AI gold rush, tool vendors and the next big thing
2017/12/27 at Mediatek
- Overview of booming AI applications, from media, entertainment, e-commerce, autonomous driving, surveillance, industrial inspection, medical imaging, bioinformatics, finance, etc., along with expert predictions of their market size and growth.
- Dissect the applications with largest size and growth into their technical components and their unmet demands.
- Among all the unmet demands and uncertainties in this AI gold rush, what should an IC design company do? I’ll briefly cover NVIDIA’s case, which most of us know well already, then supplement case studies of Qualcomm, Intel, Google TPU and other smaller firms.
Even when we have a clear target, it takes years for supporting libraries and software to be properly optimized. I’ll share some thoughts and personal experiences on how to make sequentially-ordered hardware/software/library optimization happen faster and in parallel, and the tools that the IC design house need to provide in order for it to happen.
Think different, in Finance. An outsider's two cents on how could finance majors rethink their role and value in the rapidly changing AI era, with some FinTech case studies.
Find Your Passion and Make a Difference in Your CareerAlbert Y. C. Chen
20180314 at National Taiwan Normal University.
Reflection on my own career from being inspired to work on CV/ML research during my graduate studies at NTNU, then going abroad to obtain my Ph.D. and later on my career in this field. The talk emphasizes on the importance of innovation and how to realize ones new ideas within large and small organizations.
AI gold rush, tool vendors and the next big thing
2017/12/27 at Mediatek
- Overview of booming AI applications, from media, entertainment, e-commerce, autonomous driving, surveillance, industrial inspection, medical imaging, bioinformatics, finance, etc., along with expert predictions of their market size and growth.
- Dissect the applications with largest size and growth into their technical components and their unmet demands.
- Among all the unmet demands and uncertainties in this AI gold rush, what should an IC design company do? I’ll briefly cover NVIDIA’s case, which most of us know well already, then supplement case studies of Qualcomm, Intel, Google TPU and other smaller firms.
Even when we have a clear target, it takes years for supporting libraries and software to be properly optimized. I’ll share some thoughts and personal experiences on how to make sequentially-ordered hardware/software/library optimization happen faster and in parallel, and the tools that the IC design house need to provide in order for it to happen.
Think different, in Finance. An outsider's two cents on how could finance majors rethink their role and value in the rapidly changing AI era, with some FinTech case studies.
Find Your Passion and Make a Difference in Your CareerAlbert Y. C. Chen
20180314 at National Taiwan Normal University.
Reflection on my own career from being inspired to work on CV/ML research during my graduate studies at NTNU, then going abroad to obtain my Ph.D. and later on my career in this field. The talk emphasizes on the importance of innovation and how to realize ones new ideas within large and small organizations.
Platform for Big Data Analytics and Visual Analytics: CSIRO use cases. Februa...Tomasz Bednarz
Presented at the ACEMS workshop at QUT in February 2015.
Credits: whole project team (names listed in the first slide).
Approved by CSIRO to be shared externally.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
The Power of Big Data - Transformation Day Public Sector London 2017Amazon Web Services
This session gives an in-depth look at the current state of big data at AWS. Learn about the latest big data trends and industry use cases. We’ll focus on how other organizations are using the AWS big data platform to innovate and remain competitive. Met Office also joins us to offer an inside look on how they are using AWS to enable citizens, business, and governments to consume its data on demand.
Speaker:
Ben Snively, Principal Solutions Architect, Amazon Web Services &
Jacob Tomlinson, Lead Engineer, Met Office Informatics Lab.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Using AI on a Large Scale at Doccle (presented by Bram Lerouge, CEO @Doccle)Patrick Van Renterghem
Presented by Bram Lerouge at the #futureofit seminar. Doccle is thé Belgian platform where you receive, pay, share and store your administration in one place. More and more energy, telecom and water providers, health insurance, banks, government agencies, job agencies, hospitals, B2B retailers and industries are recognising the simplicity and safety of Doccle.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Ch...Neo4j
Today’s complex data is not only big, but also semi-structured and densely connected. In this session we’ll look at how size, structure and connectedness have converged to transform the data landscape. We’ll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with practical examples drawn from the telecommunications, social media and logistics sectors.
Using Algorithmia to leverage AI and Machine Learning APIsRakuten Group, Inc.
We are entering a new era of software development. Companies are realizing that AI and machine learning are critical to success in business, both to save cost on repetitive tasks, and to enable to new features and products that would be impossible without machine intelligence. Algorithmia makes these tools available through web APIs that makes tools like computer vision and natural language processing available to companies everywhere. Kenny will talk about how sharing of intelligent APIs can improve your applications.
https://rakutentechnologyconference2016.sched.org/event/8aS5/using-algorithmia-to-leverage-ai-and-machine-learning-apis
Rakuten Technology Conference 2016
http://tech.rakuten.co.jp/
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.
