Series 1: AI Fundamentals — Chapter 6: Generative AI Technologies | AI for Non-Techies | July 2024

AI Basics

To summarize what we discussed in Chapter 2, GenAI is a specialization of AI that focuses on creating systems that can: Generating or creating new data Artificial intelligence that can express text, images, music, etc. in a human-like way. GenAI is a field that has attracted the world's attention since OpenAI released ChatGPT to the world on November 30, 2022, and thanks to this, we are all creators.

In today's post, we'll explore the origins, current state, and future of Generative AI (GenAI), highlighting its key milestones, advancements, and potential impact.

The Rise of GenAI: From its Birth to its Future

Generative AI, a rapidly evolving field of artificial intelligence, is catching the attention of researchers, developers, and the general public. The technology has the remarkable ability to create new content, such as text, images, audio, video, 3D objects, and code (and even visual representations of code, as demonstrated by Claude from Anthropic), based on given input and training data. The potential applications of generative AI span a variety of industries, from the creative arts to scientific research, and are expected to have a profound impact on society.

The Beginnings of Generative AI
The origins of generative AI date back to the 1950s, when the concept of artificial intelligence was first introduced. However, it wasn't until the 1960s that the idea of ​​using AI to generate content began to take shape. In 1966, Joseph Weizenbaum developed the ELIZA program, which demonstrated the ability to conduct natural language conversations by applying pattern matching and substitution techniques to user input.

Over the next few decades, researchers continued to explore the possibilities of generative AI. In the 1980s and 1990s, the terms “generative AI planning” or “generative planning” were used to refer to AI planning systems, particularly computer-aided process planning, that generate a sequence of actions to achieve a specific goal.

The 2000s saw the emergence of machine learning techniques such as Markov chains used to model and generate natural language, but it wasn't until the late 2000s that deep learning, a subfield of machine learning, began to make significant progress in the areas of image classification, speech recognition, and natural language processing.

The Rise of Deep Generative Models
The real breakthrough in generative AI came in 2014 with the introduction of two key advancements: variational autoencoders (VAEs) and generative adversarial networks (GANs).

VAEs and GANs were the first practical deep neural networks capable of learning generative models for complex data such as images. These models can output not only class labels for images, but entire images as well.

GANs in particular have become a popular and powerful tool in generative AI. GANs consist of two neural networks, a generator and a discriminator, that are trained competitively. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. As training progresses, the generator learns to create more realistic outputs, effectively fooling the discriminator.

The current state of generative AI
In recent years, the development and adoption of generative AI has accelerated. The introduction of large-scale language models (LLMs) such as GPT-4, GPT-4o, and Claude Sonnet has revolutionized the field of natural language processing. Trained on vast amounts of text data, these models can generate human-like text, answer questions, and even perform creative writing tasks.

The success of LLM paved the way for the development of more advanced generative AI tools such as ChatGPT, DALL-E, Leonardo.AI, Midjourney, and Claude. ChatGPT and Anthropic's Claude, developed by OpenAI, are conversational AI assistants that can participate in natural language conversations, answer questions, and write code. Midjourney and Leonardo.AI are models that can generate images from text descriptions.

Check out this quick interactive patience game I created with a real-life scenario of driving through heavy traffic in the city of Hyderabad, India.

Every time I visit my hometown, I can't help but be annoyed by the traffic and the public's attitude towards it. I've long wanted to create a video game that offers a realistic experience of driving around the city, with one goal: to inspire people to the virtue of patience. However, this game is a much simpler version of that ideal.

The game starts with a destination in Hi-Tech City, 20km away from my house. Claude consulted real map information to measure the exact distance and created his own traffic events with the goal of keeping the “patience” scale intact when he reaches the destination. Give it a try and let us know what you think. It was generated in under 3 minutes with Claude.

The impact of generative AI is already being felt across a variety of industries. In marketing and sales, companies are using generative AI to create personalized content and targeted advertising. In product and service development, generative AI is being used to generate ideas, prototypes, and even entire products.

The healthcare industry is also exploring the potential of generative AI. Researchers are using generative models to create synthetic medical data to train machine learning algorithms while protecting patient privacy. Generative AI is also being used to generate personalized treatment plans and drug designs. OpenAI and Moderna signed an agreement to use ChatGPT across the company to accelerate time to market.

Credit: OpenAI and Moderna

The Future of Generative AI
As generative AI continues to evolve, its potential impact on society is expected to grow. McKinsey Global Institute estimates that generative AI could significantly increase labor productivity across the economy, automating a range of tasks and freeing up human workers to take on more creative and strategic work.

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