Generative AI Models Explained: Types, Use Cases, and a Decision-Making Process
By the end of 2024, 85% of business leaders surveyed by MIT and Telstra plan to integrate generative AI in some of their organization’s operations. But even though businesses understand the myriad benefits this technology brings to the table, not many of them truly get how to implement it. This is especially true when we are talking not about the general concept of 'generative AI', but the actual generative AI models. How many of these models are there? How do they differ? Which one should you choose and which one to reject?
At Flyaps, we’ve helped many companies from various sectors with gen AI implementation. So it’s safe to say we have answers to these and other related questions In this article, we'll look at the best gen AI models designed for different purposes and things you and your team should consider when choosing one for your project.
How do generative AI models work?
Let’s kick things off by learning a bit more about how generative AI models work. This will help us with further comparison.
Generative AI models are like smart students learning from big books that consist of examples. They use math tricks called "probabilistic modeling" to understand these examples. Instead of memorizing stuff, they figure out how likely different things are and how they fit together. By tweaking how they learn or adjusting their settings, developers can teach them to perform tasks in different ways.
Let’s say we have a generative AI model that's trained on a large collection of physics books. The model is programmed to learn from this data and then generate new texts aimed to help physics students with complicated topics.
Suppose, students want to learn about string theory. Having received the input, the AI model starts analyzing all the sentences in its training data. Its goal is to look for specific, semantically related words and the way they appear in sentences. For example, it defines that “quantum” and “mechanics” often appear together, as well as “string” and “theory”. Based on the patterns identified, the AI model then calculates the likelihood of certain words being arranged together and form a new sentence. As a result, the AI model will generate a new sentence based on the data it found about string theory in a way that makes sense to it.
So, the general principle of how gen AI works looks quite simple. Then why do we need different AI models? Because each model performs different tasks depending on the domain and has different requirements in terms of the complexity of the training data or accuracy. Let's take a closer look at some types of models.
Types of generative AI models
Simply put, types of generative AI models represent different approaches to generating new content. They’re like digital toolboxes for creating new things like text, images, music and video. Many new tools are appearing every day as developers experiment with them. Therefore, we will only mention the most popular types of generative AI models in 2024.
Large language models (LLMs) and Small language models (SLMs)
Large language models stand behind such popular AI tools as ChatGPT and Claude, focused primarily on text generation. However, GPT-4 and some of the Gemini models can handle images too, which is quite rare and impressive in terms of language models.
LLMs are trained on large datasets and use specialized neural networks (transformers) that allow them to reflect the tone and writing style of the data they are trained on. The technology is commonly used to create chatbots and generate code but is also useful for tasks such as DNA research or sentiment analysis for search engines.
There's a smaller and more resource-efficient alternative to LLMs called small language models. Hugging Face CEO Clem Delangue recently shared his thoughts on SLMs, saying, “My prediction: in 2024, most companies will realize that smaller, cheaper, more specialized models make more sense for 99% of AI use cases. The current market & usage is fooled by companies sponsoring the cost of training and running big models behind APIs (especially with cloud incentives).”
In essence, SLMs serve the same purpose as LLMs but use less computational power and require less data for training. While they're not as effective for complex tasks, they're perfect for simpler applications like mobile apps or IoT devices. SLMs are also easier to customize. Tailoring an LLM to specific needs takes significant resources, but an SLM is ideal for smaller-scale needs.
Even big LLM providers like OpenAI see the potential of SLMs. Recently, OpenAI released GPT-4o Mini, which they claim is better and faster than other small models at reasoning tasks, with an MMLU score of 82%.
Generative adversarial networks (GANs)
GANs are used for changing images in various ways (from style to color or content) or synthetic data generation (for training other models).
GANs work based on two neural networks – the generator and the discriminator. The generator creates fake poor-quality images, while the discriminator distinguishes between real and fake images made by the generator. Basically, the two networks play a game against each other: the generator tries to produce data that is indistinguishable from real data, while the discriminator tries to get better at telling the difference. This back-and-forth duel continues until the generator creates realistic data that the discriminator can't differentiate from real data.
Transformer-based models
These models have replaced the earlier deep learning types for pattern recognition, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNN and RNN types require large, labeled datasets to train their models. Their adoption was costly and time-consuming. The transfer-based architecture has been able to overcome those challenges. Instead of relying solely on labeled data, they employ mathematical methods to identify patterns between elements. This innovative technique removes the necessity for extensive labeled datasets. Their improved architecture is based on attention mechanisms that focus on different parts of the input data when processing it. For example, the system processes each word and weighs its importance to better capture dependencies and relationships between words.
Such an approach is ideal for sequential data and tasks like sentiment analysis, machine translation, speech recognition, image captioning, image generation and object recognition.
Variational autoencoders (VAEs)
VAEs can be applied for unsupervised learning (when the algorithm doesn't need to be told what patterns to look at) and for generating new data similar to the training ones like realistic images of faces, animals, or scenery. For instance, Meta's Reality Labs, which is focused on the development of augmented reality (AR) and virtual reality (VR) technologies, uses VAEs for creating detailed face models. The model can be used for facial recognition, animation, virtual reality, medical imaging, and security systems.
VAE models consist of two main parts: an encoder and a decoder. The encoder takes input data, compresses it and keeps its essential features in the space called “latent”. The decoder takes a point from the latent space and reconstructs the original data from it. It learns to generate data similar to the input data based on the information in the latent space.
Neural radiance fields (NeRFs)
NeRFs can create a 3D picture from only a few 2D pictures by figuring out how much density is in each point in space and what color it should be. Naturally, their applications include virtual reality, augmented reality, and 3D content creation.
