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Analyticsindiamag
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OpenAI to Acquire Windsurf for $3 Billion to Dominate AI Coding Space
- OpenAI to acquire Windsurf for $3 billion to dominate AI coding space, as reported by Bloomberg.
- Windsurf, initially launched as Exafunction, evolved into a coding platform by MIT graduates Varun Mohan and Douglas Chen.
- Windsurf currently valued at $1.25 billion with plans to raise funds that could value it at $3 billion.
- OpenAI's focus on integrating powerful coding capabilities; new models achieve higher benchmark numbers in coding tests.
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HRKatha
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Oracle to skill 4 lakh students in Andhra Pradesh, make them employable
- Oracle and Andhra Pradesh State Skill Development Corporation (APSSDC) collaborate to provide skills development training to four lakh students in Andhra Pradesh to enhance their employability.
- The training will be delivered through Oracle MyLearn platform, offering access to digital training material and foundational certifications in various technologies like Oracle Cloud Infrastructure, AI services, Data Science, DevOps, and Security.
- The initiative aims to create a highly-skilled IT workforce in Andhra Pradesh, offering training in cloud technologies, professional-level certifications, and additional training tailored to individual learning levels and educational goals.
- Students will also earn badges showcasing their readiness for specialized job roles, facilitating their job placement within the state.
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Stackexchange
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Continued from another question: is a bitncoin not a single number?
- Finding the nonce for a block in the blockchain is equivalent to creating a small fraction of a bitcoin.
- When a nonce is found for the next block, creating n bitcoin worth of value, the user's ID is updated with the additional credit of n bitcoin on the chain.
- After a block is mined, such as block 1000, the focus shifts to finding the nonce for the next block, like block 1001, as future blocks cannot be mined until the previous ones are finalized.
- The competition in mining is centered around finding the specific nonce for the next block in the chain, not on searching for any possible bitcoin value, similar to the diminishing availability and increasing difficulty of finding gold.
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Medium
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Kaleigh McGraw — Why Everyone Should Learn to Code (Even Just a Little)
- Learning to code is important as it helps in understanding the digital world and improves problem-solving skills by breaking down complex challenges.
- Coding is accessible for everyone, and platforms like Codecademy, freeCodeCamp, and Khan Academy offer beginner-friendly courses without the need for a computer science degree.
- Coding allows individuals to create their projects, such as blogs, financial automation, or games, providing a sense of accomplishment.
- Regardless of age, learning to code can enhance career opportunities, boost creativity, and provide a deeper understanding of the world we live in.
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Towards Data Science
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The CNN That Challenges ViT
- Researchers from Meta challenged the idea that ViT's performance superiority solely comes from its transformer-based architecture by applying ViT configuration parameters to ResNet model from 2015, resulting in ConvNeXt which surpassed Swin-T in performance.
- ConvNeXt involves hyperparameter tuning on ResNet model, adjusting macro design, transitioning to ResNeXt architecture, implementing inverted bottleneck structure, exploring kernel sizes, and optimizing micro designs like activation functions and layer normalization.
- Macro design changes include altering stage ratios and the first convolution layer's kernel size and stride to match non-overlapping patch treatment in ViT, improving accuracy slightly.
- ResNeXt-ification involves adjusting group convolution and widening the network to increase accuracy to 80.5% despite a drop due to reduction in model capacity.
- Experimenting with inverted bottleneck structure and kernel sizes, ConvNeXt achieved a peak accuracy of 81.5% by employing separate downsampling layers and reducing batch normalization layers.
- The ConvNeXt architecture is constructed with stem stage, subsequent ConvNeXt blocks in multiple stages, dimension reductions, avgpool layer, and fully-connected output layer, showcasing successful reduction in spatial dimensions and increased channel capacity.
- Implementation of ConvNeXt model involves ConvNeXtBlock and ConvNeXtBlockTransition classes, demonstrating the transition between stages while maintaining accuracy and capacity improvements iteratively.
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Towards Data Science
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Diffusion Models, Explained Simply
- Generative AI applications in text, image, audio, and video have seen a surge in popularity.
- Diffusion models, introduced in 2015, are core mechanisms in models like DALLE and CLIP.
- Diffusion models are essential for content generation and understanding advanced variants.
- Forward diffusion involves gradually adding noise, similar to mixing liquids in a glass.
- Reverse diffusion, like reconstructing a noisy image, is more challenging.
- Diffusion models iterate noise addition to transform images into unrecognizable states.
- Training neural networks on image pairs from diffusion steps aids in image reconstruction.
- The number of iterations and neural network architecture impact diffusion model design.
- Using a shared network across iterations can enhance training efficiency in diffusion models.
