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Data Science News
Analyticsindiamag
2w
168
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Cognizant Names Sailaja Josyula as Global Head of GCC Service Line
- Cognizant has appointed Sailaja Josyula as the Global Head of its Global Capability Center (GCC) Service Line.
- Josyula, who previously held leadership roles at Cognizant from 2018 to 2024, returns to the company after a brief tenure at EY.
- In her new role, Josyula will lead a cross-functional team to support clients in building and scaling next-generation GCCs.
- Cognizant also announced plans to build a 14-acre Cognizant Immersive Learning Centre (CILC) at its Chennai campus.
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Medium
2w
165
Image Credit: Medium
Why Tauri is the Future(Everything you need to know)
- Tauri is the future of application development and offers a powerful solution for developers.
- Rust, the programming language used in Tauri, provides high speed and safety.
- Tauri's Rust backbone keeps apps lean and significantly reduces app size compared to Electron.
- Rust's zero-cost abstractions ensure high performance, outperforming JavaScript by up to 300% in CPU-intensive tasks.
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Towards Data Science
2w
280
The Case for Centralized AI Model Inference Serving
- AI models are increasingly being used in algorithmic pipelines, leading to different resource requirements compared to traditional algorithms.
- Efficiently processing large-scale inputs with deep learning models can be challenging within these pipelines.
- Centralized inference serving, where a dedicated server handles prediction requests from parallel jobs, is proposed as a solution.
- An experiment comparing decentralized and centralized inference approaches using a ResNet-152 image classifier on 1,000 images is conducted.
- The experiment focuses on Python multiprocessing for parallel processing on a single node.
- Centralized inference using a dedicated server showed improved performance and resource utilization compared to decentralized inference.
- Further enhancements and optimizations can be made, including custom inference handlers, advanced server configurations, and model optimization.
- Batch inference and multi-worker inference strategies are explored to improve throughput and resource utilization.
- Results show that utilizing an inference server can significantly boost overall throughput and efficiency in deep learning workloads.
- Optimizing AI model execution involves designing efficient inference serving architectures and considering various model optimization techniques.
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Medium
2w
168
Image Credit: Medium
How I Made $1,500 by Creating Study Guides
- The global eLearning market is worth over $399 billion and presents a lucrative opportunity for educational content creators.
- Creating study guides can be a profitable venture, especially with the help of AI-powered content creation tools.
- Platforms like Amazon's Kindle Direct Publishing (KDP) offer an easy way to reach a global audience and earn royalties.
- By focusing on crafting unique and helpful study guides, individuals can build a sustainable income stream in the eLearning market.
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Towards Data Science
2w
385
AI in Social Research and Polling
- The challenges in conducting social science research and polling are discussed, emphasizing the difficulty in obtaining a random sample of participants due to modern communication changes.
- Old methods of phone sampling for research studies are no longer effective, leading researchers to explore alternative methods such as using gig workers for polling participation.
- There is a growing reliance on AI tools in social research, but there are concerns about flawed assumptions regarding the capabilities of AI models.
- Certain AI approaches involve using language models (LLMs) to simulate human responses in polling, leading to questions about the reliability and accuracy of such methods.
- While some argue that LLMs can approximate human polling results, the potential bias and limitations of this approach raise skepticism about its effectiveness.
- The use of LLMs in polling and research poses ethical challenges, as it may undermine human participation and perpetuate a deterministic view of democratic processes.
- The shift towards AI-mediated polling raises concerns about replacing human inputs with technological mimicry, potentially marginalizing human perspectives and diminishing social participation.
- There is a critical need to address social problems rather than relying solely on AI solutions, as overlooking the complexity of human behavior and societal dynamics can have far-reaching implications.
- The discussion underscores the importance of considering broader societal impacts and ethical implications when deploying AI in social research and polling contexts.
- Using AI to address challenges in polling and research requires a nuanced understanding of its limitations and potential consequences on social engagement and democratic processes.
- Critical reflection on the societal implications of AI adoption in research and polling is essential to ensure ethical practices and preserve the integrity of democratic decision-making.
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Towards Data Science
2w
209
4 Levels of GitHub Actions: A Guide to Data Workflow Automation
- GitHub Actions is a CI/CD tool within GitHub that automates development and deployment workflows, including data workflows.
