Demystifying AI – What It Really Is and What App Developers Can Do With It

Artificial Intelligence – two simple words that have together, taken the world by storm. Half the world is excited about it, and the other half is unnerved. Whichever camp you belong to, chances are that you still wonder what exactly AI is, how it works and what it can do.

Going by AngelList’s current listings, there are more than 5000 AI startups, with an average valuation of $5 million. According to an IDC projection, the AI market will be worth more than $47 billion by 2020, and as many as 62% enterprises will adopt the technology by next year.

And since you’re on our blog, you’re probably more concerned about how AI can improve your app development, and let you create smarter, edgier, more enjoyable apps. Well today, we’ll answer all those questions, and tell you everything that falls under the ambit of Artificial Intelligence. So let’s dive right in.

What is Artificial Intelligence?

Is it robots that can think? Or apps that can know what you think? In reality, Artificial Intelligence is not a single technology but an umbrella term that covers several different technologies that aim to mimic human intelligence and appear to think just like we do. These technologies learn from our behavior and adapt, getting better with time, striving to provide near-human solutions to big and small problems, just like a personal expert. The most important technologies that all comprise AI are:

1. Machine Learning

Machine Learning (ML) is a part of artificial intelligence that comprises of systems that learn and improve with experience, without the need of explicit programming. These systems focus on developing computer programs that observe, analyze and implement data to improve their performance and personalize their functions. So in essence, the machine learning allows an app or a program to grow with you and identify patterns in your behavior, to change the way they respond according to your wishes.

How apps use Machine Learning

Have you ever wondered how Netflix is so good at understanding what you want to watch next? It’s almost as if it can read your mind. Netflix is closely watching how you react to each video you watch, and rearranges its recommendations accordingly. The more you watch, the better it knows what you want. Tinder does the same with your matches and many other apps use ML to personalize your experience.

2. Neural Networks

Explaining Neural Networks would take much longer than this blogpost can accommodate. But to get a fair idea, a neural network is a class of computing system. It is a network formed from several layers of simple processing nodes, mimicking the way the human brain works, although we are yet far from being able to replicate the brain function.

Essentially, neural networks recognize a pattern, be it in images, sounds, texts or any other format, and use the accumulated datasets to be able to identify the respective stimulus. So when and app identifies any image and tells you exactly what it is, that’s neural network at work.

How apps use neural networks

This might come as a surprise but the ever popular Shazam, that seems to be way older than the fancies of AI, was actually a great example of using neural networks to identify songs. Today, the ‘Now Playing’ on Google Pixel takes the same intelligence to a whole other level.

An app called I2S OCR is another fantastic example. It can read out absolutely any book you are holding in your hand. With a brilliant image to speech technology, it lets you scan a page on a book and translates all the text in the image to speech and reads it aloud to you.

3. Natural Language Processing

Hey Siri! What’s natural language processing?

The very fact that Siri can understand this question and give you a near perfect answer, is a feat of natural language processing. Computer science, computational linguistics and artificial intelligence come together to make our smartphones smart enough to actually have a meaningful conversation wherein they can understand speech, follow the command given, retrieve said information and render it in speech, is nothing short of magical.

How apps use natural language processing

Siri, Alexa, Google Talk and all other voice technology is based on natural language processing. Even the rather old Windows Speech to Text in Microsoft and other speech to text systems are fantastic examples. From note taking to device control, home automation and much more, NLU could very well take away the need to type on phones altogether in near future.

4. Deep Learning

At first, deep learning might seem interchangeable with machine learning. It is after all, a subset of ML and more like an improved version of it. Yet, understanding the difference between the two can enable you to go further in creating intelligent apps.

So while machine learning can parse data and use algorithms to provide highly tailored results, it will still require some guidance from a human for more nuanced searches.

Deep learning layers and structures algorithms to create an artificial neural network or ANN that can make the system more capable than machine learning. While machine learning would take verbal cues from specific words to provide personalized solutions, deep learning will slowly begin to identify other words and phrases that essentially mean the same thing and still give you appropriate results even if you didn’t say the keyword.

How apps use deep learning

Some of the biggest applications of deep learning can be found in customer service. Help bots or support bots that can answer customer questions and offer solutions like a real customer service representative would is a result if high level deep learning. Zendesk’s Answer Bot understands the context of a support ticket and responds to the queries with the appropriate answers and help articles.

Conclusion

Artificial Intelligence is already an inseparable part of our lives and there’s no looking away. The faster app developers can demystify it and begin playing with it, the better apps they will be able to develop for their audience. Apps now need to and have to observe and learn from user behavior to solve problems before the users ask. Those that don’t may soon be obsolete. We hope that this post has answered some questions for you and already set your brain ablaze with AI app ideas.