Welcome to Neural Basics, a collection of guides and explainers to help demystify the world of artificial intelligence.
However, in the background, a very complicated process takes place.
Digital equipment can capture images at resolutions and with detail that far surpasses the human vision system.
Computers can also detect and measure the difference between colors with very high accuracy.
To a computer, the above picture is an array of pixels, or numerical values that represent colors.
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Until recently, computer vision only worked in limited capacity.
Applications of computer vision
The importance of computer vision is in the problems it can solve.
It is one of the main technologies that enables the digital world to interact with the physical world.
Computer vision enablesself-driving cars to make sense of their surroundings.
Computer vision algorithms detect facial features in images and compare them with databases of face profiles.
Consumer devices use facial recognition toauthenticate the identities of their owners.
Social media apps use facial recognition to detect and tag users.
Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.
Computer vision has also been an important part ofadvances in health-tech.
Computer vision has other, more nuanced applications.
This will enable you to save internet bandwidth by only sending whats necessary to the cloud.
In fact, there was very little automation involved and most of the work was being done manually.
And the error margin was still large.
Machine learning provided a different approach to solving computer vision problems.
With machine learning, developers no longer needed to manually code every single rule into their vision applications.
Instead they programmed features, smaller applications that could detect specific patterns in images.
Machine learning helped solve many problems that were historically challenging for classical software development tools and approaches.
Deep learning relies on neural networks, a general-purpose function that can solve any problem representable through examples.
Deep learning is a very effective method to do computer vision.
Compared to previous types of machine learning, deep learning is both easier and faster to develop and deploy.
However, deep learning algorithms have their own limits, most notable among them beinglack of transparency and interpretability.
In fact, deep learning has been able to exceed human performance in image classification.
The human visual system relies on identifying objects based on a 3D model that we build in our minds.
We are also able to transfer knowledge from one domain to another.
Deep neural networks haveno notion of such conceptsand they develop their knowledge of each class of data individually.
Thats why they need to see many examples before they can develop the necessary foundations to recognize every object.
Accordingly, neural networks can make stupid (and dangerous) mistakes when not trained properly.
We know what a family is.
We know that a stretch of grass is a pleasant place to be.
It wont know what a picnic is contextually.
And it probably wont be able to tell the following picture is a drawing of an animal picnic.
We dont know whenor ifthat will ever happen.
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