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AI vs Machine Learning vs Deep Learning – Know the difference
And what actually is AI or machine learning, or deep learning? In this article, I want to zoom in on the subject, and for a moment, I would like to play fortune teller and look into the future of these technologies.
- What is artificial intelligence, and how it works?
- Machine learning is a branch of artificial intelligence.
- What is deep learning?
- What are the differences, and what brings the future for those technologies?
Before we start, there is one thing that must be established: a set of rules that have to be followed when solving problems is what we call an algorithm. In machine learning, those algorithms are used to perform calculations to find the answer.
What is AI (Artificial intelligence)
First of all, we need to establish that machine learning and artificial intelligence are not the same things, even though those notions are often used interchangeably. Artificial intelligence has a much broader scope than machine learning. It is the science together with the engineering of making computers behave like humans. It is an incorporation of human intelligence into machines. We can speak of artificial intelligence when a machine completes a task based on algorithms by making ‘intelligent’ choices. AI means to replicate the way the human brain works and things.
Generally, AI has three different levels:
- Narrow AI when the machine can perform a specific task better than a human
- General AI when the machine can perform any task with similar accuracy to a human
- Active AI when the machine beats a human in many tasks
Real-life examples of artificial intelligence are Google Maps (real-time traffic information), spam filters in email inboxes, smart cars or music streaming services.
What is Machine Learning
Machine learning is a branch of artificial intelligence. It is the study of computer algorithms that help computer programs improve / ‘learn’ through experience. ML is involved in creating algorithms that can modify themselves without any human intervention and therefore produce the desired output.
As the name suggests, machine learning is concerned with enabling computers to ‘learn’, training algorithms to make decisions.
Machine learning processes consists of training the model to use a classifier to identify the class an object belongs to. The classifier has to have data as input with labels assigned to it. The algorithm will then take the data, analyze it, find a pattern, and classify the object to the class—this process called supervised learning.
On the other hand, unsupervised learning is when the algorithms are used in pattern detection but do not have labels or output categories.
There also is reinforcement learning, where the algorithm learns from its environment and uses the observations gathered to take action.
Virtual assistants like Siri and Alexa are amazing examples of machine learning that is used daily. This also includes the popular tool Google Translate.
What is Deep Learning
Deep learning is a subset of machine learning or even a technique for realizing it. It is the next evolution of machine learning. In deep learning processes, systems learn thanks to exposure to millions of data. Deep learning networks have two or more data layers and do not have to be programmed with criteria to define items. Going further, DL can automatically discover features that can be used in further classifications.
The learning process in deep learning is done through a neural network. This is an architecture in which the layers are stacked on top of one another. Inputs go into a neuron and are multiplied by the weight. Then the result of this multiplication goes to the next layer and creates an input etc. The final layer is the output layer.
Deep learning is used in text recognition (as well as text to speech), text generation, and image classification.
Differences between ML and DL + when to use what
Since deep learning is a subset of machine learning, it can be hard to separate them.
The biggest difference between the two is that machine learning calls for so-called structured data that aids in problem-solving. Deep learning networks do not need labelled data to provide output. It uses layers of the network defining specific features of the input, e.g. an image, just like the human brain.
Now, it is important to distinguish when one of the solutions is a better option to use. Deep learning will definitely be more suitable where complex problems with a large amount of data come into the picture. On the other hand, if the data you plan to use can be easily structured, machine learning will be the better option.
What’s next for AI
With the constant development of computers, artificial intelligence is growing stronger. Machines can solve harder and harder problems, in some cases, even better than humans can.
Taking into consideration the speed of this growth, it is hard to foresee the exact future. Some people are excited about things to come. Others are scared of the existence of superintelligence that will replace them. For now, it is unlikely since the machines still need human interference, at least for now.
Artificial intelligence is most definitely a 21st-century concept. It is concerned with making machines act humanly. Machine learning is a branch of artificial intelligence, and deep learning is a subset of machine learning.
Artificial intelligence works on the whole problem, while in machine learning, a problem is divided into smaller parts that are solved individually. Deep learning is about solving a problem end to end.
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