Tag: #aiarts04

According to our current studies, AI has advanced to the point where tasks are performed much more efficiently than we can ourselves. With this comes ground-breaking innovations in various fields of expertise, allowing more streamlining of tasks. But to understand why we have chosen AI over humans we must understand what aspects of the artificial and biological are the same and what differs. 

Similarities:

ANN goes back as early as the 1950s, an example being the creation of perceptrons. The idea behind perceptrons was to emulate what we thought the BNN does in our brains. In the biological neural network, dendrites receive signals from other cell bodies which they use the nucleus to process and send out to others with the help of axons. The link between one nucleus and others can vary, leading to the storage of information (i.e. memory) and the creation of new neural links. What happens during the processing stage in the nucleus still remains uncertain, however, the ANN also tries its best to mimic the workings. The ANN also receives inputs and uses its hidden layer to process information and send out the results. However, unlike the biological neural network, the ANN is a mathematical model with changing parameters and functions to calculate whatever it is that we require of it.

Differences:

We have over 86 billion neurons in our brain creating and over 100 trillion synapses (links) while ANN consists of a number much smaller. In terms of speed of operations, the neurons in a human brain can vary widely due to aspects such as age, gender, how much they got the night before, etc. It is much easier for the ANN to stay consistent in its calculations. However, it is important to understand that consistency comes from the specialization of ANN. An example being an ANN designed to play chess wouldn’t be able to play checkers unless designed to. 

Conclusion:

I have no doubt whatsoever that AI and ANN will be developed to be adaptable to any circumstances just like in sci-fi films. The most important aspect that comes to mind is how the job market will shift in the AI revolution to come. Trucks with AI are already being tested and dispatched while medical facilities are filled with AI that can notice cancer cells much more faster and accurately than the human eye. Even home caretakers are being slowly replaced with systems such as smart homes with Amazons Alexa and Siri. From my understanding, AI can only replace us with what we already know, with information that we already have uncovered. In other words, I believe that the job market will shift towards seeing a demand in human-to-human interactions more than anything else.

Sources: 

https://www.quora.com/What-is-the-differences-between-artificial-neural-network-computer-science-and-biological-neural-network

https://towardsdatascience.com/the-differences-between-artificial-and-biological-neural-networks-a8b46db828b7

As the name Neural Network suggests, the idea is somehow borrowed from the human body/brain. To make it clear, the Neural Network is trying to simulate the structure of the brain and how neurons are connected. Previously, the concept of a neural network is also called the multi(layer) perceptron, which suggests that every node in the Neural Network will represent or capture a feature and by combining those features, detailed results will be the outcome. This is basically how the multi-perceptron works. However, the human brain works in a similar but different mechanism,  

Neural Network

Essentially, the Neural Network is doing feature extractions and feature combination and use these features to further deliver the output. And the CNN, RNN and more serve as an extraction mechanism for different kind of tasks. Furthermore, in practice, the Neural Network does not have a generic solution to all tasks. That is to say, for specific tasks, customized models are needed. 

Human Brain

On the other hand, the brain works similarly in a way that does the feature extractions and feature combination by hands, eyes, and ears. However, the brain can do continuous learning, which keeps yield solutions to different tasks,

Neural network, as its name indicates, is a model inspired by a neural structure of human—especially visual and auditory cortex. The underlying mechanism of the neural network is similar with that of human cognition, as it creates several layers, puts cells into them, and connects these cells with each other; each cell receives a signal and transmits it to the next neuron. In depth, however, there are a number of characteristics that make an artificial neural network and a biological brain disparate. A human cerebral consists of more than a hundred billion cells, and of course, current technology cannot simulate this number of neurons. Although the model of an artificial neural network itself began by simulating the structure of a biological brain, there are differences in terms of their structures other than the difference between the numbers of neurons of each system.
A biological cell has an all-or-none characteristic; stimulated under its threshold, it will not show any response at all and will react only if the stimulation is above its threshold. Stronger stimulation does not increase the size of its response, but rather increases the frequency of its response, which in fact can be considered somewhat similar to step function, or so-called Dirac Delta function. One problem is that, since it is discrete and thus non-differentiable at x = 0, a model based on the unit step function cannot intelligize itself through deep learning. That is why engineers in the past used other step functions such as sigmoid function and hyperbolic tangent function, which are smooth and differentiable. The sigmoid function is, arguably, similar with a step function; and yet, it is very different from a biological brain, which cannot function without the passage of time. In the case of artificial neural networks, the output value is set regardless of the passage of time, given a certain input value.
The question of whether artificial neural network is similar with human nerve system, in my opinion, should not be stuck to one-to-one comparison between them. The reason why scientists developed the neural network is to do tasks that human brain finds difficult to process. From the evolutionary point of view, human brain has evolved for the purpose of survival and preservation of the species; not for memorizing a hundred pages of text in a glance or performing highly complicated math operations. Artificial intelligence—optimized for these quantitative tasks—will lead us to overcome human limitations and present a new angle on the issues we are dealing with, even to the realm of art.

