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AI, Generative AI, machine learning. All of these terms have become buzzwords with the launch of Open AI’s Chat GPT at the start of the year, but what do they actually mean?

Don’t be ashamed to admit that these new terms can be confusing so let’s start with breaking down some of the key terms when we are discussing generative AI.

  • Artificial Intelligence (Al) Computer science speciality, creating systems to mimic human intelligence.
  • Machine Learning (ML) Use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
  • Large Language Models (LLMs) Deep learning algorithms. Perform a variety of Natural Language Processing tasks at scale.
  • Transformers A type of Neural Network launched in 2017 (out of Google) designed to learn contextual relationships between input data, enabling more accurate predictions (eg: words in a sentence or text sequence, pixels in an image).
  • Generative Al (GenAI) Software that automatically creates plausible text, images, sounds and code – mimicking human ability.
  • GPT: Generative Pre-trained Transformers – Neural network models that use the *Transformer architecture.

The Birth and Evolution of Generative AI

Generative AI, in essence, is a branch of artificial intelligence that specializes in crafting content – be it text, images, music, or other forms of data – that is so convincing that it’s practically indistinguishable from what a human could produce. The journey to generative AI started with the mainstream recognition of AI in the early 1950s with multiple milestones since then.

The first big breakthrough in the world of AI was the birth of neural networks which are the building blocks of generative AI. This breakthrough is said to have started before mainstream recognition and can be traced back to the 1940s. In those early days, these networks were elementary compared to today’s advanced counterparts.

However, the most important milestone for AI happened in the 1950s and 60s which saw the emergence of machine learning algorithms, which granted computers the ability to make informed decisions based on data. This set the stage for the generative AI revolution and inspired many films such as 2001: A Space Odyssey and The Matrix.

In 2017, experts at Google (Google BRAIN Team) created the ‘Transformer’, which changed the world of Al forever. This breakthrough witnessed the meteoric rise of deep learning, particularly deep neural networks, which became the driving force behind generative AI. This technology enabled AI to not only understand but also generate content with remarkable accuracy.

But what actually is Generative AI?

At its core, Generative AI entails training artificial neural networks to autonomously create content. These neural networks, often referred to as Generative Models, undergo extensive training using massive datasets. They use the patterns and information contained within the data to craft new, human-like content.

Generative AI is a bit like having a super-smart robot artist. This robot doesn’t just follow instructions; it learns by looking at tons of paintings, songs, or stories created by humans. It studies them so well that it understands what makes them special.

Then, when you ask it to create something, like a new painting or a piece of music, it doesn’t need step-by-step instructions. It uses its “knowledge” of what it has seen before to come up with something on its own. What makes Generative AI so impressive is its ability for creativity, to produce something so similar to a human that it’s hard to tell the difference.

Imagine you ask this robot to write a story. It won’t just put words together randomly. It will write a story that makes sense, with a beginning, middle and end, has characters, and might even make you feel something when you read it.

That’s the wonder of Generative AI – it’s like having a machine that’s not just good at copying but also at creating things that feel truly human, even though it’s just a computer program.

Generative AI vs. Traditional AI

Data-Driven vs. Rule-Based: Traditional AI adheres to explicit rules and programmed algorithms to make decisions. Generative AI, however, learns from data and is not bound by specific instructions. It can generate content based on patterns it has learned.

Flexibility: Traditional AI is often rigid, and limited by the predefined rules and algorithms. In stark contrast, Generative AI, empowered by deep learning, showcases remarkable flexibility. It adapts to a wide range of tasks without requiring extensive reprogramming.

Creativity: Generative AI possesses a degree of creativity that traditional AI lacks. It can generate art, compose poetry, craft music, and produce human-like text, bridging the gap between human and machine-generated content.

Learning and Adaptation: Traditional AI remains static until it’s reprogrammed or updated. Generative AI, on the other hand, continues to learn and evolve. It adapts to changing data, constantly enhancing its output.

‍So in summary…

Generative AI is a testament to the rapid evolution of AI and offers enormous potential across various domains, from content generation and artistic expression to tackling intricate challenges in healthcare and research. Its remarkable ability to mimic human creativity and adaptability positions it as a valuable addition to the AI landscape. While traditional AI retains its significance, Generative AI represents a momentous leap forward in the realm of artificial intelligence, providing us with a glimpse of a future where machines progressively emulate human intelligence and creativity.