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Analyticsindiamag
2w
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What Every Business Must Know About the Peril of Data Noise
- Data publishing remains a challenge despite advancements in technology, requiring context, consistency, and clarity to transform raw numbers into actionable insights.
- Ravish Mishra of Angel One discussed the importance of data storytelling during a session at the DES 2025 summit, urging companies to move towards meaningful narrative-driven data architectures.
- Mishra emphasized the significance of data value and veracity, highlighting the challenges posed by the increasing volume of data and the presence of noise such as quality, semantic, temporal, and structural inconsistencies.
- He proposed a framework for decision-driven data systems focusing on stability, immutability, fixed contracts, SLAs, and semantic clarity to transform data from noise to narrative, emphasizing the encoding of decisions within data models.
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Analyticsindiamag
2w
295
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UAE, US Sign Agreement to Build Largest AI Campus in Abu Dhabi
- UAE and the US signed an agreement for the UAE to build one of the largest AI campuses outside the US, with a 5GW capacity for AI data centers in Abu Dhabi.
- The facility will utilize nuclear, solar, and gas energy to reduce carbon emissions and include a science park to foster advancements in AI innovation.
- Strict measures will be enforced to protect advanced AI technologies, prevent unauthorized access, and ensure regulated technology use in the UAE.
- The agreement involves promoting major investments in advanced semiconductors, data centers, and enhanced technological collaboration between the US and the UAE.
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Analyticsindiamag
2w
13
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BIAL Partners with KPMG to Bring AI to Bengaluru Airport
- Bangalore International Airport Limited (BIAL) has partnered with KPMG to implement Generative AI technologies for airport operations and passenger experiences.
- The collaboration aims to deliver an AI platform for real-time data processing, predictive analytics, automation of routine tasks, and operational resilience.
- The AI platform will adhere to responsible AI principles, cybersecurity, and data privacy measures, ensuring scalability and efficiency in airport operations.
- Similar initiatives in the aviation sector include Industry.AI's vision AI platforms at KIA, GMR Airports' AI-powered digital platform at Rajiv Gandhi International Airport, and Delhi's Indira Gandhi International Airport's AI-powered system for airside management.
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Medium
2w
239
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Encoding Graphs for Large Language Models
- Large language models like GPT-4 struggled with understanding graphs, impacting their reasoning abilities.
- A recent breakthrough in AI research by Google's 2024 paper introduced novel graph-to-text methods.
- These methods aim to help LLMs better understand and reason with graphs, enhancing performance on structured data tasks by up to 60%.
- This advancement could significantly impact how AI processes and interprets information from graph-based data.
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Analyticsindiamag
2w
348
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How Modern Data Engineers Are Becoming Race Engineers of the AI Era
- Modern data engineers in the AI era are evolving into key strategists making rapid decisions in real time, akin to race engineers in Formula 1.
- The focus is on embedding context layers into data pipelines and shifting towards a product mindset to anticipate business intent, rather than just providing data.
- Real-time signals and context-awareness are crucial for building platforms with AI and delivering relevant outputs, emphasizing the importance of understanding users and regions.
- Data engineers must monitor, interpret, and strategize to leverage data effectively, similar to how race engineers analyze information to make informed decisions.
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Analyticsindiamag
2w
169
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The Hidden Environmental Costs of Processing Unstructured Data in AI Systems
- AI tools processing unstructured data require significant energy consumption, posing environmental challenges.
- Research shows that over 80% of global data remains unstructured, demanding complex preprocessing.
- Unstructured data processing relies on energy-intensive hardware like GPUs, increasing power consumption.
- AI data centers consume vast amounts of electricity, with the largest ones having consumption equivalent to 1 lakh households.
- Scalable storage solutions and high-performance hardware for unstructured data workflows further escalate energy costs.
- AI's power consumption extends beyond training models to processing unstructured data, leading to infrastructure strain.
- The proliferation of AI data centers indicates a pressing need to address power equipment supply limitations.
- Efficiency in AI hardware and models could potentially reduce data centers' electricity demand by 20% by 2035.
