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Generative AI Models

The rise of generative artificial intelligence and its rapid development in recent years have created a new sense of urgency for businesses to adapt. With reports suggesting that generative AI’s impact on productivity might add trillions of dollars in value to the global economy, it’s hard not to see its competitive advantage.

However, choosing the model you need among the many types of generative AI may prove challenging without expert advice. In this article, we look at model categories, applications, and benefits for different industries.

What Is a Generative AI Model?

Gen AI models belong to a type of artificial intelligence that has the ability to create a wide variety of data, including images, videos, audio, text, and 3D models. It does this by learning patterns from existing data, then using its knowledge to make predictions and generate new content that’s similar but not exactly like the original.

This creative ability is the product of training generative AI models on vast collections of existing data, something that has become possible only in the last decade. The development made generative AI a valuable tool for many industries, such as healthcare, insurance, entertainment, or product design.

Types of generative AI structures vary in size and complexity. They also have a rich and evolving history, marked by significant advancements in machine learning and artificial intelligence research. Starting as basic probabilistic models like Markov Chains back in the mid-20th century, they are now a reliable technological tool that can outperform humans in many ways.

How did this happen? The answer lies in the current process of generative AI model creation as well as their structure.

Creating a GenAI Model

Developing a generative AI model involves several stages, from data collection and preprocessing to model training and evaluation.

Defining the Aim

Simple as that, a genAI model is built to address a specific business need. Starting from the problem you want to solve, such as automating customer support or producing high-resolution images for T-shirts, will help you not to get lost in the sea of opportunities.

Collecting and Preprocessing Data

Training generative AI models on massive and diverse datasets is the key to successful development. Unfortunately, it is not always possible to track data provenance and quality even in commercially available third-party datasets. This may lead to many risks for the GenAI model, including data poisoning and copyright infringement.

Choosing the Architecture

Select an appropriate generative model architecture based on the problem domain and dataset. Common architectures include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive models, and many others we will talk about below.

Model Training, Tuning, and Evaluation

A generative AI model is trained when collected data is fed into it. Based on how well it handles the data and formulates predictions, a model’s hyperparameters are tuned again and again for optimal performance.

Next, it is put through validation and evaluated on a number of highly specific metrics. This requires a lot of computing power, time, and energy reserves. That’s why many companies choose to build on the existing generative AI foundation models.

What Are Foundation Models in Generative AI?

A foundation model here refers to a pre-trained model that serves as the basis for building more domain-specific AI models. These models are usually trained on large and reliable datasets using vast computational resources.

The idea behind a foundation model is to create a powerful starting point that can then be adjusted for specific tasks. The most well-known of them include the GPT series, BERT, XLNet, and others.

Researchers and developers use these foundation models at the outset, saving time and resources that would otherwise be needed to train a model from scratch. Fine-tuning allows them to tweak the pre-trained model for specific applications such as chatbots, modeling, translation, and more.

After testing the model on even more data, it is deployed into production to be applied in real-life cases and scenarios. At this stage, it’s essential to continuously monitor the model’s performance and ensure it meets the desired outcomes. If it doesn’t, then it will have to be retrained to adapt to changing patterns and trends.

In general, developing a generative AI model requires a deep understanding of the problem domain, expertise in model architecture selection, data preprocessing skills, and rigorous testing to ensure its effectiveness and reliability.

Generative AI Model Types

With so many types of generative AI models already available, and many more in development, it may be overwhelming to try and choose between them without any prior knowledge.

Here is a brief overview of their categories and capabilities. Generative AI models on the list represent the most popular foundation options to date and are widely integrated into various processes.

Auto-Regressive Models

Auto-regressive models made their appearance in the first half of the 20th century as purely predictive frameworks. They made forecasts about new data by going back to previous data points or regressing. In the context of generative AI, auto-regressive models refer to the type of algorithms that generate new data one element at a time, with each prediction depending on previously generated elements.

Deep Belief Networks (DBNs)

A DBN is a generative model that uses unsupervised learning to calculate complex probability distributions in data. It is built by stacking several layers of Restricted Boltzmann Machines (RBMs) — simpler models that were invented by Geoffrey Hinton in the 1980s. This greatly improves its performance and makes it useful for such tasks as learning patterns in data and algorithm optimization.

