By now, you’ve probably heard the buzz about Generative AI and how it can create content in all types of media, from text to video.
It does this by analyzing large amounts of data, which is then re-generated in ways depending on the prompts the users enter. In theory, generative AI algorithms apply a probability approach to produce new versions of data that mirror the source. The algorithms then use their programmed creative and imaginative behavior to generate original and unexpected outputs.
Time will tell if this technology’s impact on business and our natural creativity will be revolutionary. One thing is sure: Generative AI is not going anywhere, and its influence on our world is just beginning.
By 2025, generative AI will account for 10% of all data produced, up from less than 1% today.Gartner
How Does Generative AI Work?
Generative AI models and algorithms are trained on massive amounts of existing data, including books, articles, and websites. The model (a computer program trained to make predictions or decisions based on data) learns the patterns and structure of our language and can generate new content similar to what it was trained with.
It can potentially transform various industries, though it does present significant risks and challenges. As this technology evolves, the debate on using it responsibly and ethically has grown with it. In recent times many instances of deepfakes, especially with prominent personalities, have caused uproar, whether they were created using generative AI tools or video editing.
Some of the features of this technology include:
- Creating content that is hard to differentiate from human-created content.
- Automating tasks and performing them quicker than humans.
- Creating synthetic data, which can train and evaluate other AI systems.
But that’s all very generic; let’s understand a few technicalities behind how it actually works. Some cool Gen AI tools are listed below if you want to skip this section.
Neural Networks: Generative AI relies on neural networks, particularly deep neural networks, as the foundational architecture.
What are neural networks?
- These consist of interconnected nodes, similar to brain neurons, that process and transform data.
- The networks learn patterns to generate new content based on vast training data.
- It 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.
- Natural language processing (NLP) is a machine-learning technology that allows computers to interpret and understand 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.
- The other, the discriminator, is trained to distinguish between actual and generated content.
Loss Functions and Optimization: Generative AI models use loss functions to measure the difference between the generated and desired outputs.
- That means generative AI models learn by comparing their creations to real examples. They score themselves using different systems and take tiny steps to improve, getting closer to perfection like an artist learning new techniques.
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.
Have you used generative AI in your work, or any of your projects? If so, how has the reaction been, and in what ways do you see it contributing to your daily tasks. Leave a comment and let us know.