SlideShare ist ein Scribd-Unternehmen logo
1 von 49
Downloaden Sie, um offline zu lesen
Lukas Biewald
The Effect of Better Algorithms
0%
5%
10%
15%
20%
25%
Naïve Bayes Maximum
Entropy
SVM
Classifier Error Rate
Active Semi-Supervised Learning for Improving
Word Alignment
(Vamshi ACL ’10)
Real World Data
2
The Effect of Better Features
0%
5%
10%
15%
20%
25%
30%
Unigrams Bigrams Unigrams+Bigrams
Classifier Error Rate
3
The Effect of More Data
Active Semi-Supervised Learning for
Improving Word Alignment
(Vamshi ACL ’10)
Real World Data
0%
2%
4%
6%
8%
10%
12%
14%
N 2N 4N
Classifier Error Rate
4
The Effect of Cleaner Data
0%
2%
4%
6%
8%
10%
12%
14%
90% Accurate Data 95% Accurate Data 100% Accurate Data
Classifier Error Rate
5
Where Do Data Scientists Spend Their Time?
6
Source: CrowdFlower Data
Science Report 2015
CrowdFlower Data Enrichment Platform
7
Color Data
8
9
10
11
12
13
14
Apple Watch
15
Apple Watch
16
Apple Watch
17
Apple Watch
18
Collecting the Same Data Over and Over
19
Open Data
20
Make Your Data Public Setting
21
Data for Everyone
22
Data For Everyone Library
23
Data for Everyone
24
Data For Everyone
25
Open Data API
26
URL Categorization
27
Categorize URLs
28
Record Data
29
Extracting Names and Titles
30
Summarization
31
Is an Image Funny?
32
Classifying Medical Images
33
Attributes of People
34
35
Kaggle accuracy
0%
10%
20%
30%
40%
50%
60%
70%
Baseline 12-May 13-May 14-May 15-May
Accuracy
Accuracy of Best Performing Model
36
Kaggle accuracy over time
0%
10%
20%
30%
40%
50%
60%
70%
80%
Accuracy
Accuracy of the Best Performing Model
37
Kaggle Participation
0
200
400
600
800
1000
1200
1400
Number of Participating Teams
38
AI
Classifier
Output
Confident
Human in the Loop
39
Human in the Loop
Confident
Output
AI
Classifier
Human
Annotation
40
Human in the Loop
Confident
Output
AI
Classifier
Active Learning
Human
Annotation
41
Human in the Loop
Confident
Output
AI
Classifier
Active Learning
Human
Annotation
42
Active Learning
From hunch.net active learning tutorial ICML ‘09
43
Active Learning Accuracy Improvement
44
Google Cars Miles Per Disengage
45
Adaptive Cruise Control
Image source: ExtremeTech
46
Advanced Chess
Image source: Computer Chess
47
AlphaGo
48
Lukas Biewald
lukas@crowdflower.com
@L2K
Thank You

Weitere ähnliche Inhalte

Ähnlich wie Active Learning and Human-in-the-Loop

Cloud VM World Tradeshow Report
Cloud VM World Tradeshow ReportCloud VM World Tradeshow Report
Cloud VM World Tradeshow ReportCloud Cruiser, Inc
 
How to Enter the Data Analytics Industry?
How to Enter the Data Analytics Industry?How to Enter the Data Analytics Industry?
How to Enter the Data Analytics Industry?Ganes Kesari
 
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance Data
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance DataDigital Disruption Asia - Pleasing the Unpleasable with Digital Performance Data
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance DataDynatrace
 
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)Dynatrace
 
How To Plan for, Design, and Implement a Communications Dashboard
How To Plan for, Design, and Implement a Communications DashboardHow To Plan for, Design, and Implement a Communications Dashboard
How To Plan for, Design, and Implement a Communications DashboardPaine Publishing
 
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and LeadDevOps.com
 
Early Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingEarly Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingJeff Storan
 
