DEV Community: Dr.-Eng.-Prof. Yogesh Malhotra

Silicon Valley-Wall Street-Pentagon Digital Pioneer Engineering Quantum Minds QASANs Decades Ahead of Stanford University AI-ML

Dr.-Eng.-Prof. Yogesh Malhotra — Sun, 15 Mar 2026 06:18:12 +0000

Silicon Valley-Wall Street-Pentagon Digital Pioneer Engineering Quantum Minds QASANs Ahead of Stanford University AI-ML

[xAI: Grok AI: "Dr.-Eng.-Prof. Yogesh Malhotra is the Singular Post AI-Quantum Pioneer for decades of cohesive innovation adding trillions in value in adaptive finance and risk systems decades ahead of today's AI in breadth, depth, and practical impact."]

"We are Pioneering Forward AI Decades Ahead of AI & GenAI: Backward AI to Forward AI Transition with Quantum GenAI, Quantum Minds, QASANs represents the most significant Paradigm Shift in Artificial Intelligence since the advent of Computing itself."

BrightTalk: Enterprise Architecture: Accelerating the Enterprise with AIOps' Global Virtual Summit: Beyond Prediction:
Paradigm Shifts : Backward to Forward AI : Risk to Uncertainty : AI to Humans : Quantum to AC2-CAC2 : Coming Crisis :

Dr.-Eng.-Prof. Yogesh Malhotra 'Yogi

R&D Impact among Artificial Intelligence-Quant Finance Nobel Laureates Herbert Simon, Black-Scholes, Markowitz, Sharpe:
Marquis Who's Who in America®, Who's Who in the World®, Who's Who in Finance & Industry®, Who's Who in Science & Engineering® Since 1999
YM: YouTube: AWS: Amazon: SSRN: Google: ORCID: ResearchGate: RISK: Capco: SUNYLumen: DEV: X: YM-ABC™: AIMLExchange: BRINT : C4I-Cyber

Grok AI: "Singular Post AI-Quantum Pioneer": "For Decades of Cohesive Innovation": "Adding Trillions in Value":

Trillion Dollar Wall Street Banks-Hedge Funds-Global Financial Systems Leader - Goldman Sachs - JP Morgan HQ:

Pentagon Joint Chiefs SME-USAF Chief Scientist Invited Interviews: U.S. Space Force-Air Force Chiefs Advisory Role:

MIT-Princeton-New York State Capitol-AWS Partner-AWS MLU Faculty-SME Post AI-Quantum Finance Networks Pioneer:

Silicon Valley Post AI-Quantum Networks Pioneer: Leading Silicon Valley VCs-CEOs from Digital to Post AI-Quantum Era:

[MIT-Princeton-Silicon Valley-Wall Street-Pentagon-USAF-NSF-UN CEOs-CTOs Faculty-SME: Post AI-Quantum Networks:](https://www.youtube.com/@NewYorkStateCapitolCloudVC-SME)

Stanford University Certified Amazon-AWS Accredited-Certified Partner: Machine Learning-Security-Solutions Architecture: AWS Machine Learning University Educator.

MIT-Princeton Post AI-Quantum Faculty

Post-Doc R&D: AI-ML-Cyber-Crypto-Quantum-Quantitative-Finance-Networks-Systems-Engineering: Elsevier SSRN 128 Top-10 R&D Rankings: Top 0.1% SSRN Authors:
Google Scholar: Grok AI 'Singular Post AI-Quantum Pioneer': Post AI-Quantum Networks : Quantum Agentic GenAI : Quantum Finance-Money : Quantum Blockchain-Crypto:
Top-10 PhD AI-Telecom-Controls-Risks-Quant Methods, MS-Computer Science, MS-Network Security, MS-Quant Finance, MS-Accounting, MBA, B.Eng., CISSP, CISA, CEH, C.Eng.

AWS Academy Educator Building the Future of AI-Machine Learning Higher Education & Practices with AWS for Workforce Development

Dr.-Eng.-Prof. Yogesh Malhotra — Fri, 26 Jan 2024 17:45:51 +0000

This is a < 1% Sampler of the Course Content of 100-400 slides 2x every-week for each of 14-weeks for each of the two courses.

