Question

Emerging Technologies

Choose an emerging technology and prepare a report describing the technology and its impact on society. Your report should discuss:

The technology, what it does, how it works, and who uses it.
The research and development leading up to this technology.
Advantages and disadvantages to this technology.
Ways in which this technology is altering human behavior, affecting society and the environment, etc.
Remember that an emerging technology is one which is now present in society, but remains relatively new. This might include such topics as speech recognition, near field communication, virtual wallets, various social media outlets, strong artificial intelligence, nanotechnology, graphene, quantum computing, solid-state storage, biological computing, cryptography, self-driving vehicles, etc.

Exceed the Expectations: provide primary research to support your points. For example, interview an expert the field and include that expert's comments and opinions.

Format your work according to the computer science writing guide. 500 words.

Answer 1

I want to discuss something about Artificial Neural Network i.e ANN which nowadays becomes a very important part of Artificial Intelligence. To understand Neural Network, first of all, we need to know some important points. Which are :

1. What is a Neural Network?

2. What is an Artificial Neural Network(ANN)?

2. what it does, how it works, and what is its type?

3. Why we need a Neural network?

4. Advantages and disadvantages.

5. How its altering human behavior, etc.

Now let's start with What is a Neural network?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

What is an Artificial Neural network(ANN)?

In information technology (IT), an artificial neural network (ANN) is a system of hardware and/or software patterned after the operation of neurons in the human brain. ANNs -- also called, simply, neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI. Since this is a part of Artificial Intelligence so named as ANN(Artificial Neural Network).

Some commercial applications of these technologies generally focus on solving complex signal processing or pattern recognition problems. Examples of significant commercial applications since 2000 include handwriting recognition for check processing, speech-to-text transcription, oil-exploration data analysis, weather prediction, and facial recognition.

Now comes How ANN works?

An ANN usually involves a large number of processors operating in parallel 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 from 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. The tiers are highly interconnected, which means each node in tier n will be connected to many nodes in tier n-1 -- its inputs -- and in tier n+1, which provides input data for those nodes. There may be one or multiple nodes in the output layer, from which the answer it produces can be read.

Artificial neural networks are notable for being adaptive, which means they modify themselves as they learn from initial training and subsequent runs provide more information about the world. The most basic learning model is centered on weighting the input streams, which is how each node weights the importance of input data from each of its predecessors. Inputs that contribute to getting the right answers are weighted higher.

Specific types of artificial neural networks include:

• Feed-forward neural networks
• Recurrent neural networks
• Convolutional neural networks
• Deconvolutional neural networks
• Modular neural networks

Feed-forward neural networks are one of the simplest variants of neural networks. They pass information in one direction, through various input nodes, until it makes it to the output node. The network may or may not have hidden node layers, making it's functioning more interpretable. It is prepared to process large amounts of noise. This type of ANN computational model is used in technologies such as facial recognition and computer vision.

Recurrent neural networks (RNN) are more complex. They save the output of processing nodes and feed the result back into the model. This is how the model is said to learn to predict the outcome of a layer. Each node in the RNN model acts as a memory cell, continuing the computation and implementation of operations. This neural network starts with the same front propagation as a feed-forward network but then goes on to remember all processed information in order to reuse it in the future. If the network's prediction is incorrect, then the system self-learns and continues working towards the correct prediction during backpropagation. This type of ANN is frequently used in text-to-speech conversions.

Convolutional neural networks (CNN) are one of the most popular models used today. This neural network computational model uses a variation of multilayer perceptrons and contains one or more convolutional layers that can be either entirely connected or pooled. These convolutional layers create feature maps that record a region of the image which is ultimately broken into rectangles and sent out for nonlinear processing. The CNN model is particularly popular in the realm of image recognition; it has been used in many of the most advanced applications of AI, including facial recognition, text digitization and natural language processing. Other uses include paraphrase detection, signal processing, and image classification.

Deconvolutional neural networks utilize a reversed CNN model process. They aim to find lost features or signals that may have originally been considered unimportant to the CNN system's task. This network model can be used in image synthesis and analysis.

Modular neural networks contain multiple neural networks working separately from one another. The networks do not communicate or interfere with each other's activities during the computation process. Consequently, complex or big computational processes can be performed more efficiently.

Why we need an Artificial Neural network?

1. In real life situations there quite a few relationships between inputs and outputs that are non- linear and complex. Neural Networks have the ability to learn and model these non-linear and complex relationships.
2. Unlike other prediction techniques, they do not impose any restrictions on the input variables (ex: like how they should be distributed)
3. They can model data with high volatility and non-constant variance which useful in financial time series forecasting where data volatility is very high.
4. Also, they are widely used in pattern recognition because of their ability to generalize and to respond to unexpected inputs.
5. They are used in many applications such as language translation, animal recognition, text summarization, handwriting recognition, etc. which is used a lot.

Advantages and disadvantages of an Artificial Neural Network:

Advantages of artificial neural networks include:

• Parallel processing abilities mean the network can perform more than one job at a time.
• Information is stored on an entire network, not just a database.
• The ability to learn and model nonlinear, complex relationships helps model the real-life relationships between input and output.
• Machine learning means the ANN can learn from events and make decisions based on the observations.
• The ability to generalize and infer unseen relationships on unseen data means ANNs can predict the output of unseen data.

The Disadvantages of ANNs include:

• The lack of rules for determining the proper network structure means the appropriate artificial neural network architecture can only be found through trial and error and experience.
• The requirement of processors with parallel processing abilities makes neural networks hardware dependent.
• The network works with numerical information, therefor all problems must be translated into numerical values before they can be presented to the ANN.
• The lack of explanation behind probing solutions is one of the biggest disadvantages in ANNs. The inability to explain why or how behind the solution generates a lack of trust in the network.

Now comes How is it altering Human Behaviour?

After coming Artificial Neural network in the world of technology it becomes very useful for human beings to take decessions as well as to process a huge amount of data in a very short period of time. As we have already discussed the different types of applications of ANN. It makes people's life easy day by day.

Image recognition is one of the first areas to which neural networks were successfully applied, but the technology uses have expanded to many more areas, including:

• Chatbots
• Natural language processing, translation and language generation
• Stock market prediction
• Delivery driver route planning and optimization
• Drug discovery and development

These are just a few specific areas to which neural networks are being applied today. Prime uses involve any process that operates according to strict rules or patterns and has large amounts of data. If the data involved is too large for a human to make sense of in a reasonable amount of time, the process is likely a prime candidate for automation through artificial neural networks.