Machine Learning is fast becoming part of our daily lives with the growing adoption of artificial intelligence in digital networks. Machine learning and AI is so deeply integrated into the devices, virtual networks, and applications we use, that we often don’t see how many of our decisions are guided by these systems. Here are some ways that deep learning and machine learning drive our daily decisions.

Virtual Personal Assistants

Increasingly, people use voice-activated virtual personal assistants like Alexa, Siri, and Google Assistant throughout the day. For example, you may ask about your flight timings and you will get an answer based on the relevant data that the assistant has managed to parse. Google Assistant has demonstrated that it can even alert you if your flight is delayed before the airline does so, by going through the data available on airports and flights. It has further demonstrated that it can carry on a conversation with humans on the phone to successfully make restaurant or hair salon appointments with its Duplex technology.

Virtual personal assistants are driven by natural language processing, machine learning, and deep learning algorithms. Deep learning neural networks are crucial to ensure a speedy and robust Natural Language Programming (NLP) solution. These are trained to collect and analyze data from your previous interactions via deep learning. This will then help the assistant to provide you with results that will be closely aligned with your preferences.

Deep learning-driven virtual assistants and voice technology are also integrated into several platforms like search engines and smart devices that we use today, like Amazon Echo, Google Home, and smartphones.

Social Media

Social media services like Facebook rely heavily on deep learning to present you with a personalized news feed, friend recommendations, and face recognition and tagging. Pinterest also uses deep learning to extract information from images, identify objects and recommend similar pins. When a user uploads a photo with a friend, Facebook algorithms via deep learning, are trained to recognize the other person in the picture, match it with the faces in your friend list.

More importantly, it can use your information to send you relevant advertisements. Deep neural networks are used to analyze the large amounts of unstructured data that users provide via photos, likes etc. This allows the models to show the most relevant and precisely targeted advertisements to users of the social media network.

Personalized recommendations through Deep Learning

When you shop online or watch entertainment via a streaming service like Netflix, the recommendations that pop up are based on deep learning models. Netflix’s algorithms consider your viewing history and search terms to learn your preferences. Netflix can then suggest films or series similar to the kind of entertainment that you have historically shown a preference for.

The deep learning extends to picking the right kind of thumbnail image customized to the tastes of the individual user. The deep learning algorithms on Netflix, for example, will consider the user’s profile, search terms and previous movie genres chosen to exploit optimization of images. This brings a heightened level of personalization for the user.

When you shop online via a website or app, you are likely to receive similar shopping suggestions a few days later or the next time you visit the site. The system has ‘learned’ your preferences based on your past purchases, website/app navigation, and items added to cart etc. and can make product recommendations that are most suited to your personal tastes.

Online customer service

Today most websites offer the customer the option of interacting with a company representative with any queries while navigating the website. These are usually chatbots equipped with machine learning and NLP to help extract information from your queries and the current data on the website to respond to your queries satisfactorily or direct them to a human representative if necessary.

Improve email spam filtering

Spam filtering needs to be constantly updated. Deep learning helps it go beyond simple rule-based spam filtering to adapt to the more sophisticated approaches employed by spammers.

Predicting relevant emojis

Emoji usage is complex. Some are more commonly used in place of words or express emotion and are easily understood – the clapping hands emoji or the smiley face, for example. But others may have specific cultural uses – as for example the facepalm emoji – which can be confusing to some who may not be familiar with it. So how does the system predict which emoji the user is likely to pick from a library of hundreds? It does these predictions with the help of deep learning methods like convolutional neural network and other classifying approaches.
As we spend a larger portion of our time every day accessing the digital world, AI will embed itself increasingly in our daily lives. Thanks to deep learning, it can be trained to help deliver more meaningful and personalized results.