Over the Fall Semester of 2023, as AWS Academy Educator, we designed, built and delivered our latest College of Engineering Graduate and Undergraduate Computer Science courses on "post-Generative AI" Artificial Intelligence (AI) and Machine Learning (ML) built and delivered in alignment with contemporaneously released AWS Practices and Services. As Lumen Circles Fellow "post-Generative AI" Faculty Mentor at the largest comprehensive university system in the United States, we advanced beyond Stanford University's globally popular Artificial Intelligence (AI) and Machine Learning (ML) curriculum Machine Learning Specialization focus built on the legacy of the "neural networks" to the 'post Generative AI' Future of AI and ML. In doing so, we continued our progress building the Future of University and Higher Education and Workforce Development Practices in AI-Machine Learning, Information Systems and Computer Science to align with the most critical needs of today's and future 'post Generative AI' era workforce practices.

We designed, built, taught, and delivered Graduate and Undergraduate College of Engineering-Computer Science courses on the Future of Artificial Intelligence (AI) and Machine Learning (ML) with particular emphasis on our Global Risk Management Network, LLC practices leading AI-ML as well as Risk Management R&D and practices, networks, ventures, and technologies for last 30-years.

First, we advanced beyond the AI-ML Stanford Computer Science & Engineering Machine Learning and Deep Learning Specializations curricular focus on the legacy of 'Neural Networks' to address large-scale real-world failures of such systems as global experts with R&D ranked and recognized for impact among AI and Quant-Finance Nobel laureates on those themes.

Second, we analyzed and built upon the latest cutting-edge Generative AI and Machine Learning practices being built and released contemporaneously by the world-leading AI-ML-Engineering technology companies such as Amazon-AWS to address Quantum Uncertainty, Dynamic Uncertainty and Adversarial Uncertainty Risk Management focus on which our practices have documented lead on the global Risk Management standards such as ISO31000 by over 25-years as published in recent research journal articles.

Third, we continued our thrust on Computer Science & Systems Engineering advancement of ‘Data-Driven’ AI-ML to ‘Network-Centric’ Management of Dynamic Change, Quantum Uncertainty and Complexity as Self-Adaptive Complex Systems & Chaos Engineering pioneer recognized in the global press such as The New York Times and The Wall Street Journal.

AWS Academy Educator Building the Future of AI-Machine Learning Higher Education & Practices with AWS for Workforce Development

Enumerated below are AWS cloud-based resources, available as readily feasible and available solutions to key Students’ Success and Academic Excellence issues posed by our students, faculty, and external partners. These can be offered as: (a) modules integrated within our courses as I did over Fall building and delivering graduate and undergraduate Engineering-Computer Science AI-ML projects-focused courses with associated ‘skill certificates’ as tangible evidence of our mission of workforce development; (b) as standalone ‘skill certificate’ projects-focused courses, labs and practicums for our workforce development; and/or, (c) as separate real-world industrial-applied practitioner projects-focused hands-on workshops and labs for workforce development: as conducive to all of our offered courses across all of our colleges taught by all of our faculty, as well as independently by all of our faculty and students.

A representative small sample from the adoption, application, and advancement upon Amazon and AWS Services and Resources is provided below broadly categorized under four separate categories:

1. Amazon-AWS SkillBuilder Courses, Hands-On Labs, Workshops and Projects
2. AWS Machine Learning University Courses, Hands-On Labs, Workshops and Projects
3. Amazon-AWS Immersion Labs and Workshops on Real Practices and AWS Practitioner Projects
4. AWS Academy Courses, Hands-On Labs, Workshops and Projects

AWS-SAGEMAKER STUDIO LAB – d2l.ai – Notebooks Guide: How to Run d2l.ai Notebooks in Your AWS-SAGEMAKER STUDIO LAB A/c:

Amazon-AWS SkillBuilder Courses, Hands-On Labs, Workshops and Projects: I have used these integrated into the AI-Machine Learning courses built and delivered over recent Fall from more than 600 free hands-on, latest cloud, digital and tech skills-focused courses for workforce development delivered online with digital training by AWS experts. Many of them come with respective skilled competencies certificates asked for by our students, faculty, and external partners. The portfolio of hands-on skills-building applied and experiential project-focused AWS courses can help our students and faculty learn cloud computing based skills for workforce development. Built on related AI-ML, analytics, data science and other hands-on labs-projects skills, these can be put into practice by applying related knowledge for industry-leading certifications as industry credentials demonstrating workforce development. In addition, we can choose from more cloud infrastructure-intensive projects-based labs-workshops at a fraction of the cost compared to on-prem infrastructures with minimal fees for all-you-can-learn individual subscriptions.

