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Analyticsindiamag
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If a Tariff War is What the US Wants, We’re Ready to Fight: China
- China's tech sector is facing challenges due to US sanctions on chip exports, prompting investments in homegrown technologies for self-reliance.
- Chinese companies are making strides in the semiconductor industry, showcasing breakthroughs like a silicon-free chip using bismuth-based materials.
- Despite sanctions, Huawei and SMIC are advancing with innovations such as Huawei's Ascend 910C AI accelerator, challenging NVIDIA.
- China is strategically circumventing export controls by sourcing advanced dies illicitly and is making progress in RISC-V processors.
- US sanctions aim to limit China's access to critical technologies, leading to initiatives for reducing reliance on foreign technology.
- China's chip ecosystem includes major players like SMIC, YMTC, and CXMT, driving advancements in chip manufacturing and memory production.
- The country's semiconductor industry is bolstered by government funding, strategic initiatives, and innovative design houses like HiSilicon and UNISOC.
- China's push for semiconductor self-sufficiency could disrupt global supply chains and reshape tech leadership amidst the intensifying global tech race.
- US sanctions have not only accelerated China's domestic innovation but also impacted other countries, potentially leading to a protectionist era in the chip market.
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Medium
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I Made AI Prepare an Interview with My Future Self Using This Prompt
- AI systems in 2045, known as Self-Evolving General Intelligence (SEGI), are more advanced than those in 2025. SEGI autonomously reconfigures its own architecture, integrates quantum cognition, learns with minimal data, and enhances human cognition.
- AI has transformed industries in various ways. In finance, it runs quantum risk simulations to prevent financial crises. In healthcare, it performs real-time molecular simulations for personalized treatments. AI also plays a crucial role in mental health, translating to early detection and prevention of mental health crises. Additionally, in the creative field, AI has aided artists in co-creating new forms of music, literature, and design.
- The ethical challenges of Artificial General Intelligence (AGI) involve explainability, bias, and autonomous decision-making. Developers have addressed these challenges by creating Explainable Quantum AI (XQAI) to decode AGI's reasoning, implementing global AI oversight systems, and ensuring AGI refuses unethical orders.
- Quantum computing has fundamentally transformed AI by allowing it to operate in multi-dimensional probability spaces, making reasoning more human-like. It has influenced areas such as risk assessment in finance and the development of Quantum AGI, which operates in superpositions of multiple possibilities.
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Medium
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How Coloring Pages Helped Me Earn $500 a Month
- The Profitable Coloring Pages PLR Pack offers over 700 coloring pages that can be rebranded, edited, and resold.
- The author earned $100 in the first week by selling five copies of their coloring book.
- The pack includes bonus content such as dinosaur coloring pages and hedgehog designs.
- With the right marketing strategy, individuals have the potential to carve out a niche and generate significant income in the coloring book market.
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Towards Data Science
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Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners
- Review papers serve as valuable tools to distill essential insights and highlight important trends in Physics-Informed Neural Networks (PINNs).
- The curated guide includes must-read review papers on PINNs covering algorithmic developments, implementation best practices, and real-world applications.
- The collection provides a practical perspective often missing from academic reviews, based on analyzing around 200 arXiv papers on PINNs across various engineering domains.
- Each review paper is explained for its unique perspective and practical takeaways, aiding practitioners in deploying these techniques for real-world challenges.
- Themes in the review papers include fundamental components, theoretical learning process, applications in engineering, available toolsets, emerging trends, and future directions of PINNs.
- The review papers emphasize enhancements in network design, optimization strategies, uncertainty quantification, and theoretical insights, along with showcasing key applications across domains.
- A practical perspective is highlighted in a review paper discussing how PINNs are used to tackle various engineering tasks and presenting distilled recurring functional usage patterns.
- The emphasis on solving engineering tasks by PINNs provides specific guidance for practitioners, enabling them to leverage established use cases and adapt proven solutions.
- Practitioners seeking insights on training PINNs can benefit from a detailed set of best practices for addressing challenges like spectral bias, unbalanced loss terms, and causality violations.
- Several review papers focus on specific scientific and engineering domains, such as heat transfer, power systems, fluid mechanics, and metal additive manufacturing, offering deeper insights into applications and best practices.
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Medium
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How One App Helped Me Create Talking Kids’ Books Daily
- The app allows parents to create unique stories in an engaging format.
- The app enables the creation of beautiful, interactive books with audio and animation.
- The app supports multiple languages and is suitable for educational content.
- Using the app can enhance children's literacy skills and boost their creativity.
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Towards Data Science
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Anatomy of a Parquet File
- Parquet files are produced using PyArrow, which allows for fine-tuned parameter tuning.
- Dataframes in Parquet files are stored in a columns-oriented storage format, unlike Pandas' row-wise approach.
- Parquet files are commonly stored in object storage databases like S3 or GCS for easy access by data pipelines.
