Crypto exchange Binance announced the relaunch of the Binance wallet with the goal of bringing better accessibility to the Web3 world.

Crypto Exchange Binance Relaunches Binance Wallet for Better Web3 Accessibility | Coinspeaker

Air France-KLM partners with Google Cloud to enhance data, AI strategy, and customer experience, shaping the future of travel industry innovation.

Google Cloud’s Advanced AI Technology Set to Transform Air France-KLM’s Data Management, Offering Better Travel Options and Services to Millions of Global Passengers: here’s what it means for travellers - Travel And Tour World

Coca-Cola drew plenty of criticism for rolling out an uninspired and lazily AI-generated holiday ad. Redditors made a much better version.

Someone Made a Deranged Version of Coke's AI Holiday Ad and It's Way Better

Collaboration between finance, technology, and sustainability leaders is essential to sustainable value creation. But when these leaders have different objectives and approaches, it can be tough to make the long-term match.

Better Together: Matchmaking finance, technology and sustainability leaders to create more value

When you’re just starting out with programming, choosing the right computer can make a significant difference in your learning experience. As you embark on your coding journey, one of the first…

MacBook vs Windows: Which One Is Better for Starting to Learn Programming? | by Sour LeangChhean | Nov, 2024 | Medium

Both chipmakers are on track to deliver outstanding growth thanks to the robust demand for AI chips.

Better Artificial Intelligence (AI) Stock: Nvidia vs. Micron Technology

DAYTON, Ohio (WDTN)– November marks National ‘Thank a Business Month’ and turns out, one of the best ways to do that is with the Better Business Bureau! Communications VP Sheri Sword joined Living Dayton on Thursday with tips for viewers at home. Reputation can mean everything, especially ...

Celebrate ‘Thank a Business Month’ with BBB | WDTN.com

Which of these AI-driven stocks is the better investment right now?

