Generative models are an unsupervised type of ML that use artificial intelligence, probability, 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.”
Generative models can be used for text, image, video, and music generation. For example, OpenAI’s GPT-3, which was trained on over 175 billion parameters, can generate high-quality texts.
The new generation of generative models are deep generative models (DGMs) that combine traditional generative models with deep neural networks and an enormous increase in training data.
The first thing a generative network needs is an established dataset to learn from. This dataset is often comprised 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.
For example, the generative models powering AI image generators will start by generating rough images before learning colour differentiation, edges, blobs, backgrounds, textures, objects, the natural placement of objects, etc. The more they learn, the better they are at generating believable 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 are the latest class of generative models that are used for AI content generation. These generative models can generate 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 conjoined abnormally. On the contrary, AI generative 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 content generation models are so good that they understand intricate concepts like writing tones. You could ask some content generation models to write text 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:
Generative models are fairly recent, and 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.
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), generative adversarial networks (GANs), and autoregressive models.
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. Generative models are a subset of artificial intelligence.
