제12회 서울대학교 관악블록세미나 “문학적 상상력과 인공지능” 발표
(비전공자를 위한 인공지능의 최신 현황과 응용 소개)
[초록]
스토리텔링은 인간의 경험을 공유하는 수단으로 시대와 장소를 불문하고 사용되고 있습니다. 인공지능(AI) 기술의 눈부신 발전과 함께 컴퓨터가 글, 그림, 음악, 영상 등의 다양한 미디어를 이용하여 스토리텔링을 흉내 낼 수 있는 능력을 갖추기 시작하였습니다.
본 세미나에서는 이와 같은 인공지능 기반 스토리텔링 기술의 현재를 다양한 사례를 통해 살펴보고, 그 중에서 시각 정보를 중심으로 스토리를 만드는 인공지능 기술에 주목합니다. 인공지능을 통해 사람의 스토리텔링을 돕는 ‘생각하는 도우미(Thinking Aids)’가 본격적으로 성장하는 과정을 확인하실 수 있을 것입니다.
Responsible Data Use in AI - core tech pillarsSofus Macskássy
In this deck, we cover four core pillars of responsible data use in AI, including fairness, transparency, explainability -- as well as data governance.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
AI as a general-purpose technology akin to steam engines and electricity, holds the potential for profound global socio-economic change. In this talk, we delve into a new form of disruptive AI known as Generative AI (GenAI) and its revolutionary impact on how we live, work, and interact with our environment. This discussion will cover GenAI’s arrival, capability and its impact. We will also discuss the challenges and opportunities that GenAI presents to industry leaders and practitioners including the defence sector. We'll explore its potential to reshape industries, push creative boundaries, and expand consolidated knowledge -- GenAI has become the cornerstone upon which new platforms, companies, and industries are built.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Responsible Data Use in AI - core tech pillarsSofus Macskássy
In this deck, we cover four core pillars of responsible data use in AI, including fairness, transparency, explainability -- as well as data governance.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
AI as a general-purpose technology akin to steam engines and electricity, holds the potential for profound global socio-economic change. In this talk, we delve into a new form of disruptive AI known as Generative AI (GenAI) and its revolutionary impact on how we live, work, and interact with our environment. This discussion will cover GenAI’s arrival, capability and its impact. We will also discuss the challenges and opportunities that GenAI presents to industry leaders and practitioners including the defence sector. We'll explore its potential to reshape industries, push creative boundaries, and expand consolidated knowledge -- GenAI has become the cornerstone upon which new platforms, companies, and industries are built.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Understanding GenAI/LLM and What is Google Offering - Felix GohNUS-ISS
With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
Abstract:
Being the third generation of neural network models, the study of spiking neural networks is an interdisciplinary field among brain science, theoretical neuroscience, and artificial neural networks research. Recently it is gaining attention and momentum, especially in neuromorphic device design for real-time machine learning. Some of you might have heard of it, but its underneath principles probably remain unknown for most of you. In this talk, I will briefly illustrate the basic building blocks of this emerging architecture and technology.
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
🤖 Understanding 4 Waves of AI
💡 This is my humble attempt to research and correlate FOUR major waves or generations of Artificial Intelligence (AI).
🤓 I brought some generic industry use cases and researched what Salesforce offers in various Einstein services.
🥵 As a vast majority of Einstein offerings are available, it is possible to miss out on quoting some names. Please be kind, and excuse me for that.
💬 Drop your favourite AI examples, thoughts and Einstein services that correlate with this content.
#Salesforce #AI #Einstein #EinsteinAI
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
mm 2014년 가을, 美스탠퍼드대를 중심으로 인공지능 장기 연구
프로젝트인 ‘인공지능 100년 연구(AI100)’ 출범
- 인공지능 발전이 인류사회에 미칠 영향에 대한 연구 수행
mm ‘AI100’ 상임위원회는 인공지능 관련 학계·산업계 전문가와
법·정치·경제 분야 학자들로 이루어진 17인의 연구패널 구성
- 연구패널에서는 개인과 사회에 혜택을 주는 인공지능 연구,
개발, 시스템 디자인, 프로그램과 정책 개발 가이드를 제공하고,
- 인공지능의 발전과 그에 따른 기술·사회적 도전과제, 기회를
포함한 사회변화를 5년마다 평가
mm 2016년 9월, 연구패널은 최소 100년 이상 이어질 대장정 연구프로젝트의 시작을 알리며 ‘인공지능과 2030년의 삶’ 보고서 발표
- 보고서에서는 AI의 영향력과 연구 트렌드, AI가 2030년까지 북아메리카 도시에 미칠 8대 분야별 사회적 영향과 정책방향을 제시
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Understanding GenAI/LLM and What is Google Offering - Felix GohNUS-ISS
With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
Abstract:
Being the third generation of neural network models, the study of spiking neural networks is an interdisciplinary field among brain science, theoretical neuroscience, and artificial neural networks research. Recently it is gaining attention and momentum, especially in neuromorphic device design for real-time machine learning. Some of you might have heard of it, but its underneath principles probably remain unknown for most of you. In this talk, I will briefly illustrate the basic building blocks of this emerging architecture and technology.
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
🤖 Understanding 4 Waves of AI
💡 This is my humble attempt to research and correlate FOUR major waves or generations of Artificial Intelligence (AI).