Better Artificial Intelligence Stock: Palantir vs. UiPath

Better

better

SaaS is for Suckers: AI Already Builds Better Custom Software

Can WebFlux Be Better Than Spring MVC? What You Need to Know

Building applications that focus on tackling complex business problems and at the same time ensuring scalability, performance, and user-centricity may prove to be tricky. Artificial intelligence (AI) is indeed the technology that brings this change, which enables teams to put out data-driven insights plus fast-paced innovation. Our article will describe how AI-powered insights bring about revolutionary changes in product engineering; what technology has made such advances possible; and other lessons learned through actual cases showing the force of change. The Role of AI in Modern Product Engineering AI’s role in product engineering extends beyond automation. It involves extracting actionable insights from vast data repositories to enhance decision-making, streamline processes, and enable predictive capabilities. Let’s explore the core aspects where AI is making a difference: 1. Enhanced Requirements Gathering and Analysis Traditional requirements gathering can be time-intensive and error-prone. AI-powered tools like natural language processing (NLP) and sentiment analysis enable automatic parsing of customer feedback, market trends, and stakeholder input. These insights lead to accurate requirement specifications and faster product ideation cycles. Example: AI models can analyze customer reviews and feedback to identify recurring pain points, which can then guide feature prioritization during application development. 2. AI in Design Optimization AI plays a pivotal role in generating optimized designs by leveraging generative design algorithms. By analyzing design parameters, constraints, and objectives, these algorithms create multiple design variants, allowing engineers to select the most efficient and cost-effective options. Technology in Use: Generative Adversarial Networks (GANs): These models simulate design variations and enable testing for performance improvements.
AI-Driven UI/UX Optimization: AI-powered heatmaps and user interaction analytics ensure intuitive and user-friendly application interfaces. 3. Predictive Development and Proactive Maintenance AI-driven predictive analytics facilitates the identification of potential issues before they manifest. Machine learning models trained on historical application data can forecast performance bottlenecks, hardware failures, or security vulnerabilities. Key Features: Error Prediction Models: Tools like TensorFlow and PyTorch enable the development of models that identify code-level vulnerabilities.
Automated Code Refactoring: AI-powered tools like DeepCode and Tabnine assist in suggesting optimal code refactoring strategies. 4. Automated Testing and Quality Assurance AI automates extensive testing cycles by identifying test cases, generating test scripts, and even executing tests autonomously. This dramatically reduces testing time while ensuring thorough coverage. Technologies Driving Automation: AI in Functional Testing: Tools like Test.ai use machine learning to simulate user behaviors and validate application functionalities.
Performance Testing with AI: AI-driven tools like Apache JMeter integrated with predictive algorithms can simulate large-scale workloads to test application resilience. 5. AI-Driven Insights for Scalability Scalability is a cornerstone of modern product engineering. AI-powered analytics assess current system usage patterns and project future demands. This ensures proactive resource scaling, avoiding downtime or performance degradation. Implementation Example: Cloud platforms like AWS and Azure leverage AI to optimize resource allocation, enabling applications to scale seamlessly based on real-time demand patterns. Enabling Technologies for AI-Driven Product Engineering AI-driven insights in product engineering are powered by a suite of advanced technologies. Let’s look at the tools and frameworks enabling these capabilities: 1. Machine Learning and Deep Learning Machine learning (ML) models identify patterns and trends in data, which can be applied to optimize development workflows, predict system performance, and detect anomalies. Popular Frameworks: TensorFlow: For building predictive and classification models.
Scikit-learn: For statistical modeling and data mining. 2. Natural Language Processing NLP processes textual data, enabling tools to parse requirements, user feedback, and documentation. It also powers AI-driven documentation assistants. Technologies: SpaCy: For linguistic analysis.
BERT (Bidirectional Encoder Representations from Transformers): For contextual understanding in large text datasets. 3. Cloud Computing and AI Services Cloud providers offer integrated AI services that streamline application engineering. These services include automated data analytics pipelines, serverless architectures, and pre-trained models. Example Providers: AWS AI Services: For model training and deployment.
Google AI: For BigQuery ML integration. Benefits of AI-Driven Insights in Product Engineering 1. Accelerated Time-to-Market AI optimizes development cycles through automation, predictive modeling, and intelligent decision-making, enabling faster delivery. 2. Improved Application Quality With AI-driven testing and predictive maintenance, applications achieve higher performance, reliability, and user satisfaction. 3. Cost Efficiency AI optimizes resource allocation, reduces manual efforts, and minimizes rework, resulting in cost savings throughout the engineering lifecycle. 4. Enhanced Innovation AI fosters innovation by uncovering latent insights in data, inspiring novel solutions and features. Challenges and Mitigation Strategies Despite its advantages, adopting AI in product engineering comes with challenges: 1. Data Privacy and Security AI relies on extensive data collection, raising concerns about data privacy and compliance with regulations like GDPR. Solution: Implement robust data anonymization and encryption techniques. 2. Skill Gaps AI integration requires skilled personnel familiar with machine learning, data science, and software engineering. Solution: Invest in employee upskilling programs and leverage user-friendly AI platforms. 3. Integration Complexity Integrating AI into legacy systems can be complex and resource-intensive. Solution: Adopt modular AI solutions and scalable cloud-based AI frameworks. How Indium Tech Helps Accelerate Innovation in Product Engineering Indium stands at the forefront of AI-driven product engineering, offering end-to-end services tailored to the unique needs of enterprises. With a focus on innovation and operational excellence, Indium’s solutions empower businesses to achieve scalability, optimize performance, and deliver exceptional user experiences. Key Offerings: AI-Powered Application Development: Custom built AI methods integrated throughout the application lifecycle.
Comprehensive Testing Services: The use of AI to ensure quality releases through the automation of testing.
Data Analytics Expertise: Open up value insight from the enterprise data to stimulate innovations.
Cloud & DevOps Integration: AI-driven CI/CD pipelines for rigorous deployment practices. Conclusion AI-powered insights transform the limits of product engineering with new concepts about product ideation, design, and delivery. Through predictive capabilities, automation, and data-driven insights, organizations can accelerate innovation, optimize operations, and enter emerging markets more quickly than their competitors. Get in touch with Indium’s experts to lead your organization through this transformation by delivering custom product engineering solutions. Building applications that focus on tackling complex business problems and at the same time ensuring scalability, performance, and user-centricity may prove to be tricky. Artificial intelligence (AI) is indeed the technology that brings this change, which enables teams to put out data-driven insights plus fast-paced innovation. Our article will describe how AI-powered insights bring about revolutionary changes in product engineering; what technology has made such advances possible; and other lessons learned through actual cases showing the force of change. The Role of AI in Modern Product Engineering AI’s role in product engineering extends beyond automation. It involves extracting actionable insights from vast data repositories to enhance decision-making, streamline processes, and enable predictive capabilities. Let’s explore the core aspects where AI is making a difference: AI’s role in product engineering 1. Enhanced Requirements Gathering and Analysis 1. Enhanced Requirements Gathering and Analysis Traditional requirements gathering can be time-intensive and error-prone. AI-powered tools like natural language processing (NLP) and sentiment analysis enable automatic parsing of customer feedback, market trends, and stakeholder input. These insights lead to accurate requirement specifications and faster product ideation cycles. natural language processing