Machine Learning, AI And Deep Learning
Machine Learning, AI and Deep Learning: Everything is changing – faster and faster. Some people can’t keep up with it. But most people are optimistic about the constant developments. Even if we still haven’t invented the hoverboard from Back to the Future, technological developments are exciting and can be found everywhere. This also applies to digital learning.
While eLearning itself isn’t a new invention, as I’ve written in previous articles, it’s a field that’s constantly evolving. The “hoverboard of eLearning” is probably artificial intelligence, or AI for short. Therefore, this article is intended to deal with the basic knowledge necessary for understanding the current developments.
Brake For Digital Learning
A significant obstacle to eLearning is content development’s high cost (financial and time). According to a Chapman Alliance survey, an hour of eLearning content can take anywhere from 49 to 125 hours to develop. It’s easy to see how the costs add up. Although eLearning saves costs in the long run due to its repeatability, it can still pose a barrier for companies that cannot afford the initial investment. This is where the AI can intervene.
Artificial Intelligence Is Not The Same As Machine Learning
The difference between AI and machine learning is quite confusing. While many large companies use both terms interchangeably, they don’t mean the same thing. It’s related but different.
Artificial intelligence can be defined as computer systems capable of performing tasks that usually require human intelligence. These include, for example, visual perception, speech recognition, and decision-making. But what sets AI apart from machine learning?
Let’s take an example. When a computer collects information about insects, stores it, and has an ever-growing database over time; it’s considered machine learning. If this computer then independently categorizes the insects based on the information collected, you could call it artificial intelligence. This means the laptop used AI to organize the insects found on the information it gathered during machine learning. The AI , therefore, requires previous machine learning.
Machine Learning In Detail
To better understand how artificial intelligence will influence eLearning in the future, we have to delve deeper into the matter. This also requires a deeper understanding of machine learning. It is a system in which a computer can learn without explicit programming. A few years ago, what was then called AI was a set of static parameters cleverly pre-programmed by a developer.
Machine learning can be divided into three categories:
Machines are fed with data in the form of well-described questions and provided with correct solutions. After the training is complete, they will process this data and apply it to anonymous data—the accuracy results from the size of the previously learned data set.
Example: Insect datasets where the machine collects information on each species.
Machines are given specific data (i.e., in a particular category) but are not labeled or described.
Example: data on insects, but no additional information on how to understand this data.
Machines receive blank data, which is evaluated by the computer after processing. This means that the computer is told what output is intended to learn more about the decisions about that output. However, this method requires a lot of data sets to be precise.
Example: a game of chess. The machine has no pre-programmed moves (apart from the game rules), but the result (output) is weighted, so the computer knows if it has won or lost. He can apply the previously made moves (decisions) to the next game if he has won.
Deep Learning And Artificial Neural Networks
As we progress through the development of machine learning in AI, new techniques are being developed to improve effectiveness and constantly move towards genuine AI autonomy. What is meant here is an artificial neural network. Behind this is the idea of deep learning, i.e., the ability to think independently. But more on that later.
First, we need to understand what an artificial neural network even is. Put, it is a replica of the human brain and nervous system. It’s a technique used in machine learning. It consists of a network of neurons, which can be thought of as cells with multiple inputs and only one output each. The threshold value and the weighting can now be set for this artificial neuron. This means it can be specified when and which information from the inputs should be passed on. Several such neurons are then combined into networks.
To put it very simply, a neural network works like a funnel that filters all incoming information and derives a result from it. An example of this is the reverse image search on Google. If a photo of a dog playing with a ball next to a tree in a meadow is uploaded, each pixel is assigned its neuron. Depending on the weighting, the neurons decide whether the data will be passed on or not. That’s how you get the result at the end of the network, cat because all other information has been filtered out.
But only as a slight digression, because what matters to us is that artificial neural networks can be trained. The Weight and Threshold parameters are – remember, reinforced learning – modifiable. The successful image search on Google only works because you have previously fed the artificial neural network with thousands of images and from which it knew whether the result should be a dog, a tree, or a ball.
Deep Learning – Deep Neural Network
On the other hand, deep learning is more complicated as it consists of several hidden layers and creates a far more complex network called a “deep neural network.” This is where AI starts to get very interesting because deep learning is precisely the leap to self-reliance. This means that the computer no longer has to be told what to do, but examples are given to learn how to proceed in other situations—the jump from predefined steps to predefined models. Deep learning has pushed the AI field a long way lately, with incredible results in speech and image recognition.