When speaking about Artificial Intelligence (AI), it is no surprise that this branch of computer sciences is rapidly evolving. Increasing demand for efficient and accurate decision-making and problem-solving tools continues to drive development in the field. Traditional AI, also called Analytical AI, excels at analyzing data and finding patterns in it. It is used for tasks such as image and speech recognition, making predictions or recommendations in e-commerce and media, diagnosing and treating diseases in healthcare, improving traffic flow and reducing accidents in transportation, optimizing crop yields and reducing resource waste in agriculture, etc. In one of our previous articles, we discussed Artificial Intelligence Use Cases in 2022. But what is a Generative AI, and how does it differ from the Analytical AI we already deal with?
Generative AI is a machine learning type that goes beyond simply processing and analyzing data. It can create new and original content based on input or training data patterns and structures. This new content may be text, images, video, music, computer games, or even entire websites. Until recently, machines were not capable of competing with humans in terms of creative work, but the appearance of Generative AI has the potential to revolutionize industries that used to rely solely on human creativity, including social media, advertising, entertainment, law, and product design. It can make creative workers more efficient, potentially generating trillions of dollars in economic value. According to Gartner's forecast, generative AI will be responsible for creating 10% of all data by 2025 compared to less than 1% produced in 2021. As generative AI continues to improve, it may surpass human ability in certain areas of creation. Let's try to predict how far this technology can take us. And we suggest starting with analyzing different types of generative models.
There are three main types of generative models, each with its strengths and limitations, and the choice of which one should be used depends on the problem to be solved. Here are the unique characteristics and applications of each:
Generative Adversarial Networks (GANs) are neural network architectures that consist of two competing neural networks, a generator, and a discriminator. The generator tries to produce synthetic data similar to the training data, while the discriminator tries to differentiate between the synthetic data and the real data. Each side of the algorithm helps train the other. As the two networks compete, the generator improves its ability to generate realistic synthetic data. GANs have been used for various tasks, including image generation, audio and text generation, and data augmentation. GANs are particularly effective at producing high-resolution images indistinguishable from the original data. Some of the most well-known examples of GANs are Dall-E2, a deep learning model developed by OpenAI that can generate images from text descriptions. Another example is StyleGAN, an image generation model developed by NVIDIA that can create realistic, high-resolution images of faces, animals, and even medical data, such as fake chest X-rays.
Variational Autoencoders (VAEs) are generative models based on compressing data into a lower-dimensional representation and then generating new data by sampling from this compressed representation. VAEs consist of two neural networks: an encoder and a decoder. The encoder maps the input data to the latent space, while the decoder maps the latent space back to the original data space. VAEs are often used for image generation, natural language processing, and anomaly detection. They are particularly well-suited for detecting anomalies in data, as they can learn complex patterns and identify deviations from those patterns. VAEs have been used in medical datasets to detect abnormalities such as tumors and diseases. VAEs are easier to train than other models but don't usually give the best results. Some of the well-known software based on VAEs is DeepDream, a tool developed by Google that allows users to generate unique and surreal images by training VAEs on large datasets of images. Another example is MusicVAE, a breakthrough tool from Google Brain's Magenta Project for creating palettes for blending and exploring musical scores.
Transformer models are generative models that involve transforming data from a simple distribution (e.g., a standard normal distribution) into a target distribution by applying a series of invertible transformations. These models are primarily used for natural languages processing tasks, such as language translation, text generation, and language modeling, rather than for generating images or other data types. Some of the most well-known examples of transformers are GPT-3, a language generation model developed by OpenAI that can generate human-like text, and LaMDA, Google's system for building chatbots based on its most advanced large language models. Although the latter has not yet been released for commercial purposes, it is impressive based on its demos.
Now that we know the different Generative AI models let's proceed to Generative AI Use Cases and Examples.
