This document summarizes a presentation on data science and artificial intelligence. It discusses how AI is transforming businesses in many ways, including automating repetitive tasks, improving customer experiences, and driving revenue growth. It also mentions that while data is important, AI is needed to transform organizations through intelligent process optimization and innovation. The document provides examples of how various companies are applying AI in sales, customer service, and other areas. It emphasizes that AI strategies should focus on innovation, identifying high-impact use cases, and developing people's data science skills.
[Ai in finance] AI in regulatory compliance, risk management, and auditingNatalino Busa
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
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.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization’s monitoring capabilities in areas such as regulatory compliance and corporate governance.
AI systems
can adapt to changing risk environments
continually enhance the organization’s monitoring capabilities
Better manage regulatory compliance and corporate governance.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
[Ai in finance] AI in regulatory compliance, risk management, and auditingNatalino Busa
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
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.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization’s monitoring capabilities in areas such as regulatory compliance and corporate governance.
AI systems
can adapt to changing risk environments
continually enhance the organization’s monitoring capabilities
Better manage regulatory compliance and corporate governance.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
Listen to an experienced, global panel of insurance professionals present, discuss and answer your questions on the theme of “AI & Machine Learning”.
Brought to you by The Digital Insurer and sponsored by KPMG.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
This report is based on a series of interviews across the breadth of the MoD to probe the ability of the British military to cope with a growing data deluge, and identify potential applications and hurdles to their implementation.
"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Us...Dataconomy Media
"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the Author:
Hans is Scientific Director and Head of the Language Technology Lab at the German Research Center for Artificial Intelligence (DFKI). He also serves as site lead of DFKI’s Berlin branch. Hans studied at TU Berlin and the U of Texas at Austin. After research positions at SRI International in Menlo Park and IBM in Stuttgart, he became full professor for computational linguistics at Saarland U. in Saarbruecken where he taught for more than 20 years. He co-founded one print magazine and several language technology startups. Hans’s main interests in AI are foundations and applications of language and knowledge technologies. He has been leading several European and national projects in knowledge extraction, text analytics and automatic translation. His research is documented by more than 200 publications.
Just a few years back, artificial intelligence meant adaptions like Jarvis. Who would have thought that AI would soon become an application of our daily lives?
Artificial intelligence has the potential to streamline several business processes, analyze data for insights, and help in building fruitful business strategies. Hence, globally, it is being used to remediate old processes, invent new methods, and improve productivity.
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
Listen to an experienced, global panel of insurance professionals present, discuss and answer your questions on the theme of “AI & Machine Learning”.
Brought to you by The Digital Insurer and sponsored by KPMG.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
This report is based on a series of interviews across the breadth of the MoD to probe the ability of the British military to cope with a growing data deluge, and identify potential applications and hurdles to their implementation.
"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Us...Dataconomy Media
"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the Author:
Hans is Scientific Director and Head of the Language Technology Lab at the German Research Center for Artificial Intelligence (DFKI). He also serves as site lead of DFKI’s Berlin branch. Hans studied at TU Berlin and the U of Texas at Austin. After research positions at SRI International in Menlo Park and IBM in Stuttgart, he became full professor for computational linguistics at Saarland U. in Saarbruecken where he taught for more than 20 years. He co-founded one print magazine and several language technology startups. Hans’s main interests in AI are foundations and applications of language and knowledge technologies. He has been leading several European and national projects in knowledge extraction, text analytics and automatic translation. His research is documented by more than 200 publications.
Just a few years back, artificial intelligence meant adaptions like Jarvis. Who would have thought that AI would soon become an application of our daily lives?
Artificial intelligence has the potential to streamline several business processes, analyze data for insights, and help in building fruitful business strategies. Hence, globally, it is being used to remediate old processes, invent new methods, and improve productivity.
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Vertex perspectives ai optimized chipsets (part i)Yanai Oron
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning.
