Artificial intelligence is no longer just a trope in science fiction novels. It’s actually around us, make our life easier and more streamlined.
And while the complex inner workings of AI are particularly complex, there are two basic functions that are extremely popular in most tech devices these days: Deep Learning and Machine Learning.
You may have heard of one or both. Essentially, they are protocols that allow computer systems to collect and analyze data, and then make informed decisions based on the data.
This is how Instagram and Facebook know what kind of ads to show you or how Spotify can create playlists for users based on the music they listen to frequently.
But it’s not as easy as it sounds, and there are some differences between Deep Learning and Machine Learning. These topics can be quite complicated, but let’s break them down into easy-to-understand concepts.
Although they may seem like similar concepts, there are tangible differences between machine learning and deep learning.
Deep learning has a more stratified approach to data analysis than machine learning, more simplistic data analysis, and a principle of extrapolation.
But before we go too deep into either concept, we want to mention something that can help make sense of both:
Deep learning is machine learning. Kind of.
Deep Learning is essentially an extension of the principles that Machine Learning established during its development. Think of Machine Learning as the first car with a crank starter and no radio, and Deep Learning is today’s standard vehicle with modern technology.
They’re both cars, but one of them clearly has advancements beyond the original.
Believe it or not, the term “machine learning” actually dates back to 1952, starting as a virtual game of checkers that would learn his opponent’s moves the more he played.
Arthur Samuel, a developer at IBM, created the game and implemented a system in which the computer calculated the probability of every possible move it could make, based on previous experience. Thus, the more movements he made, the more precise his strategy could be.
At its most basic level, it is an artificial intelligence protocol that collects specific data and performs a function. Over time, he learns more about the data he has collected and makes better decisions in the functions he performs.
If it still sounds complicated, don’t worry, because it is. But let’s consider some examples to understand:
YouTube uses a algorithm to determine which videos to suggest to its viewers based on the videos they have 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 even more as the person speaks to them. The more voice samples the virtual assistant has, the more flexible they can be with how they recognize them and their questions.
Financial traders can use software that tracks the trends of many different stock options to create recognizable patterns that the trader can use to make predictions in trades.
The mathematical and code side of Machine Learning is quite complex, but its function is rather methodical and systematic. It is not simple in itself, but Machine Learning has its limits compared to Deep Learning.
Think of machine learning as a data analysis tool than what some would consider to be true “artificial intelligence”. It collects information from multiple sources and operates based on that data.
However, it does not add or extend the functions it performs, based on extended database, nor does it search for different forms of data to learn more. This is where the difference between Machine Learning and Deep Learning lies.
But we are getting ahead of ourselves.
As we mentioned, Deep Learning is Machine Learning, just a more extended and developed version.
Deep learning continues to absorb data and works based on what it learns, but it goes beyond machine learning with more layers in its structure of algorithms.
More algorithms means more data resources to tap into and more ways to compute information together to make a decision.
The layered structure of algorithms, or artificial neural networks, was developed on the basis of biological neural networks. Essentially, deep learning doesn’t just stop at the inbound data source provided to it; it retrieves new data streams related to the original source and analyzes each item together.
Here are some examples of deep learning:
- Autonomous cars. The goal of automated or driverless vehicles is that they themselves are able to take into account their environment and make decisions. Whether the light is green or not, are there pedestrians nearby, is there any work that affects the speed limit, is it staying on track?
- Facial recognition. Have you ever wondered how your phone’s Face ID can recognize you, regardless of your hairstyle or sunglasses? Sure, he has his limits, but he’s constantly collecting new information based on accessories, body weight, beard styles, and haircuts in order to keep up with someone’s changing appearance.
In fact, Deep Learning all over the world is constantly collecting new information so that it can make better and more informed decisions. Although this is a subset of Machine Learning, we are starting to see the difference in intensity between the two.
As we pointed out, Deep Learning is essentially an advanced form of Machine Learning, so they share some similarities.
However, if we inspect them side by side, we are using distinct differences to determine which method is best suited for the function we seek to perform.
Machine learning is anything but simple, but compared to deep learning, it could just as easily be.
Especially when you look at the CPU power that each system respectively needs to run.
Because Deep Learning has a more complex system of algorithms and neural connections (not to mention several times more data) than Machine Learning, it requires an extremely robust system.
We’re hearing potentially thousands of cores of processing power, versus machine learning which may only need a few.
This obviously needs to be taken into account when considering your resources. The amount of power that deep learning can bring is immense, but so too is the amount of power required to do so.
Due to the much more complex nature of the algorithms used by Deep Learning compared to Machine Learning, it takes much longer to train the network to recognize the data.
Deep learning can take up to several months to analyze how much data we feed. Seriously, months.
Also, the more layers we put into the network, such as the number of algorithms within its neural network, the longer it takes to process all of this information.
Machine learning is essentially a complex matching system that takes a dataset and compares it to other datasets in order to make a decision, but only at a level relative to the multiple of deep learning.
This means that while machine learning may be more limited, its preparation takes much less time.
Machine learning uses a respectable amount of information to make decisions and can actually work quite successfully even with limited data, but deep learning only gets better as it absorbs more.
The more data a deep learning protocol can absorb, the more educated it becomes. While Machine Learning usually has a cap on how much it can analyze, Deep Learning continues and gets stronger the more it absorbs.
Of course, it comes down to CPU demands and training time, because even as it gets stronger and stronger, the more it takes in, the more power and time it takes to go through everything.
Artificial intelligence is a powerful technological advance and is still in its infancy.
Machine learning has powerful and real applications and is already being used every day in the technology around us.
Deep learning has powerful implications, and while it requires hosting huge systems, it will probably one day be as common as its predecessor.
Both functions have the potential to completely change the way we use technology in the future in medical, automotive, entertainment, online shopping.
For more information on deep learning and machine learning or to increase your site’s search capabilities, visit Yext.
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Yext Inc. published this content on November 18, 2021 and is solely responsible for the information it contains. Distributed by Public, unedited and unmodified, on November 18, 2021 09:42:08 PM UTC.