What is generative AI?

Generative AI is the hot new buzzword, but what does it really mean?

What is generative AI?

In a year’s time, artificial intelligence has gone from a concept we’ve heard of occasionally to a full-fledged technology integrated into the various devices we use. Beginning from smaller devices like smartphones to larger, more complicated appliances like refrigerators, brands today are taking leaps and bounds to show off their AI prowess across various zones. 

In fact, smartphone makers like Samsung also introduced Galaxy AI with its latest S24 series and have been gradually making it available across older devices. But what exactly is generative AI and how is it different from any other type of artificial intelligence technology? We’ve heard the term being used around casually, but there’s a lot more that goes into understanding its workings. We’ve broken it down for you here. 

What exactly is generative AI, and how does it work?

Simply put, generative AI allows you to whip up content within seconds based on inputs. These inputs could consist of text, image or voice-based prompts, among other types of data.

It typically uses neural networks that identify ‘patterns’ or ‘structures’ that can then generate new content for you. This also means it can come up with unique outcomes every time you trigger a response with your prompt.

ALSO READ: I asked Suno AI, the ChatGPT-powered bot, to make me a song and here’s how it went

This usually works when you feed a large dataset of possibilities and outcomes to a generative AI model. The model will then analyse all its patterns, relationships and try to understand the rules and limitations that govern this data.

For instance, if a generative AI model has been fed a dataset of cat images, it will be able to learn and create new images of cats simply by sampling them. The final images generated are then refined through a process known as ‘interference’ to give you a life-like image of a brand-new cat. 

What does generative AI do?

Generative AI can prove to be a powerful tool in the field of content creation and its use cases span across industries. Some of the popular ones include: 

Language: This type of generative AI model is commonly observed in the field of advertising and copywriting. However, you may also find instances in code development and gene sequencing industries as well. It typically produces language-based content and is often employed to reduce manual labour. ChatGPT, Gemini or Copilot, for that matter, are a few well-known examples of a language-based generative AI model. 

Visual: Visual generative AI models are today seen in video and image generation for 3D model designing. It uses a text or voice-based prompt to come up with unique images or videos. While a lot of these may seem unique, you may find most models struggling with intricate details or understanding the concept of ‘quantities’.

That said, it is crucial to keep in mind that most of these image-generating AI bots are only the beginning of the vast possibilities that can be achieved in the future. Popular examples of this include OpenAI’s DALL-E or Midjourney. 

Auditory: AI models designed to produce music or audial outputs are some of the few new forms of generative AI available today. They are especially helpful to create unique background scores and several models also offer the option to edit or modify their piece based on a prompt. 

Companies, like Adobe, have already announced a generative AI project that can create new music. There are also platforms like Suno.AI that offer real-time text-to-music conversion at the cost of a small fee. 

What are the benefits of generative AI?

Artificial intelligence has obvious repercussions if not regulated and there have been countless discussions around our data privacy. However, there are also multiple reasons why generative AI is helpful to us. Here are some of them: 

Generative AI, for one, can create indistinguishable content, which is a 100 per cent unique. This is especially useful in the field of arts or advertising.

Secondly, generative AI can also be used to create synthetic data that can be used to train other applications. And if that’s not all, it can also unearth hidden patterns or trends for businesses that may have otherwise overlooked them within raw data. Lastly, generative AI accelerates work and saves time, leaving resources for other tasks and research. 

With AI at our fingertips, it will continue becoming simple to execute tasks that were once considered strenuous. However, it is crucial to navigate through it responsibly, ensuring the results are ethical and beneficial to everyone around us. 

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