Example: AI models can analyze customer reviews and feedback to identify recurring pain points, which can then guide feature prioritization during application development. Example: 2. AI in Design Optimization 2. AI in Design Optimization AI plays a pivotal role in generating optimized designs by leveraging generative design algorithms. By analyzing design parameters, constraints, and objectives, these algorithms create multiple design variants, allowing engineers to select the most efficient and cost-effective options. Technology in Use: Technology in Use: Generative Adversarial Networks (GANs): These models simulate design variations and enable testing for performance improvements. AI-Driven UI/UX Optimization: AI-powered heatmaps and user interaction analytics ensure intuitive and user-friendly application interfaces. Generative Adversarial Networks (GANs): These models simulate design variations and enable testing for performance improvements. Generative Adversarial Networks (GANs): AI-Driven UI/UX Optimization: AI-powered heatmaps and user interaction analytics ensure intuitive and user-friendly application interfaces. AI-Driven UI/UX Optimization: 3. Predictive Development and Proactive Maintenance 3. Predictive Development and Proactive Maintenance AI-driven predictive analytics facilitates the identification of potential issues before they manifest. Machine learning models trained on historical application data can forecast performance bottlenecks, hardware failures, or security vulnerabilities. Key Features: Key Features: Error Prediction Models: Tools like TensorFlow and PyTorch enable the development of models that identify code-level vulnerabilities. Automated Code Refactoring: AI-powered tools like DeepCode and Tabnine assist in suggesting optimal code refactoring strategies. Error Prediction Models: Tools like TensorFlow and PyTorch enable the development of models that identify code-level vulnerabilities. Error Prediction Models: Automated Code Refactoring: AI-powered tools like DeepCode and Tabnine assist in suggesting optimal code refactoring strategies. Automated Code Refactoring: 4. Automated Testing and Quality Assurance 4. Automated Testing and Quality Assurance AI automates extensive testing cycles by identifying test cases, generating test scripts, and even executing tests autonomously. This dramatically reduces testing time while ensuring thorough coverage. Technologies Driving Automation: Technologies Driving Automation: AI in Functional Testing: Tools like Test.ai use machine learning to simulate user behaviors and validate application functionalities. Performance Testing with AI: AI-driven tools like Apache JMeter integrated with predictive algorithms can simulate large-scale workloads to test application resilience. AI in Functional Testing: Tools like Test.ai use machine learning to simulate user behaviors and validate application functionalities. AI in Functional Testing: Performance Testing with AI: AI-driven tools like Apache JMeter integrated with predictive algorithms can simulate large-scale workloads to test application resilience. Performance Testing with AI: 5. AI-Driven Insights for Scalability 5. AI-Driven Insights for Scalability Scalability is a cornerstone of modern product engineering. AI-powered analytics assess current system usage patterns and project future demands. This ensures proactive resource scaling, avoiding downtime or performance degradation. Implementation Example: Implementation Example: Cloud platforms like AWS and Azure leverage AI to optimize resource allocation, enabling applications to scale seamlessly based on real-time demand patterns. Cloud platforms like AWS and Azure leverage AI to optimize resource allocation, enabling applications to scale seamlessly based on real-time demand patterns. Enabling Technologies for AI-Driven Product Engineering AI-driven insights in product engineering are powered by a suite of advanced technologies. Let’s look at the tools and frameworks enabling these capabilities: 1. Machine Learning and Deep Learning 1. Machine Learning and Deep Learning Machine learning (ML) models identify patterns and trends in data, which can be applied to optimize development workflows, predict system performance, and detect anomalies. Popular Frameworks: Popular Frameworks: TensorFlow: For building predictive and classification models. Scikit-learn: For statistical modeling and data mining. TensorFlow: For building predictive and classification models. TensorFlow: Scikit-learn: For statistical modeling and data mining. Scikit-learn: 2. Natural Language Processing 2. Natural Language Processing NLP processes textual data, enabling tools to parse requirements, user feedback, and documentation. It also powers AI-driven documentation assistants. Technologies: Technologies: SpaCy: For linguistic analysis. BERT (Bidirectional Encoder Representations from Transformers): For contextual understanding in large text datasets. SpaCy: For linguistic analysis. SpaCy: BERT (Bidirectional Encoder Representations from Transformers): For contextual understanding in large text datasets. BERT (Bidirectional Encoder Representations from Transformers): 3. Cloud Computing and AI Services 3. Cloud Computing and AI Services Cloud providers offer integrated AI services that streamline application engineering. These services include automated data analytics pipelines, serverless architectures, and pre-trained models. AI services AI services Example Providers: Example Providers: AWS AI Services: For model training and deployment. Google AI: For BigQuery ML integration. AWS AI Services: For model training and deployment. AWS AI Services: Google AI: For BigQuery ML integration. Google AI: Benefits of AI-Driven Insights in Product Engineering 1. Accelerated Time-to-Market 1. Accelerated Time-to-Market AI optimizes development cycles through automation, predictive modeling, and intelligent decision-making, enabling faster delivery. 2. Improved Application Quality 2. Improved Application Quality With AI-driven testing and predictive maintenance, applications achieve higher performance, reliability, and user satisfaction. 3. Cost Efficiency 3. Cost Efficiency AI optimizes resource allocation, reduces manual efforts, and minimizes rework, resulting in cost savings throughout the engineering lifecycle. 4. Enhanced Innovation 4. Enhanced Innovation AI fosters innovation by uncovering latent insights in data, inspiring novel solutions and features. Challenges and Mitigation Strategies Despite its advantages, adopting AI in product engineering comes with challenges: 1. Data Privacy and Security 1. Data Privacy and Security AI relies on extensive data collection, raising concerns about data privacy and compliance with regulations like GDPR. Solution: Implement robust data anonymization and encryption techniques. Solution: 2. Skill Gaps 2. Skill Gaps AI integration requires skilled personnel familiar with machine learning, data science, and software engineering. Solution: Invest in employee upskilling programs and leverage user-friendly AI platforms. Solution: 3. Integration Complexity 3. Integration Complexity Integrating AI into legacy systems can be complex and resource-intensive. Solution: Adopt modular AI solutions and scalable cloud-based AI frameworks. Solution: How Indium Tech Helps Accelerate Innovation in Product Engineering Indium stands at the forefront of AI-driven product engineering, offering end-to-end services tailored to the unique needs of enterprises. With a focus on innovation and operational excellence, Indium’s solutions empower businesses to achieve scalability, optimize performance, and deliver exceptional user experiences. Key Offerings: Key Offerings: AI-Powered Application Development: Custom built AI methods integrated throughout the application lifecycle. Comprehensive Testing Services: The use of AI to ensure quality releases through the automation of testing. Data Analytics Expertise: Open up value insight from the enterprise data to stimulate innovations. Cloud & DevOps Integration: AI-driven CI/CD pipelines for rigorous deployment practices. AI-Powered Application Development: Custom built AI methods integrated throughout the application lifecycle. AI-Powered Application Development: Comprehensive Testing Services: The use of AI to ensure quality releases through the automation of testing. Comprehensive Testing Services: Data Analytics Expertise: Open up value insight from the enterprise data to stimulate innovations. Data Analytics Expertise: Cloud & DevOps Integration: AI-driven CI/CD pipelines for rigorous deployment practices. Cloud & DevOps Integration: Conclusion AI-powered insights transform the limits of product engineering with new concepts about product ideation, design, and delivery. Through predictive capabilities, automation, and data-driven insights, organizations can accelerate innovation, optimize operations, and enter emerging markets more quickly than their competitors. Get in touch with Indium’s experts to lead your organization through this transformation by delivering custom product engineering solutions.

