Neural networks are inspired by the way the human brain works. A human brain can process huge amounts of information using data sent by human senses (especially vision). The processing is done by neurons, which work on electrical signals passing through them and applying flip-flop logic, like opening and closing of the gates for signal to transmit through. The following images shows the structure of a neuron:
The major components of each neuron are:
- Dendrites: Entry points in each neuron which take input from other neurons in the network in form of electrical impulses
- Cell Body: It generates inferences from the dendrite inputs and decides what action to take
- Axon terminals: They transmit outputs in form of electrical impulses to next neuron
Each neuron processes signals only if it exceeds a certain threshold. Neurons either fire or do not fire; it is either 0 or 1.
AI has been a domain for sci-fi movies and fiction books. ANNs within AI have been around since the 1950s, but we have made them more dominant in the past 10 years due to advances in computing architecture and performance. There have been major advancements in computer processing, leading to:
- Massive parallelism
- Distributed representation and computation
- Learning and generalization ability
- Fault tolerance
- Low energy consumption
In the domain of numerical computations and symbol manipulation, solving problems on-top of centralized architecture, modern day computers have surpassed humans to a greater extent. Where they actually lag behind with such an organizing structure is in the domains of pattern recognition, noise reduction, and optimizing. A toddler can recognize his/her mom in a huge crowd, but a computer with a centralized architecture wouldn’t be able to do the same.
This is where the biological neural network of the brain has been outperforming machines, and hence the inspiration to develop an alternative loosely held, decentralized architecture mimicking the brain.
ANNs are massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections.
One of the leading global news agencies, Guardian, used big data in digitizing the archives by uploading the snapshots of all the archives they had had. However, for a user to copy the content and use it elsewhere is the limitation here. To overcome that, one can use an ANN for text pattern recognition to convert the images to text file and then to any format according to the needs of the end-users.
