Deep Integration of Human and Machine Intelligence for Accessibility - PowerPoint PPT Presentation

Oct 28, 2023 •414 likes •640 views

Deep Integration of Human and Machine Intelligence for Accessibility Jeffrey P. Bigham Carnegie Mellon University Jeffrey P. Bigham Carnegie Mellon University Jeffrey P. Bigham @jeffbigham @jeffbigham What do blind people want to know?

Deep Integration of Human and Machine Intelligence for Accessibility Jeffrey P. Bigham Carnegie Mellon University Jeffrey P. Bigham Carnegie Mellon University Jeffrey P. Bigham @jeffbigham @jeffbigham
What do blind people want to know? vizwiz.org Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
VizWiz Dataset vizwiz.org/data Check out Microsoft’s Seeing AI! Jeffrey P. Bigham @jeffbigham
Using Devices User + AI + Crowds Bigham, Jeffrey, et al. "VizWiz: nearly real-time answers to visual questions." UIST 2010. Brady, Erin, et al. “Visual challenges in the everyday lives of blind people.” CHI 2013. X Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
Jeffrey P. Bigham @jeffbigham
What do blind people want to know? vizwiz.org Jeffrey P. Bigham @jeffbigham
Working with AI Jeffrey P. Bigham @jeffbigham
Working with AI Supporting Blind Photography Supporting Blind Photography. Jayant, Chandrika, Ji, Hanjie, White, Samuel, Bigham, Jeffrey P. ASSETS 2011. Jeffrey P. Bigham @jeffbigham
Working with AI Target What People Want to Know Wu, et al. Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service. CSCW 2017. Jeffrey P. Bigham @jeffbigham
Working with AI Errors are Important and Not Created Equa Measure and Express Confidence Well MacLeod, Bennett, Morris, Cuttrell, et al. Understanding Blind People’s Experiences with Computer-Generated Captions of Social Media Images. CHI 2017. Jeffrey P. Bigham @jeffbigham
Working with AI * Amplify Users with AI Support (blind photograph * Solve Real Problems (understand user needs) * Embrace and Understand Errors (all errors are * Measure Confidence and Express it Well Jeffrey P. Bigham @jeffbigham
The End VizWiz Dataset: http://www.vizwiz.org/data VizWiz Publications: http://www.jeffreybigham.com * Amplify Users with AI Support (blind photography) * Solve Real Problems (understand user needs) Funded by: Google, * Embrace and Understand Errors (all errors are not equal) Microsoft, NSF, NIDILRR * Measure Confidence and Express it Well Jeffrey P. Bigham @jeffbigham

Recommend

Analog IC Lay Analog IC Layout out Le Lever eraging ging Human Human and Mac and Machine

MAGICAL: T MA GICAL: Towar ard Full d Fully y Automa utomated ted Analog IC Lay Analog IC Layout out Le Lever eraging ging Human Human and Mac and Machine Intelligence hine Intelligence Biying Xu, Keren Zhu, Mingjie Liu, Yibo Lin,

786 views • 30 slides

Industrial Machine Intelligence The Golden Braid of Data Streams, AI, and Human Expertise Drew

Industrial Machine Intelligence The Golden Braid of Data Streams, AI, and Human Expertise Drew Conway Machine Learning in Oil and Gas, Canada Google releases Data Science Venn AlphaGo defeats MapReduce paper Diagram published Lee

633 views • 25 slides

deep learning for natural language processing . Sergey I. Nikolenko 1,2,3 Deep Machine

deep learning for natural language processing . Sergey I. Nikolenko 1,2,3 Deep Machine Intelligence Workshop Moscow, June 4, 2016 1 Laboratory for Internet Studies, NRU Higher School of Economics, St. Petersburg 2 Laboratory of Mathematical

815 views • 56 slides

Can Machine Think? A. M. Turing Computing Machinery and Intelligence Sabrina Hao S The

Can Machine Think? A. M. Turing Computing Machinery and Intelligence Sabrina Hao S The Imitation Game Turing Test Artificial Intelligence http://www.youtube.com/watch?v=TryOC83PH1g Digital Computers Human Computer: a person performing

356 views • 14 slides

Seeing the self in the www.hmi.unimore.it washing machine the Deep Affordance of 2.0 philosophy

Human Machine Interaction Group Seeing the self in the www.hmi.unimore.it washing machine the Deep Affordance of 2.0 philosophy in the Deep Affordance of 2.0 philosophy in the household appliance domain www.unimore.it Caterina Calefato

540 views • 15 slides

AI IN HEALTHCARE What is Artificial Intelligence (AI)? How is AI different from Machine Learning

BRINGING DEEP LEARNING TO ENTERPRISE IMAGING CLINICAL PRACTICE Esteban Rubens Global Enterpris ise Imaging Princip ipal Pure Storage @pureesteban AI IN HEALTHCARE What is Artificial Intelligence (AI)? How is AI different from Machine

552 views • 18 slides

Part 1 Advanced Computational Intelligence and Deep Machine Learning for Early Detection and

Research Problem Definition Part 1 Advanced Computational Intelligence and Deep Machine Learning for Early Detection and Diagnosis of Diseases Tarek Khorshed PhD Student, American University in Cairo 1 | Problem Definition Disease Diagnosis

280 views • 15 slides

TRADING USING DEEP LEARNING MAN VS MACHINE Orders By Algorithms 84% Orders By Human 16%

TRADING USING DEEP LEARNING MAN VS MACHINE Orders By Algorithms 84% Orders By Human 16% TRADING USING DEEP LEARNING Artificial Neural Networks Neural networks are a family of models inspired by biological brain structure and are used to

690 views • 49 slides

DataPower DataPower-MQ Integration MQ Integration Deep Dive Deep Dive Robin Wiley (Robin

