Artificial intelligence (AI) is a field of computer science studying the technology that allows computer systems to address specific input and provide value-added output. These computer programs are designed to learn from data, process information, and perform tasks typically requiring human intelligence, such as speech recognition, image classification, and language translation.
Reports suggest AI will see an annual growth of 19.1% from 2024 to 2034, with various industries like finance and health care experiencing transformations due to artificial intelligence.
But to make the most of AI, it pays to understand how it works. This guide covers the types of AI techniques available and what makes each one work.
Image Credit: GamePH/Istockphoto.
What are AI techniques?
Artificial intelligence techniques refer to the methods, algorithms, and data science approaches that allow computers to perform tasks that traditionally require human beings. These techniques help AI systems learn, make computations, identify patterns, and offer predictions.
Some techniques require reading a lot of text. In cases like this, AI uses natural language processing (NLP) to understand the meaning behind words and draw conclusions about what it reads. NLP can help with text generation, content summarization, and other text tasks.
Other techniques process information by looking at images and videos. Computer vision algorithms can analyze visual information to find patterns and give insights into what’s happening in those images, such as identifying medical problems in patient screenings.
Finally, machine learning algorithms help engineers create machines and robots that can handle tasks in the real world. These techniques use a combination of computer vision and data analysis to understand robotic devices, determine the best course of action to meet objectives, and assist in problem-solving.
Image Credit: KucherAV/istockphoto.
Types of AI techniques
Now that you understand AI techniques, let’s look at the most common ones today.
Machine learning
Machine learning is an AI technique that uses datasets, algorithms, and artificial neural networks to learn and improve results over time. It uses training data to mimic the human learning process.
The process starts with collecting data relevant to your task. Examples include financial transactions to understand accounting, legal documents to make sense of legalese, and marketing campaign headlines for copywriting.
After training the system with this data, you can feed the machine learning model new data for analysis. The AI will identify patterns and make predictions based on its prior knowledge.
Deep learning is a more advanced machine learning technique. It uses at least three neural network layers to simulate the human brain, improving AI’s decision-making and decision capabilities.
When setting up a machine learning task, AI professionals use three main types: supervised, unsupervised, and reinforcement learning.
Image Credit: anyaberkut/istockphoto.
Supervised learning
Supervised learning involves training AI models on labeled data. Think of it as providing AI with sets of questions and known answers to learn from.
For instance, consider a company that wants an AI-powered chatbot to assist its customer service team. The company provides the AI with a list of common questions and responses about its products, similar to how one might train a model for email spam detection by feeding it with emails labeled as “spam” or “not spam.”
The AI learns from this data and provides real-time responses to the company’s chatbot users. In the case of the spam detection model, it can filter incoming emails and move predicted spam messages to the spam folder.
Another instance of supervised learning is classification, like sentiment analysis on customer reviews, image classification for diagnosing medical images or driving autonomous vehicles, and facial recognition for security systems. Instead of sifting through countless reviews, you feed a machine learning program with examples of reviews and their sentiment (positive, negative, or neutral).
Once trained, the AI can analyze new reviews to provide insights into customer sentiment, identify objects in new images, or recognize faces.
Image Credit: gorodenkoff/Istockphoto.
Unsupervised learning
Unsupervised learning is a machine learning approach where you train data on unlabeled data. In this process, you provide a learning algorithm data relevant to the task you want to train for. But unlike supervised learning, there’s no output to provide.
The goal is to train an AI that independently identifies data patterns, relationships, and structures. This process is necessary for some datasets because those gathering the data may not know these patterns.
Anomaly detection is one area where this makes sense. For instance, consider a fraud team that wants to analyze financial transactions for fraud. Instead of having humans analyze each transaction, AI can train on financial data and notify financial institutions if it finds something that doesn’t match normal patterns.
Image Credit: amgun/Istockphoto.
Reinforcement learning
Reinforcement learning is a form of machine learning that allows the AI model to learn by trial and error. Instead of giving the model data and not giving it a chance to learn anything else, reinforcement learning changes things by giving the model penalties and rewards for its actions. This iterative, feedback-driven process allows an AI model to optimize over time.
Companies use reinforcement learning in various ways. One example is the creation of Deep Blue—IBM’s AI model that competed with and defeated the best chess players in the world more than 25 years ago. The AI model learned from its previous games with itself and other players to become better and gain the ability to compete with the best.
A real-world use of reinforcement learning is training autonomous vehicles. Training self-driving cars on everything they need to make good decisions on the road can be challenging. AI engineers use reinforcement learning to tell the vehicles when they make good or bad decisions, improving a car’s skills on the road.
Image Credit: Parradee Kietsirikul/Istockphoto.
Computer or machine vision
Computer vision is a subset of AI that teaches machines to understand visual data from videos and images. It aims to extract information from visuals and use that data to find patterns or take action.
Several forms of computer vision are available, including:
- Image recognition
- Object detection
- Image restoration
- Image segmentation
Each activity has unique uses. For instance, image recognition is useful for surveillance. You can tie AI systems into your camera system and use AI to recognize people—a useful case for law enforcement purposes.
Biometric recognition is another excellent application for computer vision. New security systems can recognize eyeball retinas, fingerprints, and faces to enhance security and prevent unauthorized access to locations.
When looking at computer vision, sensitivity and resolution are two main things to consider when processing data.
Image Credit: simpson33/istockphoto.
Sensitivity
Sensitivity in computer vision is an AI application’s ability to pick out small details in visual information. A low-sensitivity system may not pick up subtle clues in images or fail to work well in low lighting. However, a high-sensitivity system might be able to look at an image’s fine details and pick up on information other systems might miss.