Early Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingEarly Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingJeffrey Storan
 
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech one
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech oneHeavy, Messy, Misleading: How Big Data is a human problem, not a tech one
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech onePulsar Platform
 
Population Stability Index(PSI) for Big Data World
Population Stability Index(PSI) for Big Data WorldPopulation Stability Index(PSI) for Big Data World
Population Stability Index(PSI) for Big Data WorldJeomoan Kurian
 
Digital disruption
Digital disruptionDigital disruption
Digital disruptionJerry Tan
 
JDO 2019: Data Science for Developers - Matthew Renze
JDO 2019: Data Science for Developers -  Matthew RenzeJDO 2019: Data Science for Developers -  Matthew Renze
JDO 2019: Data Science for Developers - Matthew RenzePROIDEA
 
FIAT/IFTA MAM Survey - Results overview
FIAT/IFTA MAM Survey - Results overviewFIAT/IFTA MAM Survey - Results overview
FIAT/IFTA MAM Survey - Results overviewBrecht Declercq
 
Discovering WHY from numbers
Discovering WHY from numbersDiscovering WHY from numbers
Discovering WHY from numbersWebnographer
 
UX by the numbers: Discovering the why from numbers
UX by the numbers: Discovering the why from numbersUX by the numbers: Discovering the why from numbers
UX by the numbers: Discovering the why from numbersUXPA UK
 
Measuring Video Effectiveness - Wistiafest 2015
Measuring Video Effectiveness - Wistiafest 2015Measuring Video Effectiveness - Wistiafest 2015
Measuring Video Effectiveness - Wistiafest 2015Brendan Schwartz
 
ITESOFT Insider View | The Importance of Capture
ITESOFT Insider View | The Importance of CaptureITESOFT Insider View | The Importance of Capture
ITESOFT Insider View | The Importance of CaptureITESOFT
 
2016 Yahoo Taiwan Mobile Developer Conference
2016 Yahoo Taiwan Mobile Developer Conference 2016 Yahoo Taiwan Mobile Developer Conference
2016 Yahoo Taiwan Mobile Developer Conference Flurry, Inc.
 

Ähnlich wie Active Learning and Human-in-the-Loop (20)

Cloud VM World Tradeshow Report
Cloud VM World Tradeshow ReportCloud VM World Tradeshow Report
Cloud VM World Tradeshow Report
 
How to Enter the Data Analytics Industry?
How to Enter the Data Analytics Industry?How to Enter the Data Analytics Industry?
How to Enter the Data Analytics Industry?
 
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance Data
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance DataDigital Disruption Asia - Pleasing the Unpleasable with Digital Performance Data
Digital Disruption Asia - Pleasing the Unpleasable with Digital Performance Data
 
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)
Managing Unrealistic User Expectations (i.e Digital for Spoilt Brats)
 
The 2016 Watch List
The 2016 Watch ListThe 2016 Watch List
The 2016 Watch List
 
How To Plan for, Design, and Implement a Communications Dashboard
How To Plan for, Design, and Implement a Communications DashboardHow To Plan for, Design, and Implement a Communications Dashboard
How To Plan for, Design, and Implement a Communications Dashboard
 
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead
2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead
 
Early Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingEarly Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV Advertising
 
Early Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV AdvertisingEarly Lessons Learned in Applying Big Data To TV Advertising
Early Lessons Learned in Applying Big Data To TV Advertising
 
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech one
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech oneHeavy, Messy, Misleading: How Big Data is a human problem, not a tech one
Heavy, Messy, Misleading: How Big Data is a human problem, not a tech one
 
Population Stability Index(PSI) for Big Data World
Population Stability Index(PSI) for Big Data WorldPopulation Stability Index(PSI) for Big Data World
Population Stability Index(PSI) for Big Data World
 
