From Veo3, which can create realistic videos with sound, to OpenAI's GPT-5, which can code a whole game from just one prompt — AI models are rapidly replacing tasks that were previously only possible for humans.
We're going to answer these questions in this article — let's dig in.
An AI model is a program trained on data. It’s a type of software that can recognize patterns and make decisions without being explicitly programmed for every scenario.
At its core, an AI model is an algorithm that has been fed data and learned from it.
Once trained, these models can process information far faster than humans and spot patterns we might miss entirely.
Examples of early AI models include Chess programs in the 1950s that could respond to moves rather than follow pre-scripted sequences. Today's models power everything from your email spam filter to self-driving cars.
The answer is — no. AI models don't have consciousness, self-awareness, or subjective experiences.
At least, that’s what most researchers believe right now.
In 2022, Google engineer Blake Lemoine claimed that LaMDA, Google's Language Model for Dialogue Applications, had become sentient. After conversations where the AI expressed awareness of its existence and claimed to feel emotions, Lemoine went public with his belief that the chatbot had achieved consciousness. Google fired him shortly after for violating confidentiality policies.
Also, Anthropic researchers are working to determine whether models can access what they previously thought about and whether they can form an understanding of their own processes — abilities associated with consciousness.
That said, no studies have provided definitive evidence, at least as of yet.
☝️However, we do know that AI models process patterns in data and generate outputs based on statistical probabilities.
The belief that AI models are sentient often stems from how convincingly they mimic human conversation.
Modern language models are trained on human text, so they naturally reproduce it:
When asked about feelings, they generate responses similar to how humans discuss feelings in their training data.
But this is achieved through pattern matching: performing mathematical operations on numbers.
Many people think of names like ChatGPT when they think about AI models. However, ChatGPT is not a model. It's a web interface that allows you to communicate with a model.
Other examples of AI services include:
While other examples of AI models are:
Let’s consider 3 examples of AI models that you likely already know:
🔵 GPT. GPT stands for Generative Pre-trained Transformer. Most of OpenAI's products use models from this family. The latest version, GPT-5, can understand both text and images. It can also respond to complex questions in a way that sounds like a human.
🔵 Claude. Claude, made by Anthropic, is all about safety and helpfulness. The latest version of Claude models is the number 4 family, which includes Opus and Sonnet. It is particularly well regarded for its coding ability. For this reason, more programmers and businesses are using Claude than GPT models.
🔵 Gemini. The Gemini family of models was developed by Google. Some examples from this family of models are:
Every machine learning model is an AI model, but not every AI model uses machine learning (ML).
For example, early chess programs were AI models that followed hard-coded rules — if the opponent moves here, respond there, but there was no learning involved. These rule-based systems, also called expert systems or symbolic AI, still qualify as AI models because they simulate intelligent decision-making, but not machine learning.
Machine learning models, by contrast, improve through exposure to data. They identify patterns, adjust their parameters, and get better at their tasks over time.
How to tell if a model uses machine learning?
✅ ML models learn from past interactions. A spam filter that learns from examples of spam and legitimate emails is a machine learning model.
❌ Non-ML models use pre-determined options. A chatbot that follows a decision tree of pre-programmed responses is an AI model but not a machine learning model.
Today's most powerful AI models — GPT, Claude, Gemini — are all deep learning models.
💡 Deep learning is a subset of machine learning that uses neural networks with multiple layers to process information. These models can learn incredibly complex patterns but require a lot of data and computational power. While a traditional machine learning model might need thousands of examples, a deep learning model might need millions.
Modern AI models can be divided into these groups (though this doesn’t cover all of them):
Generative models can generate new content from scratch by learning the underlying distribution of their training data.
ChatGPT, Gemini, Claude and other models that power popular AI chatbots fall into this category. So do video, audio, and image generators.
Popular types include:
Generative models can create:
✅ Text (articles, code, conversations)
✅ Images (artwork, photos, designs)
✅ Audio (music, speech, sound effects)
✅ Video (animations, deepfakes)
✅ 3D models and synthetic data
Discriminative models focus on distinguishing between different classes Most neural networks for classification fall into this category — they’re designed to categorize, but they can’t generate something new from scratch.
Discriminative vs. Generative:
✅ Discriminative: Can tell if an email is spam
❌ Generative: Can write an email
✅ Discriminative: Can identify fraudulent transactions
❌ Generative: Can explain what a transaction is
Discriminative models typically need less computational power than generative models and often achieve higher accuracy for classification tasks.
Regression models predict numbers. They're used in predictive analytics to forecast things like:
💡 If you're predicting a number rather than a category, you need a regression model. They're interpretable, well-understood, and often the best starting point for predictive analytics.
AI model training is the process of feeding data to an algorithm so it can learn patterns and make accurate predictions.
The training process follows these steps:
Different models train differently:
But training is iterative. You rarely get it right the first time. Expect to adjust hyperparameters, add more data, or try different architectures until performance meets your needs.
Supervised learning models are AI models that were trained on labeled data — examples where you know the correct answer, such as a picture of a cat accompanied by a label “Cat.”
Common supervised learning models are further divided into these groups:
Supervised learning is very common in commercial and custom AI models because businesses can typically collect very specific labeled data for learning.
Unsupervised learning models find patterns in data without labels. They discover hidden structures on their own.
Common unsupervised learning models:
Unsupervised learning are used for exploratory analysis. For example, Netflix might use it to discover viewing patterns they didn't know existed.
The process starts by splitting available data into separate sets.
This test data remains completely untouched during training, serving as a reality check. Once training completes, the test data runs through the model to see how well it performs on examples it's never encountered.
Different types of models use different scoring methods:
Classification models use:
For example, for a spam filter accuracy might measure what percentage of emails were correctly identified.
But accuracy alone can mislead — if 99% of emails are legitimate, a model that marks everything as "not spam" scores 99% accuracy while being completely useless.
That's where precision and recall come in.
Precision asks: of all the emails marked as spam, how many actually were spam?
Recall asks: of all the actual spam emails, how many did the model catch?
Regression models use:
Regression models predicting things like stock prices or temperatures use different metrics.
Mean Absolute Error might show the model is off by an average of $5 per prediction, while Root Mean Square Error punishes larger mistakes more severely.
Testing always continues after deployment.
Companies run A/B tests, comparing the new model against existing systems with real users. They monitor for drift — when the real world changes in ways that make the training data outdated.
Once an AI model is trained and tested, it needs to be deployed somewhere users or applications can access it — on a service.
Popular deployment options include:
Which deployment method to use depends on model size, how many people developers think will interact with it, how fast it should reply, and how much the company is willing to pay to keep it running.
AI models are programs trained on data to recognize patterns and make decisions. They learn from examples rather than following hard-coded rules for every scenario.
The most powerful models today use a process called deep learning, which is a subset of Machine Learning (ML). GPT, Claude, and Gemini all fall into this category.
There are different model types, and each suits different tasks. Generative models create new content, while discriminative models classify existing data.
AI models are trained by being fed data through an algorithm, repeatedly making guesses, and labeling them as successes and failures, until they learn to make fewer mistakes.
What’s more, Pre-trained models like GPT can be fine-tuned for specific tasks, eliminating the need to train models from scratch for many applications.
