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In May of 2024, researchers at UC San Diego performed a test with three systems to produce a randomized and controlled study. By having a five-minute conversation, human evaluators were to determine whether they were speaking to humans or AI models. The tested systems included Eliza, GPT 3.5, and GPT 4, whereby the former was able to fool evaluators 54% of the time, which is nothing better than a random guessing.
This experiment poses highly important ethical and existential questions. Researchers state that even though humans were successful in nearly half of the cases, in the real world, people might not be able to delineate between machines and humans. Outside of the test setting, when you are alert and paying close attention to the conversation, making the distinction would be much more difficult.
Generative artificial intelligence models have become so smart that they can handle numerous tasks, allowing humans to focus on more complex and creative activities. The business world is actively adopting generative AI technologies to gain a competitive edge, create new products, and reduce costs. With 92% of Fortune 500 already using AI in their business processes, the technology is bound to grow and improve in the future.
In this article, we’ll discuss the main types of generative AI models, their applications, benefits, and challenges, as well as emerging trends.
Generative AI models or GenAI, is a type of artificial intelligence that generates content, including text, audio, video, and images. By learning from training data and recognizing patterns, the system can create new and unique content. Generative AI techniquesare based on deep learning, machine learning, and neural networks.
Although generative AI is nothing new, the recent breakthrough has put the technology at the forefront of news and trends. Earlier, there were generative AI models that could execute only a few tasks and deal with specific types of data. For example, some AI-powered apps were capable of enhancing image quality. However, the latest developments have drastically expanded the use cases for GenAI delivering multimodal AI models that can handle various data inputs and create different outputs.
Aside from generative AI models, let’s define some other types, namely discriminative and predictive AI models.
Discriminative models focus on conditional probability distribution and are used for classification and categorization. Unlike generative AI models that are capable of producing new content, discriminative systems excel at understanding the underlying structure of the dataset and generating similar outputs. For example, a discriminative model is good at classifying the images of dogs and cats into different groups, while a generative model can create realistic images of dogs and cats based on a prompt.
As the name suggests, predictive AI models analyze existing and historical data to derive insights and make data-based forecasts. By processing large volumes of information and analyzing data, predictive AI models can make weather forecasts, perform risk assessments, and predict market fluctuations. Such systems are used in decision-making and strategic planning processes, allowing businesses and individuals to foresee future threats, trends, and patterns.
Let’s begin by defining the most common generative AI architectures and discussing their functionality, use cases, and best examples.
Among the most common types of generative AI models are generative adversarial networks (GANs), which consist of two neural networks, including a generator and a discriminator. While the generator is in charge of content creation based on the prompt and training data, the discriminator evaluates the output to make sure it’s accurate and relevant.
The process begins with the generator creating random and mostly fake data, while the discriminator checks it against the training data. If general adversarial networks determine the information is false, incomplete, or inaccurate, the cycle repeats itself until the discriminator is satisfied. StyleGAN, a prominent image generator, exemplifies these types of generative AI, namely generative adversarial network.
Transformer models are among the main types of generative AImodels comprising multiple layers equipped with a self-attention mechanism as well as a feed-forward network. The transformer architecture allows generative AI models to identify and memorize patterns in data and contextualize them making outputs more precise. While self-attention enables generative AI models to assess the relevance of the input data and make out the relationships within it, the feed-forward network enriches the obtained information.
Transformer-based models are initially trained with a large dataset, including web content, books, various collections, articles, and other data sources. Later, the model can be fine-tuned with more specific data relevant to the task. As a result, these generative AI models output the most logical and appropriate information. The best examples of transformer-based models are those inside ChatGPT, one of the most popular large language models on the market.
Finally, one of the most ubiquitous types of generative AI is diffusion models, adding randomized noise to training data to generate new data. The noise injects variations to the data thus allowing the generative model to explore different outputs for the same input. In later steps, the generative AI model slowly removes the noise to ensure the result meets the requirements set in the prompt.
By introducing noise to the input data and then reversing the process, diffusion generative AI models can produce brand-new content. This model is widely adopted for image generation, where users request creative and unique pieces of content. One of the most well-known diffusion models is Stable Diffusion, a powerful image generation app.
From completing Schubert’s symphony and finishing Keith Haring’s iconic painting to generating new articles, images, and videos, generative AI models are capable of a myriad of tasks. In this section, we’ll explore the most popular applications of AI systems.
