SlideShare uma empresa Scribd logo
1 de 37
Baixar para ler offline
PATTERN RECOGNITION
FOR TECHNICAL COMMUNICATORS

      Kai Weber (@techwriterkai)
   & Chris Atherton (@finiteattention)
           22 September 2011
                TCUK 11
WHO ARE WE AND WHAT DO WE KNOW?

            Kai Weber                         Chris Atherton
            @techwriterkai                    @finiteattention


   Technical writer since 1988      User experience consultant


   Senior Technical Writer at       Mendeley, Skype, academia
    SimCorp, CPH, since 2008
                                     Incurable cross-disciplinarian
   Coach, trainer, mentor
                                     Ph.D. in Cognitive
   M.A. in American Studies          Neuroscience
OUR MISSION

   Helping you understand what you do …

   … so you can do what you do, better.
WHAT IS PATTERN RECOGNITION?




http://livinglifewithchemobrain.blogspot.com/2011/03/apparitions-on-toast.html
WHAT IS PATTERN RECOGNITION?

   Don’t believe that your brain is optimised to
    create patterns from apparent chaos? Watch this:
    http://www.youtube.com/watch?v=yVkdfJ9PkRQ
WHAT IS PATTERN RECOGNITION?




           Examples  rules
TOO ABSTRACT! HOW ABOUT AN EXAMPLE?

  Aardvark, J.R. (1980). Ants, and how to eat them.
    Journal of Orycteropodidae Studies, 80, 11-17.
  Barker, R. (1982). Rum babas, and what to do if you’ve got them.
    Reading: Goodnight From Him.
  Haley, W. (1955). Rock Around The Clock. New York: Decca.
  Izzard, E. (1998). Cake or Death? Gateaunomics, 10, 195-196.
  Lemur, R.-T. (2010). Strepsirrhinoplasty. Antananarivo: Raft
    Press.
  Leonard, E. (1996). Out of Sight. New York: Harper.
  Shorty, G. (in press). Okay, so they got me. Los Angeles: Cadillac.



What is this? What are the structures and rules here?
RECOGNISED PATTERNS AND RULES
  Aardvark, J.R. (1980). Ants, and how to eat them.
    Journal of Orycteropodidae Studies, 80, 11-17.
  Barker, R. (1982). Rum babas, and what to do if you’ve got them.
    Reading: Goodnight From Him. …


1. Last name, initial(s).
2. (Year of publication).
3. If journal article:
    1.   Title of article.
    2.   Title of journal, volume number, page numbers.
4. If book:
    1.   Title.
    2.   City: Publisher.
SO HOW DO WE ACQUIRE THESE RULES?

   By rote

    or

   By acquiring data (a.k.a. experience)
IMPLICIT VS. EXPLICIT KNOWLEDGE

   Gladwell, M. (2005). Blink. London: Penguin.
    http://www.gladwell.com/blink/index.html
WHY SHOULD TECH COMMUNICATORS CARE?
We do it anyway…

1. When we gather information
      Reading specs and designs
      Interviewing subject-matter experts


2. When we create and order information
      Write topics
      Structure topics into deliverables
WHY SHOULD TECH COMMUNICATORS CARE?
We do it anyway, so we might as well do it smartly!

   If we make sense of our subjects more efficiently…

   If we structure better what we need to convey…

   … we can provide better documentation!
THE PATTERN RECOGNITION EXPERIENCE




M   T   W   T   F   S   S   M   T   W   T   F   S   S   M   T   W   T
BUT HOW DO WE REACH THAT “AHA!” MOMENT?