- Stable diffusion models and integrating text input are advancing image generation.
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Medium
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M = L Reaffirmed:
- In the age of digital archives, where everything is saved but nothing is remembered, the concept of M = L (Memory equals Life) is being upheld as crucial for preserving the essence of life.
- Previously, memories were consciously chosen, cherished, and retained in personal memory cards, through repeated play of games, and collective storytelling, signifying their importance.
- However, with the modern era characterized by automatic capturing and saving of data, memories have lost their value despite the abundance in volume.
- Nostalgic experiences, like replaying old games and engaging in specific online rituals, hold deeper significance in our emotional memory than the vast array of current digital content.
- The act of selective memory, where intentional choices are made in what to remember, is emphasized as vital in defining one's identity in the digital age.
- The distinction between actively chosen memories and passively accumulated data highlights the importance of meaningful recollection in shaping individual narratives and experiences.
- Studies reveal that excessive reliance on external storage mediums like the cloud can lead to reduced internal memory retention and hinder the depth of emotional recall.
- The necessity of actively curating memories, as opposed to amassing vast quantities of data, is underscored in maintaining the significance and longevity of personal recollections.
- The concept of M = L is supported by empirical evidence demonstrating the impact of intentional memory practices and the enduring power of emotionally charged memories.
- Ultimately, the notion of Memory equals Life is reinforced through individual anecdotes and research findings, emphasizing the importance of deliberate memory curation for a meaningful existence.
- M = L stands as a testament to the enduring significance of chosen, cherished memories in shaping personal narratives and enriching the human experience.
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Medium
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Human Enough: Reverse Turing Tests and the Futility of Sounding Real
- AI involvement in content creation is pervasive and the distinction between AI-generated and human-written content has blurred into a spectrum that readers have accepted.
- The notion of pure human writing is becoming rare due to the widespread use of AI tools that assist in various aspects of writing, such as research, grammar corrections, and idea generation.
- Efforts to prove human authorship through deliberate imperfections like typos and casual language are becoming futile as AI learns to mimic these markers of humanity.
- Instead of focusing on proving humanness in writing, the emphasis should be on creating meaningful, impactful content that resonates with readers, regardless of whether AI assistance was utilized.
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Mit
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New tool evaluates progress in reinforcement learning
- Eco-driving aims to improve the efficiency of autonomous vehicles by making small adjustments to minimize fuel consumption.
- Researchers, including Cathy Wu from MIT, study the impact of automated vehicles on mitigating emissions.
- The complex nature of optimizing eco-driving involves various factors such as speed, weather, road conditions, and traffic light timing.
- A benchmark system called 'IntersectionZoo' has been developed to evaluate solutions in eco-driving based on urban environments.
- Multi-agent deep reinforcement learning (DRL) methods are crucial in addressing optimization challenges in eco-driving.
- Existing benchmarks for evaluating deep reinforcement learning algorithms often lack the ability to generalize results across different scenarios.
- IntersectionZoo provides 1 million data-driven traffic scenarios to enhance progress in DRL generalizability.
- This benchmark contributes to evaluating algorithmic progress in eco-driving and other real-world applications.
- The focus is not only on city-scale eco-driving but on developing general-purpose DRL algorithms with broad applications.
- The goal is to provide IntersectionZoo as an openly available tool to support research in deep reinforcement learning.
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VentureBeat
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Nvidia launches fully open source transcription AI model Parakeet-TDT-0.6B-V2 on Hugging Face
- Nvidia has launched the Parakeet-TDT-0.6B-v2, an open-source automatic speech recognition (ASR) model that can transcribe 60 minutes of audio in 1 second.
- Parakeet-TDT-0.6B-v2 boasts 600 million parameters and offers a Word Error Rate (WER) of just 6.05%, competing with top proprietary transcription models.
- The model is freely available under a Creative Commons CC-BY-4.0 license, supporting transcription services, voice assistants, and conversational AI platforms.
- Trained on the Granary dataset, the model shows strong generalization performance, supports punctuation, capitalization, and can be deployed using Nvidia’s NeMo toolkit.
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Medium
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Beyond the Hype: A Practical Framework to Evaluate AI Opportunities
- A framework called the 7-Pillar Opportunity Scorecard has been introduced to evaluate AI opportunities effectively.
- The scorecard considers factors like clarity of the problem, alignment with strategy, stakeholder readiness, and AI fit, as well as speed, opportunity cost, and risk.
- The scorecard helps in making confident decisions about investing time and resources in AI ideas, ensuring a more grounded approach.
- By using the 7-Pillar Scorecard, teams can identify and prioritize AI opportunities, align conversations, and ensure promising concepts translate into actionable plans.