Vertex Perspectives | AI-optimized Chipsets | Part IVertex Holdings
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning. To date, deep learning technology has primarily been a software play. Existing processors were not originally designed for these new applications. Hence the need to develop AI-optimized hardware.
Ομιλία- Παρουσίαση: Ανδρέας Τσαγκάρης, VP & Chief Technology Officer, Performance Technologies
Τίτλος Παρουσίασης: “Big Data on Linux on Power Systems”
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Industry pundits are predicting up to 50 billion connected devices by 2020, generating more data than in all of human history to date and connected via ubiquitous, connectivity such as 5G, Sigfox and NBIoT. With this comes the promise of business opportunities to deploy your Internet of Things solution. Ganga will walk you through the trends in computing that you need to be aware of, how you can get started and how working with Intel can accelerate your development and time to market.
Speaker: Ganga Varatharajan, IoT & New Technologies Manager, Intel
Data-driven enterprise requires intelligent, sentient, and resilient software systems to address information processing structures to deal with rapid fluctuations in resource demand and availability.
Communication, Collaboration and Commerce workflows at the speed of light demand always-on anti-fragile systems
Both autonomic computing and neural networks provide a next generation set of technologies to meet the needs of the data-driven enterprise at the speed of light
Crypto-Security and New Digital Asset Life-cycle Managent assures the Asset’s Confidentiality, Integrity, Availability providing Privacy & Protection of Individual Rights
Take Action: The New Reality of Data-Driven BusinessInside Analysis
The Briefing Room with Dr. Robin Bloor and WebAction
Live Webcast on July 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=360d371d3a49ad256942f55350aa0a8b
The waiting used to be the hardest part, but not anymore. Today’s cutting-edge enterprises can seize opportunities faster than ever, thanks to an array of technologies that enable real-time responsiveness across the spectrum of business processes. Early adopters are solving critical business challenges by enabling the rapid-fire design, development and production of very specific applications. Functionality can range from improved customer engagement to dynamic machine-to-machine interactions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will tout a new era in data-driven organizations, and why a data flow architecture will soon be critical for industry leaders. He’ll be briefed by Sami Akbay of WebAction, who will showcase his company’s real-time data management platform, which combines all the component parts needed to access, process and leverage data big and small. He’ll explain how this new approach can provide game-changing power to organizations of all types and sizes.
Visit InsideAnlaysis.com for more information.
A Look Under the Hood of H2O Driverless AISri Ambati
Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and production deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code (Java and C++), and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Other industry-leading capabilities include automatic data visualization and machine learning interpretability.
With Driverless AI, data scientists of all proficiency levels can train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client API from Python or R. Driverless AI builds hundreds or thousands of models under the hood to select the best feature engineering and modeling pipeline for every specific problem such as churn prediction, fraud detection, real-estate pricing, store sales prediction, marketing ad campaigns and many more.
With Bring-Your-Own-Recipe, domain experts and advanced data scientists can now write their own recipes and seamlessly extend Driverless AI with their favorite tools from the rich ecosystem of open-source data science and machine learning libraries.
In this talk, we explain how Driverless AI works and demonstrate it with live demos.
Arno's Bio:
Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators.
Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. A Journey Through The Far Side of Data Science
Ted Washburne @ Stevens Institute of Technology / Sept. 27, 2018
2. Your competition
is pursuing
opportunities for
AI & smart
automation offers
in every business
unit and going
beyond cost
savings or
productivity
improvements
The MBA is no
longer more
valuable that a
BA degree in
computer science
with machine
learning expertise
SalesForce
asserts that
“66% of a sales
rep’s time is
spent not selling”
and AI can
automate the
drudgery work
like onboarding
The younger
wealth now
have 24/7
service
expectations
that can only
be delivered by
intelligent
automation Driving revenue
growth and
profitability
Lowering the
cost of many
financial activities
to near-zero by
understanding
the critical
building blocks in
designing an AI
and smart
automation
strategy
Data alone
doesn’t help
business leaders
transform their
organizations.
