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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/

BrainNet provides the first multi-person brain-to-brain interface which allows a nonthreatening direct collaboration between human brains. It can help small teams collaborate to solve a range of tasks using direct brain-to-brain communication.

How does BrainNet operate?

The noninvasive interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver the required information to the brain. F

For now, the interface allows three human subjects to collaborate, handle and solve a task using direct brain-to-brain communication.

Two out of three human subjects are “Senders”.

The senders’ brain signals are decoded using real-time EEG data analysis. This technique allows extracting decisions which are vital in communicating in order to solve the required challenges.

Let’s take an example of a Tetris-like game–where you need quick decisions to decide whether to rotate a block or drop as it is in order to fill a line.

The senders’ signals (decisions) are transmitted to the third subject human brain via the Internet, the “Receiver” in this case.

The decisions are sent to the receiver brain via magnetic stimulation of the occipital cortex.

The receiver can’t see the game screen to decide if the rotation of the block is required.

The receiver integrates the decisions received and makes an informed call using an EEG interface regarding turning the position of the block or keeping it in the same position.

The second round of the game allows the senders to validate the previous move and provide the necessary feedback to the receiver’s action.

more at https://hub.packtpub.com/brainnet-an-interface-to-communicate-between-human-brains-could-soon-make-telepathy-real/