   *    * *     * *   !
PERCEIVING PATTERNS
                        d
        *      d d
             d




                                   t
       * *     d dd
          * ** d



                        t
                         t t t
          * * * d

                          t t
                           t
           t    t t          t t
             t      t
           t     t
   t          t
                   t           t
       t
PERCEIVING PATTERNS
                        d
        *      d d
             d




                                   t
       * *     d dd
          * ** d



                        t
                         t t t
          * * * d

                          t t
                           t
           t    t t          t t
             t      t
           t     t
   t          t
                   t           t
       t
PERCEIVING PATTERNS
                        d
        *      d d
             d




                                   t
       * *     d dd
          * ** d



                        t
                         t t t
          * * * d

                          t t
                           t
           t    t t          t t
             t      t
           t     t
   t          t
                   t           t
       t
PERCEIVING PATTERNS
                        d
        *      d d
             d




                                   t
       * *     d dd
          * ** d



                        t
                         t t t
          * * * d

                          t t
                           t
           t    t t          t t
             t      t
           t     t
   t          t
                   t           t
       t
HOW DOES PATTERN RECOGNITION WORK?
Bottom-up processing

 Experiencing
 Acquiring

 Matching

 Segmenting



… building up a representation.

But that requires lots of “data”, so…
WHAT IS THIS? HOW DO YOU KNOW?
HOW DOES PATTERN RECOGNITION WORK?
Top-down processing

 Knowing
 Generalising

 Contextualising

 Applying



… searching for confirmation.
HOW DOES PATTERN RECOGNITION WORK?
Bottom-up                   Top-down

   No prior knowledge         Uses prior knowledge

   Elements  concepts        Concepts  elements

   Emphasises relations       Emphasises context

   Slow; usually correct      Quick; sometimes wrong
WHAT IS THIS?
WHAT IS THIS? IT’S ART...




Martin Boyce:
Untitled, 2002.
WHAT IS THIS? IT’S PART OF THE SAME CHAIR!




Martin Boyce:                          Arne Jacobsen:
Untitled, 2002.                        Chair 3107, c.1952.
It is the back side of the chair where the back rest
turns into the seat, with two holes cut in and turned by 90°.
HOW DOES PATTERN RECOGNITION WORK?
Bottom-up                   Top-down

   No prior knowledge         Uses prior knowledge

   Elements  concepts        Concepts  elements

   Emphasises relations       Emphasises context

   Slow; usually correct      Quick; sometimes wrong
PATTERN RECOGNITION IN TECH COMM

   To make sense of unknown subject matter

   To overcome tech writer’s block and start writing

   To chunk topics and find reuse opportunities

   To help your readers
PATTERN RECOGNITION IN TECH COMM
To make sense of unknown subject matter
 If you have scattered, unreliable information…
   Gather all puzzle pieces and work bottom up.
     Tease out similarities until you have segments.
PATTERN RECOGNITION IN TECH COMM
To make sense of unknown subject matter
 If you have structured legacy documentation…
   Go through topic structure and analyse top down.
     Test reliability and completeness top-down.
PATTERN RECOGNITION IN TECH COMM
To overcome writer’s block and start writing
 If you lack full, consistent information…
   Start bottom-up with similar “seeds” as templates.
       Describe first what hangs together well.

                                               Making caffe latte
   About Italian coffee
                                               1.     Grind coffee.
   1.    Espresso                              2.     Steam milk and ¾ fill a latte glass.
   2.    Cappuccino                            3.     Make the espresso and pour it in.
   3.    Caffe latte                           4.     Top the drink with steamed milk.
                                               5.     Clean the steamer.


              Making hot chocolate

              1.   Pour chocolate into glass or cup
              2.   Steam the milk and pour in.
PATTERN RECOGNITION IN TECH COMM
To chunk topics and find reuse opportunities
 If you have a bunch of similar information or topics
   Identify how you can segment topics for reuse.
       Especially for similar procedures and reference info.

                                               Making caffe latte
   About Italian coffee
                                               1.     Grind coffee.
   1.    Espresso                              2.     Steam milk and ¾ fill a latte glass.
   2.    Cappuccino                            3.     Make the espresso and pour it in.
   3.    Caffe latte                           4.     Top the drink with steamed milk.
                                               5.     Clean the steamer.