Digital disruption
Digital disruptionDigital disruption
Digital disruption
 
JDO 2019: Data Science for Developers - Matthew Renze
JDO 2019: Data Science for Developers -  Matthew RenzeJDO 2019: Data Science for Developers -  Matthew Renze
JDO 2019: Data Science for Developers - Matthew Renze
 
FIAT/IFTA MAM Survey - Results overview
FIAT/IFTA MAM Survey - Results overviewFIAT/IFTA MAM Survey - Results overview
FIAT/IFTA MAM Survey - Results overview
 
Discovering WHY from numbers
Discovering WHY from numbersDiscovering WHY from numbers
Discovering WHY from numbers
 
UX by the numbers: Discovering the why from numbers
UX by the numbers: Discovering the why from numbersUX by the numbers: Discovering the why from numbers
UX by the numbers: Discovering the why from numbers
 
Measuring Video Effectiveness - Wistiafest 2015
Measuring Video Effectiveness - Wistiafest 2015Measuring Video Effectiveness - Wistiafest 2015
Measuring Video Effectiveness - Wistiafest 2015
 
[TestWarez 2017] Od testowania do monitoringu jakości – wyzwania Continuous ...
[TestWarez 2017]  Od testowania do monitoringu jakości – wyzwania Continuous ...[TestWarez 2017]  Od testowania do monitoringu jakości – wyzwania Continuous ...
[TestWarez 2017] Od testowania do monitoringu jakości – wyzwania Continuous ...
 
ITESOFT Insider View | The Importance of Capture
ITESOFT Insider View | The Importance of CaptureITESOFT Insider View | The Importance of Capture
ITESOFT Insider View | The Importance of Capture
 
2016 Yahoo Taiwan Mobile Developer Conference
2016 Yahoo Taiwan Mobile Developer Conference 2016 Yahoo Taiwan Mobile Developer Conference
2016 Yahoo Taiwan Mobile Developer Conference
 

Mehr von CrowdFlower

Building Better Models Faster Using Active Learning
Building Better Models Faster Using Active LearningBuilding Better Models Faster Using Active Learning
Building Better Models Faster Using Active LearningCrowdFlower
 
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale.
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale. CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale.
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale. CrowdFlower
 
CrowdFlower Product Webinar - Graphical Editor and Visual Reports
CrowdFlower Product Webinar - Graphical Editor and Visual ReportsCrowdFlower Product Webinar - Graphical Editor and Visual Reports
CrowdFlower Product Webinar - Graphical Editor and Visual ReportsCrowdFlower
 
How Oracle Uses CrowdFlower For Sentiment Analysis
How Oracle Uses CrowdFlower For Sentiment AnalysisHow Oracle Uses CrowdFlower For Sentiment Analysis
How Oracle Uses CrowdFlower For Sentiment AnalysisCrowdFlower
 
Humanizing The Machine
Humanizing The MachineHumanizing The Machine
Humanizing The MachineCrowdFlower
 
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...CrowdFlower
 
Virtual Data Steward: Data Management 3.0
Virtual Data Steward: Data Management 3.0Virtual Data Steward: Data Management 3.0
Virtual Data Steward: Data Management 3.0CrowdFlower
 
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 poster
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 posterExpert Crowdsourcing with Flash Teams | CrowdConf 2013 poster
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 posterCrowdFlower
 
The State of Enterprise Crowdsourcing 2013
The State of Enterprise Crowdsourcing 2013The State of Enterprise Crowdsourcing 2013
The State of Enterprise Crowdsourcing 2013CrowdFlower
 
CrowdFlower University Oct. 21 2013
CrowdFlower University Oct. 21 2013CrowdFlower University Oct. 21 2013
CrowdFlower University Oct. 21 2013CrowdFlower
 

Mehr von CrowdFlower (11)

7 Myths of AI
7 Myths of AI7 Myths of AI
7 Myths of AI
 
Building Better Models Faster Using Active Learning
Building Better Models Faster Using Active LearningBuilding Better Models Faster Using Active Learning
Building Better Models Faster Using Active Learning
 
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale.
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale. CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale.
CrowdFlower NDA Crowds - Secure, exceptional tasking at a massive scale.
 
