In this study session, we raised a topic about new trends of AI technologies following a combination of deep learning and big data.
It would call for new AI architecture and require new challenge we should do to keep up with front runners.
2019年10月25日、CTC Forum 2019@品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者:勝山 公雄(Senior Manager, Global Data Supervisory Department, Rakuten, Inc.)
2019年10月25日、CTC Forum 2019@品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者:勝山 公雄(Senior Manager, Global Data Supervisory Department, Rakuten, Inc.)
□Author
Masaya Mori, Global Head of Rakuten Institute of Technology, Executive Officer, Rakuten Inc.
森正弥 楽天株式会社 執行役員 兼 楽天技術研究所代表
□Description
そもそもなぜ人工知能(AI)をビジネスで活用する必要があるのかの視点に基づいて、AI活用戦略について述べた講演の資料です。
leewayhertz.com-How to build a generative AI solution From prototyping to pro...KristiLBurns
Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
□Author
Masaya Mori, Global Head of Rakuten Institute of Technology, Executive Officer, Rakuten Inc.
森正弥 楽天株式会社 執行役員 兼 楽天技術研究所代表
□Description
そもそもなぜ人工知能(AI)をビジネスで活用する必要があるのかの視点に基づいて、AI活用戦略について述べた講演の資料です。
leewayhertz.com-How to build a generative AI solution From prototyping to pro...KristiLBurns
Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
Building Products That Think- Bhaskaran Srinivasan & Ashish GuptaISPMAIndia
Presenters:
Bhaskaran Srinivasan, Senior Strategy Consultant
Ashish Gupta, Senior Product Manager, Google
Abstract:
This workshop is designed to introduce participants to the opportunities that Generative AI offers through the process steps of a standard NPI. The program provides insights into the capabilities and limitations of Generative AI, offering a hands-on exploration of Gen AI tools tailored for product managers. Attendees will learn how to seamlessly integrate Generative AI into their daily product management workflows, identifying opportunities and prioritizing them based on impact and feasibility. The workshop introduces a robust framework for developing Generative AI-powered products, taking into account crucial factors such as customer pain points, market segment, data and algorithm biases, transparency, user control, and privacy. To enhance the learning experience, the workshop incorporates interactive talks, case study coverage, and group-based hands-on exercises. Geared towards mid-level product managers with a foundational understanding of product management best practices, the workshop is facilitated by two seasoned speakers with expertise in product innovation.
Building a generative AI solution involves defining the problem, collecting and processing data, selecting suitable models, training and fine-tuning them, and deploying the system effectively. It’s essential to gather high-quality data, choose appropriate algorithms, ensure security, and stay updated with advancements.
Generative AI models are transforming various fields by creating realistic images, text, music, and videos. This guide will take you through the essential steps and considerations for building a generative AI model, providing a comprehensive understanding of the process.
Why So Many ML Models Don't Make It To Production?UXDXConf
Even though the future is about data, machine learning and artificial intelligence, between 80-90% of the ML models are not deployed (based on different researchers).
In this talk, I'll share the different challenges that we experienced within this space, what solutions we have implemented and any future improvements we have planned in this space.
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
How to Successfully Apply Data & AI in the Marketing Value Chain. In this session Artefact will be starting with the role of data in the current world and what it has lead to currently: Where are we with Data & Artificial Intelligence? the future is definitely here. We will make it concrete and explore where to apply Data & AI in digital marketing? What can AI do and what can't it do (yet)?Possible areas are automation, optimization and more. Artefact will make it practical to conclude and explain what using Data & AI means practically for Digital Marketing? What are the actionable next steps in Planning, setup/workflow, getting control and creative.
Dr Christoph Nieuwoudt- AI in Financial Servicesitnewsafrica
Dr. Christoff Nieuwoudt delivered a keynote on AI in Financial Services at Digital Finance Africa 2023 on the 2nd of August 2023 at Gallagher Convention Centre, Johannesburg, Midrand.
