Major Uses of Artificial Intelligence You Need to Know
Artificial Intelligence (AI), and its many sub-domains, are increasingly being used in many industries and businesses to assist in repetitive processes. There has been an increase in interest from both established tech companies and startups to use AI to make daily life easier.
AI is a key component in optimising how we interact with our smartphones and driving cars for ourselves. Machine Learning (ML), algorithms, and Natural Language Processing are commonplaces in many everyday tasks.
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Artificial intelligence: Definition
Artificial intelligence is the ability for a machine to show human-like abilities such as reasoning, planning, and creativity.
AI allows technical systems to see their environment and to deal with it. It can also solve problems and help them achieve a goal. The computer processes data that it has already collected through sensors, such as a camera, and then responds.
AI systems can adapt their behavior to a certain extent by analysing the consequences of past actions and then working independently.
Applications of AI Capabilities in Everyday Living
AI-powered devices and software powered by ML mimic human thought patterns in order to enable the digital transformation of society. AI systems can perceive the environment and solve problems. This makes everyday life easier.
These are areas in which AI can help humans meet their daily needs.
1) Voice Assistants
AI-backed Voice User Interfaces, (VUI), are use by digital assistants such as Siri, Google Home, Alexa, and Google Home to decode and process voice commands. These applications can use AI to not only rely on voice commands, but also access vast cloud storage databases. These applications are able to process thousands of lines per second of data in order to complete tasks and return tailored search engine results.
This technology is seeing a significant shift in consumer awareness. Voice assistant interfaces are quickly improving, particularly in healthcare where they can be used to diagnose certain diseases using vocal biomarkers. Chatbots based on voice are being integrated into telehealth applications to aid in screening and triage.
2) Entertainment Streaming Apps
Hulu, Spotify and Netflix are streaming giants that continuously feed data to machine learning algorithms in order to improve the user experience.
These streaming apps analyze the interaction of users with different media and recommend customized content. These apps use AI to analyze the increasing amount of user data and create customized content for each user.
AI is also important in uninterrupted streaming. It automates the allocation of servers nearest to the user. The popularity of a media piece affects how bandwidth allocation is made.
3) Personalized Marketing
To increase engagement, brands use AI-driven personalization solutions that are based on customer data. OneSpot Research found that 88% of consumers surveyed said that personalized content makes them feel more positive about a brand.
Personalized marketing via automated email and feedback forms is more effective in directing consumers to the products they want. This increases their likelihood of making a purchase. AI’s recent innovations include the use of computer vision to predict how an advertisement will perform, helping brands reach the right people while serving those who are most likely to buy those products. AI marketing apps can help prospects as well as retargeted customers depending on the stage of marketing.
4) Smart Input Keyboards
Mobile keyboard apps have been updated to include language detection and autocorrection. This makes them more user-friendly.
These apps can correct errors, switch languages and predict the next word with the aid of AI. AI programmers use the principle of “random forest” to teach these apps how to interpret the message and make precise predictions.
Swiftkey and Typewise now integrate with more than 300 languages and dialects. Recent additions include integrated search engines and real-time translation.
5) Navigation and Travel
AI programmers behind navigation apps such as Waze and Google Maps never stop working. The ML algorithms that are applied to satellite images can cross-check the vast amounts of geographic data that is being updated every second.
Researchers at MIT recently developed a navigation system that allows road features to be tagged in digital maps. This is done in real time. These digital maps can also be created simultaneously using satellite imagery, which includes information about parking spots and cycling lanes.
Convolutional Neural Networks and Graph Neural Networks have use to create imaging algorithms that simplify regular updates of routes. With the help of predictive models, AI can also use to determine routes from satellite images that have natural overgrowth.
6) Gamified Therapy
AI was a part of gaming since the days when classics like Pac-Man and Pong could use to build intuitive universes. Innovations in gaming AI, however, have tended to present more challenging challenges to gamers and not gauge their mindset.
Gamified games are being developed to assess a player’s mental strength in the face certain defeats. This allows gamers to learn how to reduce anxiety and depression.
7) Self-driving vehicles
Global corporate interest is driving large-scale innovation in the technology of Autonomous Vehicle AI. AI is advancing beyond blind-spot detection and cruise control to fully autonomous capabilities.
Deep Reinforcement Learning (DRL) is a subset in machine learning that’s being used to train vehicles to operate autonomously. Different predictive AI models are making it possible to plan your route in the face dynamic and static obstacles.
It is possible to predict accurately when vehicles around you will swerve or other unforeseen events. Simultaneous Localization and Mapping is the technology that allows for real-time location of surroundings via sensors.
8) Facial Recognition Technologies
Face ID unlock is the most common use of this technology in flagship smartphones. This technology faces the greatest challenge because of widespread concerns about its use in forensics.
Generative Adversarial Neural Networks are use to reduce error margins in facial recognition software. These neural networks can also be trained to detect the unprofessional use of Deepfake technology.
AI software is also being developed by various industries. It can detect facial expressions and determine mood and intent. Affective computing, also known as emotion AI, is a new area of interest that helps customers evaluate their experience.