AI Models: Everything You Need to Know

What Are AI Models?

Artificial intelligence models are computer programs that aim to replicate aspects of human intelligence. Developers input rules (known as algorithms) that allow the program to make decisions, notice patterns, and make predictions.

Successful models have a user-friendly interface. That means new users can interact with it without much direction.

For example, Bing Chat is an AI-powered chatbot app that can have back-and-forth conversations with users:

Bing Chat's response to "Where should I travel if I have pollen allergies?"
People type messages into the text box and the software replies—thanks to the accessible interface.

However, it’s the AI model that does the heavy lifting. It runs in the background and provides relevant answers to questions it has never encountered before.

Users don’t interact with the AI model directly. But it powers the whole experience.

Artificial intelligence is a complex topic with a lot of overlapping terminology. So, let’s clear a few things up.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Imagine artificial intelligence, machine learning, and deep learning as a tree structure.

Artificial intelligence is the main trunk. Branching off from this trunk is machine learning (ML), a significant limb of the tree. This limb further divides into smaller branches, with deep learning (DL) being one of these offshoots.

So, what's the key takeaway?

They are all part of the same tree, interconnected yet distinct. Each term represents a different aspect of the overall structure.

Here's another way to visualize it:

Image Source: Singapore Computer Society

Artificial Intelligence

Artificial intelligence is a branch of computer science that aims to simulate human intelligence in software and machines.

As far back as 2017, experts predicted AI would be able to do everything from translating essays to working in retail and performing surgery. Those forecasts gained even more steam with the creation of programs like ChatGPT.

These chatbots can’t completely match the level of a human brain yet. But they can carry out certain tasks. And already outperform humans in some areas like data science and strategy.

For example, AI can process huge volumes of data in seconds. Something that would take a human data scientist hours to do.

Machine Learning

Developers create algorithms to help programs pick up on patterns in data, similar to how humans learn. We call this process machine learning.

For example, Netflix uses machine learning to analyze movie choices and make recommendations for its subscribers.

Deep Learning

Deep learning is a more complex subset of machine learning. In this case, developers teach computers with methods inspired by the human brain (known as neural networks).

For example, healthcare image recognition (like detecting diseases in MRIs) is an example of deep learning at work. It can perform these complex tasks without human intervention.

There’s sometimes overlap among these three terms.

For example, self-driving cars utilize artificial intelligence, machine learning, and deep learning.

In all these cases, programs learn from examples and experience to make accurate decisions. Without extra help from humans.

So, all these processes are cogs in one larger AI model.

How Do AI Models Work?

AI models usealgorithms to recognize patterns and trends in data. Multiple algorithms working together comprise an AI program or “model.”

Many people use the terms “model” and “algorithm” interchangeably. But that is inaccurate.

Algorithms can work alone. But AI models can’t work without algorithms.

Human creators use artificial neural networks made up of connections or “synapses” to mimic how a brain sends information and signals via neurons. But in this case, the “neurons” are processing units in layers.

Here’s what they look like:

Image Source: IBM

Like humans, AI models are on a sliding scale of complexity and intelligence. The more training data they have to “learn” from, the more intelligent they’ll be.

Think of a model as a child.

It doesn’t know the answer to a specific question unless you provide it. You teach it enough and when you ask again, it remembers the answer.

Models can learn from thousands or millions of examples to generate predictions or classifications. So when you feed new data into them (like a question), they can predict the data you’re looking for (an answer).

But there is more than one type of AI model.

Four Categories of AI Models and Their Functions

Each of the following models falls under the umbrella of generative AI, which means they have the capability to create content, such as text or visuals.

However, each model in this list of AI models has its unique mode of operation:

1 Foundation Models

These are machine learning models that have undergone pre-training to execute specific tasks, a process known as “self-supervised learning.”
Examples of foundation models in action include OpenAI’s ChatGPT and Microsoft’s Bing Chat.

These models are trained on an extensive dataset using neural networks, enabling them to adapt to various applications, much like the human brain.

Foundation models find application in a diverse array of tasks, such as:

Responding to queries;
Composing essays and narratives;
Summarizing large amounts of information;
Generating programming code;
Tackling mathematical 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.

That’s why many foundation models are now multimodal:

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.

Here’s what the simplified process looks like:

Google’s BERT is a more sophisticated, neural network-based NLP. However, the training process involves a similar simple task that helps the model learn relationships between sentences:

Through its training, BERT learns that “The man went to the store. He bought a gallon of milk” is a logical sequence. But “The man went to the store. Penguins are flightless” isn’t.

The “large” in LLMs refers to the fact developers train them with huge datasets. Which allows them to translate, categorize, conduct sentiment analysis, and generate content.