              Making hot chocolate

              1.   Pour chocolate into glass or cup
              2.   Steam the milk and pour in.
PATTERN RECOGNITION IN TECH COMM
To help your readers
… orient themselves in your documentation.

Tables of contents, no patterns left, with patterns right.
1.    General Settings window         1.    Setting up the Trade Manager
1.1   Assets in a portfolio           1.1   Set up a portfolio
1.2   Different bank accounts         1.2   Set up bank accounts
1.3   About counterparties            1.3   Set up counterparties

2.    The Transaction window          2.    Registering transactions
2.1   Transaction window              2.1   Enter common transaction data
2.2   Stock trading                   2.2   Enter a stock transaction
2.3   Trading bonds                   2.3   Enter a bond transaction
2.4   Futures and other derivatives   2.4   Enter a derivative transaction
PATTERN RECOGNITION IN TECH COMM
To help your readers
… grasp individual topics quickly

   Structure similar items similarly for easy recognition.
   Use the same order of elements, e.g., in procedures:
       Introduction         Enter a stock transaction
       Prerequisites
                             Open the Stock Dealer window.
       Procedure            1. Enter common transaction data.
       Results              2. Enter the stock exchange.
                             3. Optionally, enter the stock series.
       Exception handling
   Apply parallelism in lists
PATTERN RECOGNITION IN TECH COMM
To help your readers
… get the most out of navigation aids

   Table of contents is a top-down aid
       Offer a coherent, consistent structure
       Assume and honour trust in the system


   Search and index are bottom-up aids
       Support not only exact matches, but also similar terms
       Make search results indicative by heading alone
PATTERN RECOGNITION IN TECH COMM
To help your readers
… in ways only you know how!

   <audience brainstorm>
FINAL WORDS OF ADVICE AND WARNING

   Keep your customers – and your job – safe!

   Apophenia: Humans are addicted to meaning.

   Some patterns refuse to be recognized

   Pattern recognition occurs in contexts

   Creating tech comm is often a top-down process…
    ... but using it is often bottom-up!
THANK YOU! KEEP IN TOUCH!




            Kai Weber                     Chris Atherton



   @techwriterkai               @finiteattention

   kaiweber.wordpress.com/      about.me/cjatherton

Mais conteúdo relacionado

Destaque

What is pattern_recognition (lecture 1 of 6)
What is pattern_recognition (lecture 1 of 6)What is pattern_recognition (lecture 1 of 6)
What is pattern_recognition (lecture 1 of 6)Randa Elanwar
 
Некоторые алгоритмы многомерной обработки изображений
Некоторые алгоритмы многомерной обработки изображенийНекоторые алгоритмы многомерной обработки изображений
Некоторые алгоритмы многомерной обработки изображенийMSU GML VideoGroup
 
Портрет по фото на заказ
Портрет по фото на заказПортрет по фото на заказ
Портрет по фото на заказkrucopa
 
A study on face recognition technique based on eigenface
A study on face recognition technique based on eigenfaceA study on face recognition technique based on eigenface
A study on face recognition technique based on eigenfacesadique_ghitm
 
JPG vs. GIF vs. PNG
JPG vs. GIF vs. PNGJPG vs. GIF vs. PNG
JPG vs. GIF vs. PNGkay2
 
Визуализация данных: как превратить числа в образы
Визуализация данных: как превратить числа в образыВизуализация данных: как превратить числа в образы
Визуализация данных: как превратить числа в образыCEE-SEC(R)
 
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов.
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов. Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов.
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов. Yandex
 
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHES
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHESSECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHES
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHESranjit banshpal
 

Destaque (13)

Csc446: Pattren Recognition (LN1)
Csc446: Pattren Recognition (LN1)Csc446: Pattren Recognition (LN1)
Csc446: Pattren Recognition (LN1)
 
What is pattern_recognition (lecture 1 of 6)
What is pattern_recognition (lecture 1 of 6)What is pattern_recognition (lecture 1 of 6)
What is pattern_recognition (lecture 1 of 6)
 
