Streamlining simple processes can boost productivity, and advances in generative AI tools are making it easier than ever.
A generative AI tool can quickly generate writing, images, music, and videos using a simple prompt. That means you don’t have to use essential team members’ bandwidth to create summaries for presentation slides or brainstorm new taglines and can instead focus on deeper, more strategic work.
Generative AI can especially benefit work and project management. For example, Atlassian Intelligence has AI capabilities that let you streamline content creation so your entire business can focus on the bigger picture.
Let’s take a closer look at generative AI tools, how they work, and how they can help teams accomplish more.
- How does generative AI work?
- History of generative AI
- Benefits of generative AI
- Types of generative AI models
- Applications of generative AI
- Limitations of generative AI
- The future of generative AI
- Enhance project management with generative AI
- Generative AI: Frequently Asked Questions
How does generative AI work?
Generative AI works by taking your input—or prompt—and using it to generate content tailored to match it. Generative AI models learn patterns and structures and use that information to create content.
The generative AI process begins with training on diverse datasets, including text, images, or other data types. This allows the model to understand language, visuals, and more. This training enables the model to recognize and replicate patterns, generating outputs that align closely with the given prompts. Generative AI can produce a wide range of outputs, from text to images, making it a versatile tool for content creation.
History of generative AI
The history of generative AI began in the 1960s with chatbots. The first chatbot — ELIZA — was created in 1966 by Joseph Weizenbaum at the Massachusetts Institute of Technology (MIT).
While chatbots have existed for several decades, 2014 marked a turning point for modern generative AI tools. A machine learning algorithm known as generative adversarial networks (GANs) empowered generative AI tools to create realistic images, videos, and audio.
Since 2014, several breakthroughs in generative AI have occurred, including natural language processing and deep learning text-to-image models. As AI tools improve, their content becomes more realistic and useful.
The introduction of ChatGPT in 2022 was a major turning point for generative AI. For the first time, the average consumer could harness the power of AI to generate anything, from basic emails to complex long-form content.
Benefits of generative AI
Generative AI tools offer several benefits, including helping you save time by streamlining content creation. This time can be spent on strategic planning and growing your business.
AI can help you:
- Kick off a new project by brainstorming and generating fresh ideas
- Improve existing content by summarizing long-winded passages, changing tone, and improving grammar
- Quickly organize data into useful formats, like bullets or tables, or to identify action items
There’s a general anxiety that AI will inevitably hinder creativity, but it has the opposite effect. AI allows you to spend less time on repetitive, manual tasks because you can quickly generate and optimize content — whether you need to rewrite a headline or simplify documentation so teammates can better understand it. As you experiment and learn how to write effective AI prompts, you can spend more of your time on high-value, creative work.
Types of generative AI models
There are three main types of generative AI models:
- Variational autoencoders
- Generative adversarial networks
- Autoregressive models
Let’s examine each type of generative AI model in more detail.
Variational Autoencoders (VAEs)
Variational autoencoders (VAEs) are a type of AI that can learn the basic features of data and create new content (like images and text) based on that information. The basic features of data are the fundamental parts that makeup something, like the shape and color in a photo or the common patterns in sentences. Variational autoencoders learn these features to create new, similar content.
They work by using two main parts: an encoder and a decoder. The encoder compresses existing data into a simplified form called latent space, where each part represents a different aspect of the original data. The decoder then takes this simplified data and reconstructs it into a form similar to the original, allowing VAEs to generate new content.
Generative Adversarial Networks (GANs)
GANs analyze a dataset and use that information to create new data similar in form or style. For example, a GAN can generate text that sounds like you because it’s trained on a collection of your writing to learn your style and syntax.
While GANs are effective generative AI tools, they have some drawbacks. They require a lot of storage and power, and training them on a dataset can lead to stability issues.
Autoregressive models
Autoregressive models use regression analysis — which maps the relationships between two variables to predict future relationships — to predict the next variable based on previous data. This is the same type of thinking you use to identify and follow a pattern of numbers to predict which number should come next.
