AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Artificial Intelligence (AI) & Machine Learning: Are You Ready?SilverTech
Long dismissed as the realm of sci-fi, artificial intelligence (AI) and machine learning have finally arrived and their potential to disrupt every industry is quickly becoming apparent. Though they work in tandem, there is a distinction to be made between the two; AI is the ability of machines to mimic human intelligence, while machine learning is the ability of computers to learn from gathered data. Despite Elon Musk’s cautions, over the next two years AI will be pervasive in everything from household appliances to digital assistants, and yes, even your website and content!
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Artificial Intelligence (AI) & Machine Learning: Are You Ready?SilverTech
Long dismissed as the realm of sci-fi, artificial intelligence (AI) and machine learning have finally arrived and their potential to disrupt every industry is quickly becoming apparent. Though they work in tandem, there is a distinction to be made between the two; AI is the ability of machines to mimic human intelligence, while machine learning is the ability of computers to learn from gathered data. Despite Elon Musk’s cautions, over the next two years AI will be pervasive in everything from household appliances to digital assistants, and yes, even your website and content!
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
Australian Legal Education in 2017: Taking Stock for an Uncertain FutureSally Kift
This presentation was made to The Future of Legal Education Workshop hosted by Griffith University's Law Futures Centre on 1 November 2017. It suggests that Australian legal education research over the last decade has positioned us well for an uncertain future. While our Law Schools cannot afford to be complacent, especially given the increasing automation of legal work and the unbundling of legal services, the strong research and evidence base to which Australian legal educators may refer provides a degree of optimism for an uncertain future. Critically, this must be a joint endeavour that engages all branches of the legal profession and the Academy working together. Students and young lawyers in particular have a vital role to play in shaping the future of their professional education. In the absence of an #OLTphoenix, Australian legal education is well-placed to be self-sustaining and self-generating.
The abstract for the session was as follows:
In 2017, Australian legal education finds itself at a crossroads. In common with its disciplinary brethren, it is being impacted by the multitude challenges and volatile policy environment facing the Australian higher education sector more broadly. As for the rest of the Academy also, Law Schools are being squeezed on numerous fronts in their quest to fund pedagogical innovation. In the meantime, law students, who continue to bear a disproportionately high percentage of their degree costs, find themselves entering an extremely competitive job market with reduced employment opportunities. And of potentially even greater import, the disruptive innovation being felt in universities is also now impacting the legal services industry itself, so much so that the halcyon days of Priestley’s dead hand (or light hand, depending on your perspective) finally look to be drawing to a close.
This presentation will review Australian legal education’s pedagogical progress over the last decade through a scholarship lens and ask how is legal education positioned in 2017 for an uncertain future? In the absence of a national body such as the Office for Learning and Teaching (OLT), which was de-funded in mid-2016, is Australian legal education research and scholarship sufficiently mature to be self-sustaining and self-generating? At the risk of being overly optimistic, it will be suggested that, in an era of stackable credentials, the quality of Australian legal education generally ranks amongst the best in the world and is well-positioned to prepare its students to take their place, personally and professionally, as global citizens in complex and dynamic legal and other workplaces.
Demystifying AI | Mathias Vercauteren | Keynote at AI 4 Business Summit | Bru...Mathias Vercauteren
Artificial Intelligence (AI) has become an exponentially growing technology over the past decades. It moved from Science Fiction to Science Fact, but a lot of companies aren’t ready yet for AI. In this dynamic and eye-opening Keynote Mathias Vercauteren gave at the AI 4 Business Summit in Brussel in 2019 (March 14), you get a real sense of the technological exponential growth and a good understanding of the proximity of AI. Vercauteren transforms your mindset and provides tactics and strategies to innovate and transform your company. Under the motto of think big, start small, he establishes a culture of Moonshot thinking & 10X your goals to enable AI in your company.
____________________________________________
Check out the video on to the One Data Partner channel here: https://www.youtube.com/channel/UCVtXnQQwfYLEgyTyJzQqfZQ?sub_confirmation=1
____________________________________________
► Subscribe to Mathias Vercauteren Channel: https://www.youtube.com/channel/UCmRXmVy9L6vqUB0-0a7Eshw?sub_confirmation=1 to learn more about data, technology and business.
____________________________________________
Mathias Vercauteren is a thought leader on the transforming relationship between business, data & innovative technology. As founder of One Data Partner and being a dynamic inspirational keynote speaker, he inspires businesses and helps them exploit the full potential of their data.
In his powerful keynotes, Mathias delivers real value to his audience by showing them how to drive breakthrough thinking and innovation within their company in a way that delivers clear, measurable and objective results.
Having worked with international companies like Hilti (electronics manufacturing), Barry Callebaut (food manufacturing) and Carrefour (retail), Mathias is a seasoned data strategist, consultant & trainer who served as a trusted advisor for different management teams and boards. He gives his clients and his audiences a true understanding of the immediacy of the current disruptive technologies and the importance of data.
____________________________________________
From Biology to Industry. A Blogger’s Journey to Data Science.Shirin Elsinghorst
What does blogging mean for Data Sciences?
What is Big Data today?
How to become a Data Scientist and what type of work results from this transformation?
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Machine Learning Introduction for Digital Business LeadersSudha Jamthe
This is Sudha Jamthe's lecture to the Masters program students of Barcelona Technology School.
Covers Machine Learning introduction of technology foundation, use cases across multiple industries, jobs and varioys business roles to create Machine Intelligence Products and Services.
ROLE OF ARTIFICIAL INTELLIGENCE IN COMBATING CYBER THREATS IN BANKINGvishal dineshkumar soni
With the advances in information technology, various cyberspaces are used by criminals to enhance cybercrime. To mitigate this cybercrime and cyber threats, the bank and financial industry try to implement artificial intelligence. Various opportunities are provided by AI techniques, which help the banking sector to increase prosperity and growth. To maintain trust in artificial intelligence, it is important to maintain transparency and explain ability. Information about customer's behavior and interest is provided by artificial intelligence techniques. Robo-advice is an automated platform that is maintained by AI. Artificial Intelligence is also involved in protecting personal data. Proper design provided by AI towards the banking sector, by which they are able to identify fraud in transactions. AI directly linked with the domain of cyber security. Various kinds of cybercrimes are prevented and identified by AI-based fraud detection systems. However, implementation and maintenance of artificial intelligence consist of the high cost. Along with this unemployment rate is increased by AI techniques.
The 2016 invited research presentation at the Princeton Quant Trading Conference proposes two new financial innovations and their interrelationships: ‘Model Risk Arbitrage’ for ‘Open Systems Finance’. It develops the new framework of Model Risk Arbitrage for profit-maximization in the emerging global financial markets characterized by unprecedented uncertainty, complexity, and, rapid discontinuous changes. It develops the new framework of ‘Open Systems Finance’ aligned with George Soros’ Reflexivity Theory based upon empirical practical experience in financial markets as contrasted from ‘Closed Systems Finance’ models characterizing most of classical and academic Finance and Economics theory.
La inteligencia artificial (IA) está demostrando ser una espada de doble filo. Si bien esto se puede decir de la mayoría de las nuevas tecnologías, ambos lados de la hoja de IA son mucho más nítidos, y ninguno de los dos es bien entendido.
Este artículo busca ayudar ilustrando primero una gama de trampas fáciles de pasar por alto. A continuación, presenta marcos que ayudarán a los líderes a identificar sus mayores riesgos e implementar la amplitud y profundidad de los controles matizados necesarios para eludirlos. Por último, ofrece una visión temprana de algunos esfuerzos del mundo real que se están llevando a cabo actualmente para hacer frente a los riesgos de IA mediante la aplicación de estos enfoques.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
Australian Legal Education in 2017: Taking Stock for an Uncertain FutureSally Kift
This presentation was made to The Future of Legal Education Workshop hosted by Griffith University's Law Futures Centre on 1 November 2017. It suggests that Australian legal education research over the last decade has positioned us well for an uncertain future. While our Law Schools cannot afford to be complacent, especially given the increasing automation of legal work and the unbundling of legal services, the strong research and evidence base to which Australian legal educators may refer provides a degree of optimism for an uncertain future. Critically, this must be a joint endeavour that engages all branches of the legal profession and the Academy working together. Students and young lawyers in particular have a vital role to play in shaping the future of their professional education. In the absence of an #OLTphoenix, Australian legal education is well-placed to be self-sustaining and self-generating.
The abstract for the session was as follows:
In 2017, Australian legal education finds itself at a crossroads. In common with its disciplinary brethren, it is being impacted by the multitude challenges and volatile policy environment facing the Australian higher education sector more broadly. As for the rest of the Academy also, Law Schools are being squeezed on numerous fronts in their quest to fund pedagogical innovation. In the meantime, law students, who continue to bear a disproportionately high percentage of their degree costs, find themselves entering an extremely competitive job market with reduced employment opportunities. And of potentially even greater import, the disruptive innovation being felt in universities is also now impacting the legal services industry itself, so much so that the halcyon days of Priestley’s dead hand (or light hand, depending on your perspective) finally look to be drawing to a close.