AI enables faster
and larger-scale
intelligent
process
optimization,
intelligent agents,
and innovation
1
Data is the
new
foundation
2
AI becomes
the new
norm
3
Innovation is
intensifying
4
AI for the
Front-Office
5
Technology
is the
Business
6
AI enabled
Customer
Experience
7
Everything
happens in
the Platform
Reach and depth of AI technology is transforming Business
3. … Intelligent Automation (Aitomation) is the key …
Complete a thorough benefit analysis
before committing to investments
Make aitomation a strategic imperative
and get senior leadership backing
Focus on AInnovation with a central
team providing Governance
Identify and rapidly scale high-
impact aitomation use cases
Artificial General Intelligence is making
significant inroads in Legal and Accounting
Develop the people capabilities needed for
maximum value (Python, Machine Learning)
Honestly asses the Competition and establish
ecosystem partners to challenge them
4. Who Made this all Possible?
Paul Werbos & The Chain Rule
• https://en.wikipedia.org/wiki/Ba
ckpropagation
• https://en.wikipedia.org/wiki/Pa
ul_Werbos
• http://explained.ai/matrix-
calculus/index.html
• differential matrix calculus, the
shotgun wedding of linear
algebra and multivariate calculus.
Silicon Graphics
-> Nvidia +
Ian Buck
• Inventor of CUDA, the
established standard for GPU
Computing worldwide. Built
engineering team from two
people into international and
matrixed organization in
numerical libraries, compilers,
system software, IDEs, profilers,
debuggers, APIs, QA, build and
release, and AI frameworks.
https://www.nvidia.com/en-us/deep-learning-ai/education/
5. Data Science Themes at SIT
Artificial
Intelligence,
Machine Learning
& Cybersecurity
"We knew it was
the Russians,
and they knew
we knew,"
Johnston told
the NYtimes of
the
cyberwarfare. "I
would say it was
the cyber
equivalent of
hand-to-hand
combat."
Biomedical
Engineering,
Healthcare & Life
Sciences
Deep Learning’s
Deepest Impact:
AI Storming
Through $6.5
Trillion
Healthcare
Industry
Complex Systems
& Networks
USAA developed
a
comprehensive
analytical
simulation
model of their
complex
operations and
the market that
is allowing
senior
executives to
explore a wide
range of
scenarios and
strategic options
and understand
long term
implications of
these decisions.
Data Science and
Information
Systems
Cognitive
knowledge
graphs encode a
model of expert
knowledge of
every domain
within a context.
This gives bots a
semantic
understanding
of the context
and helps them
to respond to
complex
queries.
Financial Systems
& Technologies
Providing an
extension of the
bank's main
quant team
covering the
whole range of
quant tasks from
numerical
algorithms to
multi-curve
building and
financial
modelling.
Resilience &
Sustainability
Utilities now use
machine learning
to classify network
assets at high risk
of failure and
manage the
complexities of
distributed energy
resource
management.
Preparation for
cyberattacks
include
Transformers,
circuit breakers, as
well as secure
warehouses to
store them in
select locations
and the
preplanned
transportation and
logistics to get
them where they
need to go as the
situation requires.
Maritime Security
HawkEye 360, a
developer of
space-based
radio frequency
(RF) mapping
and analytics
systems
develops deep
convolutional
neural networks,
Bayesian
propagation
networks and
statistical
anomaly
detection for
maritime
domain
awareness
Systems
Engineering
Research Center
*Constraint
Programming to
Incorporate
Engineering
Methodologies
into the Design
Process of
Complex
Systems.
*Recurrent Nets
to solve
Knapsack
problems in
Mission
Management.
