#PyTorch

[govindhtech]

What Is Generative Physical AI? Why It Is Important?

What is Physical AI?

Autonomous robots can see, comprehend, and carry out intricate tasks in the actual (physical) environment with to physical artificial intelligence. Because of its capacity to produce ideas and actions to carry out, it is also sometimes referred to as “Generative physical AI.”

How Does Physical AI Work?

Models of generative AI Massive volumes of text and picture data, mostly from the Internet, are used to train huge language models like GPT and Llama. Although these AIs are very good at creating human language and abstract ideas, their understanding of the physical world and its laws is still somewhat restricted.

Current generative AI is expanded by Generative physical AI, which comprehends the spatial linkages and physical behavior of the three-dimensional environment in which the all inhabit. During the AI training process, this is accomplished by supplying extra data that includes details about the spatial connections and physical laws of the actual world.

Highly realistic computer simulations are used to create the 3D training data, which doubles as an AI training ground and data source.

A digital doppelganger of a location, such a factory, is the first step in physically-based data creation. Sensors and self-governing devices, such as robots, are introduced into this virtual environment. The sensors record different interactions, such as rigid body dynamics like movement and collisions or how light interacts in an environment, and simulations that replicate real-world situations are run.

What Function Does Reinforcement Learning Serve in Physical AI?

Reinforcement learning trains autonomous robots to perform in the real world by teaching them skills in a simulated environment. Through hundreds or even millions of trial-and-error, it enables self-governing robots to acquire abilities in a safe and efficient manner.

By rewarding a physical AI model for doing desirable activities in the simulation, this learning approach helps the model continually adapt and become better. Autonomous robots gradually learn to respond correctly to novel circumstances and unanticipated obstacles via repeated reinforcement learning, readying them for real-world operations.

An autonomous machine may eventually acquire complex fine motor abilities required for practical tasks like packing boxes neatly, assisting in the construction of automobiles, or independently navigating settings.

Why is Physical AI Important?

Autonomous robots used to be unable to detect and comprehend their surroundings. However, Generative physical AI enables the construction and training of robots that can naturally interact with and adapt to their real-world environment.

Teams require strong, physics-based simulations that provide a secure, regulated setting for training autonomous machines in order to develop physical AI. This improves accessibility and utility in real-world applications by facilitating more natural interactions between people and machines, in addition to increasing the efficiency and accuracy of robots in carrying out complicated tasks.

Every business will undergo a transformation as Generative physical AI opens up new possibilities. For instance:

Robots: With physical AI, robots show notable improvements in their operating skills in a range of environments.

Using direct input from onboard sensors, autonomous mobile robots (AMRs) in warehouses are able to traverse complicated settings and avoid impediments, including people.

Depending on how an item is positioned on a conveyor belt, manipulators may modify their grabbing position and strength, demonstrating both fine and gross motor abilities according to the object type.

This method helps surgical robots learn complex activities like stitching and threading needles, demonstrating the accuracy and versatility of Generative physical AI in teaching robots for particular tasks.

Autonomous Vehicles (AVs): AVs can make wise judgments in a variety of settings, from wide highways to metropolitan cityscapes, by using sensors to sense and comprehend their environment. By exposing AVs to physical AI, they may better identify people, react to traffic or weather, and change lanes on their own, efficiently adjusting to a variety of unforeseen situations.

Smart Spaces: Large interior areas like factories and warehouses, where everyday operations include a constant flow of people, cars, and robots, are becoming safer and more functional with to physical artificial intelligence. By monitoring several things and actions inside these areas, teams may improve dynamic route planning and maximize operational efficiency with the use of fixed cameras and sophisticated computer vision models. Additionally, they effectively see and comprehend large-scale, complicated settings, putting human safety first.

How Can You Get Started With Physical AI?

