The AWS Masterclass on Artificial Intelligence by Olivier Klein

Page 2 : Introduction To AWS Cloud & Artificial Intelligence, The Road To AI


The AWS Masterclass on AI Key Points (Part 1)

Here is an exhaustive list of key takeaway points from the AWS Masterclass on Artificial Intelligence, with their individual timestamps in the video :

Introduction To AWS Cloud

  • AWS has 16 regions around the world (0:51), with two or more availability zones per region (1:37), and 76 edge locations (1:56) to accelerate end connectivity to AWS services.
  • AWS offers 90+ cloud services (3:45), all of which use the On-Demand Model (4:38) – you pay only for what you use, whether that’s a GB of storage or transfer, or execution time for a computational process.
  • You don’t even need to plan for your requirements or inform AWS how much capacity you need (5:05). Just use and pay what you need.
  • AWS has a practice of passing their cost savings to their customers (5:59), cutting prices 61 times since 2006.
  • AWS keeps adding new services over the years (6:19), with over a thousand new services introduced in 2016 (7:03).
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Introduction to Artificial Intelligence, Machine Learning, Deep Learning

  • Artificial intelligence is based on unsupervised machine learning (7:45), specifically deep learning models.
  • Insurance companies like AON use it for actuarial calculations (7:59), and services like Netflix use it to generate recommendations (8:04).
  • A lot of AI models have been built specifically around natural language understanding, and using vision to interact with customers, as well as predicting and understanding customer behaviour (9:23).
  • Here is a quick look at what the AWS services management console looks like (9:58).
  • This is how you launch 10 compute instances (virtual servers) in AWS (11:40).
  • The ability to access multiple instances quickly is very useful for AI training (12:40), because it gives the user access to large amounts of computational power, which can be quickly terminated (13:10).
  • Machine learning, or specifically artificial intelligence, is not new to, the parent company of AWS (14:14).
  • uses a lot of AI models (14:34) for recommendations and demand forecasting.
  • The visual search feature in Amazon app uses visual recognition and AI models to identify a picture you take (15:33).
  • Olivier introduces Amazon Go (16:07), a prototype grocery store in Seattle.
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The Road to Artificial Intelligence

  • The first component of any artificial intelligence is the “ability to sense the real world” (18:46), connecting everything together.
  • Cheaper bandwidth (19:26) now allows more devices to be connected to the cloud, allowing more data to be collected for the purpose of training AI models.
  • Cloud computing platforms like AWS allow the storage and processing of all that sensor data in real time (19:53).
  • All of that information can be used in deep learning models (20:14) to create an artificial intelligence that understands, in a natural way, what we are doing, and what we want or need.
  • Olivier shows how machine learning can quickly solve a Rubik’s cube (20:47), which has 43 quintillion unique combinations.
  • You can even build a Raspberry Pi-powered machine (24:33) that can solve a Rubik’s cube puzzle in 0.9 seconds.
  • Some of these deep learning models are available on Amazon AI (25:11), which is a combination of different services (25:44).
  • Olivier shows what it means to “train a deep learning model” (28:19) using a neural network (29:15).
  • Deep learning is computationally-intensive (30:39), but once it derives a model that works well, the predictive aspect is not computationally-intensive (30:52).
  • A pre-trained AI model can be loaded into a low-powered device (31:02), allowing it to perform AI functions without requiring large amounts of bandwidth or computational power.
  • Olivier demonstrates the YOLO (You Only Look Once) project, which pre-trained an AI model with pictures of objects (31:58), which allows it to detect objects in any video.
  • The identification of objects is the baseline for autonomous driving systems (34:19), as used by Tu Simple.
  • Tu Simple also used a similar model to train a drone to detect and follow a person (35:28).

Next Page > Sensing The Real World, Retrospective & Real-Time Analysis

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