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Data Science News
Medium
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Paper Explained 4: NV-Embed
- NV-Embed is an embedding model open-sourced by NVIDIA, designed for retrieval tasks.
- It is finetuned on top of Mistral 7B with innovations in architecture, training, and data curation.
- The model introduces a latent attention layer for obtaining sequence-level embeddings.
- Strategies like multi-head attention and two-stage training contribute to better performance.
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Nvidia
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Animals Crossing: AI Helps Protect Wildlife Across the Globe
- AI is playing a crucial role in wildlife conservation efforts across the globe, helping protect endangered species and their habitats.
- Organizations like Ai2, Rouxcel Technology, OroraTech, Wildlife Protection Solutions, and Conservation X Labs are using AI and NVIDIA technology to safeguard wildlife.
- Ai2's EarthRanger platform, powered by NVIDIA GPUs, aids in predicting elephant movements and tracking various wildlife to prevent conflicts and poaching.
- Rouxcel Technology in South Africa uses AI-driven RhinoWatches to protect rhinos and other endangered species, expanding its efforts to Kenya and Namibia.
- OroraTech leverages satellite imagery and AI for wildfire detection and monitoring to protect natural habitats and prevent poaching in Africa and Australia.
- Wildlife Protection Solutions uses remote cameras and AI models for real-time monitoring of animals and poachers, supporting over 250 conservation projects in 50+ countries.
- Conservation X Labs employs AI-enabled solutions like Wild Me to classify species from crowdsourced images and transform traditional wildlife monitoring tools with AI for real-time insights.
- NVIDIA technology is integral to optimizing AI models, processing vast amounts of environmental data, and enhancing wildlife protection efforts globally.
- These innovative initiatives highlight the importance of leveraging AI and accelerated computing to address environmental challenges and preserve biodiversity.
- NVIDIA's upcoming GTC AI conference will showcase how technology is enhancing conservation efforts, such as monitoring Antarctic flora and protecting the Great Barrier Reef.
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Towards Data Science
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Data Science: From School to Work, Part II
- Python is a versatile programming language known for its simplicity and large community support.
- Efficiency and maintainability of Python projects heavily rely on developer practices.
- Good code structuring, such as the src layout, is crucial for project organization and avoiding import issues.
- Adopting SOLID principles helps in improving code quality, maintainability, and scalability.
- PEP standards, notably PEP 8, play a vital role in ensuring code readability and consistency.
- PEP 257 aims to standardize the use of docstrings in Python for documenting functions, classes, and methods.
- Python's typing system, introduced by PEP 484, enhances code readability and reduces errors through explicit type declarations.
- Tools like Ruff, a linter for Python code, assist in analyzing and formatting code efficiently.
- Maintaining code readability with proper naming conventions, line length, and commenting is essential.
- Adherence to best practices in Python development ensures clear, scalable, and maintainable code for future collaborations.
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Analyticsindiamag
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Apple Delays Siri’s Major AI Update Until 2027: Reports
- Apple's planned upgrade to Siri has been delayed until at least 2027.
- Real-world usage of Apple's AI offerings is extremely low.
- Apple's competitors in the AI space include OpenAI's ChatGPT, Google's Gemini, and Microsoft's Copilot.
- Apple announced plans to invest over $500 billion in the United States over the next four years, supporting AI, silicon engineering, manufacturing, and workforce development.
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Ubuntu
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Experiment Tracking with MLFlow in Canonical’s Data Science Stack
- Experiment tracking is essential in data science, and Canonical’s Data Science Stack incorporates MLFlow for effortless experiment logging, comparison, and reproduction.
- MLFlow automatically logs experiment details like parameters, metrics, and model artifacts, facilitating easy tracking and resuming of experiments.
- To access MLFlow in Data Science Stack, run 'dss status' in the terminal, and open the MLFlow URL in a browser to interact with the MLFlow UI.
- By including a few lines of code, you can integrate experiment tracking in your training runs, logging parameters and model artifacts.
- The enhanced code snippet logs key parameters, saves the model as an artifact, and provides integrated experiment management within the MLFlow dashboard.
- Further, you can automate parameter exploration by iterating over different values, starting new MLFlow runs for each setting.
- MLFlow allows for advanced features such as visualization, model registry, and deployment pipelines to streamline experimentation and deployment workflows.
- Using MLFlow with Data Science Stack simplifies experiment tracking, enabling data scientists to focus on model building creativity rather than managing experimental details.
- By leveraging MLFlow in the Data Science Stack, users can benefit from advanced capabilities, seamless deployment support, and intuitive experiment visualization.
- Experiment confidently, compare results effortlessly, and streamline your data science workflow with MLFlow in Canonical’s Data Science Stack.
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Towards Data Science
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Avoidable and Unavoidable Randomness in GPT-4o
- The article explores randomness in GPT-4o through coin flipping prompts and analysis of determinism.
- GPT-4o's coin flips show bias resembling human tendencies observed in previous studies on coin flipping.
- Using token probabilities rather than full responses, a method for precise evaluation of GPT-4o coin flip outcomes is presented.
- Factors like temperature, seed, and system_fingerprint affect randomness in GPT-4o's responses but do not ensure determinism.
- The mixture-of-experts architecture in GPT-4o introduces non-determinism beyond controllable parameters like temperature and seed.
- GPT-3.5-turbo also exhibits non-deterministic log probabilities, indicating sources of randomness beyond mixture-of-experts.
- An experiment with 10,000 coin flips in GPT-4o reveals 42 distinct probabilities, suggesting hidden sources of non-determinism.
- The article highlights the challenge of studying non-deterministic models like GPT-4o and the limitations of controlling randomness in AI responses.
- Transparency and understanding of hidden sources of randomness in AI models remain crucial for researchers to analyze model behavior accurately.
- Mixture-of-experts models introduce randomness due to expert allocation based on batched prompts, contributing to non-determinism in AI outputs.
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Analyticsindiamag
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ReaLJam AI Lets Musicians Jam Anytime, Anywhere
- ReaLJam is an AI interface and protocol that allows musicians to have real-time jam sessions with an AI partner.
- The technology uses reinforcement learning and a user interface to create a seamless experience.
- ReaLJam can anticipate the user's melody and plan its chords accordingly, providing a waterfall display to show upcoming chords.
- The system has received positive feedback from experienced musicians and has the potential to revolutionize music creation and learning.
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Analyticsindiamag
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UBTECH Advances Humanoid Robotics with Swarm Intelligence Training at Zeekr
- Shenzhen-based UBTECH Robotics completes world's first multi-humanoid robot collaborative training program at Zeekr's 5G Intelligent Factory.
- UBTECH's Walker S1 humanoid robots demonstrated the ability to coordinate seamlessly in real-world industrial settings, performing tasks such as sorting, handling, and precision assembly.
- UBTECH developed BrainNet, a software framework enabling effective collaboration among humanoid robots.
- UBTECH plans to expand its Practical Training 2.0 program to more partner factories, accelerating the adoption of humanoid robots in intelligent manufacturing.
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Medium
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Why 10⁹+7 Rules Competitive Programming — The Magic Modulo Explained!
- In competitive programming, modular arithmetic plays a crucial role in keeping numbers manageable and avoiding overflow.
- 10⁹ + 7 (1,000,000,007) is commonly used as a mod value in competitive programming.
- 10⁹ + 7 is the preferred choice because it is the first prime number greater than 10⁹, ensuring stability and correctness in results.
- 10⁹ + 7 is smaller than 2,147,483,647, enabling more efficient computations using int rather than long long.
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Analyticsindiamag
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‘A Junior VC’ Secures ₹100 Crore Fund for Pre-Seed Investments in India
- AJVC, a micro VC firm, has secured a ₹100 crore fund for pre-seed investments in India.
- The fund aims to invest in 12-15 startups annually, focusing on emerging technologies such as AI, SaaS, and consumer technology.
- AJVC has received overwhelming response with over 5,500 applications and has already made nine investments across various sectors.
- Micro VCs are becoming increasingly important in bridging the funding gap for early-stage AI startups in India.
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Analyticsindiamag
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AWS Automates KYC and Fraud Detection—Makes Banks Failproof
- The Indian Economic Survey 2025 highlighted the increasing use of generative AI in India's banking sector.