Using Generative physical AI to create the next generation of autonomous devices requires a coordinated effort from many specialized computers:

Construct a virtual 3D environment: A high-fidelity, physically based virtual environment is needed to reflect the actual world and provide synthetic data essential for training physical AI. In order to create these 3D worlds, developers can simply include RTX rendering and Universal Scene Description (OpenUSD) into their current software tools and simulation processes using the NVIDIA Omniverse platform of APIs, SDKs, and services.

NVIDIA OVX systems support this environment: Large-scale sceneries or data that are required for simulation or model training are also captured in this stage. fVDB, an extension of PyTorch that enables deep learning operations on large-scale 3D data, is a significant technical advancement that has made it possible for effective AI model training and inference with rich 3D datasets. It effectively represents features.

Create synthetic data: Custom synthetic data generation (SDG) pipelines may be constructed using the Omniverse Replicator SDK. Domain randomization is one of Replicator’s built-in features that lets you change a lot of the physical aspects of a 3D simulation, including lighting, position, size, texture, materials, and much more. The resulting pictures may also be further enhanced by using diffusion models with ControlNet.

Train and validate: In addition to pretrained computer vision models available on NVIDIA NGC, the NVIDIA DGX platform, a fully integrated hardware and software AI platform, may be utilized with physically based data to train or fine-tune AI models using frameworks like TensorFlow, PyTorch, or NVIDIA TAO. After training, reference apps such as NVIDIA Isaac Sim may be used to test the model and its software stack in simulation. Additionally, developers may use open-source frameworks like Isaac Lab to use reinforcement learning to improve the robot’s abilities.

In order to power a physical autonomous machine, such a humanoid robot or industrial automation system, the optimized stack may now be installed on the NVIDIA Jetson Orin and, eventually, the next-generation Jetson Thor robotics supercomputer.

Read more on govindhtech.com

[limbicnation]

spaceship²€

[freddynossa]

Plataformas de Aprendizaje Automático: Las Herramientas que Impulsan la Revolución de la IA

  El aprendizaje automático (Machine Learning) se ha convertido en uno de los campos más dinámicos y transformadores de la tecnología moderna. Detrás de cada avance en inteligencia artificial, desde el reconocimiento facial hasta los vehículos autónomos, se encuentran potentes plataformas de software que permiten a desarrolladores e investigadores crear, entrenar y desplegar modelos de IA cada…

[ezyang]

Ed's parental leave: Week 2 - TTS

I played around with compiling/exporting some TTS models, specifically Kokoro and Zonos. Zonos I got to export successfully, see https://github.com/Zyphra/Zonos/pull/57. I did this in a series of public livestreams, which I also recorded and will eventually upload to YouTube. This produced some juicy guidance for the export team at Meta. It also made me realize that we really need to update our benchmark suite: it would be great to have a separate uv venv for every benchmark we want to run, this would really turbocharge the benchmark process.

[negativemind]

uvでWindows11のPython環境を管理する

以前は、WindowsにPythonを公式インストーラーで直接インストールしていたけど、 https://blog.negativemind.com/2019/03/06/python-pytorch-on-windows-10-with-visual-studio-code/ 次々と公開される研究の実装を動かして遊ぼうとすると、結局Pythonバージョンやパッケージバージョンの依存関係地獄にハマるので、プロジェクトごとに仮想環境を用意して切り分けることになる。 そんな流れで、動作が高速だと流行り(？)のuvという管理ツールを使ってみようかと↓ uv Rustで書かれた非常に高速なPythonパッケージ・プロジェクトマネージャーです。 ウォームキャッシュを使用したTrioの依存関係インストール速度の比較 ハイライト 🚀 pip, pip-tools, pipx,…

[machinelearningsite]

Gradient Boosting explained: How to Make Your Machine Learning Model Supercharged using XGBoost

Ever wondered what happens when you mix XGBoost's power with PyTorch's deep learning magic? Learn how combining these two can level up your models, with XGBoost feeding predictions to PyTorch for a performance boost.

