Introducing you to Big Data and AI
By: Lauren Toulson
Tags: Big Data, Artificial Intelligence, Ethics, Data Science, Machine Learning

The world is digital and changing towards a data economy. Everything we do leaves a digital footprint behind, a trace of our thoughts, interests and behaviours. This happens without us realising; the infrastructure of the internet is designed to communicate and exchange data as much about us as possible. Every time you open a website, you accept cookies, data packages with analytical tools are providing insight about your persona. When you share posts on social media, tag friends, buy stuff, those websites are watching and reflecting on how to optimise their site based on your behaviour. It helps them to understand our habits, so they can improve their products. For instance, analysing our search engine query improves the autofill searches next time we use it.

Here we explain some common tools that are going to become more important for data tracking in the coming years.

Machine Learning

Machine learning refers to the algorithms that allow computers to automate tasks; they are taught through different types of programming to identify information and patterns of large data sets with thousands of data points, making it infinitely quicker than human labour.

Machines can be trained in various ways:

Supervised learning

With supervised learning, data is fed into the machine learning algorithm fully labelled, this way it can learn exactly what pattens are associated with which labels, and therefore identify it when it has to identify something unlabelled. For instance, it would be fed thousands of images of animals and told which ones are cats, which are dogs. It can learn from the data its own way of recognising the patterns to identify the data.

Semi-supervised learning

Like supervised learning, some data is labelled, but it will also be trained on a mix of labelled and un-labelled data. This means that the programmers don’t have to sit and label huge quantities of data themselves, but benefit from the algorithm knowing initially where to start, by using some labelled data.

Un-Supervised learning

Unsupervised learning works without data labels, the algorithm must find the patterns within the data by itself. It often does this by clustering the data, finding hidden patterns in the data and grouping them, so it would be able to identify different patterns in cat images, and different ones for dogs. This is helpful when the programmers don’t know the labels or patterns themselves, and need the machine to do this for them — for instance, in market research it can be used to group types of customer behaviour, and allows Amazon and Netflix to provide better recommendations.

Neural Networks

A form of machine learning, deep learning, where the algorithm forms data branches in a similar way to a human brain, making thousands of connections. Deep learning is very popular for the best AI systems — For example, Facebook’s ‘DeepFace’ AI auto-tags photos. It was trained on 4.4 million Facebook photos from 4000 profiles, resulting in an accuracy of 97.25%

Issues with Machine Learning

Bias

Inequalities in society can be worsened with machine learning algorithms, either because of the unconscious bias of those who programmed it, or the data it was trained on. For example, Compas, the risk assessing algorithm, will predict that black people are twice as likely to reoffend than white people. PredPol, an algorithm that advises where police are most needed, based on data that predicts where and when crimes are most likely to take place, send police to areas populated with more minorities regardless of actual crime rates. Facial recognition softwares were initially trained on more data including white male faces, resulting in issues identifying people of colour and women. This demonstrates that bias in the data fed to machine learning is something incredibly important to overcome.

Ethics

In addition to bias, it also must be considered where that data comes from in the first place, before training AI on it. In the UK, the Data Protection act ensures that personal data must be used transparently, meaning the users consent to how it is used. In 2017, the NHS leaked patient data to the machine learning team DeepMind at Google to train an AI symptom detection system, breaking personal data rights.

Optimising Big Data in Business

Some companies are benefiting greatly from the insights that machine learning and big data can provide. For instance, H&M used data from purchases in its store to restructure their stock and store layout to suit their customers. By identifying that most of their customers in that store were women, they could promote women’s fashion and reduce costs on men’s department.

One of the leaders in big data for business is Amazon — every order feeds its algorithm with 2000 data points. They can target customers with personalised browsing by increasing or decreasing prices products they will be interested in, and promoting similar items to make the customer feel that Amazon will always cater for their needs. 35% of their annual profits comes from this personalisation method, through big data. Profits are increased by 25% annually by using live product, interest and order data and adjusting product prices every 10 minutes to suit.

Next, our series will look at how supposedly intelligent “AI” compares with human intelligence in a variety of tasks, such as playing chess, identifying health problems and creating art.

This was written by a researcher at a specialist data company. The Digital Bucket Company operates in the UK and works with clients in overcoming data challenges including privacy concerns.