This presentation will review Australian legal education’s pedagogical progress over the last decade through a scholarship lens and ask how is legal education positioned in 2017 for an uncertain future? In the absence of a national body such as the Office for Learning and Teaching (OLT), which was de-funded in mid-2016, is Australian legal education research and scholarship sufficiently mature to be self-sustaining and self-generating? At the risk of being overly optimistic, it will be suggested that, in an era of stackable credentials, the quality of Australian legal education generally ranks amongst the best in the world and is well-positioned to prepare its students to take their place, personally and professionally, as global citizens in complex and dynamic legal and other workplaces.
Demystifying AI | Mathias Vercauteren | Keynote at AI 4 Business Summit | Bru...Mathias Vercauteren
Artificial Intelligence (AI) has become an exponentially growing technology over the past decades. It moved from Science Fiction to Science Fact, but a lot of companies aren’t ready yet for AI. In this dynamic and eye-opening Keynote Mathias Vercauteren gave at the AI 4 Business Summit in Brussel in 2019 (March 14), you get a real sense of the technological exponential growth and a good understanding of the proximity of AI. Vercauteren transforms your mindset and provides tactics and strategies to innovate and transform your company. Under the motto of think big, start small, he establishes a culture of Moonshot thinking & 10X your goals to enable AI in your company.
____________________________________________
Check out the video on to the One Data Partner channel here: https://www.youtube.com/channel/UCVtXnQQwfYLEgyTyJzQqfZQ?sub_confirmation=1
____________________________________________
► Subscribe to Mathias Vercauteren Channel: https://www.youtube.com/channel/UCmRXmVy9L6vqUB0-0a7Eshw?sub_confirmation=1 to learn more about data, technology and business.
____________________________________________
Mathias Vercauteren is a thought leader on the transforming relationship between business, data & innovative technology. As founder of One Data Partner and being a dynamic inspirational keynote speaker, he inspires businesses and helps them exploit the full potential of their data.
In his powerful keynotes, Mathias delivers real value to his audience by showing them how to drive breakthrough thinking and innovation within their company in a way that delivers clear, measurable and objective results.
Having worked with international companies like Hilti (electronics manufacturing), Barry Callebaut (food manufacturing) and Carrefour (retail), Mathias is a seasoned data strategist, consultant & trainer who served as a trusted advisor for different management teams and boards. He gives his clients and his audiences a true understanding of the immediacy of the current disruptive technologies and the importance of data.
____________________________________________
From Biology to Industry. A Blogger’s Journey to Data Science.Shirin Elsinghorst
What does blogging mean for Data Sciences?
What is Big Data today?
How to become a Data Scientist and what type of work results from this transformation?
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Machine Learning Introduction for Digital Business LeadersSudha Jamthe
This is Sudha Jamthe's lecture to the Masters program students of Barcelona Technology School.
Covers Machine Learning introduction of technology foundation, use cases across multiple industries, jobs and varioys business roles to create Machine Intelligence Products and Services.
Similar to 2018 Princeton Fintech & Quant Conference: AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
ROLE OF ARTIFICIAL INTELLIGENCE IN COMBATING CYBER THREATS IN BANKINGvishal dineshkumar soni
With the advances in information technology, various cyberspaces are used by criminals to enhance cybercrime. To mitigate this cybercrime and cyber threats, the bank and financial industry try to implement artificial intelligence. Various opportunities are provided by AI techniques, which help the banking sector to increase prosperity and growth. To maintain trust in artificial intelligence, it is important to maintain transparency and explain ability. Information about customer's behavior and interest is provided by artificial intelligence techniques. Robo-advice is an automated platform that is maintained by AI. Artificial Intelligence is also involved in protecting personal data. Proper design provided by AI towards the banking sector, by which they are able to identify fraud in transactions. AI directly linked with the domain of cyber security. Various kinds of cybercrimes are prevented and identified by AI-based fraud detection systems. However, implementation and maintenance of artificial intelligence consist of the high cost. Along with this unemployment rate is increased by AI techniques.
The 2016 invited research presentation at the Princeton Quant Trading Conference proposes two new financial innovations and their interrelationships: ‘Model Risk Arbitrage’ for ‘Open Systems Finance’. It develops the new framework of Model Risk Arbitrage for profit-maximization in the emerging global financial markets characterized by unprecedented uncertainty, complexity, and, rapid discontinuous changes. It develops the new framework of ‘Open Systems Finance’ aligned with George Soros’ Reflexivity Theory based upon empirical practical experience in financial markets as contrasted from ‘Closed Systems Finance’ models characterizing most of classical and academic Finance and Economics theory.
La inteligencia artificial (IA) está demostrando ser una espada de doble filo. Si bien esto se puede decir de la mayoría de las nuevas tecnologías, ambos lados de la hoja de IA son mucho más nítidos, y ninguno de los dos es bien entendido.
Este artículo busca ayudar ilustrando primero una gama de trampas fáciles de pasar por alto. A continuación, presenta marcos que ayudarán a los líderes a identificar sus mayores riesgos e implementar la amplitud y profundidad de los controles matizados necesarios para eludirlos. Por último, ofrece una visión temprana de algunos esfuerzos del mundo real que se están llevando a cabo actualmente para hacer frente a los riesgos de IA mediante la aplicación de estos enfoques.
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Jen Q. Public: How analytics is impacting government, education and public sa...IBM Analytics
Citizens today want to create a better tomorrow for our children and grandchildren. Looking at our world, Jen Q. Public sees the opportunity that analytics, the Internet of Things and the cognitive era bring to our cities, schools and governments. This collection of stories and cartoons explore use cases related to these advancements. Read the latest entry at http://ibm.co/jenqpublic and learn more about analytics for government today http://ibm.co/governmentanalytics
Algocracy and the state of AI in public administrations.Sandra Bermúdez
AI, as technical approach to solve problems, now is deploying in social systems and public administrations. What are the effects? the challenges? should we fear? What should we do?
MIhai Bonca - Inteligenta Artificiala. Inger, demon sau oportunitateBusiness Days
Prezentarea lui Mihai Bonca in cadrul conferintei "De la provocarile prezentului la oportunitatile viitorului" din cadrul evenimentului Business Focus Iasi 2018 organizat de platforma Business Days in Iasi la data de 28 martie 2018
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
Thesis: to reconceive and more empoweringly enact relationships with authority, a new sensibility is required, that of the cryptocitizen. This is the skillset of determining oneself as an economic and political agent in the world of digital network technologies. In the cryptopolis smart city of the future, one goal could be enabling the flourishing of a multi-species society of machine, algorithm, and human.
THE SOCIAL IMPACTS OF AI AND HOW TO MITIGATE ITS HARMSTekRevol LLC
In the wake of mass automation, UBIs might be the answer low-income families and citizens might be looking towards. As automation across industries increases, the induced fear within citizens of its impact is severe. From privacy concerns through rogue AI to doomsday scenarios to more realistic concerns of misused AI and loss of jobs, pop-culture led paranoia has shaken up the world. These concerns have to be dealt with, and tech companies and businesses need to have a robust moral framework under which decisions are made, to ensure any negative externalities of implementing AI are mitigated to the maximum degree. Artificial Intelligence is a great tool to optimize businesses and make our world more efficient, but the moral imperative on all of us is to ensure it happens sides by side human sustainability, not at its expense.
Ferma report: Artificial Intelligence applied to Risk Management FERMA
FERMA brought together a group of experts from within and beyond the risk management community to develop the first thought paper about AI applied to risk management.
Their aim was to perform an initial assessment of the potential value of AI to improve enterprise risk management (ERM), and second, to understand how risk managers can be key actors in highlighting to the organisation leadership the opportunities and challenges of AI technologies.
The working group expects that corporate risk management will benefit from AI in several areas. “From its ability to process large amounts of data to the automation of certain risk management repetitive and burdensome steps, AI could allow risk managers to respond faster to new and emerging exposures. By acting in real time and with some predictive capabilities, risk management could reach a new level in supporting better decision making for senior management.”
This paper aims to guide risk managers on applying AI from a basic understanding to developing their own strategy on the implementation of AI. It includes an action guide and a template for risk managers to develop their own AI risk management roadmap.
Similar to 2018 Princeton Fintech & Quant Conference: AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning (20)
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- 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.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
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.
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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
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/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Key Trends Shaping the Future of Infrastructure.pdf
2018 Princeton Fintech & Quant Conference: AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
1. [1]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
2018 Princeton Fintech & Quant Conference
Princeton University, April 21, 2018
Princeton Presentations in AI-ML Risk Management & Control Systems
2016 Princeton Quant Trading Conference, Princeton University
How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!