As you can see, the main idea of generating new information based on learned data can be implemented with different approaches, depending on the goals or features required. With that said, let’s have a look at some examples.
When people talk about models, they usually mean a collection or group of models. Take GPT, for instance. The GPT series consists of several versions ( GPT, GPT-2, GPT-3), each with increasing model size, complexity, and capabilities. Think of models as a family tree, with different generations and branches. In the case of the GPT series, which evolved from both LLM and transformers-based types, with types being like grandparents. Types are divided into smaller groups – sets (parents). Finally, each set contains individual models (children). However, unlike with humans, each new model/child is always better than the previous one.
Since we've already mentioned GPT, let's start our list of the best gen AI models with it.
GPT (generative pre-trained transformer)
Developed by OpenAI, the GPT series is both an LLM and transformer-based. GPT models come in two types: general and custom. The famous GPT-3.5 or GPT-4 are general ones. Custom GPT you can picture as a personalized version of ChatGPT. Instead of using all of ChatGPT's features, users can pick and choose the ones they need. They can tweak the data and instructions to tailor the model to their specific needs.
Here’s what general GPTs can do:
- Generate code in different programming languages.
- GPT-4 can generate images.
- Improve itself based on users’ feedback.
Curious to learn more? Have a look at our article for details on the pros and cons of GPT, as well as the 6 other best LLMs for your business.
Stable Diffusion
Stable Diffusion are suite of models with Stable Diffusion 3 being the last one and the most advanced for now. It is also an open-source tool, where users can train their own models to generate detailed and realistic images from text descriptions. The tool can be used just like many other software where users can set it up on their computer or access it through platforms like Clipdrop and DreamStudio. They can get high-quality images simply by giving the system detailed instructions, specifying subject matter, style, and mood.
While Stable Diffusion models produce impressive results, their drawback lies in their computational cost and resource requirements. Generating high-quality images typically requires significant computational power and time.
LaMDA
LaMDA is a transformer-based model developed by Google for conversational applications. Simply put, LaMDA was created to understand and generate human-like responses in natural language. While it sounds like another chatbot model, LaMDA is more advanced in terms of the variety of topics it can carry on a conversation about. And the level of fluency is quite high – Google has put a lot of effort into making the model talk like a friend, not a robot, and encourage conversation.
Hugging Face’s BLOOM
BLOOM is an autoregressive LLM, meaning that it generates output sequentially, token by token, where each token depends on previously generated ones. The model aims to fill in missing sections of text or code. BLOOM excels at specific tasks, such as code generation or natural language completion, but its range of application is limited to these concrete tasks only, unlike more general AI models.
Meta’s Llama
These models come in different sizes, so Llama can work on everything from small phones to large cloud systems. Meta used it to create its AI assistant, which you can check out on its social media platforms. The model is often used to generate text or code.
An important plus for Llama is its community support. Although Meta does not disclose all the details of its training data, users can still access resources provided by the company itself or other developers. These resources may include documentation, tutorials, and forums where users can share knowledge and best practices for using the model.
As you can see, there is no universal model for everyone to opt for. That's why it's important not to limit your search to lists of the most popular models. So, let us tell you how you can define your model requirements and choose the right gen AI model.
How to choose the right generative AI model
Before we proceed, one thing to clarify. To choose the best generative AI model for your project, we highly recommend reaching out to professionals experienced in working with gen AI algorithms. Having hands-on experience, they will be able to match your business needs with the right technologies and approaches that will ensure the best outcome. But if you’re looking to do things on your own, here’s what a general decision-making process would look like.
Step 1. Define the key feature to perform
Choose the specific task or problem you intend to solve. Define what exact features of gen AI models can help you to achieve the goal. Is there any other cheaper or ready-made solution you can use for this purpose instead of gen AI? If not, move to the next step.
Step 2. Evaluate what model’s size the existing ecosystem can handle
Larger models often offer more features but require more computing resources. So gather information about each gen AI model that performs the functions you're interested in, including its size, performance metrics, and the computing resources required to deploy and run it.
Step 3. Evaluate the performance
Set clear performance metrics such as accuracy or speed, and assess what result each model can deliver. For example, a model can be accurate but slow, or fast but less reliable.
You can still evaluate the models even if you're not adopting them yet. Look at data and reports from reputable sources to see how well each model works in real life. Look for metrics like precision, recall, and F1 score to see how accurate the model, how well it identifies and classifies inputs. For speed, look at metrics like inference time or processing time per input. This shows how quickly the model can produce results.
Step 4. Assess risks and governance
Consider the risks associated with the use of each model. Are there potential biases or ethical concerns in the content generated? Ensure that the chosen model is compliant not only with ethical standards but also with governance policies.
Step 5. Consider the costs of the model and your budget constraints
Some models may be more expensive to deploy or maintain than others. This could include licensing fees, infrastructure, support and training. Even open-source models cost money to set up and run. This may involve setting up and configuring the model, as well as maintenance and updates.
Want to understand pricing factors and real prices? Read our article How Much Does AI Cost?
Use pre-built solutions to simplify generative AI adoption
Embracing generative AI doesn't have to be complicated. For you to implement generative AI without much hassle, we at Flyaps offer pre-built gen AI solutions designed for various industries. These solutions can be easily customized to fit your specific needs, without the need for making countless decisions about which model to use or how to integrate it into your existing systems.
Anyway, working with models is an ongoing job, as the technology changes rapidly and newer versions appear every day. So find reliable experts with experience in the field to help you keep your finger on the pulse and ensure that your solution is not outdated at the development stage.
Having trouble finding a reliable team to explore generative AI? Drop us a line!