Grasping the Mechanics of Generative AI

ChatGPT, Bing AI, and Google Bard are some of the most recognizable names in the world of consumer artificial intelligence. All three products have one thing in common—they are all generative AI products.But what exactly is generative AI, and what has sparked the recent fascination with this resurgent AI space?

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What Is Generative AI?

Generative AI, short for “generative artificial intelligence,” is a type of AI system that can generate unique or original content like text, audio, videos, or images on demand. Unlike some traditional AI systems designed for tasks like data classification or analysis, generative AI models are more concerned with producing novel or creative outputs based on the instructions they are given.

While generative AI may seem like a new technology, it has actually been around for decades. Several iterations and forms have been around at least as early as the 1960s. Artificial Intelligence is a vast field, and generative AI is just a subsection.

One of the most fascinating aspects of using a generative AI tool like ChatGPT or Google’s Bard AI is the ability of these tools to generate content that matches your request. You ask ChatGPT to write a poem in the style of William Shakespeare, and you get something strikingly similar to Shakespeare’s work. You ask it to create a speech in the style of Donald Trump, and you’d get something that impressively mimics the tone of the former US president. So, how is this possible? How is generative AI able to pull off such a fascinating feat?

How Generative AI Works

The working mechanism of a generative AI model involves a complex interplay of various deep-learning techniques and algorithms. The exact details of how a generative model works depend on its goals and the underlying architecture. For instance, a generative model for generating audio snippets will have a different working mechanism from one meant for generating videos or text.

However, at their core, most, if not all, generative models work similarly at their foundational level. They learn from a large amount of data, capture patterns and styles of the data, and then use these captured patterns to reproduce samples akin to what they’ve learned in their training data.

You can think of generative AI tools as a music composer. Imagine this music composer has listened to countless songs and studied the harmonies, melodies, rhythms, and structures of a number of musical genres. In other words, this composer has an extensive knowledge of the musical genres. With this knowledge, the composer can create original or unique music inspired by what they have learned.

So if they’ve learned a lot about pop music, you could ask them to compose a pop song, and they won’t have a problem doing so. The composed music would then be an expression of the composer’s understanding of what pop music should look like based on what they’ve learned. Similarly, the product of generative AI is an expression of the AI model’s understanding of the underlying concepts learned from its training data.

So if you want to create a generative AI model that generates pictures of cars, you’d have to feed the model a huge dataset of car images. To create an impressive model, you’d need to feed it pictures of as many car brands and models as you can think of. With enough training, the algorithm will learn what each car brand or model looks like, and we’ll be able to create images of almost any car you can think of on demand.

Popular Generative AI Models

There are hundreds of generative AI models currently in development or already being deployed in the consumer AI market. Some of the popular ones you should know of include:

1. Generative Pre-Trained Transformer (GPT)

GPT, developed by OpenAI, is among the most recognizable names in the generative AI space. Its popularity hinges on its effectiveness as a conversational AI model and the viral success of the ChatGPT chatbot, which uses GPT as its underlying technology. It is a large language model designed to generate human-like text when prompted. Typical of any generative AI model, all iterations of the GPT model have been trained on a massive amount of diverse text data.

2. Pathways Language Model (PaLM)

PaLM, an experimental large language model by Google, is a powerful generative AI model capable of performing a large range of tasks like creative writing, code generation, language translation, and a number of other text-based natural language tasks. Like GPT, PaLM was trained on a large corpus of text data sourced from a wide array of sources across the web. It is the AI model that powers Google’s Bard AI .

3. Music Language Model (MusicLM)

MusicLM is another generative AI model from Google. It is designed to generate “high-fidelity” music from simple text prompts. Trained on thousands of hours of music across various genres, the generative model can create unique music by using simple descriptions of the music you need as inputs. If you’re wondering how good it is, here’s our review of the MusicLM model .

4. DALL-E

DALL-E is OpenAI’s AI image generation model designed to create multiple styles of unique imagery from text prompts. It is a multimodal implementation of the GPT model, trained on numerous text-image pairs from various sources across the internet.

Beyond generative AI models, you’ll also find generative AI products like Midjourney, DALL-E image generator, Stable Diffusion image generator, Hugging Chat, and several other impressive AI products powered by generative AI models.

Why Has Generative AI Exploded In Popularity?

On 30th November 2022, OpenAI CEO Sam Altman announced the launch of ChatGPT in a tweet. Despite being OpenAI’s CEO, Altman was relatively unknown in the larger internet community, and his tweet came with little to no fanfare.

Five days later, ChatGPT had gathered its first one million users; it did so at a rate unheard of by any app. It gathered several million more, eventually making ChatGPT the fastest-growing app of all time . While ChatGPT is not the first generative AI product, its arrival on the AI product scene brought generative AI into public consciousness more than any other tech product before it.

While ChatGPT has been the tip of the spear in propelling the hype around generative AI, it didn’t do it alone. 2022 will be remembered as the year generative AI tools went mainstream. From conversational AI chatbots to code and art generators, the second half of 2022 was the first time multiple AI tools that had both mass appeal and practical day-to-day uses had hit the market. With these tools came the popularity of its underlying technology—generative AI.

Generative AI tools such as Bing AI, Google’s Bard, DALL-E, ChatGPT, and Midjourney have seamlessly woven themselves into the fabric of our daily lives, constantly presenting us with their remarkable creations. Whether it’s the captivating write-ups crafted by ChatGPT or the astonishingly lifelike images conjured by Midjourney, generative AI has become an ever-present companion, accompanying us day in and day out. This is the genesis of the recent popularity of generative AI.

Embrace Generative AI

The popularity of generative AI tools is not a fad. Unlike some recent technological trends that gained popularity and fizzled away with time, generative AI is a technology that holds actual practical benefits. As this resurgent technology niche snuggles its way into almost all aspects of our digital life, it’s best to seek out ways to make the best use of the technology rather than being puzzled about it.

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