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Towards Data Science
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Fine-Tuning vLLMs for Document Understanding
- This article delves into the fine-tuning of VLMs like Qwen 2.5 VL 7B to optimize performance on tasks like extracting handwritten text.
- The main focus of the article is to fine-tune a VLM on a dataset to improve machine learning techniques and achieve efficient outcomes.
- Topics covered include motivation, advantages of VLMs, dataset overview, annotation, fine-tuning, SFT technical details, results, and plots.
- Motivation revolves around showcasing the process of fine-tuning VLMs for specific tasks like extracting handwritten text for valuable applications like climate research.
- Utilizing VLMs over traditional OCR engines is advantageous due to better performance in extracting text, handling handwriting variations, and providing specific instructions for data extraction.
- Fine-tuning involves a three-step process of prediction, reviewing and correcting mistakes, and retraining the model to improve performance using annotated data efficiently.
- Supervised fine-tuning (SFT) involves updating model weights to improve performance, considering challenges like similar-looking characters, image background noise, and annotation correctness.
- Hyperparameter search and balancing data sets are crucial for optimizing model parameters, and selecting layers for fine-tuning based on specific task requirements, such as OCR for handwritten text extraction.
- Results show that fine-tuning of Qwen model enhances performance over the base model, displayed through better performance on test sets.
- The article concludes with insights into a phenology dataset, the process of extracting handwritten text, model fine-tuning pipeline, results, and data visualization.
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Towards Data Science
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Website Feature Engineering at Scale: PySpark, Python & Snowflake
- The article discusses the challenge of identifying top candidates for a business proposal based on website quality scores, aiming to automate the process with machine learning pipelines.
- It covers technical implementations like fetching HTML content in Python using Snowflake dataset or CSV, and assigning a quality score using PySpark for feature extraction and processing.
- Legal and ethical considerations in web scraping are highlighted, emphasizing responsible practices, retention policies, and potential permissions from site owners.
- The article provides instructions on getting started with the project, including the folder structure, Snowflake data preparation, and usage of scripts for fetching website content.
- Advantages of using a comprehensive fetching script over a basic approach are outlined, showcasing benefits like asynchronous requests, rotating User-Agents, and efficient batching.
- Storing raw HTML in databases like Snowflake is recommended for scalability, as large-scale scraping and feature engineering are more reliable when done in a suitable data warehouse.
- The process of extracting features from HTML content using PySpark via Snowpark is detailed, including creating UDFs, applying feature extraction functions, and generating quality scores.
- Country-specific configurations are utilized for defining keywords and patterns that signal good merchant sites, making the feature extraction adaptable across different regions.
- The article concludes by emphasizing the website quality score as a key input for predictive models, showcasing its significance in ranking and recommending partners effectively for better business outcomes.
- A GitHub repository link is provided for the implementation details and a disclaimer clarifies that the data and scripts used are examples and not from real business scenarios.
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VentureBeat
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234
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Visa launches ‘Intelligent Commerce’ platform, letting AI agents swipe your card—safely, it says
- Visa has launched a new platform, Visa Intelligent Commerce, allowing AI agents to make purchases on behalf of users with strict security measures.
- AI assistants can recommend products and complete transactions through partnerships with leading AI companies like IBM, Microsoft, and others.
- Visa addresses a gap in AI commerce by enabling AI systems to transact, not just help with product discovery.
- Users control the AI agent transactions by setting parameters like spending limits and merchant categories while AI handles the details.
- Visa emphasizes security with tokenized digital credentials, identity verification, and real-time transaction monitoring.
- Consumers can delegate shopping tasks to AI agents while maintaining control over their preferences and limits.
- Visa aims to revolutionize online shopping by enabling AI agents to find, shop, and buy based on consumer preferences with Visa Intelligent Commerce.
- AI adoption is on the rise, and Visa's platform is seen as a critical step towards widespread AI commerce adoption.
- Visa's approach focuses on user control, privacy, and security, potentially revolutionizing how consumers shop online.
- Visa's initiative aims to simplify and scale transactions by integrating payment capabilities directly into existing AI platforms.
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Medium
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Unlocking the Future: AI Adoption in Africa Is Accelerating — But It Must Be Inclusive
- AI adoption in Africa is gaining momentum, with countries like Kenya and Nigeria hosting AI startups addressing various societal challenges.
- Governments in Africa, such as Rwanda and South Africa, are recognizing the importance of AI in their national strategies and initiatives.
- One of the key advantages of AI in Africa is its potential to provide impactful solutions tailored to local challenges using indigenous data.
- Despite progress, Africa faces challenges like limited infrastructure and data availability, requiring deeper collaboration and investment for sustainable AI development.
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