AI-ML-DL: Who Said Physical Data Math Needs to Agree with Physics? AI-ML Scientists Get. . .

Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7080180389863985154

Brint.com AI-ML-DL: Who Said Physical Data Math Needs to Agree with Physics? AI-ML Scientists Get Ready for High School Math! https://lnkd.in/gTS5W9jT :
“A fundamental challenge is that the #predictions of #deep-#learning #models trained on #physical #data typically ignore #fundamental #physical #principles.”

Princeton University Presentations: #Math-#Physics of #AI-#ML-#DL-#Quant-#Finance-#Trading: ModelRiskArbitrage.com : https://lnkd.in/gcp_yHe :
#MathIsFragile #UseCarefully: #Beyond #Theory: #Real #Messes:
https://lnkd.in/gB8-BHvb
1. Know Math Underlying Your #AI-#Quantum #Models
2. More Importantly, Know Where That Math #Breaks Down
3. Most Importantly, Apply #Intuition to #GoBeyond Math

FutureOfFinance.org 1993-Current BRINT.com Goldman Sachs J.P. Morgan:

-> Start With #ModelRisk & #ModelRiskManagement

Math is #Beautiful & #Fragile: “#Model, however #beautiful, is an #artifice”:

• Princeton University Presentations in AI-ML Risk Management-Control Systems: #AI, #MachineLearning & #DeepLearning #Risk Management & #Controls: Beyond #Deep #Learning-#Generative #Adversarial #Networks: : https://lnkd.in/e_7sqj4 : ModelRiskArbitrage.com .

-> Know #Physical #Reality of #Math: #HighSchool Texts
#Mathematics for the #Nonmathematician, 1967: https://lnkd.in/g74DzQUn
• #Calculus: An #Intuitive and #Physical Approach: https://lnkd.in/gQXYsvrd
• Mathematics: The Loss of #Certainty, 1980: https://lnkd.in/g5KJuadN
• Mathematics and the Search for #Knowledge, 1985:
https://lnkd.in/gkb-r9ng

#CommonSense Beyond #Physics-#Math: #AI-#ML-#DL Don’t Have.

“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.”

“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.”

“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.”

-> Then Advance Beyond: #ModelRiskArbitrage #QuantumUncertainty:
https://lnkd.in/eR37Y89U:
SSRN: 123 Top-10 R&D Rankings: R&D Impact Among #AI-#Quant #Finance Nobel Laureates:
https://lnkd.in/ec99Zkd :

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Amazon researchers draw inspiration from finite-volume methods and adapt neural operators to enforce conservation laws and boundary conditions in deep-learning models of physical systems.
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Global Post AI-Quantum Finance & Trading Networks Pioneer Dr.-Eng.-Prof. Yogesh Malhotra is the “Singular Post AI-Quantum Pioneer” identified by Grok AI with R&D impact recognized among Artificial Intelligence (AI) and Quantitative Finance Nobel Laureates. As MIT-Princeton AI-ML-Cyber-Crypto-Quantum Finance & Trading and FinTech-Crypto Faculty-Industry Expert, and U.S. and Global Hedge Funds Advisory & Venture Capital CEO-CTO Teams Mentor, he has pioneered Silicon Valley-Wall Street-Pentagon Digital CEO-CTO Practices, Technologies, and Networks from world’s first-foremost-largest Global Digital Transformation Networks to New York State IDEA Award recognized Pentagon-USAF MVP Global Post AI-Quantum Networks pioneering Future of Finance and Trading practices as Trillion-Dollar Wall Street Hedge Funds and Investment Banks leader.