Natural Language Processing, Deep Learning, and Computer Vision
Have you ever in your life most probably as a child come across a person who claimed that he was able to talk to animals, or dogs and was able to read their mind, We all did and here we are in the future 10-20 years later where we are seeing people actually able to communicate with machines through machines, where machines with the correct data are able to predict and understand what we have in our minds.
Welcome to the world of Artificial Intelligence where we have the ability to talk to machines and vice versa. Natural Language Processing, Deep Learning, and Computer Vision are all subsets of the vast digital planet known as Artificial Intelligence.
Just like a Homosapien has many body parts each having a different function of its own, Imagine the Machine as the body then we can say that Artificial Intelligence is the measure of its own conscious intelligence, Deep Learning is the brain, and NLP or natural language processing is how converses with people or machines in its case. Computer Vision measures its ability to interpret the actions of the people it is talking to.
Let's dive a little deeper and get to know more about the ‘Anatomy of the Machine’
What is Natural Language Processing?
Natural Language Processing is a subset of Artificial Intelligence where computers analyze and derive meaning from the information that is provided to them by humans in the form of data in a smart and convenient way. In NLP, rules-based models of human language are combined with statistical, machine learning, and deep learning models. Through these technologies, computers will be able to process human language in the form of text or voice data, as well as understand the speaker's or writer's intent and sentiment.
If you consider human language in general, how hard it is to interpret what we are trying to say with so many languages being spoken all around the world combining that with the aspect how we mean a sentence to come out, it seems like a very difficult task for a machine to figure out what we are trying to say exactly.
With the invention of algorithms for Natural Language Processing, it has become somewhat possible, this is the future where we may not have talking dogs but we certainly have the ability to talk to machines.
Computer programs using NLP translate text from one language to another, respond to spoken commands and summarize large volumes of text rapidly in real-time. Computer programs using NLP translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly, service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Where is NLP used?
As I mentioned earlier the human language is filled with a vagueness which makes it extremely difficult to interpret it efficiently. A few examples of the irregularities in human language include homophones, homographs, idioms, metaphors, grammar and usage exceptions, and variations in sentence structure. Programmers must learn natural language applications to recognize and understand these irregularities from the beginning if they are to be effective.
Some of the applications of NLP are able to break down human text and voice data in ways that help the computer make sense of what it's ingesting are the following:
Automatic Speech recognition
Automatic Speech Recognition or ASR, as it’s known in short, is the technology that allows human beings to use their voices to speak with a computer interface in a way that, in its most sophisticated variations, resembles normal human conversation. Even in our day to day life we are constantly using this parameter of NLP called Automatic Speech Recognition. Whenever you reach back home after a long day at work and you ask Alexa to turn on the lights, the process of Alexa understanding what you are asking her to do is a very common application of Automatic Speech Recognition, Siri is another great example. However, even these NLP programs, despite and “accuracy” of roughly 96 to 99% can only achieve these kinds of results under ideal conditions in which the questions directed at them by humans are of a simple yes or no type or have only a limited number of possible response options based on selected keywords
Speech Tagging, Word Sense Disambiguation, Named Entity Recognition, Sentimental Analysis help in identifying other aspects of the speech like understanding the difference when a word is used as a verb or as a noun, understanding the sentiment behind what the person is saying and many other aspects to make the process of recognition perfect.
NLP Tools and approaches
Python and Natural Language Processing Toolkit
Python provides a wide range of tools and libraries for tackling specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.
Many of the tasks listed above can be accomplished using the NLTK, along with libraries for subtasks, such as sentence parsing, word segmentation, stemming, lemmatization, and tokenization (to break phrases, sentences, paragraphs, and passages into tokens that can be understood by a computer). Additionally, it includes libraries for implementing capabilities such as semantic reasoning, which enables logical conclusions to be drawn from text.
Statistical NLP, machine learning, and deep learning
Initially, NLP applications were hand-coded, rules-based systems that could perform certain tasks but could not handle a seemingly endless stream of exceptions or the increasing volume of text and voice data.
Statistical NLP combines computer algorithms with machine learning and deep learning models to extract, classify, and label elements of text and voice data and assign a statistical likelihood to each meaning. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) allow NLP systems to 'learn' as they work and extract ever more accurate meaning from raw, unstructured, and unlabeled text and voice data.
Applications of NLP can be found anywhere these days, even in our day to day life we are dealing with NLP without us even noticing it. The ability of your emails to be classified as ‘spam’ by the machine using Virtual Assistants in our IOS and android devices to translate from a different language to your own language. Natural Language Processing makes all of them possible today with reliable accuracy.
Who benefits the most by using NLP?
