Artificial Intelligence
The Basics of Machine Learning with a Focus on Neural Networks
Artificial intelligence has become a common phrase in everyday life. It is used in various settings, from education and computer science to horror and sci-fi movies. Over the years, AI has become more and more prominent in everyday life. AI has become incredibly prominent in places people might not be aware of. Alternatively, many know that AI is in voice and image recognition programs, virtual assistants (Amazon Alexa, Siri, etc.), and ChatGPT(a popular chatbot), with the increasing popularity and use of artificial intelligence worldwide. Everyone must know and understand the basics of how AI works.
Artificial intelligence is “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” In other words, making a machine works like a human brain. There are various stages of AI and its proximity to human intelligence, multiple of which are purely theoretical. Some of these stages are depicted in movies as very complicated, scary, evil things, but, in reality, it’s not as complex as it seems.
AI machines must be equipped with knowledge and experience, which can be done in two ways. The first way is a machine can be programmed with instructions similar to a recipe or a car manual. The second way is creating programs that the machine can use to learn from data. This second method is machine learning and will be discussed later in the article.
Within the category of AI, there is machine learning, which is one way that AI is trained. Furthermore, there are neural networks within machine learning, a specific type of machine learning.
What is Machine Learning?
Machine learning is a subcategory of AI that uses data and algorithms to predict solutions to challenges, learn, and adapt to situations. Its primary purpose is to replicate how humans think about something. The way it functions is that the AI is given data stored and compared with other parts of that data or different data. It is constantly optimizing itself and adjusting until a threshold of accuracy has been met. This can be done in many ways depending on the task you are trying to give the AI and how much data you have available.
The common category of machine learning that people use is Supervised machine learning. This is where developers feed the AI accurate examples of something and match it with its label. Then, the developers provide multiple properly descriptive representations of a sure thing. This helps the AI take all the examples of a properly labeled thing and match it with what is similar to the data it was fed. This is a lot more accurate because the AI knows what it wants.
The opposite would be unsupervised machine learning, where the machine tries to find patterns or trends that people aren’t explicitly looking for. This is where the AI is figuring out all the data and trying to find consistency wherever it can and goes off of that. This is not regulated with previously fed data that is accurate. The AI is making its accuracy. This type of machine learning isn’t used as much because there is much more room for error, especially with the AI trying to find patterns in something that doesn’t necessarily have a pattern.
The last primary machine learning type is reinforcement machine learning, which is just trial and error. The AI has a goal it has to reach but doesn’t know how to achieve it. So, the AI keeps failing and failing and making changes to what it’s doing until it comes to the end goal. This could take any number of times as the AI is sorting through options but doesn’t know exactly what it is sorting through. The AI knows it wants to reach the end goal. This type of learning can help in a lot of different ways. It helps us automate many other processes worldwide, making everything a lot less manual, and it can keep running 24/7.
Machine learning is the brains behind AI. It is what makes it find its patterns and constantly changes its algorithms. Machine learning is how a computer system develops its intelligence, continually evolving to do its best job at its tasks.
The Challenges of Machine Learning
What it can do is very limited when implementing this type of learning. Companies have only recently started using machine learning to its fullest, but its fullest is only what it can learn from our data. The primary issue is that the data given to the machine is different from what it needs. The system receives poor data, which doesn’t tell the machine anything. This will provide unwanted outputs because the machine doesn’t know what pattern it’s trying to look for. This will make the machine take a while to understand what it is being instructed to do. If there are too many instructions, there is too much data, so the machine can’t accurately pinpoint what it’s supposed to do. This causes the machine to take a long time to sort through all the different data types. This is called overfitting and is like trying to fit in oversized jeans.
This is the same and vice versa. If you don’t feed the AI enough data, it won’t output as much as you want. This is because there is insufficient data to make a complex enough algorithm to produce an intelligent response. It will only give you as much output as there is input. The machine will learn but can only know if given a foundation to start. That foundation must be enough to branch out and expand like a human. This process requires more data from humans than just feeding it data.
