Generative artificial intelligence and its creations are everywhere these days. Whether scrolling through your Instagram feed or catching up on your go-to blog, AI’s influence is undeniable. Type a couple ofprompt words in Bing Image Creator, and voilà! You have the perfect image for your latest TikTok or a script for a YouTube video.
But what’s this generative AI about, and how is it becoming common quickly in our everyday lives? As we explore this trending technology, we guide you through its basics and complexities.
Understanding generative AI: A brief overview
Before generative AI took the stage,artificial intelligence(AI) was mainly referred to as machine learning models. These models use data to make predictions, and they’ve been evolving for over half a century. A classic example is how they were trained with old weather data to forecast future weather patterns.
We can now apply AI to complex things like images, speech, and other advanced data types thanks to deep learning and larger data collections. Generative AI fits in this broadening scope. Generative AI is a type of AI that’s trained to recognize patterns in data. In addition to predicting, it uses these patterns to create new, original data.
The mechanics of generative AI: How it functions
Think of generative AI algorithms as a brain built using deep learning, also known as deep neural networks, under the umbrella of machine learning. The process begins by loading a deep learning model with loads of data. This could be anything relevant, like books, web articles, or business data. The goal is to make the data as diverse and complete as possible, enhancing the AI’s ability to understand and generalize.
There are multiple types of generative models, each suited to different tasks. Combining the positive attributes of each results in the ability to create more powerful models. The key element in each is converting text prompts into tokens. These tokens are numeric representations of data segments. As long as you can convert your data into this standardized token format, you can apply generative AI to create new data similar to the original.
Generative Adversarial Networks
Generative Adversarial Networks also known as GANs use two neural networks in a match-up: aGeneratorthat produces outputs and aDiscriminatorthat decides if the output is real or generated.
This cycle keeps repeating, with both networks improving each time until the fake data can pass off as real. GANs excel at creating quality outputs quickly. However, they fall short in variety. That’s why they’re more suited for generating data in specialized fields. StyleGAN, a popular image generator, is built on these models.
Variational Autoencoders
Variational Autoencoders, orVAEs, consist of two neural networks: theEncoderand theDecoder. They start by taking an image. The encoder simplifies it into a lower-dimensional representation of the image calledLatent Space.
The decoder uses this simple version to create a new image that resembles the original. This process allows for easy creation of new images. VAEs are quick at generating images but lack the detailed quality found in GAN or diffusion models.
Diffusion Models
Denoising Diffusion Probabilistic models (DDPMs), also known asdiffusion models, are generative models that work through a two-step process during training. The two steps areForward DiffusionandReverse Diffusion. The forward diffusion process slowly adds random noise to training data, while the reverse process reverses the noise to reconstruct the data samples.
By iterative refining, these models learn to generate new data samples that resemble samples in a training dataset. Stable Diffusion is an example of a diffusion-based generative AI model.
Transformer models
Transformer models are designed to process sequential input data non-sequentially and are a big part of natural language processing. They view the entire sentence simultaneously, usingAttention Mechanismto encode words as tokens and create an attention map to capture relationships between tokens.
This improves context understanding in text generation. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models. We have a detailed article onhow Transformer models work.
Multimodal models
Multimodal modelsare advanced AI systems capable of understanding and processing different data types simultaneously, like text, images, and audio. They combine multiple unimodal neural networks, each specialized in processing a specific data type. For example, they can write a description for an image you show. OpenAI’s GPT-4 is an example of these versatile models.
Utility of generative AI
The effect of generative AI is seen in its ability to automate mundane tasks, help the creative industry, and improve customer service. The field has led to new roles like prompt engineers, data annotators, and AI ethicists. Its reach extends across multiple sectors, changing the way industries like healthcare and retail function and expediting progress in previously unimaginable ways.
Generative AI’s influence on visual creativity
Generative AI is making waves in the visual arts. Generative AI creates 3D images, avatars, videos, graphs, and illustrations while offering a variety of styles and editing options.
It’s used to craft realistic images for virtual and augmented reality, develop 3D models used inAndroid consolesand games, create logos, and upscale or edit photos. In science, especially in drug discovery, it helps map new chemical compounds and molecules.
Linguistic transformation: Large language models reshaping industries
Large language models are changing the game in several industries. They help students and teachers write essays and research in education. In software development, they suggest code snippets and solve coding problems. They make global business easier by translating languages and removing communication barriers. In genomics, they contribute to understanding genetic sequences, which is key for developments in personalized medicine and genetic research.
Generative AI in audio production
Music production companies use generative AI to compose songs efficiently and at a lower cost. Generative AI models improve the audience’s experience by recognizing video content and creating corresponding sounds for a more lifelike audio effect in film and media. AI’s role in gaming and virtual environments involves composing unique music and enriching these platforms with new soundscapes.
Using synthetic data to solve data scarcity challenges
AI-generated data is helping sectors struggling with data scarcity. For example, in autonomous vehicle development, synthetic data creates rare or dangerous scenarios that are hard to capture in real life to improve the safety and reliability of these systems. In retail, artificial data analyzes customer trends without invading privacy, providing a rich, augmented set of data for training and development.
Exploring the dangers and limitations of generative AI
As generative AI continues to integrate into different sectors, it is increasingly important to understand and address the potential risks associated with its deployment.
Functional risks in generative AI: Model drift and data poisoning
Functional risks in generative AI include model drift and data poisoning.Model driftoccurs when an AI model gradually loses alignment with the domain it was trained for, leading to costly and time-consuming retraining.
Data poisoningis another risk, where malicious actors corrupt the training data, resulting in compromised AI outputs and potentially harmful decisions based on these outputs.
Operational risks in the deployment of generative AI
Operational risks involve wrongly allocating resources and accidentally sharing private information. This can happen when companies trust incorrect AI-generated advice or use compromised AI models, which wastes resources and overlooks important opportunities.
Additionally, there’s a risk of AI tools unintentionally leaking sensitive data, creating a potential security hazard if this info falls into the wrong hands.
Legal risks and challenges in using generative AI
Legal challenges in using generative AI include issues like making up information (confabulation), showing biases, breaking copyright rules, and invading privacy. If AI tools spread false info or show bias, it can harm customers, lead to legal trouble, and damage a company’s reputation.
Using copyrighted stuff in AI training can break copyright laws. There’s also a risk of AI accidentally sharing personal details, which could violate people’s privacy.
Infrastructure requirements of large-scale generative AI models
Generative AI models boast billions of parameters and require fast and efficient data pipelines to train. A major capital investment, technical expertise, large-scale data samples, and computing infrastructure are necessary to maintain and develop generative models. For example, diffusion models could require millions or billions of images to train.
Ethical and social concerns surrounding generative AI
Generative AI brings up ethical and social concerns. First, there’s the risk of making existing biases even worse, which can lead to unfair results. Then there’s the issue of jobs. Generative AI can automate tasks, which means some people might have to switch jobs or lose them, especially in areas like office work, customer service, and food service.
The origin and usage of data by generative AI systems can be problematic, especially when the data’s source is unclear or used without consent. Lastly, many generative AI systems lack transparency in handling and interpreting data, making it challenging to trust their outcomes.
The two sides of generative AI in our transformative era
Like any tool or technology, generative AI is a double-edged sword. It has the potential to revolutionizeHealth and Fitness appsby generating customized workout and diet routines. Yet, it has challenges and risks.
Andrew Ng, a leading AI expert, said, “AI is the new electricity.” Just as electricity revolutionized industries a century ago, AI will do the same today. But with great power comes great responsibility, so it’s up to us to steer this transformative era well.
