NLP, Computer Vision, AI Optimized Hardware

Updated: Nov 15

Natural Language Processing

Natural language processing (NLP) is a field of AI tasked with helping machines process, understand, and interpret human language to perform tasks. Examples of NLP include classifying, summarizing, and spell-checking text, voice text messaging, and AI assistants like Siri, Alexa, or Google Assistant. An excellent example of NLP in an application is found in Grammarly, which uses machine learning, deep learning, and NLP to improve people’s writing.

In businesses, natural language processing can be used to analyze big data in the forms of social media comments, customer support, news reports, online reviews, etc. NLP allows machines to make sense of the everyday language that humans use to make better use of all data more consistently, accurately, and in real time. It is also able to help machines classify text by mood and sentiment, which is great when used in companies for customer calls on issues or satisfaction. When this is used to understand pieces of text, businesses can then prioritize and organize data based on their own needs.

Natural language processing works by separating a text into fragments so that words and phrases can be analyzed deeper. Then computers can read and understand the text similarly to humans. Before a machine can understand language, it has to undergo tasks such as breaking up the text into smaller clauses, marking nouns, verbs, adjectives, etc., reducing words to their roots, and disregarding prepositions. Once this is done, NLP algorithms can be used to interpret the natural language and perform tasks based on what is needed. These algorithms can be in the form of machine learning or rule-based. With rule-based NLP, there are set rules for linguistics regarding grammar and sentence structure. When machine learning is used, computers are trained with statistics and example data. The benefit of using machine learning with NLP is that a computer can learn on its own once trained with initial data.

Natural language processing is important to the field of AI and is present in many modern applications. Methods for natural language processing are still being studied, and they will continue to be a large part of the future of AI. We’ll see NLP begin to play an increasingly important and mainstream role in businesses and other applications with the use of text data analysis, customer analytics, and smart assistants or chatbots.

Computer Vision

Computer vision (CV) is one of the easiest fields of AI to define but one of the hardest to execute with computers. CV in the field of AI trains computers to understand and interpret data from the visual world which includes images, videos, models, and objects. If machines can think and learn on their own, it will be important for them to also observe and understand the natural world. Human vision is intricate and complex, involving retinas, visual cortexes, and optic nerves. With it, we can tell the space between objects, the distance between ourselves and a thing, and whether something is moving or stationary. The goal of a CV is to give these abilities to machines so that more data can be processed and put to use. This can be done with cameras, data, and algorithms.

Computer vision is a process that needs abundant amounts of data to work. Machines will need to analyze data multiple times to see distinctions and recognize objects and images. Machine learning is important in CV algorithms that allow computers to teach themselves through mass amounts of visual data. Once this is achieved, a machine will be able to recognize one image from another without human intervention. As you can see, this is why deep learning plays a fundamental role in helping machines “see.” Another essential tool is a convolutional neural network or CNN. A CNN is a deep learning algorithm that can take any input image, assign importance (bias) to the aspects and objects in the image, and then differentiate these objects from each other. These algorithms are important in CV as machines need to break down images to label what they “see.” Then, predictions can be made on what is being seen, and the neural network can run computations or mathematical operations to check the accuracy.

Computer vision is a complex field that has been studied and developed for over 60 years. Today, computer vision has begun to make a breakthrough in modern technology. Computers are learning to recognize faces, objects, and places in a way that surpasses human abilities. As stated by data scientist Wayne Thompson, “Computer vision is one of the most remarkable things to come out of the deep learning and artificial intelligence world. The advancements that deep learning has contributed to the computer vision field have set this field apart.” CV is being used to detect counterfeit and defective objects including currency, enable facial recognition in security and retail applications, detect leaks and problems with pipelines, and see early signs of disease in humans and plants. In 2018, computer vision identified an estimated 1,735,350 cancer cases.

Computer vision is another exceptional example of how machine learning, deep learning, and neural networks are shaping the modern world and paving the way for more technological advancements. Computer vision is still something that is being learned, and improvements in the field should be plentiful as technology advances.

AI Optimized Hardware

Much of what is discussed with AI is the software and algorithms, but the hardware or physical devices that run the operations are equally important. More complex AI systems will require more computational power. This introduces the topic of new hardware designed specifically to optimize AI or AI-optimized hardware. This hardware is designed to accelerate the performance of neural networks and reduce energy consumption. AI hardware differs from general hardware in that it refers to accelerators: microprocessors and microchips created to allow faster and smoother processing of AI applications such as machine learning.

AI hardware is essential to conserving cost, and energy, and maximizing computational power. It also allows for cloud and edge computing. As technology advances, computational resources are becoming more in demand. This is why chips with newer and better capabilities specifically for AI are needed. New architectures, like neuromorphic chips that mimic brain cells, will allow for faster insights. Materials will need to move away from traditional CPU and GPUS.

Current machines require powerful processing power with dedicated hardware. The types of hardware widely used with AI today are the Central Processing Units (CPU), Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). The CPU handles the majority of a machine’s basic computing tasks, and the GPU is optimized to render graphics and images but can also provide the processing and computing power needed to support AI. Where a traditional GPU falls short is with convolutions. GPUs are optimized for graphics, not operations or neural networks. The central idea behind AI hardware or accelerators is that the computations will be parallel. They will contain a general-purpose GPU that, when used in parallel computing with FPGAs and ASICs specialized for AI, can deliver much faster performance rates than a CPU.

AI chips will increase speed and efficiency with the incorporation of transistors, and semiconductor devices used to amplify, control, and generate electrical signals. These new AI chips can be thousands of times more efficient in training AI algorithms than CPUs. New AI systems will require these AI-specific chips to be state-of-the-art to stay enough cost-effective, speed-efficient, and energy-conscious.

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Written by M. Abdullah Khan

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