Generative AI can create realistic images of people, animals, landscapes, and other objects. Dall-E2, Stable Diffusion, and Midjourney are the most outstanding examples of text-to-image generating software. They all can generate photo-realistic images and art from a description in natural language. Also, a generative AI model trained on a dataset of human faces might be able to create new, unique faces that look like they could be real people. Here is an example of a website producing images of people who never existed: https://www.thispersondoesnotexist.com/.
This Generative AI use case could have a vast number of applications. In the entertainment industry, it can be used for creating digital characters for movies or video games, for developing an endless number of variations for entirely personalized games for the specific player. It can be used for designing logos for businesses in different industries. In fashion, it can help create virtual models to try on clothes. It can generate synthetic medical data such as computed tomography, magnetic resonance imaging, dermoscopic, and ultrasound images to drive clinical research and protect patient privacy in healthcare.
Generated by Midjourney using keywords "watermelon growing on a tree."
Generated by Stable Diffusion using the same keywords "watermelon growing on a tree."
Generative AI can be used to restore black-and-white movies by adding color and improving their resolution. It can also enhance the quality of static images, as demonstrated by Google's models that can convert low-resolution photos into high-resolution ones. Generative AI can be used to create a video from a single photo by using machine learning algorithms to analyze the picture and generate new images based on the patterns and relationships identified in the original image. These new images can then be combined into a video sequence to create the illusion of motion. Deoldify, PixBim, and ImageColorizer are software examples that can help restore photos and produce new video content.
Black and white photo vs. its color version made in ImageColorizer
Example of photo restored and colorized with Deoldify software
Generative AI can also be used to generate text that is similar to human-written language. For example, a model trained on a large dataset of news articles might be able to create new articles that are coherent and informative. This model can fulfill tasks such as summarizing long documents, generating content for websites or social media, or helping with language translation. Jasper AI, Copy AI, and Writer are some examples of AI text generators.
Generative AI can be used to create music that is similar to human-written compositions. A model trained on a dataset of songs might be able to generate new melodies and chord progressions that sound like a human musician wrote them. It could be helpful for tasks such as creating original music for movies or video games or helping music producers develop new ideas. Try MuseNet, AmperMusic, and EcrettMusic to create your music masterpiece.
Generative AI can be used in the design industry to optimize the design of products. A model trained on a dataset of successful product designs might be able to generate new designs that are more likely to be successful based on specific criteria (e.g., cost, performance, aesthetics). It can improve manufacturing processes' efficiency or develop new products that meet specific requirements. Creo, NTopology, and Altaire are among the industry-leading tools that can revolutionize product development.
Generative AI can also automate manual coding tasks in the field of software engineering and increase productivity, allowing non-technical individuals to develop their own solutions and empowering businesses to streamline their IT processes. For example, the GENIO model-driven tool can significantly increase a professional's productivity compared to manual coding. It uses generative AI techniques to automatically generate code based on input data and desired output, reducing the need for manual coding.
There is also the recently released OpenAI Codex, a GPT-based model trained on code from GitHub and powering another OpenAI product called CoPilot. Codex translates natural language into code based on input data and desired output. It is designed to work with a variety of programming languages and can be used to automate coding tasks in software engineering. GitHub Copilot is a new AI system that provides autocomplete style and context coding suggestions while developers code. The tool uses generative AI to analyze code and identify areas where it can be improved. One of the main differences between OpenAI Codex and GitHub Copilot is the scope of their functionality. Both tools are trained on the GPT-3 language prediction model developed by OpenAI. GitHub Copilot does not actually write code instead of the developer. It provides suggestions for completing code as a developer writes it, giving multiple completion suggestions, and it is up to the developer to choose the most optimal one. The suggestions are displayed below the existing code, allowing developers to preview the entire code before accepting the advice. OpenAI Codex can only be accessed through its API or Playground and generates the whole code in response to a command. There are no alternative suggestions that users can preview or choose from, which may make Codex less appealing to developers.
