What does generative AI mean?
Generative AI refers to artificial intelligence systems that can create and generate entirely new data, content or material in a self-contained manner, as opposed to AI that can only analyze existing data.
Generative AI works by finding deep patterns and relationships in large amounts of data that it has been trained on. From these learned patterns, the AI itself can produce new output that it hasn't directly seen before, but is similar to what it has seen during training.
How is generative AI trained?
The underlying technology behind highly generative AI is neural networks and deep learning. These neural nets are trained by "feeding" them huge amounts of data within a specific domain. This could be millions of images of objects, billions of sentences of text or thousands of hours of music.
During training, the neural net finds statistical patterns in the data and learns the underlying relationships. In doing so, it builds an internal representation of the world that enables it to create new things from this model.
Types of generative AI
Some examples of generative AI include:
- Text generation - Create new coherent text based on learned language comprehension.
- Image generation - Generate new images from learned knowledge of visual objects.
- Video/audio - Generate new videos or audio using deep learning.
- 3D modeling - Design new 3D models and objects.
- Computer vision - Generate descriptions of content in images.
Big language models boost generative AI
Much of the generative AI that is currently making breakthroughs is based on so-called large language models (LLMs). These models are trained on huge amounts of text data and achieve impressive language generation capabilities.
For example, large language models like GPT-3 and PaLM can write persuasive texts, answer questions, create code and much more. Together with other generative techniques, this opens up entirely new applications for AI.
Applications of generative AI
Some possible applications of generative AI:
- Automatic text and content production
- Advanced chatbots and digital assistants
- Generating graphics, 3D models etc. for design
- Synthetic media and entertainment content (deepfakes)
- Automatic coding and software development
- Personalized recommendations and predictions
Advantages and disadvantages
Some of the benefits of generative AI are that it can:
- Automate time-consuming manual tasks
- Produce high-quality content at scale
- Personalize content and services in real-time
- Assisting people creatively with e.g. design and art
Some of the disadvantages and challenges are:
- Risk of bias, errors and misinformation in content
- Copyright and ownership issues
- Misuse for e.g. fraud and hacking
- Regulation can be a challenge
In other words, there are both huge potentials but also important considerations with generative AI that we've only just begun to see.
Future outlook for generative AI
Most experts agree that generative AI will continue to gain momentum and become more and more advanced in the coming years. As the technology matures, it will begin to disrupt many industries in earnest.
Major global tech companies are investing heavily in the development of generative AI. At the same time, efforts are being made to minimize the problems of bias and erroneous content. So the potential for generative AI is considered to be almost unlimited in the long term.
