Artificial Intelligence (AI) and its different sub-domains are rapidly being used to assist with repetitive procedures in various industries and businesses. However, established tech firms and startups are becoming increasingly interested in using AI to make everyday living easier.
AI has played a significant role in improving the way we entertain ourselves, interact with our mobile gadgets, and even drive our vehicles. More often than we realize, we come across Machine Learning (ML) algorithms and Natural Language Processing (NLP) in a variety of common tasks.
So we will be looking at 10 practical uses of AI in everyday life, but before we dive right in let’s look at the categories of AI usage
Categories for AI Usage
We can classify the way AI is utilized to improve the functionalities of day-to-day life into two broad streams.
Voice assistants, picture recognition for face unlocking in mobile phones, and machine learning-based financial fraud detection are all examples of AI software that people use every day. AI software is frequently as simple as downloading AI-capable software from an online store and does not require any additional hardware.
Drones, self-driving vehicles, assembly-line robots, and the Internet of Things are all examples of AI hardware (IoT). This entails the creation of particular gadgets with AI capabilities.
10 Applications of AI Capabilities in Day to Day Life
Artificial intelligence (AI) and machine learning (ML)-powered software and technologies are imitating human thought patterns to aid society’s digital transition. AI systems perceive their surroundings, respond to what they see, solve problems, and assist with chores to make life easier.
Here are ten areas where AI plays a significant role in assisting humans with their daily needs
1. Voice Assistants
Voice User Interfaces (VUI) powered by AI are used by digital assistants like Siri, Google Home, and Alexa to process and decipher voice requests. These apps may now use AI to not only rely on voice commands but also to access massive databases stored on cloud storage services. These apps can then analyze millions of lines of data per second to finish tasks and return personalised search engine results.
There has been a significant shift in customer understanding and familiarity with this technology. Voice assistant interfaces are quickly evolving, including applications in healthcare such as identifying disorders via vocal biomarkers. For triage and screening, voice-based chatbots are also being integrated into healthcare apps.
2. Smart Input Keyboards
To give a user-friendly experience, the latest versions of mobile keyboard apps include the features of autocorrection and language recognition.
These apps use AI to efficiently fix mistakes, assist with language switching, and forecast the next word in a non-intrusive manner. AI programmers are teaching these apps to grasp the context of the message being entered and generate accurate predictions using the “random forest” machine learning method.
Over 300 languages and dialects are now supported by apps like Typewise and Swiftkey. Recently, new features such as real-time translation and integrated search engines have been incorporated.
3. Entertainment Streaming Apps
Streaming juggernauts such as Netflix, Spotify, and Hulu are constantly pouring data into machine learning algorithms in order to provide a seamless customer experience.
These streaming apps recommend unique material based on the user’s interactions with various media. These apps construct collections of music, movies, and TV programs tailored to each individual user’s preferences using AI to comb through ever-increasing user data.
By automating the allocation of servers nearest to the user, AI also plays a key role in ensuring uninterrupted streaming. The amount of bandwidth allocated to a single piece of media changes automatically depending on its popularity.
4. Navigation and Travel
AI programmers who work on navigation apps like Google Maps and Waze never cease end. Only ML algorithms unleashed on satellite photos can successfully cross-check terabytes of geographical data that is updated every second.
Researchers at MIT recently developed a navigation model that tags road characteristics in real-time on digital maps. These digital maps are also being developed at the same time, using satellite imagery and including information about cycling lanes and parking spots.
Regular route changes have been made easier because of imaging algorithms based on Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). With the use of predictive models, AI also assists in determining paths on satellite photos covered by natural overgrowth.
5. Personalized Marketing
To increase engagement, brands deploy AI-driven personalisation solutions based on customer data. According to a poll conducted by OneSpot Research, 88 percent of customers believe that more tailored information makes them feel better about a business.
When consumers are steered to products through targeted marketing via automated e-mails and feedback forms, they are more likely to make a buy. Recent AI breakthroughs claim to employ computer vision to anticipate the performance of advertisements, assisting firms in reaching the correct individuals and serving those who require their products. Depending on the marketing stage, AI marketing tools can assist both prospects and retargeted clients.
6. Security and Surveillance
It is practically hard for a human to keep a continual eye on multiple CCTV network monitors at the same time. As a result, we’ve felt compelled to automate such monitoring operations and improve them using machine learning approaches.
AI frees up human surveillants to focus on verifying and responding to critical situations. AI video monitoring software handles the ongoing monitoring and detection aspect of surveillance. Artificial intelligence can detect abnormal activity that human eyes may overlook.
Surveillance systems in high-risk public venues, such as government buildings, use an extension of AI-based facial recognition software. Currently, liberal governments are finding ways to reduce the risk of privacy breaches from AI surveillance.
7. Gamified Therapy
From the time classics like Pac-Man and Pong were around for intuitive universe-building, AI had found a home in gaming. However, until now, gaming AI developments have primarily concentrated on presenting the gamer with more exciting tasks rather than measuring the gamer’s attitude.
Gamified apps are now being developed to test a gamer’s mental toughness in the face of inevitable defeat. This is assisting researchers in their research into ways to treat sadness and anxiety in gamers and individuals in general.
Some of these gaming applications use Virtual Reality (VR) headsets to deliver Cognitive Behavioral Therapy (CBT) for increased interaction with the person. As the game advances, AI assists these games in adapting to the user’s behavior based on studied cues.
8. Facial Recognition Technologies
Face ID unlock is the most well-known implementation of this technology, which is available on the majority of today’s top smartphone models. The most significant difficulty that this technology faces is widespread concern about racial and gender prejudice in its application in forensics.
Face recognition software is using Generative Adversarial Neural Networks (GANN) to lower the margin of error. These neural networks are also being honed to detect fraudulent Deepfake technology use.
AI software that detects facial expressions to determine mood and intent is also being developed by several sectors. Emotion AI, also known as Affective Computing, is a growing field of study for assessing client satisfaction.
9. Internet of Things
The intersection of AI with the Internet of Things (IoT) throws up a slew of possibilities for developing smarter household appliances with minimum human intervention. While IoT is concerned with devices that communicate with the internet, AI is concerned with assisting these devices in learning from data.
Create, communicate, gather, analyze, and act are the five broad phases involved in IoT-enabling. The effectiveness of the final stage, “act,” is determined by the depth of analysis, which AI greatly enhances.
AI maximizes the value of data collected by IoT devices via sensors. IoT devices can better respond to human stimuli and requirements by learning from this data across numerous iterations.
10. Self-driving Vehicles
The technology of Autonomous Vehicle AI is witnessing large-scale innovation driven by global corporate interest. AI is making innovations beyond cruise-control and blind-spot detection to include fully autonomous capabilities.
Vehicles are being taught to function independently using Deep Reinforcement Learning (DRL), a form of machine learning. Various predictive AI methods make path planning possible in the face of static and dynamic impediments.
It is being taken into account when surrounding vehicles will swerve and other such unforeseeable circumstances. Simultaneous Localization and Mapping (SLAM) is the technology that enables this with real-time sensor-based orientation to the environment.
Uses of AI: Wrapping it up
Another use of Ai is in online shopping, it provides personalised recommendations to users, based on their previous searches and purchases. Smartphones use AI to provide personalised services. AI assistants can answer questions and help users to organise their daily routines without a hassle. AI systems can help recognise and fight cyberattacks based on recognising patterns and backtracking the attacks. Lastly AI I-based language translation software provides translations, subtitling and language detection which can help users to understand other languages.