*Petri Nets for
Process
Discovery
https://www.usnews.com/news/articles/
2016-09-23/is-the-energy-grid-in-danger
Chua, L.O., Lin, G.-N
“Nonlinear programming
without computation”
8. SIT Courses & Case Studies
•Citi Cards
•Capital One
FIN 615 Financial
Decision Making
•Ceph
•Schneider Electric
•Spark
MIS 630 Database
Systems and Decision
Support
•Snowflake
•Merging Accounts
•Bad Days
MIS 636 Data
Warehousing and
Business Intelligence
•Knapsack
•HSBC RPA
BIA 650 Process
Optimization and
Analytics
•BMW China
•Coin-OR
•Visa Fraud
•Morgan Stanley
High Frequency
BIA 670 Risk
Management: Methods
and Applications
•Kaggle
•Numpy
•Pandas
•Seaborn, Keras
BIA 652
Multivariate Data
Analytics
•Schneider Electric
•Unmanned ground
vehicle
• Capital One
BIA 664 Data and
Information Quality
•TV Advertising
•Direct Mail
BIA 654 Experimental
Design
•MBA Forecasts
•Asurion Trouble
Prediction
MIS 637 Knowledge
Discovery in Databases
•DBS Treasury
•Hastie, Stork
BIA 656 Statistical
Learning and Analytics
•Flu prediction
•Amobee link
prediction
•Alternative
Influence Network
BIA 658 Social Network
Analytics
•DoubleClick
Attribution
•Yelp reviews
BIA 660 Web Mining
•PwC Audit.ai
•Automated Feature
Engineering
BIA 662 Cognitive
Computing
•A template for
understanding Big
Debt Crises
•by Ray Dalio
BIA 670 Risk
Management: Methods
and Applications
•Omnicom
•HP Forecast
•Lowes Hardware
•Cambridge
Analytica
BIA 672 Marketing
Analytics
•Walmart Labs
•Nike
•NV Energy
•Hawkeye360
BIA 674 Supply Chain
Analytics
•DBS ATM Maint.
•Insurance
Telematics
•ASW
BIA 676 Data Streams
Analytics: Internet of
Things
•Spark killed Hadoop
•MapReduce is dead
•DataBricks
•SparkFlows.io
BIA 678 Big Data
Technologies
•Next Best Action
Models for Wealth
Management
•HR systems
BIA 686 Applied
Analytics in a World of
Big Data
https://www.youtube.co
m/watch?v=g6oIQ5MXBE4
https://aws.amazon.com/rekognition/
9. Useful Algorithms For Your BI Career
Reinforcement
Learning
Dueling Deep Q
Network
Robots for
unloading ships,
warehouse
forklifts,
harvesting crops
Recommender
Systems
Netflix Prize
A quantum-
inspired classical
algorithm for
recommendation
systems.
Ewin Tang, July 10,
2018
Segmentation
Decision Tree
Clustering (k-
means or EM)
& factoextra
•Avoid “lazy”
dimensionality
reduction, like
principal
components
Forecasting
Tsintermittent
Recurrent NN
XGBoost
•http://docs.h2o.ai/
driverless-
ai/latest-
stable/docs/usergu
ide/time-
series.html?highlig
ht=forecast
Regression
GLM/GBM
Classifiers
Trees or XGBoost
Logistic Regression
Convolutional
Neural Nets
NLP
Word2Vec
Truncated SVD
OR
Coin-OR branch
and cut
Knapsack
DS is IT + Biz + software engineering + design
Get to know Docker, Kuberflow, Kubernetes, and SeldonA 2018 paper in Nature
cited AlphaGo's
approach as the basis for
a new means of
computing potential
pharmaceutical drug
molecules
10. Systems Engineering Research
• I authored a paper on a Hopfield (recurrent) system that we had built at Lockheed Research
Labs, along with a few other contributors and got it accepted to a Neural Networks conference
in Maryland – as a poster paper
• Some weeks later, I am getting a new clearance and told to report to a building with no
windows.
• The project has something to do with Mission Management and it has a challenge in the area
of solving a really big knapsack optimization problem over multiple time windows
• The problem often arises in resource allocation where there are financial constraints and is studied in
fields such as combinatorics, computer science, complexity theory, cryptography, applied
mathematics, and daily fantasy sports.
