Data Science & Gen AI Updates

What is SSO? Single Sign-On is a sophisticated authentication process that allows users to access multiple applications with one set of login credentials. Think of it as a master key for your digital workplace! The SSO Process in 3 Simple Steps: 1. User logs in once with single credentials 2. SSO verifies identity and issues an authentication token 3. Token grants access to multiple applications or services How SSO Actually Works? (Technical Flow) 1. User initiates login through SSO login page 2. SSO redirects to Identity Provider (like Google/Facebook) 3. User enters credentials 4. Identity Provider authenticates user 5. Auth token (SAML) is generated 6. Token is validated 7. Access granted to multiple services seamlessly Why SSO Matters: Without SSO: - Multiple logins for different services - Password fatigue - Increased security risks - Time wasted on credential management - Higher support costs With SSO: - One-time authentication - Seamless access to all services - Enhanced security - Improved user experience - Reduced IT overhead Key Benefits: • Enhanced Security • Reduced Password Fatigue • Improved User Experience • Lower IT Support Costs • Centralized Access Control • Better Compliance Management Pro Tips for Implementation: 1. Choose the right SSO protocol (SAML, OAuth, OpenID Connect) 2. Plan your identity provider strategy 3. Consider multi-factor authentication integration 4. Map out your application ecosystem 5. Plan for scalability ⚠️ Implementation Considerations: - Identity provider selection - Protocol compatibility - Failover planning - User directory integration - Security monitoring Perfect For: - Enterprise organizations - Companies with multiple SaaS applications - Businesses prioritizing security - Organizations scaling their digital infrastructure Have I overlooked anything? Please share your thoughts—your insights are priceless to me. Credit:- Brij kishore Pandey

Data Architecture: The Building Blocks of Success Behind every well-designed data system lies critical architecture artefacts that guide and shape its foundation. These artefacts ensure clarity and provide the roadmap for scaling and optimising data-driven solutions. Here are the essential artefacts every data architect should master: ➤ Reference Architectures -  High-Level Architecture Diagrams: A bird’s-eye view of the organisation’s data landscape. -  Architecture Blueprints: Detailed designs, from data warehouses to real-time analytics. -  Architecture Principles: The guiding rules for creating effective data solutions. ➤ Solution Design -  Detailed Design Documents: Comprehensive plans covering components, interactions, and data flows. -  Integration Catalog: A directory of internal and external data producers and consumers. ➤ Data Models -  Conceptual, Logical, and Physical Models: From high-level relationships to database implementation details. ➤ Data Flows -  Data Flow and Process Flow Diagrams: Illustrating how data moves and transforms. -  Pipeline Designs: Specifications for ETL/ELT and data pipelines. ➤ Decision Records -  Architecture Decision Records (ADRs): Documenting the rationale behind critical architectural choices. -  Technical Debt Register: Tracking known debt and its impact. ➤ Design Patterns -  Reusable Design Patterns and Pattern Application Guides: Ready solutions for everyday challenges like sharding or caching. ➤ Data Standards -  Data Naming Conventions and Quality Standards: Ensuring consistency, clarity, and reliability across the board. What did I miss? Cheers! Credit : Deepak Bhardwaj

100 AI Tools to replace your tedious work: 1. Research - ChatGPT - YouChat - Abacus - Perplexity - Copilot - Gemini 2. Image - Fotor - Stability AI - Midjourney - Microsoft Designer 3. CopyWriting - Rytr - Copy AI - Writesonic - Adcreative AI 4. Writing - Jasper - HIX AI - Jenny AI - Textblaze - Quillbot 5. Website - 10Web - Durable - Framer - Style AI 6. Video - Klap - Opus - Eightify - InVideo - HeyGen - Runway - ImgCreator AI - Morphstudio .xyz 7. Meeting - Tldv - Otter - Noty AI - Fireflies 8. SEO - VidIQ - Seona AI - BlogSEO - Keywrds ai 9. Chatbot - Droxy - Chatbase - Mutual info - Chatsimple 10. Presentation - Decktopus - Slides AI - Gamma AI - Designs AI - Beautiful AI 11. Automation - Make - Zapier - Xembly - Bardeen 12. Prompts - FlowGPT - Alicent AI - PromptBox - Promptbase - Snack Prompt 13. UI/UX - Figma - Uizard - UiMagic - Photoshop 14. Design - Canva - Flair AI - Designify - Clipdrop - Autodraw - Magician design 15. Logo Generator - Looka - Designs AI - Brandmark - Stockimg AI - Namecheap 16. Audio - Lovo ai - Eleven labs - Songburst AI - Adobe Podcast 17. Marketing - Pencil - Ai-Ads - AdCopy - Simplified - AdCreative 18. Startup - Tome - Ideas AI - Namelix - Pitchgrade - Validator AI 19. Productivity - Merlin - Tinywow - Notion AI - Adobe Sensei - Personal AI 20. Social media management - Tapilo - Typefully - Hypefury - TweetHunter