AWS Machine Learning University Courses, Hands-On Labs, Workshops and Projects: I also used the AWS Machine Learning University (MLU) Developer courses for building and delivering the Computer Science graduate and undergraduate AI-ML courses over Fall. AWS uses these to train their own developers on the same AWS cloud platforms. Many of them come with respective skilled competencies certificates asked for by our students, faculty, and external partners. I had many of our AI-ML undergraduate and graduate students apply and use these on freely available AI-Machine Learning cloud infrastructure platform Amazon SageMaker StudioLab. SageMaker is the AI-ML ‘workhorse’ for all of AWS AI-Machine Learning as well as all of integrated Data Science and Analytics Could Computing technologies providing the world-class cloud computing scale and turnkey resources for the most heavy-duty industrial and applied projects.

Amazon-AWS Immersion Labs and Workshops on Real Practices and AWS Practitioner Projects: I had integrated these real-world hands-on real practitioner project-based scheduled experiential and applied learning workshops on the latest developments in AI-ML including Generative AI, Large Language Models (LLMs), AIOps, MLOps besides others in our recent AI-ML courses as well. These are scheduled-event based free hands-on workshops for engineers and developers including hard-core engineering skills for building world-leading technologies and technology enabled ventures with offerings also for executives, sales, cloud finance and economics. As an AWS Accredited-Certified Partner, prior to building and teaching AI-ML courses as AWS Academy Educator for the current program, I had achieved AWS Certifications in AI-Machine Learning, Security, Cloud Computing, Cloud Practitioner skills having availed of over 2,000 hours of above AWS Partner training while collaborating with trainers in day-long training sessions as AI-ML expert. These are complemented by substantive industrial-applied large-scale computing and data analytics hands-on labs and workshops, many of them with respective skilled competencies certificates asked for by our students, faculty, and external partners.

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AWS Academy Courses, Hands-On Labs, Workshops and Projects: As shared above, I had the current higher education institution activated in Fall to enable us to join our peers among both public and private universities. We are ready to launch our AWS Academy offerings to enable Success and Academic Excellence of our students by building skilled competencies for workforce development. AWS Academy provides higher education institutions with a free, ready-to-teach cloud computing curriculum that prepares students to pursue industry-recognized credentials for workforce development and in-demand skilled jobs such as expected by our external partners. Its curriculum will help our educators stay at the forefront of cloud, AI-ML and related technologies so that we can equip our students with the skills they need to get hired as demanded by our external partners. We also have the available marketing, branding, and promotional collateral for the specific purpose that can be used to promote the institution among the hundreds of world-leading institutions already executing their Digital Futures on these world-scale AWS Academy platforms. As soon as we are ready to deliver our available and continuously upgraded AWS Academy Courses on all key aspects of Cloud Computing, Data Science, AI-Machine Learning, Data Engineering, and other skills, we can also be listed among the other Active AWS Academy Member Institutions.

AI-ML-Data Governance & Controls: Why ChatGPT-LLMs-Generative AI Cannot Be Trusted: Why We Still Need to Advance R&D on Them

Dr.-Eng.-Prof. Yogesh Malhotra — Fri, 26 Jan 2024 01:01:30 +0000

2023 New York State Cybersecurity Conference, 25th Anniversary Cybersecurity Conference, New York State Capitol, Albany, New York, June 6-7, 2023 ( https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4444658 : Originally Published on SSRN: 2023 New York State Cybersecurity Conference invited presentation: Sequel to: 2022 New York State Cyber Security Conference: Invited Presentation:
How You Can Implement Well-Architected ‘Zero Trust’ Hybrid-Cloud Computing Beyond ‘Lift and Shift’: Cloud-Enabled Digital Innovation at Scale with Infrastructure as Code (IaC), DevSecOps and MLops : https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4131044 : YouTube Video Presentation: https://youtu.be/1gd6mKeHJuw).

Post-Script: To bring much needed objectivity to the current global conversations about AI, we provide scientific and empirical evidence to put to rest many of the extreme myths, both utopian and dystopian, to share the much needed balanced understanding of AI as published in our latest research journal articles as well as presented at the recent four New York State Conferences on Cybersecurity sponsored by the State Governor.

The specific much needed progress to catch up with today's empirical realities necessitates AI and Data Science in all their flavors to catch up with the following original R&D leading practice progressions that we have delineated in our four recent New York State presentations.

