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Data Science News
Towards Data Science
2w
229
🚪🚪🐐 Lessons in Decision Making from the Monty Hall Problem
- The Monty Hall Problem is a brain teaser offering lessons in Decision Making, especially relevant for data scientists.
- The problem involves choosing between three doors, one with a prize and two with goats, and deciding whether to stick with the initial choice or switch.
- By applying common sense, Bayesian analysis, and causal models, the optimal strategy is to always switch doors after the host reveals a goat.
- Lessons learned from the Monty Hall Problem include the importance of updating beliefs with new information and shifting from fast to deep thinking.
- The problem demonstrates the counterintuitive nature of probabilities and the need to be comfortable with ambiguity in decision-making.
- Insights gained from the problem can be applied in real-world data science scenarios, emphasizing the value of critical thinking and subjective decision-making.
- Various examples, analogies, visualizations, and simulations help elucidate the solution and enhance understanding of probability concepts.
- The article concludes with a reflection on embracing ignorance, humility in learning, and the diverse approaches to problem-solving.
- Overall, the Monty Hall Problem serves as a valuable tool for improving decision-making skills and thinking processes, particularly in data science.
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Medium
2w
143
Image Credit: Medium
Practical implementation on adding Alpha on a strategic allocation using Black-Litterman model…
- The combination of Strategic Asset Portfolio (SAP) and Tactical Asset Portfolio (TAP) forms the final optimal portfolio according to the Black-Litterman model.
- Traditional Mean-Variance Optimization faces challenges like making precise forecasts, which are addressed by the Black-Litterman model.
- Black-Litterman model allows for classification-based approach to construct investor views and incorporates Sharpe Ratios for SAP and TAP to determine expected returns.
- The optimization problem in Black-Litterman model is formulated as a convex optimization framework with risk aversion coefficients and penalties for turnover and covariance shrinkage.
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Medium
2w
104
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Why Tech Ethics Needs More Than Engineers
- Algorithms play a significant role in shaping interactions and decisions in today's world, yet they are often assumed to be impartial and neutral, which is not the case.
- The individuals building and utilizing these algorithms need to ask critical questions about fairness, inclusion, and the impacts of coded biases in these systems.
- Issues of bias and inequality are evident in algorithmic risk assessment tools, as they reflect and sometimes reinforce existing societal prejudices and power dynamics.
- Ethics in technology must not be an afterthought but should be integrated at the very beginning, addressing the human factors, power structures, and values that influence the development and implementation of algorithms.
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6 Likes
Medium
2w
344
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Protecting Data Privacy in a World of Threats
- Data privacy is crucial in today’s digital world due to the escalating threats of identity theft and corporate espionage.
- Hardware-level encryption, such as self-encrypting drives (SEDs) following Opal SSC specification, provides a robust defense against data breaches.
- Opal SEDs store encryption keys on the drive, ensuring that even if a device is stolen, data remains inaccessible without proper credentials.
- Opal Lock by Fidelity Height offers specialized management tools for Opal SEDs, ensuring efficient encryption configuration and maintenance to maximize data security.
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Medium
2w
348
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“Learning AI the Right Way: Why Every Beginner Should Start Here”
- The author shares their experience of beginning their AI learning journey with foundational knowledge and then turning to YouTube videos for a deeper understanding.
- The author describes the pivotal moment when they started reading a technical book that focused on the fundamental questions about AI, such as the concept of thinking and the interdisciplinary nature of AI.
- The book provided the author with a comprehensive understanding of AI, leading them on a journey through the ideas that shaped the field.
- The author emphasizes the importance of approaching AI with a broad perspective, incorporating various disciplines like philosophy, neuroscience, and economics, and recommends the book 'Artificial Intelligence: A Modern Approach' for those interested in exploring AI further.
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Medium
2w
204
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Data Mesh Architecture: Decentralizing Data Ownership
- Data Mesh architecture decentralizes data ownership and treats data as a product owned by domain teams.
- Key principles of Data Mesh include domain-oriented ownership, data as a product, self-serve data platform, and federated computational governance.
- Advantages of implementing Data Mesh include scalability, agility, domain expertise utilization, reduced bottlenecks, and improved data quality.
- Challenges of adopting Data Mesh include cultural shift, skills distribution, governance complexity, technology infrastructure, and organizational alignment.
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12 Likes
VentureBeat
2w
78
Arm is rebranding its system-on-a-chip product designs to showcase power savings for AI workloads, targeting a surprising sector
- Arm, a UK-based chip designer, is rebranding its system-on-a-chip product designs to focus on power savings for AI workloads.
- Arm, known for providing SoC architecture used by tech giants like Nvidia, Amazon, and Google, aims to delve into the AI sector.
- The company's shift from a component supplier to a platform-first entity emphasizes its ecosystem to aid in scaling AI efficiently.
- Arm's historical proficiency in creating low-power chips positions it well for powering AI training and inference tasks.
- As AI workloads grow in complexity and power needs, Arm is restructuring its offerings around complete compute platforms.
- Arm is retiring previous naming conventions and introducing new product families organized by market segments.
- The rebranding aligns with Arm's successful Q4 results, crossing $1 billion in quarterly revenue and showing strong growth in royalties and licensing.
- Arm's focus on AI, automotive, and cloud hyperscalers like Google and Amazon reflects its strategic growth areas.
- The new branding aims to provide naming clarity and performance tiers to meet the rising demand for energy-efficient AI compute.
- Arm's rebranding and platform integration offer streamlined paths for selecting compute architectures optimized for AI workloads.
