Human Perception vs. Neural Networks: What’s The Difference?
Artificial Neural Networks (ANNs) are at the base of technologies that make machines intelligent. These are highly inspired by the human nervous system and thus are used and improved to make machines and computers as intelligent as humans. Biological Neural Networks allow our five senses to work correctly, thus helping us “perceive” things right. That’s precisely the function of ANNs in robots or computers.
Neural Network Perception
Due to several reasons, there are several differences in how humans perceive things and how machines do it. These differences have a significant effect on the performance of ANNs. Neural networks use stimuli to know how a specific thing looks, feels, smells, tastes, or sounds like. Understanding the stimuli properly is what makes the basis of perception. Scientists have been working to improve ANNs to mimic the exact behavior of the human nervous system when it interacts with an external stimulus. Many recent studies have found several differences in the working of the two. The next step would be to minimize the differences.
Human perception Vs Neural Networks
Now will see the major differences in Human perception vs Neural Networks and sees what the researchers have found in their study.
The most significant and easily noticeable difference is the size of Biological and Artificial NNs. A neuron is a cell of the human brain that processes and transmits information, while a Perceptron is the mechanic version of the neuron that can be thought of as the basic unit of an ANN. Our brain contains about 86 billion neurons, while the number of perceptrons in different ANNs are somewhere between 10–1000.
A single perceptron can be thought of as a one-directional neural network that is incapable of solving complex two-directional or three-directional problems. That’s where the idea of layers comes in. Scientists have been working to connect several perceptrons to make “hidden” layers of neural networks that can then be used to compare and compute outputs to complex problems. Until the size of ANNs is increased, there is little hope that machine perception can equal that of human’s.
Other than that, we also have to take into account different kinds of connections that perceptrons should make with each other. Merely increasing the number and layers of cells or units is not always beneficial to the perception of a machine.Growth
Brain fibers grow and expand to communicate with other neurons. Neuroplasticity allows for the creation of new connections or areas to transfer and change functionality. Also, neural connections can strengthen or weaken depending on their importance. This means the biological neurons physically change as our experience increases. On the other hand, ANNs have a predefined configuration in which no new neurons or connections can be introduced or removed. During the training of an ANN, only the weights of the inputs or connections change. The networks begin with random weight values and will gradually attempt to reach a point where more weight changes no longer affect performance.
Due to this reason, we have to devise an extremely efficient way of calculating and changes weights that can mimic human behavior. Otherwise, only a certain level of intelligence in attainable with the current algorithms and systems.
The brain utilizes about 20 percent of all the energy of the human body. Physically, the adult brain runs on approximately 20 watts, which is almost enough to light a bulb. However, a single Nvidia GeForce Titan X GPU operates alone on 250 watts. Using a power supply instead of just food, which humans need to function. It is thus clear that our artificial or mechanical systems are much less efficient than natural ones. The heating up of computers is another thing that makes functioning difficult. A lot of work needs to be done in this domain to decrease the power and energy consumption of our systems.
What Lies Ahead?
Deep Learning and Artificial Intelligence’s research trend is bending towards studying the precise nature of human experience. Moreover, determining how it varies from the perception of neural networks. For a Neural network researcher, it is not sufficient to only understand algorithms and technology. Also, one must have some experience with the characteristics of basic human perception. They not only need to understand what and how humans do but also what they don’t do that makes them capable of perceiving their environments easily. This knowledge will help them improve their ANNs, which will prove to be life-changing for humans!