Некоторые алгоритмы многомерной обработки изображений
Некоторые алгоритмы многомерной обработки изображенийНекоторые алгоритмы многомерной обработки изображений
Некоторые алгоритмы многомерной обработки изображений
 
File types pro forma(1)
File types pro forma(1)File types pro forma(1)
File types pro forma(1)
 
Digital imaging
Digital imagingDigital imaging
Digital imaging
 
7 3-2
7 3-27 3-2
7 3-2
 
Портрет по фото на заказ
Портрет по фото на заказПортрет по фото на заказ
Портрет по фото на заказ
 
A study on face recognition technique based on eigenface
A study on face recognition technique based on eigenfaceA study on face recognition technique based on eigenface
A study on face recognition technique based on eigenface
 
CSC446: Pattern Recognition (LN6)
CSC446: Pattern Recognition (LN6)CSC446: Pattern Recognition (LN6)
CSC446: Pattern Recognition (LN6)
 
JPG vs. GIF vs. PNG
JPG vs. GIF vs. PNGJPG vs. GIF vs. PNG
JPG vs. GIF vs. PNG
 
Визуализация данных: как превратить числа в образы
Визуализация данных: как превратить числа в образыВизуализация данных: как превратить числа в образы
Визуализация данных: как превратить числа в образы
 
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов.
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов. Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов.
Анализ изображений и видео. Поиск по подобию, поиск нечетких дубликатов.
 
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHES
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHESSECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHES
SECURE IMAGE RETRIEVAL BASED ON HYBRID FEATURES AND HASHES
 

Semelhante a Kai Weber & Chris Atherton - Pattern recognition for technical communicators - tcuk11

Pattern recognition for technical communicators
Pattern recognition for technical communicatorsPattern recognition for technical communicators
Pattern recognition for technical communicatorsamelio
 
Pattern recognition for UX - 13 April 2013
Pattern recognition for UX - 13 April 2013Pattern recognition for UX - 13 April 2013
Pattern recognition for UX - 13 April 2013amelio
 
Discussion 8
Discussion 8Discussion 8
Discussion 8Kevin Lee
 
Computer and Generations
Computer and GenerationsComputer and Generations
Computer and GenerationsToufiqueAhmed13
 
Rebirth of Slick: Why Great Design Will Make People Love Your Company
Rebirth of Slick: Why Great Design Will Make People Love Your CompanyRebirth of Slick: Why Great Design Will Make People Love Your Company
Rebirth of Slick: Why Great Design Will Make People Love Your CompanyKelsey Ruger
 
5 insights on social media, which airlines in must tap on
5 insights on social media, which airlines in must tap on5 insights on social media, which airlines in must tap on
5 insights on social media, which airlines in must tap onSimpliFlying
 
Startup Live Prague Pitch Workshop
Startup Live Prague Pitch WorkshopStartup Live Prague Pitch Workshop
Startup Live Prague Pitch WorkshopCan Ertugrul
 
StoryScaping(tm) Short Version
StoryScaping(tm) Short VersionStoryScaping(tm) Short Version
StoryScaping(tm) Short VersionGaston Legorburu
 
Giving Opinions Opinion Writing, Persuasive Writing
Giving Opinions Opinion Writing, Persuasive WritingGiving Opinions Opinion Writing, Persuasive Writing
Giving Opinions Opinion Writing, Persuasive WritingSue Ganguli
 
Presentation microsoft
Presentation microsoftPresentation microsoft
Presentation microsoftLauraGrenade
 
Giving Code a Good Name
Giving Code a Good NameGiving Code a Good Name
Giving Code a Good NameKevlin Henney
 
Deep Learning (DL) from Scratch
Deep Learning (DL) from ScratchDeep Learning (DL) from Scratch
Deep Learning (DL) from ScratchAziz416788
 
