Generative AI, or artificial intelligence able to generate novel content, has the potential to revolutionize mobile and web apps. This new technology is exciting because content is an important part of any app.
With generative AI, you can create content that is more engaging and more relevant to your users. This includes text, images, music, and other types of multimedia. Even entire conversations can be generated by AI. In this article, we'll explore how generative AI can help people spawn amazing new app ideas and bring those ideas to life.
Definition of Generative AI
Generative AI is a type of artificial intelligence that is able to generate new content based on a set of input parameters. This is in contrast to other types of AI. Supervised learning, for example, relies on training data to make predictions. Instead of predicting things or categorizing things, generative AI creates things.
Examples of Generative AI
Generative AI is already being used in a variety of industries and applications. Below are several examples of ways this incredible technology is being used today.
Creating mockups and prototypes of product designs. Generative AI can be used to quickly and easily generate mockups or prototypes of product designs, such as app interfaces or website layouts. This gives designers and developers a sense of how the product will look and function before building it, saving time and resources.
Generating marketing materials. Generative AI can be used to create marketing materials such as promotional videos, social media posts, or advertisements. This helps businesses reach potential customers and generate interest in their products or services.
Writing news articles. Generative AI can be used to write news articles or other types of content. This helps media companies and other organizations produce content more efficiently, or cover topics that would be difficult or time-consuming for human writers to research and write about.
Translating text from one language to another. Generative AI can be used to translate text from one language to another, helping to make information more accessible to a wider audience.
Generating personalized recommendations. Generative AI can be used to personalize recommendations for users, such as generating playlists or meal plans. This improves the user experience and make it easier for people to find content or products that are relevant to their interests.
Providing real-time transcriptions of speech. Generative AI can be used to provide real-time transcriptions of speech, adding captions to videos and transcripts to audio recordings. This makes content more accessible to people who are deaf or hard of hearing.
Generating responses to customer inquiries. Generative AI can be used to generate responses to customer inquiries in a customer service setting. This enables businesses to provide faster and more efficient customer support.
Beyond these examples, there are many other ways that generative AI can be used to create new content. Creating new art, replicating human voices, and generating new music are just a few of the other applications of this technology.
Generative AI's Potential for New Apps
Generative AI has the potential to be a powerful tool for app development in a number of ways. Every step of product development, from ideation to development to marketing, can be expedited or enhanced by generative AI.
Generating New App Ideas
First, it can be used to generate new app ideas and concepts. For example, an AI can generate a list of potential app ideas based on user input, such as a specific problem or need that the app is intended to address. It can also create rough prototypes of app interfaces and features, which can then be refined and developed further by human designers and developers.
Consider the following interaction with OpenAI's ChatGPT, a chat bot powered by GPT-3. I asked the bot for ideas for a new app that can utilize OpenAI's text generation API within the B2B space.
ChatGPT is particularly interesting for text generation as it retains the context of your conversation. This means that followup questions can be asked to get more specific information about the app ideas that were generated.
Once you have an idea you like, you can ask ChatGPT to produce additional content to pitch your idea to your team.
You can take the idea even further with ChatGPT, asking it to assist you in obtaining verbal commitments to purchase from your first customers.
Improved App Designs and User Experiences
Generative AI can also be used to optimize the design and functionality of an app. New AI tools could be used to create mockups or prototypes of app interfaces, which can help developers see what their app might look like and get a sense of how it might function. It could also be used to generate new app features or functionality, such as by suggesting new features to add to an existing app or coming up with entirely new app concepts.
While the output of these tools is not a replacement for human designers, it can be used to help designers and developers come up with new ideas and improve their designs.
The above example was not particularly innovative or outside of the realm of what a human might devise, but it does show how generative AI can be used to assist designers in their work. Instead of an app designer spending their own time devising a signup flow from scratch, ChatGPT can give our designer a rough idea they can iterate upon (with or without ChatGPT's continued assistance).
Text generation is not the only generative AI tool worth considering for improving your app ideas. Image diffusion models such as DALL-E 2 are capable of producing unique, high quality images of practically anything you can imagine, which can power interesting capabilities within your app. Uizard is a free tool that generates mockups of app interfaces, which can be used to help designers and developers visualize what their app might look like and get a sense of how it might function with less effort than was previously required.
Personalized App Content
App personalization can be taken further than ever with the help of generative AI. The user experience can be tailored to the individual user, with anything in the app catering to the user's needs and preferences. AI services can generate customized recommendations or tailored content based on the user's preferences and history.
Certain user characteristics, behaviors, or actions could be used as inputs to an AI model to generate personalized content. For example, messages encouraging the user to make a purchase could be generated to use a different tone or different vocabulary based on the user's personality. A personalized onboarding experience could be generated based on the user's demographic information: a Millennial might be greeted with a different message than a Baby Boomer. AI could even determine that the user may be interested in certain features more than others and adjust the screen layout to prioritize the features that the user is most likely to use.
Improved Accessibility and Inclusiveness
Generative AI can also be used to generate assistive features within an app, such as automatic captions for videos or audio transcriptions for voice notes. Complex articles can be summarized into more digestible text, aiding comprehension for people with reading difficulties. The text of an app can be automatically translated into other languages, making it more accessible to people who speak different languages. These capabilities could help make apps more accessible and user-friendly for people with disabilities or other needs.