Arguably the most widespread application of various types of generative AI models is text generation tasks. Users can input a prompt or insert an article into the large language models and request a summary, paraphrasing, analysis, or other actions. Generative models can perform an array of tasks, from condensing long documents and rewriting texts to answering questions and creating new content.
Aside from that, such generative AI models are capable of generating human-like responses, adjusting to different tones of speech and styles, translating texts into multiple languages, and enhancing grammar or writing style. Furthermore, they can generate natural language data from structured outputs such as statistics, graphs, tables, etc.
Image generation can be categorized as text-to-image and image-to-image. Text-to-image translation produces visual content, from drawings to realistic images based on textual data. Apps like Stable Diffusion, Midjourney, and Dall-e can generate various images from the user prompt. The more detailed the prompt, the better the end result. You can mention the type of media, color palette, mood, objects, etc., in order to create something that fits your vision.
Image-to-image translation converts one image into another based on user prompts. Using these generative AI models, you can transform the style or genre of the image, add some details to it, tweak colors and shadows, and much more.
Different types of generative AI can also facilitate audio generation, including text-to-speech and audio-to-audio. Generative adversarial networks are often used to process the input data with natural language processing techniques. Later, the text is analyzed for duration, pitch, and sounds which are then converted into acoustic features. These types of generative AI models are used for music creation and dubbing as well as to increase customer experience for various apps and services.
Audio-to-audio generation transforms audio data based on the input. At first, audio is converted to spectrograms which are then processed with image models to extract relevant features. Later, the system can generate a new audio file, remove the content or style to replace it with the user prompt or target various voice features. You can change the voice characteristics, audio style, recording quality, and more. For example, you can remove noise or distortion thus enhancing the quality of the audio file.
Video generation involves the creation of new videos based on text prompts. First, generative AI models analyze the input to plan out a storyboard and create individual frames. Later, the system connects the frames to ensure consistency and adherence to the prompt. From enhancing quality and resolution to increasing or decreasing the frame rate and restoring corrupted videos, video generation is quickly evolving.
Synthetic data generation is the process of creating datasets that mimic real-world data and can be used to enhance the capabilities of different types of generative AI models. The process begins with an analysis of the original dataset to gain insights into its statistical properties and distributions. Using generative adversarial networks, you can generate synthetic data samples and improve their quality with the discriminator. Later, the new data is compared to the original to ensure it’s valid and consistent.
Large language models continuously require new data to grow smarter, and acquiring more samples for training can be extremely pricey and time-consuming. Moreover, at some point, companies will run out of high-quality data, rendering their improvement attempts obsolete. Synthetic data generation can supplement limited real datasets, create sensitive medical data for healthcare, generate data for various tests and hypotheses, and more.
In addition to the aforementioned applications, generative AI models can aid in drug discovery, language translation, code generation, and numerous other aspects. For example, they can help clinicians transform MRI images into CT scans. Not only do CT scans expose patients to significantly higher levels of radiation, but they are also a lot more expensive compared to MRIs. Using MRI-to-CT generation, doctors can accelerate their diagnostics procedures and protect patients from unnecessary harm.
Generative AI models can also contribute to VR/AR development by allowing users to generate characters and environments for video games, simulators, and other tools. You can also create new product designs, generate marketing texts, and utilize chatbots to streamline customer support.
Generative AI models are designed to create new data or content that resembles the patterns and characteristics of the input data they were trained on. At their core, various types of generative AI models use complex algorithms and neural network architectures to generate text, images, or other types of media. For instance, models like Generative Pre-trained Transformer (GPT-4) are based on transformer architecture, which employs self-attention mechanisms to weigh the importance of different words or features in a sequence. During training, the model processes vast amounts of data and learns to predict the next element in a sequence based on the context provided by preceding elements. This capability allows it to generate coherent and contextually relevant content.
Training generative models involves two main phases: pre-training and fine-tuning. In the pre-training phase, the model learns from a diverse and extensive dataset, capturing general patterns and relationships within the data. For example, a language model might be trained on a mixture of books, articles, and websites to understand various writing styles, topics, and vocabularies. Fine-tuning, on the other hand, adapts pre-trained the model to specific tasks or datasets by exposing it to more targeted examples. This step allows the model to refine its output to better match the nuances and requirements of particular applications, such as generating legal documents or creating art.