Big Data LDN 2018: THE PATH TO ENTERPRISE AI: TALES FROM THE FIELDMatt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 11:50 - 12:20
Speaker: Romain Fouache
Organisation: Dataiku
About: Enterprise AI is a target state where every business process is AI-augmented and every employee is an AI beneficiary. But is that really attainable? And, if so, what is the path to get there? In this talk, Kurt Muehmel, VP Sales Engineering at Dataiku, will share learnings from the field, describing how companies of different sizes and across different sectors have begun this journey. Some are farther along than others, and by making the right decisions now and avoiding stumbling blocks, you can to supercharge your quest to this AI-fuelled future.
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
In my presentation, I will summarize the applied and practical aspects of creating sustainable software products. What does it mean - "green" software for users and developers? I want to explain how creating “green” software can be driven by multiple organizational layers. And how building “green” software products can help the organization increase overall software product efficiency.
This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
Functional Programming in Pattern-Match-Oriented Programming Style <Programmi...Rakuten Group, Inc.
Research Scientist Satoshi Egi gave a presentation, Functional Programming in Pattern-Match-Oriented Programming Style, at the 2021 <Programming> conference (March 22-26). The presentation focuses on his 2020 research paper, which advocates a new programming paradigm called pattern-match-oriented programming.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
7. 7
• GAN, Adversarial Network
• AlphaGo Zero (Deep RL)
• AICO (ad banner generator & CTR predictor)
• Predictive Learning
Real
Example
Generated
Example
Generator
Noise
Source
Discriminator
GAN Real
Fake
10. 10
Other projects will be organized under one vision.
Pitari
AIris
RCP
Creative
AI
Projects for the platform
Fraud
Detection
Data-based
Trading
Delivery
Optimization
Language
Learning
Next Candidates
13. 13
Machine translation
Automatic Speech Recognition for Product Voice Search
Product Data science
Sentiment Analysis
+
Category Grocery & food
Subcategory Wine 我们真的很有诚意
了。
你说我一个老总都
亲自跑了好几趟了。
Machine
translationAI organizing Chaotic Data AI understanding multi-languages
AI understanding speechAI understanding Voice of Customers
Voice recognition
14. 14
RIT Machine Translation matches Human Translation
Rated by bi-lingual speakers on
a 5-point scale for adequacy
and fluency
RIT
Human
Google
for English to Spanish / Portuguese / French / Polish / German / Italian
And, we‘re starting English to Japanese.
16. 16
Face recognition Gender, Age, Emotion Recognition
Product Recognition
AI understanding Face AI understanding User visually
AI understanding Object visuallyAI generating Digital Contents
Contents Generation (Creative AI)
17. 17
SNEAKER SALE Up to 30% off
Generation Prediction
30
Sneaker Sale
Up to
OFF
%
Sneaker
up to 30% off
Sale
Sneaker
up to 30% off
Sale
30
Sneaker Sale
Up to
OFF
%
Image Segmentation
Images
Text
Styles
Assisting Graphic
Design Process
18. 18
Mature-Level
At leveraging Deep Learning
Vision
Voice
(ASR)
Language
Voice
(TTS)
Big Gap, but bridgingSome Gap
The strategies of each Program Management are different.
21. 21
Artificially increase the volume of the training dataset to improve accuracy. It is good for when data is
insufficient, quality is low, or data is imbalanced to a specific category.
Small Dataset
Small Dataset Big Dataset
Data
Augmentation
Data is enough
Accuracy is increased
Data is insufficient
23. 23
Data augmentation example :
• Mixup is combination of two
training data
Method Sample Image
*C. Summers et al., "Improved Mixed-Example Data Augmentation", 2018
24. 24
A model developed for a task is reused as the starting point for a model on a second task. By
transferring, we can get improved result with small dataset.
Concept Use Cases
Big Dataset
Small Dataset
Pre-Training
Re-Training
Output
Transfer
Autonomous cars
Realization of automatic cars by deep learning
*Preferred Research (https://research.preferred.jp/2016/01/ces2016/)
43cm
20
cm
(ex. Flower image)
(ex. Animal image)
TOYOTA / Preferred Networks
25. 25
With a pretrained model with Japan`s Ichiba data, transfer learning can help extract prospective
customer, product recommendation and purchase prediction in US market .
