Users expect more and more from the mobile app industry and these expectations are met by Artificial Intelligence (AI). Instead of bombarding users with ads, AI-driven mobile apps predict the behavior of users and make recommendations accordingly. But they do more than that.
In this article, we will talk about AI and its impact on the mobile app industry. We will cover the following topics:
- Are AI and Machine Learning (ML) the same or different?
- Are Siri, Alexa AI or Machine Learning?
- How are Artificial Intelligence and Machine Learning impacting the mobile app industry?
- How are AI and ML used today?
You may have heard a lot about Artificial Intelligence and Machine Learning. Are they the same thing? What are they? When you get the answer to these questions, you will also know how to build an AI or how to build an ML.
Sometimes, the two are mistakenly used interchangeably. However, they are not the same. AI is a broader concept. It applies data to build a smarter world. On the other hand, Machine Learning is the current application of AI. It is just a subfield of AI. To clarify more, let's examine the subsets of AI.
Artificial Intelligence is a branch of computer science to build smart machines capable of performing tasks otherwise performed by human intelligence.
One of the godfathers of AI, John McCarthy, described AI as computer science to make intelligent machines.
Other definitions say it is the ability of machines to mimic human intelligence.
How does it work?
Basically, AI discovers patterns in complex data that would otherwise be impossible to process by the human brain. AI machines process data and act on those learnings. Tasks are automated at an unimaginable rate and scale.
AI is a broader category. It has subsets described in this chart.
As you can see, Machine Learning is only a subcategory of AI. Let's have a look at each subsection.
Deep Learning is a subset of Machine Learning. It applies artificial neural networks while processing data. They are much like the neural networks in the human brain.
Deep Learning is based on neural networks. These are computer systems that mimic human neurons and result in perception.
Cognitive computing aims to recreate the human through the process in a computer model. Cognitive computing understands human language and recognizes images.
Natural Language Processing
Natural Language Processing or NLP enables computers to recognize and reproduce human speech.
It is a subset of AI to recognize, process, and interpret visual data, including graphs, tables, and pictures within PDF documents.
Lastly, we come to Machine Learning or ML. ML enables computers to learn from experience without being explicitly programmed. Algorithms are the backbone of ML. These algorithms can analyze data and make predictions.
For example, if you are a user of Netflix, you may have noticed that Netflix offers you movies similar to what you have watched. Or Uber offers you the best route for your drive. This is machine learning that analyzes your behavior based on previous data and comes up with recommendations. ML is also applied in life science, disease diagnosis, drug development, and so on.
Let’s take the example of Siri, Alexa, and other smart assistants. These are very real applications of artificial intelligence. They rely on natural language processing (NLP) , natural language generation (NLG) and machine learning (ML). What does this mean in practice?
NLP is the ability of a machine to “read” and “understand” the content. They can do that by writing or speaking. On the other hand, NLG is the process when a machine creates content for the human mind.
And here comes machine learning. This basically means that machines can learn from data (improve if needed) rather than being programmed by humans on what to do with the data.
Every time Alexa or Siri makes a mistake, they “learn from it” and improve the response the next time. Let’s suppose you ask Alexa to turn off the light or set the thermostat to cool. If an error was made, the smart speaker takes that data and learns from it. If you approve of Alexa’s behavior, it will continue repeating it.
Naturally, AI and Machine Learning are impacting the mobile app industry. Let's have a look at how.
AI-powered machines can process a vast amount of data about the user behavior, preferences, or pain points. This information is a powerful tool in the hands of retailers that can build their business according to customer needs. These customers are divided into target groups for custom app content and features.
Another aspect of AI and ML-powered devices is automation. A lot of tasks previously performed by humans are now performed by machines. Take the example of chatbots. According to Acquire, 1.4 billion people are users of messaging apps and prefer chatbots as a faster way of communication.
The app industry speedily integrated chatbots into the mobile app industry. According to Juniper Research, by 2022 the aggregate saving for businesses from chatbots will be over $8 billion per year.
Feedback-Driven Design and Development
Few businesses launch their product into the market without testing it. Minimum Viable Product is a buzzword and a lot of businesses invest in this aspect of development. AI and ML algorithms help to collect information about user behavior. The designers and developers make more effective decisions based on data-driven insights.
Enhanced Authentication of Apps
Since the advent of online commerce, the idea of data security is a key concern both for businesses and consumers.
AI and ML technologies are being used in the mobile app industry for the secure authentication of apps. Take the example of voice and face recognition or biometric authentication. These are all by-products of AI and ML.
AI and ML technologies are also applied to detect abnormal behavior or irregularities in user behavior thus detecting and preventing fraud.
There are incredible examples of how AI and ML are used today. Looking at these examples you may have a better idea of how to build an AI and how to build an ML for your own product.
Perhaps the most amazing AI-powered app is Google Maps. It can detect location data from smartphones. This app has access to a vast amount of data and can reduce commutes with its proprietary algorithms.
Have you ever thought about how Uber determines the price of your ride? How does it match you with other customers to minimize detours? The answer is that Uber KNEW the answer to how to build an ML that satisfies the customers. It's machine learning that helps Uber and Lyft to optimize their rides.
In the future, self-driving cars will shorten your commute even further. Good news? The number of cars on the road will be reduced by up to 75%, and smart traffic lights will reduce wait times by 40%.
And think about the security of your data. AI-based face recognition and biometric systems keep track of data and provide people safety in their financial transactions. So are the security cameras and the surveillance systems that are used to keep cities and the population safe.
In 2021, Amazon purchased a young robotics company called Kiva Systems to employ robots for carrying shelves of products and reading barcodes. This reduced the time Amazon warehouse workers have to walk on hard concrete floors. It was some ten years ago. Think how advanced warehouse technology is today.
The robotic vacuum Roomba 980 model uses artificial intelligence to scan room size, identify obstacles and efficiently clean the rooms while needing no human assistance.
And lastly, let's mention the Hanson-created Sophia which can efficiently communicate with natural language and use facial expressions to convey human-like emotions.
In short, AI and ML are transforming the industries starting from medicine to retail. Isn't that amazing? Click here for more examples of AI and ML.
It should be clear now how incredible AI and ML are and what prospects are open for app development. How to build an AI? How to build an ML? These questions are easier to answer today. And contrary to most people’s views, it does not cost a fortune to build an AI or ML app.
Today, a lot of companies have already adopted AI in their strategy for a better mobile experience. Personalization, feedback generation, automation, and customer satisfaction are just a few benefits AI and ML can provide to the new mobile app industry.