new police department record. The data-driven operation was hailed a huge success.

Arguably, the world’s most famous and closely guarded mathematical formula is Google’s PageRank algorithm. While the exact way Google organises search results remains a closely guarded secret, the broad strokes of how the algorithm works are known. Google’s trademarked algorithm assigns each web page a relevancy score.

The task of sifting through all those pages to find helpful information is monumental. Yet Google’s search engine generates results in a fraction of a second thanks to its algorithm. It instantaneously identifies web pages that contain the keywords used in a search request and then assigns a rank to each page. Higher ranked pages appear further up in Google’s results page.

Algorithms are also being used to save lives. Living kidney donors and medically compatible transplant candidates have been successfully matched using computer algorithms. These kidney exchange programs have overcome the traditional problem of a willing donor (usually a loved one) not being compatible with the intended recipient of their organ. 

In the past, these unmatched donor-recipient pairs had nowhere to turn. Now, they can join other incompatible pairs on a kidney database that creates a chain of donors and recipients. By pairing a selfless donor - who originally wanted to donate to a family member who was not a match - with a complete stranger, the matching algorithm results in a greater number of transplants. 

The use of algorithms also extends to financial markets. High-frequency automated stock trading relies on algorithms to decide - based on criteria related to price, timing, volumes, etc. - what and when to buy or sell. The system enables firms to execute more than 100,000 trades in a second for a single customer - a speed that is impossible for humans to match. 

When driving your car, algorithms figure out the best route for you to take. The science of route planning pulls together information such as length of road, time of day, volume of traffic, speed limits and road blocks to generate an estimated time of arrival. Sat-navs determine the shortest distance from where we are to where we would like to go.

It’s clear that we are living in an algorithmic society. These mathematical formulas will progressively make more decisions for us and help solve more of our problems. From what we read, to whom we date and how we invest, you will not be able to escape algorithms. And if there’s not an algorithm for something already, there soon will be!

*Circa 1600 BC, the Babylonians developed the earliest known mathematical algorithms for factorization and finding square roots.

 

Regards
Paul J. Thomas, CEO

Comments

avatar John Clark
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Well done Paul
As always your blogs are very thought provoking
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CEO Paul Thomas