Кто такой хороший Drupal-разработчик
Кто такой хороший Drupal-разработчикКто такой хороший Drupal-разработчик
Кто такой хороший Drupal-разработчикDrupalSPB
 
Who is a Good Drupal Developer?
 Who is a Good Drupal Developer? Who is a Good Drupal Developer?
Who is a Good Drupal Developer?Kate Marshalkina
 

Semelhante a Kai Weber & Chris Atherton - Pattern recognition for technical communicators - tcuk11 (20)

Pattern recognition for technical communicators
Pattern recognition for technical communicatorsPattern recognition for technical communicators
Pattern recognition for technical communicators
 
Pattern recognition for UX - 13 April 2013
Pattern recognition for UX - 13 April 2013Pattern recognition for UX - 13 April 2013
Pattern recognition for UX - 13 April 2013
 
Discussion 8
Discussion 8Discussion 8
Discussion 8
 
Computer and Generations
Computer and GenerationsComputer and Generations
Computer and Generations
 
Lshort
LshortLshort
Lshort
 
Rebirth of Slick: Why Great Design Will Make People Love Your Company
Rebirth of Slick: Why Great Design Will Make People Love Your CompanyRebirth of Slick: Why Great Design Will Make People Love Your Company
Rebirth of Slick: Why Great Design Will Make People Love Your Company
 
Os Goodger
Os GoodgerOs Goodger
Os Goodger
 
Digital Storytelling Handout
Digital Storytelling HandoutDigital Storytelling Handout
Digital Storytelling Handout
 
5 insights on social media, which airlines in must tap on
5 insights on social media, which airlines in must tap on5 insights on social media, which airlines in must tap on
5 insights on social media, which airlines in must tap on
 
Web As A Platform
Web As A PlatformWeb As A Platform
Web As A Platform
 
Startup Live Prague Pitch Workshop
Startup Live Prague Pitch WorkshopStartup Live Prague Pitch Workshop
Startup Live Prague Pitch Workshop
 
Coding Standards
Coding StandardsCoding Standards
Coding Standards
 
StoryScaping(tm) Short Version
StoryScaping(tm) Short VersionStoryScaping(tm) Short Version
StoryScaping(tm) Short Version
 
Giving Opinions Opinion Writing, Persuasive Writing
Giving Opinions Opinion Writing, Persuasive WritingGiving Opinions Opinion Writing, Persuasive Writing
Giving Opinions Opinion Writing, Persuasive Writing
 
General Talk on Pointers
General Talk on PointersGeneral Talk on Pointers
General Talk on Pointers
 
Presentation microsoft
Presentation microsoftPresentation microsoft
Presentation microsoft
 
Giving Code a Good Name
Giving Code a Good NameGiving Code a Good Name
Giving Code a Good Name
 
Deep Learning (DL) from Scratch
Deep Learning (DL) from ScratchDeep Learning (DL) from Scratch
Deep Learning (DL) from Scratch
 
Кто такой хороший Drupal-разработчик
Кто такой хороший Drupal-разработчикКто такой хороший Drupal-разработчик
Кто такой хороший Drupal-разработчик
 
Who is a Good Drupal Developer?
 Who is a Good Drupal Developer? Who is a Good Drupal Developer?
Who is a Good Drupal Developer?
 

Mais de amelio

Mission Statements in der Doku - tekom 150914
Mission Statements in der Doku - tekom 150914Mission Statements in der Doku - tekom 150914
Mission Statements in der Doku - tekom 150914amelio
 
Kai Weber - Unstructured documentation to structured topics - stc 140519 - p...
Kai Weber  - Unstructured documentation to structured topics - stc 140519 - p...Kai Weber  - Unstructured documentation to structured topics - stc 140519 - p...
Kai Weber - Unstructured documentation to structured topics - stc 140519 - p...amelio
 