• While researching the problem, I was concerned about a limitation of Hopfield networks
getting stuck in local minima and not finding the globally optimal solution
• Researching all the way back to the 1970’s, I found a paper by a Berkeley professor named
Chua that had a better behaved network for this problem
• The network could be modeled in the popular circuit modeling software “SPICE”, using a Sun SPARC
workstation, like was done at Analog Devices and Linear Technology (merger last year)
• SPICE (Simulation Program with Integrated Circuit Emphasis) is a general-purpose, open source analog electronic
circuit simulator. MatLab can do this now
• We found that we could set up very large constraint matrices and completely random starting
points and the system would find an almost perfect solution in under a second and then spend
the next 10 seconds converging to the optimal solution
• Far faster than a Cray using the traditional linear programming ‘Greedy Algorithm’
12. Financial Systems & Technologies
NBA: Recommended Solution for Client Investment & Trading Tech / Treasury and Markets
Sales Person
Counterparty
Client
Product
Counterparty
Market Data
Quantitative
Product Info
Market Data
Revenue
P&L Records
Unstructured
Data (WWW)
Counterparties clustered by similarity
(clients and non-clients)
Graph or relations
• Salesperson x CPTY as nodes
• Products related to each graph edge
Temporal structure of relations:
Mutations of the graph in time
Market Regime
Switches
• Regime-driven behaviour (SP, CPTY)
• Regime-driven revenue and risks
Contracts/products parameters templates
for each SP/CPTY in time
Client Request
Historical Data Knowledge Discovery OUTCOMES
•Stable transitions?
•Periodic events?
•Hidden connections?
•Recognized states?
•Unstructured outlier?
•Atypical states series?
•Unrecovered gaps?
•Unstable behaviour?
Patterns
Anomalies
Client Existing Data
Required Extra Data for Future Phases
Discovered KnowledgeKey: NBA Data
NBA Model
MODELS
13. Data Science and Information Systems
• https://databricks.com/session/moving-ebays-
data-warehouse-over-to-apache-spark-spark-as-
core-etl-platform-at-ebay
• Snowflake - Beyond Hadoop: Modern Cloud Data
Warehousing
• Captial One / NY / Sr. Software Engineer
• All of our infrastructure runs on AWS, and we are
eyeing other cloud providers too. We use Elasticsearch
and ELK stack, Redis, PostgreSQL, Redshift, and
Snowflake. We do analytics using H2O, Spark and
MLlib, Databricks/EMR, TensorFlow and Keras. We
build awesome products for our users that use this
data. We write microservices in Go and Node.js,
orchestrated by Kubernetes, with user experiences
written in React and TypeScript. We embrace
serverless. Your hands-on expertise in at least some of
these tools will be valuable, as well as your track
record of providing effective technical guidance.
14. CPA + AI
Accounting jobs are not going away – the skill set is changing
• Business Setting
• PwC partnered with H2O.ai to build a revolutionary bot that uses AI and machine
learning to ‘x-ray’ a business, analyzing billions of data points in milliseconds,
seeing what humans can’t, and applying judgement to detect anomalies in the
general ledger. Called GL.ai, it is the first module of PwC’s Audit.ai.
• Approach
• GL.ai harnesses PwC’s global knowledge and experience, embedding it in
algorithms trained to replicate the thinking and decision-making of expert auditors.
• It examines every uploaded transaction, every user, every amount and every
account to find unusual transactions (indicating potential error or fraud) in the
general ledger, without bias or variability.
• Impact
• Experience confirms that GL.ai speeds up the audit process, generates insights that
boost efficiency, and provides comfort that attention is being focused on areas of
true risk. These benefits are a direct result of GL.ai’s ability to analyze huge
amounts of data, not limited by sampling.
• The next Audit.ai modules are in development. They are set to revolutionize the
audit, enhancing client service, quality and efficiency, and giving our people more
time to do what machines can’t: thinking strategically and engaging,
communicating and building the relationships needed to turn data insights into
business action.
