Artificial intelligence and Machine Learning are two very hot buzzwords right now and they have become an integral part of our daily routine. But does that mean that we understand these technologies well? Do we know the difference between them? Are we clear about what they stand for individually? Raise your hand if you have been struggling with these questions. Okay, now bring down your hand, buddy, we can’t see it!
Both these technologies are often used interchangeably by people and the perception of them being the same is an epidemic. So, this piece will help you understand these technologies better and it will discuss a few points on the basis of which you can differentiate between them.
In short, the best statement to clear the air between the technologies is:
AI is a broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
Machine Learning is an application of AI based around the idea that we should be able to give machines access to data and let them learn for themselves.
What is Artificial Intelligence (AI)?
Artificial intelligence is the science of making computers behave in ways that, until recently, required human intelligence to function. AI today is symbolized with Human-AI interaction gadgets.
Artificial Intelligence can be interpreted to mean incorporating human intelligence to machines.
Artificial intelligence is a broad concept that includes everything from Good Old-Fashioned AI (GOFAI) to futuristic technologies such as deep learning. Whenever a machine completes a task based on a set of rules that solve problems, such an “intelligent” behavior is what is called AI.
A great application of Artificial Intelligence is Apple’s personal assistant – Siri. Siri is the friendly voice-activated computer that interacts with the user on a daily basis. It is your virtual friend that helps you find information, gives directions, adds events to calendars, helps send messages, etc.
What is machine learning?
Machine learning is the study of algorithms that allow computer programs to improve automatically through learning and experience. Machine learning works with small to large data-sets, examines and compares the data to find common patterns and explore nuances.
Machine learning can be interpreted to mean empowering computer systems with the ability to “learn”.
Machine Learning is a subset of AI, in fact, it is a technique for realizing AI.
It is a method of training algorithms such that they can learn how to make decisions. For instance, if you provide a machine learning model with songs that you like, along with their audio statistics, it can automate and generate a recommender system to give you suggestions for the music that you’ll enjoy in future. This is what companies like Netflix and Spotify practice to improve their user experience.
The key differences between AI and ML:
|Artificial Intelligence||Machine Learning|
|It works like a computer program that does smart work.||A simple concept where a machine takes data and learns from it.|
|The goal is to simulate natural intelligence and solve a complex problem.||The goal is to learn from data on a certain task in order to maximize the performance of a machine on this task.|
|Develops a system to mimic human behavior to respond to certain circumstances.||Creates self-learning algorithms.|
|The aim is to increase success and not accuracy.||The aim is to increase accuracy, not success.|
A Case Of Branding?
Artificial Intelligence and ML certainly has a lot to offer to the industry. With a promise of automating mundane tasks as well as offering creative insight, industries in every sector are reaping the benefits. AI and ML are being sold consistently, and lucratively.
Machine Learning has been seized as an opportunity by marketers. After AI has been around for so long, it is possible that it has started to be seen as something “old hat” even before achieving its true potential. And Machine Learning certainly gives marketers something new and shiny to work with.
The fact that we will eventually develop human-like AI has been treated inevitably by technologists. Certainly, today we are closer than ever and moving towards the goal rapidly.