Five Promising AI areas for App Developers

Over the past few years, we’ve seen some truly impressive advancements in AI and machine learning. I remember that 20 years ago, when I was studying Artificial Intelligence, even finding faces in images was a major challenge—something that smartphones now do effortlessly in real time. The same goes for speech recognition: once considered nearly impossible, it’s now embedded in tools like Siri, Alexa, Google Assistant, and even real-time translation apps. While voice assistants are still not perfect—as this video humorously shows—it’s clear that AI has come a long way. Today, AI opens the door to exciting new applications that were unimaginable just a few years ago. In this post, we explore key areas of AI and ML that app developers can leverage to create smarter, more engaging experiences. First, let’s look at Image Recognition…

Image Recognition #

The first area that started the whole AI revolution is image recognition. Before 2012, it was quite difficult to recognize objects in images. The ImageNet dataset was created to benchmark image recognition algorithms, and it was a big challenge for researchers. AlexNet changed this and showed that deep learning could be used to distinguish between a large number of classes in images. Since then, we have seen many improvements in image recognition, object detection, and image segmentation. SAM2 is a great example of this, as it can segment images into different objects, opening up new possibilities that help us select and manipulate objects in images easily. Moreover, SAM2 is open source and can be easily embedded in your applications.

Natural Language Processing #

The second area is natural language processing (NLP). NLP has been around for a long time, but it has seen huge leaps in the last few years. It helps us process text data. It can be used for classification tasks like sentiment analysis, topic classification and understanding the context of text. It can also be used to compare texts for similarity and summarize them. These models also help us with question answering and searching large corpora of text data.

Speech Recognition and Synthesis #

The third area is speech recognition and synthesis. Speech recognition has been around for a long time, but it has become much more accurate and reliable in the last few years. It helps us transcribe audio data into text, which can be used for many applications like voice assistants, transcription services, and real-time translation. It can also be used to generate speech from text, which is useful for accessibility and voice assistants. The latest models can even generate speech that sounds very natural and human-like.

Recommendation Systems #

The fourth area is recommendation systems. Recommendation systems have been around for a long time, but they have become much more sophisticated and accurate in the last few years. Think about how Netflix recommends movies and TV shows based on your viewing history, or how Spotify suggests music based on your listening habits. These systems use machine learning algorithms to analyze user behavior and preferences, and provide personalized recommendations that help users discover new content they might like. This is a great way to enhance user experience and engagement in your applications.

Generative AI #

The last area is generative AI. Generative AI can help you create new images, text, or even video content. Models like DALL-E, Midjourney, and Stable Diffusion can generate images from text descriptions, which can be used for creative applications like art, design, and marketing. It opens up possibilities for creativity on tasks that would normally take a lot of time to learn. Other models also can generate text descriptions for images. Similarly, text-based models like ChatGPT can generate text based on prompts, which can be used for content creation, chatbots, and virtual assistants. It can help your users write better, faster, and more creatively. It can also help summarize text and assist you in writing code. Last, generative AI can also be used to create new music, which can be used for creative applications like music production, sound design, and game development.

We will explore these areas in more detail in the coming weeks, and look at how we can use them in our applications. Next time, we’ll build a simple app using CoreML to add smart image recognition to your iOS app.