#043 Knowledge Graphs Won't Fix Bad Data

How AI Is Built

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محتوای ارائه شده توسط Nicolay Gerold. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Nicolay Gerold یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Fixable

1
How to get your confidence back 41:05

21 weeks پیش41:05

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41:05

When negative feedback shakes your confidence, it can be difficult to get back to feeling like yourself at work. In this episode, Anne and Frances help a struggling listener who has spent years toning herself down in the workplace after being told that she was too assertive — now, she feels that her modest approach is holding her back. Together, they use Anne and Frances’s “trust triangle” framework to explore how empathy, authenticity, and logic can help you rebuild confidence and trust with your colleagues, and share helpful confidence hacks for getting comfy with discomfort. What problems are you dealing with at work? Text or call 234-FIXABLE or email fixable@ted.com to be featured on the show. For the full text transcript, visit ted.com/podcasts/fixable-transcripts Want to help shape TED’s shows going forward? Fill out our survey ! Hosted on Acast. See acast.com/privacy for more information.…

حدود یک سال پیش 1:10:59

MP3•خانه قسمت

Metadata is the foundation of any enterprise knowledge graph.

By organizing both technical and business metadata, organizations create a “brain” that supports advanced applications like AI-driven data assistants.

The goal is to achieve economies of scale—making data reusable, traceable, and ultimately more valuable.

Juan Sequeda is a leading expert in enterprise knowledge graphs and metadata management. He has spent years solving the challenges of integrating diverse data sources into coherent, accessible knowledge graphs. As Principal Scientist at data.world, Juan provides concrete strategies for improving data quality, streamlining feature extraction, and enhancing model explainability. If you want to build AI systems on a solid data foundation—one that cuts through the noise and delivers reliable, high-performance insights—you need to listen to Juan’s proven methods and real-world examples.

Terms like ontologies, taxonomies, and knowledge graphs aren’t new inventions. Ontologies and taxonomies have been studied for decades—even since ancient Greece. Google popularized “knowledge graphs” in 2012 by building on decades of semantic web research. Despite current buzz, these concepts build on established work.

Traditionally, data lives in siloed applications—each with its own relational databases, ETL processes, and dashboards. When cross-application queries and consistent definitions become painful, organizations face metadata management challenges. The first step is to integrate technical metadata (table names, columns, code lineage) into a unified knowledge graph. Then, add business metadata by mapping business glossaries and definitions to that technical layer.

A modern data catalog should:

Integrate Multiple Sources: Automatically ingest metadata from databases, ETL tools (e.g., dbt, Fivetran), and BI tools.
Bridge Technical and Business Views: Link technical definitions (e.g., table “CUST_123”) with business concepts (e.g., “Customer”).
Enable Reuse and Governance: Support data discovery, impact analysis, and proper governance while facilitating reuse across teams.

Practical Approaches & Use Cases:

Start with a Clear Problem: Whether it’s reducing churn, improving operational efficiency, or meeting compliance needs, begin by solving a specific pain point.
Iron Thread Method: Follow one query end-to-end—from identifying a business need to tracing it back to source systems—to gradually build and refine the graph.
Automation vs. Manual Oversight: Technical metadata extraction is largely automated. For business definitions or text-based entity extraction (e.g., via LLMs), human oversight is key to ensuring accuracy and consistency.

Technical Considerations:

Entity vs. Property: If you need to attach additional details or reuse an element across contexts, model it as an entity (with a unique identifier). Otherwise, keep it as a simple property.
Storage Options: The market offers various graph databases—Neo4j, Amazon Neptune, Cosmos DB, TigerGraph, Apache Jena (for RDF), etc. Future trends point toward multimodel systems that allow querying in SQL, Cypher, or SPARQL over the same underlying data.

