When you ask Alexa to dim the lights, you're harnessing the power of Artificial Intelligence (AI).
Once a tapestry of science fiction, AI now weaves itself into the fabric of everyday life, mimicking human intelligence.
But do you know the applications of the branches of AI in our world? Keep reading this article to understand how AI is creating a smarter and more efficient future!
1. Expert Systems
Expert Systems are a branch of AI designed to simulate the decision-making ability of human experts.
At their core, they are intelligent systems consisting of a knowledge base and inference engine.
This knowledge base stores curated knowledge from human experts.
Likewise, the inference engine applies rules to the knowledge to solve complex tasks and problems.
Think of it as a library brimming with books written by the best minds in a field.
This example would be coupled with a librarian adept at finding the right knowledge for any specific query.
In healthcare, Expert Systems can assist doctors and nurses by suggesting diagnoses and treatments while monitoring patients' evolution.
For example, MYCIN, created by Edward Shortliffe, is capable of diagnosing bacterial infections and recommending antibiotics.
On the other hand, virtual assistants enable users to get quick, tailored answers and support specific to their needs.
Lastly, financial institutions rely on these systems for fraud detection, risk assessment, and portfolio management.
2. Natural Language Processing
Natural Language Processing (NLP) focuses on the intersection between computers and human language.
This branch of AI powers virtual assistants and intelligent machines to comprehend, interpret, manipulate, and generate human language.
Imagine you're talking to a friend who not only understands your words.
They can also grasp the underlying emotions, respond appropriately and sometimes predict what you're about to say.
That's what NLP strives to achieve but between humans and computers!
A common example of NLP is Amazon's virtual assistant, Alexa.
It understands spoken requests and answers accordingly through Speech Recognition.
You can ask for weather updates, playing music, or controlling smart home devices.
Alexa leverages NLP to decipher your natural language commands and perform the desired actions.
Likewise, NLP-powered customer service chatbots can answer questions and troubleshoot problems.
Machine Translation tools leverage NLP to break down the grammatical structure of a language and rebuild it in another.
3. Neural Networks
Neural Networks (NNs) are a powerful Machine Learning subfield that follows a set of linear instructions. They work similar to neurons in the human brain!
These interconnected nodes process information and learn from data by passing signals back and forth through the network.
Neural Networks excel at tasks that involve recognizing complex patterns. Examples include identifying objects in images or understanding spoken language.
Although this pattern recognition ability to process language may sound similar to NLP, NNs go further into diverse applications!
Imagine looking at a picture of a cat. Your brain can instantly recognize the cat based on its shape, fur texture and facial features.
NNs achieve similar features by analyzing vast amounts of image data.
That makes them ideal for applications like facial recognition. In fact, NNs are behind tagging suggestions on social media platforms!
This ML subfield is being used in autonomous vehicles or self-driving cars.
Tesla, with the help of NVIDIA, enables object detection so cars navigate complex road environments.
This use of NNs make roads safer for drivers and pedestrians.
4. Robotics
Robotics blends engineering and science to design, build and program robots to perform tasks.
These tasks can be either autonomously or with human supervision.
Robots can move and perform actions independently, often equipped with sensors to perceive and interact with their environment.
A compelling example of robotics is Eyepick's AI automation and Soft Robotics' grippers' dexterity.
This application sorts tomatoes based on their quality, color and other features.
Just imagine a robotic arm capable of helping farmers streamline routine tasks.
You could turn manual processes of the agriculture field into modern food-processing, sorting and packing operations!
5. Machine Learning
Machine Learning focuses on algorithms and statistical models for computers to perform tasks without explicit instructions.
Through Reinforcement Learning and Unsupervised Learning, machines can learn from data and make predictions.
The wide range of applications go from product recommendation systems to fraud detection and image recognition.
Streaming services like Spotify use ML to analyze a user's listening history, including favorite songs, artists, and genres.
By identifying patterns, the algorithms can recommend similar music or create specific playlists.
If you frequently listen to upbeat music during workout routines, the algorithm might suggest a high-energy playlist.
This data-driven approach to music selection personalizes the listening experience and keeps users engaged.
6. Fuzzy Logic
Fuzzy Logic is a branch of Artificial Intelligence (AI) that mimics human reasoning by accounting uncertainties.
Imagine deciding how to wash your clothes. The process would go beyond the clean/dirty dichotomy.
Factors could include fabric type, dirt level and desired water temperature.
Fuzzy Logic excels in these situations, making it well-suited for applications with inherent uncertainty.
It helps handle ambiguity and complexity for a more nuanced evaluation process.
FL also enables decision-makers to consider both quantitative and qualitative factors simultaneously.
This approach can lead to more informed and comprehensive evaluations of product ideas.
In Product Development, it can lead to more accurate, efficient and reliable methods for evaluating product ideas.
Overall, Fuzzy Logic is a powerful tool for AI systems.
They're key in making nuanced decisions in complex control systems or uncertain situations!
7. Computer Vision
Computer Vision empowers machines to interpret and understand visual information from the real world.
By mimicking human visual perception, Computer Vision systems can analyze images and videos to extract meaningful insights.
This technology finds applications in diverse areas, including facial recognition, self-driving cars, medical image analysis, and quality control in manufacturing.
In healthcare, Computer Vision algorithms can analyze X-rays and MRIs to detect abnormalities with remarkable accuracy.
This capability could be key in aiding in early diagnosis and treatment.
