Although often times used interchangeably, you might actually artificial intelligence and machine learning is not exactly the same despite the prevailing trends to merge the two.
The reality might seem slightly blur for a novice, but if you want to understand a true difference between the terms, first off it’s essential to address the atom of the molecule and define the smallest part of the groups.
Do you see where we’re going with this? And you’d be right to guess that we mean to say algorithm.
Algorithm entitles an assembly of certain rules, steps for one to take when dealing with a particular problem. Now attention: in machine learning these algorithms are like conductors that take charge of the data sets and computations, orchestra to produce solutions to the problems at hand, tunes that, combined together, become music.
As for the conductor, it takes years of experience to become able to bring musicians in sync and do it well, same is true for algorithms that first need to be trained in order to perform calculations efficiently and with the right answer at the end, right?
Bear with us, because we’re going to show you how to distinguish between artificial intelligence and machine learning and what role algorithm plays to help solve the mystery.
First off, AI is kind of a parallel reality where machines (computers) are like copycats trained to be a little more human and copy the behavior of latter. So when machines work on challenges based on algorithms in an “intelligent” way, this is what we know as AI. In broader terms, artificial intelligence is an art that focuses on developing “smart machines” that can operate and reflect on things similar to humans. You might wonder what those reactions could be? These might be speech recognition, planning, learning, and solving problems.
At the same time, machine learning development refers to a part of AI that deals with the ability of machines to receive a set of data and learn with further changes made to initial algorithms taking into account a broader knowledge obtained from the information under processing.
So layer upon layer, the input information is getting refined with algorithms becoming smarter with each progression.
Then there’s one more term worth introduction and it is neural networks. Think about it this way: neural networks are like a web of interlinked algorithms that resemble the human brain structure.
Similar to the brain’s natural ability to spot and acknowledge patterns and thus, assist the humans to cluster the whole bunch of information from the outer world, neural networks perform the same functions when it comes to computers.
Here’s another way to think about it. On the one hand, our brain is always on the look to derive the sense from the information it is digesting. In order to achieve this goal, it needs to put tags and assign data types to sort out the inputs. On the other hand, when we end up in an unfamiliar situation, our immediate reaction would be to try and compare it to something that is already known to us so we can more or less understand what we deal with here. That’s the job of neural networks when it comes to computers stepping onto terra incognita.
The bottom line is neural network is a kind of system the main function of which is to classify the inputs. For instance, if there’s a set of pictures with distinct categories of objects on them, the neural network would assign each picture a category. Let’s say the pictures with Ford, BMW and Honda etc would go to “cars” category and lion, dog, and a horse would be tagged as “animals”. Such systems use the information they have access to assign categories and determine the affiliations to certain classes of objects.
In a nutshell, you can view artificial Intelligence as a wider discipline and idea of computers being able to work on different tasks “intelligently”. What does intelligently mean here? It is about a machine being able not only to process the initial inputs but after some time “growing their expertise” and lowering the chances for mistakes taking into account refined information that gets cleared with each learning session that follows. The important thing to note of here is to “feed” the machines high-caliber data in order to obtain quality results as an output.
To sum it up, machine learning is a part of AI and a very niche component of it. The basic idea of machine learning is rooted in the concept of creating situations where machines can not only process data but also learn on their own without human tweaking being a part of the process.