Artificial intelligence enables us to search for a video on YouTube using a voice command, view similar patterns of reels on social media, generate content through ChatGPT, and see shopping options while browsing the internet. Let’s dive deeper into artificial intelligence applications in real life.
How AI Works In Real Life
AI in simple words means inserting human potential to a machine, and developing its brain just like a human for brainstorming. To understand this concept, we need to understand, how artificial intelligence actually works.
AI is a field of computer science that simulates the human brain. We need to input information into an AI system, one of which is data as a source. The AI system processes it and creates a model using pre-trained methods, providing results based on the data, essentially mimicking human brain functions. The more data that is input into AI, the more useful and accurate it becomes. However, all AI systems require large data sets, as big data is the most important component of artificial intelligence.
Artificial intelligence requires four major processes to work.
- Machine Learning
- Neural network
- Data processing
- Algorithms
Machine Learning
Machine learning is considered the foundation of AI. It involves teaching machines how the human brain actually works. AI uses all the data input into machine learning models to create data sets, enabling the system to learn how to perform specific tasks, make decisions without traditional software programs, and provide predictions. While we can easily understand data in machine learning through artificial intelligence, thoroughly analyzing that data still requires software programming and algorithms
Data exists in large forms, which can be visualized as a pyramid of data. To process this data, we need mathematical models. Image classification is one example of artificial intelligence applications in real life. For instance, while browsing a website, we often encounter CAPTCHAs that instruct us to select images with traffic lights, stairs, or other objects among a different set of images. All of this is an example of machine learning.
Neural Networks
Neural networks, the next major component of AI, serve as its building blocks. Inspired by the human brain, neural networks enable machine learning to work. Just as neurons in the brain interconnect, many hidden layers form neural networks. Data processing occurs within these layers. As data passes through each layer, it enters a deep learning phase, integrating all layers and data to produce the best results.
Data Processing
Data is crucial for artificial intelligence systems; it serves as the fuel for AI models. It is not possible to train AI models without datasets. There should be a wide range of information in the data, without any missing relevant fields. It’s important to maintain data consistency and accuracy to effectively perform AI modeling. Data should be updated regularly without any irrelevant information. To train AI systems, we typically need to provide three types of inputs.
Structured Data
Present data in standard formats, such as dates, addresses, credit card numbers, numeric series, or other standardized input methods.
Unstructured Data
There might be specific information missing in unstructured formats, such as text, images, and videos. AI systems identify patterns in this type of data and process it using natural language processing (NLP) and other data processing techniques.
Semi-structured Data
This data is considered unstructured when it does not have a predefined model. Such data often uses formats like JSON, XML, or CSV files. These formats help leverage the benefits of unstructured source data, making it easier to train AI models.
Algorithms
Algorithms serve as the backbone of AI and are essential for problem-solving. These mathematical procedures define how AI learns, improves decision-making, and tackles various problems. Algorithms are critical in transforming raw data into useful insights, benefiting both clients and companies.
Artificial Intelligence Applications in Real Life
We use technology daily, often every moment, as surrounded by it. It has made our lifestyle easier and more efficient. When we wake up, many of us check our phones for social media updates. Some people use biometric locks to unlock their smartphones, while others use Face ID. Apple iPhones feature 3D technology that projects 30,000 infrared dots. Machine learning algorithms then verify whether the scanned data matches the stored data. Only after this verification does the smartphone decide whether to unlock the phone.
How AI Personalizes Content and Protects Users
Thera re different artificial intelligence applications in real life. Algorithms in our smartphones are aware of our interests and filter data accordingly. We receive recommendations based on our daily browsing habits, routines, posts, pictures, voice commands, and location. AI knows what we like to explore. Additionally, from our history, we get shopping recommendations, friend suggestions, food choices, and news updates. All of these are personalized for us. Moreover, to protect us from cyberbullying and fake news, AI and machine learning technologies continuously work behind the scenes.
When we arrive at the office, one of the first tasks is checking emails. To draft an email, we often need to ensure correct spelling and proper sentence structure. There are several spell-check tools that assist with this. These tools utilize natural language processing (NLP) and artificial intelligence (AI) to improve our writing. Additionally, when we receive emails, AI helps filter and block suspected spam, keeping our inboxes cleaner. Antivirus software on our system, powered by machine learning algorithms, further protects our email accounts from potential threats.
We rely on Google to explore almost anything and type our queries into the search bar. It’s hard to go a day without it. Artificial intelligence makes the specific results we get for any question possible. Similarly, the ads we see on blogs or videos are powered by AI. It analyzes our browsing patterns and uses that information to show us relevant ads.
Artificial intelligence & IoT
Our homes are becoming smarter every day with devices like voice-controlled assistants, sensor-activated lights, automatic temperature and cooling systems, and smart refrigerators that alert owners about what’s spoiling and what needs to be restocked. All of this is part of the Internet of Things (IoT) which is one of the artificial intelligence applications in real life.
For instance, many of us prefer shopping on Amazon for everything from home accessories to electronic devices. AI operates 24/7, and if we frequently buy items from Amazon, its AI system learns our preferences and provides tailored recommendations. Sponsored brands also get displayed at the top based on these insights.
When we install apps, they often request permissions for access to things like the microphone, camera, gallery, and files, and we typically grant them. If you casually mention that your laptop isn’t working, your smartphone can detect this through voice recognition, and AI processes the information, leading to related product recommendations.
When we want to relax, we often choose to watch series, movies, or dramas on OTT platforms. These platforms use artificial intelligence to provide recommendations based on our viewing history. Therefore, we can say that our daily lives are driven or supported by AI, influencing our choices.
We have discussed all four processes of AI, but it’s important to highlight “Machine Learning.” While we’ve touched on this topic, there are additional programs designed to teach machines based on three different types of machine learning.
- Unsupervised Learning
- Supervised Learning
- Reinforcement Learning
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