Juan Sequeda:

LinkedIn
data.world
Semantic Web for the Working Ontologist
Designing and Building Enterprise Knowledge Graphs (before you buy, send Juan a message, he is happy to send you a copy)
Catalog & Cocktails (Juan’s podcast)

Nicolay Gerold:

00:00 Introduction to Knowledge Graphs 00:45 The Role of Metadata in AI 01:06 Building Knowledge Graphs: First Steps 01:42 Interview with Juan Sequira 02:04 Understanding Buzzwords: Ontologies, Taxonomies, and More 05:05 Challenges and Solutions in Data Management 08:04 Practical Applications of Knowledge Graphs 15:38 Governance and Data Engineering 34:42 Setting the Stage for Data-Driven Problem Solving 34:58 Understanding Consumer Needs and Data Challenges 35:33 Foundations and Advanced Capabilities in Data Management 36:01 The Role of AI and Metadata in Data Maturity 37:56 The Iron Thread Approach to Problem Solving 40:12 Constructing and Utilizing Knowledge Graphs 54:38 Trends and Future Directions in Knowledge Graphs 59:17 Practical Advice for Building Knowledge Graphs

58 قسمت

#Tech #Nicolay Gerold #Technology #Llm #Machine Learning #Data Engineering

How AI Is Built

#043 Knowledge Graphs Won't Fix Bad Data

How AI Is Built

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

Metadata is the foundation of any enterprise knowledge graph.

By organizing both technical and business metadata, organizations create a “brain” that supports advanced applications like AI-driven data assistants.

The goal is to achieve economies of scale—making data reusable, traceable, and ultimately more valuable.

A modern data catalog should:

Integrate Multiple Sources: Automatically ingest metadata from databases, ETL tools (e.g., dbt, Fivetran), and BI tools.
Bridge Technical and Business Views: Link technical definitions (e.g., table “CUST_123”) with business concepts (e.g., “Customer”).
Enable Reuse and Governance: Support data discovery, impact analysis, and proper governance while facilitating reuse across teams.

Practical Approaches & Use Cases:

Start with a Clear Problem: Whether it’s reducing churn, improving operational efficiency, or meeting compliance needs, begin by solving a specific pain point.
Iron Thread Method: Follow one query end-to-end—from identifying a business need to tracing it back to source systems—to gradually build and refine the graph.
Automation vs. Manual Oversight: Technical metadata extraction is largely automated. For business definitions or text-based entity extraction (e.g., via LLMs), human oversight is key to ensuring accuracy and consistency.

Technical Considerations:

Entity vs. Property: If you need to attach additional details or reuse an element across contexts, model it as an entity (with a unique identifier). Otherwise, keep it as a simple property.
Storage Options: The market offers various graph databases—Neo4j, Amazon Neptune, Cosmos DB, TigerGraph, Apache Jena (for RDF), etc. Future trends point toward multimodel systems that allow querying in SQL, Cypher, or SPARQL over the same underlying data.

Juan Sequeda:

LinkedIn
data.world
Semantic Web for the Working Ontologist
Designing and Building Enterprise Knowledge Graphs (before you buy, send Juan a message, he is happy to send you a copy)
Catalog & Cocktails (Juan’s podcast)

Nicolay Gerold:

58 قسمت

#Tech #Nicolay Gerold #Technology #Llm #Machine Learning #Data Engineering

All episodes

How AI Is Built

1
#051 Build systems that can be debugged at 4am by tired humans with no context 1:05:52