- Benefits of GitHub Actions in data workflows include setting up data science environments, streamlining data integration and transformation, and automating machine learning model training.
- GitHub Actions is free for public repositories and provides 2,000 free minutes per month for individual accounts with private repositories.
- GitHub Actions offers templates, community resources, and support forums for easy implementation.
- GitHub Action building blocks include Events, Workflows, Runners, and Runs, allowing for automation directly within repositories.
- The article presents 4 levels of GitHub Actions implementation for data workflows, starting from a simple workflow to a secure pipeline workflow.
- Level 1 introduces a basic setup with manual triggers and Python script execution.
- Level 2 adds environment setup and runs workflows automatically on code pushes to the main branch.
- Level 3 involves scheduled jobs and dynamic date handling for periodic data fetching.
- Level 4 enhances security and performance through secrets and environment variables management.
- GitHub Actions' versatility in building dynamic data pipelines offers a streamlined approach to data solutions and accelerates the development lifecycle.
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Towards Data Science
2w
48
Agentic AI: Single vs Multi-Agent Systems
- Agentic AI involves programming with natural language through large language models (LLMs) for automating tasks, allowing for more dynamic decision-making.
- LLMs serve as a communication layer on top of structured systems and data sources, interpreting natural language but not inherently validating facts.
- Agentic AI excels in interpreting nuanced language for tasks such as customer service and research but may not be ideal for structured tasks like precise calculations.
- LangGraph, Agno, Mastra, and Smolagents are agentic AI frameworks worth exploring, with LangGraph being a popular choice among developers for building workflows.
- Single-agent workflows involve one LLM accessing multiple tools to make decisions, while multi-agent workflows distribute tasks among different agents, offering more control and precision.
- Single-agent setups are easier to start with but may lack precision for complex tasks, while multi-agent systems require careful architecture design for effective data flow and collaboration.
- Using cheaper LLMs for most agents in a multi-agent system and reserving more advanced models for crucial tasks can help optimize costs and performance.
- Building multi-agent workflows requires thoughtful architecture and data flow planning, with each agent responsible for specific tasks and interactions among different agents.
- Improvements such as parsing user queries into structured formats, ensuring agents use tools effectively, enhancing summarization, handling errors, and implementing long-term memory can enhance workflow efficiency.
- State management, particularly isolating short-term memory for each team or agent, is crucial for optimizing performance and cost in agentic systems.
- Exploring different agentic workflows, such as single vs. multi-agent setups, can offer insights into the level of control, precision, and complexity achievable in automating tasks with AI.
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Medium
2w
233
Image Credit: Medium
The Rise and Fall of Enterprise AI: How to Get Value Out of It Again
- The rise and fall of enterprise AI is not due to broken technology, but rather strategic misalignment, poorly framed problems, and a lack of rigor in execution.
- The success of AI projects depends on solving what truly matters for someone at the right time, with a structured process and a focus on using data to drive decisions.
- AI governance is essential, as AI is already shaping various sectors such as hiring, healthcare, finance, and education. However, most companies lack a framework for AI governance and only a small percentage of universities teach it.
- Consumer AI and enterprise AI differ significantly, and the virality of tools like ChatGPT has created misleading expectations in business environments. The future of AI is not just technological, but also relies on human involvement and augmentation rather than automation.
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Medium
2w
250
Image Credit: Medium
AI Bias Uncovered: Tackling Inequality in Hiring, Healthcare & Beyond
- AI bias is a hidden force, shaping our world in ways we often don’t realize.
- Understanding AI bias could change your perspective on fairness and justice.
- AI systems, trained on biased data, can perpetuate and amplify existing social inequalities.
- Examples of AI bias include misdiagnosing conditions in people with darker skin tones and favoring male candidates in hiring processes.
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Medium
2w
255
Image Credit: Medium
Why You Should Start Exploring NLP Today
- NLP is already deeply embedded in our daily digital interactions.
- NLP is used for automation and can free up time for more meaningful work.
- NLP helps turn unstructured text data into actionable insights.
- NLP promotes inclusivity and can break down language barriers.
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Medium
2w
43
Decoding the Crypto Rebound: What 2025 Feels Like On-Chain
- Big players are now actively involved in blockchain, moving tokens and staking positions.
- DeFi is shifting towards long-term stability rather than rapid gains and crashes.