Ever felt like your model’s nearly perfect but needs that little extra “boost”? Your model is not too bad to be thrown away in trash but also not good enough to get a green signal for deployment. I mean, you can just deploy it anyway if it is the last day of your internship. But, for those you still like to keep their jobs, I’ve got exactly what you need for your “almost good” machine learning…

[ingoampt]

Comparing TensorFlow (Keras), PyTorch, & MLX - Day 46

  Comparing Deep Learning on TensorFlow (Keras), PyTorch, and Apple’s MLX Deep learning frameworks such as TensorFlow (Keras), PyTorch, and Apple’s MLX offer powerful tools to build and train machine learning models. Despite solving similar problems, these frameworks have different philosophies, APIs, and optimizations under the hood. In this post, we will examine how the same model is…

[2ribu]

Evolusi Framework AI: Alat Terbaru untuk Pengembangan Model AI di 2025

Kecerdasan buatan (AI) telah menjadi salah satu bidang yang paling berkembang pesat dalam beberapa tahun terakhir. Pada tahun 2025, teknologi AI diperkirakan akan semakin maju, terutama dengan adanya berbagai alat dan framework baru yang memungkinkan pengembang untuk menciptakan model AI yang lebih canggih dan efisien. Framework AI adalah sekumpulan pustaka perangkat lunak dan alat yang digunakan…

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

🚀Motivation comes from the sense of longing something or someone. May it be in terms of money, affluence or to woo someone 😇

🌟Start asking yourself Questions like:

📍Are you happy with your current situation? Is this the best that you can do?

Question this to yourself whenever you are weary.

If the answer to the above question is yes, then set new goals. Raise your bar.

But if you have the answer as No, then here are some things that you can do.

1. Focus on what you want more. There has to be something that you would want far more than others. Set that as your target.

2. Make it fun. Believe me you don’t want to do what you dont like.

3. Treat yourself with every step closer to your goal.

4. Fill yourself with a positive attitude. Always hope for better for that is one thing that gives us strength to move forward.

5. Once achieved your goal, set a new target.

The most important thing in life is moving forward; doing things that we haven’t. The thrill of the unknown and variety of possibilities of life that you can uncover will always keep you motivated. 🙏🏻✨🥰

[yourtechdietblog]

Unleash the Power of Deep Learning with PyTorch

PyTorch is revolutionizing the world of AI and machine learning with its flexibility, dynamic computational graphs, and GPU acceleration. Whether you're starting your deep learning journey or refining your expertise, PyTorch provides the perfect platform.

✨ Highlights from Our Comprehensive Guide:

What is PyTorch?: A dynamic framework by Facebook's AI Research Lab (FAIR) designed for building deep learning models.

Easy Installation: Get started with just: pip install torch

Tensors Simplified: The backbone of PyTorch, with GPU acceleration for faster computation.

Building Neural Networks: Use torch.nn to create and train custom architectures.

Autograd: Simplify gradient calculations for backpropagation.

Advanced Libraries: Explore TorchVision for vision and TorchText for NLP tasks.

🚀 Why PyTorch? Its user-friendly design, strong community support, and advanced functionalities make it ideal for research and development. From training models to saving and deploying them, PyTorch empowers you at every step.

[justposting1]

Top AI Tools to Start Your Training in 2024

Empower Your AI Journey with Beginner-Friendly Platforms Like TensorFlow, PyTorch, and Google Colab The rapid advancements in artificial intelligence (AI) have transformed the way we work, live, and learn. For aspiring AI enthusiasts, diving into this exciting field requires a combination of theoretical understanding and hands-on experience. Fortunately, the right tools can make the learning…

[usaii]

Pytorch Vs Tensorflow? How To Pick The Best Deep-Learning Frameworks | Infographic | USAII®

Elaborate take on PyTorch and TensorFlow to build a frictionless AI career. Make the most of the top AI ML certifications to understand core deep learning frameworks.

Read more: https://shorturl.at/Odd53

Deep learning library, deep learning frameworks, TensorFlow, PyTorch developers, AI and ML engineer, AI career, Top AI ML Certifications, Machine Learning Certification, AI skills