Artificial Neural Network: an Overview

“Machine Intelligence is the last invention that humanity will ever need to make.”

-  Nick Bostrom

We probably agree with the eminent speaker above as that’s what AI technology is all about. Artificial Intelligence is an umbrella term that is widely used when talking about technologies and software like ANN or Machine vision or speech recognition and whatnot. Fortunately, we’re living in times where we have access to possibly everything with a click of a button. A rundown on this interesting topic is going to be really gripping as it involves the brain and its incredible ways of execution into the technical fields. Resolving sophisticated signal processing or pattern recognition challenges is where these technologies find commercial usage. Handwriting recognition, speech-to-text transcription, weather prediction, and facial recognition are just a few examples.

Since 2010, AI professionals have all the training data and computer resources they need to operate large artificial neural networks all thanks to big data movement and parallel computing. It would be doubly interesting to know how Artificial Neural Networks (ANN) function?

An ANN usually involves a large number of processors operating and arranged in tiers. The first tier receives the raw input information- analogous to optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it, rather than the raw input- in the same way, neurons further from the optic nerve receive signals from those closest to it. The last tier produces the output of the system. Each processing node has its own small sphere of knowledge including what it has seen and any rules it was originally programmed with or developed for itself. These tiers are highly interconnected which means each node in tier n will be connected to tier n-1 (its inputs) and in tier n+1 which provides input data for those nodes.

Artificial neural networks are popular for being adaptive, which simply means that they modify themselves as they learn from initial training and subsequent runs provide more information about the world. Inputs that contribute to getting the right answers are weighted higher.

ANN & their learning mechanisms:

Being an adaptive mechanism, ANN training consists of providing input and telling the network what the output should be. For example- to build a network that identifies the faces of actors, the initial training might be a series of pictures including actors, non-actors, masks, statuary, and animal faces. Each input is accompanied by matching identification, such as actor’s names or ‘not actor’ or ‘not human’ information. Providing the answers allows the model to adjust its mental weightings to learn how to do its job better. In defining the rules and making determinations, neural networks use several principles including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Biased data sets are an ongoing challenge in training systems that find answers on their own by recognizing patterns in data. If the data feeding the algorithm isn’t neutral and almost no data is, the machine propagates bias.

TYPES OF ARTIFICIAL NEURAL NETWORKS:

Neural networks are synonymously considered as deep learning, inclusive of many layers between input and output, also called hidden layers. An AI engineer masters all the networks in order to succeed in tricky computation situations. Variations on the classic neural network design allow various forms of forwarding and backward propagation of information among tiers.

Types of ANN are:

• FEED-FORWARD NEURAL NETWORKS
  It is the simplest variant of neural networks as it passes information in one direction through input nodes until it makes it to the output node. These are used in technologies such as facial recognition and computer vision.
  
  
• RECURRENT NEURAL NETWORKS
  In this model, each node acts as a memory cell & remembers all processed information in order to reuse it in the future. It is used in text-to-speech conversions where it self-learns and continues working towards correct prediction during backpropagation.
• CONVOLUTIONAL NEURAL NETWORKS
  Text digitization, signal processing, NLP, and image classification are some of the advanced applications of CNN as it uses a variation of multilayer perceptron & creates feature maps that record a region of the image which is broken into rectangles.
  
  
• DECONVOLUTIONAL NEURAL NETWORKS
  As the name suggests, it is a revered CNN model and aims to find lost features or signals that may originally have been considered unimportant to CNN’s systems.
  
  
• MODULAR NEURAL NETWORKS
  In this, the networks do not interfere or communicate with each other’s activities during the computation process. Consequently, big and complex computational processes can be performed more effectively.

ADVANTAGES OF ANN:

• Having talked about the different types of Neural networks, let’s look into the advantages these ANN possess:
• The information is stored on an entire network, not just a database.
• No restrictions are placed on input variables such as how they should be distributed.
• Parallel processing abilities mean that the network performs more than one job at a time, a multitasker indeed.
• It has a high fault tolerance that simply means that the corruption of one or more cells of ANN won’t stop output generation.
• ANN being an intelligence system can learn from events and make decisions based on the observations.
• The ability to learn hidden relationships in the data without commanding any fixed relationship means ANN can better model highly volatile data and non-constant variance as well.
• ANN can predict the output of unseen data as its ability to generalize and infer unseen relationships is tremendous.

DISADVANTAGES OF ANN:

• It is hardware dependant.
• It only understands numerical information so all the problems must be translated to numerical values before they’re presented to the ANN.
• Lack of rules means appropriate ANN architecture can only be found by trial and error or experience.
• The inability to explain the why or how behind the solution generates a lack of trust in the network.

Have you ever wondered what forms the basis of May I know your page that meta directed you to when you were busy scrolling through or how your online signatures are verified? Nowadays, Neural networks are regulating some key sectors including finance, healthcare, and automotive. These Artificial neurons function in a way similar to the human brain. They can be used for image recognition, character recognition, and stock market predictions.

Below are some of the Applications of ANN:

• Facial recognition
• Stock market prediction
• NLP, translation & language generation
• Chatbots & social media
• Aerospace & defense
• Drug discovery & healthcare development
• Signature verification & handwriting analysis
• Weather forecasting

Neural networks have a myriad of applications from facial recognition to weather forecasting, the interconnected layers can do a lot of things with some simple inputs. ANN algorithms have simplified the assessment and modified the traditional algorithms. With humanoid robots like Grace on their way, the world can expect some sci-fi movies to turn into reality pretty soon.