Weber, Kai - Why you need a tech comm mission statement - tekom 131107
Weber, Kai - Why you need a tech comm mission statement - tekom 131107Weber, Kai - Why you need a tech comm mission statement - tekom 131107
Weber, Kai - Why you need a tech comm mission statement - tekom 131107amelio
 
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - public
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - publicAtherton & Weber - Bake your own taxonomy - tcuk 130924 - public
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - publicamelio
 
Kai Weber - Addicted to Meaning - tcuk 130925 - public
Kai Weber - Addicted to Meaning - tcuk 130925 - publicKai Weber - Addicted to Meaning - tcuk 130925 - public
Kai Weber - Addicted to Meaning - tcuk 130925 - publicamelio
 
Kai Weber - Addicted to meaning - tcworld 121023 public
Kai Weber - Addicted to meaning - tcworld 121023 publicKai Weber - Addicted to meaning - tcworld 121023 public
Kai Weber - Addicted to meaning - tcworld 121023 publicamelio
 

Mais de amelio (6)

Mission Statements in der Doku - tekom 150914
Mission Statements in der Doku - tekom 150914Mission Statements in der Doku - tekom 150914
Mission Statements in der Doku - tekom 150914
 
Kai Weber - Unstructured documentation to structured topics - stc 140519 - p...
Kai Weber  - Unstructured documentation to structured topics - stc 140519 - p...Kai Weber  - Unstructured documentation to structured topics - stc 140519 - p...
Kai Weber - Unstructured documentation to structured topics - stc 140519 - p...
 
Weber, Kai - Why you need a tech comm mission statement - tekom 131107
Weber, Kai - Why you need a tech comm mission statement - tekom 131107Weber, Kai - Why you need a tech comm mission statement - tekom 131107
Weber, Kai - Why you need a tech comm mission statement - tekom 131107
 
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - public
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - publicAtherton & Weber - Bake your own taxonomy - tcuk 130924 - public
Atherton & Weber - Bake your own taxonomy - tcuk 130924 - public
 
Kai Weber - Addicted to Meaning - tcuk 130925 - public
Kai Weber - Addicted to Meaning - tcuk 130925 - publicKai Weber - Addicted to Meaning - tcuk 130925 - public
Kai Weber - Addicted to Meaning - tcuk 130925 - public
 
Kai Weber - Addicted to meaning - tcworld 121023 public
Kai Weber - Addicted to meaning - tcworld 121023 publicKai Weber - Addicted to meaning - tcworld 121023 public
Kai Weber - Addicted to meaning - tcworld 121023 public
 

Último

Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 

Último (20)

Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 

Kai Weber & Chris Atherton - Pattern recognition for technical communicators - tcuk11

