Artificial Intelligence is no longer just a trope in science fiction novels. It's actually around us, making our lives easier and more streamlined.
And while the intricate inner workings of AI are notably complex, there are two basic functions that are extremely popular in most technological devices these days: Deep Learning and Machine Learning.
You may have heard of one or both of these. Essentially, they are protocols that allow computer systems to pick up and analyze data and then make educated decisions based on the data.
This is how Instagram and Facebook know what kind of advertisements to show you or how Spotify can build playlists for users based on the music that they listen to frequently.
But it isn't as simple as it seems, and there are differences between Deep Learning and Machine Learning. These topics can be fairly complicated, but let's break them down into easy-to-understand concepts.
The Basics
While they may seem like similar concepts, Machine Learning and Deep Learning do have tangible differences.
Deep Learning has a more layered approach to data analysis than Machine Learning, a more simplistic data parsing, and an extrapolation principle.
But before we get too far down the road on either concept, we want to mention something that may help make sense of the two:
Deep Learning is Machine Learning. Sort of.
Deep Learning is essentially an expansion of the principles that Machine Learning established when it was developed. Consider Machine Learning as the first car with a hand crank starter and no radio, and Deep Learning is today's standard vehicle with modern technology.
They're both cars, but obviously one has advancements beyond the original.
Machine Learning
Believe it or not, the term "Machine Learning" actually goes all the way back to 1952, starting as a virtual game of checkers that would learn its opponent's moves the longer it played.
Arthur Samuel, a developer at IBM, was creating the game and implemented a system in which the computer calculated the probability of each possible move it could make, based on previous experience. So the more moves it made, the more accurate its strategy could be.
At its most basic level, it's an Artificial Intelligence protocol that takes in specific data and performs a function. Over time, it learns more about the data it has collected and makes better decisions in the functions it makes.
If that still sounds complicated, don't worry because it is. But consider a few examples to wrap your head around it:
YouTube uses an algorithm to determine what videos to suggest to its viewers based on the videos they've already clicked on, combined with other viewers who have watched similar videos.
Virtual Assistants like Siri or Cortana are able to recognize the voice and questions of their users the more that the person speaks to them. The more samples the virtual assistant has of the voice, the more flexible it can be with how it recognizes them and their questions.
Finance Traders may use software that tracks trends of many different stock options to create recognizable patterns that the trader can use to make predictions in trades.
The math and code side of Machine Learning is quite complex, but its function is rather methodical and systematic. It isn't simple in and of itself, but Machine Learning has its limitations compared to Deep Learning.
Consider Machine Learning more of a data analysis tool than what some would consider a true "artificial intelligence." It collects information from multiple sources and operates based on that data.
However, it does not add to or expand the functions that it does, based on extended data, nor does it seek different data forms to learn more. Here lies the difference between Machine Learning and Deep Learning.
But we're getting ahead of ourselves.
Deep Learning
As we mentioned, Deep Learning is Machine Learning, just a more expansive and developed version.
Deep Learning still takes in data and proceeds to function based on what it learns, but it takes it further than Machine Learning with more layers within its structure of algorithms.
More algorithms mean more data resources to pull from and more ways to compute the information together to come to a decision.
The layered structure of algorithms, or artificial neural networks, was developed based on biological neural networks. Essentially, Deep Learning doesn't just stop at the incoming source of data given to it; it picks up new streams of data related to the original source and analyzes each piece together.
A few examples of deep learning would be:
Self-Driving Cars. The goal with automated, or driverless, vehicles is that they themselves will be able to take in their surroundings and make decisions. Whether or not the light is green, are there pedestrians nearby, is there construction that affects the speed limit, is it staying in the proper lane?
Facial Recognition. Have you ever wondered how your phone's Face ID can recognize you regardless of your haircut or sunglasses? Of course, it has its limitations, but it's constantly taking in new information based on accessories, body weight, beard styles, and haircuts in order to keep up with someone's regularly changing appearance.
In fact, Deep Learning everywhere is constantly taking in new information in order to be able to make better and more educated decisions. While it is a subset of Machine Learning, we start to see the difference in intensity between the two.
Major Differences
As we've highlighted, Deep Learning essentially is an advanced form of Machine Learning, so they do share similarities.
However, if we inspect them side by side, we use distinct differences to determine which method might best suit the function that we're looking to fulfill.
Computation Requirements
Machine Learning is anything but simple, but when compared to Deep Learning, it might as well be.
Particularly when you look at how much CPU power each system needs respectively to work.
Because Deep Learning has a more complex system of algorithms and neural connections (not to mention multiple times more data) than Machine Learning, it requires an extremely heavy-duty system.
We mean potentially thousands of cores of processing power, compared to Machine Learning which may only need a few.
This obviously has to be taken into mind when considering your resources. The amount of power that Deep Learning can bring to the table is immense, but so is the amount of power that it requires to do so.
Training Time
Because of the vastly more complex nature of the algorithms Deep Learning uses compared to Machine Learning, it requires much more time to train the network to recognize data.
Deep Learning can take up to several months to analyze the amount of data we feed into. Seriously, months.
As well, the more layers that we introduce to the network, as in the number of algorithms within its neural network, the longer it takes to process all of that information.
Machine Learning is essentially a complex matching system that takes in a set of data and compares it to other sets of data in order to make a decision, but only on one level compared to Deep Learnings multiple.
This means that while Machine Learning may be more limited, it takes much less time to prepare.
Data Points
Machine Learning takes in a respectable amount of information in order to make decisions and can actually function fairly successfully even with limited data, but Deep Learning only gets better the more that it takes in.
The more data that a Deep Learning protocol can absorb, the more educated it becomes. While Machine Learning typically has a ceiling of how much it can analyze, Deep Learning continues and becomes stronger the more that it takes in.
Of course, this comes back to the CPU requirements and Training Time because while it gets stronger and stronger, the more that it takes in, the more power and time it requires to go through all of it.
In Conclusion
Artificial Intelligence is a powerful advancement in technology and is realistically still in its infancy stage.
Machine Learning has powerful, real-world applications and is already being used every day in technology all around us.
Deep Learning has powerful implications, and though it requires immense systems to be housed will likely one day be just as common as its predecessor.
Both functions have the potential to completely change the way we use technology in the future in the medical field, the automotive industry, entertainment, online shopping- Just about every operation in life may have artificial intelligence in one form or another involved one day.
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