There are also such model types as Variational Autoencoders (VAEs) and GANs. VAEs learn to encode input data into a compressed representation and then decode it back into a new but similar output, ensuring diversity and coherence. This technique allow to use compressed but still informative intermediate representation of the original data for other tasks. GANs consist of two networks—a generator and a discriminator—that compete against each other; the generator creates new content, while the discriminator evaluates its authenticity. Through this adversarial process, GANs can produce highly realistic and innovative outputs.
Another family of models are Diffusion models. They are a class of generative models used to create data, such as images, by gradually denoising a random noise input through a series of steps. They work by learning to reverse a diffusion process: starting with clean data, random noise is added in small steps until the data becomes unrecognizable. During training, the model learns how to reverse this process by predicting the noise that was added. During generation, the model starts with pure noise and iteratively denoises it, eventually producing new data that resembles the original data.
How can generative AI models aid you in advancing your business? From streamlining content creation and customer support to facilitating process automation and product development, let’s dive into some of the benefits of the technology.
Synthetic data generation allows developers to create brand-new data that simulate real-world information. Using generative AI models, you can generate data to supplement existing datasets while preventing any privacy violations. Especially in industries that deal with sensitive data like finances or healthcare, sourcing high-quality data can be an arduous process. Companies generate synthetic data to unlock major cost-saving benefits, facilitate further expansion and development of generative AI models, and eliminate the need for data privacy and confidentiality concerns.
Generative AI models can also be used to build chatbots and virtual assistants to streamline and automate customer support. Their advanced natural language processing capabilities allow these systems to excel at content creation and deliver responses that closely mimic human interactions. AI-powered chatbots are available 24/7, offer support in several languages, adhere to brand voice and policies, and can handle multiple customer requests at the same time.
From ideation and conceptualization to fleshed-out articles, songs, and visual artwork, generative AI models are capable of delivering creative content. There are numerous apps that can compose a song from just a few words, write a poem based on a prompt, or generate a painting from a text input. These capabilities are invaluable in marketing and aid companies in scaling and tailoring their content creation to target various audiences. Using a generative AI model, you can automate draft generation, adjust tone of voice and style, enhance SEO, schedule posting, and supplement your textual content with images and videos.
Similar to text generation, generative AI models can create or refine code based on your requirements. Although no single tool can build a complex app from scratch, it can help you with certain stages of software development. For instance, AI models can aid in the ideation process, prototyping, and user experience simulation. Moreover, the latest releases of ChatGPT 4.0 and Claude 3.5 are capable of creating coherent and functional code for smaller apps or their parts.
Whether you’re relying on generative models to improve your content strategy, create new articles, get inspiration, or streamline coding, you can automate numerous tasks. Process automation reduces operational costs, shortens time to market, and eliminates human error. Additionally, humans can focus on more analytical activities that are more critical to the business.
Now let’s focus on some of the drawbacks you may face when dealing with generative AI.
Nowadays, businesses can integrate a generative AI model via an API (application programming interface) to avoid building an internal system. APIs make technology more user-friendly and allow companies to take advantage of new services without spending much. However, this type of integration makes your systems more vulnerable to malicious attacks. Especially in the workplace, working with generative AI models can expose your company to numerous risks and jeopardize your reputation and revenue in case of sensitive data leaks.
Mode collapse is a large concern across generative AI models, including generative adversarial networks, diffusion, and transformer models. This occurs when the system begins to output limited or generic data to satisfy requirements. In a generative adversarial network, a discriminator forces the generator to only focus on a narrow set of outputs making the model less effective.
This phenomenon also occurs in transformer models, these systems can also exhibit similar behavior. The model may begin to generate repetitive text patterns due to relying on high-probability tokens. This leads to more safe and generic responses making the system less diverse and creative. Moreover, image models can also struggle to generate complex data. Imbalances in the training data and the complexity of the data distribution may lead to biases and errors in the output data.
Another concern regardinggenerative AI models is the potential power concentration in the industry. The organizations that built foundational models have accumulated significant influence in the space which allows them to monopolize the entire sector. In the future, it’s vital to ensure open access to these technologies, foster further growth in the field, and encourage innovation in the application layer. For example, regulations surrounding the AI industry and continuous efforts toward democratizing technology can aid us in preventing monopolization.
