Advanced mathematics

📓 book

@programming_books_it

New Russian Radar Can Spot Tiniest Drones
https://sputnikglobe.com/20240622/new-russian-radar-can-spot-tiniest-drones-1119060515.html

Extract Musical Notes from Audio in Python with FFT - YouTube
https://www.youtube.com/watch?v=rj9NOiFLxWA

🤖 Musculature for Robots

A new type of “muscle” for robots has been developed, resembling human musculature. These rubber “muscles” can move efficiently thanks to individual servo motors, and the designers have also created special rotating shafts to facilitate their movement.

🤖@science #robots

🤖 Realistic Robo-Hand by Clone Robotics 🤖

Clone Robotics has developed a highly realistic robotic hand powered by electricity and heat. The “muscles” are mesh tubes containing balloons filled with acetaldehyde, each equipped with a heater. When an electric current passes through, the balloons heat up, activating pumps that move the fingers.

@science

Machine Learning: The Basics

📚 Book

@datascienceiot

76-page survey paper on Prompting Techniques ✨

Explores structured understanding and taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities.

📌 The paper focuses on discrete prefix prompts rather than cloze prompts, because prefix prompts are widely used with modern LLM architectures like decoder-only models. It excludes soft prompts and techniques using gradient-based updates.

📌 The paper identifies 58 text-based prompting techniques broken into 6 major categories:

1) In-Context Learning (ICL) - learning from exemplars/instructions in the prompt

2) Zero-Shot - prompting without exemplars

3) Thought Generation - prompting the LLM to articulate reasoning

4) Decomposition - breaking down complex problems

5) Ensembling - using multiple prompts and aggregating outputs

6) Self-Criticism - having the LLM critique its own outputs

📌 For ICL, it discusses key design decisions like exemplar quantity, ordering, label quality, format, and similarity that critically influence output quality. It also covers ICL techniques like K-Nearest Neighbor exemplar selection.

📌 Extends the taxonomy to multilingual prompts, discussing techniques like translate-first prompting and cross-lingual ICL. It also covers multimodal prompts spanning image, audio, video, segmentation, and 3D modalities.

📌 More complex techniques like agents that access external tools, code generation, and retrieval augmented generation are also taxonomized. Evaluation techniques using LLMs are discussed.

📌 Prompting issues like security (prompt hacking), overconfidence, biases, and ambiguity are highlighted. Two case studies - benchmarking techniques on MMLU and an entrapment detection prompt engineering exercise - are presented.

https://arxiv.org/abs/2406.06608

@opendatascience

Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

📚 Book

@datascienceiot

Forecasting in Economics, Business, Finance and Beyond

📚 Book

@datascienceiot

Fundamentals of Data Engineering

📌 Book
📌Download

@datascienceiot

Neural Networks and Deep Learning

📓 book

@datascienceiot

Collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics

📖 Github

@datascienceiot

🌆 AI system to generate 3D worlds from a single image. Check out some early results on our site, where you can interact with our scenes directly in the browser!

World Labs aims to address the challenges many creators face with existing genAI models: a lack of control and consistency. Given an input image, our system estimates 3D geometry, fills in unseen parts of the scene, invents new content so you can turn around, and generalizes to a wide variety of scene types and artistic styles.

Most generative models predict pixels. Predicting a 3D scene instead has many benefits: the scene won’t change if you look away and come back, and it obeys the basic physical rules of 3D geometry. The simplest way to visualize the 3D scene is a depth map, where each pixel is colored by its distance to the camera.

https://worldlabs.ai/blog

@opendatascience

Biomimetic Robots: The Future Is Here, Straight Out of “Westworld”

Meet Clone Alpha, the world’s first humanoid robot with biomimetic architecture. Equipped with synthetic organs and artificial muscles powered by water, this innovation brings us a step closer to the sci-fi visions of “Westworld.”

🔬 What is Biomimetic Architecture?
The term “biomimetic” refers to mimicking human biological systems. Here’s what makes Clone Alpha revolutionary:
• Artificial Muscles (Myofibers): These water-powered muscles contract by up to 30% in less than 50 milliseconds, generating a kilogram of force from just 3 grams of material.
• Polymer Skeleton: Mimicking the human body with 206 bone-like components, connected by joints and ligaments, all supported by a hydraulic vascular system driven by a compact 0.5-kilowatt pump.
• Cybernetic Nervous System: Using “Cybernet” technology, Clone Alpha features a visual-motor system with four depth cameras for precise environmental tracking.

These features enable unmatched speed, precision, and agility, making the android’s movements indistinguishable from those of humans.

🛠️ Behind the Scenes
While Clone Alpha remains classified and has not yet been unveiled to the public, some details have been leaked:
• The biomimetic hand has been showcased, demonstrating dexterity by lifting a 7-kilogram dumbbell with ease (Watch the demo here).
• Orders are already being taken for the first 300 units, set to be delivered in early 2025. Pricing details remain speculative.

Why It Matters
A year ago, the idea of androids with such biomimetic architecture seemed like Hollywood fiction. Today, it’s on the brink of becoming reality. If Clone Alpha lives up to its promises, we could see projects akin to “Westworld” emerging as early as 2025.

Want to explore more? Check out Clone Robotics’ official pages:
• Clone Robotics Hand
• Clone Robotics Android

The countdown to the android revolution has begun! 🦾

#science #robotics

They’re not ants—they’re tiny robots! Scientists in South Korea have developed entire swarms of miniature magnetic robots that work together like ants, even lifting objects several times their own size.

Each microrobot measures around 600 micrometers and is made of an epoxy body embedded with neodymium-iron-boron (NdFeB) particles, allowing the swarm to be guided by magnetic fields. Thanks to this setup, these robots can accomplish complex tasks in challenging environments—such as performing minimally invasive procedures on clogged arteries (as demonstrated in the video).

“Although the research results are promising, the swarms will need a higher degree of autonomy before they are ready for real-world applications. Magnetic microrobot swarms require external magnetic control and cannot autonomously navigate complicated or enclosed spaces like actual arteries. Future studies will focus on enhancing microrobot swarm autonomy, for example by controlling their movements and trajectories with real-time feedback,” says Professor Jeon Jae Wi from the Department of Organic and Nano Engineering at Hanyang University (Seoul, South Korea).

#science #robots

From Junior to Senior Developer with ChatGPT

ChatGPT and similar AI tools can significantly aid developers by analyzing code, suggesting improvements, writing tests, and more. Their effectiveness depends on clear, specific prompts. While they are not designed to solve new or niche problems independently, they excel in tasks like code contextualization, reviews, and documentation. Tools like GitHub Copilot leverage additional context to provide more relevant suggestions, bridging the gap between junior and senior developer roles.

🔗 https://www.codemotion.com/magazine/ai-ml/from-junior-to-senior-developer-with-chatgpt