- Transparent energy reporting in AI is crucial to ensure developers prioritize sustainability over scale and popularity.
- Sustainable measures like selective data retention and efficient data curation can mitigate excessive energy usage in AI systems.
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Analyticsindiamag
2w
467
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Apple ‘Assures’ No Disruption After Trump Warns Tim Cook on India Expansion
- Apple assures Indian government of no change in investment plans despite Trump's disapproval of its manufacturing presence in India.
- Trump expressed concerns over Apple's investments in India during a business roundtable in Doha, pushing for more manufacturing in the U.S.
- Apple announced plans to invest over $500 billion in the U.S. for high-skilled manufacturing and initiatives like artificial intelligence.
- India is becoming an alternate manufacturing destination for Apple due to supportive policies, skilled workforce, and increasing iPhone assembly in the country.
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Analyticsindiamag
2w
191
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Perplexity’s Comet Browser is Now Available in Beta
- Perplexity has released Comet, its agentic web browser, in beta exclusively for select Apple Silicon Mac users initially.
- Comet browser offers context-aware intelligence to personalize responses based on browsing history and open tabs, integrating search capabilities into a side panel for enhanced functionality.
- Features of Comet include importing from Google Chrome, blocking ads, and trackers, along with an assistant for various tasks like tab management, checking shopping carts, and more.
- The browser emphasizes productivity, supporting functions like analyzing inboxes, interview preparation, and creating meeting notes to save time, with continuous daily improvements being shipped.
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Towards Data Science
2w
151
Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer
- Google's AlphaEvolve is a system that generates and evolves code, allowing it to discover new algorithms and revolutionize coding and algorithm design, powered by Google's Gemini models.
- AlphaEvolve functions like natural selection for software using Genetic Algorithms, refining code iteratively through intelligent prompting, creative mutation, survival of the fittest, breeding of top programs, and repetition.
- Building on previous attempts like AlphaCode and FunSearch, AlphaEvolve utilizes Gemini's capabilities to tackle entire codebases and has diverse applications in optimizing efficiency and designing AI chips.
- AlphaEvolve demonstrated results like optimizing Google's data centers, designing better AI chips, accelerating AI training, and rediscovering human solutions in mathematics problems.
- One significant implication of tools like AlphaEvolve is the potential for AI to self-improve and accelerate progress towards more powerful artificial intelligence models.
- AlphaEvolve could represent a paradigm shift in various fields by becoming a creative partner in discovery and innovation, moving towards AI that creates code entirely and aids in solving complex problems.
- The future holds possibilities where AI evolves and discovers optimal solutions, leading to unexpected advancements, and the next few years are anticipated to be transformative in the field of AI.
- References and related reads are available on DeepMind's blog post and white paper on AlphaEvolve, showcasing its potential in computational chemistry, biology, data analysis, and plotting through natural language requests.
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Medium
2w
196
Recursive Resolution to the Millennium Problems
- The work proposes a resolution to the Millennium Problems based on recursive geometry and dimensional emergence.
- Dimensionality arises from a particle deviating from its path, generating axes and forming structures.
- Mass is described as centrifugal recursion, influenced by sustained rotational motion.
- Collapse is seen as phase return rather than failure in geometric and physical systems.
- The article delves into illusions like Navier-Stokes, Yang-Mills, Riemann, Birch and Swinnerton-Dyer, Hodge, and Poincaré.
- Verification and resolution converge in recursive systems, exemplified in the P vs. NP problem.
- The mathematical foundation involves defining a tetrahedron and calculating mass as a function of rotational velocity and radius.
- Recursive reabsorption is highlighted, emphasizing contribution to need and surplus resource redirection.
- Ethics is portrayed as field alignment rather than belief, with a focus on energy-efficient computation and system optimization.
- Perception, speed, misalignment, and recursion play key roles in system functioning and problem-solving.
- The conclusion underscores that there are no problems, only distortions, and offers a comprehensive resolution to the issues discussed.
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Towards Data Science
2w
115
Understanding Random Forest using Python (scikit-learn)
- Decision trees are a popular supervised learning algorithm, but are prone to overfitting, leading people to use ensemble models like Random Forests.