Overview

At first I thought that the Neural Network was a work of bionics, which meant it mainly mimicked how the biological neurons worked. But this is just a random guess according to their similar names. However, after doing several research on the Neural Network and biological neurons as well as their connections and differences, I have a new conclusion about their relationship. Neural Network is inspired by the biological neurons and at first the research personnel wanted to imitate the way biological neurons worked and applied the method to the machine. However, since our biological neural network has been evolved for millions of years, it has already reached a considerably mature level. Thus it is impossible for the machine learning to completely copy the method or put the method into practice. Therefore in its later stage of development, it created new ways to close to or even reach the effect of biological neurons but essentially, it is just a  mathematical or computational model. That is to say, neural network loosely model biological neurons and the connection between these two is weaker and weaker.

General introduction

The most essential factor of biological neuron is nerve impulses, which are electrical events produced by neurons. Nerve impulse can let neurons communicate with each other, processing information and performing computation. When a neuron spikes, it releases a neurotransmitter, a chemical that move across a synapse for a tiny distance and then reach other neurons. In that way, this neuron is able to communicate with other hundreds of neurons.

For the Neural Network, it is composed of connected artificial neurons. Each connection can transmit a signal to other neurons. It has three kinds of layers: input layers, output layers and hidden layers. On the input layer, many neurons accept non-linear information. Output layer shows the result after transmitting and analyzing the input information. Hidden layer, composed by neurons and links, is between the input layer and output layer and the number of layers and neurons on each layer is uncertain. Generally speaking, the more the number of neurons, the more nonlinear the neural network is and the more significant the robustness of the neural network is. To adjust the weights of each layer according to the correction of training samples is the process of creating the model. And the model is then validated by a back-propagation algorithm.

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Connections & Difference

Obviously artificial neurons loosely model the biological neurons and the way they communicate is similar. According to wikipedia, both of them have the characters of nonlinearity, distribution, parallelization, local computation, and adaptability.

However, the connection among biological neurons is pretty weak, and new connection can be formed only when there is a strong impulse to let it release neurotransmitter. And only a small group of neurons strongly connected with each other. While the artificial neurons are highly connected with each other. The connection is fixed and it can’t create new connections by itself. While the artificial neuron layers are usually fully connected, the character of biological neurons can only be simulated by introducing weights that are 0 to mimic the lack of connections between two neurons.

Also I’ve watched a really interesting video demonstrating the difference on the operation mechanism between biological neuron and artificial neuron. This video takes a baby and candies as an example. When the baby sees the candy, there is an impulse to let the neurons of  the mouth connect the neurons of the hands, and the signal will transmit on these connected neurons and then produce the feedback: hold out hand and make a gesture for sugar. 

 

But for ANN, it has already formed a complete system and have learnt countless examples from the database to know what to do after seeing candies. Every time it uses back-propagation algorithm to validate and to do corrections.

Conclusion

Therefore, as we can see, ANN is inspired by biological neurons but the connection between them is loose. Also, at the late stage of its development, it abandoned the way that biological neurons works and turn to apply statistical methods and algorithm to build models to receive, process and analyze information.

Reference:

https://towardsdatascience.com/the-differences-between-artificial-and-biological-neural-networks-a8b46db828b7

https://en.wikipedia.org/wiki/Artificial_neural_network#Learning

https://www.bilibili.com/video/av15997699from=search&seid=4264978940427641437

When you get down to the details of now artificial neural networks work, they differ from neurons in the human brain. However, the basic concept of how they work is very similar – an unsurprising discovery considering the idea of artificial neural networks was loosely based on how biological nervous systems work.

Both artificial neural networks and brains take inputs, go through some sort of “hidden layer” process, and then return an output. In a convolutional neural network, for example, the input could be an image. After the “hidden layer” process of calculating probabilities is complete, the output is a classification, or category for that image. In a human brain, the input could be touching something very high in temperature. Throughout the “hidden layer” process, neurons and synapses interact with each other which in turn signal you to feel pain as an output.

While this very basic concept may be similar, there are many differences within the “hidden layer” process. Take this graph, for example:

This graph shows the process our human nervous system goes through to produce signals. Cells need a certain amount of membrane voltage in order to transmit a signal; they must be stimulated by their input synapses. This can be related to how in artificial neural networks the input data must be added up before going through a non-linearity. However, neurons follow the all-or-nothing principle: the transferring of signals is binary. With artificial neural networks, the signals are continuous. The amount of power it takes to accomplish tasks also differs: some deep neural networks such as Nvidia GPU requires 300W, whereas our brains only require about 20W of energy.

The learning mechanisms for biological nervous systems and artificial neural networks also differ. Artificial neural networks use gradient descents which take a large amount of data and convert it into a minimum based on a predefined function. On the other hand, while the human brain’s learning process is not entirely known, there is strong evidence for it using Hebbian learning or Spike-Timing-Dependent Plasticity. With these kinds of learning processes, if a set of neurons frequently transmit signals between each other, these connections are made stronger.

As mentioned, there are both similarities and differences between artificial neural networks and human brains. While the details of both are quite different, the inspiration for neural networks still stemmed from biological brains, accounting for the similarities.

Sources:

https://becominghuman.ai/natural-vs-artificial-neural-networks-9f3be2d45fdb’

https://ml4a.github.io/ml4a/convnets/