Over the years, artificial intelligence (AI) has contributed to outstanding innovations in the tech industry. The advancement of deep learning has helped in a wide range of applications such as machine translation, image classification, and protein folding prediction.
SwissCognitive Guest Blogger: Swamini Kulkarni – “AI In Computer Vision Finds Applications in Healthcare, Security, Manufacturing, And Much More”
Having said that, AI has opened a new branch of science such as computer vision where AI can train computers to interpret and capture information from an image or video data. With the use of machine learning models to images, computers can classify objects and identify them. The most widely used application is face recognition in a smartphone, where computer vision unlocks the smartphone after recognizing the owner’s face. The applications of computer vision are limitless. From improving selfies to detecting lesions in medical images, computer vision is set to revolutionize several industrial verticals.
Current Scenario of AI Computer Vision
According to Allied Market Research, the global AI in the computer vision market is expected to reach $207.09 billion by 2030, growing at a CAGR of 39.6% from 2021 to 2030. An increase in demand for computer vision systems in automotive applications, high demand for quality inspection and automation, and a surge in demand for emotion AI drive the market growth.
In layman’s terms, computer vision is the ability of an AI system to “see” objects like humans. It has been increasingly gaining interest and funding for R&D. Computer vision does not limit to mimicking humans in identifying objects but it outmatches the human visual system. Moreover, researchers aim to develop AI systems that can automate some tasks that demand visual cognition. While this needs a huge amount of multi-dimensional data and analysis, it is much more complex than a simple understanding of binary information. Thus, developing AI computer vision to recognize visual information is much more complex.
However, over the last few years, AI has developed tremendously. The use of deep learning and the artificial neural network has made it easier to mimic human vision. What’s more, these technologies have made computer vision more adept at recognizing patterns from images than humans. Some researchers strongly believe that AI computer vision is far better at recognizing patterns in the field of healthcare than human physicians.
Thus, AI computer vision technology has become more common in various industries, and in the future, it would offer unimaginable outcomes that outsmart humans. Today, computer vision is powered by deep learning algorithms that use neural networks to make sense of images. This network is trained using the colossal amount of image data that helps the algorithm to break down information and understand everything contained in an image. These networks scan pictures pixel by pixel to memorize them and understand the pattern. This helps in the classification of images by understanding characteristics including colors and contours. This process is repeated again and again and with each iteration, AI computer vision improves at offering the right output.
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Currently, computer vision is used in healthcare in diagnosing diseases by analysing CT scans and other medical images. Moreover, it has gained importance in security and manufacturing where computer vision can be used in retinal and fingerprint scanning for security purposes and quality insurance in manufacturing processes by inspecting the final product.
Facial recognition and fingerprint scanning are the widely used applications of AI computer vision technology. These technologies are now part of today’s smartphones and doors and help in improving security. The next big thing in computer vision is self-driving vehicles where the AI systems would detect obstacles in order to navigate. These cars are equipped with LiDAR and ultrasound sensors which can help them get a better understanding of their surroundings.
With the increased investment and funding for R&D in computer vision, the technology would open a broad range of applications in the future. Not only will AI computer vision would be easier to train but also, they will be able to discern more images than now. In the future, AI computer vision will be used in combination with natural general intelligence (AGI) and artificial superintelligence (ASI) to offer them the ability to process data even better than humans.
Keeping the current advancements in computer vision in mind, it is safe to say that the future of computer vision is more about exploring new abilities. The future of computer vision would define the next generation of artificial intelligence systems that resemble humans exactly. However, there are many challenges to overcome to do so.
A few decades ago, AI was considered a myth and a part of science fiction but today it is a part of our daily lives. Thus, it is no wonder that in the future, computer vision become far more advanced and accurate in recognizing patterns, images, videos, or any visual information for that matter.
About the Author:
Swamini Kulkarni holds a bachelor’s degree in Instrumentation and control engineering from Pune University and works as a content writer at Allied Market Research. She is deeply fascinated by the impact of technology on human life and loves to talk about science and mythology. When she is not glued to the computer, she loves to read and travel.
Over the years, artificial intelligence (AI) has contributed to outstanding innovations in the tech industry. The advancement of deep learning has helped in a wide range of applications such as machine translation, image classification, and protein folding prediction.
SwissCognitive Guest Blogger: Swamini Kulkarni – “AI In Computer Vision Finds Applications in Healthcare, Security, Manufacturing, And Much More”
Current Scenario of AI Computer Vision
According to Allied Market Research, the global AI in the computer vision market is expected to reach $207.09 billion by 2030, growing at a CAGR of 39.6% from 2021 to 2030. An increase in demand for computer vision systems in automotive applications, high demand for quality inspection and automation, and a surge in demand for emotion AI drive the market growth.
In layman’s terms, computer vision is the ability of an AI system to “see” objects like humans. It has been increasingly gaining interest and funding for R&D. Computer vision does not limit to mimicking humans in identifying objects but it outmatches the human visual system. Moreover, researchers aim to develop AI systems that can automate some tasks that demand visual cognition. While this needs a huge amount of multi-dimensional data and analysis, it is much more complex than a simple understanding of binary information. Thus, developing AI computer vision to recognize visual information is much more complex.
However, over the last few years, AI has developed tremendously. The use of deep learning and the artificial neural network has made it easier to mimic human vision. What’s more, these technologies have made computer vision more adept at recognizing patterns from images than humans. Some researchers strongly believe that AI computer vision is far better at recognizing patterns in the field of healthcare than human physicians.
Thus, AI computer vision technology has become more common in various industries, and in the future, it would offer unimaginable outcomes that outsmart humans. Today, computer vision is powered by deep learning algorithms that use neural networks to make sense of images. This network is trained using the colossal amount of image data that helps the algorithm to break down information and understand everything contained in an image. These networks scan pictures pixel by pixel to memorize them and understand the pattern. This helps in the classification of images by understanding characteristics including colors and contours. This process is repeated again and again and with each iteration, AI computer vision improves at offering the right output.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Currently, computer vision is used in healthcare in diagnosing diseases by analysing CT scans and other medical images. Moreover, it has gained importance in security and manufacturing where computer vision can be used in retinal and fingerprint scanning for security purposes and quality insurance in manufacturing processes by inspecting the final product.
Facial recognition and fingerprint scanning are the widely used applications of AI computer vision technology. These technologies are now part of today’s smartphones and doors and help in improving security. The next big thing in computer vision is self-driving vehicles where the AI systems would detect obstacles in order to navigate. These cars are equipped with LiDAR and ultrasound sensors which can help them get a better understanding of their surroundings.
With the increased investment and funding for R&D in computer vision, the technology would open a broad range of applications in the future. Not only will AI computer vision would be easier to train but also, they will be able to discern more images than now. In the future, AI computer vision will be used in combination with natural general intelligence (AGI) and artificial superintelligence (ASI) to offer them the ability to process data even better than humans.
Keeping the current advancements in computer vision in mind, it is safe to say that the future of computer vision is more about exploring new abilities. The future of computer vision would define the next generation of artificial intelligence systems that resemble humans exactly. However, there are many challenges to overcome to do so.
A few decades ago, AI was considered a myth and a part of science fiction but today it is a part of our daily lives. Thus, it is no wonder that in the future, computer vision become far more advanced and accurate in recognizing patterns, images, videos, or any visual information for that matter.
About the Author:
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