15. Complex Systems & Networks
Prescriptive Models combine:
•Known system facts, structure, and process
•Facts derived from data and statistical analysis
•Causal hypotheses (business judgment and assumptions)
•Dynamics of the system
•To not only predict the behavior, but to also to tell you why the system behaves that way
•Point to actions you can take today
•Prepare for responses if certain events materialize (real options)
•Help to point out wrong assumptions
•Explore a wide range of outcomes (scenarios)
Hybrid Modeling
•Discrete Event Simulation – Models how entities flow through a process and consume resources. Good for finding bottlenecks and
throughput/volume issues for well defined processes and non-adaptive entities (PROCESS CENTRIC)
•System Dynamics (Causal Loop) – Models aggregate causal relationships between system components to study system level behavior.
Incorporates non-linear causation, feedback loops, delays, system interdependencies, and soft variables. Good for broad, system level
understanding of dynamic behavior (SYSTEM CENTRIC)
•Agent Based Modeling – Models individual agents and how they react to external stimuli and their relationships with other agents. The
complex dynamic system level behavior emerges from the interactions of simple agents following simple rules. Agents have biases and
bounded rationality. They adapt and learn, but operate in a noisy uncertain environment (INDIVIDUAL CENTRIC)
Superior Models
•Far Higher Granularity, Time
•Skills/Groups, Missing Effects
•Abandonment behavior, Multi-skilled Sales Reps, Routing logic
•Also Incorporate Call and Sales Rep Attributes, Attribute based routing, Individual Agent Behaviors
Business Results
•Benefits
•Increased Revenue due to reduced abandons
•Improved customer satisfaction due to reduced wait times
•Reduction in hiring and training costs
•Risk Reduction
•Better understanding of operational risks and where they might surface
•This allows USAA to design mitigation strategies that are more proactive than reactive
•Investment Prioritization
•Reduced/Avoided rework due to better sequencing of work
•Better allocation of resources to create most value
How this Relates to Strategy and Value
8
EXTERNAL
FACTORS
LEADERSHIP
DECISIONS
PROCESS
OUTCOMES/
MEASURES
(KEIs)Competition
GDP
Capability
Investments
Hire Staff
Customer SAT
Quality
Profit
contacts apps products
rework
VALUEOperational ProcessesStrategic Choices
Risks
Causal Hypotheses
-Analytics
-Business Judgment
-Assumptions
Example II – Portfolio Roadmap and Dependencies
15
* - Notional Data
Incorporate data analytics as well as
business judgment in your models
Using Data Science and Simulation to Create Business Value
Dr. Bipin Chadha - Data Scientist
USAA Enterprise Data Analytics Office
Nov. 2015
16. Biomedical Engineering, Healthcare & Life Sciences
Best Opportunities to Healthcare’s Needs
Clinical
• Value Stream
Mapping/Design, Leading
Kaizen and Relentless Root
Cause Analysis
• Personalization of care
using claims and biometric
data (Apple watch?) models
• Predict outcomes and
adverse events
• Accountable care – Medical
Economics
Enterprise
• Cybersecurity
• Payment Integrity
• Pricing Optimization
• Fraud, AML, KYC
• Intelligent RPA with Petri
Nets for Process Discovery
Marketing & Sales
•Rep-Broker-Sponsor Attribution
Modeling
•Real-time targeting in the call center
•CLV, health behavior/activation models,
experiment design
•Attribution models
•Develop audience segmentations, core
value propositions, messaging strategy
for the different segments, measure
impact and efficacy of marketing
investments
•End to end delivery of behavior change
campaigns
•Chatbots & knowledge graphs
•Customer Journey Analytics
•Direct-to-Consumer marketing & sales
Member
Experience
• Advocacy Analytics
• Flu region prediction (with
active listening of Social
Media) and Next Best
Action messaging
• Churn prediction
• Personalized Robo-advisors
provide greater
convenience and insightful,
real-time recommendations
17. Artificial Intelligence, Machine Learning & Cybersecurity
Detecting Fraud or Cybersecurity Transactions Involves Monitoring Multiple Views Simultaneously
Endpoint authentication
Is the session being
compromised?
