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Data Science News
Medium
1w
250
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AI’s Hidden Water Problem: How Artificial Intelligence Consumes Water and What It Means for Our…
- AI consumes water primarily through data centers and cooling systems, as well as semiconductor manufacturing.
- Data centers use cooling systems that consume billions of gallons of water annually, with Google's data centers using 5 billion gallons of fresh water for cooling in 2022.
- Semiconductor manufacturing, essential for AI chips, can use up to 10 million gallons of ultrapure water per day.
- To make AI more water-efficient, companies can improve cooling technologies, transition to renewable energy, promote transparency and accountability, and innovate in semiconductor manufacturing.
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Towards Data Science
1w
111
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Least Squares: Where Convenience Meets Optimality
- The popularity of Least Squares stems from its simplicity and its alignment with key statistical principles.
- The Least Squares approach is favored for its mathematical clarity, providing closed-form solutions for optimization.
- However, its reliability diminishes when data deviates from theoretical assumptions, like outliers in the distribution.
- Least Squares is especially useful in Linear Regression, offering optimal coefficients estimation through OLS.
- The conventional mean and median in statistics are directly linked to L2 and L1 losses, respectively.
- Under the Gauss-Markov theorem, the OLS estimator is acclaimed as the Best Linear Unbiased Estimator.
- OLS minimizes variance and maintains unbiasedness, outperforming other linear estimators.
- Least Squares is also equivalent to Maximum Likelihood Estimation in normal error scenarios.
- However, the reliance on normal errors makes Least Squares less effective in the presence of outliers.
- To address this limitation, robust loss functions like Huber or Tukey are recommended for outlier resilience.
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Medium
1w
261
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Exploring the Evolution and Classifications of Artificial Intelligence
- Artificial Intelligence (AI) is rapidly evolving, impacting various sectors with enhanced efficiency and capabilities.
- Understanding the classifications of AI is crucial to grasp its current applications and future prospects.
- AI types can be categorized based on capabilities and functionalities, displaying distinct characteristics and applications.
- From Narrow AI to Artificial General Intelligence (AGI), AI is reshaping industries like healthcare, finance, and the creative arts.
- AGI aims to replicate human intelligence across various tasks, enabling reasoning, problem-solving, and real-time decision making.
- Super AI, a hypothetical advanced form, surpasses human capabilities, raising ethical and existential questions on control and humanity's future.
- AI systems operate in various types, including Basic AI, Limited Memory AI, and Full Theory of Mind AI.
- AI advancements span domains like emotion recognition, self-awareness, and understanding human behavior, posing ethical challenges.
- AI applications include statistical techniques, natural language processing, visual data analysis, and robotics integration.
- Industries such as healthcare, finance, creative arts, and legal frameworks are undergoing significant transformations with AI integration.
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Analyticsindiamag
1w
1
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Western Union Teams Up with HCLTech to Shift to AI-Led Platform Model
- Western Union has partnered with HCLTech to enhance its financial services through an AI-driven platform operating model.
- The collaboration designates HCLTech as Western Union’s largest preferred partner, focusing on digital transformation, platform innovation, and operational efficiency.
- As part of the agreement, Western Union will use HCLTech’s AI-powered solutions, FENIX 2.0 and AI Force, to transition to a platform-centric model, enhancing agility and scalability.
- The partnership also aims to accelerate Western Union’s platform and channel transformation by utilising HCLTech’s digital, cloud, and AI solutions to improve customer experiences and support data-driven decision-making.
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Medium
1w
29
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It’s Either Greater Than Or Less Than
- The puzzle requires algebraic manipulation and knowledge of Euler's number.
- Two boxes in the middle are to be filled with either > or <.
- The series expansion of e^x is used to solve the puzzle.
- The solution involves substituting x = 1 into the series expansion.
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Analyticsindiamag
1w
211
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IAF Signs Contract with IG Drones to Enhance Airbase Safety
- The Indian Air Force (IAF) has signed a contract with IG Drones to implement a Bluetooth Low Energy (BLE) tool tracking system.
- The system aims to enhance airbase safety and efficiency by streamlining tool management and inventory tracking.
- The BLE system will help minimize Foreign Object Debris (FOD) damage by enabling real-time tracking and automated alerts for tools left on runways.
- The system offers an extended charging cycle of six to eight months, reducing maintenance efforts and ensuring uninterrupted operations.
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Analyticsindiamag
1w
42
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LLMs Hit a New Low on ARC-AGI-2 Benchmark, Pure LLMs Score 0%
- ARC Prize has announced the ARC-AGI-2 benchmark to evaluate AI models' human-like intelligence.
- The benchmark poses greater challenges by factoring in efficiency and performance.
- Non-reasoning models (Pure LLMs) scored 0%, while human participants achieved a perfect score of 100%.
- OpenAI's o3 reasoning model received the highest score of 4.0%, but will not be released as a standalone model.
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Towards Data Science
1w
358
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From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities
- The article discusses the development of a Morphological Feature Extractor to enhance AI's recognition capabilities by mimicking human visual recognition processes.
- Traditional CNNs lack the structured trait separation seen in human recognition, leading to difficulties in distinguishing similar objects.
- The Morphological Feature Extractor focuses on body proportions, head shape, fur texture, tail structure, and color patterns to help AI understand and recognize objects better.
- Different analyzers within the extractor address specific features like body proportions, head features, tail features, fur texture, and color patterns.
- The Feature Relationship Analyzer connects these morphological features to improve breed differentiation, similar to how human intuition works.
- The article highlights the importance of the residual connection in allowing different information channels to complement each other for improved recognition accuracy.