As an example, let's ask ChatGPT to simplify that last paragraph into a more digestible version:
Challenges and Limitations
While generative AI has the potential to be a powerful tool for app development, there are a few challenges and limitations to consider.
One challenge is the need for high-quality data. Generative AI must be trained on a data set that reflects the type of content you wish to generate. This is not a small task. GPT-3 was trained on 175 billion parameters, or 800 gigabytes of data.
Depending on your desired use case, you may only need to use an existing trained model, eliminating the data problem. However, some applications that use generative AI may require user input to also be of a high level of quality. For example, if you wish to generate professional images of app designs, standard models may need more guidance than a general text-to-image diffusion model provides. An image-to-image model, which depends on the user providing a starting image to improve upon, would require users to provide reasonably drawn designs to begin with. Uizard, for example, requires users to build a wireframe to help their AI generate a high-quality mockup of an app interface.
Another challenge is bias in trained models. AI models will reflect whatever biases were present during training, either due to underrepresented or overrepresented concepts in the training data or some bias introduced in the training method itself. Failing to recognize these biases can lead to unintended consequences.
Beyond training biases, generative AI models can produce incorrect or misleading information. Large language models such as GPT-3 are designed to generate human-like text but are not necessarily knowledgeable on any particular topic. As a result, they may sound authoritative on a topic when in fact they are not designed to actually know the topic and are just writing reasonable sounding but factually incorrect responses that are similar to concepts they learned from their training data. This could lead people to believe or act upon incorrect information because the AI appears to know what it's talking about.
In addition, generative AI used for open-ended responses to end users' questions could provide problematic responses. For example, a customer support chat bot intended to help users troubleshoot common problems with an app may encounter an unsolvable problem. In some cases, the AI may suggest a solution that is not helpful or appropriate, such as suggesting the user try another app. As another example, GPT-3 may give a programmer a code snippet that does not work due to changes made in utilized software libraries since the model was trained.
Finally, there are practical limitations to consider when using generative AI. Some bloggers are using generative text to write blog posts, but most models have a limited amount of text they can generate at once. Therefore, it's currently easy to detect a purely generated post, and a purely AI-generated post is unlikely to rank well in organic search results. Long-form content is also difficult to produce without a human collaborator guiding the AI through multiple rounds of writing prompts.
When incorporating generative AI into your app idea, it's important to test out ways the AI will be used. If you are using a text-to-image model to generate app designs, you should test the quality of the designs it produces. The way you anticipate the model working—or the way users want to use your app—may not reflect how the model actually works.
Generative AI Tools and Platforms
There are a number of generative AI tools and platforms that can be used to power new capabilities in apps. While text generation is perhaps the simplest to understand, there are many other types of generative AI that can be used to power new features in your app.
Generative Text Tools
Generative text tools can create idea lists, suggest names for products, create personalized content, and write documentation for your app idea.
Open AI's GPT-3 is a large language model that can be used to generate text, images, and code. The Open AI API lets you access GPT-3 programmatically, which is perfect for powering new capabilities in your app.
ChatGPT is a chat bot that uses GPT-3 to generate responses to user questions. It's a great way to experiment with GPT-3 and see how it works. ChatGPT is a particularly powerful tool for brainstorming new ideas, as it retains the context of previous questions and answers within "conversations." Simply write messages to ChatGPT to ask questions, to instruct it to write for you, or to ask it to summarize text for you.
Generative Image Tools
Generative image tools can create app designs, offer design inspiration, and generate unique images for your blog posts.
Open AI's DALL-E 2 is a text-to-image model that can generate images based on text descriptions. Similar to GPT-3, DALL-E 2 can be accessed via the Open AI API.
Midjourney is another image diffusion model. It is a web-based service that can generate impressive scenes with relatively simple text prompts.
Stable Diffusion is a freely distributed image diffusion model that can be used to generate images from text prompts. Several web services have launched to make it easy to use the model, or you can run Stable Diffusion on your own computer.
DreamBooth lets you train a generative AI model on images of yourself to generate new images of yourself through text-to-image tools like Stable Diffusion.
Uizard is a free tool that generates mockups of app interfaces, which can be used to help designers and developers visualize what their app might look like and get a sense of how it might function with less effort than was previously required.
Other Generative Tools
There are countless other generative AI tools that can be used to power even more interesting app ideas. Generative AI models exist for all sorts of tasks and creative work. The following represents a small sample of the broad range of tools available.
Topaz Video AI performs video enhancement tasks such as deinterlacing, upscaling, and motion interpolation. This can help you increase the resolution of low-quality videos, restore old footage, or create smooth slow motion effects.
AIVA is a creative music assistant that can help you generate novel music in several different styles and genres.
Murf is a text-to-speech tool that generates realistic voices to read text you provide. This can be used for voiceover work, to create audio books, or to generate audio presentations.
GitHub Copilot is a programming assistant that can suggest entire functions or code snippets based on existing code. Copilot greatly speeds up development of simple features and can be used to guide development of complex features.
Conclusion
Generative AI has the potential to be a powerful tool for app development, helping product managers, designers, and developers come up with new ideas and bring those ideas to life. While there are challenges and limitations to consider, such as the need for high-quality data and the potential for bias or incorrect information, the potential benefits of using generative AI are significant. As this technology continues to advance, we can expect to see even more exciting and innovative app ideas being developed using generative AI.