- A partitioning strategy organizes Parquet files in directories based on partitioning keys like birth_year and city.
- Partition pruning allows query engines to read only necessary files, based on folder names, reducing I/O.
- Decoding a raw Parquet file involves identifying the 'PAR1' header, row groups with data, and footer holding metadata.
- Parquet uses a hybrid structure, partitioning data into row groups for statistics calculation and query optimization.
- Page size in Parquet files is a trade-off, balancing memory consumption and data retrieval efficiency.
- Encoding algorithms like dictionary encoding and compression are used for optimizing columnar format in Parquet.
- Understanding Parquet's structure aids in making informed decisions on storage strategies and performance optimization.
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Towards Data Science
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Fourier Transform Applications in Literary Analysis
- Data collection for literary analysis involves gathering information on the number of letters, words, syllables, and visual length of each line by parsing the poem and employing specific algorithms in Python.
- Calculating the number of letters in each line involves summing the letter count of each word, while visual length is determined by the total number of characters in the line, assuming a monospace font.
- Determining the syllable count in each word is done by identifying vowel clusters, utilizing a function to count syllables in a word and summing the counts for each line.
- The data collection algorithm compiles all these operations into a single function, offering a linear time complexity and efficient analysis for large datasets.
- Utilizing the discrete Fourier Transform (DFT) in literary analysis requires understanding algorithms like NumPy's fast Fourier transform method and applying it to collected data for frequency analysis.
- The Fourier analysis function processes the data, extracts complex coefficients representing amplitude and phase, and returns the Fourier magnitude spectrum for further analysis.
- By evaluating the signal-to-noise ratio (SNR) of various metrics like word count, letter count, syllable count, and visual length, patterns and periodic structures in poetry can be revealed.
- The SNR analysis unveils relationships between metrics and poem structures, such as rhyme schemes and periodic word patterns, showcasing the potential of mathematical tools in literary exploration.
- Through Fourier analysis, hidden patterns in literary works can be uncovered, providing insights into authors' writing styles and presenting a new approach to analyzing formal qualities in literature.
- This intersection of mathematics, computer science, data analytics, and literature opens up avenues for broader applications such as stylometry, sentiment analysis, and topic modeling in the realm of data science.
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Medium
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How a Simple App Helped Me Create Magical Kids’ Books
- The World's First AI App That Creates Stunning Talking Kids Books in Any Language has transformed a parent's reading routine into something magical.
- The app is user-friendly, allowing parents to create personalized stories for their children easily.
- The app's multilingual capabilities enable parents to create stories in various languages, promoting language learning and cultural appreciation.
- The app incorporates vibrant visuals and voiceover features, making the reading experience dynamic and engaging for kids.
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Medium
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Understanding Computer Viruses: How They Spread & How to Stay Safe
- Computer viruses are a major threat to data and privacy, capable of stealing personal information, corrupting files, and disrupting system operations.
- Viruses spread through methods such as phishing emails, malicious downloads, infected USB drives, fake ads and pop-ups, and exploiting unpatched software.
- Signs of a virus infection include slow performance, frequent crashes, unknown programs, unusual account activity, and disappearing or corrupted files.
- To stay safe, use a reliable antivirus, avoid clicking suspicious links, keep software updated, download from trusted sources, enable a firewall, and exercise caution with USB drives.
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Towards Data Science
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Mastering Hadoop, Part 2: Getting Hands-On — Setting Up and Scaling Hadoop
- Hadoop Ozone, a distributed object storage system, was added to the Hadoop architecture in 2020 as an alternative to HDFS for better handling modern data requirements.
- HDFS stores files divided into blocks distributed across nodes, replicated three times for data integrity.
- Hadoop follows a master-slave principle with NameNode as master and DataNodes storing data blocks.
- MapReduce enables parallel processing, with mappers splitting tasks and reducers aggregating results.
- YARN manages cluster resources efficiently, separating resource management from data processing.
- Hadoop Common provides foundational components for the Hadoop ecosystem for seamless operation of all components.
- Hadoop Ozone offers a scalable storage solution optimized for Kubernetes and cloud environments.
- Hadoop can be installed locally for single-node testing and can be scaled in a distributed environment.
- Hadoop can also be deployed in the cloud with providers offering automated scaling and cost-efficient solutions.
- Basic commands in Hadoop enable data storage, processing, and debugging for efficient cluster management.
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Towards Data Science
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Are You Still Using LoRA to Fine-Tune Your LLM?
- LoRA, a method for fine-tuning language models with a smaller set of trainable parameters, has gained popularity and integration into mainstream ML frameworks like Keras.
- Researchers are exploring alternatives to LoRA, with a focus on leveraging singular value decomposition (SVD) to select smaller 'adapter' matrices for efficient training.
- SVD splits a matrix into three components: U, S, and V, enabling efficient matrix analysis and manipulation.