DataPower DataPower-MQ Integration MQ Integration Deep Dive Deep Dive Robin Wiley (Robin Wiley Training) Capitalware's MQ Technical Conference v2.0.1.6 Your Presenter: Robin Wiley Senior Instructor, IBM Messaging Products MQ

457 views • 35 slides

Machine Learning and Deep Contemplation of Data Joel Saltz Department of Biomedical Informatics

Machine Learning and Deep Contemplation of Data Joel Saltz Department of Biomedical Informatics Stony Brook University CCDSC October 5, 2016 From BDEC: Domain: Spatio-temporal Sensor Integration, Analysis, Classification

540 views • 53 slides

Reverse-engineering human intelligence, and engineering more human-like AI Josh Tenenbaum MIT

Reverse-engineering human intelligence, and engineering more human-like AI Josh Tenenbaum MIT Computational Cognitive Science Group CSAIL Department of Brain and Cognitive Sciences March 2013 A success story: Intelligence as statistics

379 views • 5 slides

CREATING MORAL ROBOTS Katherine Steen DEFINING TERMS Robot programmable machine that

CREATING MORAL ROBOTS Katherine Steen DEFINING TERMS Robot programmable machine that mimics human activities AI capability of a machine to imitate intelligence human behavior Autonomous independent/power to make own

410 views • 22 slides

Artificial Intelligence Artificial Intelligence Artificial Intelligence Study and design of

Artificial Intelligence Artificial Intelligence Artificial Intelligence Study and design of machines that can think like a human being Attempt to understand human intelligence Artificial Intelligence But how do you define intelligence?

517 views • 15 slides

WITH TECHNOLOGY FACE ATTRIBUTES NETWORK CAMERA WH WHAT AT IS IS DE DEEP EP LEARNING

EPHRAIM SECURITY WITH TECHNOLOGY FACE ATTRIBUTES NETWORK CAMERA WH WHAT AT IS IS DE DEEP EP LEARNING EARNING Artificial Intelligence Machine Learning Deep Learning DEEP COOPERATION BETWEEN EPHRAIM AI & INTEL CPU/FPGA

569 views • 29 slides

Everything you want to know about Istio Machine Intelligence Modern Infrastructure

Everything you want to know about Istio Machine Intelligence Modern Infrastructure http://mi2.live What is MI2? MI2 Webinars focus on the convergence of machine intelligence and modern infrastructure . Every alternate week, I deliver

238 views • 20 slides

for DSLs Integration Thomas Degueule INRIA, France Mechanical Airlines Structure Human-

Towards Language Interfaces for DSLs Integration Thomas Degueule INRIA, France Mechanical Airlines Structure Human- Machine Avionics Interaction Environmental Aerodynamics Impact Multiple Concerns Propulsion Safety System

834 views • 31 slides

Machine Intelligence at Google Scale: Vision/Speech API, TensorFlow and Cloud ML Kaz Sato Staff

Machine Intelligence at Google Scale: Vision/Speech API, TensorFlow and Cloud ML Kaz Sato Staff Developer Advocate +Kazunori Sato Tech Lead for Data & Analytics @kazunori_279 Cloud Platform, Google Inc. What well cover Deep learning

919 views • 45 slides

Build Your First ML Model with Azure Machine Learning Service Machine Intelligence Modern

Build Your First ML Model with Azure Machine Learning Service Machine Intelligence Modern Infrastructure http://mi2.live What is MI2? MI2 Webinars focus on the convergence of machine intelligence and modern infrastructure and. Every

1.14k views • 17 slides

Accelerating drug discovery with deep neural networks literature review Tobias Sikosek Senior

Accelerating drug discovery with deep neural networks literature review Tobias Sikosek Senior Data Scientist In Silico Unit (Heidelberg) Deep Learning Artificial intelligence Machine learning Deep learning Renewed focus on multi-layer

836 views • 50 slides

63b Integration Massage: Swedish, Passive Stretches, BMTs, and Deep Tissue 63b Integration

63b Integration Massage: Swedish, Passive Stretches, BMTs, and Deep Tissue 63b Integration Massage: Swedish, Passive Stretches, BMTs, and Deep Tissue Class Outline 5 minutes Attendance, Breath of Arrival, and Reminders

211 views • 10 slides

for Machine Vision From AI to ML to DL Source: Whats the Difference Between Artificial

Demystifying Machine Learning for Machine Vision From AI to ML to DL Source: Whats the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning ? by Michael Copeland Demystifying Machine Learning for Machine Vision

130 views • 11 slides

ARTIFICIAL INTELLIGENCE (AI): WHAT YOU NEED TO KNOW AND HOW IT WILL CHANGE HUMAN HISTORY

ARTIFICIAL INTELLIGENCE (AI): WHAT YOU NEED TO KNOW AND HOW IT WILL CHANGE HUMAN HISTORY Sheldon Hochberg Friendship Heights Village Center October 23, 2017 1 Artificial intelligence is shaping up as the next industrial revolution,

1.1k views • 67 slides

Horizontal Integration of Warfighter Intelligence Data A Shared Semantic Resource for the

Horizontal Integration of Warfighter Intelligence Data A Shared Semantic Resource for the Intelligence Community Barry Smith, University at Buffalo, NY, USA Tatiana Malyuta, New York City College of Technology, NY William S. Mandrick, Data

676 views • 34 slides

Effectiveness of Deep Learning Vs. Machine Learning in a Health Care Use Case RxToDx A Data

Effectiveness of Deep Learning Vs. Machine Learning in a Health Care Use Case RxToDx A Data Science Machine Learning/Deep Learning Show Case Dima Rekesh/Julie Zhu/Ravi Rajagopalan Optum Technology November, 2017 Analytics Machine

483 views • 32 slides