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Introduction

1.1 AI – Artificial Intelligence

AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human. It refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more.

1.2 ML – Machine Learning

Machine learning is a subset of artificial intelligence that automatically enables machines to extract knowledge from data and learn from it autonomously. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions.


Connection Between AI And ML

AI is a broader concept that encompasses any technique that allows computers to mimic human intelligence. ML is one of the ways to achieve this goal within the field of AI. ML is considered a subset of AI because it focuses on the development of algorithms that enable computers to learn patterns from data and make predictions or decisions without being explicitly programmed. ML is a key component of many AI systems. Instead of being explicitly programmed to perform a task, an AI system using ML learns from examples and experiences. ML algorithms allow AI systems to improve their performance on a specific task over time as they are exposed to more data. This adaptability is a crucial aspect of intelligent systems.


Difference Between AI And ML

Artificial Intelligence

  • AI allows a machine to simulate human intelligence to solve problems
  • Goal - to develop an intelligent system that can perform complex tasks
  • Builds systems that can solve complex tasks like a human
  • It has a wide scope of applications
  • It uses technologies in a system so that it mimics human decision-making
  • It works with all types of data: structured, semi-structured, and unstructured
  • It systems use logic and decision trees to learn, reason, and self-correct

Machine Learning

  • ML allows a machine to learn autonomously from past data
  • Goal is to build machines that can learn from data to increase the accuracy of the output
  • Trains machines with data to perform specific tasks and deliver accurate results
  • Machine learning has a limited scope of applications
  • It uses self-learning algorithms to produce predictive models
  • It can only use structured and semi-structured data
  • It systems rely on statistical models to learn and can self-correct when provided with new data

Key Components

4.1 Rule-Based Systems

Traditional AI systems use predefined rules and logic to perform tasks.

4.2 Expert Systems

Utilize knowledge bases and reasoning engines to mimic human decision-making in specific domains.

4.3 Natural Language Processing (NLP)

Enables machines to understand and generate human language.

4.4 Computer Vision

Allows machines to interpret and understand visual information.


Benefits

  • Wider data ranges: Analyzing and activating a wider range of unstructured and structured data sources.
  • Faster decision-making: Improving data integrity, accelerating data processing, and reducing human error for more informed, faster decision-making.
  • Efficiency: Increasing operational efficiency and reducing costs.
  • Analytic integration: Empowering employees by integrating predictive analytics and insights into business reporting and applications.

Applications

AI

  • Virtual Personal Assistants (example - Siri, Alexa)
  • Autonomous Vehicles
  • Game Playing (e.g., chess-playing programs)
  • Robotics
  • Healthcare Diagnostics

ML

  • Spam Filters
  • Recommendation Systems (e.g., Netflix recommendations)
  • Image and Speech Recognition
  • Natural Language Processing
  • Predictive Analytics

Other Applications

  • Software and Tools: AI-ML development often involves the use of specialized software libraries and tools. TensorFlow, PyTorch, scikit-learn, and other frameworks are employed for building and deploying machine learning models. Understanding and utilizing these tools require technical proficiency.
  • Algorithm Designs: Designing effective machine learning algorithms involves a solid understanding of mathematical concepts, linear algebra, and statistics. Technical expertise is necessary to design algorithms that can learn patterns from data and generalize well to new, unseen data.
  • Hardware Integration: As AI-ML models become more complex, there is a need for specialized hardware acceleration (e.g., GPUs, TPUs) to speed up training and inference. Integrating these hardware components into the AI-ML workflow requires technical knowledge.
  • Data Processing and Analysis: AI-ML heavily involves processing and analyzing large datasets. Technical skills are essential to manipulate and preprocess data, extract relevant features, and ensure that data is suitable for training and testing machine learning models.

Conclusion

In summary, AI seeks to create intelligent systems, while ML is a specialized technique within AI that emphasizes learning from data. Both fields are interconnected, with AI encompassing various approaches, including ML, to achieve the creation of intelligent machines that can perform tasks autonomously.