A quick guide to generative AI models
Over the past few years, Generative Artificial Intelligence (GenAI) has been considered one of the biggest breakthroughs. In personal and professional spheres alike, generative AI models have presented new horizons for creativity and innovation, from image generation to music composition to text generation.
Let's explore how generative AI models work, and how they're shaping various industries (and potentially: your developer career).
What is generative AI?
Generative AI is a branch of AI that falls under AI. Unlike traditional AI, where models are fed on old training data for discovering relevant patterns, generative AI models create new data that resemble the original training data.
Deep generative AI models exploit the basic structure and traits of the training data by using their discovery to create new and original content. They can synthesize realistic images and generate coherent, contextually relevant text, such as articles, stories, and poetry, mimicking human writing styles with impressive fidelity. Additionally, these models can compose music, create synthetic data to enhance machine learning models, and produce content across fields like marketing, advertising, and entertainment. They are also used for tasks like design optimization, anomaly detection, and personalized recommendations.
These models also play a significant role in data augmentation, increasing the training datasets’ diversity and richness by generating synthetic data. This operation largely increases the accuracy and generalizability of the machine learning models.
Generative AI (GenAI) is an exciting new frontier of technology that opens up seemingly endless creative possibilities. This course provides a glimpse of generative models’ capability by showcasing some of their most impressive applications. It will empower you to leverage GenAI and large language models (LLMs) like DALL·E and GPT-2. You’ll learn about the evolution of machine translation systems, from the early 1950s to the current state-of-the-art generative models. You’ll learn about the building blocks of Transformer networks, including CNNs, RNNs, etc. This will be supplemented by an overview of the components of a GenAI system. Next, you’ll learn about transformer models and their variations: Vision Transformers (ViT) and multimodal transformers. You’ll explore the state-of-the-art models for text, image, and video generation models through the practical exercises. You’ll dive deep into the impact of GenAI across fields and industries, fueling the development and launch of GenAI-based products.
How do generative AI models work?
Generative AI models utilize deep learning techniques to create new datasets that are equally close to real-world examples, if not better.
In such instances, these models are provided with huge pictures, text, and audio datasets. Data learning occurs through optimization, in which the model discovers hidden patterns and structures in that data. This implies adjusting interior settings to lower the difference between the computed outputs and the given data.
Each learning forms the data representation in a low-dimensional latent space in which similar samples are grouped. This latent space is designed to collect characteristics and relations among different data sample instances, allowing detection of the structure in the dataset.
In addition, training the generative AI model may generate new data points by sampling from the acquired latent space. Through manipulation of parameters or by imposing specific constraints, users can tailor the output to meet their needs, for example, by choosing style, content, or theme.
Some generative AI models involve a feedback loop that enables programmers to use the generated outputs to infuse the model with new capabilities. Using this repetition method, we can obtain data of the highest quality and realism as time passes.
Finally, the assessment process is designed to evaluate pictures and graphics against previously established guidelines, including visual quality, unity, and importance. With the aid of such an evaluation method, the working process of the generative AI model would be examined, and weak points would be revealed.
From learning the intricacies of large datasets, these models are capable of producing content in almost every field with a very high level of credibility and logical conformance.
Generative AI model training
Generative AI models are trained by gathering large datasets relevant to the model’s application. After cleaning and standardizing the data, a suitable model architecture is chosen, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs).
During training, the model learns to create new data instances by decreasing the gap between its outputs and real-world examples. This includes iteratively modifying the model’s parameters using optimization methods such as stochastic gradient descent (SGD). Validation on a second dataset confirms the model’s performance and generalizability. Once trained, the model may produce new material using previously learned patterns and structures.
Types of generative AI models
Various types of generative AI models perform specific tasks. The most popular types are as follows:
Variational autoencoders (VAEs)
This is a type of neural network that learns a compressed representation of the input data, called a latent space, and can then generate new examples by sampling from this latent space.
Generative adversarial networks (GANs)
GANs are a type of neural network that can generate new data similar to a given dataset. GANs are trained in an adversarial process where a generator network generates data samples, and a discriminator network evaluates the generated samples and determines if they are real or fake. The generator network is trained to improve its ability to generate realistic data by trying to trick the discriminator network. It is trained to identify the actual data from the generated data correctly. They have been used for various applications, such as generating realistic images, videos, and audio.
Transformers
This neural network is used extensively for natural language processing (NLP) tasks, such as language translation and text generation. Transformers rely on self-attention mechanisms to learn contextual relationships between words in a text sequence. They are faster to train and easily parallelizable.
