“As soon as it works, no one calls it AI anymore …”
You wake up, refreshed, as your phone alarm goes off at 7:06am, having analysed your previous night’s sleep to work out the best point to interrupt your sleep cycle. You ask your voice assistant for an overview of the news, and it reads out a curated selection based on your interests. Your local MP is defending herself—a video has emerged which seems to show her privately attacking her party leader. The MP claims her face has been copied into the footage, and experts argue over the authenticity of the footage. As you leave, your daughter is practising for an upcoming exam with the help of an AI education app on her smartphone, which provides her with personalised content based on her strengths and weaknesses in previous lessons.
On your way to work, your car dashboard displays the latest traffic information, and estimates the length of your journey to the office, based on current traffic conditions and data from previous journeys. On arrival, you check your emails, which have been automatically sifted into relevant categories for you. A colleague has sent you several dense legal documents, and software automatically highlights and summarises the points most relevant to a meeting you have later. You read another email, sent by your partner, asking if he can borrow your bank login details to quickly check something. On closer inspection you decide it is probably a fake, but still, you hesitate before deleting it, wondering briefly how the spammers captured his writing style so unerringly.
You have other things to worry about though, as you head to a hospital appointment. However, after a chest x-ray, you are surprised when the doctor sits you down immediately afterwards, explaining that you look to have a mild lung infection—you had expected it to take weeks before the results came back.
Your relief is short lived—a notification on your phone warns you of suspicious activity detected on your bank account, which has been automatically stopped as a result. You call the bank, and someone called Sarah picks up, and helps you order a replacement card. Except, you soon realise, Sarah is not human at all, just a piece of software which sounds just like a real person. You are a little unnerved you did not realise more quickly, but still, it got the job done, so you do not particularly mind.
After a quick detour to the local supermarket, where the products on the shelves have all been selected automatically based on previous customer demand, current shopping trends and the likely weather that day, you drive home. On your way back, your car detects signs that you are feeling slightly agitated, and chooses some music you have previously found relaxing. After dinner, you and your partner watch a film suggested by your TV, which somehow strikes just the right note for both of your normally divergent tastes. After dozing off, your house, predicting you are asleep by now, turns off the bathroom light and turns on the washing machine, ready for another day.
1 Betrand Meyer, ‘John McCarthy’, Communications of the ACM (28 October 2011): [accessed 8 March 2018]
2 A range of smartphone apps exist which can track sleep cycles by monitoring bed movements or snoring, and use machine learning to attempt to wake you up during lighter periods of sleep. See for example, Brenda Stolyar, ‘Sleep Cycle app for Android will soon allow users to track sleep using sound’, Digital trends (14 February 2018): [accessed 8 March 2018]
3 The Amazon Echo and Google Home devices are just two of the many home AI assistants currently on the market with this feature.
4 Lyrbird.ai, a US-based start-up, has used an AI voice emulation system to replicate the voices of former US President Barack Obama and current President Donald Trump. Other AI software is allowing users to swap faces into pre-existing video footage with relatively little technical skill necessary.
5 Software known as ‘Intelligent Tutor Systems’, such as Tabtor, Carnegie Learning and Front Row, is increasingly being used to track a learner’s progress and provide them with lessons and personalised content based on this.
6 Most digital map services in use today use machine learning to predict traffic flow speeds and provide an estimated time until arrival.
7 Many email services in use today, including Google’s Inbox and Microsoft Outlook, use AI to categorise emails by type and priority.
8 A range of ‘lawtech’ businesses have begun offering software which examines legal documents for relevant information, and can assist with the preparation of legal contracts.
9 A recent report from 26 academic and industry experts warned of the malicious applications of AI, including mass ‘spear phishing’ attacks. Current spear phishing attacks, whereby fraudulent emails are personalised to an individual target, usually in a bid to steal sensitive information, currently require significant human labour, but by automating this process, these attacks could be scaled-up in the near future. See Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, University of Cambridge, Center for a New American Security, Electronic Frontier Foundation and OpenAI, The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (February 2018): [accessed 1 March 2018]
10 AI-powered radiology software is beginning to enter limited usage, which can automate aspects of x-ray analysis, and significantly reduce the amount of time needed to get useful results from scans.
11 Companies like MasterCard and Visa have been using machine learning algorithms to detect fraudulent patterns of spending in debit and credit cards, and automatically freeze cards in response.
12 A number of companies are using AI-powered chatbots, which can handle routine interactions with customers, and recently NatWest began experimenting with ‘Cora’, an in-branch AI personality, which can help with basic customer queries.
13 A number of UK supermarket chains are now using machine learning algorithms to better predict customer demand for particular products, cutting down on unnecessary waste and missed sales.
14 Emotion recognition is currently a significant area of growth in AI development, and a number of facial recognition companies have claimed that their systems have achieved human or near-human levels of emotion recognition.
15 Online film and TV streaming services, such as Netflix and BBC iPlayer have used machine learning algorithms to suggest what to watch based on previous viewing preferences and a range of other factors.
16 Smart home hubs, which can control a range of different smart systems such as lighting and home appliances, are becoming increasingly commonplace, and are using machine learning to detect and automate household functions based on personal habits and behaviour.