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4 Likes
Medium
2w
335
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Hinge Loss: Understanding and Implementing it from Scratch
- Support Vector Machines (SVMs) use hinge loss to confidently separate data points into distinct classes.
- SVMs aim to find the best line with the widest margin between classes, even allowing for some errors with soft margins.
- Hinge loss measures the model's confidence in its decisions, encouraging a clear margin of difference between classes.
- While logistic regression uses log loss for smooth and probabilistic decisions, SVMs use hinge loss for sharp decision boundaries.
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Analyticsindiamag
2w
74
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Why INDMoney is Using Golang to Power Real-time Data Streaming
- INDMoney is using Go programming language, the messaging system NATS, and a WebSocket engine to deliver high-frequency market data at extraordinary speeds.
- The company's objective is to keep traders updated in real-time with latencies below 200 milliseconds as milliseconds matter in trading opportunities.
- INDMoney's system employs microservices powered by Go programming language for efficient and minimal resource consumption infrastructure.
- Key components include multicast streaming, NATS for reliable message delivery, protocol optimization using Protobuf, and client-side sampling for precise measurements.
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4 Likes
Analyticsindiamag
2w
287
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What Skills Do You Need to Be a Data Engineer in 2025?
- Data engineering professionals are facing challenges in adapting to rapid technological changes due to the increasing demand for advanced skills, especially in the era of AI.
- Prakash Rajagopalan highlighted the impact of LLMs and generative AI on the industry, emphasizing the reshaping of the data engineering lifecycle and processes.
- Key areas for professionals include rethinking the data engineering lifecycle, adapting to new demand patterns, and leveraging AI tools effectively for tasks such as data modelling and code generation.
- Organisations and individuals need to focus on upskilling and adapting to new ways of working, embracing an AI-first approach to augment work processes with AI tools in the coming years.
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17 Likes
Medium
2w
143
Image Credit: Medium
What is Machine Learning? A Simple Explanation with Real-Life Examples.
- Machine Learning is a part of computer science where computers learn from data like humans learn from experience.
- Arthur Samuel coined the term Machine Learning in 1959, defining it as the ability for computers to learn without explicit programming.
- Machine Learning helps businesses and systems become smarter and faster by analyzing vast amounts of data.
- Examples of Machine Learning in everyday life include facial recognition for phone unlocking and personalized video suggestions on platforms like YouTube.
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8 Likes
VentureBeat
2w
213
Image Credit: VentureBeat
You.com’s ARI Enterprise crushes OpenAI in head-to-head tests, aims at deep research market
- You.com has launched ARI Enterprise, claiming it outperforms OpenAI in head-to-head tests and excels in accuracy on independent benchmarks.
- ARI achieved 80% accuracy on the FRAMES benchmark, surpassing major competitors.
- This advanced research platform offers 4x greater depth and breadth and connects to internal corporate data sources.
- ARI Enterprise provides 35% more insights and facts per research project, with enhanced integration capabilities.
- Early adopters include venture capital firms, consulting agencies, and research institutions.
- The platform's interactive approach involves collaboration with users to refine research plans and guide analysis.
- Founder Richard Socher emphasizes ARI's role in augmenting analysts' efficiency, not replacing them.
- You.com believes ARI Enterprise democratizes access to high-quality research and transforms professional roles.
- The company raised $99 million to challenge Google's search dominance and has reported significant revenue growth.
- ARI Enterprise changes how businesses process information, offering comprehensive, verified analysis in minutes.
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12 Likes
Medium
2w
113
Image Credit: Medium
The Simplest Way to Understand Ocean Protocol’s Compute-to-Data
- Private data is valuable for research and innovation in industries like healthcare and finance, but using it while maintaining privacy is crucial.
- Ocean Protocol's Compute-to-Data allows for the value of data to be unlocked without giving it away, enhancing collaboration, innovation, and earning opportunities.
- Compute-to-Data (C2D) enables algorithms to access and process data without exposing the raw information, promoting data privacy in applications like healthcare, finance, and AI development.
- Key components of Compute-to-Data include data providers, data consumers, and compute providers, offering benefits like privacy preservation, monetization opportunities, and enhanced compliance with data protection regulations.
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6 Likes
Analyticsindiamag
2w
91
Image Credit: Analyticsindiamag
Using Huawei AI Chips Could Lead to Criminal Charges, US Warns
- The US warns against the use of Huawei AI chips, stating it could lead to criminal charges under US export control laws.
- The Bureau of Industry and Security rescinded the AI Diffusion Rule issued by former US President Biden, aiming to strengthen export controls on semiconductors globally.
- Huawei's Ascend processors face stringent export restrictions due to suspicions of utilizing US technology, complicating the company's efforts to create advanced AI chips and smartphones.
- The Trump administration previously imposed restrictions on Huawei in 2019, impacting the company's chip market, but it has since recovered with Chinese government support.
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5 Likes
Analyticsindiamag
2w
353
Image Credit: Analyticsindiamag
Twilio Partners With Microsoft To Advance Conversational AI
- Twilio and Microsoft announced a strategic partnership to boost conversational AI solutions globally.
- The collaboration aims to empower Twilio developers and Microsoft-managed customers to create AI-driven customer interactions.
- Twilio's communication and AI expertise, combined with Microsoft Azure AI's infrastructure, will enhance conversational AI adoption.
- New innovations include AI-powered agents, tools for customer service agents, and multi-modal solutions to improve digital customer interactions.
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