Knight Reconsidered Again: Risk, Uncertainty, & Profit beyond ZIRP & NIRP
2015 Princeton Quant Trading Conference, Princeton University
Future of Finance Beyond 'Flash Boys': Risk Modeling for Managing Uncertainty in an
Increasingly Non-Deterministic Cyber World:
Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era
Yogi
Dr. Yogesh Malhotra
Post-Doctoral R&D in AI, Machine Learning & Deep Learning
Marquis Who's Who in the World® 1999-, Marquis Who's Who in America® 2002-,
Marquis Who's Who in Finance & Industry® 2001-, Marquis Who's Who in Science & Engineering® 2006-
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892
http://www.linkedin.com/in/yogeshmalhotra
www.FutureOfFinance.org
2. [2]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
The European Parliament Think Tank's Research Policy document 'Should we fear artificial
intelligence?' reflects the ongoing mainstream debate between the Utopian and Dystopian
aspects of AI and Machine Learning. "Powerful AIs can in principle be given nearly any goal,
which is a source of both risk and opportunity. There are myriad possible malicious uses of AI
and many ways in which it might be used in a harmful manner unintentionally, such as with
algorithmic bias. Perhaps most fundamentally, the control problem will have to be addressed –
that is, we will need to learn how to ensure that AI systems achieve the goals we want them to
without causing harm during their learning process, misinterpreting what is desired of them,
or resisting human control." Third in the series of the Princeton Presentations on AI and
Machine Learning Risk Management & Control Systems, the current presentation develops
fundamental guidance on the design, development, and implementation of AI, Machine
Learning, and Deep Learning Models and Methods. The 2018 Princeton presentation will focus
on "the control problem" which is a critical prerequisite for AI systems to have positive impacts
by further developing upon my prior two presentations that pioneered Cyber-Finance-Trust™
Model Risk Management & Model Risk Arbitrage™ practices at prior Princeton Quant Trading
Conferences. Starting with the first technical report on the Bitcoin Blockchain Cryptographic
Proof of Work; spanning latest developments in AI, Machine, Learning, Deep Learning, and,
Generative Adversarial Networks; and, hedge fund algorithmic trading, the presentation
generates interesting insights about the most critical role of risk management controls. Such
role of risk management controls is most critical in not only getting the best out of AI, but also
ensuring that the worst fears about the AI do not really come true.
Abstract
3. [3]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
4. [4]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Adaptability-Generalizability
Past Prediction vs. Future Anticipation
KMS &
Risk Management Controls Self-Adaptive Complex Systems
AI-ML
Knowledge Management Systems
Sense Making
Past vs. Future
‘Historical Data’
Known vs.
Unknown
5. [5]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
6. [6]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
7. [7]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"Recently, such probabilistic, statistical, and numerical methods related concerns are in globally
popular press related to cybersecurity controls and compliance. Earlier, similar probabilistic,
statistical, and numerical methods related concerns were in the global popular press in the context
of the global financial crisis. Future questions focused on the underlying assumptions and logic
may focus on related implications for compliance, controls, valuation, risk management, etc.
Likewise, recent developments about mathematical entropy measures shedding new light on
apparently greater vulnerability of prior encryption mechanisms may offer additional insights for
compliance and control experts. For instance, given related mathematical, statistical and numerical
frameworks, analysis may also focus on potential implications for pricing, valuation and risk
models. The important point is that many such fundamental assumptions and logic underlying
widely used probabilistic, statistical, and numerical methods may not as readily meet the eye."
Interpretability, Explainability, and, Model Risk are Related Issues
Hence, they need to be addressed together for AI and Machine Learning
Future of Bitcoin & Statistical Probabilistic Quantitative Methods:
Global Financial Regulation (Interview: Hong Kong Institute of CPAs)
http://yogeshmalhotra.com/Future_of_Bitcoin.html
Bitcoin Protocol: Model of ‘Cryptographic Proof’ Based
Global Crypto-Currency & Electronic Payments System
http://yogeshmalhotra.com/BitcoinProtocol.html
January 20, 2014
December 04, 2013
GDPR
8. [8]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Harvard Business Review: If Your Data Is Bad,
Your Machine Learning Tools Are Useless
In addition to Data, the challenges of accurate
AI-ML Models and Methods are equally, if not
even more so, critical given that they are hidden
from the users' eyes (WWW: Society of Actuaries in
Ireland: Cybersecurity & Cyber-Finance Risk
Management - Yogesh Malhotra, PhD)
https://lnkd.in/eDb897h "[T]he approaches to
mitigate operating risk associated with the use of
models need to evolve to reflect recent trends in the
Finance Industry. In particular there are a number of
new areas where it is not possible for the "human eye"
to necessarily detect material flaws: in the case of
models operating over very small time scales in high
frequency algorithmic trading, or for portfolio risk
measurement models where outputs lack
interpretability due to highdimensionality and complex
interactions in inputs, the periodic inspection of
predicted versus realized outcomes is unlikely to be an
effective risk mitigate." https://lnkd.in/eV79T6C
9. [9]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.europarl.europa.eu
/thinktank/en/document.html?
reference=EPRS_IDA(2018)6
14547
http://www.europarl.europa.eu/
RegData/etudes/IDAN/2018/61
4547/EPRS_IDA(2018)614547
_EN.pdf
Adaptability-Generalizability
Past Prediction vs. Future Anticipation
KMS &
Risk Management Controls
Self-Adaptive Complex Systems
AI-ML
Knowledge Management Systems
Creativity, Imagination,
Innovation, Intuition,
Insight
Known vs.
Unknown
Routine, Structured, Procedural
Non-routine, Unstructured, Non-procedural
With Great Power Comes Great Responsibility
10. [10]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI-ML Risk Management & Controls Most Critical
Lesser Concern about the Next ‘AI Winter’
Greater Concern about the ‘Nuclear Winter’*
11. [11]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI-ML Risk Management & Controls Most Critical Interpretability
vs.
Sense Making
Past vs. Future
12. [12]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI-ML Risk Management & Controls Most Critical
13. [13]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI-ML Risk Management & Controls Most Critical
Adaptability-Generalizability
SACS
4 AI Types
Human Driving in Most
Unpredictable Environments
Past vs. Future
14. [14]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www.linkedin.
com/feed/update/urn:
li:activity:639162502
6721890304
*M5: What is being
Human?: Qualities such
as "freedom of will,
intentionality, self-
consciousness, moral
agency and a sense of
personal identity."
http://www.robotics
-openletter.eu/
15. [15]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www.linkedin.com/feed/update/
urn:li:activity:6391798889275547648
16. [16]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
1998 First Quant MIS- IT PhD on
KMS & Risk Management Controls
Cybernetic & Control Systems
http://www.aacsb.edu//media/aacsb/publications/
research-reports/impact-of-research.ashx?la=en
*
20-Year R&D
Adaptability-
Generalizability
SACS
Past Prediction vs. Future Anticipation
17. [17]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
My LinkedIn Page
accessible also from
my Home Page:
https://www.linkedin.c
om/pulse/dear-ceo-ai-
machine-learning-
advice-top-industry-
leading-malhotra/
MASTER REFERENCE FOR MOST TERMS & CONCEPTS
http://www.kmnetwork.com/RealTime.pdf
Adaptability-
Generalizability
SACS
KMS &
Risk Management Controls
Sense Making
Past vs. Future
‘Historical Data’
Malhotra, Y., Integrating
Knowledge Management
Technologies in Organizational
Business Processes: Getting Real
Time Enterprises to Deliver Real
Business Performance, Journal of
Knowledge Management, Vol. 9,
Issue 1, April 2005, 7-28.
Past Prediction vs.
Future Anticipation
Known vs. Unknown
20-Year R&D
KMS-Controls
Risk Mgmt.
Strategies
Technologies
People
Processes
18. [18]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.yogeshmalhotra.com/
publications.html
http://www.brint.org/expertsystems.pdf
Malhotra, Y., Expert Systems for
Knowledge Management: Crossing
the Chasm between Information
Processing and Sense Making,
Expert Systems with Applications: An
International Journal, 20(1), 7-16,
2001.
https://www.linkedin.com/in/
yogeshmalhotra/
Adaptability-Generalizability
Past Prediction vs. Future Anticipation
KMS &
Risk Management Controls
Self-Adaptive Complex Systems
AI-ML
Knowledge Management Systems
Sense Making
Past vs. Future
‘Historical Data’
19. [19]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
20. [20]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
MIT Technology Review: The GANfather: The
man who’s given machines the gift of
imagination MIT AI-Strategy Executive Guide
(continued) https://lnkd.in/eknKzm5
Malhotra, Yogesh, "Knowledge Management
in Inquiring Organizations" (1997). AMCIS
1997 Proceedings. 181. https://lnkd.in/eKR3p8s
https://lnkd.in/eGbhayW "Hegelian inquiry
systems are based on a synthesis of multiple
completely antithetical representations that are
characterized by intense conflict because of the
contrary underlying assumptions. Knowledge
management systems based upon the Hegelian
inquiry systems, would facilitate multiple and
contradictory interpretations of the focal
information. This process would ensure that the
focal information is subjected to continual re-
examination and modification given the
changing reality. Continuously challenging the
current 'company way,' such systems are
expected to prevent the core capabilities of
yesterday from becoming core rigidities of
tomorrow." https://lnkd.in/eQNXzkN
21. [21]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Example of Latest on GANs:
At Least 20-Years Behind!
Not in MATH,
But in INTUITION...
See: Derman on Models & Intuition
Key Problems of AI-ML Models:
Socio-Psychology & Learning Constructs
- Correct AI-ML REPRESENTATION?
- Valid & Reliable MEASURES?
- Valid & Reliable RELATIONSHIPS?
Recipe for the Next AI-ML Crisis
“Baked” in underlying METHODs
And MODELs
And assumed as a GIVEN
Concern Less about the
‘Next AI Winter’
but More about the
‘Next AI Nuclear Holocaust’
If
Risk Management Controls
are Non-existent or Bypassed
22. [22]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
• Malhotra, Y., Galletta, D.F., and, Kirsch, L.J. How Endogenous Motivations Influence
User Intentions: Beyond the Dichotomy of Extrinsic and Intrinsic User Motivations,
Journal of Management Information Systems, Summer 2008, Vol. 25, No. 1, 267-299.