DeepMind Released AlphaFold 3 Inference Codebase, Model Weights and An On-Demand Server DeepMind recently released the inference codebase, model weights, and an on-demand server for AlphaFold 3. This release makes it easier for researchers and developers worldwide to integrate the power of AlphaFold into their workflows. Compared to its predecessor, AlphaFold 2, AlphaFold 3 offers a more sophisticated architecture capable of predicting the joint structure of biomolecular complexes, including proteins, DNA, RNA, ligands, ions, and even chemical modifications. This version is designed to accommodate highly complex interactions within biological systems, and the release includes access to model weights, allowing researchers to directly replicate or extend the existing capabilities. AlphaFold 3 introduces a diffusion-based architecture, significantly improving accuracy for predicting biomolecular interactions. Unlike AlphaFold 2, which mainly focused on proteins, AlphaFold 3 employs a generalized architecture capable of predicting structures for a broader range of biomolecular types. The new “pairformer” replaces AlphaFold 2’s “evoformer” as the central processing module, simplifying the process and improving efficiency. The system operates by directly predicting atomic coordinates using a diffusion model, removing the need for specific torsion angle predictions and stereochemical handling that added complexity in earlier models.... Read the full article here: https://lnkd.in/guSckD8v Paper: https://lnkd.in/eES3JrJW Codebase: https://lnkd.in/gEjDY2ce

Machine Learning Explained in a Nutshell #machinelearning

Explore, Try AI agent : #PromptQL which is a natural language API that executes Python and SQL-like queries on top of structured, unstructured, and any data behind an API. The most fascinating aspect of it is how it works: To answer a query, PromptQL creates an execution plan to access and operate the data it has access to. The best way to understand how this works is with an example. Imagine we write the following request: "Write an email to my latest customer describing the product they bought." The attached diagram shows every step that PromptQL will execute behind the scenes: 1. It will start by writing a SQL query to retrieve the latest customer from the database. 2. Using the customer ID, it will write a second SQL query to retrieve the list of products bought by this customer. 3. Using the list of products, it will retrieve their information from a vector store. 4. Finally, it will ask an LLM to write an email using the customer and the description of every product. PromptQL will automatically decide where to fetch each piece of information and will use Python to orchestrate the entire query plan. If it doesn't generate a good plan, you can nudge it in the right direction by improving the initial prompt. This is very impressive, especially compared to RAG applications, which are much less powerful and expressive than this. There are a few other characteristics: • PromptQL remembers previous interactions and uses this memory to solve complex workflows. • It has a high tolerance for failures and can automatically self-heal and improve the data it uses. • It can use any LLM, including your own. Here is much more information about PromptQL: shortclick.link/v9pbx7

What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective (University of Maryland, October 2024, 136 pages) Paper: https://lnkd.in/gkXVXDXV Abstract: "What makes a difference in the post-training of LLMs? We investigate the training patterns of different layers in large language models (LLMs), through the lens of gradient, when training with different responses and initial models. We are specifically interested in how fast vs. slow thinking affects the layer-wise gradients, given the recent popularity of training LLMs on reasoning paths such as chain-of-thoughts (CoT) and process rewards. In our study, fast thinking without CoT leads to larger gradients and larger differences of gradients across layers than slow thinking (Detailed CoT), indicating the learning stability brought by the latter. Moreover, pre-trained LLMs are less affected by the instability of fast thinking than instruction-tuned LLMs. Additionally, we study whether the gradient patterns can reflect the correctness of responses when training different LLMs using slow vs. fast thinking paths. The results show that the gradients of slow thinking can distinguish correct and irrelevant reasoning paths. As a comparison, we conduct similar gradient analyses on non-reasoning knowledge learning tasks, on which, however, trivially increasing the response length does not lead to similar behaviors of slow thinking. Our study strengthens fundamental understandings of LLM training and sheds novel insights on its efficiency and stability, which pave the way towards building a generalizable System-2 agent." GitHub: https://lnkd.in/g8_ahZjg