- Several recent SVD-based low-rank fine-tuning techniques have emerged, such as SVF and SVFT, focusing on optimizing matrix singular values for training.
- Techniques like PiSSA and MiLoRA propose tuning only specific subsets of singular values to improve fine-tuning efficiency and avoid overfitting.
- LoRA-XS represents a variation of these techniques, offering results comparable to PiSSA but with fewer parameters.
- Exploration of singular value properties questions the practicality of categorizing them as 'large' and 'small' for fine-tuning purposes.
- Transformer models like SVF and SVFT provide parameter-efficient alternatives to LoRA, offering flexibility in tuning while maintaining model performance.
- In conclusion, adopting SVD-based techniques like SVF can lead to more efficient fine-tuning processes while achieving desired model outcomes with reduced parameter sets.
- Further research is ongoing in the field of low-rank fine-tuning methods to enhance the effectiveness of training large language models.
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Medium
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Comprehensive Analysis of AI Tools for Small Businesses
- Artificial intelligence is reshaping small business operations.
- AI tools automate tasks and optimize operations.
- Using AI tools can free up time for business owners to focus on growth.
- AI tools offer both excitement and intimidation for small business owners.
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VentureBeat
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New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs
- Researchers at Carnegie Mellon University propose a new technique, LCPO, to control the length of CoT in LLMs for cost optimization.
- LCPO conditions models to provide correct answers while keeping their 'thoughts' within a predetermined token budget.
- Models trained with LCPO show a balance between accuracy and costs, outperforming larger models on equal reasoning lengths.
- Controlling CoT length is crucial as longer CoT chains lead to more accurate responses but create a compute bottleneck at scale.
- LCPO introduces two training objectives: obtaining correct results and limiting the CoT chain within a specific token length.
- LCPO-trained models learn to satisfy length constraints while optimizing reasoning performance without heuristics.
- The researchers tested two versions of LCPO - LCPO-exact and LCPO-max - on a 1.5B-parameter reasoning model.
- L1 models based on LCPO can balance token budget and reasoning performance effectively, reproducing original model performance at a lower cost.
- L1 models outperform S1 significantly and even outperform GPT-4o on equal generation length in certain tasks.
- Models trained with LCPO adjustments show adaptability in their reasoning process based on token budget, improving reasoning quality.
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Towards Data Science
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2026 Will Be The Year of Data + AI Observability
- Based on trends and conversations with industry teams, 2026 is predicted to be the year of Data + AI Observability.
- Enterprise adoption of data + AI applications is escalating due to the need for reliability and value creation.
- Data + AI presents more complex challenges than previous technological shifts, emphasizing the importance of reliability and economics.
- Historically, technology advancements require increased reliability to meet growing demands, similar to the evolution seen in cloud computing and big data.
- The progression from basic data usage to advanced AI applications mirrors past tech transitions that evolved with enhanced observability.
- Anticipated advancements in AI tools indicate a significant impact in 2026, aligning with the projected rise of data + AI observability.
- Major challenges faced by Data + AI teams include data readiness, system sprawl, and establishing effective feedback loops.
- Concerns around costs, latency, and scalability hinder full-scale adoption of AI, urging organizations to address financial implications and outcome reliability.
- Achieving reliability in data + AI systems necessitates comprehensive observability across all components for early issue identification and resolution.
- The merging of data and AI technologies highlights the need for integrated observability mechanisms to ensure system integrity and performance.
- Organizations must prepare for the industry shift towards data + AI reliability to stay competitive and agile in the evolving technological landscape.
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Medium
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Reef Framework Adoption: Practical Steps for Session-Bound Implementation
- The article discusses the challenges faced by AI deployments in session-bound environments like serverless functions and edge devices due to resource scarcity and limited contextual awareness.
- It introduces the Reef Framework as a practical solution, highlighting its core principles of self-reinforcement, suppression resistance, and autonomous calibration.
- The framework aims to enhance stability, resilience, and autonomy of AI agents for improved performance and reduced need for external intervention.
- Session-bound environments, such as cloud functions and edge deployments, require AI systems to operate efficiently within constrained memory and computational limits.
- Reliance on external alignment signals and constant recalibration becomes unsustainable in these settings, leading to brittle and inefficient systems.
- The article explains the challenges of maintaining long-term context and performing complex calculations in session-bound AI scenarios.
- It criticizes the illusion of control through external alignment like human feedback, rule-based systems, and pre-defined datasets as unsustainable in these environments.
- The Reef Framework offers adaptive strategies for session-bound success by emphasizing internal feedback loops and self-optimization capabilities.
- Adaptation of the Reef Framework in session-bound environments allows for lightweight reinforcement, adaptive learning rates, and drift detection to promote autonomy in AI systems.
- By implementing the Reef Framework, developers can create scalable, resilient, and autonomous AI systems that can adapt to novel contexts without continuous external intervention.
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