Computer automation couldn't exist without data science as a field. On the other hand, all those fancy machine learning subroutines would be rather useless if there wasn't at least some way to get them to do practical work. Unfortunately, the interaction between all of these fields makes it pretty difficult to define them.
Chances are that everyone might agree that automation involves controlling a process without constantly having to manually input data into it. Business automation narrows that definition a bit to focus on the kinds of things that managers have to deal with in a real-world scenario. IS department staffers would more than likely also agree that automation couldn't stand without massive developments in machine learning technologies.
Putting these concepts into words, however, has proven more challenging than some experts would have liked to admit.
Deep Learning vs. Machine Learning
One of the biggest buzz phrases these days seems to be deep learning, which has ensured that some people have completely conflated it with machine learning as a whole. The two aren't necessarily the same thing, however. The difference between deep learning and machine learning has traditionally been poorly defined, but it's best to look at deep learning as one discipline in a much larger field. Machine learning is a massive subject that could be broached from multiple angles. Data scientists could utilize either supervised or unsupervised machine learning algorithms and expect a result from them. The individual algorithms themselves, however, may belong to other paradigms, of which deep learning is one.
You might say that all deep learning is machine learning but not all machine learning is deep learning. Truly deep machine algorithms are going to employ neural networks in some way and they're going to analyze data using a strict logical structure that at least somewhat approximates the way in which humans draw conclusions. It's theoretically possible to design a very simple machine learning process that relies simply on recording outputs in a binary tree until a program gets a complete map of a process and can therefore automate it.
While deep learning algorithms can achieve the same goals, they do so with a much greater level of sophistication than most machine learning techniques.
Automating Business Processes Using a Neural Network
Autoencoders, which are algorithms that mirror their inputs into an output space, are perhaps the most common basis for deep learning systems. These shrink the data encoded into the input into a representation that they can then reconstruct later. Unlike simple compression utilizes, this technique is lossless and the initial inputs can be reassembled. That's making it an attractive option for those who want to automate their visualization tools using a focused form of machine learning.
Speech synthesis and robotic control chores are often managed by recurrent neural networks, which utilize time series and sequences of information as inputs to predict the correct next action. In a network designed this way, each composite node consists of an independent input layer, hidden layers and a component output node attached to it. Each of these layers stack next to each other and receives input on a separate basis. Edge prediction becomes possible using this sort of paradigm by parsing inputs and figuring out the odds that they'd match any specific matrix.
Convolutional neural networks that work on two-dimensional images have employed the use of these tactics to find Zip codes and phone numbers even when conventional optical character recognition systems were unable to manage. Currently, this level of coding is incorporating deep learning technology, which itself is a subset of machine learning, in order to achieve an autonomous process. Emerging developments could take that one step further.
Edge Computing with a Neural Paradigm
Data scientists have been designing fully autonomous ocean-going vessels, which are required to think in three dimensions as opposed to two. Ironically, this sort of development isn't all that new, yet it's only just now being applied in this way. As early as 1943, the McCulloch-Pitts concept proposes that a machine learning algorithm could be instantiated that mirror human thought processes in real-time.
Thus far, one of the real challenges to uniting deep learning procedures with business automation has been one of hardware, but the fact that any of these algorithms could be deployed using a modern high-level programming language makes them remarkably portable. As representatives of the semiconductor industry roll out additional advancements in the field, it's highly likely that these learning techniques could simply be ported to increasingly strong hardware in the future. Operating system-neutral implementations will help to further improve the portability of these software algorithms.
While deep learning may only be one small subset of a greater machine learning field, it's certainly one that's going to become increasingly important at the industry continues to mature.
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