Surveillance is one area where this matters a lot. People have unique characteristics, but a surveillance system using facial recognition that isn’t sensitive enough may give off too many false positives. A surveillance system works well only with the ability to look at the fine details of a person’s face.
Image Credit: Ekkasit919/istockphoto.
Resolution
Resolution is the level of detail a computer vision system can capture and process. You can measure resolution by looking at the number of pixels captured on the camera, with a higher resolution offering more detail and pixels.
High-resolution images are vital for correctly identifying an image’s detail. Not having enough details means you won’t get good results and may miss important parts of an image—details you can’t afford to miss in high-risk situations, such as quality control in manufacturing, inspection of infrastructure, or scientific analysis where missing minute details could lead to significant errors or oversights.
The issue many companies face is balancing resolution with computational resources. Finding the best resolution for your needs means understanding how much detail you need to capture for the best results.
Image Credit: Tero Vesalainen/Istockphoto.
Natural language processing (NLP)
NLP is a field of AI that uses text to create meaningful interactions between machines and humans. NLP uses algorithms and text models to help machines read human language, interpret it, and take action based on its understanding.
These abilities make NLP useful in almost every industry. For instance, workers can use AI tools like ChatGPT—from OpenAI and Microsoft—to ask questions in a chat window and get results based on the data available to the AI model.
Several tasks with NLP make text content easier to work with and understand: text preprocessing, part-of-speech tagging, named entity recognition, and sentiment analysis.
Image Credit: Aree Sarak/istockphoto.
Text preprocessing
Text preprocessing is translating raw text into a format that machines understand. The goal is to extract the most relevant information during this process. It happens in three steps:
- Tokenization. Break down text into individual words and phrases.
- Stemming. Reduce words to their base form (e.g., “running” and “runs” to “run”).
- Stop word removal. Eliminate words irrelevant to the meaning of a piece of text (e.g., “the,” “is,” and “and”).
This process leaves the AI with the words that make a section of text meaningful for further processing.
Image Credit: Djordje Krstic/istockphoto.
Part-of-speech tagging
Part-of-speech tagging is the process of taking a sentence and understanding the function of each word in it. You look at each word and define its purpose based on the context—is it a noun, a verb, an adverb, or another element?
This process is also useful in word-sense disambiguation (WSD). You’ll use this process to determine the correct meaning of a word with multiple meanings.
The word “bear” is a great example. It can be a noun meaning the animal in an outdoor setting or a verb indicating an individual is supporting some kind of load. Without that kind of understanding, NLP applications can’t understand the text.
Image Credit: hapabapa/istockphoto.
Named entity recognition (NER)
Named entity recognition (NER) is recognizing and extracting specific entities in text. These are people, places, schools, and other entities.
This application helps analyze data to determine the essential items. For instance, legal analysts can analyze large documents to find the areas that talk about specific people, places, and things.
Google also heavily uses NER to show search results. It identifies the key entities on website pages to learn what they’re about and determine how they relate to the search—allowing Google to show the most relevant results for search queries.
Image Credit: Boy Wirat/istockphoto.
Sentiment analysis
Sentiment analysis is analyzing text to determine the writer’s sentiment. It helps look at the emotion someone is trying to convey—are they positive, negative, or neutral?
This type of analysis is excellent for learning about the public’s overall opinion about something. For instance, a public event that makes the news is likely to be discussed on social media and other platforms.
Media platforms can take those comments and run them through a sentiment analysis NLP program. From that, they can see how people feel about an event without analyzing every comment.
Image Credit: Shutthiphong Chandaeng/istockphoto.
Automation and robotics
Automation and robotics are two things that don’t need to use AI. In the past, automation tasks and robots performed predefined tasks set by the end user. This ability offered a lot of flexibility in reducing workloads, but it was limited by what the user could program and teach software.
AI changes things by giving expert systems the ability to learn. New intelligent systems can examine their surroundings and digest new information to take new actions that users do not create.
These abilities give automation and robotic tasks more capabilities by allowing them to navigate unknown surroundings without human intervention—like autonomous cars and advanced robotic tasks in manufacturing.
Image Credit: SweetBunFactory/istockphoto.
Applications of AI techniques
After understanding the types of AI available, let’s examine the applications of artificial intelligence used today.
- Health care. New tools allow researchers to quickly analyze medicines to look for new and better alternative treatments, like protein folding techniques for quick analysis of new disease proteins.
- Finance. Personalized financial tools, including fraud detection, give people better insights into their finances. Bank of America’s Erica is one virtual assistant in finance.
- Business. Big data AI technologies allow businesses to analyze more data for campaign optimization and automate marketing campaigns.
- Transportation. Autonomous vehicles use deep learning and computer vision to drive farther distances and become safer on the road.
- Logistics. Understand how much inventory you need and when you need it by forecasting demand and achieving optimal inventory levels.
Image Credit: gorodenkoff/Istockphoto.
Let AI help solve real-world problems
There’s no one way to train AI algorithms. You have NLP for text-based problems, computer vision for image tasks, and machine learning for data analysis. With all the options available, you have access to a large toolbox to handle whatever problem you face in your industry.
AI research continues to advance, developing new methodologies and improving existing ones. The field of AI is constantly evolving from neural network nodes to advanced Python libraries. As a subset of computer science, AI offers powerful solutions for complex problems across various industries.
This article originally appeared on Upwork.com and was syndicated by MediaFeed.org.
Image Credit: Jacob Wackerhausen/istockphoto.
More from MediaFeed
4 Pros & 4 Cons to AI Automation for Businesses
Image Credit: yacobchuk/Istockphoto.