Every business is looking for a game-changer in data science, machine learning, and AI. Most organizations are also looking for ways to tap into open-source and commercial data science tools such as Python, RStudio, Apache Spark, Jupyter, and Zeppelin notebooks, to accelerate predictive and machine learning model building and deployment while leveraging the scale, security and governance of the Hortonworks Data Platform and other commercial platforms.
Ana Maria Echeverri will demonstrate how to accelerate data science, machine learning, and deep learning workflows by using IBM Watson Studio, an integrated environment for data scientists, application developers, and subject matter experts. This suite of tools allows to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale while using Open Source skills (i.e.: Python) and expanding into cognitive capabilities through access to Watson APIs to build AI-powered applications. If you love Python and want to tap into the power of IBM Watson, this is the session for you.
Platform for Big Data Analytics and Visual Analytics: CSIRO use cases. Februa...Tomasz Bednarz
Presented at the ACEMS workshop at QUT in February 2015.
Credits: whole project team (names listed in the first slide).
Approved by CSIRO to be shared externally.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
The Power of Big Data - Transformation Day Public Sector London 2017Amazon Web Services
This session gives an in-depth look at the current state of big data at AWS. Learn about the latest big data trends and industry use cases. We’ll focus on how other organizations are using the AWS big data platform to innovate and remain competitive. Met Office also joins us to offer an inside look on how they are using AWS to enable citizens, business, and governments to consume its data on demand.
Speaker:
Ben Snively, Principal Solutions Architect, Amazon Web Services &
Jacob Tomlinson, Lead Engineer, Met Office Informatics Lab.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Using AI on a Large Scale at Doccle (presented by Bram Lerouge, CEO @Doccle)Patrick Van Renterghem
Presented by Bram Lerouge at the #futureofit seminar. Doccle is thé Belgian platform where you receive, pay, share and store your administration in one place. More and more energy, telecom and water providers, health insurance, banks, government agencies, job agencies, hospitals, B2B retailers and industries are recognising the simplicity and safety of Doccle.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Ch...Neo4j
Today’s complex data is not only big, but also semi-structured and densely connected. In this session we’ll look at how size, structure and connectedness have converged to transform the data landscape. We’ll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with practical examples drawn from the telecommunications, social media and logistics sectors.
Using Algorithmia to leverage AI and Machine Learning APIsRakuten Group, Inc.
We are entering a new era of software development. Companies are realizing that AI and machine learning are critical to success in business, both to save cost on repetitive tasks, and to enable to new features and products that would be impossible without machine intelligence. Algorithmia makes these tools available through web APIs that makes tools like computer vision and natural language processing available to companies everywhere. Kenny will talk about how sharing of intelligent APIs can improve your applications.
https://rakutentechnologyconference2016.sched.org/event/8aS5/using-algorithmia-to-leverage-ai-and-machine-learning-apis
Rakuten Technology Conference 2016
http://tech.rakuten.co.jp/
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.
Every business is looking for a game-changer in data science, machine learning, and AI. Most organizations are also looking for ways to tap into open-source and commercial data science tools such as Python, RStudio, Apache Spark, Jupyter, and Zeppelin notebooks, to accelerate predictive and machine learning model building and deployment while leveraging the scale, security and governance of the Hortonworks Data Platform and other commercial platforms.
Ana Maria Echeverri will demonstrate how to accelerate data science, machine learning, and deep learning workflows by using IBM Watson Studio, an integrated environment for data scientists, application developers, and subject matter experts. This suite of tools allows to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale while using Open Source skills (i.e.: Python) and expanding into cognitive capabilities through access to Watson APIs to build AI-powered applications. If you love Python and want to tap into the power of IBM Watson, this is the session for you.
Digital Transformation and Innovation on http://denreymer.com
- Merging the Real World and the Virtual World
- Intelligence Everywhere
- The New IT Reality Emerges
http://www.gartner.com//it/content/2940400/2940420/january_15_top_10_technology_trends_2015_dcearley.pdf
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.
We were founded in 2011 with headquarters in Israel and Ukraine. Our specialization lies in organizing and managing offshore dedicated teams for outstaffing purposes in different business and tech areas, as well as developing complex sophisticated software projects.
Similar to Video AI for Media and Entertainment Industry (20)
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Tips for would-be founders, technical or non-technical, before rolling up their sleeves and develop their products! From various ways of "pretotyping" to accurately gauge target customer's response, lean method, minimum viable product, feature selection, planning a product with robust data cycle, coping with delays, and guiding a team of rockstar engineers to build the right product and build the product right. Some personal experienced shared at the end as case studies.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Practical computer vision-- A problem-driven approach towards learning CV/ML/DLAlbert Y. C. Chen
Practical computer vision-- A problem-driven approach towards learning CV/ML/DL
Albert Chen Ph.D., 20170726 at Academia Sinica, Taiwan
Invited Speech during Academia Sinica's AI month
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
DevOps and Testing slides at DASA ConnectKari Kakkonen
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I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Video AI for Media and Entertainment Industry
1. Video AI for Media and
Entertainment Industry
Albert Y. C. Chen, Ph.D.
Vice President, R&D
Viscovery
2. Albert Y. C. Chen, Ph.D.
陳彥呈博⼠士
• Experience
2017-present: Vice President of R&D @ Viscovery
2016-2017: Chief Scientist @ Viscovery
2015: Principal Scientist @ Nervve Technologies
2013-2014 Computer Vision Scientist @ Tandent
2011-2012 @ GE Global Research
• Education
Ph.D. in Computer Science, SUNY-Buffalo
M.S. in Computer Science, NTNU
B.S. in Computer Science, NTHU
3. Viscovery = Video Discovery
Optical Character
Recognition
Offline
Recognition
2013
2014
Product Recognition
2015
Video Content related
Advertisements
2017
Wearable Devices
Video Content Discovery &
Interaction
2016
Leading provider of Video AI analytic products
4. Current AI does not “solve it all”
appl.
layer
tech
layer
infra
layer
solution
platform
libraries
modules
data
machine computing power
data accumulation via open API
AI/DNN library AI/DNN library
gen purpose
platforms
gen purpose
platforms
app-specific
platforms
app-specific
platforms
app app app app app
HW
co.
VerticalAIStartups
agri. manu. med. fin. retail trans.
E.g., 1: Google, Amazon, FB, 2: IBM, 3: Walmart, 5: NVidia
5. Vertical AI
Solving industry-specific problems by combining
AI and Subject Matter Expertise.
• Full Stack Products
• Subject Matter Expertise
• Proprietary Data
• AI delivers core value
(Bradford Cross, 2017/06/14)
7. Media & Entertainment
Industry’s challenge
• Internet Era: Make content free, maximize traffic,
ad revenue waiting at the end of the rainbow?
• It worked for nearly 20 years, with Google and
Facebook being the only beneficiary; they control
75% of digital ad revenue, 99% of future growth.
• Is this business model still working? Does it work
for others? The latest unicorns from Silicon Valley
are suggesting otherwise.
11. The curveball: App Stores
and News Syndicators!
• News Republic (acquired for 57M use, Aug 2016)
• 12.5 million daily active users
• 60k USD annual revenue
• 今⽇日頭條 (toutiao.com)
• 80 million daily active users.
• 1B USD annual revenue.
12. Pay source, or pay platform?
• Platform:
• More focus, less distraction: news focus on
content instead of customer service, software
development, etc.
• Potential Problem:
• Facebook and Google control 75% of all traffic
and 99% of expected future growth?
13. Netflix
• Netflix spends $250m USD yearly on
personalization and content recommendation.
• 104m subscribers worldwide; 52m in US (75%
market penetration, #1 in US, Youtube #2 at
53%)
• Netflix subscribers watch 19 days per month, for
28H/month (#2, less than Dish’s 47 H/month)
17. The evolution of methods for
monetizing text/video content
Struggling
Traditional
Media
Free Content
Ad Revenue
Subscription
Revenue
2000 2005 2010
Do nothing?
Sitting Duck.
Improve
Ad Revenue?
Ad Tech
now
Video
Content-related
ads
Own platform?
shared
platform, licensed
content?
tailored
recommendations
(improve UX & stickiness)
(user & video
content related
recommendations)
Video Data
Mining
18. If we already have such precise
indexing of video content
Jay Chao
singing A
dancing B
wearing C
with items D
in front of E
at time F?• We will disrupt:
• advertisement
• e-commerce
• online video platform ecosystem
• screenwriting, film producuction and film editing..
19. Video content-related
advertisements
Previous moment: dining scene Insert Food Deliver Service ad Next Moment: dining scene
饿了了吗?快点饿了了么!
Food Delivery Service Ad:
Previous moment: dining scene Insert KFC ad Next second: dining scene
炸鸡红包快
来抢!
Restaurant Ad:
24. Mining Video Content with
Computer Vision
• 85% of data are unstructured, e.g., videos.