AI Is Making it Easier to Engineer Better Products—Here's How

AI ukax juk’amp suma yänak ingeniero lurañax juk’amp jasakiw —Akanx Kunjams lurasi

AI gør det nemmere at konstruere bedre produkter – her er hvordan

KI erleichtert die Entwicklung besserer Produkte – so geht‘s

La IA facilita la creación de mejores productos: aquí te contamos cómo

Az AI megkönnyíti a jobb termékek tervezését – íme, hogyan

AI מקל על הנדסת מוצרים טובים יותר - הנה איך

AI によってより優れた製品の開発が容易になる — その方法とは

AI ले अझ राम्रो उत्पादनहरू इन्जिनियर गर्न सजिलो बनाउँदैछ—यहाँ कसरी छ

AI maakt het makkelijker om betere producten te ontwikkelen - en wel zo

AI gör det enklare att konstruera bättre produkter – så här

AI inereneriň has gowy önümlerini has aňsatlaşdyrýar - Ine

AI بہتر پروڈکٹس کو انجینئر کرنا آسان بنا رہا ہے — یہ طریقہ ہے۔

Are you ready to level up your skills as a developer in 2025? In this article, I show 10 books that will help you become a better programmer, enhance your coding practices, and improve your overall software engineering skills. Whether you’re a beginner or an experienced developer, these books cover essential topics like clean coding, debugging, system design, productivity, and more. My selection is based on suggestions and recommendations of top developers worldwide. If you are ready, then let’s dive in. 1. Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin I still believe this book must be in your collection, even if it is a bit old. This book emphasizes the importance of writing code that is not only functional but also easy to read and maintain. It provides actionable principles, rules, and examples to help developers craft professional-grade software. From naming conventions to refactoring techniques, Clean Code is a goldmine of practical advice. It’s essential for improving code readability, maintainability, and professionalism. If you want to elevate your coding standards and write code that others can easily work with, this book is a must. 2. The Pragmatic Programmer: Your Journey to Mastery by Andrew Hunt and David Thomas The Pragmatic Programmer covers everything from debugging techniques to automation and career advice. It encourages critical thinking and problem-solving while providing actionable strategies to tackle real-world challenges in software development. This book teaches you how to think like a pragmatic developer — someone who focuses on efficiency, adaptability, and continuous improvement. 3. Code Complete: A Practical Handbook of Software Construction by Steve McConnell Often referred to as the “manual of software engineering,” Code Complete dives deep into the principles of writing high-quality code. It covers topics like design patterns, testing strategies, debugging techniques, and much more. If you want an all-encompassing resource on writing bug-free, maintainable code while understanding the entire software development lifecycle, this is it. 4. Why Programs Fail: A Guide to Systematic Debugging Debugging can be one of the most time-consuming aspects of programming. This book teaches systematic approaches to identifying and fixing bugs quickly without relying on trial-and-error methods like excessive console logging. Debugging is one of the most critical skills for any developer. This book will save you countless hours by teaching you how to debug efficiently. 5. Deep Work: Rules for Focused Success in a Distracted World by Cal Newport In Deep Work, Cal Newport explains how cultivating focus can lead to extraordinary results in both personal and professional life. He provides actionable strategies for minimizing distractions and maximizing productivity. As developers, our work often requires long periods of uninterrupted focus. This book will teach you how to protect your attention and get into “the zone.” 6. Refactoring: Improving the Design of Existing Code by Martin Fowler Martin Fowler’s Refactoring introduces the concept of systematically improving the design of existing code without altering its functionality. The book provides a catalog of over 70 refactoring techniques, each with step-by-step instructions, motivations, and examples. It also discusses how to recognize “code smells” that indicate opportunities for refactoring and emphasizes the importance of testing during the process. The second edition includes updated examples in JavaScript, making it accessible to modern developers. This book is essential for developers who want to enhance the readability, maintainability, and scalability of their codebases. It teaches how to refactor safely and efficiently while minimizing the risk of introducing bugs. 7. System Design Interview by Alex Xu This is my favorite book about system design. This book breaks down complex system design concepts into digestible chunks, making it easier for developers to understand distributed systems, scalability, and performance optimization — key topics in technical interviews at top companies. If you’re preparing for system design interviews or want to deepen your knowledge of scalable architecture, this book is invaluable. 8. Don’t Make Me Think: A Common Sense Approach to Web Usability by Steve Krug This book focuses on creating web interfaces that are simple and user-friendly. It emphasizes usability testing and offers practical advice on designing websites that users can navigate effortlessly without confusion or frustration. For web developers aiming to improve user experience (UX), this is an essential read that simplifies complex usability concepts into actionable advice. 9. Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma This classic introduces 23 design patterns that solve common software design problems. It explains when and how to use these patterns effectively while improving code reusability and scalability. Understanding design patterns is crucial for building robust object-oriented systems that are easy to maintain and extend over time. 10. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans This book introduces the concept of aligning your software model closely with business domains. It provides strategies for tackling complexity using DDD principles like ubiquitous language and bounded contexts. If you’re working on complex systems or collaborating closely with business stakeholders, this book will teach you how to bridge the gap between technical solutions and business needs effectively. 10. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans This book introduces the concept of aligning your software model closely with business domains. It provides strategies for tackling complexity using DDD principles like ubiquitous language and bounded contexts. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans If you’re working on complex systems or collaborating closely with business stakeholders, this book will teach you how to bridge the gap between technical solutions and business needs effectively. Video :-) https://www.youtube.com/watch?v=F0fVOnfDZtM&embedable=true Conclusion Set the goal to read these books in 2025, and your programming skills will never be the same. ;) If you want to share your favorite books, please do so in the comment below. If you want to buy the book, you can search in Google or use my link where I collect everything together: 10 Books to Read in 2025 to Become a Better Developer. P.S. This page contains affiliate links. If you make a purchase through these links, I may earn a commission at no additional cost to you. ;) Good luck! ;) Are you ready to level up your skills as a developer in 2025? In this article, I show 10 books that will help you become a better programmer, enhance your coding practices, and improve your overall software engineering skills. Whether you’re a beginner or an experienced developer, these books cover essential topics like clean coding, debugging, system design, productivity, and more. 10 books My selection is based on suggestions and recommendations of top developers worldwide. If you are ready, then let’s dive in. 1. Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin I still believe this book must be in your collection, even if it is a bit old. This book emphasizes the importance of writing code that is not only functional but also easy to read and maintain. It provides actionable principles, rules, and examples to help developers craft professional-grade software. From naming conventions to refactoring techniques, Clean Code is a goldmine of practical advice. Clean Code It’s essential for improving code readability, maintainability, and professionalism. If you want to elevate your coding standards and write code that others can easily work with, this book is a must. 2. The Pragmatic Programmer: Your Journey to Mastery by Andrew Hunt and David Thomas The Pragmatic Programmer covers everything from debugging techniques to automation and career advice. It encourages critical thinking and problem-solving while providing actionable strategies to tackle real-world challenges in software development. The Pragmatic Programmer This book teaches you how to think like a pragmatic developer — someone who focuses on efficiency, adaptability, and continuous improvement. 3. Code Complete: A Practical Handbook of Software Construction by Steve McConnell Often referred to as the “manual of software engineering,” Code Complete dives deep into the principles of writing high-quality code. It covers topics like design patterns, testing strategies, debugging techniques, and much more. Code Complete If you want an all-encompassing resource on writing bug-free, maintainable code while understanding the entire software development lifecycle, this is it. 4. Why Programs Fail: A Guide to Systematic Debugging Debugging can be one of the most time-consuming aspects of programming. This book teaches systematic approaches to identifying and fixing bugs quickly without relying on trial-and-error methods like excessive console logging. Debugging is one of the most critical skills for any developer. This book will save you countless hours by teaching you how to debug efficiently. 5. Deep Work: Rules for Focused Success in a Distracted World by Cal Newport In Deep Work , Cal Newport explains how cultivating focus can lead to extraordinary results in both personal and professional life. He provides actionable strategies for minimizing distractions and maximizing productivity. Deep Work As developers, our work often requires long periods of uninterrupted focus. This book will teach you how to protect your attention and get into “the zone.” 6. Refactoring: Improving the Design of Existing Code by Martin Fowler Martin Fowler’s Refactoring introduces the concept of systematically improving the design of existing code without altering its functionality. The book provides a catalog of over 70 refactoring techniques, each with step-by-step instructions, motivations, and examples. It also discusses how to recognize “code smells” that indicate opportunities for refactoring and emphasizes the importance of testing during the process. The second edition includes updated examples in JavaScript, making it accessible to modern developers. Refactoring This book is essential for developers who want to enhance the readability, maintainability, and scalability of their codebases. It teaches how to refactor safely and efficiently while minimizing the risk of introducing bugs. 7. System Design Interview by Alex Xu This is my favorite book about system design. This book breaks down complex system design concepts into digestible chunks, making it easier for developers to understand distributed systems, scalability, and performance optimization — key topics in technical interviews at top companies. If you’re preparing for system design interviews or want to deepen your knowledge of scalable architecture, this book is invaluable. 8. Don’t Make Me Think: A Common Sense Approach to Web Usability by Steve Krug This book focuses on creating web interfaces that are simple and user-friendly. It emphasizes usability testing and offers practical advice on designing websites that users can navigate effortlessly without confusion or frustration. For web developers aiming to improve user experience (UX), this is an essential read that simplifies complex usability concepts into actionable advice. 9. Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma This classic introduces 23 design patterns that solve common software design problems. It explains when and how to use these patterns effectively while improving code reusability and scalability. Understanding design patterns is crucial for building robust object-oriented systems that are easy to maintain and extend over time. 10. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans This book introduces the concept of aligning your software model closely with business domains. It provides strategies for tackling complexity using DDD principles like ubiquitous language and bounded contexts. If you’re working on complex systems or collaborating closely with business stakeholders, this book will teach you how to bridge the gap between technical solutions and business needs effectively. 10. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans This book introduces the concept of aligning your software model closely with business domains. It provides strategies for tackling complexity using DDD principles like ubiquitous language and bounded contexts. Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans If you’re working on complex systems or collaborating closely with business stakeholders, this book will teach you how to bridge the gap between technical solutions and business needs effectively. Video :-) https://www.youtube.com/watch?v=F0fVOnfDZtM&embedable=true https://www.youtube.com/watch?v=F0fVOnfDZtM&embedable=true Conclusion Set the goal to read these books in 2025, and your programming skills will never be the same. ;) If you want to share your favorite books, please do so in the comment below. If you want to buy the book, you can search in Google or use my link where I collect everything together: 10 Books to Read in 2025 to Become a Better Developer . 10 Books to Read in 2025 to Become a Better Developer 10 Books to Read in 2025 to Become a Better Developer P.S. This page contains affiliate links. If you make a purchase through these links, I may earn a commission at no additional cost to you. ;) P.S. This page contains affiliate links. If you make a purchase through these links, I may earn a commission at no additional cost to you. ;) Good luck! ;)

