Generative AI examples are rapidly growing as this emerging AI technology quickly gains adoption. Already, generative AI examples are found in industries ranging from healthcare to manufacturing to finance to marketing.
Additionally, examples of generative AI tools are also growing, as developers work to evolve the original technology to create new software.
Why is it so popular? Generative artificial intelligence is a technology used to generate new content based on previous data. Current generative AI tools enable users to develop new images, text and more by inputting data.
In this guide, we’ll discuss examples of generative AI throughout key industries, as well as example of generative AI tools that are moving this new technology forward.
Also see: Generative AI Companies: Top 12 Leaders
Generative AI Examples for Key Industries
Whether ChatGPT or Bing AI, generative AI tools have many use cases across critical industries such as education, finance and advertising.
Healthcare is an industry ripe for change. And many healthcare organizations are currently implementing generative AI in various ways. For example, generative AI can be used by physicians to develop custom care plans for patients that will improve health outcomes.
Another healthcare use case for generative AI is the improvement of images resulting from MRI, CT and PET scans. Current AI tools can slightly edit patient scans to improve their quality and speed up rendering, resulting in faster response times to injuries.
Another industry that will benefit from the use of generative AI is manufacturing. For example, generative AI algorithms can be used to generate product ideas based on certain specifications. In many cases, this process is much faster than the traditional design process which must be completed manually.
Generative AI is also useful for processes such as predictive maintenance. Algorithms can detect and alert leaders when machines or equipment may need part maintenance before breakdowns occur. This prevents costly downtime for manufacturers.
The agriculture industry can also benefit from the predictive maintenance capabilities enabled by AI algorithms. However, other use cases exist specific to farming.
For example, agriculture leader John Deere has used AI to train field cameras to recognize bad plants during harvest. Algorithms then instruct the equipment to remove the bad plants, improving overall yields.
Other use cases involve using images to report on the state of crops in the field and using satellite data to predict future weather patterns.
The finance industry is currently harnessing the power of generative AI, with Bloomberg recently unveiling its own AI model, BloombergGPT.
Various use cases exist for generative AI in the finance sector. For example, in banking, AI chatbots can support bank customers through financial transactions. In investing, generative AI tools can analyze financial data and prepare insights and financial strategies to consider.
In education, generative AI can be used to develop custom learning plans for students based on their grades and overall understanding of various subjects. Generative AI tools such as ChatGPT can also support students with complex assignments such as term papers by being a starting point for brainstorming (though admittedly, ChatGPT is also abused by some students.)
For busy educators, generative AI holds promise for simplifying tedious daily tasks such as building lesson plans, outlining assignments, generating rubrics, building tests and more.
As technology advances, so do cyber risks. The cybersecurity industry must evolve too, so organizations can stay protected from breaches and cybercrime. Generative AI could improve cybersecurity in many ways.
For example, generative AI can be used to simulate risky environments that cybersecurity professionals can use to test their security policies and controls. AI tools can also analyze past data for trends to identify potential security risks. As a result, teams can mitigate these risks to improve their security posture.
Advertising & Marketing
One industry that seems nearly synonymous with AI is advertising and marketing, especially when it comes to digital marketing. Many marketers feel AI can reduce the amount of time spent on manual tasks to make room for enhanced creativity.
For example, marketers are currently using AI tools such as ChatGPT to generate briefs for content development and develop copy for search advertisements.
Other use cases include generating branded images to use in ads, developing content ideas based on SEO keywords, writing shareable summaries for long-form articles and even translating advertisements.
Also see: AI’s Drawbacks: IP to Ethics
Examples of Generative AI Tools
Generative AI uses machine learning algorithms to analyze large amounts of data, “learn” from it and develop new content from what it gleans. This process can be used to create everything from news articles to stock photography.
Currently, there’s a wide range of generative AI tools on the market, from ChatGPT to Google’s Bard.
ChatGPT is an AI natural language processing chatbot developed by OpenAI that’s trained to “read” prompts and provide a human-like response. ChatGPT was “trained” by analyzing all forms of content found across the internet.
ChatGPT can be used for simple queries such as “who signed the Declaration of Independence” or for more complex tasks such as finding errors in code.
While ChatGPT’s functions can be beneficial, there are some drawbacks to consider. First, ChatGPT was trained using information published up to 2021. It doesn’t understand any more recent data. This means ChatGPT is prone to giving false answers that look and sound like the truth.
Also see: GPT-4 vs. ChatGPT: AI Chatbot Comparison
DALL-E is similar to ChatGPT in that it uses natural language processing to generate new content in the form of images. Also created by OpenAI, DALL-E works using prompts entered by its users.
Users can enter a descriptive prompt into DALL-E and receive a detailed image only seconds later. For example, prompts can range from “a simple sunset” to “a watercolor-style fall sunset landscape featuring purples and oranges.” Both prompts would result in very different outputs.
The images generated by DALL-E are currently being used for everything from book covers to stock photography for websites.
Also see: What Is Natural Language Processing?
LaMDA is a large language model (LLM) developed by Google. LaMDA stands for “language model for dialogue applications” and was built to engage in true “conversation” with its users. Google engineered LaMDA to understand the context of a conversation and provide human-like dialogue.
LaMDA is built on Transformer, a neural network also invented by the team at Google. The result is a model that’s trained to understand words and how they relate to other words in conversations. LaMDA is the LLM currently in use by Google Bard, a conversational AI chatbot similar to ChatGPT.
Google Bard was released on the heels of OpenAI’s ChatGPT. Bard is meant to function similarly to ChatGPT, however, there is a key difference. While ChatGPT was developed using information from the web before 2021, Bard is able to pull information directly from the web.
The result is a chatbot that can be used in tandem with Google search to find relevant and updated content. Of course, there’s a current waitlist for users who wish to try it out. Google Bard was built using Google’s LaMDA LLM, which enables it to interact conversationally with its users.
Bing is a search engine by the tech giant Microsoft – it has always run a distant second to Google. However, in February of 2023, Microsoft announced the new Bing, which features an AI chatbot that can give answers to queries alongside search results.
Bing’s chatbot works the same way as ChatGPT and Google Bard. Users can simply insert queries into the Bing search box and receive answers instantly on the search results page.
Bing AI was built using GPT-4, which is the newest large language model developed by OpenAI.
On a related topic: What is Generative AI?
Examples of Generative AI Technology: Generative AI vs. Discriminative AI
Beyond generative AI, other types or models of AI exist and are in use today. An example of another common models is discriminative AI. This form of AI is most often used to classify. In other words, it’s used to discriminate between different classes of data.
For example, discriminative AI enables logistic regression, an algorithm used to determine a binary (two) outcome, such as “yes or no” or “left or right.”
Generative AI is different than discriminative AI in that it generates. Generative AI can develop new data or content based on previous data. It goes beyond simple classification. For example, you can enter a prompt into a chatbot and the algorithm will give you brand-new content based on that prompt.
Also see: Generative AI Startups
The Bottom Line: Generative AI Examples are Growing
Generative AI and tools such as ChatGPT and Google Bard have many examples across critical industries such as cybersecurity and manufacturing. And more use cases are expected to come to light over time.
While the future of generative AI will improve many industry-specific processes, we must move forward with caution. Even as positive examples abound, the power of generative AI and other models is not yet fully understood.
For those who decide to implement AI into their organizations, it’s critical to do so strategically and with cautious optimism.