Generative Adversarial Networks (GANs)

Introduced by Ian Goodfellow et al. in 2014, GANs revolutionized the field of generative modeling. This framework involves a competition between two neural networks, the generator and the discriminator. The generator’s task is to create new data based on the existing data points, such as images, while the discriminator tries to distinguish between the original and fake data. Through this adversarial process, GANs learn to generate highly realistic synthetic outputs.

Convolutional Neural Networks (CNNs)

The primary application of CNNs is connected with visual data and includes such tasks as image classification, generation, and object detection. They consist of multiple layers that learn to recognize useful sequences and their importance in the original input to iterate and build on.

Recurrent Neural Networks (RNNs)

One of the early models from the 1980s, RNNs were developed by John Hopfield to be capable of memorizing patterns in data and using these memories to process it. They are widely used in tasks where context and temporal dependencies are crucial, such as speech recognition, language modeling, and time series prediction.

Long Short-Term Memory Networks (LSTMs)

LSTMs are a type of RNN architecture designed to address higher-level problems. They can learn long-term dependencies in data sequences by using their ability to selectively remember or forget information over time. This makes LSTMs a common model type for speech recognition, machine translation, and text generation.

Variational Autoencoders (VAEs)

The concept of VAEs first emerged in the 1990s, and since then it has come a long way in gaining popularity.

In short, VAEs are complex models with two parts — an encoder and a decoder — working together to generate new, realistic data points from learned patterns. An encoder converts given input into a smaller and denser representation of the data. It preserves only the essential features that the decoder needs to successfully reconstruct the original input. All irrelevant information is discarded, which leads to high efficiency of VAEs as generative AI models.

Diffusion Models

Diffusion models are generative models that learn the probability distribution of data by looking at how it spreads or diffuses throughout a system. These models have shown great results in generating high-quality images and videos, unparalleled in comparison to any other model type. However, diffusion models typically take much longer to train than variational autoencoders due to their large-scale architecture, so it’s a trade-off to consider prior to implementation.

Transformer Models

Since their introduction by Vaswani and colleagues in 2017, transformers have been responsible for a paradigm shift in natural language processing (NLP).

With their attention mechanisms, transformers are able to focus on the important parts of data sequences. This allows them to process and generate coherent and contextually relevant text. Models like BERT and GPT are based on transformers and are capable of tasks such as text classification, translation, and text generation.

In practice, all types of generative AI models can be combined to leverage their strengths. For example, using a CNN to extract features from images and then passing these features to an RNN for sequence modeling on top of these extracted features leads to producing more realistic data samples.

Examples of Popular Models

Apart from their incredible productivity, generative AI, together with large language models, offer users a chance to create multimodal outputs. The idea refers to taking one type of input, e.g. text, and generating a completely different one — music, pictures, or even videos.

Below, you can find information about some of the most sought-after genAI models and LLMs, their functions, and applications.

Visuals and Sound

DALL-E (OpenAI): An impressive text-to-image model that combines techniques from computer vision and natural language processing to generate images. Since its initial release in 2021, more performant versions DALL-E 2 and DALL-E 3 have become available.

Stable Diffusion (Stability AI): Based on diffusion technology, this model is capable of generating unique photorealistic images, animations, and videos based on a user prompt. It can be fine-tuned to meet your specific needs with just a handful of images through transfer learning and is available to everyone under a permissive license. These points differentiate Stable Diffusion from its predecessors.

As of now, the latest version, Stable Diffusion 2, is on the market, while the waitlist for the early preview of SD 3 has just been opened.

Jukebox (OpenAI): An AI model capable of generating music in different genres and with various instruments. According to its developers, if the model is provided with the genre, artist, and lyrics as input, it can output a new music sample produced from scratch.

Imagine 3D (Luma AI): Generates a 3D model with a full-color texture based on a text prompt. It’s claimed to produce higher quality 3D assets than some other genAI models with similar functionality due to using real-time imaging for reference.

These few examples alone showcase the incredible versatility and creativity of generative AI models, ranging from art and music generation to word-building. They offer a glimpse into the potential of AI technology to augment human professionals and inspire new forms of expression.

Generative AI and Large Language Models

The difference between generative AI and large language models (LLM) is their focus. Both are machine learning models that can produce novel outputs, but LLMs are primarily concerned with natural language processing (NLP). This includes text generation, analysis, translation, and even code completion.