🤓 I brought some generic industry use cases and researched what Salesforce offers in various Einstein services.
🥵 As a vast majority of Einstein offerings are available, it is possible to miss out on quoting some names. Please be kind, and excuse me for that.
💬 Drop your favourite AI examples, thoughts and Einstein services that correlate with this content.
#Salesforce #AI #Einstein #EinsteinAI
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
mm 2014년 가을, 美스탠퍼드대를 중심으로 인공지능 장기 연구
프로젝트인 ‘인공지능 100년 연구(AI100)’ 출범
- 인공지능 발전이 인류사회에 미칠 영향에 대한 연구 수행
mm ‘AI100’ 상임위원회는 인공지능 관련 학계·산업계 전문가와
법·정치·경제 분야 학자들로 이루어진 17인의 연구패널 구성
- 연구패널에서는 개인과 사회에 혜택을 주는 인공지능 연구,
개발, 시스템 디자인, 프로그램과 정책 개발 가이드를 제공하고,
- 인공지능의 발전과 그에 따른 기술·사회적 도전과제, 기회를
포함한 사회변화를 5년마다 평가
mm 2016년 9월, 연구패널은 최소 100년 이상 이어질 대장정 연구프로젝트의 시작을 알리며 ‘인공지능과 2030년의 삶’ 보고서 발표
- 보고서에서는 AI의 영향력과 연구 트렌드, AI가 2030년까지 북아메리카 도시에 미칠 8대 분야별 사회적 영향과 정책방향을 제시
인공지능(AI)과 사용자 경험(UX)
담론 I. 드라마로 본 AI & UX
담론 II. 도전과제로 본 AI & UX
담론 III. 변방성 질문으로 본 AI & UX
사례연구 #1-1. 지능형 패션 프로파일링 및 UX
사례연구 #1-2. 지능형 패션 추천 시스템 및 UX
사례연구 #2. 지능형 시니어 맞춤 UX
오컴 Clip IT 세미나 1회차 "머신러닝과 인공지능의 현재와 미래"
1. 인공지능과 머신러닝
- 영화 및 애니메이션에 나타나는 친화적 인공지능과 적대적 인공지능, 그리고 감성적 인공지능
- 강한 인공지능과 약한 인공지능의 차이
- 인공지능과 머신러닝의 관계
2. 딥러닝과 강화학습
- 인공지능의 중요 열쇠이자 머신러닝의 세부 이론인 딥러닝과 강화학습에 대한 개괄 소개
3. 인공지능에 대한 우리의 자세
- 과연 인공지능은 완벽한가?
- 과연 인공지능은 인간 전문가를 대체할 수 있을까?
- 데이터의 중요성
2017년 라이트브레인의 네 번째 UX 트렌드 리포트 ‘UX Discovery 제4호’
2017년 상반기동안 라이트브레인 UX1 컨설팅그룹에서 수집하고 선별한 의미있는 UX 사례들을 총 200여 페이지, 13개 분야로 나누어 정리하였으며 아이템별로 라이트브레인만의 UX 인사이트를 함께 수록했습니다.
UX 관련 업계 종사자들이 보다 양질의 정보를 접할 수 있도록 돕고, 눈앞에 다가온 4차산업혁명시대를 전망하고 준비하시는 데 있어 귀한 인사이트를 얻고 더 많은 도전을 할 수 있도록 지원하고자 라이트브레인 블로그와 슬라이드쉐어를 통해 1,2부로 나눠 바로 보실 수 있도록 순차적으로 공개하며,
뉴스레터 구독자분들에 한해 무료로 리포트를 pdf파일로 보내 드리고 있습니다.
PDF파일 무료구독 신청접수 :
http://www.rightbrain.co.kr/CMS/discovery4/
- Agenda-
Part-1
Artificial Intelligence
Robot
Car
ChatBot
VUX
Part-2 (곧 공개됩니다)
Wearable
New Interaction
VR/AR
IoT/Product
Commerce
New App
Healthcare
Similar to 스토리텔링을 위한 시각 중심의 인공지능(AI for Visual Storytelling) (20)
36. • Web Browser: 현재는 구글 크롬(Chrome)만 가능합니다
• Google Photos 계정
– 사용자가 자신의 Google Photos에 생성되어 있는 앨범에서
사진을 골라 스토리를 생성할 수 있습니다.
– 구글 계정이 있다면 https://photos.google.com/에서 쉽게
앨범을 생성할 수 있습니다.
SurroMind Robotics Confidential
실행 환경 – 접속 전에 준비해주세요
37. • 크롬 웹브라우저에 URL 입력
http://bit.ly/albumstoryteller
– 최초 접속시 보안 관련 경고창이 뜹니다. “안전 페이지로
돌아가기” 버튼 왼쪽의 ‘고급’을 선택하여 접속해주세요.
• 구글포토를 사용하는 계정으로 로긴해주세요
• 튜토리얼을 한 번 확인하시고 사용해주세요
• 스토리 삭제
– 웹 브라우저의 캐쉬에 저장되어 있습니다. 웹 캐쉬를 지우면
스토리가 삭제됩니다
– 지인과 공유하고 싶은 스토리는 SNS 공유 기능을 통해 미리미리
공유해주세요
• 메뉴에서 설문과 버그리포트를 통해 피드백
부탁드립니다.
SurroMind Robotics Confidential
Album Storyteller 접속