While generative AI has the potential to bring about significant change and innovation, it does come with its own set of challenges that we will have to consider. The main challenges, in our opinion, are:
Limited control and predictability: Generative AI systems have limited control and predictability because they are based on machine learning models, which are complex and can be difficult to change once they have been trained on a dataset. These models are designed to be flexible and adaptable, learn patterns and relationships in the data, and generate new diverse outputs based on their learning. At the same time, because of this flexibility, the outputs of these models are not always easy to interpret or predict, and they may not meet users' expectations.
Lack of originality and creativity: Generative AI can generate new content based on existing data but are not capable of independent thoughts or creativity like humans. They simply follow the patterns and relationships that they have learned from the data without the ability to generate genuinely novel ideas or concepts.
Biased content: AI is the potential for bias in the data used to train the models. If the data used to train a generative AI model is biased, the model will likely produce biased results. For example, if a generative AI model is trained on data that is predominantly from a particular race or gender, it may produce biased content toward that group. It could have serious consequences, such as stereotypes or discrimination.
Moral and ethical considerations: Generative AI technologies have the potential to create fake content that looks convincingly real and thus could damage someone's reputation or even incite violence. It might be especially harmful to misuse by governments or corporations as there is a risk that it could be used to manipulate public opinion, spread disinformation, exert control over individuals, influence the outcome of elections, etc. Another ethical consideration is the potential for generative AI to create misleading or harmful content, especially in sensitive areas such as healthcare, finance, and the justice system. In these areas, the consequences of misleading content could be particularly severe. For example, if a generative AI model is used to diagnose medical conditions or make financial decisions, any errors in the model could have serious consequences for individuals. Will it be possible to insert this video here? https://www.youtube.com/watch?v=gLoI9hAX9dw&ab_channel=BloombergQuicktake%3AOriginalsс
Job displacement: beyond the content it produces, the use of Generative AI may result in job displacement, particularly in industries where the technology can automate tasks or generate content that humans previously created. It could lead to job losses in various fields, including content creation, journalism, marketing, and other areas where generative AI may be applied. While it is difficult to predict how generative AI will impact employment in the long term, it is essential for workers and employers to be aware of the potential for job displacement and consider how they can adapt to the changing job market in the face of these emerging technologies. With new technologies constantly appearing, we prefer to think that when one door closes, another door almost always opens.
Ownership and intellectual property violations: Generative AI has the potential to impact content ownership and intellectual property significantly. With the ability to generate new content based on existing data, generative AI could create original works that could be difficult to distinguish from those created by humans. It could raise questions about who owns the rights to such content and how it should be protected. Additionally, generative AI could be used to create copies of existing works without permission, leading to issues around copyright infringement. As a result, content creators and intellectual property holders need to be aware of the possible impact of generative AI and take steps to protect their rights in the face of these emerging technologies.
Overall, AI generative software is a new tool we have to learn how to use wisely. It is becoming increasingly sophisticated and is being used in a wide range of applications, from content creation and marketing to research and software development, with the potential to revolutionize industries and solve complex problems by creating original and novel content. As Generative AI continues to advance, it will significantly enhance many aspects of our lives. Still, with its potential, we also wonder what price we will have to pay for it and how we distinguish what content is real or human-based from the generativity produced. These questions are yet to be answered in the coming future. Considering the speed of development in this field, this future is over the corner. It might seem somewhat worrying, but it will surely be very exciting.
By the way, we used Stable Diffusion AI Generative software to create the main image for this article. But hold on, could it be that the entire article is actually the product of Generative AI? Who knows.
Micro frontends have gained popularity in recent years for their ability to break down monolithic frontends into smaller, more manageable pieces. But have you ever wondered how individual micro frontends work together to create a seamless and unified application?Read more
When speaking about Artificial Intelligence (AI), it is no surprise that this branch of computer sciences is rapidly evolving. Increasing demand for efficient and accurate decision-making and problem-solving tools continues to drive development in the field.Read more