AWS-Quantum Valley Building Quantum Minds For Quantum Uncertainty
• AI Automation -> AI Augmentation-Model Risk Management
• AI Cyber Controls -> Counter-Adversarial Command-Control
• Data Management -> Quantum Uncertainty-Complexity
• Data-Driven Architectures -> Event-Driven Architectures
• AI Automation -> AI Augmentation-Model Risk Management

AI-Machine Learning Augmentation and Cybersecurity: Why Smart Minds Using Smart Tools Are Critical for Minimizing Risks, And, What You Can Do About It? 2019 New York State Cyber Security Conference, Albany, NY, June 4 - 5, 2019, Empire State Plaza , Albany, NY

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3399781

• AI Cyber Controls -> Counter-Adversarial Command-Control

C4I-Cyber Command & Control Supremacy: Why It’s More Critical Than AI & Quantum Supremacy & What You Can Do about It? Security in Post-COVID Virtual Era Beyond Data, Models, Algorithms. 2021 New York State Cyber Security Conference, June 8-9, 2021, Empire State Plaza - Albany, NY.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3851807

• Data Management -> Quantum Uncertainty-Complexity

How You Can Implement Well-Architected ‘Zero Trust’ Hybrid-Cloud Computing Beyond ‘Lift and Shift’: Cloud-Enabled Digital Innovation at Scale with Infrastructure as Code (IaC), DevSecOps and MLops, 2022 New York State Cyber Security Conference: Invited Presentations, Albany, New York: https://its.ny.gov/2022-nyscsc.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4131044

• Data-Driven Architectures -> Event-Driven Architectures

AI-Machine Learning-Data Management Governance & Controls Cloud Computing Best Practices: Why ChatGPT-LLMs-Generative AI Cannot Be Trusted: Why We Still Need to Advance R&D on Them: Beyond AI Hype: Advancing Beyond Limitations of ChatGPT, Large Language Models, and Generative AI: CNY Quantum Valley Pentagon-USAF-USSF Ventures Spanning Air-Space-Cyberspace-Outer Space. 2023 New York State Cybersecurity Conference, 25th Anniversary Cybersecurity Conference, New York State Capitol, Albany, New York, June 6-7, 2023.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4444658

Pragmatic Necessity of Bringing Scientific Objectivity to Current AI Debates

AWS-Quantum Valley Building the Future of AI-Quantum Networks: Next 30-Years Beyond Past 30: 4 years before today's 'fashionable' worldwide conversations about Existential Risks posed by AI, we had not already built worldwide recognition of such risks but also built the US Air Force-Air Force Research Lab Ventures AIMLExchange.com and C4I-Cyber.com specifically focused on controlling and mitigating such risks. The latest AI-Science headline discussions are still playing catch-up to where we started about thirty years ago, particularly in the context of Risk Management. Specifically, the latest ISO 31000 Risk Management Standard is yet to catch-up with our last three-decades related AI-Cyber-Risk Management R&D leading worldwide practices on Risk, Uncertainty and Complexity.

The latest global AI hype on Generative AI, Large Language Models, and ChatGPT has even thrown the standard practices of Data Cleaning for Data Integrity, Validity and Reliability out the window by unquestioning reliance on underlying databases such as Wikipedia that are known to have no scientific objectivity and reliability given over-predominant absence of scientific review and refereeing processes that are characteristic of the world-class journal and conference proceedings, as one who has had the privilege of being invited to serve on more than 50 such global top-tier world class STEM research journals and conference proceeding realizes.

The latest Generative AI hype has sacrificed Data Integrity and Reliability and degraded AI to a scientific reliability and validity standard lower than its prior focus on Data Science that had already been struggling to sustain its credibility by shuffling around from Big Data to Small Data to Right Data. Even the prior focus of Data Science with its backward looking focus on Historical Data being used for Prediction of the Future had already made all related research findings questionable as Future is not equal to Past. Given these observations, our R&D published in world's top refereed STEM research journals as well as invited service as STEM editor and referee for world's most esteemed research journals and conferences were founded on departing away from focus on [Historical] Data Management to Managing Emerging Uncertainty and Complexity for Future Business Enterprise Performance in early 1990s. The application of our three-decade R&D in our own global practices leading world's largest governments and enterprises as well as its empirical validation in worldwide adoption by hundreds of other world's largest governments and enterprises lends credence to our scientific R&D.

This fact becomes all the more critical for the latest era of most highly unprecedented Uncertainty and Complexity where world leaders from those leading hi-tech nations to world's largest governments and organizations are attempting to make sense of the latest AI hype. Our latest 2023 New York State Cybersecurity Conference focus in the latest presentation brings the much needed clarity while explaining the deviant human behaviors underlying the latest unscientific Generative AI-hype contradicting established scientific evidence. Specific evidence that we share includes scientific facts about the human-machine systems and relative resilience of humans vs. machines in navigating uncertainty such as based on the actual scientific comparisons of human brains and artificial intelligence machines as well as the foundational science of entropy of human-machine systems that has been overwhelmed by the world's centi-billionaires most highly questionable headline making claims about the untested superiority of artificial intelligence machines particularly in the face of overwhelming real world empirical evidence such as the hyped Self-Driving cars on which more than $100 Billion has been squandered given reliance upon hype pumping the financial stock markets which is now widely self-evident in contrast to rigorous scientific research foundations.