Top 10 Software Development Books to Become a Better Software Developer in 2025

በ2025 የተሻሉ የሶፍትዌር ገንቢ ለመሆን 10 ምርጥ የሶፍትዌር ልማት መጽሐፍት።

Los 10 mejores libros de desarrollo de software para convertirse en un mejor desarrollador de software en 2025

10 parasta ohjelmistokehityskirjaa, joista tulee parempi ohjelmistokehittäjä vuonna 2025

Os 10 mellores libros de desenvolvemento de software para converterse nun mellor programador de software en 2025

2025 में बेहतर सॉफ्टवेयर डेवलपर बनने के लिए शीर्ष 10 सॉफ्टवेयर डेवलपमेंट पुस्तकें

2025 年に優れたソフトウェア開発者になるためのソフトウェア開発に関するベスト 10 書籍

10 geriausių programinės įrangos kūrimo knygų, kurios padės tapti geresniu programinės įrangos kūrėju 2025 m

Top 10 softwareontwikkelingsboeken om in 2025 een betere softwareontwikkelaar te worden

Epamusoro gumi Mabhuku Ekuvandudza Software Kuti Ave Zvirinani Software Developer muna 2025

2025 இல் சிறந்த மென்பொருள் உருவாக்குநராக மாறுவதற்கான சிறந்த 10 மென்பொருள் மேம்பாட்டு புத்தகங்கள்

ኣብ 2025 ዝሓሸ ሶፍትዌር ዲቨሎፐር ንምዃን ዝሕግዙ ብሉጻት 10 መጻሕፍቲ ምዕባለ ሶፍትዌር

Iincwadi ezili-10 eziphezulu zoPhuhliso lweSoftware ukuze ube nguMphuhlisi weSoftware oNgcono ngo-2025

This New AI Tool Claims to Solve Data Problems Better Than Anything Else—Here’s Why That Matters

Bu yeni AI aləti məlumat problemlərini hər şeydən daha yaxşı həll etdiyini iddia edir - bu, niyə vacibdir

Dieses neue KI-Tool soll Datenprobleme besser lösen als alles andere – deshalb ist das wichtig

Esta nueva herramienta de inteligencia artificial afirma resolver problemas de datos mejor que cualquier otra cosa: esta es la razón por la que es importante

AI tresna berri honek datu-arazoak beste ezer baino hobeto konpontzen dituela dio - Hona hemen zergatik garrantzitsua den

Alat AI Baru Ini Mengklaim Dapat Memecahkan Masalah Data Lebih Baik Daripada Apa Pun—Inilah Alasannya

この新しい AI ツールは、他の何よりも優れたデータ問題の解決を主張しています。これが重要な理由です。

ეს ახალი ხელოვნური ინტელექტის ინსტრუმენტი ირწმუნება, რომ მონაცემთა პრობლემებს სხვაზე უკეთ წყვეტს - აი, რატომ არის ეს მნიშვნელოვანი

Бұл жаңа AI құралы деректер мәселелерін кез келген нәрседен жақсырақ шешеді деп мәлімдейді - бұл неге маңызды

මෙම නව AI මෙවලම වෙනත් ඕනෑම දෙයකට වඩා හොඳින් දත්ත ගැටළු විසඳීමට හිමිකම් කියයි - මෙන්න එය වැදගත් වන්නේ ඇයි

Ichi Chitsva cheAI Tool Chinoti Kugadzirisa Matambudziko eData zvirinani pane chero chimwe chinhu-Hechi ndicho chikonzero nei zvichikosha.