  • 1. PATTERN RECOGNITION FOR TECHNICAL COMMUNICATORS Kai Weber (@techwriterkai) & Chris Atherton (@finiteattention) 22 September 2011 TCUK 11
  • 2. WHO ARE WE AND WHAT DO WE KNOW? Kai Weber Chris Atherton @techwriterkai @finiteattention  Technical writer since 1988  User experience consultant  Senior Technical Writer at  Mendeley, Skype, academia SimCorp, CPH, since 2008  Incurable cross-disciplinarian  Coach, trainer, mentor  Ph.D. in Cognitive  M.A. in American Studies Neuroscience
  • 3. OUR MISSION Helping you understand what you do … … so you can do what you do, better.
  • 4. WHAT IS PATTERN RECOGNITION? http://livinglifewithchemobrain.blogspot.com/2011/03/apparitions-on-toast.html
  • 5. WHAT IS PATTERN RECOGNITION?  Don’t believe that your brain is optimised to create patterns from apparent chaos? Watch this: http://www.youtube.com/watch?v=yVkdfJ9PkRQ
  • 6. WHAT IS PATTERN RECOGNITION? Examples  rules
  • 7. TOO ABSTRACT! HOW ABOUT AN EXAMPLE? Aardvark, J.R. (1980). Ants, and how to eat them. Journal of Orycteropodidae Studies, 80, 11-17. Barker, R. (1982). Rum babas, and what to do if you’ve got them. Reading: Goodnight From Him. Haley, W. (1955). Rock Around The Clock. New York: Decca. Izzard, E. (1998). Cake or Death? Gateaunomics, 10, 195-196. Lemur, R.-T. (2010). Strepsirrhinoplasty. Antananarivo: Raft Press. Leonard, E. (1996). Out of Sight. New York: Harper. Shorty, G. (in press). Okay, so they got me. Los Angeles: Cadillac. What is this? What are the structures and rules here?
  • 8. RECOGNISED PATTERNS AND RULES Aardvark, J.R. (1980). Ants, and how to eat them. Journal of Orycteropodidae Studies, 80, 11-17. Barker, R. (1982). Rum babas, and what to do if you’ve got them. Reading: Goodnight From Him. … 1. Last name, initial(s). 2. (Year of publication). 3. If journal article: 1. Title of article. 2. Title of journal, volume number, page numbers. 4. If book: 1. Title. 2. City: Publisher.
  • 9. SO HOW DO WE ACQUIRE THESE RULES?  By rote or  By acquiring data (a.k.a. experience)
  • 10. IMPLICIT VS. EXPLICIT KNOWLEDGE  Gladwell, M. (2005). Blink. London: Penguin. http://www.gladwell.com/blink/index.html
  • 11. WHY SHOULD TECH COMMUNICATORS CARE? We do it anyway… 1. When we gather information  Reading specs and designs  Interviewing subject-matter experts 2. When we create and order information  Write topics  Structure topics into deliverables
  • 12. WHY SHOULD TECH COMMUNICATORS CARE? We do it anyway, so we might as well do it smartly!  If we make sense of our subjects more efficiently…  If we structure better what we need to convey…  … we can provide better documentation!
  • 13. THE PATTERN RECOGNITION EXPERIENCE M T W T F S S M T W T F S S M T W T
  • 14. BUT HOW DO WE REACH THAT “AHA!” MOMENT? * * * * * !
  • 15. PERCEIVING PATTERNS d * d d d t * * d dd * ** d t t t t * * * d t t t t t t t t t t t t t t t t t
  • 16. PERCEIVING PATTERNS d * d d d t * * d dd * ** d t t t t * * * d t t t t t t t t t t t t t t t t t
  • 17. PERCEIVING PATTERNS d * d d d t * * d dd * ** d t t t t * * * d t t t t t t t t t t t t t t t t t
  • 18. PERCEIVING PATTERNS d * d d d t * * d dd * ** d t t t t * * * d t t t t t t t t t t t t t t t t t
  • 19. HOW DOES PATTERN RECOGNITION WORK? Bottom-up processing  Experiencing  Acquiring  Matching  Segmenting … building up a representation. But that requires lots of “data”, so…
  • 20. WHAT IS THIS? HOW DO YOU KNOW?
  • 21. HOW DOES PATTERN RECOGNITION WORK? Top-down processing  Knowing  Generalising  Contextualising  Applying … searching for confirmation.
  • 22. HOW DOES PATTERN RECOGNITION WORK? Bottom-up Top-down  No prior knowledge  Uses prior knowledge  Elements  concepts  Concepts  elements  Emphasises relations  Emphasises context  Slow; usually correct  Quick; sometimes wrong
  • 24. WHAT IS THIS? IT’S ART... Martin Boyce: Untitled, 2002.
  • 25. WHAT IS THIS? IT’S PART OF THE SAME CHAIR! Martin Boyce: Arne Jacobsen: Untitled, 2002. Chair 3107, c.1952. It is the back side of the chair where the back rest turns into the seat, with two holes cut in and turned by 90°.
