How does AI work? Right now, some of the tools we access daily, from ChatGPT to AI smart speakers, seem almost magic. Some AI models are so advanced that users believe they’re self-aware. The good news is they’re not (as far as we know).
Artificial intelligence (AI) systems in all of their forms simply rely on various algorithms to process data and achieve specific outcomes. The algorithms they use vary depending on the type of AI you’re accessing. For instance, an intelligent IVR system in a contact center combines machine learning, natural language processing, and analysis.
So, what’s actually going on behind the scenes when you talk to ChatGPT, or ask your AI-powered CRM to assess customer behavior?
Understanding Artificial Intelligence: What is AI?
One of the reasons it’s so difficult to answer the question, “How does AI work?” comprehensively, is that AI isn’t just one technology. Artificial Intelligence is a broad term in the computing industry that describes the discipline of programming machines to replicate human intelligence.
AI systems can assess data and make connections to perform various tasks, like recognizing images or speech or making decisions. Artificial intelligence is usually divided into two categories: one we can explain and the other we can’t (for now).
Weak or Narrow AI
Weak or narrow AI is the term used to describe the AI most of us know today. The term “weak” is slightly misleading as these models can accomplish a lot. However, they’re named “weak” because they only perform specific tasks.
The two common forms of weak AI you might be familiar with are reactive machines and limited-memory AI. Reactive machines are tools that can perceive the world around them, using data, and react to situations. They can’t retain memory, which means they don’t learn and improve over time, using techniques like machine learning.
Limited memory machines, on the other hand, use machine and deep learning to imitate how the human brain works. They can store data and learn from it when making decisions. Essentially, they use what they know and have discovered to “predict” what they should do next. Examples of this form of AI appear in various generative AI and conversational AI apps.
Strong AI or Artificial General Intelligence
Strong AI, sometimes referred to as “Artificial General Intelligence,” is a form of AI we can’t yet explain because it doesn’t exist (that we know of). Theorists say that this type of AI would have consciousness and self-awareness similar to those of humans.
In this landscape, there are two commonly theorized forms of strong AI. “Theory of Mind” AI systems would essentially be able to understand human emotions and thought processes and make their own decisions based on what they learn.
Self-aware AI, or “sentient AI,” would be able to understand its existence and purpose and come up with opinions and thoughts on its own, regardless of its existing data sets. It would be able to identify and respond to and even predict people’s emotions.
How Does AI Work? The Core Components
Obviously, AI comes in many different forms. In addition to strong and weak AI, we have countless different AI solutions intended for different purposes. There are generative AI tools like ChatGPT and Dall-E, conversational AI systems, smart assistants, chatbots, and more.
Despite their differences, however, many AI solutions rely on similar technologies and core components, such as:
- Data sets: AI thrives on data and needs significant volumes to function effectively. In recent years, we’ve seen an increase in the amount of data actually used for AI enablement, with the rise of large language models and connections to the Internet of Things. Without data, AI simply can’t exist, making it fundamental to the functionality of every AI tool.
- Computing systems: All AI solutions rely on computing systems, such as graphical processing units (GPUs) and central processing units (CPUs). These allow AI systems to perform the millions of calculations they need to interpret data. The more advanced the computing system, the more effective the AI will be.
- Algorithms: Algorithms are the statistical rules that define how artificial intelligence handles the data it is given. They essentially guide the thought processes of an artificial intelligence system and influence how it makes decisions.
Many AI solutions today also rely on APIs (Application Programming Interfaces), which allow intelligence to be added to software applications and programs. They may also leverage connections with other technologies, from IoT-connected devices (like sensors) to analytics systems and hardware (such as cameras, speakers, and microphones).
How Does AI Work? The Basic Steps
So, how does AI work? Again, the exact functionality of any AI system will vary depending on its intended purpose. However, virtually all forms of artificial intelligence operate with the same series of critical steps.
Step 1: Data Collection and Input
To create an AI model, developers must first collect large volumes of valuable data in text, audio, videos, and more. This data is sorted into categories and often labeled, to assist AI systems in understanding its purpose. Rules or algorithms are created to determine how the AI system transforms data into actionable information.
Step 2: Data Processing
Once an AI system is fed with the right data, it needs to be able to assess that information, encode it, and use it to identify patterns. The collected data generally undergoes a comprehensive analysis process in a very short time. One AI computer software solution boasted the ability to run 1.1 quintillion calculations per second.
