Have you ever uploaded a photo on Facebook with your friends? You might have noticed how Facebook automatically highlights faces and prompts friends to tag in the photo. But how does Facebook know which of your friends is in this photo?
The answer is – Artificial intelligence. Facebook uses facial recognition powered by Artificial Neural Networks to suggest to you whom you should tag in the post.
Artificial Neural Networks is the main tool used in Machine learning. It has been gaining popularity at a very fast pace with Deep Learning, Data Science and Machine Learning being around in the past few years.
Artificial Neural networks have taken over a lot of work that was considered manual effort which made us realize that Artificial neural networks are biologically inspired by the human brain and our nervous system.
So in this blog, we’ll discuss how an Artificial Neural Network models the brain and the difference between an artificial neural network and the human brain
Biological Neuron Explained
A neuron is the foundational building block that builds up the human nervous system.
The neurons are just like some other cells in the human body but what makes them different is their ability to transmit information throughout the body.
Basically, a biological neuron is divided into 3 main parts.
- The Dendrites: The dendrites are the branch-like structures that receive signal from the other neurons. They process these signals and transfers the information to the core of the neuron.
- The Cell body: This cell body of a neuron comprises the nucleus (the part of cell which contains its genetic material). In the cell body, takes place the synthesis and degradation of the neural proteins and membranes.
- The Axon: The axon looks like a thin fiber that goes beyond a neuron. It transmits electric signals to carry out sensory perception and movement across long distances.
What is Artificial Neural Networks (ANNs)
ANN’s had started with background work in the late nineteenth and early twentieth century. At that time there were no mathematical theories or algorithms about neural networks.
They had started with researching interdisciplinary work in Psychology, Physics, and Neurophysiology.
In the last two decades, ANN has been touching the roofs. It is in every other field and a lot of research work has been done with new papers being published now and then.
Considering ANN as omnipresent, it is available in every field ranging from the environment to electronics.
What is Biological Neural Network (BNNs)?
In the Biological Neural network, neurons are working inside a human brain which are connected by synapses activated for the specific function they ought to carry out.
Early studies of BNN’s have been done around the 1800s in terms of psychology but the first rule of neuronal learning and the meaning of BNN are coined by Hebb in 1949, in the Hebbian theory.
The connections between the neurons in the human brain are much more complicated than the artificial ones.
There are two basic kinds of connections between neurons present in the biological brain called synapses, both electrical and chemical.
Synapses help the connection of neurons in overlapping and interlinking the neural circuits. Consider the Biological Neural Network to be a connective bridge in the difference between a neural network and the human brain.
Now that we know what is BNN and ANN, let us now figure out the key differences when it comes to artificial neural network vs human brain.
The difference between Artificial Neural Network and Biological Neural Network
The biological brain and Artificial Neural Networks are two of the most controversial aspects of analysis in the field of Neural Network research. But there have been some postulations regarding the working difference between ANN and the human brain.
- SIZE: In the human brain, there are 86 billion neurons, and more than 100 trillion synapses to pass on electrical signals throughout a biological body. But on the other hand, the number of neuron in the artifical neural network is way more lesser in numbers. One layer perceptron network consists of several perceptrons that are not connected to one another. They receive inputs on their “dendrites” and generate output on their “axon” branches.
- SIGNALS: Based on the action potential, biological synapses either carry a signal or they don’t. An artificial neuron accepts continuous values and apply a simple non-linear, easily differentiable function (an activation function) on the sum of its weighted inputs to restrict the outputs’ range of values.
- TOPOLOGY: The artificial layers add up one by one, instead of being a part of a network that has nodes that don’t add up synchronously. On the other hand, in biological networks, neurons parallelly fire asynchronously with a small portion of highly connected neurons and a large amount of lesser connected ones.
- POWER CONSUMPTION: The biological brain consumes about 20% of the overall human body’s energy. An adult brain operates on about 20 watts, and in comparison to this, artificial builds can’t even match the efficiency level of a biological brain.
- SPEED: In the biological neuron, electric signals travel at varied speeds depending on the type of the nerve impulse. The speed usually ranges from 0.61 m/s to 119 m/s. On the other hand, an artificial neuron emits signal by the continuous, floating point number values of synaptic weights.
Artificial Intelligence’s Impact on Future
Now after going through this, You would’ve got a clear understanding of how the Artificial brain works, how does an artificial neural network models the brain, how it is spreading across every field possible, and the difference between a neural network and the human brain.
Artificial Intelligence is like the driver for all the emerging industries we have today. It encapsulates all the prominent concepts like Big data, Deep Learning, Data Science and Machine Learning.
A recent study from Redwood Software and Sapio Research said they believe that 60 per cent of businesses can be automated in the next five years. Gartner Analytics reported that AI will produce more jobs than it will displace.
Dennis Mortensen, CEO and founder of x.ai, maker of AI-based virtual assistants says “I look at our firm and two-thirds of the jobs here didn’t exist a few years ago.” For many reasons, the future with AI is the future most of the world sees and is working towards.
Also Read: Skills Required for Artificial Intelligence
So you have finally made it to the end of this article which shows how much you want to make it big in AI. Why leave it here when you can easily learn how Artificial Intelligence works and become an expert in it?
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