That’s why fields like healthcare are implementing them rapidly. Many healthcare LLMs use the BERT architecture:

BioBERT: A domain-specific model pre-trained on biomedical data
ClinicalBERT: A domain-specific model pre-trained on Electronic Health Records (EHRs) from intensive care patients
BlueBERT: A domain-specific model pre-trained on clinical notes and abstracts from the online database PubMed
All these programs can understand, classify, and respond to patient queries faster and more efficiently.

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.

Here’s what the process looks like, simplified:

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.

However, different diffusion model tools generate different images for the same input.

For example, here are images from Stable Diffusion, DALLE-2, and Midjourney for the prompt “Cherry blossom near a lake, snowing”:

Why do they differ?

Because the companies creating these cutting-edge AI tools have different architectures, objectives, and training mechanisms.

So each model refers to separate, varying datasets when combining features for a “lake” or “cherry blossom.”

**Examples of Popular Marketing Tools That Use AI Models
**
People use different AI models to create tools for a range of complex tasks. Let’s look at popular options small business owners and marketers would find most helpful:

*ChatGPT: GPT-3.5 *

ChatGPT is OpenAI’s advanced chatbot that uses the latest GPT LLM to generate relevant, human-like responses to prompts.

For example, here’s how it responded to the prompt “Explain how you work in a few lines:”

GPT stands for Generative Pre-trained Transformer:

Generative: Means it generates content
Pre-trained: Means the OpenAI team inputted data (known as pre-training) to help the system understand and respond to specific tasks
Transformer: Means it uses deep learning capabilities to consider the context of words and predict what comes next
ChatGPT uses the GPT-3.5 model for free users and the latest GPT-4 version for paid plans.

Ask ChatGPT a question, and it’ll answer you conversationally.

But that’s not all it does. The tool can also:

Create marketing content (e.g., social media posts, email newsletters, or landing page copy)
Write cold email templates
Break down complicated concepts in simple terms
Translate text into multiple languages
Create spreadsheet formulas and solve math problems
Summarize and categorize huge documents and meeting notes
ChatGPT can generate inaccurate and sometimes biased information. So always double-check any content you use it to create (especially for marketing purposes).

Google Bard: PaLM 2

Bard is Google’s free experimental chatbot that uses the second version of an LLM called Pathways Language Model (PaLM).

Its original AI model was the Language Model for Dialogue Applications (or LaMDA for short). However, PaLM 2 is better at reasoning, translating, and coding.

Google designed Bard to be a complementary experience to Search. It works by searching the web in real time for answers. Then, uses its findings to converse with users.

For example, here’s how it responded to the prompt “What’s the weather like in Monticello, Utah?”:

s there any answer you’re not sure about or want to explore further? Visit Google’s search engine directly within the interface with a single click.

Bard can help you:

Come up with marketing ideas
Discover relevant tips and tricks
Switch up your writing’s tone
Translate English into multiple languages
Summarize text and data
Generate content (e.g., ecommerce product page copy)

When it quotes or includes images, Bard links to sources and citations. This sourcing is a helpful feature other popular chatbots are missing.

DALL-E 2: GLIDE

DALL-E 2 is OpenAI’s text-to-image generator that uses a multimodal model called GLIDE. It stands for Guided Language to Image Diffusion for Generation and Editing.

OpenAI used the GLIDE model to improve the original DALL-E. And allow DALL-E 2 to have higher image resolutions and higher-quality photorealism.

DALL-E 2 produces AI images from text prompts. The visuals look like human-created sketches, illustrations, paintings, and photos.

For example, here’s what it came up with for the prompt “a photo of a spiky hedgehog laying in the grass”:

The tool will always generate four variations of AI images that it thinks best match your prompt.

You can use DALL-E 2 images in all types of marketing content. For example:

Blog articles
Social media posts
Landing pages
Email newsletters
Community forums

Heinz Ketchup even created an entire marketing campaign around DALL-E 2:

Stable Diffusion XL Playground: Stable Diffusion
Stable Diffusion XL is an AI image generator that uses Stable Diffusion’s API. It’s an open-source model, which means its code is available to the public. So any creator can use its capabilities to set up models and build tools.

That’s why many users believe Midjourney (another popular AI image generator) uses the Stable Diffusion model. But the team hasn’t confirmed that.

You can create free images using Stable Diffusion XL in its online Playground. Enter your prompt, choose your style, and generate a result.

For example, here’s what it came up with for “a horse running through a candy cane forest” in cinematic style:

Stable Diffusion XL's image generated for the “a horse running through a candy cane forest” prompt
Want images without watermarks?

You’ll need Stable Diffusion’s official AI application, DreamStudio.

Like DALL-E, you can use Stable Diffusion’s tools to add visuals to any marketing material.