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Understanding Artificial Intelligence is confusing because it is not a separate technology. AI is a whole complex of technologies that lead to one goal - to imitate human intelligence as much as possible.
Early on, it seemed to us that such technologies would be the call of the future, but in fact we have quietly moved to their daily use in many industries.
Computer vision and deep learning are very popular topics today, their use makes our lives more convenient. Starting from guessing our wishes and the perfect selection of products on marketplaces, ending with unmanned vehicles - all this is already today.
In this article, we will help you understand this complex AI system and differences between its subcategories.
Table of Content:
As we mentioned before, AI is not a separated technology, its systems use plenty of data to achieve their goals and are constantly learning and improving.
Each learning cycle of Artificial Intelligence leads to new insights that help the system become more precise and reliable.
Different systems of Artificial Intelligence are responsible for different goals - vision, learning and decision making. And to achieve these revolutionary goals it uses components.
Machine Learning, literally, is constantly improving the way a machine learns. Just like a human being, having experienced a mistake or going through a cycle of action, a machine is able to make conclusions and improve, which doesn’t need detailed programming.
Deep Learning is a subtype of ML that uses neural networks. The inspiration behind the use it comes from the human brain. In this vein, the machine tries to find connections between data.
Neural Networks are a process that is in constant search of associations and meanings. Neural connections help systems process large datasets and identify patterns among them.
Cognitive Computing is a system responsible for communication between humans and machines. The main tasks are to analyse the speech, language and images.
Natural Language Processing - responsible for the language in its various manifestations. It can be either oral or written language. Any interaction between a machine and a person begins with a way of communication between them. This happens when we talk with Google or Siri voice assistants, as well as bots.
Computer Vision is responsible for the eyes. The computer has the ability to perceive visual data and analyse them, and the speed and efficiency of this process already exaggerates the human one at times.
Finally, we have gone through all the systems of artificial intelligence, now let's go through the most popular ones and find out what is the difference between them.
Computer vision is that AI system that aims to mimic human eyes, and it does it even better! The benefit of this technology is possibility to recognize thousands of objects at the same time, which the human eyes and brain are not capable of. In addition to recognition, it can analyse and distinguish objects, even if they differ in minor details.
In addition to covering many objects at the same time, the problem of human eyes in memory. We can remember a maximum of 10 objects, unlike AI, which is unlimited, capable of simultaneously processing and collecting information.
Collecting data and analyzing it is good, but there is another feature that makes computer vision even more efficient - notifying about events. Modern businesses use this advantage in order to automatically receive notifications and quickly react to some events, like theft or defect.
The computer vision technology market is gradually expanding because every industry is opening up new opportunities to automate boring routine processes that require human attention, especially visually.
Researches report that in 5 years the computer vision market will reach $19.1 billion. And it is not at all surprising to see facial recognition, security systems and production quality on the list of the most popular models.
Computer vision uses convolutional neural networks (CNNs) to identify images at the pixel level. In order to find and understand the relationships between images, using other neural networks - recurrent ones (RNNs).
There are three stages in the process of working with computer vision models:
First we get an image, the resource can be a date set of images, real time video or 3D technology.
Processing - This is where deep learning models come in to help automate the process. The model learns itself, becomes accurate and reliable, if you provide it with a large number of necessary images.
At the last stage of the computer vision model, the object is identified or classified.
Let's take a look at the most popular CV algorithms.
Image Classification - the basic algorithm that determines to which class an object belongs. Using the model, it learns to classify visual data using groups of images, such as the cat class or the dog class.
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Object Detection - this is a technique that allows the model to identify objects in an image by classifying them and determining their place in an image or video.The extended type allows you to recognize several objects at once in one image.
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Pose Estimation - this algorithm is dividing the human body by joints and using this to determine the pose. This is one of the key models used in sports.
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Image Segmentation - an algorithm that divides an image into parts that are similar in pixels, making it easier for the model to identify objects in these parts.
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Face Detection - an algorithm that allows you to identify a human face in a photo or video. It is part of the object detection, and it is not strange, because the face itself is a separate object - the nose, lips, eyes.
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Optical Character Recognition (OCR) -Â an algorithm that converts text from visual or scanned formats into machine-readable text. It is used by many services, for example, Google Translate uses a photo to identify the text and automatically translate it.
Machine learning is a discipline of artificial intelligence that allows systems to learn without prior training or programming, using past experience.
The main difference between machine learning and conventional development is that a machine learning can process huge datasets without the need to train it. We write a classical program and explain to it how to work, and models can learn themselves based on past processing cycles.
Machine learning is data-driven and the goal is to get as close as possible to automatic operation without the need for human intervention.
Today, computer learning exists in many areas of life, and sometimes we don't even notice it.
These technologies are very close and often work together to improve object recognition algorithms.
The specific difference is in the task, because the concept of machine learning is very abroad and is used in many tasks and techniques. Machine learning is guided by statistical principles and algorithms to produce models that can infer solutions from input data.Computer vision, in turn, is focused on the task of using the camera and working with images.
Examples of machine learning applications can be programs in financial institutions that work with clients in a personalized way, conduct analytics and forecasts. And also medicine, where a computer can quickly scan all existing information about a patient, draw conclusions or highlight patterns.
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Deep learning is an extension of machine learning, the difference is in the globality and ways of solving problems. This technology uses artificial neural networks and plenty of labeled data to process. Algorithms understand and process information in the same way as the human brain.
Deep learning is the most exciting technology, because it is currently the closest to the main goal of artificial intelligence - to be like a person. It is this technology that works when we use our voice to ask devices to do actions - phones, TVs and speakers.
The difference between deep learning and computer vision is the same in concept and operation. There is a real difference between ML and DL but we will tell you more about it later.
You can see the comparison table Deep Learning and Computer Vision below.
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The difference between all systems of Artificial Intelligence at first glance seems complicated. But if you take it apart in parts and especially in time intervals, you can see how they are used in different ways and follow different goals.
Artificial intelligence as a system itself appeared back in the 1950s, more active use of machine learning in the 80s, and a breakthrough in machine self-sufficiency with the help of deep learning is recorded in the 2010s.
As these technologies develop, they require less and less human intervention and programming, which greatly simplifies our task.
But don't rush to rejoice, we are still far from reaching a truly independent intellect.
There are three types of artificial intelligence, depending on its capabilities:
Limited, general and super.
But so far we have managed to create only a limited one that can only solve a specific type of problem.
We at ByteAnt are ready to help you develop and implement new technologies in your business that automate many processes and reduce waste.
Contact us to develop a custom computer vision/machine learning model and start your journey into the future today.
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