  • 26. HOW DOES PATTERN RECOGNITION WORK? Bottom-up Top-down  No prior knowledge  Uses prior knowledge  Elements  concepts  Concepts  elements  Emphasises relations  Emphasises context  Slow; usually correct  Quick; sometimes wrong
  • 27. PATTERN RECOGNITION IN TECH COMM  To make sense of unknown subject matter  To overcome tech writer’s block and start writing  To chunk topics and find reuse opportunities  To help your readers
  • 28. PATTERN RECOGNITION IN TECH COMM To make sense of unknown subject matter  If you have scattered, unreliable information…  Gather all puzzle pieces and work bottom up.  Tease out similarities until you have segments.
  • 29. PATTERN RECOGNITION IN TECH COMM To make sense of unknown subject matter  If you have structured legacy documentation…  Go through topic structure and analyse top down.  Test reliability and completeness top-down.
  • 30. PATTERN RECOGNITION IN TECH COMM To overcome writer’s block and start writing  If you lack full, consistent information…  Start bottom-up with similar “seeds” as templates.  Describe first what hangs together well. Making caffe latte About Italian coffee 1. Grind coffee. 1. Espresso 2. Steam milk and ¾ fill a latte glass. 2. Cappuccino 3. Make the espresso and pour it in. 3. Caffe latte 4. Top the drink with steamed milk. 5. Clean the steamer. Making hot chocolate 1. Pour chocolate into glass or cup 2. Steam the milk and pour in.
  • 31. PATTERN RECOGNITION IN TECH COMM To chunk topics and find reuse opportunities  If you have a bunch of similar information or topics  Identify how you can segment topics for reuse.  Especially for similar procedures and reference info. Making caffe latte About Italian coffee 1. Grind coffee. 1. Espresso 2. Steam milk and ¾ fill a latte glass. 2. Cappuccino 3. Make the espresso and pour it in. 3. Caffe latte 4. Top the drink with steamed milk. 5. Clean the steamer. Making hot chocolate 1. Pour chocolate into glass or cup 2. Steam the milk and pour in.
  • 32. PATTERN RECOGNITION IN TECH COMM To help your readers … orient themselves in your documentation. Tables of contents, no patterns left, with patterns right. 1. General Settings window 1. Setting up the Trade Manager 1.1 Assets in a portfolio 1.1 Set up a portfolio 1.2 Different bank accounts 1.2 Set up bank accounts 1.3 About counterparties 1.3 Set up counterparties 2. The Transaction window 2. Registering transactions 2.1 Transaction window 2.1 Enter common transaction data 2.2 Stock trading 2.2 Enter a stock transaction 2.3 Trading bonds 2.3 Enter a bond transaction 2.4 Futures and other derivatives 2.4 Enter a derivative transaction
  • 33. PATTERN RECOGNITION IN TECH COMM To help your readers … grasp individual topics quickly  Structure similar items similarly for easy recognition.  Use the same order of elements, e.g., in procedures:  Introduction Enter a stock transaction  Prerequisites Open the Stock Dealer window.  Procedure 1. Enter common transaction data.  Results 2. Enter the stock exchange. 3. Optionally, enter the stock series.  Exception handling  Apply parallelism in lists
  • 34. PATTERN RECOGNITION IN TECH COMM To help your readers … get the most out of navigation aids  Table of contents is a top-down aid  Offer a coherent, consistent structure  Assume and honour trust in the system  Search and index are bottom-up aids  Support not only exact matches, but also similar terms  Make search results indicative by heading alone
  • 35. PATTERN RECOGNITION IN TECH COMM To help your readers … in ways only you know how!  <audience brainstorm>
  • 36. FINAL WORDS OF ADVICE AND WARNING  Keep your customers – and your job – safe!  Apophenia: Humans are addicted to meaning.  Some patterns refuse to be recognized  Pattern recognition occurs in contexts  Creating tech comm is often a top-down process… ... but using it is often bottom-up!
  • 37. THANK YOU! KEEP IN TOUCH! Kai Weber Chris Atherton  @techwriterkai  @finiteattention  kaiweber.wordpress.com/  about.me/cjatherton