The Impact of Artificial Intelligence on Modern Computing
Artificial intelligence (AI) is a key factor in modern computing
Artificial intelligence (AI) is a key factor in modern computing innovation, enabling computers to perform tasks that are usually associated with human intellectual processes, such as reasoning. AI can help computers perceive their environment, solve problems, and act to achieve a goal. Here are some ways AI is used in modern computing:
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| AI On Modern Computing |
- Optical character recognition (OCR)
- Natural language processing (NLP)
- Computer vision
- Software development
- OPTICAL CHARACTER RECOGNITION(OCR):
Uses AI to extract text and data from images and documents, turning unstructured content into structured data that can be used by businesses. Optical character recognition (OCR) is a technology that converts images of typed, handwritten, or printed text into machine-readable text. OCR can be used to extract text from documents, photos, scene photos, and even subtitle text. OCR is also known as text recognition or text extraction.
OCR can be used to automate data capture from forms, which can reduce data entry costs and improve accuracy. It can also be used to eliminate paper and reduce manual identification and data entry into other systems. This can help businesses reduce costs, accelerate processes, and improve service.
OCR has its origins in 1913 when Dr. Edmund Fournier d'Albe invented the Optophone, which could scan and convert text into sound for people who are visually impaired. The technology became more prominent in the 1990s when historical newspapers were digitized, and has continued to advance with the rise of smartphones and electronic documents.
- NATURAL LANGUAGE PROCESSING(NLP):
Teaches computers to understand and produce written and spoken language, similar to humans. NLP combines computer science, linguistics, machine learning, and deep learning to help computers analyze unstructured text or voice data and extract relevant information. NLP is used for things like spam detection and virtual assistants.
NLP techniques:
Sentiment analysis: Classifies the emotional intent of text, such as whether the sentiment is positive, negative, or neutral. This technique is also known as opinion mining or emotion AI. It can be used to analyze customer reviews or identify signs of mental illness in online comments.
Machine translation: Automatically translates text from one language to another. This technique is useful for providing translation services and multilingual support in global applications.
Named entity recognition: Identifies and categorizes named entities, such as people, locations, dates, and organizations.
Topic modeling: Groups similar words and phrases together to identify the main topics or themes in a collection of documents or text.
- COMPUTER VISION:
Teaches machines how to interpret the visual world by analyzing visual information like camera images and videos. Computer vision systems can use deep learning models to learn to identify and classify objects. Computer vision is a type of artificial intelligence (AI) that allows computers to interpret and analyze visual inputs, similar to how humans see and understand the world. It uses machine learning and neural networks to identify and classify objects in digital images and videos, and then allows computers to react to what they see.
Computer vision can be used for many applications, including:
- Facial recognition: Uses optical input to analyze images, particularly faces, and can be used for face identification, grouping, and verification
- Object detection: Uses machine learning or deep learning to locate objects in images or videos, and can be used in manufacturing to monitor production lines, or in connected home cameras to detect people and objects in real time
- Image segmentation: Partitions a digital image into groups of pixels to inform object detection.
Other applications of computer vision include:
ARTIFICIAL INTELLIGENCE ON MODERN COMPUTING:
- Self-driving cars
- Robotic automation
- Medical anomaly detection
- Sports performance analysis
- Manufacturing fault detection
- Agricultural monitoring
- Plant species classification
- SOFTWARE DEVELOPMENT:
AI can automate routine tasks, optimize resource utilization, and improve the overall project workflow. This can lead to more efficient development processes and higher-quality software outcomes. AI can also help enhance software performance and user experience.
AI is also used in other areas, such as finance, where banks, insurers, and financial institutions use it for applications like detecting fraud, conducting audits, and evaluating customers for loans. AI is also used in autonomous systems like self-driving cars and drones.
Artificial intelligence (AI) and machine learning (ML) are changing how software developers approach problem-solving and innovation at every stage of the software development life cycle (SDLC). AI can help developers improve efficiency, save time and money, and create better software:
1.Coding:
AI can help developers write better code by identifying patterns, spotting errors, and recommending fixes. It can also automate tasks like debugging and testing, and suggest changes or explain why a line of code is incorrect.
2.Design:
AI can help developers improve the design process and ensure products meet user expectations. For example, AI can help analyze user requirements and brainstorm solutions during the planning and design phases.
3.Testing:
AI can automate test creation and execution, and accurately detect UI/UX issues.
4.Project optimization:
AI can help developers create scalable and efficient workflows to reduce time-to-market and save money.
Writer: Lithikaa Maruthasalam
BCA Student at Dr SNS rajalakshmi college of arts and science