Det här nya AI-verktyget hävdar att det löser dataproblem bättre än något annat - här är varför det är viktigt

Ushbu yangi AI vositasi ma'lumotlar muammolarini hamma narsadan yaxshiroq hal qilishni da'vo qilmoqda - bu nima uchun muhim

Authors:
(1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences;
(2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences;
(3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences;
(4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. Editor's note: This is Part 5 of 10 of a study on improving the evaluation and comparison of tools used in natural language preprocessing. Read the rest below. Table of Links Abstract and 1. Introduction and related works NLPre benchmarking 2.1. Research concept 2.2. Online benchmarking system 2.3. Configuration NLPre-PL benchmark 3.1. Datasets 3.2. Tasks Evaluation 4.1. Evaluation methodology 4.2. Evaluated systems 4.3. Results Conclusions

Appendices
Acknowledgements
Bibliographical References
Language Resource References 3. NLPre-PL benchmark 3.1. Datasets NKJP1M (Przepiórkowski et al., 2018) The NKJP1M subcorpus of the Polish National Corpus (Przepiórkowski et al., 2012) is manually annotated according to the NKJP tagset (Szałkiewicz and Przepiórkowski, 2012) and afterwards modified in line with the Morfeusz tagset (Woliński, 2019). This balanced subset of thematic- and genre-diverse texts and transcriptions is used to train Polish POS taggers. NKJP1M is maintained in two formats: TEI[8] and DAG.[9] These two formats are accepted by older NLPre tools but not modern ones. We thus convert NKJP1M to the CoNLL-X format (Buchholz and Marsi, 2006) preserving the original segmentation, POS tags and morphological features (i.e. the Morfeusz tagset), and to the CoNLL-U format10 with UD tags, Morfeusz tags (XPOS) and UD morphological features. Since there is no generally accepted split of NKJP1M into training, development and testing subsets, we uniformly divide NKJP1M in all formats (i.e. DAG, TEI, CoNLL-X and CoNLL-U) pursuant to the formulated splitting heuristics. Each document in the subcorpus contains multiple paragraphs of continuous textual data. To avoid possible information leakage, we treat each such paragraph as an indivisible unit. To ensure that the subsets include paragraphs of varying length, we investigate the distribution over the number of segments in each paragraph. Since it is akin to Gaussian distribution, we decide to not exclude any data, and we divide the paragraphs into K = 10 buckets of roughly similar size and then sample from them with respective ratios of 0.8:0.1:0.1 (corresponding to train, dev, and test subsets). This data selection technique assures similar distribution of segments number per paragraph in three subsets, hereafter byName. For creating our second split, hereafter byType, we consider the type of document a paragraph belongs to. We first group paragraphs into categories equal to the document types, and then we repeat the above-mentioned procedure per category (see the summary of NKJP1M and data splits in Table 1). PDB-UD (Wróblewska, 2018) Polish Dependency Bank is the largest collection of Polish sentences manually annotated with dependency trees and afterwards converted into UD representations in line with the UD annotation schema (de Marneffe et al., 2021). PDB-UD slightly correlates with NKJP1M, i.e., a subset of the PDB-UD sentences comes from NKJP1M, and the language-specific tags (XPOS) in PDB-UD match the Morfeusz tagset. PDB-UD is typically used to train NLPre systems for Polish. In NLPre-PL, we use the original PDB-UD data without any modifications and its standard split (see the statistical summary of PDB-UD in Table 1). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [8] http://nlp.ipipan.waw.pl/TEI4NKJP. [9] https://github.com/kawu/concraft-pl#data-format [10] https://universaldependencies.org/format.html Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. Authors: Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. Editor's note: This is Part 5 of 10 of a study on improving the evaluation and comparison of tools used in natural language preprocessing. Read the rest below. Editor's note: This is Part 5 of 10 of a study on improving the evaluation and comparison of tools used in natural language preprocessing. Read the rest below. Editor's note: This is Part 5 of 10 of a study on improving the evaluation and comparison of tools used in natural language preprocessing. Read the rest below. Editor's note: This is Part 5 of 10 of a study on improving the evaluation and comparison of tools used in natural language preprocessing. Read the rest below. Table of Links Abstract and 1. Introduction and related works Abstract and 1. Introduction and related works NLPre benchmarking NLPre benchmarking 2.1. Research concept 2.1. Research concept 2.2. Online benchmarking system 2.2. Online benchmarking system 2.3. Configuration 2.3. Configuration NLPre-PL benchmark NLPre-PL benchmark 3.1. Datasets 3.1. Datasets 3.2. Tasks 3.2. Tasks Evaluation Evaluation 4.1. Evaluation methodology 4.1. Evaluation methodology 4.2. Evaluated systems 4.2. Evaluated systems 4.3. Results 4.3. Results Conclusions

