Humans think in a complex way due to their intelligence. As the years have gone by they evolved and human intelligence then came forth a conceptual idea of describing a human’s way of thinking as a symbolic system into the computer system. With time computers are utilized to perform tasks in a more optimized way compared to humans, in particular, the repetitive task category, this is so due to humans consuming more energy and resources than said machines. This is much more relevant in this day and age as computers continue to play a more important role in our daily lives as the increased dependence of humans on the computer's capabilities.
Artificial intelligence in short AI is major technology advancement, making computer systems capable of the human's thinking conceptualize. Many subsets of AI technology began to appear due to its increasing complexity and one of them is Machine Learning, its main capability is to study the user behavior through their inputs. At present, most of the powerful solutions and tools of mobile app development rely on these two technologies, as they open up more and more possibilities.
Both Artificial intelligence and Machine Learning have already been inseparable and fully integrated into technologies for building intelligent and highly user-centric apps. But they come with differences that are imported to be noted down.
AI ultimate represents the larger spectrum of the broader technology fields which enables computer systems such as programs, data, apps to use the concept of human thinking to solve various problems. As such there are multiple subsets of AI ranging from technologies like Machine Learning to Natural Language assistants on our mobile devices. Machine learning is part of the whole known as AI technology, it gathers a database on the user behavior which is presented in a complex algorithm that is compiled through historical data, this enables an accurate prediction of possible outcomes without being explicitly programmed to do so.
The computer vision, in the beginning, is now turned into a prototype camera with the usage of ML algorithms. This helps the app find faces using the camera and allows it to add ears, dogs, and other filters. The filter uses AI to detect and add said filters through your face and movement will not disrupt it.
Tinder is a dating app. By using the ML algorithms finding an accurate match according to the prerequisites the user has entered is made possible. With more and more users it will learn what appeals more to the user and help the user prioritize preferred choices.
The integration of AI and ML technologies has been implemented by multiple mobile apps, they utilize intelligent automation of a variety of tasks. Chatbots are known to be fast in customer support and services, this is so due to them being based on AI and ML. The chatbot is able to gather data about the user through conversation and interaction thus giving the best possible output for better interaction. As more and more businesses realize its potential in being able to provide excellent proactive customer service more and more is being implemented in their system.
AI and ML collect data through algorithms to identify consumers’ positive and negative behaviors towards certain functionalities of a product thus able to give data-driven insights into what is more preferent and what is not thus able to give forth more effective solutions in the future.
AI and ML open up new opportunities for innovation, promoting possible apps with services that provide user interaction such as Chatbot. In addition, personalized user experience is made possible using the algorithm data collected.