Thinking about AI means thinking about self-driving cars, remembering HAL9000, Terminator and I Robot movies. Luckily, AI’s scenario is a lot less dreadful — and way nearer to us. In fact, Artificial Intelligence became the most important part of our life and we have been using it on a daily basis for years, almost without realising it.
You don’t believe it, right?
Let’s follow Dan in its daily routine journey to discover how much AI is around us!
Here we have Dan, Millennial and technology addicted.
He wakes up listening to his favourite Spotify playlist, that he has discovered last week thanks to the app’s suggestions.
Spotify’s algorithm analyzes the playlists of its subscribers all over the world, comparing them and then formulating playlists. The system scans millions of users and compares the tastes of each one, then crosses the data and finds songs to recommend. It uses Natural Language Processing (NLP), to understand what people say and comments about albums and musicians, Collaborative Filtering, to create the recommendation model, Machine Learning, to recognise patterns and improve predictions, and many others kind of algorithms.
He goes under the shower, where he says: “Alexa, please, read for me today’s most important news” and Alexa starts with his favourite newspaper.
Here you have speech recognition, natural language processing (NLP, a procedure of converting speech into text then natural language understanding (NLU), to recognise intentions, and transform the request into action.
Dan prepares a brewing coffee while he walks in the kitchen. The coffee’s almost run out. Time for a new order on Amazon!
While Dan is ordering his favourite coffee blend, Amazon suggests he may be interested in a new pair of high-quality headphones. It’s what he needs!
Amazon, as well as Spotify, uses collaborative filtering, to get recommendations in real-time. “This type of filtering matches each of the user’s purchased and rated items to similar items, then combines those similar items into a recommendation list for the user”. This is a data-driven marketing approach that sells!
“35% of Amazon.com’s revenue is generated by its recommendation engine.” (McKinsey)
Time to go out: he’s just started a new job, so he hasn’t memorised the route yet. No problem: Dan calls Siri and asks it (her?) to open Google Maps and lead him to his brand new, shining office.
Siri is able to recognize voice queries, giving relevant answers to questions, send messages and make calls. It works similar to Alexa. Google Maps’ algorithms suggest the most convenient routes and means of transportation for the selected destination, by analysing in real-time thousands of data such as traffic, accidents, time scheduling, etc.
Before sitting at his desk, Dan changes the temperature of lights, because he prefers subtle and warm tones.
Ok, let’s check the emails. He loves Gmail because he can see which ones are important and which ones still need an answer. He almost forgets to look at his Google Calendar, but there’s no problem: notification on his phone reminds him of all the main tasks of the day.
Gmail uses machine learning to stop unwanted email or spam from entering your inbox, to protect the system from malware and phishing. The system can analyze and learn from previous examples to make decisions as precise as possible.
Recently it is also suggesting you most popular sentences to use while you are writing. It also detects if you mentioned some keywords and suggests actions. An example? Gmail shows you an alert if you mention an attachment and you don’t include it in the email.
But first, let’s book a table at that amazing little Italian restaurant for lunch. It looks popular on Tripadvisor! Let’s contact the restaurant using their chatbot. “Hi, I want to book a table today for 1 pm.” et voilà, five minutes of chatting and he’s done.
Many intelligent chatbots are using Artificial Intelligence to as Siri can do. They understand language with NLP and NLU, recognise intentions and sort out the right answer or action to meet the user request.
Oh God, it’s five o’clock already! It’s time to leave, also because he wants to stop and give a try to that new amazing SPA that he saw on a Facebook ad. Before going home, he orders also groceries on Amazon, trying that new brand of pasta that the system recommended him after his last purchase.
Facebook uses AI to show relevant ads thanks to machine learning and predict those ads that users are likely to click on.
It’s time to go home and relax. Well, it’s been a really busy day.
“Alexa, please, turn the lights off, put some jazz music on and wake me up at 8.00 am. Thank you!”
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