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Model Compression: Make Your Machine Learning Models Lighter and Faster

Model compression has become essential due to the increasing size of models like LLMs, and this article explores four key techniques: pruning, quantization, low-rank factorization, and knowledge distillation.
Pruning involves removing less important weights from a network, either randomly or based on specific criteria, to make the model smaller.
Quantization reduces the precision of parameters by converting high-precision values to lower-precision formats, such as 16-bit floating-point or 8-bit integers, leading to memory savings.
Low-rank factorization exploits redundancy in weight matrices to represent them in a lower-dimensional space, reducing the number of parameters and enhancing efficiency.
Knowledge distillation transfers knowledge from a complex 'teacher' model to a smaller 'student' model to mimic the behavior and performance of the teacher, enabling efficient learning.
Each technique offers unique advantages and can be implemented in PyTorch with specific procedures and considerations for application.
The article also touches on advanced concepts like the Lottery Ticket Hypothesis in pruning and LoRA for efficient adaptation of large language models during fine-tuning.
Overall, model compression is crucial for deploying efficient machine learning models, and combining multiple techniques can further enhance model performance and deployment.
Experimenting with these methods and customizing solutions can lead to creative approaches in enhancing model efficiency and deployment.
The article provides code snippets and encourages readers to explore the GitHub repository for in-depth comparisons and implementation of compression methods.
Understanding and mastering model compression techniques is vital for data scientists and machine learning practitioners working with large models in various applications.

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Towards Data Science

ACP: The Internet Protocol for AI Agents

ACP (Agent Communication Protocol) enables AI agents to collaborate across teams, frameworks, and organizations, fostering interoperability and scalability.
It is an open-source standard facilitating communication among AI agents, with the latest version released recently.
Key features of ACP include REST-based communication, no requirement for specialized SDKs, and offline discovery.
ACP supports asynchronous communication by default, with synchronous requests also being supported.
ACP enhances agent orchestration across various architectures while not managing workflows or deployments.
Challenges addressed by ACP include framework diversity, custom integrations, development scalability, and cross-organization considerations.
A real-world example showcases how ACP facilitates seamless communication between manufacturing and logistics agents across organizations.
Creating an ACP-compatible agent is simplified, requiring minimal code to make it discoverable, process requests, and integrate with other ACP systems.
Comparison with other protocols like MCP and A2A highlights ACP's emphasis on agent-to-agent communication, flexibility, and vendor-neutrality.
The roadmap for ACP focuses on areas such as identity federation, access delegation, multi-registry support, agent sharing, and deployment simplification.
The development of ACP encourages community contributions to shape the standard for improved agent interoperability.

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Towards Data Science

435

The Dangers of Deceptive Data Part 2–Base Proportions and Bad Statistics

The article is a follow-up to a previous piece on deceptive data and explores statistical concepts leading to data misinterpretation.
It emphasizes the distinction between correlation and causation, highlighting the need for controlled trials to establish causation.
An example involving cigarette consumption and life expectancy illustrates the correlation vs. causation fallacy.
The base rate fallacy is discussed using an example from a medical school scenario to demonstrate the importance of base proportions in statistics.
The article mentions the Datasaurus Dozen dataset, showing how summary statistics alone may not reveal the full picture of the data.
It stresses the significance of understanding uncertainty and explains how error bars represent uncertainty, not errors in data.
The article cautions against mistrusting correct data due to misinterpretation of uncertainty and the societal implications of such misunderstandings.
Key takeaways include being cautious of deceptive data, considering base rates, exploring data beyond summary statistics, and interpreting uncertainty accurately.
Understanding these concepts equips individuals to approach data science problems effectively and make informed decisions.
The article concludes by encouraging readers to stay vigilant against deceptive data and misinformation.

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Engadget

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How to watch the Android Show ahead of Google I/O 2025

Google's annual I/O developer conference is approaching on May 20, with two major events: The Android Show: I/O Edition on May 13 and I/O 2025 on May 20.
The Android Show: I/O Edition will be live-streamed on Google's Android YouTube channel on May 13 at 1PM ET / 10AM PT, giving insights into potential updates for Android 16.
I/O 2025 starts with Google's keynote on May 20 at 1PM ET, featuring updates on Android, Search, Google Workspace, Gemini, Android XR news, and experimental projects like Project Astra.
Both events can be watched on Google's YouTube channel or via live coverage on Engadget, with an emphasis on Material 3 Expressive and Gemini updates.

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Medium

240

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Why Fundamentals Matter More Than Ever in the AI Era

In the AI era, there is a temptation to skip learning fundamentals due to the ease of using AI-generated code.
Relying too much on AI can lead to communication issues, mysterious bugs, and project instability.
Fundamental building blocks are essential to prevent teams from crumbling under pressure and being stuck without AI.
AI coding helpers like GitHub Copilot can be beneficial, but understanding the basics is crucial for leadership and handling unexpected challenges.

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Medium

Understanding JavaScript Scope: The Magic Behind Variable Visibility

JavaScript scope determines where variables and functions are accessible, with some having all-access privileges while others are limited to specific functions or blocks.
There are three main types of scope in JavaScript: global scope, function scope, and block scope.
Scope follows a chain in JavaScript, where if a variable is not found in the immediate scope, it searches upward until it reaches the global scope.
Closures in JavaScript allow inner functions to 'remember' variables even after their parent function has finished executing, showcasing the power of closures in maintaining variable visibility.

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Tech Radar

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Apple will reportedly take on Ray-Ban Meta glasses in 2027

Apple is reportedly developing a new chip to power smart glasses, expected to be ready by 2027.
The upcoming smart glasses from Apple are likely to rival Ray-Ban Meta glasses.
The company is also still working on a more advanced pair of smart glasses.
Apple's focus on smart glasses with the new chip and potential competition with Meta suggests continued innovation in the wearables market.