• Malhotra, Y. and Galletta, D.F., A Multidimensional Commitment Model of
Volitional Systems Adoption and Usage Behavior, Journal of Management
Information Systems, Summer 2005, Vol. 22, No. 1; 117-151.
• Malhotra, Y., and, Kirsch, L.J., Personal Construct Analysis of Self-Control in IS
Adoption: Empirical Evidence from Comparative Case Studies of IS Users & IS
Champions. Proceedings of the First INFORMS Conference on Information Systems
and Technology, 105-114, Washington, DC, May, 1996.
• Malhotra, Y., Expert Systems for Knowledge Management: Crossing the Chasm
between Information Processing and Sense Making, Expert Systems with
Applications: An International Journal, 20(1), 7-16, 2001. (Holland Communication
- 1995) Example of Latest on GANs:
At Least 20-Years Behind!
Not in MATH,
But in INTUITION...
See: Derman on Models & Intuition
Example of Latest in Generative Adversarial Networks – 20 Years earlier
Research Applied by NASA, Big Banks, and, Top Intelligence Agencies
Artificial Curiosity, Intrinsic Motivation, Information Seeking Behavior, Reward Function
http://www.yogeshmalhotra.com/
publications.html
Sense Making
Past vs. Future
‘Historical Data’
KMS &
Risk Management Controls
23. [23]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Stanislav Petrov was on duty in a secret
command centre outside Moscow on 26
September 1983 when a radar screen showed
that five Minuteman intercontinental
ballistic missiles had been launched by the
US towards the Soviet Union.
Red Army protocol would have been to order
a retaliatory strike, but Petrov – then a 44-
year-old lieutenant colonel – ignored the
warning, relying on a “gut instinct” that told
him it was a false alert.
It later emerged that the false alarm was the
result of a satellite mistaking the reflection of
the sun’s rays off the tops of clouds for a
missile launch.
“We are wiser than the computers,”
Petrov said in a 2010 interview with the
German magazine Der Spiegel.
“We created them.”
“false alarm”
‘fake news’
24. [24]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
LET US DO A THOUGHT EXPERIMENT
DOTs: WHAT IS ITS “MEANING”?FEATURE
MATH vs.
INTUITION
25. [25]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
LET US DO A THOUGHT EXPERIMENT
LINEs: WHAT IS ITS “MEANING”?FEATURE VECTOR
MATH vs.
INTUITION
26. [26]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
LET US DO A THOUGHT EXPERIMENT
PLANEs: WHAT IS ITS “MEANING”?FEATURE MAP
Interpretability
vs.
Sense Making
Past vs. Future
MATH vs.
INTUITION
Known vs.
Unknown
27. [27]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
CUBEs:
WHAT IS ITS “MEANING”?
STACKED
FEATURE MAP
The Building Blocks of
Interpretability
Interpretability techniques are
normally studied in isolation.
We explore the powerful
interfaces that arise when you
combine them
and the rich structure of this
combinatorial space.
MATH vs.
INTUITION
28. [28]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Labrador retriever and tiger cat
Several floppy
ear detectors seem to be
important when
distinguishing dogs,
whereas pointy ears are
used to classify "tiger cat".
https://distill.pub/2018/building-blocks/
The Building Blocks of
Interpretability
Interpretability techniques are
normally studied in isolation.
We explore the powerful
interfaces that arise when you
combine them
and the rich structure of this
combinatorial space.
MATH vs.
INTUITION
29. [29]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://devblogs.nvidia.com/deep-
learning-nutshell-core-concepts/
Deep Learning in a Nutshell
consolidation
30. [30]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www.theverge.com/20
18/4/11/17224984/artificial-
intelligence-idxdr-fda-eye-
disease-diabetic-rethinopathy
31. [31]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www.fda.gov/N
ewsEvents/Newsroom/
PressAnnouncements/
ucm604357.htm
32. [32]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
33. [33]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www.wsj.com/articles
/the-key-to-smarter-ai-copy-
the-brain-1523369923
34. [34]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why ‘Humans-in-the Loop’ are Even More Critical for Interpretability
https://ssrn.com/abstract=2940467
Socio-Technical
Systems
Malhotra, Yogesh,
Advancing Cognitive
Analytics Using
Quantum Computing for
Next Generation
Encryption (Presentation
Slides) (March 24,
2017). Available at
SSRN: https://ssrn.com/a
bstract=2940467
35. [35]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why Interpretability is complicated
Why ‘Humans-in-the Loop’ are Even More Critical for Interpretability
Malhotra, Y., Bringing the Adopter
Back Into the Adoption Process: A
Personal Construction Framework
of Information Technology
Adoption. Journal of High
Technology Management Research,
10(1), 1999, 79-104.
Socio-Technical
Systems
36. [36]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why Interpretability is complicated
Why ‘Humans-in-the Loop’ are Even More Critical for Interpretability
Socio-Technical
Systems
37. [37]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Socio-Technical
Systems
Sense Making
Past vs. Future
‘Historical Data’
Adaptability-Generalizability
Self-Adaptive Complex Systems
AI-ML -KMS
Known vs.
Unknown
LET US DO A THOUGHT EXPERIMENT
38. [38]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Socio-Technical
Systems
Sense Making
Past vs. Future
‘Historical Data’
Adaptability-Generalizability
Self-Adaptive Complex Systems
AI-ML -KMS
Known vs.
Unknown
LET US DO A THOUGHT EXPERIMENT
39. [39]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Socio-Technical
Systems
Sense Making
Past vs. Future
‘Historical Data’
Adaptability-Generalizability
Self-Adaptive Complex Systems
AI-ML -KMS
Known vs.
Unknown
LET US DO A THOUGHT EXPERIMENT
40. [40]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
What Caused the Failure of the Socio-Technical System? 3 Key Systems Failed
Perfect Weather Conditions and Perfect Road Conditions in AZ
What Would Happen in the “Typical” “Zero-Visibility” Winter Weather in Central NY?
When 65 MPH I-90 “Thruway” Traffic Drives ‘Normally’ in Day at 10 MPH for Safety
Or When All Traffic is Off the 65 MPH I-90 “Thruway” as it’s Frozen.
Socio-Technical
Systems
Adaptability-
Generalizability
Self-Adaptive
Complex Systems
AI-ML -KMS
Sense Making
Past vs. Future
‘Historical Data’
Known vs.
Unknown
41. [41]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"When you do physics you're playing against God; in
finance [just like all other sociotechnical systems],
you're playing against God's creatures.“
- Emanuel Derman
[Generalized Model Risk Management:
Bayesian vs. VaR: https://lnkd.in/eGr9eCi ]
"While robot cars are being created to follow traffic rules,
interactions with humans continue to present hurdles.
Pedestrians, in particular, can confuse systems because
they are "unpredictable"."
“The computer vision systems are incredibly
brittle in these cars. There’s a strong, high
probability that the computer vision system
failed to detect the person.”
Tempe Police confirmed in a press conference
that the Uber vehicle was traveling at around
40mph (with no signs yet that it was slowing
down) when it struck the pedestrian.
42. [42]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
MATH vs.
INTUITION
"Not everything that counts can be counted,
and not everything that can be counted counts."
"As far as the laws of mathematics refer to reality,
they are not certain, and as far as they are certain,
they do not refer to reality."
"If you give a pilot an altimeter that is
sometimes defective he will crash the plane.
Give him nothing and he will look out the
window. Technology is only safe if it is
flawless.” NNT
43. [43]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
44. [44]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SPACE
+
CYBERSPACE
‘OFFENSIVE’
‘DEFENSIVE’
Analysis of full-motion video data from tactical aerial
drone platforms such as the ScanEagle and medium-
altitude platforms such as the MQ-1C Gray Eagle and
the MQ-9 Reaper.
Project Maven: First operational use of deep learning
AI technologies in the defense intelligence enterprise.
Malhotra, Yogesh,
Cognitive Computing
for Anticipatory Risk
Analytics in
Intelligence,
Surveillance, &
Reconnaissance (ISR)
(January 28, 2018).
Available at
SSRN: https://ssrn.com
/abstract=3111837
MATH vs.
INTUITION
https://thebulletin.org/project-maven-brings-ai-fight-against-isis11374
Algorithmic Warfare Cross-Functional Team
45. [45]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
“Maven is designed
to be that pilot
project, that
pathfinder, that
spark that kindles
the flame front of
artificial
intelligence across
the rest of
the Department.”
https://thebulletin.org/project-maven-brings-ai-fight-against-isis11374
With Great Power Comes Great Responsibility
MODELS
RISKS
ISR
SIGNALS
Data in Transit
Data in Use
Malhotra, Yogesh, Cognitive
Computing for Anticipatory
Risk Analytics in
Intelligence, Surveillance, &
Reconnaissance (ISR)
(January 28, 2018).
Available at
SSRN: https://ssrn.com/ab
stract=3111837
MATH vs.
INTUITION
46. [46]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Maven’s success is clear proof that AI-ML-DL is ready to revolutionize
many national security missions even if DoD is not yet ready for the
organizational, ethical, and strategic implications of that revolution.
Having met sky-high expectations of the DoD, it’s likely
to spawn 100 copycat ‘Mavens’ in ISR.