- AWS is aiding banks in addressing security and scaling challenges by offering solutions for generative AI integration.
- Companies like Dhan, HDFC, Fibe, and Axis Bank have begun leveraging generative AI through AWS for various benefits.
- Dhan automated 25% of KYC processes, reducing wait times by 50% and operational costs by 30% using AWS.
- Razorpay used generative AI to reduce payment failures and launched Ray Concierge for simpler payment gateway setups.
- Generative AI finds applications in fraud detection, customer experience enhancement, document summarization, and process automation in the BFSI sector.
- AWS ensures resiliency in UPI payments through availability zone architecture and automatic scaling.
- With a focus on security, AWS employs security-by-design principles and offers systems like landing zones for secure code development.
- AWS plans to invest $8.3 billion in cloud infrastructure in the AWS Asia-Pacific (Mumbai) Region to boost cloud computing in India.
- AWS aims to train developers to drive adoption of generative AI services and contribute significantly to India's GDP and job market.
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Medium
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The Causal Revolution in Machine Learning: Moving Beyond Correlation to Causation
- Causal Machine Learning focuses on identifying cause-and-effect relationships in data, moving beyond correlation.
- Traditional ML excels at finding correlations but lacks the ability to distinguish genuine causal relationships.
- Judea Pearl's 'ladder of causation' illustrates the progression from correlation to causality.
- Causal ML goes beyond association to understand interventions and counterfactuals, enabling more nuanced insights.
- Techniques like causal graphs and counterfactual reasoning help machines reason about cause and effect like humans.
- Causal ML's focus on causation over correlation leads to more reliable decision-making and intervention strategies.
- Traditional ML's reliance on correlation can lead to flawed decision-making, misallocation of resources, and perpetuation of biases.
- Causal ML techniques, such as SCMs and counterfactual reasoning, offer more accurate predictions with less data.
- Causal ML reduces bias, enables targeted interventions, and enhances decision-making in real-world applications.
- Challenges in implementing causal ML include modeling complexity, data quality, biased data, and integration with existing ML infrastructure.
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Medium
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Malware Detection Using Machine Learning Methods on the APIMDS Dataset-1: Preparation of the…
- Traditional methods for malware detection are no longer sufficient in cybersecurity, leading to the adoption of machine learning-based approaches.
- The article focuses on using Machine Learning algorithms with the APIMDS dataset for malware detection.
- The dataset contains API call sequences of malware samples classified by Kaspersky AntiVirus.
- Each row in the dataset corresponds to a software sample, with API call sequences being the main components.
- The article discusses the challenges faced due to varying column lengths in the dataset when using Pandas for data processing.
- The manual operation involves organizing the data to represent API calls as columns with binary values for presence.
- The process includes cleaning the dataset, adjusting the malware_class column to differentiate between harmless and harmful software.
- After preparation, the dataset consists of 17268 rows and 1165 columns for machine learning analysis.
- Analysis reveals the most frequently used API calls in the dataset, showcasing critical calls for malware detection.
- The dataset is now clean and ready for model training using machine learning techniques for malware detection.
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Analyticsindiamag
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Flora Launches AI Platform with More Creative Controls, Encourages ‘AI Haters’ to Try it
- Flora is a new startup founded by Weber Wong, aiming to bring together various creative platforms under one roof.
- Flora offers the best AI models and three types of blocks (text, image, and video) that allow users to have creative control over the generative process.
- The platform supports real-time collaboration and sharing of projects.
- Flora is available for free with restrictions on projects, and the pricing starts at $16 per month.
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Analyticsindiamag
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Progress Appoints Ed Keisling as Chief AI Officer to Drive AI Strategy
- Progress, a global software company, has appointed Ed Keisling as its Chief AI Officer (CAIO) to advance its AI strategy and enhance its product portfolio.
- Keisling, previously the senior vice president of engineering for infrastructure management at Progress, brings extensive experience in system architecture, cloud computing, and infrastructure management.
- Keisling aims to ensure that Progress' customers fully leverage AI's transformative potential by providing them with the tools, processes, and expertise to drive further value with their products.
- The role of Chief AI Officer is becoming essential in corporate boardrooms as AI is increasingly central to business strategy and success.
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