If you could have dinner w/any computer scientist, past or present, who would it be?
RESEARCH
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Optimization Engines Learn Physical Constraints Beyond Mathematics
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Your optimization engine just learned the difference between mathematically possible and physically realistic.
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Top AI Papers: Agents, LLMs, and Coding Automation
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The Top AI Papers of the Week (April 6 – 12) – Memento
– Neural Computers
– The Universal Verifier
– Agent Skills in the Wild
– Memory Intelligence Agent (MIA)
– Single-Agent vs Multi-Agent LLMs
– Scaling Coding Agents via Atomic Skills Read on for more: -
Deterministic AI Creativity Respects Thermodynamic Laws
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The deterministic foundation ensures AI creativity never violates the laws of thermodynamics or material science.
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Genetic editing breakthrough transforms rare disease treatment paradigm
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The turning point for rare diseases, which affect >300 million people around the world.
A call to get rid of its many structural obstacles, to consider it as molecular surgery unlike drug treatments
gift link: https://
nytimes.com/2026/04/09/opi
nion/genetic-editing-diseases-health-care.html?unlocked_article_code=1.aVA._Ngc.rcD9QiLWrfIM&smid=url-share
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Scientists Hijacked Nvidia CUDA for Deep Learning Research
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Nvidia did not "sell its gaming architecture to a handful of scientists" A handful of scientists such as Patrice Simard et MSR, Andrew Ng, and later Geoff Hinton, hijacked CUDA and implemented convnets and backprop on them. It took several years for Nvidia to realize there was
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Linear Algebra for Artificial Intelligence and Machine Learning
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Linear Algebra for Artificial Intelligence! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/LA-for-Al-Intelligen…
→ View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC
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Training Neural Networks with Optical Backpropagation Technology
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Training Neural Network on Optical Backpropagation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Linnainmaa, S. I. (1976). Taylor expansion of the accumulated rounding error. BIT Numerical Mathematics, 16(2), 146–160. Published June 1976. Retrieved March 10, 2025, from doi.org/10.1007/BF01931367 Lvovsky, A. I., Guo, X., & Spall, J. (2025). Training neural networks with end-to-end optical backpropagation. Advanced Photonics, 7(1), 016004. Published February 4, 2025. Retrieved March 10, 2025, from doi.org/10.1117/1.AP.7.1.016… Nielsen, M. A. (2015). How the backpropagation algorithm works. In Neural networks and deep learning. Published 2015. Retrieved March 10, 2025, from neuralnetworksanddeeplearnin… Spall, J., Guo, X., & Lvovsky, A. I. (2025). The optical implementation of backpropagation (Oxford, Lumai). Semiconductor Engineering. Published February 4, 2025. Retrieved March 10, 2025, from semiengineering.com/the-opti…
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Tacotron: TensorFlow Implementation for Machine Learning
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Tacotron: Tensorflow Implementation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Tecotron [Translated from EN to English]
→ View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC
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Bayesian Neural Networks: Big Data and Machine Learning Guide
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Bayesian Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Bayesian-Neural-Nets
→ View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC