Generative AI Meaning
What Is Generative AI?
Generative AI (gen AI) defines a category of artificial intelligence technology that can create new forms of creative content based on a variety of inputs. Gen AI, for instance, generates content such as text, images, videos, and audio.
While this technology dates back to chatbots in the 1960s, it was not until around 2014 that generative AI could produce high-quality, convincingly authentic outputs. This was made possible by the inception of generative adversarial networks (GANs) – a kind of machine learning algorithm.
Gen AI’s improved capability unlocks a world of new possibilities such as writing software code, accelerating pharmaceutical research, creating rich educational content, developing new products, and transforming supply chains.
However, Gen AI has also raised concerns about cybersecurity attacks and deepfakes – that is, digitally manipulating synthetic media to convincingly mimic another person’s likeness. For instance, a malicious actor can create a deepfake image to access their victim’s stolen devices using facial recognition. Technological solutions such as digital identities, deep learning algorithms, and image, video, and audio analysis can be used to identify inconsistencies in deepfakes that are imperceptible to the human eye.
Some popular gen AI tools include Github Copilot, ChatGPT, Dall-E, Jasper AI, and Gemini (formerly known as Bard).
How Does Generative AI Work?
Generative AI relies on machine learning (ML) models known as deep learning models. These are algorithms that simulate the human brain’s decision-making and learning processes. Thus, you can think of gen AI as an ML model trained to respond to users’ natural language questions. This also means that the accuracy and quality of the output are heavily reliant on the data used to train the ML model.
Users typically query gen AI models using descriptive input, whether textual or imagery, that the algorithm can process. The model then encodes patterns and relationships in vast amounts of data and uses that information to understand the users’ queries. It then generates relevant to satisfy the user query. For instance, for textual responses, the ML model uses natural language processing (NLP) techniques to convert raw characters into comprehensible sentences, entities, or parts of speech. The AI algorithms can also transform images into visual elements or vectors.
New-gen AI tools allow users to submit their queries in plain language and customize the results after an initial response, such as the desired tone or style.
What Are the Use Cases for Gen AI?
Some of the applications of gen AI models include:
- Redesigning business processes
- Automating email responses
- Simplifying the data augmentation process
- Enhancing responses to technical questions
- Automating content creation
- Breaking down complex information.