The terms such as ‘Deep Learning’, ‘Machine Learning’ and ‘Artificial Intelligence’, are all around us and sometimes used interchangeably. However, there are several differences among them. The hype nowadays is all about ‘Machine Learning’ and ‘Artificial Intelligence’ so it is not uncommon that the differences between them are neglect. For example, the terms ‘Artificial Intelligence’ and ‘Machine Learning’ are used just about everywhere algorithms are used to analyze any data. Therefore, before we delve into the definition and comparison of AI, Machine Learning and Deep Learning, we need to go to the basics and understand the concept of algorithm, which is the core of all these terms.
Understanding the Role of Algorithms
Algorithms are a series of rules or steps which need to be followed while solving problems. The algorithms in Machine Learning are designed to take an input of data and analyze it in order to find a trend or an answer. The analysis can have a wide range of complexity starting from very simple. Algorithms are developed to complete the analysis of the data and deliver the results with as much efficiency as possible. An algorithm cannot be considered good if it takes a longer time to analyze the data than a human would, or if the results provided by it are incorrect.
Thus, algorithms are developed and trained to classify the data and process it. Depending on the quality of the training put into the algorithm, the accuracy and efficiency of the algorithm are measured. However, though Deep Learning, Machine Learning, and AI, all use algorithms to analyze data, all the algorithms used in the analysis of data is not always a part of them.
All Learnings are Algorithms, All Algorithms are Not Learnings!
Sadly, nowadays, these terms are used as buzzwords when people mean to refer to algorithms which are used for analyzing data and making a prediction based on it. What people fail to understand is that Machine Learning or Deep Learning is not simply limited to using an algorithm in order to predict a future outcome, but also includes the using of the prediction to improve itself and its future predictions. An algorithm needs to have both these properties before it can be declared to be Deep Learning or Machine Learning.
Looking Into The Terms
- Machine Learning is a subset of Artificial Intelligence, which makes the latter a broader concept. It refers to the computers mimicking the cognitive functions in humans. Machines are said to be carrying out tasks in an ‘intelligent’ manner when they function based on algorithms.
- But for it to be called Machine Learning, it has to, in addition to following algorithms, be able to receive sample data and learn from it with little to no human assistance. They even have the ability to alter the algorithms based on their learnings.
- Deep Learning is the subset of Machine Learning, which means it includes all the properties of the Machine Learning and takes it one step further. It analyses the data in multiple, deep layers of neural networks, which is how it got its name. The layers are a part of a nested hierarchy or decision trees, wherein the result of analyzing one question often leads to multiple layers to analyze related questions.
Deep learning networks cannot simply be trained. They require huge quantities of data to analyze, in order to function. What is interesting is that Deep Learning networks can carry out several tasks without any human assistance and need not even be programmed to define the task. For example, Deep Learning learned to play video games in a matter of a few hours, without being specifically programmed for it. It was simply given complete control of the game while playing and it learned the rules on its own and figuring out hacks to win. In a couple of hours, it outperformed the professional players and after a few more, it was playing at a level unachievable by humans.
What are Neural Networks?
The independent learning of Deep Learning to achieve cognitive skills similar to humans is possible partly by using Neural Networks. Neural Networks are a set of algorithms which are based on the model of the human brain. Similar to the brain recognizing patterns and helping people to classify and categorize data. Neural Networks function the same way for computers.
The Scope of the Learning Algorithms
There are several companies which focus on Deep learning and Machine Learning to carry out several tasks such as coloring black and white images, real-time translation, bringing sound to silent movies, playing video games, and a plethora of other activities.
The huge advancement of Deep Learning and Machine Learning in the last decade could only be possible because of the huge increase in the amount of data available for them to analyze. Without significant data, Deep Learning or Machine Learning would not be able to perform any such tasks. On the other hand, It must also be ensured that the data they receive is legitimate and of high quality.