“I don't think honestly there is any aspect of Department that is not
ripe for introducing some type of AI and machine learning into it.”
Agile Manifesto + Quant Models Manifesto + CyberISR
“Convolutional Neural Networks are doomed” – Geofferey Hinton
Malhotra, Yogesh, Cognitive Computing for Anticipatory
Risk Analytics in Intelligence, Surveillance, &
Reconnaissance (ISR)
(January 28, 2018). Available at
SSRN: https://ssrn.com/abstract=3111837
With Great Power Comes Great Responsibility
SPACE
+
CYBERSPACE
‘OFFENSIVE’
‘DEFENSIVE’
47. [47]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://thebulletin.org
/daniel-ellsberg-
dismantling-
doomsday-
machine11539
Lesser Concern
about the Next
‘AI Winter’...
Greater Concern
about the
‘Nuclear Winter’*
48. [48]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.dailymail.co.
uk/sciencetech/article-
5603367/AI-studies-
CCTV-predict-crime-
happens-rolled-
India.html
49. [49]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
“I think the way we’re doing
computer vision is just
wrong,” he says. “It works
better than anything else at
present but that doesn’t mean
it’s right.”
Dynamic Routing Between Capsules
https://arxiv.org/abs/1710.09829
Matrix capsules with EM routing
https://openreview.net/forum?id=HJWLfGWRb¬eId=HJWLfGWRb
“I think the way
we’re doing
computer vision
is just wrong.”
50. [50]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
MATH vs.
INTUITION
“Imagine a face. What are the
components? We have the face oval,
two eyes, a nose and a mouth. For a
CNN, a mere presence of these
objects can be a very strong
indicator to consider that there is a
face in the image. Orientational
and relative spatial relationships
between these components are not
very important to a CNN.”
=
https://www.cs.toronto.edu/~hinton/csc2535/notes/lec6b.pdf
https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
Internal data representation of a convolutional neural network does not take into
account important spatial hierarchies between simple and complex objects.
"As far as the laws of
mathematics refer to reality,
they are not certain, and as far
as they are certain, they do not
refer to reality."
“Certainly the statement 2 x (1/2) = 1 is arithmetically correct. But do two half-sheets of paper
make one whole sheet and do two half-shoes make one whole shoe?” – Morris Kline
51. [51]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www2.deloitte.com/insights/us/en/deloitte-review/issue-20/augmented-intelligence-human-
computer-collaboration.html
MATH vs.
INTUITION
What’s HARD?
What’s EASY?
Computationally?
Intuitively?
Computationally:
Routine,
Structured,
Procedural
Intuitively:
Non-routine,
Unstructured,
Non-procedural
"Though machines are, in speed, accuracy, and endurance, superior to the human brain, one should
not infer, as many popular writers are now suggesting, that machines will ultimately replace brains.
Machines do not think. They perform the calculations which they are directed to perform by people
who have the brains to know what calculations are wanted.” - Morris Kline
52. [52]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://www2.deloitte.com/insights/us/en/deloitte-review/issue-20/augmented-intelligence-human-computer-
collaboration.html
53. [53]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
54. [54]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Harvard Business Review: If Your Data Is Bad,
Your Machine Learning Tools Are Useless
In addition to Data, the challenges of accurate
AI-ML Models and Methods are equally, if not
even more so, critical given that they are hidden
from the users' eyes (WWW: Society of Actuaries in
Ireland: Cybersecurity & Cyber-Finance Risk
Management - Yogesh Malhotra, PhD)
https://lnkd.in/eDb897h "[T]he approaches to
mitigate operating risk associated with the use of
models need to evolve to reflect recent trends in the
Finance Industry. In particular there are a number of
new areas where it is not possible for the "human eye"
to necessarily detect material flaws: in the case of
models operating over very small time scales in high
frequency algorithmic trading, or for portfolio risk
measurement models where outputs lack
interpretability due to highdimensionality and complex
interactions in inputs, the periodic inspection of
predicted versus realized outcomes is unlikely to be an
effective risk mitigate." https://lnkd.in/eV79T6C
55. [55]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
56. [56]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"Patrick Winston, a professor of AI and
computer science at MIT, says it would be more
helpful to describe the developments of the past
few years as having occurred in “computational
statistics” rather than in AI. One of the leading
researchers in the field, Yann LeCun, Facebook’s
director of AI, said at a Future of Work
conference at MIT in November that machines
are far from having “the essence of intelligence.”
That includes the ability to understand the
physical world well enough to make predictions
about basic aspects of it—to observe one thing
and then use background knowledge to figure
out what other things must also be true. Another
way of saying this is that machines don’t have
common sense." "The computer that wins at Go
is analyzing data for patterns. It has no idea it’s
playing Go as opposed to golf, or what would
happen if more than half of a Go board was
pushed beyond the edge of a table... "
AI has No ‘Common Sense’...
No Sense for ‘Sense Making’...
No Sense of ‘Meaning’...
57. [57]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
“I personally think the problem of intelligence is
the greatest problem in science. AlphaGo is one
of the two main successes of AI, and the other is
the autonomous-car story. Very soon they’ll be
quite autonomous. Is this getting us closer to
human intelligence? " Tomaso Poggio, a
professor at the McGovern Institute for Brain
Research at MIT said these programs are no
closer to real human intelligence than before.
"These systems are pretty dumb." He says no one
knows how to make a broader general
intelligence, like what humans have, and you
can’t do it by “gluing together” existing
programs that play games or categorize images.
A self-driving Go player would bring us no closer
to a "general" AI, or one that can think for itself
and solve many kinds of novel problems. “We
have not yet solved AI by far. This is not
intelligence," says Poggio. He thinks the next AI
breakthroughs are going to come from
neuroscience, something he works on as head of a
10-yr, $50 million program called the Center for
Brains, Minds, and Machines, which is exploring
how the brain creates human visual awareness.
This is not intelligence
58. [58]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"Insofar as certainty of knowledge is concerned, mathematics serves as an ideal, an ideal
toward we shall strive, even though it may be one that we shall never attain. Certainty may be
no more than a phantom constantly pursued and interminably elusive.“ – Morris Kline
https://www.linkedin.com/pulse/designing-smart-
minds-using-tools-utopian-view-ai-yogesh-/
http://www.linkedin.com/in/yogeshmalhotra
Fischer Black and the Revolutionary Idea of Finance
Hedge Funds Trading and Risk Management
On Fischer Black: Intuition is a Merging of the
Understander with the Understood – Emanuel Derman
A Man for All Markets – Ed Thorp
"Future strategic advantage and competitive performance will not derive from simply adoption and
use of new information and communication technologies. Rather, they will be determined by smart
minds using smart technologies, with greater emphasis being on smart minds.
59. [59]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Starting from original article on AI & ML inspired by
Genetic Algorithms pioneer Dr. John Holland that
outlined these key distinctions 20 years ago:
https://lnkd.in/eDE-W3z... To recent observations
in: Making AI & Deep Learning Work Better:
Designing 'Smart Minds' Using 'Smart
Tools': https://lnkd.in/gcp_yHe . Conclusions in this
week's MIT-Strategy discussions on AlphaZero,
AlphaGoZero, and, AlphaGo: "From a Strategic and
Psychological perspective, the 'games' humans are
capable of imagining and playing are at a different
level as compared to machines, only, if we can
recognize so, as discussed in Module 5 with
reference to [my] articles such as on AI & Machine
Learning Strategy and Psychological Games."
Response to: "we're always outdated..." To never be
outdated, always "Know Forward" instead of
"Knowing Backward"... use Real
Intelligence... How: "Obsolete what you know
before others obsolete it and profit by creating the
challenges and opportunities others haven't even
thought about." - Inc. Magazine Interview, Inc.
Technology special issue #3, 1999.
https://lnkd.in/dhrXpwq
BEYOND THE MASTER ALGORITHM
60. [60]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
More on Partnering 'Smart Minds' with 'Smart Tools':
Making AI & Deep Learning Work Better: Designing 'Smart
Minds' Using 'Smart Tools': https://lnkd.in/gcp_yHe .
MIT Sloan Management Review: "Companies are
succeeding with AI by partnering smart machines with
smart people who are learning how to take advantage of
what those machines can do. In short, AI implementation
success depends on your ability to hire and develop problem-
solvers, equip them with data (and potentially AI), and then
empower them to actually solve problems. Note that
addressing skill requirements this way may well require
major changes to your existing hiring and development
practices. Companies that view smart machines purely as a
cost-cutting opportunity are likely to insert them in all the
wrong places and all the wrong ways. These companies will
automate existing processes rather than imagine new ones.