Challenges of generative AI models
Some of the key challenges faced by generative AI models include:
Generating High-Quality Outputs | Despite the improvement in computer-generated graphs, producing authentic and realistic content continues to pose a big challenge. Models usually find it hard to generate outputs that are well organized visually and contextually. |
Avoiding Mode Collapse | Mode collapse arises when the model cannot capture the whole training data space, and one can get a repetitive or standard output. Addressing this barrier will provide a basis for reaching various audiences. |
Addressing Bias and Fairness | When fed biased data, generative AI models can accidentally reflect it in the outputs, which can be harmful or unjust. Preventing bias from showing up in this content is crucial in the process of building ethical AI. |
Ensuring Data Privacy | One more thing that needs to be addressed is privacy. AI tools may create realistic images or text, raising concerns about data privacy because an individual may be identified or misrepresented in that content. The issue of privacy and protecting the user while creating useful results is a multifaceted issue. |
Security Vulnerabilities | Generative AI models can be misused through adversarial attack detection, in which the malicious actors manipulate the inputs to generate unintended or harmful outputs. Protecting model security and resistance to attacks is vital for moving generative AI from a simulated environment into a real-life situation. |
Benefits of generative AI models
The following are some of the benefits of generative AI models:
Content Generation | These models can generate high-quality content such as images, text, and audio, streamlining creative processes and enabling the production of engaging and personalized content at scale. |
Data Augmentation | Generative AI can create synthetic data to augment existing datasets, improving the performance and robustness of machine learning models trained on limited data. |
Personalization | By generating personalized recommendations and experiences based on user preferences and behavior, generative AI enhances user engagement and satisfaction in applications like e-commerce and content recommendation. |
Automation | Generative AI fosters innovation by enabling the exploration of new ideas, designs, and concepts that may not have been feasible or practical through traditional methods. |
Problem-Solving | These models can tackle complex problems in diverse domains, offering novel solutions and insights through tasks like image synthesis, text generation, and simulation. |
Cost Saving | Generative AI helps businesses save time and resources while improving operational efficiency by automating tasks and accelerating the creative process. |
Generative AI offers several benefits for businesses. Some of them are listed below:
Content Creation | With Generative AI, we can generate high-quality content at scale for marketing, advertising, and branding purposes. |
Templates for Sales | It can create personalized templates for sale pitches, presentations, and communication materials. |
Data Privacy | Generative AI can aid in protecting sensitive data by generating synthetic data for testing and analysis while preserving privacy. |
Product Design and Optimization | It optimizes industrial design and production by analyzing data to create innovative designs, improve product quality, and boost competitiveness. |
Examples of generative AI tools and models
Several AI models have emerged recently and are becoming increasingly popular. Let’s look at a few examples:
GPT-3
GPT-3 (Generative Pre-trained Transformer 3) is a language processing AI model developed by OpenAI, capable of generating very complex text. It can take a small amount of input to produce relevant and useful responses. GPT-3 has 175 billion parameters, making it one of the largest and most powerful language models ever created. It has many applications, including text completion, summarization, translation, question-answering, and more.
ChatGPT
ChatGPT is an
DALL⋅E
This is a generative AI model developed by OpenAI that can create images from textual descriptions. It is based on the GPT-3 architecture. Based on textual prompts, DALL
Midjourney AI
Midjourney is a generative AI model developed by an independent research lab. The model’s goal is to convert imagination into art. The generated art style is dream-like and appeals to users interested in fantasy, gothic, and sci-fi themes.
Stable Diffusion
Stable Diffusion, created by Stability AI, is a text-to-image diffusion model. It generates photorealistic images based on text descriptions and allows manipulation of existing photos by removing or adding new details.
Learning Generative AI skills
We would be amiss not to address the common fear that a technology capable of writing code could replace developers, but this is not the case. Generative AI cannot replace human judgment, and has been known to make mistakes. These models are far from perfect, and they require supervision from human subject matter experts. Because of this, we'll still need human developers in an AI-driven future.
That said, the future also demands that developers have Generative AI skills that enable them to leverage these technologies to be more productive. And if you haven't started already, learning Gen AI skills will be crucial to staying in-demand with the needs of the tech industry.
We have several courses, Skill Paths, and projects that get you building your Generative AI skills.
Some of our most popular Generative AI courses are:
Onward to an AI-driven future!