NLP is used to understand the structure and the meaning of human language, which then is transformed into rule-based, machine learning algorithms that solve specific problems and perform desired tasks. Content marketers, strategists, and writers can use it to understand how computers understand the human language and use this knowledge when crafting their own pieces of writing. This promotes the basic idea of web 3.0 where instead of a centralized system people are supporting a decentralized system where the jobs are categorized and hired on the basis of a person’s creativity and ability to innovate and learn new things.
What is deep learning?
Deep Learning on many of the sites on Google is defined as a ‘subset of machine learning’. I have come across the statement so many times but it only makes sense to me to say that it is only partially correct. In my humble opinion I feel like Deep Learning is another field of technology which has a similar job as machine learning but is better and faster than it and if till now people have only been trying to explore more about deep learning by considering it as a subset of machine learning then there is a very vast potential in this field that is yet to be explored.
Deep learning attempts to mimic the human brain albeit far from matching its ability enabling systems to cluster data and make predictions with incredible accuracy.
Deep learning is at the core of many artificial intelligence (AI) applications and services that automate analytical and physical tasks without requiring human intervention. The technology behind deep learning is behind everyday products (like digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (like self-driving cars).
How is deep learning different from machine learning?
Deep learning distinguishes itself from classical machine learning by the type of data that it works with and the methods in which it learns.
Machine learning algorithms use structured, labeled data to make predictions that are defined from input data and organized into tables. However, if it does use unstructured data, it typically goes through some pre-processing to organize it into a structured format before using it.
Deep Learning eliminates some of the pre-processing that is typically involved with machine learning. Using these algorithms, unstructured data, such as text and images, can be ingested and processed, and feature extraction can be automated, reducing the need for human expertise. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize them by “lion”, “dog”, “chameleon”, et cetera. Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another. In machine learning, this hierarchy of features is established manually by a human expert.
Where deep learning is used?
Assisting the law
Deep learning algorithms can analyze and learn from transactional data to identify dangerous patterns that indicate possible fraudulent or criminal activity. Speech recognition, computer vision, and other deep learning applications can improve the efficiency and effectiveness of investigative analysis by extracting patterns and evidence from sound and video recordings, images, and documents, which helps law enforcement analyze large amounts of data more quickly and accurately.
Many organizations incorporate deep learning into their organizations. Deep learning algorithms can analyze and learn from transactional data to identify dangerous patterns that indicate possible fraudulent or criminal activity. . Chatbots used in a variety of applications, services, and customer service portals are a straightforward form of AI. Traditional chatbots use natural language and even visual recognition, commonly found in call center-like menus. However, more sophisticated chatbot solutions attempt to determine, through learning, if there are multiple responses to ambiguous questions. Based on the responses it receives, the chatbot then tries to answer these questions directly or route the conversation to a human user.
Virtual assistants like Apple's Siri, Amazon Alexa, or Google Assistant extends the idea of a chatbot by enabling speech recognition functionality. This creates a new method to engage users in a personalized way.
What is Computer Vision?
Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.
It is a subset of Artificial Intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs and take actions or make recommendations based on that information.
How does computer vision work?
Computer Vision requires a lot of data to work. It runs analyses of data over and over until it discerns distinctions and ultimately recognizes images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.
Machine learning and deep learning are essential technologies for computer vision. By feeding data into algorithmic models, computers can learn to distinguish one image from another. This process is helped by convolutional neural networks (CNNs), which break down images into pixels that are given tags or labels. Through a series of iterations, the computer runs convolutions and checks the accuracy of its predictions until it starts to recognize images in a way similar to humans.
How Computer Vision and AI Help Fight Climate change?
Artificial Intelligence has a lot of applications in the modern industrial world. AI can help monitor and detect, manage, and advance environmental science technology to help fight climate change. The computer vision field of AI can also help even more with the added ability of being able to see and comprehend complex visual data that might take humans months or years to notice on their own. Computer Vision can be used to detect and monitor climate change shifting factors. By monitoring patterns that might otherwise elude the naked eye, by constant and consistent observation beyond human capability, computer vision programs can detect subtle shifts that may be a result of climate change.
One of the biggest benefits of utilizing AI in the fight against changes in Earth's climate is the ways in which AI can detect and even suggest ways to reduce carbon emissions.
One creative way to use computer vision to fight climate change is to be able to monitor and manage certain factors such as traffic flow.
Computer vision can help manage and reduce the impact of climate change in a number of ways. For example, it can be used to more efficiently distribute electricity, which can in turn help reduce carbon emissions. Additionally, computer vision can be used to monitor for potential dangers related to the power grid, such as power surges or areas at risk for flooding or wildfires. By detecting these dangers early on, computer vision can help prevent catastrophic failures that could cause damage to the environment or people.
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