CrowdFlower Product Webinar - Graphical Editor and Visual Reports
CrowdFlower Product Webinar - Graphical Editor and Visual ReportsCrowdFlower Product Webinar - Graphical Editor and Visual Reports
CrowdFlower Product Webinar - Graphical Editor and Visual Reports
 
How Oracle Uses CrowdFlower For Sentiment Analysis
How Oracle Uses CrowdFlower For Sentiment AnalysisHow Oracle Uses CrowdFlower For Sentiment Analysis
How Oracle Uses CrowdFlower For Sentiment Analysis
 
Humanizing The Machine
Humanizing The MachineHumanizing The Machine
Humanizing The Machine
 
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...
Productive Out-of-the-Box | Tooling with Yeoman to Rapidly Develop Ember.js A...
 
Virtual Data Steward: Data Management 3.0
Virtual Data Steward: Data Management 3.0Virtual Data Steward: Data Management 3.0
Virtual Data Steward: Data Management 3.0
 
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 poster
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 posterExpert Crowdsourcing with Flash Teams | CrowdConf 2013 poster
Expert Crowdsourcing with Flash Teams | CrowdConf 2013 poster
 
The State of Enterprise Crowdsourcing 2013
The State of Enterprise Crowdsourcing 2013The State of Enterprise Crowdsourcing 2013
The State of Enterprise Crowdsourcing 2013
 
CrowdFlower University Oct. 21 2013
CrowdFlower University Oct. 21 2013CrowdFlower University Oct. 21 2013
CrowdFlower University Oct. 21 2013
 

Kürzlich hochgeladen

YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 

Kürzlich hochgeladen (17)

YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 

Active Learning and Human-in-the-Loop

Hinweis der Redaktion

  1. Over 200,000 Records
  2. 59,000 records
  3. Unlike humans, artificial intelligence has no ego, so it can make an unbiased estimate of its confidence - Where it’s confident we use its answer, because hardware CPUs get cheaper+faster every year and human CPUs don’t - Where it’s not confident we use a human because in real business applications 80% accuracy isn’t good enough
  4. I think we can and should apply this to every business process We start with a machine learning classifier. Unlike humans, artificial intelligence has no ego, so it can make an unbiased estimate of its confidence - Where it’s confident we use its answer, because hardware CPUs get cheaper+faster every year and human CPUs don’t - Where it’s not confident we use a human because in real business applications 80% accuracy isn’t good enough A huge side benefit is that the human labels can be reused used to improve the machine learning classifier over time. We didn’t invent any of this, lot’s of people are talking about this and thinking about this, including many people in the room. But looking at the industry we see a lot more people talking about it than actually doing it. We are going to make this setup so easy that you will have no excuse for not doing it.
  5. I think we can and should apply this to every business process We start with a machine learning classifier. Unlike humans, artificial intelligence has no ego, so it can make an unbiased estimate of its confidence - Where it’s confident we use its answer, because hardware CPUs get cheaper+faster every year and human CPUs don’t - Where it’s not confident we use a human because in real business applications 80% accuracy isn’t good enough A huge side benefit is that the human labels can be reused used to improve the machine learning classifier over time. We didn’t invent any of this, lot’s of people are talking about this and thinking about this, including many people in the room. But looking at the industry we see a lot more people talking about it than actually doing it. We are going to make this setup so easy that you will have no excuse for not doing it.
  6. A huge side benefit is that the human labels can be reused used to improve the machine learning classifier over time.
  7. handed control to the driver 272 times and a test driver felt compelled to intervene 69 times
  8. In the field of chess computers passed humans a long time ago. But if you really want to make a great chess playing algorithm you would still use a human and computer together. There is a subculture of folks who still play “Advanced Chess” and this is actually where the highest quality chess games take place. - Still situations where humans are better