From 4b5a7d5d77550fa1968547541f3f5d4519e700b9 Mon Sep 17 00:00:00 2001
From: Jake Howard <git@theorangeone.net>
Date: Fri, 28 Oct 2022 16:03:22 +0100
Subject: [PATCH] Add in some speaker notes

---
 src/index.html | 94 +++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 90 insertions(+), 4 deletions(-)

diff --git a/src/index.html b/src/index.html
index f1aef3b..af09bcc 100644
--- a/src/index.html
+++ b/src/index.html
@@ -14,47 +14,133 @@
             <p class="has-text-primary is-size-2">🧍 Jake Howard</p>
             <p class="has-text-primary is-size-6">🐦 <a class="has-text-primary" href="https://twitter.com/realorangeone">@RealOrangeOne</a></p>
             <p class="is-size-4">👨‍💻 Senior Systems Engineer @ <span class="has-text-torchbox">Torchbox</span></p>
+            <aside class="notes" data-markdown>
+              - Who am I?
+                - Systems @ Torchbox
+                - We're hiring btw
+              - In my spare time...
+            </aside>
           </section>
           <section data-background-image="https://studentrobotics.org/images/content/blog/sr2022/arena.jpg">
             <h1 class="r-fit-text text-shadow">I Build Robots*</h1>
             <p class="text-shadow">* I help <em>others</em> build robots</p>
+            <aside class="notes" data-markdown>
+              - I build robots
+              - Help _others_ build robots
+            </aside>
+          </section>
+          <section data-background-iframe="https://www.youtube-nocookie.com/embed/xiDS58Htuh4?autoplay=1&start=24">
+            <aside class="notes" data-markdown>
+              - Student Robotics
+              - Charity to help students get into STEM
+              - Autonomous robotics competition
+              - 16 - 19 year olds
+              - Always looking for sponsors
+            </aside>
           </section>
-          <section data-background-iframe="https://www.youtube-nocookie.com/embed/xiDS58Htuh4?autoplay=1&start=24"></section>
         </section>
         <section>
           <section data-background-image="https://live.staticflickr.com/8718/17123916289_1cbc4c5210_k.jpg">
-            <h1 class="r-fit-text text-shadow">Robots need to see things</h1>
+            <h1 class="r-fit-text text-shadow">Environment</h1>
+            <aside class="notes" data-markdown>
+              - Robots need to sense their environments
+              - As humans, we rely quite a lot on sight
+              - Competitors, as humans, also do the same
+            </aside>
           </section>
           <section data-background-image="https://live.staticflickr.com/2837/33771948196_3cf1b5e3e5_k.jpg">
+            <aside class="notes" data-markdown>
+              - Sight is a powerful sense
+              - Ultrasound sensors can't distinguish between objects
+              - Switches are dull
+              - Eyes can detect objects, get distances, colour etc
+              - Only if they know what they're doing
+            </aside>
           </section>
         </section>
         <section data-background-image="https://images.unsplash.com/photo-1504639725590-34d0984388bd?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1000&q=80">
           <h2 class="text-shadow">Computer Vision</h2>
+          <aside class="notes" data-markdown>
+            - Requires the world of computer Vision
+            - Been around since late 1960s
+            - Universities pioneering early AI
+              - Not machine learning, the old kind
+              - Lots of `if` statements
+            - Some techniques unchanged to this day
+          </aside>
         </section>
         <section>
           <section data-background-image="./img/object-recognition.png">
             <h2 class="text-shadow">Object Recognition</h2>
             <h4 class="text-shadow">The <span class="has-text-success">hot new thing</span></h4>
+            <aside class="notes" data-markdown>
+              - More modern machine learning
+              - 2 stages
+                - First you train a model
+                - Use that model to detect things
+              - Not quite ideal for our use case...
+            </aside>
           </section>
           <section data-background-image="./img/not-hotdog.png">
             <h2 class="text-shadow">Prone to errors</h2>
+            <aside class="notes" data-markdown>
+              - Prone to errors
+              - Different lighting / shadows can affect detection
+              - Black box
+            </aside>
           </section>
           <section data-background-image="https://images.unsplash.com/photo-1558494949-ef010cbdcc31?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1000&q=80">
             <h2 class="text-shadow">Computationally intensive</h2>
+            <aside class="notes" data-markdown>
+              - Tonnes of computation
+              - However is done once upfront
+              - Fairly fast to detect
+              - Our robos are just Raspberry Pis
+            </aside>
+          </section>
+          <section>
+            <h2 class="r-fit-text">What else?</h2>
+            <aside class="notes" data-markdown>
+              - What other options do we have?
+            </aside>
           </section>
         </section>
         <section>
           <section data-background-image="https://april.eecs.umich.edu/media/apriltag/apriltagrobots_overlay.jpg">
             <h2 class="text-shadow">Fiducial Markers</h2>
+            <aside class="notes" data-markdown>
+              - Enter fiducial markers!
+              - Look sorta like QR codes
+              - Just a single number
+              - Simpler, so they're easier to detect
+            </aside>
           </section>
           <section data-background-image="https://docs.opencv.org/4.x/singlemarkersaxes.jpg">
-            <h2 class="text-shadow">Pose Estimation</h2>
+            <h2 class="text-shadow">Location</h2>
+            <aside class="notes" data-markdown>
+              - Accurately detect edges, see where in our FoV it is
+              - If we know how big it's meant to be, derive distance
+              - We know it's a square, derive angles
+            </aside>
           </section>
           <section data-background-image="https://live.staticflickr.com/2827/32969459924_164c509e20_k.jpg">
             <h2 class="text-shadow r-fit-text">We put them <strong>everywhere</strong>!</h2>
+            <aside class="notes" data-markdown>
+              - Abundance of sources
+                - Arena walls
+                - Game props (tokens etc)
+              - Location of any of them
+              - If you know where the marker is, you know where you are
+            </aside>
           </section>
           <section>
-            <h2 class="r-fit-text">How do they work?</h2>
+            <h2 class="r-fit-text">How do fiducial markers work?</h2>
+            <aside class="notes" data-markdown>
+              - All well and good knowing they exist
+              - Tools out there to help make it easier
+              - Not good enough
+              - How does it actually _work_?
+            </aside>
           </section>
         </section>
       </div>