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Towards Data Science

125

The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help

AutoML simplifies machine learning by automating modeling processes, but it can lead to issues like hidden architectural risks, lack of visibility, and system design problems.
AutoML tools make it easy to deploy models without writing code, but they can result in unintended consequences when critical issues arise.
The lack of transparency and oversight in AutoML pipelines can cause subtle errors in behavior and hinder debugging efforts.
Traditional ML pipelines involve intentional decisions by data scientists, which are visible and debuggable, unlike AutoML systems that bury decisions in opaque structures.
AutoML platforms often disregard MLOps best practices like versioning, reproducibility, and validation gates, leading to potential infrastructural violations.
AutoML may encourage score-chasing over validation, where experimentation is prioritized without rigorous testing and model understanding, leading to deployment of flawed models.
Issues like lack of observability in AutoML systems can cause monitoring gaps, impacting critical functionalities like healthcare, automation, and fraud prevention.
While AutoML can be effective when properly scoped and governed, it requires version control, data verification, and continuous monitoring for long-term reliability.
The shadow side of AutoML lies in its tendency to create systems lacking accountability, reproducibility, and monitoring, highlighting the importance of human-governed architecture.
AutoML should be viewed as a component rather than a standalone solution, emphasizing the need for control and oversight in machine learning workflows.

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Dev

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How to make money from web3

1. Crypto Trading involves buying and selling cryptocurrencies like Bitcoin and Ethereum through platforms like Binance and KuCoin.
2. Airdrops offer free tokens to early blockchain platform users, requiring beginner-level skills and early interaction with projects such as Arbitrum and Optimism.
3. Staking allows users to lock crypto to secure networks and earn rewards, with examples including ETH staking and Solana staking for beginners.
4. Building or launching web3 projects like dApps, tokens, NFT projects, or protocols can be profitable for advanced individuals through token sales and transaction fees.

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Dev

440

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Transform Your Website into a Mobile App with AI: A Step-by-Step Guide

Converting your website into a mobile app can significantly boost engagement, user experience, and conversions in today's mobile-first world.
AI-powered tools have made this transformation accessible, affordable, and requiring minimal technical knowledge.
Benefits include faster loading, smoother navigation, offline access, improved engagement, higher conversion rates, and powerful marketing capabilities.
Platforms like Twinr, Instance, and Appy Pie leverage AI for website-to-app conversion, offering various features and customization options.
The step-by-step process involves evaluating your website, choosing the right AI tool, creating the app foundation, customizing the interface, adding mobile-specific features, testing thoroughly, preparing for app store submission, and launching and promoting.
Real-world success stories include H&M, Calendly, and Canva successfully transitioning websites into thriving mobile apps.
Challenges like performance issues, feature parity, user adoption, and maintenance burden can be addressed with proper strategies.
Future-proof your mobile app by focusing on regular updates, user feedback, performance monitoring, and staying current with platform changes.
Transforming your website into a mobile app using AI tools is a powerful way to enhance your digital presence and user engagement.
By taking advantage of tools like Instance, Twinr, or Appy Pie, businesses can make this transition successfully and cater to the growing mobile app market.

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Tech Radar

198

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Pinterest's new AI tools help you shop by visuals and vibes

Pinterest has introduced new AI tools for visual search to explore fashion by vibe, mood, or aesthetic.
Users can now click on highlighted outfit pieces in Pins to find similar items with descriptive terms provided by Pinterest.
The AI tools aim to curate personalized shopping ideas using generative AI and Visual Language Models.
Pinterest plans to expand these AI features beyond women's fashion, including a refinement bar for tweaking results and starting visual searches directly from the app.

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11 Likes

Medium

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I Got My First Backend Job with Java — Here’s What I Wish I Knew

Lessons learned by a new backend developer working with Java.
Mastering Java's syntax is not enough, understanding its ecosystem and philosophy is crucial for backend development.
Java is robust, enterprise-grade language designed for scalability, reliability, and maintainability.
Building systems with Java requires handling real-world complexity.

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5 Likes

Medium

229

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The Death of Bloatware: Why Lean Tools Are the Future

Companies are expanding their platforms and adding new features out of fear of being labeled as 'single-feature' products that can be replaced easily.
This trend has led to bloatware, making products vulnerable rather than stronger, as it invites leaner competitors to offer better alternatives.
Not all expansion is bad, but smart companies expand with discipline, focusing on delivering real value and solving problems effectively.
The future belongs to tools that remain lean, focused, and essential, knowing their target audience and unique value proposition.

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4 Likes

Pymnts

195

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Bitcoin Surpasses $100K Mark Amid New Trade Deal

Bitcoin surged past the $100,000 mark, driven by optimism surrounding a new trade deal and easing tariff tensions.
The price of bitcoin previously hit a record high of $109,000 in January but fell due to market downturn caused by tariffs imposed by President Trump.
The new trade agreement between the U.S. and UK could facilitate faster customs procedures and reduce barriers for various exports.
Legislation such as the End Crypto Corruption Act and the GENIUS Act are being considered to regulate stablecoins and address concerns related to digital assets.

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Popsci

222

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Flailing tube man-inspired robot uses no electronics

Engineers have created a tiny soft 'robot' inspired by flailing tube men, capable of walking, hopping, and swimming without the need for electronics.
The robot harnesses the physics behind its four soft, tubular legs powered by a continuous stream of air, working together to create a rhythmic gait.
The design showcases decentralized intelligence, adapting to obstacles and transitioning between land and water movement patterns autonomously.
The low-energy, high-speed robot offers a promising alternative to expensive robots, with potential applications in smart pills, wearable exosuits, and operations in extreme environments like space.

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