The Power and Impact of Generative AI

Artificial Intelligence (AI) is increasingly a part of the world around us, and it’s rapidly changing our lives. It offers a hugely exciting opportunity, and sometimes, it can be more than a little scary. And without a doubt, the big development in AI making waves right now is generative AI.

Just like it sounds, it’s AI that can create, from words and images to videos, music, computer applications, and even entire virtual worlds. What makes generative AI different and special is that it puts the power of machine intelligence in the hands of just about anyone.

The Democratization of AI

We’re used to using AI-powered applications and tools in our everyday lives. Google uses it to find the information we need; Amazon uses it to suggest things we should buy; Netflix uses it to recommend movies; Spotify uses it for music – it’s all powered by AI. But the new generation of generative AI tools goes even further, giving us the power to build and create in amazing ways. With a little practice, we can even use them to build our own AI-powered apps and tools. Because it breaks down the technical barriers, it can truly be seen as the beginning of the long-awaited democratization of AI.

What is Generative AI?

The term AI, as it’s used today, refers to computer algorithms that can effectively simulate human cognitive processes – learning, decision-making, problem-solving, and even creativity. It’s this last and perhaps most human quality where generative AI comes into the picture. Like all modern AI, generative AI models are trained on data. They then use that data to create more data, following the rules and patterns they’ve learned.

For example, if you train it on pictures of cats, it will learn that a cat has four legs, two ears, and a tail. Then, you can tell it to generate its own picture of a cat, and it will come up with as many variations as you need, all following those basic rules.

One distinction that’s worth understanding is the difference between generative AI and discriminative (or predictive) AI. Discriminative AI focuses mainly on classification, learning the difference between “things” – cats and dogs, for example. This is what’s used in recommendation engines like those used by Netflix or Amazon to distinguish between things you might want to watch or buy and things you’re unlikely to be interested in. Or in navigation apps to distinguish between good routes from A to B and ones you should probably avoid.

Generative AI, instead, focuses on understanding patterns and structure in data and using that to create new data that looks like it.

Possibilities with Generative AI

The first use cases for generative AI typically involved creating text and images, but as the technology has become more sophisticated, a world of possibilities has opened up. Here are some of them:

• Images: Many generative AI tools – such as Midjourney or Stable Diffusion – can take a natural language (i.e., a human language) prompt and use it to generate a picture.
• Text: ChatGPT, Google’s Bard, and Meta’s Llama are generative text tools that can be used to write anything from essays and articles to plays, poems, and novels.
• Coding: Tools like ChatGPT, Microsoft’s GitHub Copilot, and Amazon’s CodeWhisperer make it easy for anyone to generate computer code with very little technical knowledge.
• Audio: Generative AI tools can create human-like voices (voice synthesis), allowing computers to speak words that have never before been uttered by a human, as well as music and sound effects.
• Video: Emerging tools allow us to create and edit video simply by describing what we want to see.
• Data augmentation: Generative AI makes it easy to create entirely synthetic data sets for use in training other AI models that follow real-world rules without conferring privacy and data security obligations.
• Virtual environments: Generative AI can greatly accelerate the design of virtual reality (VR) environments or video game worlds.

How Does Generative AI Work?

Generative AI is a product of machine learning (ML), a field of AI where algorithms get better at their jobs the more data they are fed. Models used in generative AI applications include Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders, Diffusion models, and Transformer Models.

As generative AI becomes more advanced, it has been used in various creative and practical applications. It has brought art, music, and design to new levels, facilitating the creation of masterpieces combining human creativity and AI’s capabilities. It has revolutionized industries such as automotive design and drug discovery. However, generative AI also raises ethical and social concerns, such as deepfakes and the impact on jobs and copyright.

As the pace of generative AI development accelerates, we must address these issues and find solutions that ensure its responsible and beneficial use in society.