Ready to start using AI at work? If you’re looking to incorporate AI into your daily workflows, it’s helpful to understand the different types of AI models you could be using. 

Because AI can get confusing really fast. 

This article explores the various types of artificial intelligence, categorized by technology, capability, functionality, and purpose, providing a comprehensive overview of the field. It’ll help you understand which types of AI will be most beneficial to use throughout your work day. 

The different types of AI 

By technology

• Machine Learning: Machine learning (ML) is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. It relies on algorithms and statistical models to analyze and draw inferences from data. Key types of machine learning include:
  • Supervised Learning: Involves training a model on labeled data, where the desired output is known. Common techniques include linear regression, classification, and support vector machines. Supervised learning is used in image recognition, fraud detection, and more.
  • Unsupervised Learning: Involves training a model on unlabeled data, where the output is not predefined. Techniques include clustering and association, often used for customer segmentation and anomaly detection.
  • Deep Learning: A specialized form of ML that uses artificial neural networks with many layers (deep networks). It excels in natural language processing (NLP) and image recognition.

• Natural Language Processing (NLP): Enables machines to understand and process human language.
• Robotics: Combines AI with mechanical systems to perform tasks in the physical world.
• Expert Systems: Mimic human decision-making in specialized fields, using rule-based systems.

By capability

• Weak AI (Narrow AI): Designed to perform specific tasks with human-like abilities. It uses supervised and unsupervised learning. Examples include voice assistants like Siri and Alexa. Weak AI cannot perform beyond its programmed capabilities.
• Strong AI (General AI): A hypothetical form of AI with human-like intelligence, understanding, and reasoning. It can perform any intellectual task that a human can. Strong AI remains a concept that has not yet been realized.
• Superintelligent AI: An advanced form of AI that surpasses human intelligence in all aspects. This type of AI could potentially outperform humans in every field, from scientific research to social skills. It is currently theoretical.

By functionality

• Reactive AI: The most basic form of AI, which reacts to specific inputs with pre-defined responses. It does not have memory or the ability to learn from past experiences. Examples include IBM’s Deep Blue chess computer. It typically uses rule-based systems.
• Limited Memory: Can store past experiences and use them to inform future decisions. Most modern AI systems, such as self-driving cars, fall into this category. They use supervised and unsupervised learning methods.
• Theory of Mind: An advanced form of AI that can understand and interact with human emotions and thoughts. It involves complex interactions and is still in the experimental stage.
• Self-aware AI: A theoretical form of AI with self-consciousness and awareness. It would possess the ability to understand its existence, much like humans. This concept remains speculative and is a subject of philosophical debates.

By purpose

• Generative AI: Focuses on creating new content, such as images, music, or text. It relies on unsupervised learning to generate realistic outputs. Key types of generative AI include:
  • Generative Adversarial Networks (GANs): Two neural networks (generator and discriminator) compete to create realistic data, enhancing the generator’s output over time.
  • Variational Autoencoders (VAEs): Encodes data into a latent space, then decodes it to generate new data, ensuring variations similar to the original data.
  • Diffusion Models: Generate data by reversing a noise-corruption process, producing high-quality samples through iterative denoising.
  • Multimodal AI: Generates cross-modal content, integrating multiple data types like text, images, and audio.
  • Recurrent Neural Networks (RNNs): Suitable for sequential data generation, maintaining memory of previous inputs for coherent outputs.
  • Flow Models: Use invertible transformations to map data into a latent space, enabling precise data generation and density estimation.
  • Neural Radiance Fields (NeRFs): Generate 3D scenes from 2D images by modeling the radiance emitted from each point in space.
• Predictive AI: Uses data to predict future outcomes, employing techniques like linear regression and time series analysis. It is widely used in areas like demand forecasting and risk assessment.
• Assistive AI: Designed to assist humans in various tasks, enhancing productivity and efficiency. It includes tools like virtual assistants and recommendation systems, often using supervised learning.
• Conversational AI: Powers chatbots and virtual assistants, enabling human-like interactions through NLP and dialogue management. It uses a combination of supervised, unsupervised, and deep learning techniques.

