What is Generative AI?

Generative AI

Generative AI refers to algorithms, such as ChatGPT, that can be used to create new content in various forms, including audio, code, images, text, simulations, and videos. Recent breakthroughs in this field have the potential to revolutionize the way we approach content creation.

Generative AI systems belong to the broader category of machine learning. Here's how a system like ChatGPT describes what it can do:

Are you ready to take your creativity to the next level? Look no further than Generative AI! This beautiful form of machine learning allows computers to generate all sorts of new and exciting content, from music and art to entire virtual worlds. And it's not just fun; generative AI has numerous practical applications, such as creating new product designs and optimizing business processes. So why wait? Unleash the power of generative AI and see what amazing creations you can come up with!

Does that paragraph sound a bit off to you? Maybe not. It's grammatically correct, the tone is smooth, and the narrative flows.

What are ChatGPT and DALL-E?

That's why ChatGPT (GPT stands for Generative Pre-trained Transformer) is getting so much attention now. It's a free chatbot that can generate answers to almost any question asked. Developed by OpenAI and released for public testing in November 2022, it has been hailed as the best AI chatbot ever created. And it's popular too: over a million people signed up to use it in just five days. Astonished fans have shared examples of the chatbot writing computer code, college-level essays, poems, and even telling decent jokes. Shaking things up among the many content creators from copywriters to lifelong professors, there are also some who are trembling.

While many people have fears about ChatGPT (and AI and machine learning more broadly), machine learning has shown great potential. Over the past few years, machine learning has made an impact in various industries, from medical image analysis to high-resolution weather forecasting. According to a 2022 survey by McKinsey, the adoption of AI has more than doubled in the past five years, and investments in the field of AI are rapidly growing. It's clear that generative AI tools like ChatGPT and DALL-E (an AI tool for generating art) have the potential to change the way a range of jobs are done. However, the full extent of this impact is still unknown, as are the risks. But there are some questions we can answer, such as how generative AI models are built, what kind of problems they are best suited to solve, and how they fit into the broader category of machine learning. Read on to find out more.

What's the difference between machine learning and AI?

Artificial intelligence, as the name suggests, involves machines mimicking human intelligence to perform tasks. You may have interacted with AI without even realizing it—voice assistants like Siri and Alexa are based on AI technology, and so are chatbots that pop up to help you navigate websites.

Machine learning is a subset of AI. Practitioners develop AI through machine learning, where models learn from patterns in data without human guidance. The massive volume and complexity of data being generated today, which is beyond human capacity to manage, have led to increased potential and demand for machine learning.