AI in Mobile Apps
Artificial Intelligence is the biggest thing in the world right now, according to Statista by 2025, AI would have a market cap of 85 billion US dollar.
What is Artificial Intelligence?
Artificial Intelligence is intelligence demonstrated by the machines. Any task that requires human intelligence for the completion which can be completed by machines can be categorised as AI.
AI has been already used in several critical fields like credit card fraud detection, advanced problem-solving in physics etc. There are two types of AI, Narrow AI and General AI.
We see narrow AI around us every day; it can do specific tasks without the need for human intelligence. Unlike Narrow AI, General AI is the future; we usually see GAI in movies where it can do multiple tasks and think on its own.
Is Machine learning and AI the same?
No, Machine learning is one of the subfields of AI. It can recognise hidden patterns from the data without explicitly programmed where to look.
Other subfields of AI are Neural Network, Deep learning, Computer vision, Natural language processing.
But how is any of this related to the Mobile Apps? What are the technologies used in AI?
Let’s answer these questions now!
The most popular AI technologies that can be used in mobile apps are
· Speech Recognition Technology
This is one of the most popular AI technologies that is already used by several apps. The personal AI assistant bots such as Google Assistant, Apple Siri or Amazon Alexa primarily works with speech Recognition Technology. The Speech Recognition technology is slowly becoming a must-have feature in all the apps due to its ease of use.
Chatbots are again used by several web and mobile applications. It can interact with people and educate them about the process or help them get any specific information. Virtual assistants like Google Assistant, Apple Siri, Amazon Alexa are advanced chatbots in the industry that can perform a wide range of tasks.
· Natural Language Processing (NLP)
NLP concerns interactions between the computer and human language on how to process a huge amount of natural language data. Some of the examples are Optical Character Recognition, Speech Recognition, Text-to-Speech Recognition and Word segmentation.
Biometrics can help computers or the software to understand and analyse human physical behaviour. Most common applications are in gesture control, voice and sensory recognition. It can be used in market research; organisations like 3VR, Affectiva, Agnitio, FaceFirst etc. used this technology.
· Emotion Recognition
Another new technology that can help machines to read human emotion using advanced image processing and audio data. The subtle voice changes intonation are captured, and the Machine can analyse the data.
· Image Recognition
Image recognition is already widely used by a lot of apps. Google uses this technology in a lot of its apps like Google Photos to automatically organise and search for images, Google Lens for live recognition of the pictures. The other applications include detecting license plates, diagnosing diseases, authenticating users etc.
· Machine Learning Platforms
Suppose an app needs to implement forecasting and classification. In that case, there are various ML platforms providers from Amazon, Google, Microsoft etc. These tools, combined with AI developers, can help businesses across industries in several ways.