Deep fakes powered by generative AI offer businesses innovative ways to create personalized and dynamic content at scale. By using AI-generated video and audio, companies can produce tailored marketing campaigns, customer service interactions, or training materials, enhancing engagement and efficiency. They enable cost-effective virtual spokespersons, eliminating the need for live actors or voiceovers. Additionally, deep fakes can simulate realistic scenarios for product demonstrations or virtual events, improving customer experience. For entertainment and media industries, AI-driven content can create hyper-realistic simulations for films or games.
However, deep fakes can also be leveraged for scams and fraudulent schemes. There have already been numerous cases in which a criminal generated a deep fake of a celebrity or executive to trick people into giving up their sensitive data or sending money. The emergence of deep fakes is the reason for countless scams, fraud, and misinformation. Thus, businesses must also consider ethical and security concerns to ensure responsible use of the technology.
Since generative AI can learn by itself, it can be difficult to figure out how and why it came to a certain decision. The ever-growing complexity of architecture comprising billions of parameters paired with at times unpredictable behavior exhibited by the models is referred to as the black box effect. As a result, users cannot be sure how the system arrived at a particular answer and whether it was driven by biases or misconceptions. The lack of transparency can lead to user mistrust, legal and ethical implications, as well as the emergence of fake data.
Other concerns include the ability of a generative AI model to hallucinate and produce factually incorrect or biased outputs, muddying the waters and spreading misinformation across the internet. Computational power is another issue during the training process, polluting the environment even further and putting millions of lives at risk.
Finally, sourcing data from the web presents additional ethical considerations. Content creation is a strenuous task, requiring experience, talent, and time. Many painters, writers, and other artists are concerned that companies use their content to train the models without regard for their work. They advocate for a more transparent and consensual data collection that doesn’t ignore the effort that went into their work.
Finally, let’s take a look at the future of the technology and discuss emerging trends in the generative AI space.
Multimodality in generative models translates into versatility, allowing users to interact with different modalities of a system, including text, video, audio, and image content. The increasing processing power, advances in neural networks, and current market interests demand more versatile generative AI models. These can smoothly toggle between various tasks like natural language processing, video and image generation, computer vision, and more. The more holistic context understanding, more natural interaction, and the ability to perform multiple tasks make the models more creative and useful across departments and businesses.
With the recent release of Meta’s Llama generative AI model, the demand for open-source technology is only growing. Open source makes large language models more accessible to the general public and smaller businesses, encourages contributions from experts, and drives innovation in the AI space. Open-source technology also introduces better transparency and builds trust within the community, further facilitating the development of generative models.
Personalization has become an integral part of many industries, from sales and marketing to healthcare and scientific research. For instance, medical scientists are working on tailoring treatment plans to enhance the quality of healthcare services. Medicine operates with statistics and even a drug that works for the majority of the population, there are always exceptions. Hyper-personalized content can help organizations tailor messaging and communication to individual situations. With more effective and precise strategies, companies can improve engagement and drive the patient experience and outcomes.
Human-in-the-loop (HITL) is an emerging concept that combines AI’s outputs with human judgment, ethics, and creativity. By feeding precise prompts to generative models and refining the outputs, humans can provide valuable feedback and strengthen the system. Especially for more complex models, distilling the responses with human expertise can significantly improve the quality of responses and reduce hallucinations and fake data responses.
As mentioned, generative AI models raise concerns for data privacy and confidentiality, interfering with the adoption of the technology. In the coming years, the industry will develop robust regulations and rules to minimize cyber attacks and safeguard sensitive data. For example, the Artificial Intelligence Act entered into force in August of 2024 and aims to establish a common framework for legal and ethical considerations within the EU. Such initiatives are integral for the development of safe and ethical generative AI models that can be used across countries, industries, and use cases.
Bring your own AI (BYOAI) is a practice of creating a custom generative AI model tailored to an organization’s needs, industry, and objectives. This trend is especially vital for companies that require hyper-personalized content or specific tasks in law, medicine, and finances. While universal AI models carry numerous applications and benefits, specialized systems are essential for innovation within smaller niches.
If you would like to stay on course with the latest technological developments and harvest the numerous benefits that generative AI models can offer, get in touch with NIX. We’re a software development agency with years of experience in the AI space. From customizing a generative model to guiding you through the AI adoption process, our team is there to provide support. Leverage AI capabilities to improve customer experience, drive innovation, and reduce operational costs with NIX’s expertise and customer-centric approach.
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