They will cut jobs rather than upgrade roles. These are the
companies who will find that implementing AI is little more
than a reprise of the ERP nightmare."
https://lnkd.in/dBHEYXh
61. [61]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI: Model Risk Management to counter Spurious ML
"Patterns": MIT AI-Strategy Executive Guide
(continued) https://lnkd.in/eknKzm5 "All models are
wrong, some models are useful." Problem: FT:
Spurious correlations are kryptonite of Wall St’s AI
rush https://lnkd.in/ednTEiS "Machine learning is a
valuable tool to analyse vast data sets. But it really is
just data mining to find patterns. Sometimes a signal
might make money for a few days or weeks, and when
it disappears or even leads to losses it can be hard to be
certain whether it was arbitraged away by other
traders, or if it was spurious from the start. Although
data mining is often used simply to mean looking for
patterns in huge data sets, for quants the term typically
has negative connotations, implying a selective hunt for
data points to support a specific thesis. It is frequently
used interchangeably with the more technical
expression “overfitting”, building a faulty model on a
bedrock of shaky data." Model Risk Management:
Model Risk Management Paper (JP Morgan) (follow up
to MIT Sloan Management Review Paper)
https://lnkd.in/eGr9eCi Model Risk Management
Presentation (Princeton) https://lnkd.in/eyP9Npd
Model Risk Arbitrage™ Presentation (Princeton)
https://lnkd.in/dJ-Gnxx https://lnkd.in/ednTEiS
62. [62]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Yogesh Malhotra, PhD, 2016www.yogeshmalhotra.com
Malhotra, Yogesh, Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &,
Intelligence: CROs-CSOs Keynote: Enterprise Risk Management to Model Risk Management: Understanding
Vulnerabilities, Threats, & Risk Mitigation (September 15, 2015). Available at SSRN:
https://ssrn.com/abstract=2693886.
All Models are Wrong...
Some Models are Useful.
Why Intuition is most critical for System Performance
63. [63]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Yogesh Malhotra, PhD, 2016www.yogeshmalhotra.com
Malhotra, Yogesh, Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &,
Intelligence: Enterprise Risk Management to Model Risk Management: Understanding Vulnerabilities, Threats, &
Risk Mitigation (Presentation Slides) (September 15, 2015). Available at SSRN: https://ssrn.com/abstract=2693886.
Why it is most critical to remember that Model is Not the Reality
64. [64]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Yogesh Malhotra, PhD, 2016www.yogeshmalhotra.com
• Embrace subjectivity
• Acknowledge uncertainty
• Integrate objective &
subjective info
Why ‘Common Sense’ is most critical to know how wrong a Model can be
65. [65]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
66. [66]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Yogesh Malhotra, PhD, 2016www.yogeshmalhotra.com
Model use entails model risk (Derman, 1996; Morini, 2011) because
a statistical model is used for risk estimation. The problem of model
risk for any risk model such as VaR results from the fact that risk
cannot be measured, but must be estimated using a statistical model
(Boucher et al., 2014; Danielsson et al., 2014) . Using a range of
different plausible models which can be robustly discriminated
between, the variance between corresponding range of estimates is a
succinct measure of model risk (Danielsson et al., 2014). We apply
this notion of multi-model comparison of estimates and extend it to
multi-methods comparison to manage model risk advancing
estimation of cyber risk related loss beyond the limitations of VaR
discussed earlier.
Why it is most critical to manage model risk using Model Risk Management
67. [67]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"The world has too much texture than
[quants] can squeeze into the framework
they're used to. I see a huge incidence of
pure speculative gambling on the part of
these folks who are hired on the strength
of their knowledge of quantitative
methods."
"You're worse off relying on misleading
information than on not having any
information at all. If you give a pilot an
altimeter that is sometimes defective he
will crash the plane. Give him nothing
and he will look out the window.
Technology is only safe if it is flawless."
"To me, VaR is charlatanism because it tries
to estimate something that is not
scientifically possible to estimate, namely
the risks of rare events. It gives people
misleading precision that could lead to the
build up of positions by hedgers. It lulls
people to sleep."
http://www.yogeshmalhotra.com/risk.html
"The only Constant used to be Change...
Even it is not Constant anymore...."
68. [68]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.actuaries.org/
ASTIN/Colloquia/Helsink
i/Presentations/Embrechts
.pdf
https://www.wired.com/2009/02/wp-quant/
At the heart of it all was Li's formula. When
you talk to market participants, they use
words like beautiful, simple, and, most
commonly, tractable...
Li's approach made no allowance for
unpredictability: It assumed that correlation
was a constant rather than something
mercurial...
“They didn't know, or didn't ask. One reason was
that the outputs came from "black box" computer
models and were hard to subject to a
commonsense smell test. Another was that the
quants, who should have been more aware of the
copula's weaknesses, weren't the ones making the
big asset-allocation decisions. Their managers, who
made the actual calls, lacked the math skills to
understand what the models were doing or how
they worked.”
“The most dangerous part is when people
believe everything coming out of it.” - Li
69. [69]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
XXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXX
70. [70]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.yogeshmalhotra.com/risk.html
71. [71]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
72. [72]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://ssrn.com/abstract=2538401
73. [73]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://ssrn.com/abstract=2553547
74. [74]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://ssrn.com/abstract=3081492
75. [75]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
National Association of Insurance Commissioners Expert Paper
Most Models are Wrong.
Some Models are Useful.
- Derman
https://ssrn.com/abstract=3081492
76. [76]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
77. [77]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
GUIDING THE BIG-4 CONSULTING BEST PRACTICES ABOUT ‘BEST PRACTICES’
78. [78]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
79. [79]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
“Machine learning models are vulnerable to adversarial
examples: small changes to images can cause computer
vision models to make mistakes such as identifying a
school bus as an ostrich. However, it is still an open
question whether humans are prone to similar mistakes.”
80. [80]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
“Images on the Right are slightly
distorted versions of the images on
the Left.”
“The difference between Left and
Right set of images is imperceptible to
the human eye.”
“However, where human eye
sees the SAME OBJECT on
the Right, the Convolutional
Neural Network sees an
OSTRICH
for all the three images.”
81. [81]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
What Caused the Failure of the Socio-Technical System? 3 Key Systems Failed
Perfect Weather Conditions and Perfect Road Conditions in AZ
What Would Happen in the “Typical” “Zero-Visibility” Winter Weather in Central NY?
When 65 MPH I-90 “Thruway” Traffic Drives ‘Normally’ in Day at 10 MPH for Safety
Or When All Traffic is Off the 65 MPH I-90 “Thruway” as it’s Frozen.
Socio-Technical
Systems
Adaptability-
Generalizability
Self-Adaptive
Complex Systems
AI-ML -KMS
Sense Making
Past vs. Future
‘Historical Data’
Known vs.
Unknown
82. [82]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
MATH vs.
INTUITION
"Not everything that counts can be counted,
and not everything that can be counted counts."
"As far as the laws of mathematics refer to reality,
they are not certain, and as far as they are certain,
they do not refer to reality."
https://alexiajm.github.io/GANs/
83. [83]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://developers.googleblog.co
m/2018/04/text-embedding-
models-contain-bias.html
https://papers.nips.cc/paper/6228-
man-is-to-computer-programmer-
as-woman-is-to-homemaker-
debiasing-word-embeddings.pdf
84. [84]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://datavizblog.com/2013/07/27/why-you-should-never-trust-a-data-visualization/
“The mathematician really
creates models of reality. Each
model has a limited applicability.
Moreover, one must distinguish
between the mathematical model
and the physical world or
between mathematical theories
and physical reality.”
Morris Kline
“That one can draw
pictures to represent what
one is thinking about in
geometry has its
drawbacks. One is prone to
confuse the abstract
concept with the picture
and to accept unconsciously
properties of the pictures.”
85. [85]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
It’s hard to explain to people who haven’t worked with
machine learning, but we’re still back in the dark ages when
it comes to tracking changes and rebuilding models from
scratch. It’s so bad it sometimes feels like stepping back in time
to when we coded without source control...
This is an optimistic scenario with a conscientious researcher,
but you can already see how hard it would be for somebody
else to come in and reproduce all of these steps and come
out with the same result. Every one of these bullet points is an
opportunity to inconsistencies to creep in. To make things
even more confusing, ML frameworks trade off exact
numeric determinism for performance, so if by a miracle
somebody did manage to copy the steps exactly, there would
still be tiny differences in the end results!
In many real-world cases, the researcher won’t have made
notes or remember exactly what she did, so even she won’t be
able to reproduce the model. Even if she can, the frameworks
the model code depend on can change over time, sometimes
radically, so she’d need to also snapshot the whole system she
was using to ensure that things work.
https://petewarden.com/2018/03/19
/the-machine-learning-
reproducibility-crisis/
86. [86]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
https://petewarden.c
om/2013/07/18/why
-you-should-never-
trust-a-data-
scientist/
87. [87]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Arguably the world's greatest
mathematician, he worked out a
solution to one of the seven great
unsolved mathematical problems, the
Poincaré conjecture, in 2002. It was a
magnificent achievement. Honours,
cash, offers of world lecture tours and
lucrative teaching posts were hurled
at the Russian theorist.
But Perelman turned down the lot,
including the Fields medal, the
mathematical world's equivalent of a
Nobel prize, and a million dollars in
prize money that the Clay Institute
wanted to give him for his work.
Since then, he has announced he has
given up the study of mathematics
altogether and has cut off
communications with all journalists
and nearly all his friends.
https://www.theguardian.com/books/2011/ma
r/27/perfect-rigour-grigori-perelman-review
88. [88]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.pravdareport.com/science/tech/28-04-
2011/117727-Grigori_Perelman-0/
According to the newspaper, both
Russian and foreign special services
are showing interest in Perelman's
discoveries. The scientist has learned
some super-knowledge which helps
realize creation. Special services need
to know whether Perelman and his
knowledge may pose a threat to
humanity. With his knowledge he can
fold the Universe into a spot and
then unfold it again. Will mankind
survive after this fantastic process?