A challenge that might come about after letting the machine learn is that it starts to believe something incorrectly. Since it is a very new technology, we will have to hold its hand a lot as it processes data because it will sometimes be wrong, especially if there is biased data or garbage data that only adds to the confusion. Another confusing thing about machine learning is that after we feed it data, the machine’s output may or may not be correct. If the AI is outputting a bunch of data, there is only so much we can ensure proper data. Especially if we aren’t sure how the AI came to its conclusions, a lack of explain-ability is also found in machine learning algorithms, which reduce the credibility of the algorithms.
Maintaining a brain with a world of different data is very time-consuming. It is a constant, lengthy, expensive, resource-consuming process for the developers. And that’s only if they have customer interest in their product. It is rare to see a lot of commercial uses of machine learning, but they are becoming more popular as more funding and work goes into it, such as Tesla’s self-driving cars. It still makes plenty of mistakes, but it’s getting upgraded and implemented more and more.
How we benefit from using Machine Learning
Though still semi-new, Machine learning still serves a lot of uses in the world. With proper data given, AI is very good at matching patterns that can be used differently. It is unique because it can be autonomous and constantly running. It will continuously improve and learn, and it can handle and process more data more efficiently and effectively and even use decision trees to take action on the information.
It will recognize patterns and features that humans may overlook because machines are often more accurate than humans, given the proper circumstances. It’s so good at identifying patterns that it can make predictions from the past. This can be used to predict the future in many instances. It stores a lot of data and always makes use of it. If you have facial recognition on your phone, machine data learns your face and uses it as a security feature. It’s a constantly growing and changing brain that has a great deal of potential.
What are neural networks, and how do they work?
Neural networks are a subcategory of artificial intelligence and machine learning. It was inspired by the structure of the human brain and how neurons send signals to one another. It is composed of different layers of nodes. A node is the name for an artificial neuron, and many are used in neural networks.
There are three sections of nodes in neural networks: the input layer, the hidden layers, which are often multiple layers, and the output layer. Nodes are composed of various parts: input data, weights, threshold, and output data. The input data is the data received by a node, and it is the data that gets a weight. A weight is a number assigned to data to help determine the importance of that data. This means that the larger the weight, the more impact data has on the output.
After all the data has been assigned weights, all the weights are added. After the final sum of the weights of the input data is found, that sum is put through the threshold. If the sum of the weights is greater than the threshold, the data becomes the output data of the node. This causes the node to be activated and the data to be passed onto the next node. If the sum of the weights is less than the threshold, the data is not passed on to the next node. When the data is passed onto the next node and not to any other node, it is called a feedforward network. Almost all neural networks are feedforward networks.
Current uses and examples of neural networks
One typical example of neural network technology is Amazon Alexa, Google Echo, and other digital assistants. Most people have encountered one of these devices at least once. Neural networks are explicitly used in the speech recognition aspect of these devices. Neural networks can analyze human speech despite varying speech patterns, pitch, tone, language, and accent.
Another instance of where neural networks are used in technology is in Google’s search engine. Neural networks help google understand what someone is searching for better. Google uses neural networks to compare the words in the search bar to what those words are in Google. This is used to create a better search result closer to what the searcher is trying to find. These are only two examples of the many ways and places that neural networks are used in everyday technology.
Takeaway
Machine learning and neural networks have significantly impacted AI in our society. They are both interconnected and affect each other greatly. Moreover, they both have largely shaped AI into what it is today and will continue to shape it in the future. AI is used in everyday life, so it is essential to understand how it works and where it is used. Understanding how AI works and how it is incorporated into our daily lives is vital to positively using AI.
Additionally, having this knowledge helps one understand that AI has pros and cons. Artificial intelligence will continue to develop and grow more prominent in society. People must stay informed about AI and its impact and usage.