The Geometry of Concepts: Sparse Autoencoder Feature Structure Yuxiao Li, Eric J. Michaud, David D. Baek, Joshua Engels, Xiaoqing Sun, Max Tegmark Massachusetts Institute of Technology 2024 https://lnkd.in/gn6Rk-ck In this study, researchers explored the structure of concepts in large language models using sparse autoencoders (SAE), which break down information into high-dimensional vectors. They identified three main levels of structure within this "concept universe": Atomic-Level Structure: At the smallest scale, they found "crystals" of concepts, where relationships between ideas form geometrical patterns like parallelograms or trapezoids. A famous example of this is the analogy "man-woman-king-queen." These geometric relationships became clearer when distracting factors like word length were removed using a technique called linear discriminant analysis. Brain-Level Structure: At an intermediate level, they observed that certain concept clusters, like math and code, formed distinct regions, or "lobes," similar to how different regions of the brain handle specific functions. They used various metrics to measure this modularity, showing that related concepts tend to group together spatially more than random chance would suggest. Galaxy-Level Structure: At the largest scale, the researchers found that the overall structure of the concept space isn't uniform. Instead, it follows a power law, meaning some areas are denser with information than others. This effect is most pronounced in the middle layers of the model, and they also analyzed how the organization of these concept clusters changes across different layers. In essence, this work shows that the concepts understood by large language models have a rich, organized structure, much like the way our brains and the universe are organized at different scales. "This work expands on recent works finding structures in SAEs, and we are excited to dive deeper in future works to understand why some of these structures emerge!" #AI #machinelearning #neurons #cognition #LLM #sparseautoencoders

Say Goodbye to Endless Job Searches. With ChatGPT, you can crack your dream job in just 2 weeks. Check out these 10 powerful ChatGPT prompts to enhance your chances of landing an interview: [1] Review Your Job Descriptions Prompt: Simply copy and paste the job description you're targeting into ChatGPT and inquire: "Highlight the 5 most important responsibilities in this job description: [Insert Job Description]" [2] Building Connections on LinkedIn for Job Opportunities: Prompt: Create a message to connect with a professional at [Company] on LinkedIn, discussing my interest in the [Title] position and how my background in [Specific Field/Technology] makes me a strong candidate. [3] Enhance Your Resume Bullet Points: Prompt: Elevate your approach by refining your bullet points. Take a bullet point from your resume. Paste it into ChatGPT and state: "Please rewrite this bullet in under 20 words using compelling language and measurable metrics from my resume: [Paste Resume]" [4] Check if your Resume aligns with the Job Description: Prompt: Review if my skills and the job description for the [Title] position at [Company] match? Tell mismatch percentage. Job description: [paste text/link] My Skills: [Add your Skills] [5] Update your Resume Prompt: Update my resume for the [Title] role at [Company] by focusing on relevant skills mentioned in the job description. Job Description: [copy/paste job description] Current Resume: [copy/ paste current resume] [6] Craft Your Cover Letter Prompt: ChatGPT can also assist you in crafting an exceptionally personalized cover letter. "Please write a personalized cover letter for this [Job Title] at [Company]. Here's the job description: [Paste Job Description]. And here is my resume: [Paste Resume]." [7] Get Ready for Your Interview Prompt: Provide me a list of [number] interview questions based on job description. Job description: [paste text/link] [8] Practice a Mock Interview Prompt:: Conduct a technical mock interview for the [Job Role]. I am applying for this position. Ask me 15 questions related to [Specific Field/Technology], one after the other, gauging my expertise. [9] How to Introduce Yourself during an Interview Prompt: Prepare a brief introduction about myself focusing on my experiences in [Specific Field/Technology] for the [Title] interview at [Company]. [10] Follow-Up Email Prompt: Craft a follow-up email to inquire about the status of your application for the [Title] role at [Company].

AI agents are not a type of robot, software, or simple automation. AI agents are a broad category and aren’t limited to LLMs. From reinforcement learning agents in robotics, computer vision agents for surveillance, to predictive maintenance agents in manufacturing...They’re all AI agents focused on achieving different goals. AI agents are autonomous digital entities designed to make decisions, adapt, and take actions independently to achieve specific goals within a dynamic framework. And they are not the same as Agentic AI or Agentic Workflows. Think of AI agents as the autonomous 'doers' in a system. Agentic AI is the overarching concept of AI behaving autonomously and goal-oriented, often involving multiple agents or agentic workflows. Agentic workflows provide the adaptive structure that enables AI agents to achieve their goals. In short, AI agents operate within agentic workflows, and Agentic AI refers to the entire approach of autonomous, goal-driven AI systems. We’re going to see and work with more and more AI agents on a daily basis. Also, this is a topic I’ve been especially interested in lately, and I’ve invited Prof. Jia Li for a deeper conversation about it. I’ll be sharing more details about the interview soon—it’s going to be an interesting one! __________________ Credit Alex Wang