Alex Rhatushnyak

Unleashing the Future with Physics-Informed Neural Networks in Issue 8 of the Data & AI Magazine Imagine blending the precision of physics with the learning power of neural networks. This isn't sci-fi—it's happening now with Physics-Informed Neural Networks (PINNs). Francesco Gadaleta, Ph.D., founder of Amethix Technologies and Intrepid AI, shares his groundbreaking insights in the latest issue of Data & AI Magazine. With career-spanning roles like Chief Data Officer at Abe AI and a key member of Johnson & Johnson's Advanced Analytics Team, Francesco's expertise is as diverse as it is deep. From healthcare to finance, Francesco's work spans multiple industries, proving the limitless potential of AI. But how do physics and neural networks merge to create something revolutionary? Here's a snapshot: - Traditional physics models = precision but complex equations. - Neural networks = data-driven, adaptable, no need for explicit formulas. - PINNs = The best of both worlds, offering unparalleled accuracy and capability. Think of modelling aircraft dynamics or robotic operations—equations and data come together in ways never before possible. Are you curious to know how PINNs are transforming complex problem-solving and pioneering closed-form solutions? Dive deeper into this innovation by subscribing for FREE to Data & AI Magazine on the link below! https://lnkd.in/emYGNEND #DataandAIMagazine #PINNs

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Robust determination of cubic elastic constants via nanoindentation and Bayesian inference https://lnkd.in/gEcRgQJq

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Looking to run end-to-end machine learning workloads without external dependencies? Be sure to check out Census on GitHub! This sample code will show you how to use Intel Distribution of Modin and Intel Extension for Scikit-learn to build and run performant #ML workloads. https://intel.ly/3ygmSFr #MachineLearning #Developer #oneAPI

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If you're running on constrained hardware and/or with no GPU, LLaMA 3.2 1B models with 4-bit quantization work surprisingly well! The model is only 800MB. Here's an easy-to-try example with txtai! Example: https://lnkd.in/etd5w-9Q

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Google's quantum leap in computing power! Google's latest Sycamore quantum processor boasts 70 qubits, a significant jump from the 53 qubits of its 2019 version. This translates to a staggering 241 million times greater processing power! #exponentialgrowth #quantumrevolution Google's Sycamore quantum processor achieved a milestone in demonstrating quantum supremacy. In a specific task, Sycamore completed a calculation in 200 seconds that would take a classical computer 10,000 years. While there's still debate on the exact timeframe, this achievement showcases the immense potential of quantum computers. Google is actively involved in finding real-world applications for quantum computing. They recently launched a $5 million XPRIZE Quantum Applications competition to incentivize the development of practical quantum algorithms. Any ideas? Essentially, they still have no idea what to do with a computer this powerful. Although, I shudder to think what would happen if the NVIDIA Blackwell Superchip bumped into a computer this powerful. The AI singularity could be closer than we think.

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Introduction Unit testing is a foundational practice in software development involving testing individual components of code to verify that they function correctly. By writing unit tests, developers can ident...  #statology

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To improve imaging of neural activity, a new camera chip design allows for optimizing each pixel’s timing. This maximizes the signal-to-noise ratio when tracking a real-time visual indicator of neural voltage. #neuroscience #microscopy #brainscience #brains #MIT

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The parameters of compiler optimizations interact in complex and non-trivial ways, and finding optimal combinations remains an open research challenge. There are several approaches to discovering effective combinations of these optimization parameters. Recently, Michael Canesche (Cadence Design Systems) and Gaurav Verma (Stony Brook University) have had promising results using auto-tuning with coordinate descent. To visualize this process, imagine that each optimization represents a 'dimension' in a search space. The combination of all optimizations and their respective parameters forms a vector in this space. Now, consider that each point in this space is mapped to a real number representing the performance of a kernel (assuming a fixed input). Coordinate descent is a search technique that assumes the performance curve is convex. Based on this assumption, it aims to find the optimal point by varying one optimization parameter at a time. Explore as a Storm, Exploit as a Raindrop: On the Benefit of Fine-Tuning Kernel Schedulers with Coordinate Descent: https://lnkd.in/dgxxfdYR The Droplet Search Algorithm for Kernel Scheduling: https://lnkd.in/dBfQid9z

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LLVM is an open-source project with a pretty large code base. The acme in terms of code quality, considering its size and open-source nature. After all, it's the developers of compiler tools who know best about language features and their proper use. Their top-notch code is always a challenge for our analyzer, and we always accept it with pleasure. Follow the link - https://lnkd.in/eiEj59gm #cpp #compiler #programming #IT

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In this post, Rami Krispin introduces the Multi-Stage build approach for setting up a lightweight dockerized Python development environment:

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MicroFlow is a Rust-based tinyML framework that optimizes AI for microcontrollers, ranging from the 32-bit SAM V71 to 8-bit ATmega328, and outperforms even TensorFlow Lite in terms of memory utilization.

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Python 3.13 is out!  Biggest features are the new interactive REPL, experimental support for turning off GIL, and a JIT compiler. Here are the release notes: https://lnkd.in/g8Z7keqm

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From this part of a Busy Developer's Guide to Quantum Computing you will learn how programming a quantum computer can actually look.

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🚀 Check out the latest Medium post by our student Tianyunxi Yin: "Quantization Fundamentals for Model Compression". Explore the essentials of quantization and learn how it helps in compressing machine learning models for better efficiency without compromising performance. Read more here: https://lnkd.in/gHbbR_J4 #USFMDS #DataScience #MachineLearning #ModelCompression #Quantization

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Learn the basics of nonlinear dimensionality reduction, kernel PCA, and multidimensional scaling by following along Biman Chakraborty's clear, hands-on Python tutorial.

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What are the biggest benefits for engineers of working with 3D Deep Learning?   In conversation with Microsoft’s HPC and AI Specialist Dr. Lukasz Miroslaw, our co-founder and CTO, Théophile Allard explains that for engineers using Neural Concept's Engineering Intelligence platform, speed is only one side of the equation—enhanced performance is the other.   With Engineering Intelligence at the heart of their process, engineers can explore a greater number of design and simulation variants faster while optimising for the best possible performance outcomes.     🔗 Check out the video of Théo and Lukasz in conversation here: https://lnkd.in/eUbNfT8X   #neuralconcept #engineering #3d #deeplearning #microsoft #ai #machinelearning #ev #automotive #aerospace #electronics #renewableenergy #oem #productdesign #engineeringintelligence  

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