Is this accounts'
behavior normal for this
channel?
Is this accounts’
behavior normal for all
channels?
Are multiple accounts
behavior showing
correlated behaviors
across multiple
channels?
Endpoints
Merchant POS
ATM
Online Purchase
Acquirer
third-party online
payment platform
Cloud Communication
Network
Production Fraud
Scoring
•Endpoint authentication
•Is the session being
compromised?
•Is this accounts' behavior
normal for this channel?
•Is this accounts’ behavior
normal for all channels?
•Are multiple accounts
behavior showing
correlated behaviors across
multiple channels?
Transaction histories
with fraud categorization
•Machine learning fraud
models
•Cross account and cross
channel graph analytics
Issuers
Banks
Alternative Payments
Enablers
Blockchain & Bitcoin
18. A Unified Approach to Interpreting Model Predictions
Scott Lundberg, Su-In Lee
19. What Capital One is Looking For
FINANCE ASSOCIATE
Finance
• Analyze financial metrics and performance
• Develop, improve and / or automate reporting and analysis to provide insight into business trends
• Play a key role in evolving product and strategy decisions by providing finance analysis and forecasts
• Prepare for the future by learning new Tech skills
Accounting
• Participate in the external financial reporting process, including the quarterly earnings release and
securitization trust reporting
• Perform financial and operational audits, testing controls and identifying efficiencies
• Monitor/Enhance business specific analytics in support of external and internal financial reporting
• Evaluate and engage in Robotics Process Automation (RPA) projects across the Controller’s
Organization.
Preferred Qualifications:
• Bachelor’s degree in Finance, or Economics, or Business, or Accounting
• A demonstrated interest in financial management and technology aptitude
• At least 6 months of experience or course work in Financial Planning & Analysis (FP&A)
• Aptitude with technologies such as Python, SQL and R is strongly preferred
DATA SCIENTIST INTERN
On any given day, you might:
• Evaluate open source and internally-developed modeling and analytics tools using real business data
• Integrate internal data with external data sources and APIs to discover and implement actionable
insights
• Design and craft rich data visualizations to communicate stories to customers and company leadership
We'd love to find someone who is…
• 1. Intellectually curious. You ask why, you explore, and you are excited to imagine and create new
ideas by inventing self-adaptive models or by tapping into unstructured data sources. You love mining
data for insights into behaviors, intent and sentiment.
• 2. A builder. You are passionate about delivering better experiences and better products to our
customers and have a deep sense of ownership for your craft.
• 3. An experimental scientist. You love putting on your lab coat and trying new things, new
combinations of tools, techniques and feature engineering approaches even if you sometimes fail.
Basic Qualifications:
• - At least 6 months of experience or course work in open source programming languages for data
analysis
• - At least 6 months of experience or course work in inferential statistics or machine learning
Preferred Qualifications:
• - Direct experience with either Python or R, plus one other general purpose programming language
such as Java or C/C++
• - Experience or course work with large scale data analysis
20. Further Reading Worthy Of Becoming The
Basis Of Your Thesis
• https://amp-businessinsider-
com.cdn.ampproject.org/c/s/amp.businessinsider.com/why-attitude-is-
more-important-than-iq-2017-2
• https://www-theverge-
com.cdn.ampproject.org/c/s/www.theverge.com/platform/amp/2018/9/5/
17822562/google-dataset-search-service-scholar-scientific-journal-open-
data-access
• https://www.liebertpub.com/doi/full/10.1089/big.2018.0083
• https://blogs.microsoft.com/blog/2018/07/19/powering-our-customers-
the-innovation-story-behind-microsofts-earnings/
• https://www.fastcompany.com/40590772/my-three-decades-at-disney-
taught-me-not-to-fear-automation
32. Cheat Sheets for AI, Neural
Networks, Machine Learning,
Deep Learning & Big Data
https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-
deep-learning-big-data-678c51b4b463