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Since the early years of artificial intelligence, scientists have dreamed of creating computers that can “see” the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence.

 

But like many other goals in AI, computer vision has proven to be easier said than done. In the past decades, advances in machine learning and neuroscience have helped make great strides in computer vision. But we still have a long way to go before we can build AI systems that see the world as we do.

 

Biological and Computer Vision, a book by Harvard Medical University Professor Gabriel Kreiman, provides an accessible account of how humans and animals process visual data and how far we’ve come toward replicating these functions in computers.

 

Kreiman’s book helps understand the differences between biological and computer vision. The book details how billions of years of evolution have equipped us with a complicated visual processing system, and how studying it has helped inspire better computer vision algorithms.

 

Kreiman also discusses what separates contemporary computer vision systems from their biological counterpart.

Hardware differences

Biological vision is the product of millions of years of evolution. There is no reason to reinvent the wheel when developing computational models. We can learn from how biology solves vision problems and use the solutions as inspiration to build better algorithms.

 

Before being able to digitize vision, scientists had to overcome the huge hardware gap between biological and computer vision. Biological vision runs on an interconnected network of cortical cells and organic neurons. Computer vision, on the other hand, runs on electronic chips composed of transistors

 

Architecture differences

There’s a mismatch between the high-level architecture of artificial neural networks and what we know about the mammal visual cortex.

Goal differences

Several studies have shown that our visual system can dynamically tune its sensitivities to the common. Creating computer vision systems that have this kind of flexibility remains a major challenge, however.

Current computer vision systems are designed to accomplish a single task.

Integration differences

In humans and animals, vision is closely related to smell, touch, and hearing senses. The visual, auditory, somatosensory, and olfactory cortices interact and pick up cues from each other to adjust their inferences of the world. In AI systems, on the other hand, each of these things exists separately.

 

read more at https://venturebeat.com/2021/05/15/understanding-the-differences-between-biological-and-computer-vision/