Understanding Generative AI

The development of generative AI has an interesting history marked by significant breakthroughs. Although gaining widespread attention in 2022, its evolution began with autoencoder neural networks in 2006 and continued through the adoption of generative AI models such as DALL E, ChatGPT by Open AI, Kubrick, Journey, and others.

"Any sufficiently advanced technology is simply undistinguishable from magic." – Arthur C. Clark

Generative AI is not only transforming almost every profession but also changing the way we create. We can reduce repetitive processes to focus on more creative and strategic activities, which is at the core of the importance of generative AI. Some of the big players in writing these generative AI models, engines, are Open AI, NVIDIA, Google, Meta, and universities like UC Berkeley and LMU Munich.

How generative AI is different than other types of AI

We need to know where Generative AI fits in the broader AI landscape. AI involves the theory and development of computer systems capable of performing tasks that typically require human intelligence. Machine Learning grants computers the ability to learn without explicit programming. Deep learning, a type of machine learning, utilizes artificial neural networks, enabling them to process more complex patterns than traditional Machine Learning. Generative AI, a subset of Deep Learning, generates new data instances based on a learned probability distribution of existing data. Thus, generative models generate new content, such as text, imagery, audio, and synthetic data.

How Generative AI Works

Generative AI operates by using techniques such as neural networks and deep learning algorithms to analyze patterns in a large dataset. During the training process, the model learns the underlying rules governing the content and generates new data by sampling from a learned probability distribution. The model continuously adjusts its parameters to maximize the probability of producing accurate output. For instance, if trained on cat images, a generative model can create new cat images through a process called "inference," refining the output to align better with desired results. In inference, the model adjusts its output to match the user's preferences, ensuring that the generated content becomes more realistic.

Source:

[1] Google Cloud Tech. 2023. Introduction to Generative AI.

[2] Hiren Dhaduk. 2024. How Does Generative AI Work: A Deep Dive into Generative AI Models

[3] Pinar Seyhan Demirdag. 2023. What Is Generative AI?

[4] Popova Zhuhadar, L. A. 2023. Comparative View of AI, Machine Learning, Deep Learning, and Generative AI.