We might still conjure images of fantasy films featuring robots and cyborgs when we think of artificial intelligence (AI).

Realistically, AI is a blossoming part of the tech industry and integrating itself into our day-to-day lives. With increased access to AI technology, small businesses and entrepreneurs can leverage AI apps in new and exciting ways. 

Chances are you’ve already used an AI app.The chat option on a website for a customer service question is a great example of basic AI. Do you use your Alexa to check the weather every morning? That’s AI-powered too. 

But AI apps aren’t just for big tech or coding geniuses. These apps automate mundane processes and help increase profit margins. AI apps have the potential to impact industry in the way Henry Ford’s assembly line did.

This technology is still within reach if you’re interested in AI apps but don’t know how to code. For example, it’s possible to adapt Chat GPT code from Open AI to generate emails for your small business reminding customers of sales or lingering carts.

No Code MBA strives to support entrepreneurs and level the playing field when it comes to tech skills. In this article, we’ll walk you through the basics of AI app development and how No Code MBA can help your business launch an AI app to meet your needs.

Understanding the Basics of AI App Development

There are four broad categories of AI apps to consider for business purposes: text generation, image generation, computer-vision, and embeddings,. Let’s break each of these ideas down.

Text Generation

Text generation AI is based on a Large Language Model (LLM). LLMs are some of the most advanced AI available today. LLMs work by analyzing large amounts of text to learn patterns and structures of language.

When LLMs write text, they are really predicting what words or phrases should come next based on the training data used by the creators. Each next word is generated in real-time based on which word is most likely to come next as defined by the LLM training words.

It’s similar to how humans learn to talk and write by learning from teachers, parents, and friends. Babies can only imitate sounds, words, and phrases that have been introduced to them.

Text generation AI can help with a variety of tasks: 

• Classification (such as categorizing words or phrases) 
• Generate text based on a short description
• Translation
• Question answering

The best example of natural language processing (NLP) AI right now is ChatGPT by Open AI. This program takes a prompt or question and extrapolates it. For example, if you ask, “What caused WW2?” it provides a written answer with complete sentences. It should o provide similar answers if you typed “What caused World War II” or “what caused the second world war?” 

To include text generation AI in your app, we recommend using OpenAI’s GPT-3 API.

Image Generation

Image generation AI, like DALLE-2 and Stable Diffusion, is like having a magic robot that can create pictures of anything you can imagine. Similar to text generation, image generation takes written prompts and turns them into images.

The range of images can vary in complexity as demonstrated by comedian John Oliver - it can truly create anything you can think of! 

Image generation AI does not simply copy and paste existing images, but instead creates entirely new images based on the images it was trained on. Similar to text-generation AI, it learns by looking at a huge volume of images. 

However, it is possible that some generated images could resemble existing images or concepts. Many artists are raising copyright concerns around this visual AI tech. However, it could help entrepreneurs and businesses create logos and visual branding at a fraction of the current cost for professional work.

If you want to include Image Generation in your own apps, we recommend using OpenAI or Stable Diffusion APIs.

Computer Vision AI

Computer vision AI is like a robot that can see and understand images and video similarly to a human. There are two types of computer-vision-based AI: still image and movement based.

Anyone who’s gone through secure logins on a website has interacted with computer-vision based AI. When you are asked to select parts of an image with cars or traffic lights for site security, you are teaching AI what these objects are and are not. 

Computer vision AI can identify objects, such as cars or trees. In addition, it analyzes facial expressions and body language to understand how humans are feeling or what they might be thinking.

Movement-based AI works by analyzing video data frame-by-frame to track movement of objects or people. This can be used in a variety of different ways, such as sports analysis to track the movement of players and self-driving cars to identify other cars or pedestrians. 

To add computer-vision AI into your no-code app, we recommend using APIs like Tensorflow.

Embeddings

The final AI model we’ll discuss is embedding models. These models maximize the content (such as text) AI can process. For example, on this site you can ask a book about its contents. The AI app generates responses based on the embedded model which scans and analyzes the book's text.

Open AI’s technology uses embedded modeling to analyze text strings and how they relate to one another. For example, “public schools need more funding” and “public schools are chronically underfunded” say the same thing but use slightly different words. AI with text embedding should be able to understand that.

AI apps are complex but no-code tools put them within reach for entrepreneurs willing to try something new. Different no-code tools offer different strengths when building an AI app.

There are two broad AI app categories to consider: user-facing AI apps and internal AI apps. To integrate AI functionality into an external-facing app that handles data, user-interface, and login we recommend using Bubble in combination with APIs like OpenAI and Stable Diffusion. To run simple AI predictions in workflows that use data from outside apps like Google Sheets, Zapier has an OpenAI integration..

In summary, if you’re building a soup-to-nuts AI app for public use, we recommend using Bubble. If you need an AI app to assist with back-end tasks to automate your enterprise (not customer-facing), Zapier is the better choice.

Planning and Designing Your AI App

With an understanding of the types of AI apps available, determining the best style for your needs will be your first concrete step. It’s most practical to decide based on what features you need for your site, or application programming interfaces (APIs). Then you’ll want to decide how to build it, in this case which no-code tool you’ll use.

You’ll pick which APIs you need based on what your site requires and utilize the appropriate no-code tool. For a chatbot using text-to-text generation, you’d want to use GPT3 for no-code solutions, while for text-to-image generation you’d employ DALLE-2 or Stable Diffusion. If you’re looking to modify existing images, say proofs or plans of some kind, Replicate has a great API to integrate into your no-code app..

Building Your AI App

With an understanding of how to leverage AI app components to help your business, it’s time to get started. No Code MBA offers a course with an overview of everything discussed in this article: Building Apps with AI.

In this course you’ll get:

• In-depth video tutorials on how to execute each step of the app building process
• Several AI app feature tutorials including how to build an image generator and how to fine-tune models
• More details and nuance than outlined here
• One-on-one chat support if you encounter a roadblock

Unlike other courses that focus on instruction through theory, No Code MBA’s course teaches through real-world examples. 

The total course time is six hours with 33 easy-to-follow lessons. Even if you’ve never built an app or website before, this course will walk you through exactly how to build AI-powered apps from start to finish.

What’s Next?

If you’re excited (or nervous) about the possibilities AI apps unlock, you’re not alone. 2022 saw a sharp uptick in the accessibility and popularity of AI with Dalle-2 and Chat GPT. What will 2023 and beyond bring?

For example, Microsoft Bing is set to launch an AI chat app in early 2023. Bing’s AI winnows out the vast number of search results to provide the best answer. As discussed in NYTimes The Daily, this technology could drastically shift how search engines and websites operate.

Beyond chatbot improvements, writers at the MIT Technology Review predict three more significant changes for AI. First, government regulations regarding AI will emerge and limit the industry. Second, AI startups will emerge, moving the development of AI technology out of big labs. Finally, AI use in pharmacology will increase and evolve.

Changes in our everyday technology happen fast; why wait until your business is big enough to afford a software developer to integrate AI apps? No Code MBA’s Building Apps with AI Course can help you get started right away.