The healthcare industry continues to try to find new Tools for Improving Quality. One of those tools is Artificial Intelligence. In this instance, when I say artificial intelligence, I’m referring to machine learning as well. Artificial intelligence and Machine learning are terminologies that are often used interchangeably. They both have to do with big data and both exist in the same space. They are however not the same thing is they have different processes and applications.
Artificial Intelligence and
Neural Networks as a Tool
for Improving Quality
You’ve definitely heard the term artificial intelligence at some point. Either in a movie, or you’ve seen it in the news. As I already said, artificial intelligence is different from machine learning and not only is it different, it is also a lot more border. Artificial INtelligence can be simply defined is the process where machines carry out tasks based on algorithms, in an intelligent manner.
Machine learning, on the other hand, is a subset of AI and it is when a machine has the ability to receive a set of data, review that data, and learn from itself. In machine learning, the machine changes algorithms as they learn more about the information they are processing.
Artificial intelligence is a broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans. When machines carry out tasks based on algorithms in an “intelligent” manner, that is AI.
The idea of machine learning and artificial intelligence is achieved through the use of neural networks. Neural networks can be defined is a series of algorithms that a modeled to mimic the human brain and function is such.
Think of JARVIS in Iron Man and the Avengers Movies. JARVIS is a neural network. Neural networks can recognize patterns in everything they process and uses this patterns to classify information. When any new information is inputted into a neural network, it tries to categorize it with already existing items and patterns. This is similar to what the human brain does. There are many benefits to having a neural network besides helping to defeat Ultron. A neural network can —
- Extract meaning from complicated data
- Detect trends and identify patterns too complex for humans to notice
- Learn by example
- Speed advantages
Machine Learning as a
Tool for Improving Quality
Machine Learning is basically the idea that systems can learn new behavior without being told explicitly by a programmer what that behavior ought to be. The behavior is expressed in terms of models, which are themselves the result of examining data. In a networking environment, if the goal of a machine is to automate workflows as part of adaptive or predictive operations. This is unlike generalized algorithms which are simply building blocks. Workflows are not ubiquitous.
Deep Learning as a Tool for Improving Quality
Deep learning is considered is a subset of machine learning and is the name implies, it involves going levels deeper in terms of machine learning. Deep learning is often also referred to is deep neural networks. A neural network might have just one layer but in deep learning, a deep neural network will one or two layers. The layers can be seen as a nested hierarchy of related concepts or decision trees.
The answer to one question leads to a set of deeper related questions. Deep learning systems learn from an exposure to millions of data points. In deep learning, the systems are not programmed with the criteria that define terms, instead, they are exposed to a large amount of data and are able to identify the characteristics themselves.
The Algorithm of Quality
I’ve mentioned algorithm several times already and it’s worth taking time to define what it is is it is used frequently in both artificial intelligence, machine learning, and deep learning. In machine learning, algorithms take in data and perform calculations to find an answer. The calculations can be very simple or they can be more on the complex side. Either way, algorithms help deliver the answered in a very efficient manner.
An algorithm system also has to be trained and its efficiency depends on how well it’s trained. Algorithms need to be trained to learn how to classify and process information. Machine learning involves using algorithms but not all use of algorithms can be classified is machine learning. Using an algorithm to predict an outcome of an event is not machine learning. Using the outcome of your prediction to improve future predictions is.
In order to contextualize machine learning algorithms, you have to train it. It is important to have a training system that is consistent across all environments so this way, data can be brought in from different environments and the system would not be overwhelmed and would be able to understand it and aggregate it as part of the entire network’s solution.
So, how do you contextualize an algorithm? In ML-speak, you train it. This is where data comes in. If the thing being trained is common and consistent across all or even many environments, then the data can come from many places and be aggregated as part of the networking solution. But if the behavior is very specifically based on the actual deployment both the devices and the surrounding infrastructure, applications, and tools then the generalized algorithm has to be fed very contextualized data.