Appendices
Acknowledgements
Bibliographical References
Language Resource References Conclusions Appendices
Acknowledgements
Bibliographical References
Language Resource References Conclusions Appendices Acknowledgements Bibliographical References Language Resource References Appendices Acknowledgements Bibliographical References Language Resource References 3. NLPre-PL benchmark 3.1. Datasets NKJP1M (Przepiórkowski et al., 2018) The NKJP1M subcorpus of the Polish National Corpus (Przepiórkowski et al., 2012) is manually annotated according to the NKJP tagset (Szałkiewicz and Przepiórkowski, 2012) and afterwards modified in line with the Morfeusz tagset (Woliński, 2019). This balanced subset of thematic- and genre-diverse texts and transcriptions is used to train Polish POS taggers. NKJP1M is maintained in two formats: TEI[8] and DAG.[9] These two formats are accepted by older NLPre tools but not modern ones. We thus convert NKJP1M to the CoNLL-X format (Buchholz and Marsi, 2006) preserving the original segmentation, POS tags and morphological features (i.e. the Morfeusz tagset), and to the CoNLL-U format10 with UD tags, Morfeusz tags (XPOS) and UD morphological features. NKJP1M Since there is no generally accepted split of NKJP1M into training, development and testing subsets, we uniformly divide NKJP1M in all formats (i.e. DAG, TEI, CoNLL-X and CoNLL-U) pursuant to the formulated splitting heuristics. Each document in the subcorpus contains multiple paragraphs of continuous textual data. To avoid possible information leakage, we treat each such paragraph as an indivisible unit. To ensure that the subsets include paragraphs of varying length, we investigate the distribution over the number of segments in each paragraph. Since it is akin to Gaussian distribution, we decide to not exclude any data, and we divide the paragraphs into K = 10 buckets of roughly similar size and then sample from them with respective ratios of 0.8:0.1:0.1 (corresponding to train, dev, and test subsets). This data selection technique assures similar distribution of segments number per paragraph in three subsets, hereafter byName. For creating our second split, hereafter byType, we consider the type of document a paragraph belongs to. We first group paragraphs into categories equal to the document types, and then we repeat the above-mentioned procedure per category (see the summary of NKJP1M and data splits in Table 1). PDB-UD (Wróblewska, 2018) Polish Dependency Bank is the largest collection of Polish sentences manually annotated with dependency trees and afterwards converted into UD representations in line with the UD annotation schema (de Marneffe et al., 2021). PDB-UD slightly correlates with NKJP1M, i.e., a subset of the PDB-UD sentences comes from NKJP1M, and the language-specific tags (XPOS) in PDB-UD match the Morfeusz tagset. PDB-UD is typically used to train NLPre systems for Polish. In NLPre-PL, we use the original PDB-UD data without any modifications and its standard split (see the statistical summary of PDB-UD in Table 1). PDB-UD This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [8] http://nlp.ipipan.waw.pl/TEI4NKJP. [9] https://github.com/kawu/concraft-pl#data-format [10] https://universaldependencies.org/format.html

New Framework Promises to Train AI to Better Understand Hard-to-Grasp Languages Like Polish

Table of Links Abstract and 1. Introduction 2. Preliminaries and 2.1. Blind deconvolution 2.2. Quadratic neural networks 3. Methodology 3.1. Time domain quadratic convolutional filter 3.2. Superiority of cyclic features extraction by QCNN 3.3. Frequency domain linear filter with envelope spectrum objective function 3.4. Integral optimization with uncertainty-aware weighing scheme 4. Computational experiments 4.1. Experimental configurations 4.2. Case study 1: PU dataset 4.3. Case study 2: JNU dataset 4.4. Case study 3: HIT dataset 5. Computational experiments 5.1. Comparison of BD methods 5.2. Classification results on various noise conditions 5.3. Employing ClassBD to deep learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.5. Feature extraction ability of quadratic and conventional networks 5.6. Comparison of ClassBD filters 6. Conclusions Appendix and References 5.3. Employing ClassBD to deep learning classifiers Given that ClassBD serves as a signal preprocessing module, it possesses the flexibility to be integrated into various backbone networks. In the context of this experiment, we assess the classification performance by incorporating ClassBD into four widely recognized deep learning classifiers: ResNet [85], MobileNetV3 [86], WDCNN [54], and Transformer [87]. It is important to note that some networks were initially designed for image classification, so some parameters are revised to accommodate the 1D signal input. The properties of these backbone networks are illustrated in Table 11. Firstly, the classification results on the PU dataset are illustrated in Table 12. Predominantly, ClassBD has demonstrated its efficacy in enhancing performance. For instance, in the case of N09M07F10 with -4dB noise, the application of ClassBD results in an improvement exceeding 10% across all backbones. On average, when the SNR is -4dB, ClassBD achieves F1 scores of 87.60%, whereas the raw networks only yield 64.07%. Subsequently, we also test these models to the JNU and HIT datasets, setting the SNRs to -6dB and -4dB respectively. As depicted in Table 13, ClassBD exhibits commendable performance on both datasets, and all backbones experience a performance boost. Specifically, on the JNU dataset, the F1 scores exceed 90% post the employment of ClassBD, irrespective of the backbone performance. Furthermore, a substantial improvement is observed in the Transformer. The raw Transformer initially yields F1 scores around 50% on the JNU and HIT datasets, which, after the application of ClassBD, escalates to an average F1 score of 90%. Finally, the results on the three datasets suggest that ClassBD can function as a plug-and-play denoising module, thereby enhancing the performance of deep learning classifiers under substantial noise. Considering both performance and model size, the simplest backbone, WDCNN, achieves consistent performance under all conditions. Consequently, we recommend it as the backbone for bearing fault diagnosis. Authors:
(1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China;
(3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China;
(4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Preliminaries and 2.1. Blind deconvolution 2. Preliminaries and 2.1. Blind deconvolution 2.2. Quadratic neural networks 2.2. Quadratic neural networks 3. Methodology 3. Methodology 3.1. Time domain quadratic convolutional filter 3.1. Time domain quadratic convolutional filter 3.2. Superiority of cyclic features extraction by QCNN 3.2. Superiority of cyclic features extraction by QCNN 3.3. Frequency domain linear filter with envelope spectrum objective function 3.3. Frequency domain linear filter with envelope spectrum objective function 3.4. Integral optimization with uncertainty-aware weighing scheme 3.4. Integral optimization with uncertainty-aware weighing scheme 4. Computational experiments 4.1. Experimental configurations 4.1. Experimental configurations 4.2. Case study 1: PU dataset 4.2. Case study 1: PU dataset 4.3. Case study 2: JNU dataset 4.3. Case study 2: JNU dataset 4.4. Case study 3: HIT dataset 4.4. Case study 3: HIT dataset 5. Computational experiments 5.1. Comparison of BD methods 5.1. Comparison of BD methods 5.2. Classification results on various noise conditions 5.2. Classification results on various noise conditions 5.3. Employing ClassBD to deep learning classifiers 5.3. Employing ClassBD to deep learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.5. Feature extraction ability of quadratic and conventional networks 5.5. Feature extraction ability of quadratic and conventional networks 5.6. Comparison of ClassBD filters 5.6. Comparison of ClassBD filters 6. Conclusions 6. Conclusions Appendix and References Appendix and References 5.3. Employing ClassBD to deep learning classifiers Given that ClassBD serves as a signal preprocessing module, it possesses the flexibility to be integrated into various backbone networks. In the context of this experiment, we assess the classification performance by incorporating ClassBD into four widely recognized deep learning classifiers: ResNet [85], MobileNetV3 [86], WDCNN [54], and Transformer [87]. It is important to note that some networks were initially designed for image classification, so some parameters are revised to accommodate the 1D signal input. The properties of these backbone networks are illustrated in Table 11. Firstly, the classification results on the PU dataset are illustrated in Table 12. Predominantly, ClassBD has demonstrated its efficacy in enhancing performance. For instance, in the case of N09M07F10 with -4dB noise, the application of ClassBD results in an improvement exceeding 10% across all backbones. On average, when the SNR is -4dB, ClassBD achieves F1 scores of 87.60%, whereas the raw networks only yield 64.07%. Subsequently, we also test these models to the JNU and HIT datasets, setting the SNRs to -6dB and -4dB respectively. As depicted in Table 13, ClassBD exhibits commendable performance on both datasets, and all backbones experience a performance boost. Specifically, on the JNU dataset, the F1 scores exceed 90% post the employment of ClassBD, irrespective of the backbone performance. Furthermore, a substantial improvement is observed in the Transformer. The raw Transformer initially yields F1 scores around 50% on the JNU and HIT datasets, which, after the application of ClassBD, escalates to an average F1 score of 90%. Finally, the results on the three datasets suggest that ClassBD can function as a plug-and-play denoising module, thereby enhancing the performance of deep learning classifiers under substantial noise. Considering both performance and model size, the simplest backbone, WDCNN, achieves consistent performance under all conditions. Consequently, we recommend it as the backbone for bearing fault diagnosis. Authors: (1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China; (3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; (4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. Authors: Authors: (1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China; (3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; (4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv

