Jaartal correctie

Keynote toegevoegd

content/evenementen/nluug/voorjaarsconferentie-2017/_index.md

@@ -27,7 +27,7 @@ event_organizers:
 event_attendance_mode: "offline"
 event_images:
 - /afbeeldingen/locaties/bunnik/postillion-hotel-bunnik-baan-van-fectio.jpg
-event_description: "Voorjaarsconferentie 2018 van de vereniging NLUUG in het Postillion Hotel te Bunnik"
+event_description: "Voorjaarsconferentie 2017 van de vereniging NLUUG in het Postillion Hotel te Bunnik"
 event_members_only: true
 event_register_link: "https://nluug.nl/aanmelden/index.cgi?action=event"
 event_status: "scheduled"
@@ -76,9 +76,9 @@ event_schedule:
     - column:
       talk:
         speaker: Jane Stewart Adams
-        title: "Simple, Distributed, Scalable: What ants, starlings, and slime mold can teach us about computers."
+        title: "Simple, Distributed, Scalable: What ants, starlings, and slime mold can teach us about computers"
         keynote: true
-        link: 
+        link: talks/jane-stewart-adams-simple-distributed-scalable/
       center: true
       size: 3
   - row:

content/evenementen/nluug/voorjaarsconferentie-2017/talks/jane-stewart-adams-simple-distributed-scalable.md

@@ -0,0 +1,31 @@
+---
+categories:
+- presentaties
+date: 2017-05-16T10:31:43+02:00
+description: ""
+layout: event-talk
+tags:
+- cloud
+- workloads
+title: "Jane Stewart Adams - Simple, Distributed, Scalable: What ants, starlings, and slime mold can teach us about computers"
+speakers:
+- jane-stewart-adams
+presentation:
+  filename: 
+recording:
+  platform: youtube
+  url: https://www.youtube.com/watch?v=R91aTvaHbbs
+---
+
+## Abstract
+
+There are biological analogs for many of our computational problems. Slime mold grows optimal networks, fruit fly brains select maximal independent sets during development, and swarms use distributed search to efficiently find food. These biological systems have inspired several algorithms and protocols, but there is much more to be leveraged.
+
+
+In this talk, we’ll examine a handful of biological systems that have, over many cycles of evolution, arrived at very simple algorithms that yield incredibly complex collective behaviors. By better understanding when, where, and how these algorithms emerge in natural systems, and how to spot them, we can better apply them to our computational problems, without having to wait for many cycles of evolution.
+
+## Biography
+
+Jane Stewart Adams is a data scientist, engineer, and writer living in Brooklyn. She has an undergraduate degree from New York University in complex systems, and a master's degree, also from New York University, in urban data science.
+
+Her writing has appeared in the Wall Street Journal, and she has several open source projects and artworks that focusing on Python, data science, and data stewardship. She works at Two Sigma doing data things to data.