• Previously, videos need manual tagging before its
content can be indexed and further utilized.
• Computer Vision is the AI subfield that focuses on
recognizing and understanding visual content.
25. What algorithms do we need?
Face Motion
Image
scene Text Audio Object
Semantics
26. Where are we now?
• Face
• Object
• Scene
• Logos
• Text
• Audio
• Motion
• Semantics
27. Where are new now?
Face Recognition
• 1 to 1: 99%+
• 1 to 100: 90%
• 1 to 10,000:
50%-70%.
• 1 to 1M: 30%.
LFW dataset, common FN↑, FP↓
28. Where are we now?
Image Scene Classification
• MIT Places 365
dataset.
• top-5 accuracy
rates >85%.
29. Where are we now?
Object Detection & Classification
• ImageNet Large Scale Visual
Recognition Challenge (ILSVRC)
• 1000+ classes, 1.2M images.
0
0.125
0.25
0.375
0.5
11 12 13 14 11 12 13 14
classification
error
classification
+localization error
30. Putting things together is not
trivial and often very messy.
Classical Workflow:
1. Data collection
2. Feature Extraction
3. Dimension Reduction
4. Classifier (re)Design
5. Classifier Verification
6. Deploy
Modern Brute-force workflow
1. Data collection
2. Throw everything into a Deep Neural Network
3. Mommy, why doesn’t it work ???
31. Classical Problem #1:
Curse of Dimensionality
坐
ze
sit
って
앉다
sentarse
• Number of Variables vs Number of Samples
Q. Who would make such naive mistakes?
A. Many “newbies” repeatedly do so.
32. Example 1-1:
illegal parking detection
legal parking samples x100 illegal parking samples x100
Let’s train a 150-layer Res-Net!!!
What could possibly go wrong?
33. Example 1-1:
illegal parking detection
• Data: try cleaner data
• Feature: fine-tune with pre-trained model; don’t
train from scratch
• Classifier overfitting: beware of statistical
coincidences,
35. Example 1-2: Smart Photo
Album with Google Cloud Vision
No effective distance measure for thousands,
if not millions of dimensions (tags); would be
approximately zero most of the time.
36. Classical Problem #2:
Overfitting Data
• Make sure your deep learning algorithm is
learning better features for data, not overfitting
the data with complex classifiers.
37. Luckily, we’re in AI startup boom!
(BCG AI Report, 2016/10)
appl.
layer
tech
layer
infra
layer
solution
platform
libraries
modules
data
machine computing power
data accumulation via open API
AI/DNN library AI/DNN library
gen purpose
platforms
gen purpose
platforms
app-specific
platforms
app-specific
platforms
app app app app app
HW
co.
VerticalAIStartups
agri. manu. med. fin. retail trans.
E.g., 1: Google, Amazon, FB, 2: IBM, 3: Walmart, 5: NVidia
38. Vertical AI Startups
Solving industry-specific problems by combining
AI and Subject Matter Expertise.
• Full Stack Products
• Subject Matter Expertise
• Proprietary Data
• AI delivers core value
(Bradford Cross, 2017/06/14)
40. TOP 5 TAGS COMPARISON
TAG
AD PLACEMENT
VALUE
TAG
AD PLACEMENT
VALUE
Person Low
Coulee Nazha
(actress)
High
Anime Low Sean Sun (actor) High
Screenshot Low Back of smartphone High
Cartoon Low Female Medium
Adult Medium Young Medium
“FIRST LOVE” DRAMA SERIES SCENE
Competitive Analysis
Baidu vs. Viscovery
TOP 5 TAGS COMPARISON
TAG (Man’s Face)
AD PLACEMENT
VALUE
TAG
AD PLACEMENT
VALUE
Age: 32 Medium Necklace High
Asian Medium Baseball cap High
Male Medium Bracelet High
Not smiling Low (inaccurate) Ziwen Wang High
Examples of Vertical AI
beating General Purpose AI
41. Use AI to turn unstructured
video data into a gold mine!
60 mins0 mins
服饰 汽⻋车
代⾔言⼈人
聚会
⼿手机
居家
z
CTR: 0.2%
60 mins0 mins
旅游 活⼒力力 汽⻋车
⼯工作 聊天
z
60 mins0 mins
学习
using only physical tags
for recommendation
CTR: 0.9%
CTR: 2.0%
z
z
Smartphone Ad physical plus abstract
and emotional tags
physical, abstract and
emotional tags plus feedback
客厅
欢乐客厅
聊天⼯工作⼿手机 代⾔言⼈人 欢乐旅游