In the 1950s, a scientist named Frank Rosenblatt introduced the concept of a perceptron with the help of earlier work prosed by power tool manufacturers McCulloch and Walter.
Perceptron was modeled on the human brain’s neurons and takes multiple inputs and generates a single output by applying arithmetic operations. Rosenblatt calculated perceptron’s output by simply multiplying each input with a value called weight and then summing all the values.
That calculated value was then compared with a threshold value and assigned 0 or 1 if the calculated value was less or greater than some threshold value, respectively. The weight multiplied with each input is basically the degree of importance given to each specific input. That was the start of the initial concept of a perceptron.
The diagram of perceptron given above is specific, which generates a single output by taking three inputs, while in general, it can take any number of inputs to generate output.
The arithmetic operation performed by a perceptron can be represented by the following mathematical formula:
z = w1x1 + w2x2 + w3x3.
This calculated value z is then compared with the threshold value to decide the final output of the perceptron. By combining a large number of neurons, over and over, a complex giant neural network is formed.
The size of neural networks also increased with time, leading to the ability to solve more complex data-driven problems. For example, the architecture AlexNet, popularly known for solving image recognition limitations, has 650,000 neurons. A simple problem ends up far from simple calculations.
With the increasing amount of intelligent, independent systems in human settings, the capacity of systems to perceive, comprehend and anticipate transportation conduct is becoming more and more crucial.
Specifically, for self-driving vehicles, service robots and sophisticated surveillance systems, anticipating and planning future positions of dynamic agents are essential tasks.
There are many other significant areas of implementation such as translation, speech recognition, image compression and character recognition, etc. Let’s discuss each one of them in detail.
Machine Translation was introduced globally initially through Google, but the concept was older than that. The research work underlying the principles of natural language started as early as the 1950s.
Natural language processing (NLP) is one of the fastest-growing areas of artificial intelligence (AI) and machine learning (ML), and it is on course to radically transform the way we interact with the digital world.
The challenge in implementing natural language processing solutions lies in training algorithms to better understand the many nuances in natural speech. How can we teach a computer to understand things like irony, colloquialisms, and context? How can we train algorithms to recognize the myriad different accents out there? How will these solutions deal with errors and ambiguities?
These are just some of the questions that make the need clear for extensive data labeling and training throughout the model development process.
For an NLP model to be helpful in a real-world scenario, it needs to be trained using vast amounts of labeled data prepared to the highest standards of accuracy and quality. But data labeling for machine learning is a demanding and time-consuming job, hence the value of working with a supervised learning and data labeling service. This will help you scale your workforce, giving you time to focus on innovation.
Speech recognition, also known as speech-to-text conversion, converts human speech into documented text. Speech recognition can easily be mistaken with the term voice recognition, but they are two different applications. Speech recognition enables the verbal speech to a written format. On the other hand, voice recognition merely identifies a person’s voice.
There are numerous computation methods and algorithms that work to recognize speech and improve the model’s accuracy. These are hidden Markov models, natural language processing, N-grams, speaker diarization and neural networks.
Speech recognition applications are found in various industries like sales, technology, healthcare, automotive, and security. There are multiple deep learning techniques that can be implanted in speech recognition, but it depends on the task and the required output.
These are artificial neural networks (ANN), convolution neural networks (CNN), and recurrent neural networks (RNN).
The difference between ANN vs CNN vs RNN can be defined as:
A group of numerous neurons or perceptrons also known as feed forward networks. ANN is the simple form of a neural network that passes information in one direction and receives the results on the other side.
This type of network is feasible for image data, tabular data, and text data. However, there could be some limitations, including hardware requirements or unexpected behavior of the network.
These are some of the most widely used deep learning models. CNN is different from the ANN as multiple convolution layers could be pooled or connected.
They used spatial features and feature maps to generate ultimate output for non-linear processing. This type of model is mainly used for sequential inputs and images. However, CNN sometimes generates inappropriate results for an object’s unusual position and shape; considerable data is also needed to train such networks.
These are complex networks because they work as bidirectional (feed the result backward to improve the results). During this process, RNN self-learns in case of incorrect output and each node performs as a memory cell.
In the case of speech recognition, RNN is preferable to the other two networks because they perform better with time sequence prediction. However, the training of RNN is a difficult task due to a vanishing and exploding gradient problem.
Data compression is widely used all over the internet. For example, the movie you share, watch, the music you like and the blog you read.
Compression methods are responsible for sharing the content efficiently and quickly. Without image compression, the bandwidth and time cost would be so expensive. The compression ratio is highly dependable on the model capacity. However, deep learning models play an important role in the optimization of probabilistic high-dimensional models.
These advancements lead to lossless compression methods. A powerful method is to join autoregressive models with entropy coders to achieve a good compression ratio.
In the current digitalized world, editing, storing, and researching in a digital script is much more efficient than spending long hours on handwritten documents.
Moreover, it’s not just hectic but also leads to missing the information. Now we are lucky to have such computers doing such extensive tasks and performing better than before. There are extensive applications to extract text from images, such as in-vehicle number plate recognition, converting handwritten text into digital text, passport recognition, and more.
Artificial intelligence is a promising field striving for better technology and a fully digitalized future, and tools utilizing technologies like logic, probability, optimization, statistics, and more help develop it even further.
Deep learning holds promise for many other fields, ranging from linguistics, neuroscience, security to medicine. Although deep learning models may possess biases towards serval applications, it’s hoped that deep learning innovations will eventually overcome these limitations and improve almost every aspect of daily life.
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