ClassBD Boosts Deep Learning Classifiers for Better Fault Diagnosis

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI Just Got Better at Watching Videos

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution;
(2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion and References A Discussion: Selection Mechanism B Related Work C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results 3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. We overcome this with a hardware-aware algorithm that exploits the memory hierarchy on modern hardware (Section 3.3). We then describe a simple SSM architecture without attention or even MLP blocks (Section 3.4). Finally, we discuss some additional properties of selection mechanisms (Section 3.5). 3.1 Motivation: Selection as a Means of Compression We argue that a fundamental problem of sequence modeling is compressing context into a smaller state. In fact, we can view the tradeoffs of popular sequence models from this point of view. For example, attention is both effective and inefficient because it explicitly does not compress context at all. This can be seen from the fact that autoregressive inference requires explicitly storing the entire context (i.e. the KV cache), which directly causes the slow linear-time inference and quadratic-time training of Transformers. On the other hand, recurrent models are efficient because they have a finite state, implying constant-time inference and linear-time training. However, their effectiveness is limited by how well this state has compressed the context. To understand this principle, we focus on two running examples of synthetic tasks (Figure 2). • The Selective Copying task modifies the popular Copying task (Arjovsky, Shah, and Bengio 2016) by varying the position of the tokens to memorize. It requires content-aware reasoning to be able to memorize the relevant tokens (colored) and filter out the irrelevant ones (white). • The Induction Heads task is a well-known mechanism hypothesized to explain the majority of in-context learning abilities of LLMs (Olsson et al. 2022). It requires context-aware reasoning to know when to produce the correct output in the appropriate context (black). In summary, the efficiency vs. effectiveness tradeoff of sequence models is characterized by how well they compress their state: efficient models must have a small state, while effective models must have a state that contains all necessary information from the context. In turn, we propose that a fundamental principle for building sequence models is selectivity: or the context-aware ability to focus on or filter out inputs into a sequential state. In particular, a selection mechanism controls how information propagates or interacts along the sequence dimension (see Section 3.5 for more discussion). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion and References 6 Conclusion and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work B Related Work C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results E Experimental Details and Additional Results 3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. We overcome this with a hardware-aware algorithm that exploits the memory hierarchy on modern hardware (Section 3.3). We then describe a simple SSM architecture without attention or even MLP blocks (Section 3.4). Finally, we discuss some additional properties of selection mechanisms (Section 3.5). 3.1 Motivation: Selection as a Means of Compression We argue that a fundamental problem of sequence modeling is compressing context into a smaller state. In fact, we can view the tradeoffs of popular sequence models from this point of view. For example, attention is both effective and inefficient because it explicitly does not compress context at all. This can be seen from the fact that autoregressive inference requires explicitly storing the entire context (i.e. the KV cache), which directly causes the slow linear-time inference and quadratic-time training of Transformers. On the other hand, recurrent models are efficient because they have a finite state, implying constant-time inference and linear-time training. However, their effectiveness is limited by how well this state has compressed the context. To understand this principle, we focus on two running examples of synthetic tasks (Figure 2). • The Selective Copying task modifies the popular Copying task (Arjovsky, Shah, and Bengio 2016) by varying the position of the tokens to memorize. It requires content-aware reasoning to be able to memorize the relevant tokens (colored) and filter out the irrelevant ones (white). • The Induction Heads task is a well-known mechanism hypothesized to explain the majority of in-context learning abilities of LLMs (Olsson et al. 2022). It requires context-aware reasoning to know when to produce the correct output in the appropriate context (black). In summary, the efficiency vs. effectiveness tradeoff of sequence models is characterized by how well they compress their state: efficient models must have a small state, while effective models must have a state that contains all necessary information from the context. In turn, we propose that a fundamental principle for building sequence models is selectivity: or the context-aware ability to focus on or filter out inputs into a sequential state. In particular, a selection mechanism controls how information propagates or interacts along the sequence dimension (see Section 3.5 for more discussion). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Why Compressing Information Helps AI Work Better

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources D Differences with Glaze Finetuning In Section 4.1 and Figure 2, we discussed the brittleness of Glaze protections against small changes in the finetuning script. We also found our finetuning setup to be better at baseline style mimicry from unprotected art (see Figure 19). Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources D Differences with Glaze Finetuning In Section 4.1 and Figure 2, we discussed the brittleness of Glaze protections against small changes in the finetuning script. We also found our finetuning setup to be better at baseline style mimicry from unprotected art (see Figure 19). Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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