4 Types of AI Models and What They Do

All the below models are types of generative AI. Which means they can generate content, like text or images.?

But each one on this AI models list works a little differently:

1. Foundation Models

Foundation models are machine learning models pre-trained to perform tasks. We call this process “self-supervised learning.”

Popular tools like OpenAI’s ChatGPT and Microsoft’s Bing Chat utilize foundation models, for example.?

Developers train foundation models on a vast amount of data with neural networks. So, the model can adapt to different use cases when you need it to. (Like a human brain can.)

People use foundation models across a wide range of scenarios. For example:

• Answering questions
• Writing essays and stories
• Summarizing chunks of information
• Generating code
• Solving math problems

2. Multimodal Models

Multimodal models learn from multiple types (or “modes”) of data like images, audio, video, and speech. Because of that, they can respond with a greater variety of results.

A popular type of multimodal AI is a vision-language model. It “sees” visual inputs (like pictures and videos) through a process called computer vision.

In other words, it can extract information from visuals.

These hybrids can caption images, create images, and answer visual questions. For example, the text-to-image generator DALL-E 2 is a multimodal AI model.

Learning from a more extensive range of mediums allows these models to offer more accurate answers, predictions, and decision-making. It also helps them better understand the data’s context.

For example, “back up” can mean to move in reverse. Or make a copy of data.?

A model that has “seen” and understands examples of both will be more likely to make the right prediction.

If a user is talking about computers, they’re more likely referring to the data version. If a user is talking about a car accident video, the AI system assumes it’s likely directional.

3. Large Language Models

Large language models (LLMs) can understand and generate text. They use deep learning methods combined with natural language processing (NLP) to converse like humans.

Two branches comprise natural language processing:

• NLU: Natural language understanding
• NLG: Natural language generation

Both of these working together allow AI models to process language similarly to people.

How?

They learn from millions of examples to accurately predict the next word in a phrase or sentence. For example, the autocomplete feature on your cellphone is a type of NLP.

4. Diffusion Models

Diffusion models split images into tiny pieces to analyze patterns and features. They can then reference these pieces to create new AI-generated images.

The process involves adding “noise” to break up images. Then, reversing and “denoising” the image to generate new combinations of features.

Let’s say a user asks for a picture of an elephant. A diffusion model recognizes elephants have long trunks, large ears, and round bodies.

So it can refer to all the images it’s learned from to recreate these features.

Source this article : https://www.semrush.com/blog/how-to-find-youtube-influencers/