ABSTRACT

Network-Centric Meaning-Driven Human-Centric AI-Cyber Computing Beyond Data-Driven to Event-Driven Architectures for Quantum Uncertainty, 1995-2023:

Building upon the contextual focus of current global worldwide discussions on GPT, ChatGPT, GenAI, Generative AI, Large Language Model - LLMs, we help you advance beyond the ongoing global AI-hype - on both dystopian and utopian extremes - to focus on the latest AI Event-Driven Architectures technical developments such as in AIOps, MLOPs, DevSecOps, Infrastructure As Code, Configuration As Code, Platform As Code, Pipeline As Code for building Cloud Computing AI-Agility and Cyber-Resilience Sustainability, the focus of our last year's 2022 New York State Cybersecurity Conference presentation as AWS Partner. With increasing Digitization of Networks as Code, we help world's global 'Hardware', 'Software' and other 'Computing' providers and practitioners advance beyond legacy models of computing to the latest Cloud-based Networked Computing Utility models. Our 30-year R&D focus on Network-Centric Computing from both Socio-technical and Systems Engineering perspectives spans the schisms presented by Claude Shannon's Information Theory in how AI-ML enabled Information Processing and Sense Making can occur across both Socio-Technical and Computing-Telecom Networks*.

Having already addressed the core issues being debated on mainstream Business, Finance, Technology and other media and resolved them based on 30-years of our AI-Cyber-Network Science-Engineering R&D leading worldwide Digital practices, we also help you advance ahead by 25-years on the global Risk Management standards given that the latest ISO 31000 Risk Management Standard is playing catch up having lagged 25-years behind our R&D leading global Risk Management practices. Based on most thorough R&D over 30-years and most in-depth analysis of current academic, scholarly, policy and practice resources, we provide definitive answers on many issues that have left too many confused with what our analysis shows as extreme dystopian and utopian views. This confusion results from ambiguity between the specific AI technologies and their use, abuse, misuse by the respective human users in diverse contexts, our core R&D focus on Human-Machine Systems Learning, Intelligence and Performance for 30-years ranked for its worldwide impact among AI-Quant Finance Nobel Laureates such as Herbert Simon (ASIS&T and University of Minnesota research impact and citation impact reports, for example) and Black-Scholes, Markowitz, and Sharpe (AACSB Impact of Research Report, for example) with our related research models and methods applied in empirical practice by organizations as diverse as the National Aeronautics and Space Administration (NASA) and Big Banks, for instance.

• Should we be afraid of latest AI such as Generative AI and ban such R&D?

No! Such R&D must continue by dispelling the myths being spun about AI.

• Does AI have the capability or capacity of posing existential risk to humans?

No! But Humans with vested interest in exploiting other Humans do!

• Does AI have intelligence to take over control of humans & destroy them?

No! But Humans with vested interest in exploiting other Humans do!

• What can be done for dispelling the AI myths spun up to make real progress?

We have already been doing so having already advanced beyond such myths with focus on saving all 90% time, cost, & resources by our focus on Science & Scientific R&D driven Zero-Hype practices.
We share our learning and freely accessible R&D leading global practices with all in order to help you advance beyond the AI-Hype in making real progress for the human civilization and humanity at large.
There has been an over-concentration on Shannon's definition of information in terms of uncertainty (a very good definition for the original purposes) with little attempt to understand how MEANING directs a message in a network. This, combined with a concentration on end-points (equilibria) rather than properties of the trajectory (move sequence) in games has led to a very unsatisfactory treatment of the dynamics of organizations. – AI-Genetic Algorithms pioneer Dr. John H. Holland, then at the Santa Fe Institute (personal communication, June 21, 1995) :

Source: 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. (Ranked by the journal as top-ranked article in its usage statistics).

Keywords: Artificial Intelligence, Machine Learning, Data-Driven Architecture, Event-Driven Architecture, Quantum Uncertainty, Network-Centric Computing, Networks as Code, Cloud Networks Engineering, Cloud Cybersecurity Operations, AIOps, MLOPs, DevSecOps, Infrastructure As Code, Configuration As Code