- AI is being used in blockchain analytics to identify hidden patterns and market shifts.
- More projects are embracing DAO models, prioritizing collaboration and trust in decision-making.
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2 Likes
Medium
2w
21
Image Credit: Medium
Kaizen for Code: Ultra-Fast, Ultra-Reliable Software Engineering through Continuous Improvement
- Software teams can boost speed, quality, and cost-effectiveness by applying manufacturing principles like kaizen and assembly line techniques to software development.
- Drawing parallels between manufacturing and software engineering reveals strategies to accelerate development cycles with improved reliability.
- The dilemma of speed versus quality in software development mirrors historical manufacturing challenges addressed by assembly line innovations.
- Toyota's Toyota Production System (TPS) demonstrates continuous improvement through small changes, akin to modern software development practices.
- Software value streams, similar to manufacturing processes, require analysis for efficiency improvements and restructuring.
- Establishing a software assembly line involves infrastructure design with tools like Terraform, Docker, and Kubernetes for consistency.
- Continuous Integration (CI) tools automate build processes, providing feedback to developers and ensuring quality components advance in the pipeline.
- Testing strategies, including unit, integration, end-to-end, performance, and security testing, are integrated into every stage of the software assembly line.
- Feature flags offer flexibility by enabling controlled feature releases and rapid experimentation in software development.
- Modular architectures, shared libraries, and design systems enhance software development efficiency through standardized, reusable components.
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VentureBeat
2w
21
I asked an AI swarm to fill out a March Madness bracket — here’s what happened
- A new generative AI technology, conversational swarm intelligence (hyperchat), enables teams to engage in real-time conversations and converge on AI-optimized solutions.
- Hyperchat breaks large groups into parallel subgroups with AI agents known as 'conversational surrogates' to distill and share insights within the groups.
- Enterprise teams are already using a commercial platform called Thinkscape®, powered by hyperchat technology, for optimized deliberations in real-time.
- In a public test, 50 random sports fans utilized Thinkscape to create a March Madness bracket, performing exceptionally well in the ESPN contest.
- Studies showed that hyperchat increased group intelligence significantly, with groups scoring an effective IQ of 128 versus 100 working individually.
- Comparisons between standard chat and hyperchat revealed that the latter led to increased productivity, collaboration, and better solutions among groups.
- Hyperchat, with the addition of 'contributor agents', enables hybrid collective superintelligence by combining human expertise and real-time factual content from AI.
- This technology has the potential to transform collaboration by allowing real-time conversations among teams of any size, even in large companies with hundreds of members.
- Louis Rosenberg, the founder of Immersion Corp and Unanimous AI, is at the forefront of advancing conversational swarm intelligence for collective decision-making.
- The success of the 50 sports fans in creating a March Madness bracket highlights the effectiveness of harnessing collective intelligence through hyperchat technology.
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Medium
2w
56
Python Cheat Sheet
- Variables and Data Types
- Data Structures
- Conditional Statements
- Loops
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VentureBeat
2w
380
Image Credit: VentureBeat
Emergence AI’s new system automatically creates AI agents rapidly in realtime based on the work at hand
- Emergence AI has introduced a new AI agent creation platform that allows users to specify tasks via text prompts and then generates necessary agents in real time.
- This platform is a no code, natural language, AI-powered multi-agent builder that aims to simplify and speed up data workflows for enterprise users.
- It enables the creation of agents that can anticipate related tasks and autonomously generate new agents to fulfill specific enterprise needs.
- The platform orchestrates multiple agents without human coding, creating a new level of autonomy in enterprise automation.
- Emergence AI's technology focuses on automating data-centric enterprise workflows like ETL pipeline creation, data migration, and analysis.
- The platform integrates large language models' code generation abilities with autonomous agent technology, aiming to fill the gap in code production.
- It emphasizes interoperability, allowing integration with leading AI models and frameworks, while enabling enterprises to bring their own models into the platform.
- Safety features like guardrails, verification rubrics, and human-in-the-loop oversight are incorporated to ensure responsible use of the platform.
- Emergence AI's platform maintains human oversight to validate key decisions and provides clear checkpoints for enterprises to retain control over automated processes.
- The company plans to further update the platform in May 2025 to support containerized deployment in any cloud environment and enhance agent creation through self-play.
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