ChatGPT, DALL-E, what is next? OpenAI has done everything in its power to keep the entire tech community on its toes. Despite futile attempts by other tech giants to steal OpenAI’s thunder, it kept bringing on new waves of innovation, securing its spot as the leading advocate for AI research. Of course, the growing similarity between emerging technologies leaves us wondering: What do these state-of-the-art tools have in common? The answer is right there: the most stunning advancements in artificial intelligence are largely attributed to generative AI— a technology that can create new content from scratch, whether AI-generated art, images, text, or audio files. Fortunately for us, the technology is not as complicated as it sounds; with generative AI, a model studies and identifies patterns that are connected to the input to produce similar content. However, with each passing day, these models are evolving and learning further based on their mistakes, which begs the question: How much more are they capable of with continuous training?
In this blog post, we will be covering generative AI from A-Z, highlighting why you and your industry should or should not adopt it.
A brief overview of generative AI and how it emerged
Generative AI’s abilities go beyond fun mobile applications and avatars, as they are now being used to create art pieces and design, produce code, write blogs, and generate all kinds of different high-quality content.
By using unsupervised and semi-supervised learning algorithms, generative AI processes enormous amounts of data to generate its own outputs. One example is how with large language models, computer programs can now easily understand texts and generate new content. The neural network that is at the core of generative AI can pick up on the traits of a specific image or text and then exert it when needed.
Through generative AI, computers can predict the most relevant patterns to input, allowing them to output corresponding content. During the training, a limited number of parameters are given to the generative AI models, enabling them to make their own conclusions and highlight features present in the training data. However, to get the most out of generative AI, human involvement is still essential, and that is both at the start and end of the training.
Applications of generative AI
Now that we have summed up how generative AI works let’s take a look at some of its most-used applications.
Generative AI is frequently utilized in creative sectors, specifically to create art and generate images. In fact, AI-generated visuals are a whole new category of art nowadays. These models can be trained on a large number of paintings and later be used to generate new ones with similar features and slight variations in style.
For example, if you want your AI to produce works similar to Leonardo Da Vinci, you will need to provide it with as many paintings of Da Vinci as possible. Once that is done, the model’s neural network observes and takes in the characteristics of those art pieces to reproduce similar works. The same goes for models that generate text, produce music, etc. With generative AI, another feature you get is changing one kind of image into another, meaning modifying the style or specific areas of the image. This occurs when the generative AI model copies the characteristics and aesthetic of your preferred painting and gives you an alternative version. It can also work with rough sketches or wireframes and offer a finalized version of the design.
Video games are benefiting from generative AI through its generation of new levels, dialogue options, maps, and new virtual worlds. Generative AI can provide new experiences for players by building immersive worlds for them to explore, like cities, forests, and even new planets. One example is Scenario which allows game developers to train their generators to produce images according to the particular model of their games. Generative AI’s intervention could lead to an increase in the number of games that are created annually, which also means new genres that would not have been invented without the help of generative AI.
Image instance creation
Generative AI is perhaps best known for its ability to produce fake realistic-looking photographs of people. When the input data is an image of someone’s face, the model gets trained on it and then generates fake images/photographs with the same faces.
With generative AI models, healthcare professionals can identify health issues early on and get to create treatments on time. Generative AI is also being used in healthcare applications for its ability to revert MRI scans into CT scans. Generative adversarial networks (GANs) are also learning to create fake versions of underrepresented data, which is later used in training and developing a model. They are also handy for identifying data and improving its privacy and security.
Generative AI can even work with audio data, altering the sound of musical genres or human voices. With its intervention, a musical piece can be transformed from one genre to another, for example, rock into classical music, and vice-versa. Musico is an example of an AI-driven software engine that generates music, making use of gestures, motions, codes, and much more. Musico's engines have the ability to create according to the user’s preferences, and that varies from musical sketches to full songs.
The ever-growing demand for personalized web and email content for marketing purposes is a reason enough for having more automated solutions. When it comes to writing, more specifically copywriting, it seems like generative AI has a lot to offer. By teaching generative AI algorithms to grasp the main ideas and look for keywords, copywriters can now create better headlines and make sure to grab the readers’ attention. Generative AI’s ability to generate content can improve your work, help you explain concepts more efficiently, and help you come up with new ways to produce higher-quality writing. Despite speculations on how generative AI is taking over industries such as copywriting, when looking at it from up close, one can see that they are nothing more than mere assistants for humans, making sure their works reach their full potential. An example is Copy.ai, a generative AI that can create blogs with optimized texts, social media posts with high conversion rates, and engaging emails from scratch. Furthermore, such tools can take care of the itty bitty aspects of copywriting, such as repetitive tasks, bullet lists, and subheadings, and leave the creative aspect of it to humans.
Media and advertising
Generative AI can modify content creation through super-resolution. In the media industry, combining machine learning techniques with marketing techniques can lead to improved content generation. Predictive targeting is an example of a marketing technique that utilizes both AI and machine learning, foreseeing a customer’s next decision by analyzing their old data and behavior patterns. Synthesia is another example of a well-known generative AI company that implements new synthetic media technology for visual content creation, and it does so by using minimum skills and cost. By utilizing generative AI, both marketing and advertising industries can produce more personalized content, they can better understand the market and consumer behavior, and come up with more efficient campaigns.