Do we need to control the Universe at
all?
http://www.claymath.org/library/proceedings
/cmip19.pdf
89. [89]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Reflecting on Math, Theory vs. [Socio-]Physical Reality -
Morris Kline
“One of the first difficulties in applying statistics is to decide the meaning of
the concepts involved.”
“In the search for a method of proof, as in finding what to prove, the
mathematician must use audacious imagination, insight, and creative ability.
His mind must see possible lines of attack where others would not.”
“When creating a mathematical
proof, the mind does not see the
cold, ordered arguments which
one reads in texts, but rather it
perceives an idea or a scheme
which when properly formulated
constitutes deductive proof. The
formal proof, so to speak,
merely sanctions the conquest
already made by the intuition.”
90. [90]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Reflecting on Math, Theory vs. Physical Reality
"In my lifetime, I have never bought
any stock, much less their derivatives.
I deposit money only in an ordinary
bank account, and I have rarely even
had a fixed deposit account.”
Kiyosi Itô, Founder of Itô Calculus aka
Stochastic Calculus of Quantitative
Finance
“There is nothing so practical as a good theory.”
- Kurt Lewin
“There is nothing so practical as good practice of theory.”
- Yogesh Malhotra
- (A Personal Constructivist Corollary)
"Is then mathematics a collection of diamonds
hidden in the depths of the universe and
gradually unearthed one by one or is it a
collection of synthetic stones manufactured by
man but nevertheless so brilliant that it
bedazzles those mathematicians who are
already partially blinded by pride in their own
creations? Several considerations incline us to
the latter point of view.“
- Morris Kline
"One should question the extent to
which mathematics really represents
the physical world. It treats those
physical concepts which can be
represented by numbers or
geometrical figures. But physical
objects possess other properties was
well. We do not usually think of
human beings as chunks of matter
moving in space and time.“
- Morris Kline
"All scientific work depends upon measurement.
However, all measurements are approximate.“
- Morris Kline
91. [91]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Reflecting on Math, Theory vs. Physical Reality - Morris Kline
"One finds among the supreme mathematicians
men, such as Newton, Lagrange, and Laplace,
who even cared little or nothing for
mathematics proper, but felt compelled to take
up mathematical problems in order to solve
physical problems."
"If herds of cattle behaved like volumes of
gases or like raindrops, then the arithmetic
would not apply, and it is only through
experience that we learn how they do
behave. Hence, we have no guarantee that
arithmetic per se represents truths about
the physical world."
"The mathematician really creates
models of reality. Each model has a
limited applicability. Moreover, one
must distinguish between the
mathematical model and the physical
world or between mathematical
theories and physical reality."
"Human nature is a more complicated
structure than a mass sliding down an
inclined plane or a bob vibrating on a
spring."
"Suppose, next, that one raindrop is added
to another raindrop. Do we now have two
raindrops? If one cloud is joined to another
cloud do we now have two clouds? One may
protest that in these examples the merged
objects have lost their identity, and that the
addition process of arithmetic does not
contemplate such loss. And precisely for
this reason, arithmetic in the normal sense
no longer applies."
92. [92]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
SHOULD WE FEAR ARTIFICIAL INTELLIGENCE
CURRENT GLOBAL CONTEXT & BACKGROUND
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 1
SENSE MAKING vs. INFORMATION PROCESSING
AI, MACHINE LEARNING, DEEP LEARNING, GANs: 2
SENSE MAKING vs. INFORMATION PROCESSING
AI-ML-DL BIASES, INTERPRETIBILITY, EXPLAINABILITY
WITH GREAT POWER COMES GREAT RESPONSIBILITY
AI-ML-DL and VaR: WHY ALL ‘MODELS’ ARE WRONG
“THIS IS NOT INTELLIGENCE”, NOT ‘COMMON SENSE’
RISK MODELING TO UNCERTAINTY MANAGEMENT
WHEN ‘BEST PRACTICES’ BECOME ‘WORST PRACTICES’
AI-ML-DL-GANs: KNOWING ‘MATH’ AND ITS ‘REAL’ LIMITS
RELY UPON INTUITION TO GO BEYOND LIMITS OF ‘MATH’
OUTLINE OF PRESENTATION
Need Copy of Presentation? Contact via LinkedIn: http://www.linkedin.com/in/yogeshmalhotra
93. [93]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why Interpretability is complicated
Why ‘Humans-in-the Loop’ are Even More Critical for Interpretability
Malhotra, Y., Bringing the Adopter
Back Into the Adoption Process: A
Personal Construction Framework
of Information Technology
Adoption. Journal of High
Technology Management Research,
10(1), 1999, 79-104.
Socio-Technical
Systems
94. [94]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why Interpretability is complicated
Why ‘Humans-in-the Loop’ are Even More Critical for Interpretability
Socio-Technical
Systems
95. [95]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Why Machine Learning Doesn’t ‘Make Sense’ Or Sense ‘Meaning’
Malhotra, Y., Bringing the Adopter
Back Into the Adoption Process: A
Personal Construction Framework
of Information Technology
Adoption. Journal of High
Technology Management Research,
10(1), 1999, 79-104.
96. [96]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Cognition
ActionAffect
Socio-Psychology and Neuroscience of ‘Making Sense’ and Sensing ‘Meaning’
Damásio presents the "somatic
marker hypothesis", a proposed
mechanism by which emotions
guide (or bias) behavior and
decision-making, and positing
that rationality requires emotional
input. He argues that René
Descartes' "error" was the dualist
separation of mind and body,
rationality and emotion.
https://en.wikipedia.org/wiki/Descartes%27_Error
“Damasio’s essential insight is that feelings are
“mental experiences of body states,” which arise as
the brain interprets emotions, themselves physical
states arising from the body’s responses to external
stimuli. (The order of such events is: I am threatened,
experience fear, and feel horror.) He has suggested
that consciousness, whether the primitive “core
consciousness” of animals or the “extended” self-
conception of humans, requiring autobiographical
memory, emerges from emotions and feelings.”
https://www.technologyreview.com/s/528151
/the-importance-of-feelings/
“Thinking, feeling, and deciding are the
most intimately human of all things, and
yet we understand them hardly at all.”
https://www.technologyreview.com/s/528221
/peering-inside-the-workings-of-the-brain/
97. [97]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Cognition
ActionAffect
How Humans “Make Sense” Where every “aspiring”
‘Data Scientist’ starts by rote
Function Form
“In the search for a method of proof, as in finding what to prove, the
mathematician must use audacious imagination, insight, and creative ability.
His mind must see possible lines of attack where others would not.”
Morris Kline
98. [98]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Harvard Business Review: If Your Data Is Bad,
Your Machine Learning Tools Are Useless
In addition to Data, the challenges of accurate
AI-ML Models and Methods are equally, if not
even more so, critical given that they are hidden
from the users' eyes (WWW: Society of Actuaries in
Ireland: Cybersecurity & Cyber-Finance Risk
Management - Yogesh Malhotra, PhD)
https://lnkd.in/eDb897h "[T]he approaches to
mitigate operating risk associated with the use of
models need to evolve to reflect recent trends in the
Finance Industry. In particular there are a number of
new areas where it is not possible for the "human eye"
to necessarily detect material flaws: in the case of
models operating over very small time scales in high
frequency algorithmic trading, or for portfolio risk
measurement models where outputs lack
interpretability due to highdimensionality and complex
interactions in inputs, the periodic inspection of
predicted versus realized outcomes is unlikely to be an
effective risk mitigate." https://lnkd.in/eV79T6C
99. [99]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
"Recently, such probabilistic, statistical, and numerical methods related concerns are in globally
popular press related to cybersecurity controls and compliance. Earlier, similar probabilistic,
statistical, and numerical methods related concerns were in the global popular press in the context
of the global financial crisis. Future questions focused on the underlying assumptions and logic
may focus on related implications for compliance, controls, valuation, risk management, etc.
Likewise, recent developments about mathematical entropy measures shedding new light on
apparently greater vulnerability of prior encryption mechanisms may offer additional insights for
compliance and control experts. For instance, given related mathematical, statistical and numerical
frameworks, analysis may also focus on potential implications for pricing, valuation and risk
models. The important point is that many such fundamental assumptions and logic underlying
widely used probabilistic, statistical, and numerical methods may not as readily meet the eye."
Interpretability, Explainability, and, Model Risk are Related Issues
Hence, they need to be addressed together for AI and Machine Learning
Future of Bitcoin & Statistical Probabilistic Quantitative Methods:
Global Financial Regulation (Interview: Hong Kong Institute of CPAs)
http://yogeshmalhotra.com/Future_of_Bitcoin.html
Bitcoin Protocol: Model of ‘Cryptographic Proof’ Based
Global Crypto-Currency & Electronic Payments System
http://yogeshmalhotra.com/BitcoinProtocol.html
January 20, 2014
December 04, 2013
GDPR
100. [100]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
AI-ML Risk Management & Controls Most Critical
Lesser Concern about the Next ‘AI Winter’
Greater Concern about the ‘Nuclear Winter’*
101. [101]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
http://www.europarl.europa.eu
/thinktank/en/document.html?
reference=EPRS_IDA(2018)6
14547
http://www.europarl.europa.eu/
RegData/etudes/IDAN/2018/61
4547/EPRS_IDA(2018)614547
_EN.pdf
Adaptability-Generalizability
Past Prediction vs. Future Anticipation
KMS &
Risk Management Controls
Self-Adaptive Complex Systems
AI-ML
Knowledge Management Systems
Creativity, Imagination,
Innovation, Intuition,
Insight
Known vs.
