The Technology Behind XR | Virtual and Augmented Reality

Table of Contents:
02:08 - The Six Elements of XR Design
02:31 - Technical components
02:58 - The Six Elements of XR Design
03:07 - Technical components
03:09 - Hey
03:26 - Viewing Method: Stereoscopy
04:28 - Binocular Vision
04:38 - Stereoscopy vs Normal Image
04:54 -
05:28 -
05:30 -
05:50 - Stereoscopic VR
06:42 - Head Mounted AR Displays
08:17 - What ‘could’ be here within a decade?
08:20 - Expand into haptics?
09:59 -
10:41 - Scent?
12:06 - Taste?
13:55 - 2. Tracking Sensors
14:35 - Accelerometers
15:19 - Issues
15:58 - Gyroscopes
16:26 - Cameras
17:01 - 3. Computing Power
17:52 - 4. Controllers
18:27 -
18:39 - AR
18:48 - Augmented and Mixed Reality
19:05 -
19:19 -
20:13 - AR uses similar/ the same tracking sensors to VR
(i.e., MEMS in your phone)
20:28 - Tracking Sensors: Gaze-Adaptive Augmented Reality
21:03 - AR Tracking Approaches
22:48 - Interfaces facilitate manipulating content
23:03 -
23:34 - AR + AI is the future?
24:46 - What else is Missing?
25:41 - AI
25:42 - Data is Everywhere!
26:08 - To Act On Data, We Must Make Sense of It
26:16 - 1. Measure
26:43 -
27:01 -
27:03 - Assumptions
27:12 - Is Boris Johnson an honest Prime Minister?
28:31 - 2. Organise
29:18 - Big Data
29:30 - What is an Algorithm?
29:43 -
30:26 -
30:38 - Current AI (simplified)
31:41 -
31:58 - 3. Synthesise
33:05 - Algorithms Going Deeper
34:15 - Algorithms That Evolve…
34:43 - Algorithms That Evolve…
37:01 -
37:07 - Remember: We can predict sleep (from IOT data)
39:47 - Example: Can AI Find a Good Cookie?
40:36 -
41:37 -
41:50 -
42:10 -
42:25 -
42:37 - Machine Learning
42:42 - We can identify people from their walk pattern
43:28 - 4. Apply
43:39 - Neural Networks
44:00 - Welcome Neural Networks
44:25 - Hierarchy Maps
44:26 - Example: FindFace
45:08 - You Are Training Neurones
45:21 - Unsupervised Machine Learning
45:48 -
46:02 - Unsupervised Machine Learning
46:05 -
46:45 - Unsupervised Machine Learning = Generalisation
46:59 - Machine Learning’s Limit
48:07 - Neural Networks are Taking Over Machine Learning/ Artificial Intelligence
48:23 - Learning is the ability to recognise and eventually reproduce patterns
48:30 - Example: Captain Beefheart (Frownland)
48:54 -
49:01 -
49:44 -
51:03 -
51:35 - Predictions Can Be Wrong
51:37 - Pre-Mortum on Illness Prediction
53:18 - A Brave New World?
55:04 -
55:08 - Path Dependance
55:10 -
55:12 - Beware Spurious Correlations - they wreck Machine Learning

[@mdandry]

Great video 👍🏻
I am just getting into AR/MR in a quest to build an environment in my mind. I appreciate the time you put into this video. It was very informative.

[@DesigneLearningTutorials]

Glad it was helpful!