AI types chart

Category	Type	Description	Learning Types
Capability	Weak AI (Narrow AI)	Task-specific, limited intelligence	Supervised, Unsupervised
	Strong AI (General AI)	Human-like intelligence, theoretical	Not yet realized
	Superintelligent AI	Surpasses human intelligence, theoretical	Theoretical
Functionality	Reactive AI	Responds to inputs without memory	Rule-based
	Limited Memory	Can learn from past experiences	Supervised, Unsupervised
	Theory of Mind	Understands human emotions	Experimental
	Self-aware AI	Possesses self-consciousness	Theoretical
Purpose	Generative AI	Creates new content	Unsupervised
	Predictive AI	Predicts future outcomes	Supervised
	Assistive AI	Assists humans in tasks	Supervised
	Conversational AI	Human-like interactions	Supervised, Unsupervised, Deep Learning
Technology	Machine Learning	Learning from data	Supervised, Unsupervised
	Deep Learning	Artificial neural networks	Deep Learning
	Natural Language Processing	Understanding human language	Supervised, Unsupervised
	Robotics	AI-driven mechanical systems	Various AI techniques
	Expert Systems	Decision-making in specialized fields	Rule-based

Types of AI models 

In recent years, AI models have made big strides in various fields, thanks to advancements in machine learning and deep learning. Among these, Large Language Models (LLMs) have gained particular attention for their capabilities in natural language understanding and generation. Here, we explore some of the most popular AI models, including LLMs.

1. GPT-3 and GPT-4 (Generative Pre-trained Transformer)

• Description: Developed by OpenAI, GPT-3 and GPT-4 are state-of-the-art LLMs known for their ability to generate coherent and contextually relevant text. They are used in applications ranging from chatbots to content generation.
• Applications: Natural language processing, text summarization, language translation, conversational AI.

2. BERT (Bidirectional Encoder Representations from Transformers)

• Description: Created by Google, BERT is a transformer-based model designed to understand the context of words in a sentence by looking at both directions (left and right). It’s widely used for tasks that involve understanding the nuances of language.
• Applications: Question answering, sentiment analysis, search engine optimization.

3. T5 (Text-To-Text Transfer Transformer)

• Description: Developed by Google, T5 is a versatile model that frames all NLP tasks as a text-to-text problem. It has been fine-tuned for various tasks, including translation, summarization, and question answering.
• Applications: Text generation, translation, summarization, question answering.

4. RoBERTa (Robustly optimized BERT approach)

• Description: An optimized version of BERT by Facebook AI, RoBERTa enhances BERT’s performance by training on more data and longer sequences. It is known for its robust language understanding capabilities.
• Applications: Text classification, natural language inference, token classification.

5. Transformers in Vision (ViT)

• Description: ViT applies the transformer architecture to image recognition tasks. It splits images into patches and processes them like sequences of words, enabling powerful image analysis capabilities.
• Applications: Image classification, object detection, medical imaging.

6. DALL-E

• Description: Another creation by OpenAI, DALL-E generates images from textual descriptions. It leverages a combination of NLP and image generation techniques to create highly detailed visuals based on prompts.
• Applications: Creative arts, design, advertising.

7. CLIP (Contrastive Language–Image Pretraining)

• Description: CLIP is an AI model that connects text and images, understanding them jointly. It can perform various tasks without specific fine-tuning, such as image classification and zero-shot learning.
• Applications: Image and video retrieval, cross-modal tasks, zero-shot classification.

Getting started with AI in your organization

To integrate AI into your organization, start by identifying specific areas where AI can enhance efficiency and productivity. Implement machine learning models to automate tasks, use NLP for data analysis, and consider deep learning for complex problem-solving. 

GenAI tools like GoSearch Enterprise Search can help streamline your knowledge management, making it easier to find and share work information! With features like advanced NLP,  deep learning, and multimodal AI, GoSearch can transform how your team accesses and utilizes data.

Click here to learn more about GoSearch or schedule a demo.

FAQs

What are the 4 types of AI technology? 

The four main types of AI technology are machine learning, deep learning, natural language processing, and robotics.

What type of AI is ChatGPT? 

ChatGPT is a type of conversational AI that uses deep learning, specifically transformer-based neural networks, for natural language understanding and generation.