Could Machine Learning Practitioners Prove Deep Math Conjectures?
Many of us have solid foundations in math or have an interest in learning more, and are passionate about solving difficult problems during our free time. Of course, most of us are not professional mathematicians, but we may bring some value to help solve some of the most challenging mathematical conjectures, especially the ones that can be stated in rather simple words. In my opinion, the less math-trained you are (up to some extent), the more likely you could come up with original, creative solutions. Not that we could end up proving the Riemann hypothesis or other problems of the same caliber and popularity: the short answer is no. But we might think of a different path, a potential new approach to tackle these problems, and discover new theories, models and techniques along the way, some applicable to data analysis and real business problems. And sharing our ideas with professional mathematicians could have benefits for them and for us. Working on these problems during our leisure time could also benefit our machine learning career, if anything. In this article, I elaborate on these various points.
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Matemático Autodidacta
6moA Non-Trivial Zero. y counterexample. Demostration: If: σ = 0.99970141973107 R = i(-0.2443504425376) σ' = -0.00029858026893 N = i(-0.2443504425376) When: s = [(σ + R) / ( σ' + N)] Then: s It is a non-trivial Zero. And it is also a couterexample to: Reiman'n Hypothesis. Given the: ζ(s) = 0 When: t = σ + R t' = σ' + N Them: s = t / t' When: σ ≠ 1/2 σ' ≠ 1/2 Then: Reiman'n Hypothesis It is ambiguous. Since the condition is sufficient but not necessary. Then: Is it True or false...? Mathematician: Rodolfo Nieves
Generative ai | ML/NLP | Python
10moI found this article intriguing as it highlights the potential for non and professional mathematicians to contribute to solving challenging problems. The idea that less math-trained individuals could offer fresh perspectives is thought-provoking. I'm curious about how such collaborations between amateurs and professionals could be facilitated effectively.
Sr. Principal/Grp Product Leader - Strategy & Transformation, Data & AI-ML/LLM-GenAI Ops, Multi-Agent AI, Advanced Analytics Technologies | Product-Services Delivery | Research Scientific Officer | Professor of Practice.
3yVincent Granville: Just catching-up a bit here on a previous edition (ML Practitioners & Conjectures) that I bookmarked. The answer is emphatically “No”! Many ML “practitioners” (with those fancy certifications) that I have encountered on various projects, could not even interpret the equations/algorithms they select/use or explain the dynamics & analyses underlying their data, models, & outcomes, let-alone prove the Conjectures. However, they seemed very good at using the AutoML, ML software & tool-kit and applying the tool to one particular “use-case”, but not others. Is this current approach (to training analysts & deploying ML) sub-serving to a canned compartmentalization of ML analytics based on over-specialized use-cases?
Étudiant à Lycée Moderne Azaguie
3yVery good
Enterprise Head of Analytics & AI @McKesson | World 50 | American Society for AI
3yWow two of my absolutely favorite topics, conjectures are such fun - easy to interpret yet hard to prove. Now that ML could prove them (if at all), that may take some of the fun away :)