SuperAnnotate’s SuperGen feature is another example of generative AI. Our platform now also allows you to generate synthetic data, which you can use as an addition to your existing datasets. All you need to do is log in, type out the desired prompt, and click submit.
If you find any of the results applicable to your dataset, you can generate similar but by selecting the respective image and clicking the Generate similar button below.
By leveraging SuperGen, you can add diversity to your data and potentially minimize dataset bias before it goes to training.
How generative AI is governed
With the emergence of generative AI, we have also witnessed different approaches to AI governance. Within private sectors, there are two main approaches; the first is companies self-governing the space by limiting release strategies, keeping an eye out on the use of the models, and limiting access to their products. Newer organizations, on the other hand, believe that generative artificial intelligence models are for democratizing access as they have the potential to positively impact both the economy and human society.
When looking at public sectors, there are no regulations concerning the growth of generative AI models, yet that does make more room for issues concerning the invasion of privacy, intellectual property, and copyright. As generative artificial intelligence datasets are usually retrieved from the internet, they are used without permission from the creators/artists, which can appear to be original when in reality, it is fabricated and can cause plagiarism.
Problems concerning generative AI
As beneficial as generative artificial intelligence gets, its growth does raise legal, moral, and ethical questions. Let's have a look at some of the biggest concerns:
With generative AI producing unlimited amounts of content, especially art pieces, the internet will shortly be filled with paintings that are unrecognizable from the original ones. This also raises the issue of it replacing humans when it comes to many creative workforces, such as freelancers or commercial artists who work in publishing, entertainment, and even advertising.
As generative AI models are being fed large datasets such as articles, books, and websites, there is a huge chance that the information they are being given is biased, and that makes it hard to filter credible content completely. With this, models can easily create deep fakes, reinforce machine learning bias, and share misleading content across platforms.
The internet is filled with scammers and people who are trying to steal your data, and generative artificial intelligence can be used by such people to successfully cause damage to users or, at the very least, circulate spammy news online.
The need for large amounts of training data
As stated above, generative AI algorithms need large amounts of training data so they can perform their tasks with high accuracy. However, it is challenging for GANs to generate entirely new content; they can only combine what they picked up in new different ways and give a fresh output. With GANs being hard to control, generative artificial intelligence models are not always stable, and they can give out unexpected outcomes.
Benefits of generative AI
There has to be a reason for engineers to go gown the generative road and for users to love the end product, right? So, here are some of the advantages of generative artificial intelligence:
Generative modeling helps reinforcement machine learning models in exhibiting less bias. They also give these models the ability to grasp complex ideas.
Automated content variety
Generative AI can take in any type of content, whether text, image, video, code, etc. It can also give answers to questions and output new content, including translations, summaries, and analyses. This is a big time-saver for students and researchers, as they can access more content and information in less time.
Personalized content creation
Once generative AI models go through training, they can start producing personalized content upon their users’ preferences. This can benefit businesses in creating content that is more likely to reach their target audiences, as they are specifically tailored to their own preferences.
Generative AI techniques
As we covered earlier on, in order to produce content, generative artificial intelligence lets machines find the underlying pattern related to the input, and it does so through a few different techniques.
- GANs: Generative adversarial networks are made up of two neural networks, a generator and a discriminator network. The generator network produces fresh data that is similar to the source data, whereas the discriminator network separates the generated data from the source.
- Variational auto-encoders: With this technique, you get to encode the input into compressed code as the decoder duplicates the code’s information and stores its distribution as another, much smaller dimensional portrayal of the compressed data.
- Transformers: Mainly trained to comprehend images or languages, transformers can also learn to classify tasks and generate content. A well-known example of this model is GPT-3, which imitates cognitive attention and measures the significance of the input data parts.
Future of generative AI
When most of the AI systems we have today are used as classifiers, what distinguishes the generative AI apart is its ability to be creative and use that creativity to produce something new. Not only are generative AI tools revolutionizing the marketing and art industries, but their applications are becoming more and more practical in fields such as pharmaceuticals, medtech, and biotech, with many experts predicting the development of new solutions within the next few years. Generative AI is more than NLP tasks such as language translation, text summarization, and text generation, with OpenAI’s ChatGPT as the biggest proof (reaching millions of users in just a few days). Although it is still in its development stages, there is more room for generative AI to grow and transform the way we make use of the internet.
With the way things are going now, it is foreseeable that AI's implementation will have long-lasting effects on future businesses, whether that revolves around simple everyday tasks or collaborations on large-scale developments.
With generative AI being able to create and reproduce high-quality works, automate content, and even address medical issues, we can see how it will revolutionize the way things work within the arts, robotics, advertisement, and healthcare sectors. As concerning as this may sound to some, it is as important for us to keep an open mind about it and be mindful that for a generative model to operate, human input is a must; hence we are still in control.
Training a top-performing generative AI model requires annotation, effective visualization, and curation of voluminous datasets (hundreds of thousands of data items and beyond). Though it is extremely challenging to find a single tool that covers all stages of the pipeline individually — this is where SuperAnnotate steps in to offer an all-in-one solution for all your data needs.