AI Models

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Generative artificial intelligence

Generative artificial intelligence (Generative AI or GenAI) is a subfield of artificial intelligence that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom in the 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Copilot, DeepSeek, Google Gemini and Grok; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Veo, LTX and Sora. Technology companies developing generative AI include Alibaba, Anthropic, Baidu, DeepSeek, Google, Lightricks, Meta AI, Microsoft, Mistral AI, OpenAI, Perplexity AI, xAI, and Yandex. Companies in a variety of sectors have used generative AI, including those in software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, and product design. Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes. Generative AI may lead to mass replacement of human jobs. The tools themselves have been described as violating intellectual property laws, since they are trained on copyrighted works. Many generative AI systems use large-scale data centers whose environmental impacts include e-waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.

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• AI Models: Types, Examples, and Everything You Need to Know

  ## What Are AI Models? AI models are programs that are trained on data sets to recognize patterns. Then, developers add in algorithms, which are rules that help the program make decisions. Based on the patterns a program recognizes, it makes decisions or predictions on its own. Each AI model is trained to perform a specific task. Examples of common tasks that AI models can do include running and compiling marketing campaign reports, generating computer code, recognizing letters and numbers in texts, and entering data. The more input and training data a model receives, the more accurately it can execute its task. The goal of an AI model is to complete the task effectively without any further human intervention. [...] ## Examples of AI Models AI models are quickly changing the way every industry functions. Some of the most powerful and popular AI models that currently exist are: [...] Hugging Face. Hugging Face is a data science tool that focuses on natural language processing and allows users to test and deploy AI models. With in-browser tools, users can train, test,, and deploy the machine learning models they’re building. The company’s goal is to make AI more accessible for everyone. It’s become especially popular after partnering with AWS. Now, companies and individuals can use Hugging Face to demo models, fine-tune algorithms, research best practices, share data sets, and build prototypes. Even the smallest companies can build AI models to save money and automate their repetitive tasks, sort data, make custom tools that gauge social media sentiments, and create chatbots.

• What Is an AI Model? | Microsoft Azure

  Once trained, a well-built AI model can perform a wide spectrum of tasks—from identifying objects in photos to forecasting financial markets—at a speed and scale that go far beyond human capabilities alone. These abilities vary depending on the type of model and the data it’s been trained on, but in the right context, they can transform industries and workflows. For example, a natural language processing model might answer a complex customer service question in seconds, while a deep learning model could scan thousands of images to detect anomalies in manufacturing. How AI models are built [...] ## Key takeaways AI models use algorithms and machine learning to perform tasks like classification, prediction, and content generation. Common AI model types include classification, regression, generative, and foundation models. AI models are used in industries like healthcare and manufacturing to improve efficiency, reduce costs, and drive innovation. Choosing the right model depends on your business goals, use case, data availability, and cost. ### Learn how AI models work and how they’re built [...] ## AI model defined An AI model is the engine inside an artificial intelligence system that learns from data to perform tasks. It combines algorithms, training data, and learned parameters to transform raw inputs into outputs like recognizing speech, predicting equipment failures, or generating new product designs. AI models work at the intersection of artificial intelligence and machine learning, where algorithms continually learn from data to deliver more accurate predictions and better responses over time. ## Key takeaways

• What is an AI model? | Google Cloud

  # What is an AI model? Last Updated: 12/04/2025 An artificial intelligence (AI) model is a computer program or algorithm that has been trained on a large dataset of information. This training process allows the AI model to learn patterns and relationships in the data so that it can make predictions or decisions about new data that it has never seen before. Think of it like this: imagine you're teaching a child to identify different types of animals. You might show them pictures of cats, dogs, birds, and fish, and tell them the name of each animal. Over time, the child will learn to identify these animals on their own, even if they've never seen a particular cat or dog before. An AI model works in a similar way. [...] AI models are loosely modeled after the way humans think, mimicking our ability to learn, reason, and make decisions. However, unlike humans, AI models can process vast amounts of data and identify subtle patterns that we might miss. This capability makes them particularly well-suited for taking on complex problems that require analyzing intricate datasets, which can lead to more efficient and accurate solutions compared to traditional methods. Want to explore our models? New customers get up to $300 in free credits to try Vertex AI and other Google Cloud products. Get started for free Learn to train an AI model ## AI models versus deep learning and machine learning models [...] ## Pre-trained AI models Pre-trained AI models, sometimes referred to as foundational models, are AI models that have already been trained on a large set of data. They are often used as a starting point for building new AI models, as they can save developers a lot of time and effort. When tackling more common AI tasks, using a pre-trained model can be a great alternative to building a model from scratch. They can be used directly or fine-tuned for specific use cases. If you need to perform a task that is similar to the task that the pre-trained model was trained on, it is often faster and easier to fine-tune a pre-trained model than it is to train a new model from scratch.

• What Is an AI Model? | IBM

  Artificial Intelligence # What is an AI model? ## What is an AI model? An AI model is a program that has been trained on a set of data to recognize certain patterns or make certain decisions without further human intervention. Artificial intelligence models apply different algorithms to relevant data inputs to achieve the tasks, or output, they’ve been programmed for. Simply put, an AI model is defined by its ability to autonomously make decisions or predictions, rather than simulate human intelligence. Among the first successful AI models were checkers- and chess-playing programs in the early 1950s: the models enabled the programs to make moves in direct response to the human opponent, rather than follow a pre-scripted series of moves. [...] ## Foundation models Also called base models or pre-trained models, foundation models are deep learning models pretrained on large-scale datasets to learn general features and patterns. They serve as starting points to be fine-tuned or adapted for more specific AI applications. Rather than building models from scratch, developers can alter neural network layers, adjust parameters or adapt architectures to suit domain-specific needs. Added to the breadth and depth of knowledge and expertise in a large and proven model, this saves significant time and resources in model training. Foundation models thus enable faster development and deployment of AI systems. [...] ### Generative models Generative algorithms, which usually entail unsupervised learning, model the distributionof data points, aiming to predict the joint probabilityP(x,y) of a given data point appearing in a particular space. A generative computer vision model might thereby identify correlations like “things that look like cars usually have four wheels” or “eyes are unlikely to appear above eyebrows.”

• AI Model Types: Past, Present and Predictions for the Future | IBM

  Models continue to evolve. Reasoning models, for example, are fine-tuned to break down complex problems, applying reinforcement learning techniques that incentivize these LLMs to generate smaller, intermediate “reasoning steps” before arriving at a conclusion. Meanwhile, world models learn computational representations of the real world, including causal relationships, physical dynamics and spatial characteristics. These learning algorithms can help physical AI systems like robots and self-driving cars better perceive and navigate their environments in real time. [...] ## Where the next generation of AI models are headed The rapid pace of development in artificial intelligence means that different types of AI models keep cropping up, making it difficult to predict what to expect in the coming years. But David Cox, VP for AI models at IBM Research, has noticed an encouraging trend of small language models (SLMs) outshining their larger counterparts, with compute- and energy-intensive models compressed “by a factor of almost 10 every six to nine months,” he observes. Such shrinkage makes SLMs faster and more efficient to run on compact hardware. “It’s going to be much more widespread because we can pack more into smaller packages,” Cox adds. [...] ## Classic AI model types are here to stay Traditional AI models have been around for decades. Two of these fundamental paradigms include classification models and regression models. While classification models predict discrete categories, regression models predict continuous values. Both fall under supervised learning, a machine learning (ML) technique that relies on labeled data for model training.