The AWS Masterclass on Artificial Intelligence by Olivier Klein

Page 3 : Sensing The Real World, Retrospective & Real-Time Analysis

The AWS Masterclass on AI Key Points (Part 2)

Connecting Things and Sensing the Real World

  • Cloud services like AWS IoT (37:35) allow you to securely connect billions of IoT (Internet of Things) devices.
  • Olivier prefers to think of IoT as Intelligent Orchestrated Technology (37:52).
  • Olivier demonstrates how the combination of multiple data sources (maps, vehicle GPS, real-time weather reports) in Bangkok can be used to predict traffic as well as road conditions to create optimal routes (39:07), reducing traffic congestion by 30%.
  • The PetaBencana service in Jakarta uses picture recognition and IoT sensors to identify flooded roads (42:21) for better emergency response and disaster management.
  • Olivier demonstrates how easy it is to connect an IoT devices to the AWS IoT service (43:46), and use them to sense the environment and interact with.
  • Olivier shows how the capabilities of the Amazon Echo can be extended by creating an Alexa Skill using the AWS Lambda function (59:07).
  • Developers can create and publish Alexa Skills for sale in the Amazon marketplace (1:03:30).
  • Amazon Polly (1:04:10) renders life-like speech, while the Amazon Lex conversational engine (1:04:17) has natural language understanding and automatic speech recognition. Amazon Rekognition (1:04:29) performs image analysis.
  • Amazon Polly (1:04:50) turns text into life-like speech using deep learning to change the pitch and intonation according to the context. Olivier demonstrates Amazon Polly’s capabilities at 1:06:25.
  • Amazon Lex (1:11:06) is a web service that allows you to build conversational interfaces using natural language understanding (NLU) and automatic speech recognition (ASR) models like Alexa.
  • Amazon Lex does not just support spoken natural language understanding, it also recognises text (1:12:09), which makes it useful for chatbots.
  • Olivier demonstrates that text recognition capabilities in a chatbot demo (1:13:50) of a customer applying for a credit card through Facebook.
  • Amazon Rekognition (1:21:37) is an image recognition and analysis service, which uses deep learning to identify objects in pictures.
  • Amazon Rekognition can even detect facial landmarks and sentiments (1:22:41), as well as image quality and other attributes.
  • You can actually try Amazon Rekognition out (1:23:24) by uploading photos at CodeFor.Cloud/image.

Retrospective and Real-Time Data Analytics

  • AI is a combination of 3 types of data analytics (1:28:10) – retrospective analysis and reporting + real-time processing + predictions to enable smart apps.
  • Cloud computing is extremely useful for machine learning (1:29:57) because it allows you to decouple storage and compute requirements for much lower costs.
  • Amazon Athena (1:31:56) allows you to query data stored in Amazon S3, without creating a compute instance to do it. You only pay for the TB of data that is processed by that query.
  • Best of all, you will get the same fast results even if your data set grows (1:32:31), because Amazon Athena will automatically parallelise your queries across your data set internally.
  • Olivier demonstrates (1:33:14) how Amazon Athena can be used to run queries on data stored in Amazon S3, as well as generate reports using Amazon QuickSight.
  • When it comes to data analytics, cloud computing allows you to quickly bring massive computing power to bear, achieving much faster results without additional cost (1:41:40).
  • The insurance company AON used this ability (1:42:44) to reduce an actuarial simulation that would normally take 10 days, to just 10 minutes.
  • Amazon Kinesis and Amazon Kinesis Analytics (1:45:10) allows the processing of real-time data.
  • A company called Dash is using this capability to analyse OBD data in real-time (1:47:23) to help improve fuel efficiency and predict potential breakdowns. It also notifies emergency services in case of a crash.

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