Japan EC data
US EC data
Pre-Training
Transfer
・User purchase history
・Browsing history
・Review
・Advertisement click count
・Product search history…
Prediction (in US market)
・Prospective customer extraction
・Product recommendation
・Purchase prediction …
Re-Training
27. 27
Ensemble learning method is techniques that create multiple models and then combine them to improve
prediction accuracy.
Concept Use Cases
Predict demand forecast with high accuracy by using
multiple learning model.
Manufacturer
*FUJITSU website “FUJITSU Business Application Operational Data Management & Analytics”
Prediction accuracyModel B
Output
Model C
Model A
Ensemble
Normal Output
Model FUJITSU
Predict demand
forecast
28. 28
Predict USD/JPY, NK225 and JGB on daily or weekly basis from past data by using machine learning.
Ensemble learning is used as a method, and accuracy is improved.
Index
・・・
Model B
Model C
Model A
Past Data Future Prediction
Accuracy
Ensemble
learning
Nikkei
225
Bond
Currency
Index
・・・
Nikkei
225
Bond
Currency
Input Machine Learning Output
Predict price
29. 29
Classify product catalog by using machine learning.
Ensemble learning is used as a method, and accuracy is improved.
Product catalog data Classification (Taxonomy)
Input Output
・Title
・Product description etc.
Model B
Model C
Model A
Accuracy
Ensemble
learning
(XGBoost)
Machine Learning
30. 30
Detect merchants which can repay money from EC data with machine learning.
Ensemble learning is used as a method, and accuracy is improved.
EC data Credibility Score
Input Output
Tons of inputs
• Can repay
• Cannot repay
Judge
MerchantsModel B
Model C
Model A
Accuracy
Ensemble
learning
Machine Learning
30
Sneaker Sale
Up to
OFF
%
31. 31
Multi-modal learning is a model to learn from multiple data source(text, image, voice, etc.).
It is expected to high accuracy than model which learn from single source
Concept
Text
Voice
Image
Multi-modal
learning
Increase accuracy of fraud item detection by using
multimodal model : image, product name, description and
price.
EC
Robotics
Develop ASVR(Audio-Visual Speech Recognition), which
has high noise-robust with combination of sound and video
signals,
Use Cases
*Waseda University, Ogata tetsuya (https://pdf.gakkai-web.net/gakkai/ieice/icd/html/2017/view/I_01_02.pdf)
*Mercari, Engineering Blog “https://tech.mercari.com/entry/2018/04/24/164919”,
Text
Multi-
modal
source
Single
source
Voice
Image
(Video)+
Honda Research Institute
Mercari
Image Text
(Product name, Description etc. )
+
32. 32
Item Genre Classification : with Multi-modal learning
Classifier based on
CNN/RNN
Final Result
Text Data
• Item Title
• Item Description
Image Data
LSTM
CNN
33. 33
Reinforcement Learning is machine learning on how software agents to take action in environment to maximize
some notion of reward. Agents find optimal action model through trial and error.
Software
Agents
User etc.
Action Feedback
Concept Use Cases
Find optimal action model
Alibaba has adopted reinforcement learning to
improve commodity search
EC
Tech
DeepMind’s AlphaGo beat champion in Go game.
*Sigmoidal (https://sigmoidal.io/alphago-how-it-uses-reinforcement-learning-to-beat-go-masters/)
*Analytics India Magazine (https://www.analyticsindiamag.com/how-alibaba-is-applying-virtual-taobao-to-simulate-e-commerce-environment/)
Google
Alibaba
35. 35
Example (Human case)
Skill of riding bicycle
= Stand up + Ascend or descend a staircase etc.
Meta learning is approach of learning to learn.
It learn a variety of tasks from small amounts of data by utilizing past learning.
• Learn task quickly from small amounts of data
by utilizing past learning
• Meta learning is deep learning
algorithm close to human
*Nikkei X TECH (https://tech.nikkeibp.co.jp/dm/atcl/mag/15/00189/00003/)