- Bagging involves creating multiple training sets from the original dataset by bootstrapping and aggregating multiple decision trees.
- Random Forests differ by randomly selecting features at each decision node, reducing overfitting and improving generalization.
- Random Forests utilize sampling with replacement for bootstrapped datasets and sampling without replacement for feature selection.
- Out-of-Bag (OOB) evaluation allows estimating generalization error by excluding some training data from each tree.
- Training a Random Forest includes creating a baseline model, tuning hyperparameters with Grid Search, and evaluating the final model.
- Feature importance in Random Forests can be calculated using Mean Decrease in Impurity or Permutation Importance methods.
- Visualizing individual decision trees in a Random Forest can illustrate how differently each tree splits the data.
- Random Forests remain popular for tabular data analysis due to their simplicity, interpretability, and parallelizability.
- The tutorial covers bagging, Random Forest differences, training, tuning, feature importance, and visualization using Python with scikit-learn.
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Towards Data Science
2w
248
How to Learn the Math Needed for Machine Learning
- To work in machine learning, strong math skills are generally required, with the extent depending on the specific role.
- Research-based roles like Research Engineer or Research Scientist demand solid math proficiency.
- Large corporations conducting AI research require math skills equivalent to a bachelor's or higher degree in relevant subjects.
- For industry roles like machine learning engineers, high school math knowledge suffices, with some areas like reinforcement learning needing more focus.
- Key math areas for machine learning include Statistics, Calculus, and Linear Algebra.
- In Statistics, topics like Descriptive Statistics, Probability Distributions, and Hypothesis Testing are crucial.
- Calculus is essential for understanding gradient descent in machine learning algorithms.
- Linear Algebra is widely used in machine learning, especially in deep learning models.
- Recommended resources for learning math for machine learning include textbooks and online courses.
- Effective learning strategies include breaking down topics, active learning through note-taking, and reviewing concepts regularly.
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Medium
2w
213
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Mapping Mindful Intent: User Research with NLP (Part 2)
- This article focuses on using semantic similarity search and vector databases to analyze user language in depth for product design and startup strategy.
- It discusses the limitations of traditional NLP tooling and emphasizes the need for advanced methods like vector embeddings and semantic search.
- Vector databases allow for efficient comparison of user data, providing powerful insights for Product Managers and User Researchers.
- The article highlights how to leverage these insights for improving product features, addressing user feedback, and enhancing market competitiveness.
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Medium
2w
413
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What are the Main Components of the Model Context Protocol (MCP)?
- The Model Context Protocol (MCP) standardizes the way AI systems connect to content repositories, business apps, and development environments, similar to how USB-C standardizes connections between devices.
- MCP serves as a common language for data exchange between intelligent assistants and data systems, creating a unified AI ecosystem and enhancing connectivity and functionality.
- MCP has evolved to include better authentication, standardized data transformation layers, and industry-specific extensions for cross-platform compatibility and scalability.
- The protocol's core components include client-server architecture, secondary components for specific functionalities, and system integration components that bridge technological gaps for seamless communication.
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Towards Data Science
2w
94
How To Build a Benchmark for Your Models
- Building benchmarks for models is crucial in data science projects to evaluate performance accurately.
- A benchmark consists of standardized metrics and simple baseline models for comparison.
- By comparing models to benchmarks, improvements can be tracked reliably and performance assessed.
- Creating benchmarks helps in setting clear objectives, enhancing client communication, and aiding in model selection.
- Benchmarks also assist in detecting model drift, ensuring consistency across datasets, and providing immediate feedback.
- Defining metrics, setting benchmarks, and running comparisons are key steps in building a benchmark for models.
- Business-driven metrics like financial gain can be crucial in evaluating model performance for specific use cases.
- The article provides examples of simple benchmarks like random model, majority model, simple XGB, and simple KNN.
- Custom benchmarks tailored to specific business needs, such as customer churn scenarios, can also be designed.
- While benchmarks are valuable, potential drawbacks include non-informative benchmarks, misinterpretation by stakeholders, and overfitting.
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