Applications of Artificial Intelligence In Day To Day Life

Posted by ongraph · May 9, 2017 · 6 Min read

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Artificial intelligence with technological innovations is beginning to find its way into our day-to-day life. Without our consciousness, such sophisticated technologies even entered into daily lives long back. Now, AI is significantly affecting how we live, work and entertain ourselves. Our lives have been significantly changed by machine learning and AI and its influence are likely to grow in coming years. In the following write-up, we mention areas where we are already addicted to personal assistance powered by AI and machine learning.

(In our previous post Artificial Intelligence and how they are empowering Search for Mobile, Web Apps, we have already discussed that how machine learning and artificial intelligence along with Deep Learning are helping companies like Google forming a better future for people. It’s deep learning that informs computer about data that help computer make a decision about other data.)

Here are some of the examples where we are trying to illustrate how AI is transforming our daily lives

  1. Email

You experience AI whenever you access your email accounts. Spam filters in your email inbox and Smart Email Categorization that you experience with Gmail are AI-powered. These spam filters filter out messages with particular words like ‘online pharmacy’ that come from unknown sources. However, complicated cases like a user might consider daily ‘deals email’ as spam that might be a welcome sight in the inboxes of others. All such cases are looked into by Machine Learning and transformed into AI allowing some Emails to be important for someone while they may be spam for others. By taking advantage of machine learning algorithms, Gmail successfully filters 99.9% of spam.

A similar approach is applied to categorize emails into primary, social, and promotional inboxes as well as labeling emails as important. In 2015, Google also introduced a next-generation email interface that begins smart reply. With machine learning, the system automatically suggests three different brief responses to answer the email. As of early 2016, 10% of mobile inbox user’s emails were sent via smart reply.

  1. Social Networking

Facebook, Pinterest, Instagram, and Snapchat are popular social networking platforms and are the biggest applicants of AI technology. Facebook uses Artificial Neural Networks and ML algorithms and recognizes faces. Every user also experiences a personalized newsfeed where they get ads that are relevant to their personal interests. With the AI initiative, Facebook claims to understand with near-human accuracy the textual content of several thousand posts per second. Facebook serves deep-text in Facebook Messenger where it can detect intent like – ‘I need a ride’ and it allows a user to hail an Uber from within the app. Google also implemented such a service in Google Map and now a user can jump to taxi booking app while searching route in Google Map.

An emoji-to-text translation (for instance, a laughing emoji could replace “lol”) on Instagram uses ML. Machine learning empowers the app to identify the contextual meaning of emojis. This way Instagram auto-suggest emojis and emoji hashtags. Alike, Printerest can automatically identify objects in images and then recommend visually similar pins. Snapchat also facilitates users to add animated effects or digital masks which adjust when their faces move. The app introduced facial filters, called lenses in 2015. The technology uses machine learning to track movements in the video.

  1. Online Shopping

Online retailers increase revenue tremendously while introducing AI and MI technology in their online stores. The personalized online shopping experience for a user by helping them find and buy products they are interested in. The online stores now can learn from past search patterns and make new searches easy. E-commerce web applications sites even quickly return a wide list of the most relevant products as well as personalized recommendations on the home page, bottom of item pages, and through email too. Online retails do such things by using artificial neural networks that help them generate product recommendations. This helps companies increase product sales.

Not only this but AI is also used for fraud prevention in online credit card transactions. In a press release announcing the rollout of its AI technology, online retailers leverage machine learning technology to increase approvals for genuine transactions and deliver an overall better consumer shopping experience.

  1. Mobile Use

Mobile Apps and devices are everyone’s personal assistant and their AI makes it a smart companion to carry it for every moment. We all have made a lot of fun from AI features i.e. voice-to-text at the beginning where our device converts the audio, that we speak, into text. Nowadays, it is a relatively routine task and helps many professionals save time. Google uses artificial neural networks to power voice search. Now as voice-to-text technology is accurate enough to use for basic conversation, it is adopted widely as a control interface for a new generation of smart personal assistants.

With Siri and Google Now, we have simply experience phone assistants, but Amazon has worked upon the model and expanded it with the announcement of complementary hardware and software components. Alexa is empowered with AI technology accepts voice commands to create a to-do list, order items online, set reminders, and answer questions (Via internet searches). In addition to this, Echo smart speakers allow you to integrate Alexa into your living room and use voice commands to ask natural language questions, order pizza, play music, hail an Uber, and integrate with smart home devices.

  1. Smart Cars

Have you ever wondered how Google Map continuously updates you about the live traffic conditions every time you are on the road and even sends notifications about traffic statuses about the routes you mostly follow? When you enable location access to the app, it is able to analyze in real-time whether you are moving or stuck in traffic. The app pool such data from millions of users around the clock, including those who are traveling on the same route as yours. The app even calculates the number of cars on the same route, and how fast they are moving to tell you exactly where and what time can you expect heavy traffic. The more people using the app, the more accurate data would be. Interestingly, all the data is sent to Google servers anonymously, which means if they have access to your location, the information is secure.

Google Maps even suggest the fastest routes to and from work through incorporating user-reported traffic incidents like construction and accidents. Besides this, Uber and other taxi apps can determine the price of your ride. They also minimize the wait time once you hail a cab. Have you thought that how do these services optimally match you with other passengers to minimize detours? Obviously, it’s ML and Uber’s Head of Machine Learning Danny Lange confirmed Uber’s use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection.

In the above write-up, we have only scratched the surface of examples of AI and ML in day-to-day life. AI has deeply integrated into human lives. For example, causal chess players regularly use AI-powered chess engines to analyze their games and practice tactics.

How you enrich your life with an AI assistant? Let us know via the comment section below.

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