Unknown
Routine, Structured, Procedural
Non-routine, Unstructured, Non-procedural
With Great Power Comes Great Responsibility
102. [102]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
Adaptability-Generalizability
Past Prediction vs. Future Anticipation
KMS &
Risk Management Controls Self-Adaptive Complex Systems
AI-ML
Knowledge Management Systems
Sense Making
Past vs. Future
‘Historical Data’
Known vs.
Unknown
103. [103]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
The European Parliament Think Tank's Research Policy document 'Should we fear artificial
intelligence?' reflects the ongoing mainstream debate between the Utopian and Dystopian
aspects of AI and Machine Learning. "Powerful AIs can in principle be given nearly any goal,
which is a source of both risk and opportunity. There are myriad possible malicious uses of AI
and many ways in which it might be used in a harmful manner unintentionally, such as with
algorithmic bias. Perhaps most fundamentally, the control problem will have to be addressed –
that is, we will need to learn how to ensure that AI systems achieve the goals we want them to
without causing harm during their learning process, misinterpreting what is desired of them,
or resisting human control." Third in the series of the Princeton Presentations on AI and
Machine Learning Risk Management & Control Systems, the current presentation develops
fundamental guidance on the design, development, and implementation of AI, Machine
Learning, and Deep Learning Models and Methods. The 2018 Princeton presentation will focus
on "the control problem" which is a critical prerequisite for AI systems to have positive impacts
by further developing upon my prior two presentations that pioneered Cyber-Finance-Trust™
Model Risk Management & Model Risk Arbitrage™ practices at prior Princeton Quant Trading
Conferences. Starting with the first technical report on the Bitcoin Blockchain Cryptographic
Proof of Work; spanning latest developments in AI, Machine, Learning, Deep Learning, and,
Generative Adversarial Networks; and, hedge fund algorithmic trading, the presentation
generates interesting insights about the most critical role of risk management controls. Such
role of risk management controls is most critical in not only getting the best out of AI, but also
ensuring that the worst fears about the AI do not really come true.
Abstract
104. [104]
Model Risk Management in AI, Machine Learning & Deep Learning
AI, Machine Learning & Deep Learning Risk Management & Controls
Beyond Deep Learning and Generative Adversarial Networks...
Copyright, Dr. Yogesh Malhotra, 2018www.yogeshmalhotra.com
Princeton Fintech and Quant Conference @ , April 21, 2018
Conference sponsors include:
2018 Princeton Fintech & Quant Conference
Princeton University, April 21, 2018
Princeton Presentations in AI-ML Risk Management & Control Systems
2016 Princeton Quant Trading Conference, Princeton University
How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!
Knight Reconsidered Again: Risk, Uncertainty, & Profit beyond ZIRP & NIRP
2015 Princeton Quant Trading Conference, Princeton University
Future of Finance Beyond 'Flash Boys': Risk Modeling for Managing Uncertainty in an
Increasingly Non-Deterministic Cyber World:
Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era
Yogi
Dr. Yogesh Malhotra
Post-Doctoral R&D in AI, Machine Learning & Deep Learning
Marquis Who's Who in the World® 1999-, Marquis Who's Who in America® 2002-,
Marquis Who's Who in Finance & Industry® 2001-, Marquis Who's Who in Science & Engineering® 2006-
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892
http://www.linkedin.com/in/yogeshmalhotra
www.FutureOfFinance.org
Editor's Notes
Presentation 1: Saving the Global Financial and Trading Systems, Markets.
Presentation 2: Saving the Global National and Global Economic Systems.
Presentation 3: Saving the World.
With Great Power Comes Great Responsibility...
Of those designing, testing, validating, qualifying, and, deploying AI...
My greatest concern is about that responsibility of the various Humans...
Other risk may be plausible, but the greatest risks would most likely result from inadequate focus on that key responsibility of Humans.
Ian Goodfellow – went to a bar and he was kidding with his friends and thought about GANs and he came home and couldn’t sleep and wrote about the first paper which became his PhD thesis...
I took a more boring approach – as a PhD student – I was looking at all statistical models and was thinking about PREDICTION – to me with the start of the WWW – the world looked very uncertain, very messy, where most classical statistical models that I was studying wouldn’t apply... My early thinking on Model Risk Management – just around the time Emanuel Derman was thinking about his paper at Goldman Sachs... I came to know the term MRM much later... But all the work went into developing the framework of why MRM is needed at all levels of analysis and what are the “gaps” between Models and “reality” at different levels of analysis. I came across Churchman’s work that helped me distinguish between the “two world’s of business” – Lockean/Leibnitizian Static, Deterministic and thus Predictable world... And Hegelian/Kantian Dynamic, Non-Deterministic and thus Uncertain / Unpredictable World...
Curiosity is essential for most jobs and careers...
In fact most job ads typically write so...
Have you seen any job ad so far asking you need to be ‘artificially curious’!
Curiosity is essential for most jobs and careers...
In fact most job ads typically write so...
Have you seen any job ad so far asking you need to be ‘artificially curious’!
MACHINES PROCESS THE RED-GREEN-BLUE OR RGB COLOR VALUE OF EACH PIXEL OR A BUNCH OF PIXELS FOR ANY LOW-LEVEL OR HIGH LEVEL “FEATURE” – IN CONTRAST TO HUMANS...
Why blind reliance and total devotion to theoretical Math is dangerous?Why ignorance of Math particularly aversion to Math is also dangerous?
This is where the “Rubber Meets the Road” – “Theory meets Reality”
https://www.digitaltrends.com/cool-tech/could-ai-based-surveillance-predict-crime-before-it-happens/
It’s already common for law enforcement in cities like London and New York to employ facial recognitionand license plate matching as part of their video camera surveillance. But Cortica’s AI promises to take it much further by looking for “behavioral anomalies” that signal someone is about to commit a violent crime.
The software is based on the type of military and government security screening systems that try to identify terrorists by monitoring people in real-time, looking for so-called micro-expressions — minuscule twitches or mannerisms that can belie a person’s nefarious intentions. Such telltale signs are so small they can elude an experienced detective but not the unblinking eye of AI.
Going directly to the brain
Cortica’s AI software monitors people in real-time, looking for micro-expressions — minuscule twitches or mannerisms that can belie a person’s nefarious intentions.
To create such a program, Cortica did not go the neural network route(which despite its name is based on probabilities and computing models rather than how actual brains work). Instead, Cortica went to the source, in this case a cortical segment of a rat’s brain. By keeping a piece of brain alive ex vivo (outside the body) and connecting it to a microelectrode array, Cortica was able to study how the cortex reacted to particular stimuli. By monitoring the electrical signals, the researchers were able to identify specific groups of neurons called cliques that processed specific concepts. From there, the company built signature files and mathematical models to simulate the original processes in the brain.
The result, according to Cortica, is an approach to AI that allows for advanced learning while remaining transparent. In other words, if the system makes a mistake — say, it falsely anticipates that a riot is about to break out or that a car ahead is about to pull out of a driveway — programmers can easily trace the problem back to the process or signature file responsible for the erroneous judgment. (Contrast this with so-called deep learning neural networks, which are essentially black boxes and may have to be completely re-trained if they make a mistake.)
Initially, Cortica’s Autonomous AI will be used by Best Group in India to analyze the massive amounts of data generated by cameras in public places to improve safety and efficiency. Best Group is a diversified company involved in infrastructure development and a major supplier to government and construction clients. So it wants to learn how to tell when things are running smoothly — and when they’re not.
A 4-Year old who has been shown a few faces and told that they were faces wouldn’t make the mistake made by the CNN.
How OBJECTIVE and SUBJECTIVE can be linked to better UNDERSTAND and MANAGE UNCERTAINTY
Human Factor: Challenger O-Rings story – Human factor in managerial controls and culture as well as intuition, common sense, and experience of the engineers... That Models are not expected to have... These are human traits... Not traits of machines or math!
Search for the General AI Artificial general intelligence (AGI), or Broad AI, as contrasted with most AI of today which is Narrow AI. Supervised Learning, or, Training Data are NOT Experience... Hence, current focus of AI – particularly beyond Convolutional Networks based on Backpropagation and Gradient Descent, and, beyond Supervised Learning and Training Data – such as in AlpohaGo Zero and Nurevolution and Reinforcement Learning... Beyond focus on Big Data and Big Computing to More Robust Algorithms....
In certain contexts, a 4-year old child has greater intelligence as compared with NLP AI despite the latest reports about Big IT firms creating new benchmarks on the standardized reading comprehension tests.
INTEGRAL – 2 Ways – Domain Knowledge, Subjective Experience, Intuition...
Also Multi-Theoretical Frameworks of Human-Machine Systems – A Unified Theory of Sorts...
He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide. For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels. His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored. Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li's formula hadn't expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.
With Great Power Comes Great Responsibility...
Of those designing, testing, validating, qualifying, and, deploying AI...
My greatest concern is about that responsibility of the various Humans...
Other risk may be plausible, but the greatest risks would most likely result from inadequate focus on that key responsibility of Humans.