Types of Artificial Intelligence & AI Models That You Should Know In 2024

Let's begin with the basics, which I'm sure you already know.

Artificial Intelligence: What Is It?

Artificial intelligence is the process of developing intelligent machines by analyzing vast amounts of data. These systems are designed to learn from past mistakes and experiences, mimicking human behaviour. As a result, AI can improve the efficiency, speed, and accuracy of human efforts. By utilizing sophisticated algorithms and techniques, AI can enable computers to make decisions on their own. This is achieved through deep and machine learning, which are the fundamental building blocks of artificial intelligence.

Now that you know what AI really is, let’s look at what are the different types of artificial intelligence.

1. Based on Capabilities

Narrow AI (Weak AI)

This type of AI is designed to perform a narrow task (e.g., facial recognition, internet searches, or driving a car). Most current AI systems, including those that can play complex games like chess and Go, fall under this category. They operate under a limited pre-defined range or set of contexts.

General AI (Strong AI)

A type of AI endowed with broad human-like cognitive capabilities, enabling it to tackle new and unfamiliar tasks autonomously. Such a robust AI framework possesses the capacity to discern, assimilate, and utilize its intelligence to resolve any challenge without needing human guidance.

Superintelligent AI

This represents a future form of AI where machines could surpass human intelligence across all fields, including creativity, general wisdom, and problem-solving. Superintelligence is speculative and not yet realized.

2. Based on Functionalities

Reactive Machines

These AI systems do not store memories or past experiences for future actions. They analyze and respond to different situations. IBM's Deep Blue, which beat Garry Kasparov at chess, is an example.

Limited Memory

These AI systems can make informed and improved decisions by studying the past data they have collected. Most present-day AI applications, from chatbots and virtual assistants to self-driving cars, fall into this category.

Theory of Mind

This is a more advanced type of AI that researchers are still working on. It would entail understanding and remembering emotions, beliefs, and needs, and depending on those, making decisions. This type requires the machine to understand humans truly.

Self-aware AI

This represents the future of AI, where machines will have their own consciousness, sentience, and self-awareness. This type of AI is still theoretical and would be capable of understanding and possessing emotions, which could lead them to form beliefs and desires.

3. Based on Technologies

Machine Learning (ML)

AI systems are capable of self-improvement through experience, without direct programming. They concentrate on creating software that can independently learn by accessing and utilizing data.

Deep Learning

A subset of ML involves many layers of neural networks. It is used for learning from large amounts of data and is the technology behind voice control in consumer devices, image recognition, and many other applications.

Natural Language Processing (NLP)

This AI technology enables machines to understand and interpret human language. It's used in chatbots, translation services, and sentiment analysis applications.

Robotics

This field involves designing, constructing, operating, and using robots and computer systems for controlling them, sensory feedback, and information processing.

Computer Vision

This technology allows machines to interpret the world visually, and it's used in various applications such as medical image analysis, surveillance, and manufacturing.

Expert Systems

These AI systems answer questions and solve problems in a specific domain of expertise using rule-based systems.

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.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Think of artificial intelligence, machine learning, and deep learning as one big tree.?

The trunk is AI. And one of its biggest branches is machine learning (ML). But that big branch splits into several smaller branches. One of them is deep learning (DL).

What’s the bottom line?

All are connected. But each term doesn’t refer to the same process.

Here’s what it looks like:

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.

With deep learning, things get even more specialized.

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 use algorithms 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:

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.

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.

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 that 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: This means it generates content
• Pre-trained: This means the OpenAI team inputted data (known as pre-training) to help the system understand and respond to specific tasks
• Transformer: This 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 to create (especially for marketing purposes).

Google Gemini

Gemini is a family of multimodal large language models developed by Google DeepMind, serving as the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, and Gemini Nano, it was announced on December 6, 2023, positioned as a competitor to OpenAI's GPT-4. It powers the chatbot of the same name.

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:

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.?

I hope you will like this article. Enjoy Friends.