Artificial Intelligence and machine learning are two super close terms we hear every now and then. Yes, we agree that both of these sound similar and related to each other in several cases. Yet there are significant differences between them. This blog today will find out and analyze the differences on a critical note. Read it carefully to be able to identify which aspects make AI and ML different from each other.
What exactly Artificial Intelligence is?
A complete guide to Artificial Intelligence is here. Starting from definition to examples, we have jotted down everything about it. Take a look.
Definition of Artificial Intelligence
First, let’s kickstart this guide with a neat overview of Artificial Intelligence before you learn artificial intelligence & machine learning differences. AI is a specialized field in computer science which enables an entire computer system to copy human intelligence. To be very specific, the term means “ Human-made Thinking power.”
Types of Artificial Intelligence
When it comes to the types of Artificial Intelligence, it’s divided into 4 broad categories:
Reactive AI – This is the most fundamental type of AI. It’s programmed to release a predictable output on the basis of the input it receives.
Limited Memory AI – Next comes Limited Memory AI, which learns and memorizes from the past and develops experimental knowledge. The Limited Memory AI observes historical data or actions combined with pre-programmed information doing complex tasks.
Theory Of Mind AI – With Theory Of Mind AI, the computer system acquires genuine decision-making abilities similar to humans. The best part of this type is it understands behaviour as well as emotions.
Self-Aware AI – This is the most advanced AI type where machines become aware of self-emotions in addition to that of the surroundings. A level of consciousness and intelligence play here powerfully. But if you are thinking about building bots without AI, that’s not possible.
Examples of Artificial Intelligence
Want some prominent examples of Artificial Intelligence. We have brought here some of the real-life ones in the bullet points below:
The Recommendation Engine Of Netflix – An example of Reactive AI
Autonomous Vehicles – An example of Limited Memory AI
Autonomous cars are able to interpret drivers and pedestrians mental states to predict their behaviour – An Example of Theory Of Mind AI.
The Robot made by a group of people from Columbia University was able to create a self-stimulation of ownself without any prior knowledge – Self-Aware AI.
Some other popular examples of Artificial Intelligence we come across in our day-to-day lives include Conversational Bots, Alexa, Siri, Cortana, etc. Moreover, the agents of AI are Simple Reflex Agent, Model-based reflex agent, Goal-based agent, Utility-based agent, and Learning agent.
The way AI is spreading across the planet like a spider web, it’s quite evident that AI is the future of the world.
What is Actually Machine Learning?
After AI, here comes a description of Machine Learning. Read the sections below to know in detail about it.
Definition of Machine Learning
First of all, ML is nothing but a small part of Artificial Intelligence. It makes the computer system capable of learning and memorizing past experiences or data. Nonetheless, it’s not perfectly programmed.
Types of Machine Learning
There are three broad categories of Machine learning. We have shed lights in each of them below:
Reinforcement Learning – Reinforcement machine learning takes into account the inspiration from how human beings in their daily life learn from data. The algorithm in it keeps scaling itself up by learning from new situations through a trial-and-error method.
Supervised Learning – Supervised learning is the most basic type of ML where the algorithm learns from accurately labelled data. If used in the right situation, supervised learning is extremely powerful.
Unsupervised Learning – Do you know what’s the greatest advantage of working with unlabeled data? It means that to make a given dataset machine-readable, there is no requirement of human labor.
Examples of Machine Learning
Below are laid out some real-life examples of machine learning to give you better clarity. Take a look:
Image Recognition – Image recognition is a widely popular real-world example of machine learning. For instance, tagging a person on social media by assigning his name to his photographed face.
Speech Recognition – Speech recognition simply refers to the “text to speech” system like voice dialing, voice search, etc.
Statistical Arbitrage – Analyzing massive datasets, algorithmic trading evaluating a market microstructure, identifying real-time arbitrage opportunities.
What are the differences between Artificial Intelligence and Machine Learning?
Most common people use AI and ML as synonymous and are unaware of the differences. It can be said that Artificial Intelligence is a wide sector consisting of a plethora of concepts. However, ML is a very small part but heavily unlike the former.
Below we have presented the key differences between Artificial Intelligence and Machine Learning:
Definition – AI enables a machine to mimic human behaviour. On the other hand, Machine Learning is a branch of AI allowing machines to remember past data and learn from it.
Objective – The objective of AI is to make smart computer systems just like humans for solving complex problems. Oppositely, the core objective of ML is making the machines learn from the data to get authentic output.
Classification – The 4 broad types of AI are reactive AI, Limited Memory AI, Self-Aware AI, and theory of mind AI. Furthermore, we train machines to perform a specific task and generate accurate results.
Example – Examples of AI applications include chatbots, Siri, Alexa, Netflix Recommendations, Autonomous cars, etc. The instances of ML applications are Google Search Algorithms, Facebook Auto Tagging Suggestions, etc.
Classification – The 4 broad types of AI are reactive AI, Limited Memory AI, Self-Aware AI, and theory of mind AI. Oppositely, the 3 principal types of ML are Supervised learning, Unsupervised learning, and reinforcement learning.
The Ending Notes
That’s all about the differences between Artificial Intelligence and Machine Learning. Nevertheless, both of them are interlinked and are counterparts to each other in spite of having differences. Understanding these dissimilarities is crucial if you are from any form of tech domain. Corporates are now gradually aligning AI in their operations, resulting in exponential developments. As a result, AI, including ML, is a lucrative career option these days.
Do you have any viewpoints or questions to share with us? Leave them in the comment area below; we will be happy to hear your out.
Author’s Bio – Alisha Jones is an online entrepreneur by profession and a passionate blogger by heart. She is on a mission to help digital businesses grow online. She shares her journey, insights, and experiences at Online Health Media & Follow The Fashion. If you are an entrepreneur, digital marketing professional, or simply an info-holic, then this blog is for you.
