Getting Familiar with Generative AI
Generative AI is a technology that can create content, including text, imagery, audio, video, and artificial data. It analyses large data sets to perform tasks and generates new content through prompts.
Generative AI algorithms apply probabilistic approaches to produce new instances that mirror the original data, typically with the capacity to demonstrate creative and inventive behavior beyond what was explicitly designed.
By 2025, generative AI will account for 10% of all data produced, up from less than 1% today - as reported by Gartner.*
It's hard to say where we are headed with it, as the technology is rapidly evolving, and people are still getting used to it. Let's take a closer look to get ourselves familiar.
Want to learn more about Artificial Intelligence?
The Tech Behind Generative AI
Generative AI models and algorithms are trained on massive datasets of existing data, including books, articles, and websites. The model learns the patterns and structure of human language and can generate new content similar to what it was trained with.
Some of the features of this technology include:
- Generative AI models can create new content that is indistinguishable from human-created content.
- Generative AI models can automate and accelerate many tasks that humans currently do.
- Generative AI models can be used to create synthetic data, which is used to train and evaluate other AI systems.
But that's all very generic; let's get a little bit more technical and learn more about how this technology is built.
- Neural Networks: Generative AI relies on neural networks, particularly deep neural networks, as the foundational architecture. These networks consist of interconnected nodes, similar to neurons, that process and transform data. These networks learn patterns to generate new content based on vast data used for training. The network then adjusts its parameters to minimize errors between the actual and desired output. This process is repeated until the network is able to predict the result for new data examples accurately.
- Natural Language Processing: Generative AI uses natural language processing (NLP) to analyze and generate human-like language. NLP is a subfield of AI that focuses on the interaction between computers and human language. It enables generative AI to understand and generate text, speech, and other forms of human communication.
- Adversarial learning: Many generative AI models use adversarial learning, a technique where two neural networks are trained to compete against each other. One network, the generator, is trained to create new content, while the other, the discriminator, is trained to distinguish between actual and generated content.
- Loss Functions and Optimization: During training, generative AI models use loss functions to measure the difference between the generated and desired outputs. The goal is to minimize this loss function through optimization techniques like gradient descent (which is an optimization algorithm used in machine learning and deep learning to reduce the cost function of a neural network model during training). The choice of loss functions and optimization strategies is critical for model training and performance.
The above are just a few technical points on how this technology is built, tested and deployed for us to use.
Generative AI can potentially transform various industries while presenting significant risks and challenges. As the technology evolves, it will be essential to address data security and trust to ensure that generative AI is used responsibly and ethically.
McKinsey reports that generative AI could enable labor productivity growth of 0.1 to 0.6% annually through 2040, depending on the rate of technology adoption.**
Generative AI Sites
Here are a few sites that use generative AI technology to power their services.
- Synthesia: An AI video platform for creating videos. It allows you to turn text into high-quality voiceovers with one click.
- Bing Image Creator: Bing Image Creator is an AI image generator powered by OpenAI's latest DALL-E technology. It allows users to type in a prompt and generate an image instantly.
- Bardeen: This AI automation tool enhances your productivity and saves time. It is a no-code AI workflow automation platform that replaces repetitive tasks such as notifications, joining meetings, sending email reminders, scheduling, and data extraction with shortcuts.
- Designs.ai: Designs.ai uses artificial intelligence to generate variations and millions of design assets. Its tools can customize designs, making them suitable for different purposes, from branding to social media content creation and creative projects.
- Copy.ai: Copy.ai is an AI-powered copywriting tool that generates high-quality marketing copy and content for businesses. It uses deep learning algorithms to understand user behavior and craft human-like copy that will most likely attract and convert the target audience.
Experience comprehensive and easy-to-grasp reports with Livedocs. Sign up today.
Subscribe to our blog today for product announcements
and feature updates, straight to your inbox.
Purpose Driven Design, How Metrics Shape User Experience
Discover how purpose-driven design and metrics enhance user experience for optimal results.
How Data Analytics Can Illuminate Consumer Sentiments
How do you measure what people feel about your brand? Using Sentiment Analysis that's how.