- By integrating the Morphological Feature Extractor, model accuracy in distinguishing similar-looking dog breeds significantly improved.
- Heatmaps demonstrate how the extractor refocuses the model's attention to key features, leading to more reliable predictions and reduced misclassifications.
- The concept of Morphological Feature Extractors can extend beyond dog breed identification, potentially benefiting other domains requiring recognition of fine-grained differences.
- Challenges and areas for improvement exist in refining the methodology, emphasizing the need for continuous development in AI feature recognition.
- Overall, the approach of Morphological Feature Extractors represents a step towards AI thinking more like humans, focusing on crucial features for improved recognition and decision-making.
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Analyticsindiamag
1w
12
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How DataRobot is Pushing AI Use Cases to Production
- DataRobot unveiled its enterprise AI suite to simplify AI application creation and deployment.
- CEO Debanjan Saha stresses the need for AI investments to deliver tangible business value.
- DataRobot focuses on identifying AI use cases that drive significant business impact.
- Robust governance and monitoring features in the AI suite address the confidence gap in AI deployment.
- DataRobot aims to make AI more accessible by lowering the bar for participation.
- The enterprise AI suite includes composable AI applications and agents for various business needs.
- Advanced AI observability features, including guard models, enhance safety and reliability in production environments.
- DataRobot enables faster AI deployment and cost reduction for organizations like CVS Health and BMW Group.
- In India, DataRobot has demonstrated increased productivity and accelerated value from AI with companies like Razorpay.
- DataRobot's approach bridges predictive, generative AI, and agentic AI to meet enterprise needs.
- The enterprise AI suite facilitates collaboration among teams for innovation and rapid scaling of AI applications.
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Analyticsindiamag
1w
223
Microsoft Introduces Security Copilot Agents That’s Set To Get ‘Smarter’ Over Time
- Microsoft has launched Security Copilot agents and Microsoft Purview, an AI-powered data security investigations and analysis platform.
- The Security Copilot agents aim to automate tasks and manage the increasing volume and complexity of cyberattacks.
- The agents provide autonomous and adaptive automation, continuously learning and improving over time.
- Microsoft Security Copilot agents will be available in preview from April 2025.
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Analyticsindiamag
1w
42
How Axtria is Helping the Life Sciences Industry Achieve an AI-Driven Future
- The life sciences industry is leveraging AI to develop treatments for rare diseases that lack effective therapies.
- Axtria, a New Jersey-based company, is leading the charge in the AI-driven future of the industry.
- Axtria's GenAI Solutions, built on cloud platforms, enable scalability, predictive analysis, and personalized customer engagement.
- Axtria's AI-driven software, such as Axtria DataMAx, helps solve data integration and analytics challenges for life sciences organizations.
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Analyticsindiamag
1w
63
Is Indian Edtech Still Worth the Bet After 2,150 Failed Startups?
- India's edtech sector has experienced a tumultuous time in recent years, with a significant number of failures.
- A recent cohort study by WTFund 2024 showed that 15% of the total startup applications were in the edtech sector.
- Between 2015 and 2024, a total of 2,780 Indian edtech startups shut down, with 2,150 closures occurring between 2020 and 2024.
- Despite the failures, there are still successful edtech companies in India such as Physics Wallah and Vedantu, which are preparing for IPOs.
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VentureBeat
1w
186
Midjourney’s surprise: new research on making LLMs write more creatively
- Midjourney, known for its AI image generators, released new research on training text-based large language models (LLMs) to write more creatively.
- The collaboration with New York University introduces two new techniques, DDPO and DORPO, to expand the range of possible outputs while maintaining coherence and readability.
- The research goes beyond academic exercises and could fuel a new wave of LLM training among enterprise AI teams, product developers, and content creators.
- By incorporating deviation, the models learn to produce high-quality but more varied responses, ensuring AI-generated stories explore a wider range of characters, settings, and themes.
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VentureBeat
1w
304
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DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI
- Chinese AI startup DeepSeek has released a new large language model, DeepSeek-V3-0324, under an MIT license allowing commercial use.
- The model can run on Apple's Mac Studio with M3 Ultra chip, achieving over 20 tokens per second.
- DeepSeek's launch lacked typical fanfare, with no whitepaper or marketing, but the model has shown improvements over its predecessor.
- DeepSeek-V3-0324 operates with a MoE architecture, activating only 37 billion out of its 685 billion parameters for specific tasks, enhancing efficiency.
- The new model incorporates MLA and MTP technologies, boosting output speed by nearly 80%.
- With a 4-bit quantized version offering reduced storage footprint, it can run on high-end consumer hardware, challenging traditional AI infrastructure.
- Chinese AI companies like DeepSeek opt for open-source licensing, contrasting with Western companies keeping models behind paywalls.
- This strategy enables rapid transformation and AI innovation in China, with tech giants like Baidu, Alibaba, and Tencent also embracing open-source models.
- DeepSeek-R2, an advanced reasoning model, is anticipated to build upon DeepSeek-V3-0324, potentially competing with models like GPT-5 from OpenAI.
- By democratizing access to AI technology through open-source models, DeepSeek is reshaping the future of AI development and adoption globally.
- DeepSeek's approach reflects a broader trend towards making AI more accessible and empowering a wider range of developers and researchers in the field.
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Medium
1w
164
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How to Automate Data Labeling with Google Bert
- YouTube ads are a popular platform for companies to advertise products and drive revenue.
- Some companies aim to protect their brand image by avoiding placing ads on harmful content.
- A Data Scientist developed an automated labeling system to predict if a video is harmful or not.
- This system aims to improve efficiency and reduce the time required for manual labeling.
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