Generative models are an unsupervised type of machine learning model that use artificial intelligence, probability distribution, and statistics to produce a computer-generated representation of a targeted variable calculated from prior observations, input or datasets. This means they can generate new or synthetic data after being trained on a real dataset hence the name “generative.”
This is what's powering the current wave of AI content creation. See, generative modeling can be used to create a wide range of content, from text to images. For example, OpenAI's GPT-3, which was trained on billions of parameters, can generate high-quality articles in seconds. Then there are generative AI programs like Midjourney, that can produce photorealistic images with a simple text prompt.
In this article, we're going to discuss generative models, and how they relate to AI content creation. We'll go over what generative models are (and what they're not), how they work, and their challenges and limitations.
Before we can fully understand generative models and how they relate to AI content creation, it's essential to define what generative models are not: discriminative models. While they're both types of statistical models, there are some key differences that set them apart.
The goal of generative modeling is to learn the joint probability distribution of a dataset. In other words, it determines the likelihood of more than one event, say A and B, happening at the same time.
When it comes to language tasks, this means that generative models use the joint distribution to determine the likelihood of seeing a particular set of words and phrases together in a text. Basically, it learns the patterns and structures within an existing dataset, and uses them to create new content.
For example, if you ask a generative model to write a poem about the moon, it would use its knowledge of language, poetry, and the moon to create a brand-new poem.
Discriminative modeling, on the other hand, would take a different approach to this moon poem. A discriminative model is more of a traditional machine learning model. It deals with conditional probability, where it determines the likelihood of A happening based on the fact that B already occurred.
This is the key difference between generative and discriminative models: generative modeling uses the joint probability of an entire dataset to create new content, while discriminative modeling uses conditional probability to distinguish between different types of data.
So, when it comes to our moon poem, discriminator networks would identify what features would distinguish a moon poem from others. It would then be able to classify whether a given poem is about the moon or not.
See, while a discriminative approach can help you classify or tell the difference between different types of data, it can't necessarily use this information to generate unique content. And it's this distinction that makes a generative approach ideal for content creation.
The first thing a generative network needs is an established dataset to learn from. This dataset is often made up of real and not synthetic data.
So, to train a generative model, you need to collect a large amount of existing data in certain domains. The data could comprise texts, images, or sounds, depending on what you want to generate.
Generative models learn from the established dataset before coming up with new data of their own. They learn from the natural features of the data and understand the categories and dimensions of the datasets.
Take AI image generation, for example. AI image generators will start by generating rough images before learning color differentiation, edges, blobs, backgrounds, textures, objects, the natural placement of objects, etc. The more they learn, the better they are at generating believable, realistic images.
Generative models are tweaked and worked on until their accuracy is very high, and they can generate synthetic data that is almost indistinguishable from real-life, human-made data. The more parameters used to train the dataset, the more accurate the model is.
That said, there is the problem of overfitting. Overfitting occurs when the synthetic data generated by the model is too close to the original data.
At first glance, this might seem like the end goal, but overfitting means that the model is not as creative as it should be.
The essence of generative models is their creativity in data generation. So you should avoid overfitting at all costs. Cross-validation and other techniques will help avoid overfitting during generative model training.
Large language models (LLMs) are the latest class of generative AI models used for content generation. These deep learning algorithms can create artificial text content and large bodies of sentences. ChatGPT is a prime example of this.
The generated sentences are not incoherent ramblings or meaningless sentences strung together. On the contrary, generative AI models can write great essays, business plans, and even compose poems using natural language processing. They can also summarize books and explain difficult concepts.
AI generative models are so good that they understand intricate concepts like writing tones. You could ask some content generation models to write original content like a child or a top-level professor, and you would get astonishing results.
Popular language models include GPT-3, BLOOM, BERT, and GPT-NeoX. These models are trained using large chunks of texts from datasets comprising Wikipedia, books, webpages, historical documents, academic papers, etc.
A more niched large language model is the fine-tuned language model. Fine-tuned language models are smaller than large language models. But they are well-suited for composing texts within a specific subject matter or industry.
Some fine-tuned language models may be designed specifically for medicine, while some may be trained for simple historical quizzes. Fine-tuned language models require less computing power and take less time to train.
Here are the major generative model limitations and challenges:
While many advancements have been made concerning AI generative models, they still remain quite inaccurate. Many people can still tell the difference between human-generated work and AI-generated work.
Generative models can only be trained using powerful computer graphics processing units (GPUs) and central processing units (CPUs). These make them very expensive to develop and train. It has also hampered the ability of several individual researchers to work on and improve these models.
Using generative models has given rise to several ethical issues. Issues like plagiarism, deepfakes, students using AI for assignments as opposed to learning on their own, copyright issues, and much more.
Since AI generative models are fairly recent, there haven't been strict governing bodies and regulatory institutions to create standardized rulesets to govern the use and misuse of the models. That said, hopefully, a defining code of conduct concerning the ethics of generative models and their usage will be available to protect every party involved very soon.
There are valid fears and concerns that using generative models will put millions of people out of work. AI generative models could affect copywriters, blog writers, literature writers, speech writers, poets, artists, and even musicians.
Generative models will continue to improve and see far more use cases than what we have now. We will see more generative model applications in engineering, architecture, real estate, and advanced data visualization.
Also, the rise of open-source research in artificial intelligence and machine learning will enable AI generative models to become more accessible to more people. Computer hardware and software will get better, and just about anyone will be able to take part in it.
Fun fact, a research study by Gartner predicts that by 2025, over 30% of discovered drugs and materials will be fueled by generative models.
We are still in the early stages of generative model usage, but we can expect many more applications in the near future.
Examples of generative models are variational autoencoders (VAEs), Bayesian networks, diffusion models, restricted Boltzmann machines (RBMS), generative adversarial networks (GANs), and autoregressive models.
While it is a type of neural network architecture, convolutional neural networks are mainly used as discriminative models, especially when it comes to images. CNNs usually focus on classifying, detecting, and recognizing images.
They can be used to plagiarize creative work and impersonate individuals. They also cause an increase in bias and can cause unemployment.
Generative models are trained on large datasets containing the data they need to imitate or generate. Repeated and lengthy training causes the models to understand every dimension and facet of the data. Generative models can generate images, text, videos, and music.
They require huge computational power, and there could be issues with training stability.
Generative models use neural networks to train and improve themselves. They're a subset of artificial intelligence.