While autoregressive models aren’t frequently used to generate images and other content, they’re great for predicting weather — or, for project and work management, what your next task should be.
Applications of generative AI
As generative AI tools become more powerful, individuals and businesses everywhere are turning to AI to streamline work so teams can focus on project collaboration and other vital tasks.
Countless businesses of all sizes also use generative AI chatbots to interact with customers. These chatbots can interpret and respond to customers’ questions, allowing human agents to focus on customers who may require more hands-on help.
Generative AI can be critical in project management by helping you brainstorm and organize ideas. You can use AI to generate catchy headline ideas, brainstorm product names, and manage data. AI can analyze project requirements, break them into actionable tasks, and assign deadlines, ensuring every team member knows their responsibilities and timelines.
Generative AI can also facilitate better teamwork and communication. AI-powered tools can automatically summarize meeting notes, highlight key points, and assign follow-up tasks, ensuring everyone is on the same page and boosting collaboration.
Limitations of generative AI
While generative AI tools are powerful in many ways, they have drawbacks and limitations.
Some of the most popular generative AI tools — including new ChatGPT models — can now access real-time web data. However, even with this improvement, generative AI can still produce inaccurate, outdated, or misleading information, known as a “hallucination,” where the AI generates responses that sound convincing but are actually false.
Hallucinations occur because the AI generates content based on patterns in its training data without a true understanding of the information.
Hallucinations aren’t a major concern if you use AI as a jumping-off point for the creative process, like brainstorming slogans or product names. However, if you’re using AI to help write informative, authoritative content, it’s essential to carefully craft your prompts and thoroughly review the AI’s output for accuracy.
The future of generative AI
As AI tools become commonplace in our daily lives, some of the brightest minds are working to build better generative AI tools for the future.
Personal AI is one area being explored. Instead of using general data from the Internet for training, personal AI leverages individual user data to offer a more personalized experience. One example of personal AI is Google Gemini—an AI-powered personal assistant that integrates with Gmail, Docs, and other Google services. Google Gemini can enhance productivity by sorting emails, summarizing content, drafting responses, and more, using AI to streamline personal and professional tasks.
AI tools are relatively new, which means rules and regulations are still being implemented — and more government regulations surrounding AI are expected in the coming years. Companies like Atlassian are also dedicated to responsible technology principles to ensure AI empowers teams and contributes to better societal outcomes.
Enhance project management with generative AI
As generative AI tools improve, AI plays an increasingly important role in business operations. From content creation and marketing to project management and collaboration, AI can help you save time by streamlining workflows.
With Atlassian Intelligence, generative AI lets you summarize text, organize data and ideas, adjust the tone for improved communication, and even create original content.
Learn more about how Atlassian Intelligence is transforming what is possible in teamwork.
Generative AI: Frequently Asked Questions
What is the difference between OpenAI and generative AI?
OpenAI is a research organization focused on artificial intelligence. Generative AI is a subset of AI models capable of generating tabular synthetic data. OpenAI has developed ChatGPT and Sora, a text-to-video generative AI tool.
Any AI tool that can take a collection of information and use it to generate original content is generative AI. OpenAI develops generative AI tools like ChatGPT.
What is the difference between machine learning and AI?
Machine learning and AI aren’t the same, but they’re closely intertwined. Machine learning is a subset of AI that uses training algorithms to learn from datasets. Some real-world examples of machine learning include facial recognition, product recommendations, and email spam filtering.
Think of machine learning as part of AI’s foundation. While some types of AI don’t use machine learning, machine learning models are a cornerstone of generative AI tools.
Is ChatGPT a generative AI tool?
ChatGPT is a language learning model developed by OpenAI, and it’s considered a generative AI tool because it can process your input and generate human-like text based on your prompt. ChatGPT can write blog posts, emails, academic papers, and more. Unlike some generative AI tools, ChatGPT can’t be used to generate images or videos.
Like any generative AI tool, ChatGPT results rely on your input. Detailed prompts can help you generate original content specifically tailored to your needs.