How_AIs_Work_Elementary (1)
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Supervised AIs learn through example training with labeled data, while unsupervised AIs find patterns and structure in unlabeled data. AIs analyze vast amounts of numerical data to learn without direct instruction, discovering hidden rules and grouping similar information, though humans must provide oversight to ensure AIs are working as intended and their outputs can be trusted.
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The document discusses the basics of data and AI. It explains that data contains rows and columns, with each row representing an example for the AI to train on. Features describe the examples, while labels categorize them. It provides an example of using text data describing feelings as features to train a model to predict if a feeling is happy or sad. The document also defines an AI service as a program that takes new questions or data as input and uses a pre-trained model to generate predictions or answers that can be used by an application.
Ai basics
The document discusses artificial intelligence and machine learning. It defines AI as how computers mimic human brains, and provides examples like Google Translate, Alexa, and Siri. Machine learning is described as a subset of AI where machines learn from data, and can be supervised by providing examples, unsupervised with no information given, or reinforcement using a reward system. Deep learning is presented as a sophisticated version of machine learning.
Ai lifecycle and navigator
The document discusses the AI workflow and different software options that can be used. The typical AI workflow involves identifying a problem, preparing data, developing and training models, testing models, deploying models, connecting to applications, and monitoring and optimizing models in an iterative process. It then shows diagrams of different software flows using Amazon SageMaker and Scikit-Learn Serverless for AWS, Google Cloud AI Platform for Google Cloud, and using AWS services like S3, SageMaker, and Lambda. It promotes the company's own AI Navigator software for building, deploying, and managing AI applications on AWS at scale.
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Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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Chapter 4
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Chapter 5
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- 2. How an AI works
- 3. Supervised AIs learn by example - Where the right answer is given Big idea #1 AIs need to learn Dog Cat Unsupervised AIs learn without answers - Like grouping things that look similar
- 4. Big idea #2 AIs learn Patterns โ They can find patterns in the data โ They can decide which patterns are the most important โ They can find hidden rules
- 5. Big idea #3 They can learn from all sorts of data! Pictures Sound Text Numbers Even videos
- 6. Big idea #4 They Only Know Numbers Everything has to be converted to a number! Images โ Pixels โ Numbers Text โ Words โ Numbers
- 7. Big idea #5 Humans Have to Use Them Carefully โข Humans need to be able to trust the AI โข Humans need to apply judgement and decide what to teach the AI โข It's important to know how well the AI is learning and working