As a subspecies of generative AI, LLMs also require vast amounts of data to train and improve.

Due to all the text data available on the Internet, such as books, social media posts, and even webpages themselves, we have recently seen significant advancements in their development. Here are the most well-known ones.

GPT Series (Open AI): As advanced transformer models, GPT series have a broad general knowledge and high reasoning abilities. The latest iteration, GPT-4 is a large multimodal model that accepts both text and image inputs to produce novel textual output like conversations, essays, summaries, and code chunks.

BERT (Google): This model was specifically designed for natural language understanding tasks. Its full name is Bidirectional Encoder Representations from Transformers, meaning that it can learn context from both left and right sides of a word to use it in text classification, question answering, and sentiment analysis.

Other developers, such as HuggingFace and Meta have been able to create more optimized versions of BERT, such as DistilBERT and RoBERTa suited for a variety of NLP tasks.

LLaMA (Meta): In an effort to democratize access to generative AI and LLMs, the company released this state-of-the-art foundational model in 2023. It is available in several sizes and versions, the latest being LLaMA 2. The distinctive features of this model are openness to modifications and freedom of use.

BLOOM (BigScience): Able to generate text in 46 natural languages and 13 programming languages, BLOOM is the largest existing language model with over 170 billion parameters. It is widely available now through the HuggingFace ecosystem and can be used for research and language processing tasks at scale.

Gemini (Google): Formerly known as Bard, this innovative multimodal LLM is able to seamlessly integrate with a number of environments — from data centers to mobile devices. It can generalize, produce, and combine different types of information including text, code, audio, image, and video. The first version Gemini 1.0 is available in three sizes, offering a high degree of flexibility.

These modern LLMs represent the cutting edge of natural language processing research, enabling a variety of applications from conversation generation to content creation. They continue to push the boundaries of what is possible in understanding and producing human-like text.

Generative AI models have found a broad use across a number of industries. They are responsible for optimization, automation, and new creative trends at unparalleled scale and speed.

In art and design, models like Stable Diffusion, Midjourney, and DALL-E are used to create visually stunning artwork, help designers iterate on new production patterns, and generate new content like music, blog posts, and videos.

Healthcare, one of the most conservative industries in terms of adopting new technologies, has also benefited from genAI introduction. Generative models now assist human professionals in medical imaging, diagnostics, and drug design.

Generative models in finance and banking are responsible for predicting investment risks and opportunities, algorithmic trading, and fraud prevention. With cybersecurity as a primary concern, this sphere utilizes such strengths of generative AI models like GANs, RNNs, and transformers as anomaly identification, intrusion detection, and predicting security threats.

Retail and e-commerce have equally been revolutionized by genAI technologies through personalized shopping recommendations, dynamic pricing, automated customer service, and inventory management optimization.

In marketing, an adjacent sphere, generative AI is indispensable for efficient content creation, customer segmentation, and ad generation.

As far as education and training are concerned, AI has come a long way since its initial application in automatic grading systems. Now, AI chatbots are used to answer student questions and act as personalized tutors. Generative models create immersive simulations, virtual labs, and training scenarios for hands-on learning and generate synthetic data for research.

Significant advances in energy and sustainability have been achieved thanks to AI. It enabled reliable climate modeling for sustainability planning, optimization of energy consumption and distribution across smart grids, and enhanced building and urban design.

Efficient logistics and transportation are now nearly unthinkable without generative models. Passenger identification, route optimization, smart vehicles, and real-time supply chain tracking — these are just a few examples of genAI capabilities in this sphere.

What Problems Do Generative AI Models Solve?

The biggest advantage of using generative AI models in industry is being able to rely on them to resolve the eternal issues of productivity and time vs. effort trade-off. More and more companies are choosing to train their employees in the use of genAI technologies to enhance their efficiency and automate repetitive tasks.

Indeed, no employee can be everywhere at once to provide customer support, offer personalized recommendations, and create tailor-made content. By entrusting these tasks to AI, businesses can significantly reduce operational costs and free up human resources for higher-level tasks.

According to Google Analytics, in our digitalized world, data is king. Generative AI models are able to take an organization’s data and make it work. Forecasting demand, iterating product designs, and enabling innovative practices — all this gives an enterprise a bold competitive advantage with the help of genAI.

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Last modified: April 10, 2024

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