People use words like Artificial Intelligence and Machine learning in their routine conversations, whereas the last “Artificially intelligent machine” that they came across was a Customer service Chatbot.
It’s not their fault per se; these words are in trend, so they spitball whatever information they can grasp from social media. And that is the reason why most of the time, they end up using these words synonymously.
To get it clear, we have compiled a blog that will draw a line between artificial intelligence and machine learning and list out the similarities and differences that stand between them.
Artificial Intelligence is indeed a vast concept, and Machine learning is just a subset of it. But still, there are some differences that might seem imperceptible but are fascinating to learn.
Let’s start with the basic definition: AI or Artificial intelligence, in simple terms, can be defined as the ability of a computer or a machine to think and perform tasks just as human intelligence.
ML or Machine Learning, on the other hand, can be defined as the ability of a machine to learn from past data without being explicitly designed for the same.
Creating AI aims to build a system that would work by mirroring a human mind; in this human endeavor, we try to create man-made humans. These advanced beings would be not only supremely intelligent but also immortal, and their intelligence could be transferred to a new body(machine).
In ML, the machines are programmed to analyze pre-fed data, which builds up from that. The machine uses structured and semi-structured data to produce an accurate result, or if there are no results, it will help predict the outcome.
AI, as from the definition it can be guessed, has a broader range in terms of its application; it can be used to perform ‘N’ numbers of tasks, as it is a self-learning intelligence, and the algorithm is such that it requires no external factor to learn and complete a task.
On the other hand, ML is created to perform a specific or a few specific functions. It is also self-taught, but the range is comparatively shorter than that of AI, as it circulates its search around the pre-fed, past, or, say, historical data. It might generate results independently, but it will be limited to a specific function.
AI aims at maximizing the reach of intelligence, whereas the primary motto of ML is to focus on accuracy and recognizing patterns.
ML is used by many brands dealing in the service sector; their names are Google, Uber, Netflix, Spotify, and many more; these services obtain the user’s data, and with the help of ML, they try to make a user-friendly or more personalized recommendation engine for the user.
AI is used in functions like Voice assistance or face recognition, which, if we look closely, could be found in our homes in devices like ECHO and Google Homes.
At last, it can be summed up by saying that all Machine learning is artificial intelligence, but not all artificial intelligence is machine learning.