The world of AI has a lot of things around it to thank for its existence in our technological landscape of today. Not only have humans spent decades of research perfecting the mathematical calculations to make these wonderfully complex learning algorithms work but during this time we have looked further than our own species as inspiration to make the next generation of intelligent presence on our planet. Mother Nature, and all that it encompasses, has it’s roots firmly planted in the workings of Artificial Intelligence — and it’s here to stay.

Aren’t Sir David Attenborough’s wildlife documentaries just incredible? They go into incredible, high definition detail about the behaviours and properties of the Earth’s many inhabitants, and they allow us to understand how they fit into the natural ecosystem and work together in order to allow our planet to flourish — to make it Earth. Now I’m no Sir David Attenborough, but I’m still going to take you on a wildlife documentary of my own. The star creatures in question are none other than those Artificial Intelligence algorithms that are inspired directly by Mother Nature’s own. But first, I need to introduce you to two algorithm concepts. Search/Pathfinding and Predictive Modelling.

SEARCH (PATHFINDING) ALGORITHMS

Search algorithms are essentially programs that are designed to find the best/shortest route to an objective. For example, the travelling salesman problem is a typical search optimisation issue where you are given a list of cities and distances between those cities. You must search for the shortest route for the travelling salesman, whilst visiting each city once to minimise travel time and expenditure (ensuring that you return to the origin city). Real world applications of this problem are delivery trucks. Imagine 100 people in London made an online order and all the boxes are loaded into one van. The courier (let’s say…DPD), must now calculate the most efficient route (balancing distance/time taken) to deliver those packages from the depot (eventually returning to the depot) to ensure that the company is wasting as little time and money as it can during the delivery process.

PREDICTIVE MODELLING ALGORITHMS

Today, predictive modelling is where all the hype is. Data Scientists everywhere are shouting ‘Neural Networks!’ from the rooftops of their cosy office buildings and companies like Google are running around trying to solve the world’s problems with different variations of these complex little ‘artificial brains’. Essentially predictive modelling uses statistics in order to predict outcomes. You often hear Data Scientists attempting to solve two kinds of predictive modelling problems, Regression and Classification. Regression is the dark art of finding the correlation between two sets of variables, and classification is the process of determining the probability of a dataset belonging to a different set.