Various methodologies are used to determine how AI systems process data, from neural networks to machine and deep learning (we’ll cover these shortly).
Step 3: Outcomes and Assessments, and Adjustments
After the data is processed, the computer uses the patterns it has identified to predict potential outcomes and “take action.” During this stage, the AI solution is programmed to determine whether specific data is a “pass” or “fail.” In other words, it identifies whether that data matches previous patterns and leads to effective decision-making.
When data sets are considered a “fail”, advanced AI systems learn from the mistake, and the process is repeated again with different conditions. Sometimes, algorithms are adjusted, or the data set might be updated based on specific needs.
Finally, the AI synthesizes the insights it has gained from all of the data sets to make predictions based on outcomes and adjustments for assessments. This allows developers to incorporate updates into the algorithms before moving forward.
How Does AI Work? Core Concepts
Broadly, artificial intelligence combines huge volumes of data with rapid processing and intelligent algorithms, allowing the software to synthesize human intelligence. There are a few central concepts or processes involved in the functionality of AI tools, such as:
Machine Learning
Machine learning (ML) has a significant impact on how many AI tools operate today. ML is a branch of artificial intelligence that enables computers to learn and improve by analyzing data, imitating how human beings learn.
There are a lot of different machine learning techniques, such as:
- Supervised learning: Supervised learning involves training your model on pre-labeled data, which combines inputs with desired outputs. The algorithm learns from these labeled data points to predict outputs for new, unseen data. This method is usually used to create tools for speech recognition and image classification.
- Unsupervised learning: Unsupervised learning allows machines to find patterns in unlabeled data. The algorithm looks for patterns and relationships to make predictions about new data. This is more common in anomaly detection and clustering applications.
- Semi-supervised learning: Semi-supervised learning uses smaller labeled data sets to help guide an algorithm’s classification process when interacting with unlabeled datasets. Semi-supervised learning usually solves the problem of not having enough labeled data to train a machine learning algorithm.
- Reinforcement learning: With reinforcement learning, there’s a similar approach to supervised learning, but there’s no sample data. Instead, the model uses trial and error, and successful outcomes are reinforced. IBM used this approach when it created the Watson system that won the Jeopardy challenge in 2011.
Deep Learning and Neural Networks
The terms deep learning and machine learning are often used interchangeably. However, if you’re asking, “How does AI work?” it’s worth understanding the nuances between the two.
Deep learning leverages huge neural networks with various layers of processing units and nodes. While deep machine learning can draw on labeled datasets, it doesn’t need them. It can ingest unstructured data in raw form and understand how categories differentiate.
The neural networks in deep learning simulate the structure and behavior of the human brain. They’re made up of nodes containing input layers, hidden layers, and a layer for output data. Each node connects to another and holds a specific weight and threshold. When a node is activated, it passes data along to the next level.
Notably, there are also different types of neural networks. A neural network consisting of more than three layers is a “deep neural network,” while any network with three layers only would be a basic neural network.
Computer Vision, Natural Language Processing, and More
Alongside machine learning, deep learning, and neural networks, several other technologies influence how AI solutions work. For instance, computer vision is a form of AI algorithm based on machine learning that uses pattern recognition and deep learning to recognize visual content. It allows machines to process, analyze, and understand images.
Natural Language Processing (NLP) allows computers to understand and create spoken or written human language. NLP empowers machines to analyze all forms of human language using computer science, deep learning, and linguistic strategies.
Cognitive computing, another component of AI systems, allows computer models to mimic how a human brain works for specific tasks, like analyzing text, images, or speech. This is a common technology used in conversational and generative AI applications.
How Does AI Work? Finishing Thoughts
AI is a complicated concept, even today. Artificial Intelligence is constantly evolving, and the way applications work to process data and complete tasks is always advancing. For now, we know most AI systems work by processing and responding to large amounts of data on a basic level.
However, new algorithms are being developed to influence how future systems respond to data, paving the way for ever-more powerful models. The growing complexity of AI, and how it works, is part of why many regulatory bodies and governments are focusing heavily on explainable AI.
Most governments want developers to be able to explain (in depth) how their systems come to certain conclusions to avoid future issues with privacy, security, and ethics. Most developers can do this, but the concept raises concerns about what might happen if we can no longer explain AI.
If we can’t answer the question “How does AI work?” in the future, this could signal a move to new forms of sentient artificial intelligence.