11 روز پیش1:05:52

1:05:52

Nicolay here, Today I have the chance to talk to Charity Majors, CEO and co-founder of Honeycomb, who recently has been writing about the cost crisis in observability. "Your source of truth is production, not your IDE - and if you can't understand your code there, you're flying blind." The key insight is architecturally simple but operationally transformative: replace your 10-20 observability tools with wide structured events that capture everything about a request in one place. Most teams store the same request data across metrics, logs, traces, APM, and error tracking - creating a 20X cost multiplier while making debugging nearly impossible because you're reconstructing stories from fragments. Charity's approach flips this: instrument once with rich context, derive everything else from that single source. This isn't just about cost - it's about giving engineers the connective tissue to understand distributed systems. When you can correlate "all requests failing from Android version X in region Y using language pack Z," you find problems in minutes instead of days. The second is putting developers on call for their own code. This creates the tight feedback loop that makes engineers write more reliable software - because nobody wants to get paged at 3am for their own bugs. In the podcast, we also touch on: Why deploy time is the foundational feedback loop (15 minutes vs 15 hours changes everything) The controversial "developers on call" stance and why ops people rarely found companies How microservices made everything trace-shaped and killed traditional metrics approaches The "normal engineer" philosophy - building for 4am debugging, not peak performance AI making "code of unknown quality" the new normal Progressive deployment strategies (kibble → dogfood → production) and more 💡 Core Concepts Wide Structured Events : Capturing all request context in one instrumentation event instead of scattered log lines - enables correlation analysis that's impossible with fragmented data. Observability 2.0 : Moving from metrics-as-workhorse to structured-data-as-workhorse, where you instrument once and derive metrics/alerts/dashboards from the same rich dataset. SLO-based Alerting : Replacing symptom alerts (CPU, memory, disk) with customer-impact alerts that measure whether you're meeting promises to users. Progressive Deployment : Gradual rollout through staged environments (kibble → dogfood → production) that builds confidence without requiring 2X infrastructure. Trace-shaped Systems : Architecture pattern recognizing that distributed systems problems are fundamentally about correlating events across time and services, not isolated metrics. 📶 Connect with Charity: LinkedIn Bluesky Personal Blog Company 📶 Connect with Nicolay: LinkedIn X / Twitter Website ⏱️ Important Moments Gateway Drug to Engineering : [01:04] How IRC and bash tab completion sparked Charity's fascination with Unix command line possibilities ADHD and Incident Response : [01:54] Why high-pressure outages brought out her best work - getting "dead calm" when everything's broken Code vs. Production Reality : [02:56] Evolution from focusing on code beauty to understanding performance, behavior, and maintenance over time The Alexander's Horse Principle : [04:49] Auto-deployment as daily practice - if you grow up deploying constantly, it feels natural by the time you scale Production as Source of Truth : [06:32] Why your IDE output doesn't matter if you can't understand your code's intersection with infrastructure and users The Logging Evolution : [08:03] Moving from debugger-style spam logs to fewer, wider structured events oriented around units of work Bubble Up Anomaly Detection : [10:27] How correlating dimensions reveals that failures cluster around specific Android versions, regions, and feature combinations Everything is Trace-Shaped : [12:45] Why microservices complexity is about locating problems in distributed systems, not just identifying them AI as Acceleration of Automation : [15:57] Most AI panic could be replaced with "automation" - it's the same pattern, just faster feedback loops Non-determinism as Genuinely New : [16:51] The one aspect of AI that's actually novel in software systems, requiring new architectural patterns The Cost Crisis : [22:30] How 10-20 observability tools create unsustainable cost multipliers as businesses scale SLO Revolution : [28:40] Deleting 90% of alerts by focusing on customer impact instead of system symptoms Shrinking Feedback Loops : [34:28] Keeping deploy-to-validation under one hour so engineers can connect actions to outcomes Normal Engineer Design : [38:12] Building systems that work for tired humans at 4am, not just heroes during business hours The Instrumentation Habit : [23:15] Always looking at your code in production after deployment to build informed instincts about system behavior Progressive Deployment Strategy : [36:43] Kibble → Dog Food → Production pipeline for gradual confidence building Real Engineering Bar : [49:00] Discussion on what actually makes exceptional vs normal engineers 🛠️ Tools & Tech Mentioned Honeycomb - Observability platform for structured events OpenTelemetry - Vendor-neutral instrumentation framework IRC - Early gateway to computing Parse - Mobile backend where Honeycomb's origin story began 📚 Recommended Resources "In Praise of Normal Engineers" - Charity's blog post "How I Failed" by Tim O'Reilly "Looking at the Crux" by Richard Rumelt "Fluke" - Book about randomness in history "Engineering Management for the Rest of Us" by Sarah Dresner…

How AI Is Built

1
#050 Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 1:06:58

۵ weeks پیش1:06:58

1:06:58

Nicolay here, Most AI developers are drowning in frameworks and hype. This conversation is about cutting through the noise and actually getting something into production. Today I have the chance to talk to Paul Iusztin, who's spent 8 years in AI - from writing CUDA kernels in C++ to building modern LLM applications. He currently writes about production AI systems and is building his own AI writing assistant. His philosophy is refreshingly simple: stop overthinking, start building, and let patterns emerge through use. The key insight that stuck with me: "If you don't feel the algorithm - like have a strong intuition about how components should work together - you can't innovate, you just copy paste stuff." This hits hard because so much of current AI development is exactly that - copy-pasting from tutorials without understanding the why. Paul's approach to frameworks is particularly controversial. He uses LangChain and similar tools for quick prototyping - maybe an hour or two to validate an idea - then throws them away completely. "They're low-code tools," he says. "Not good frameworks to build on top of." Instead, he advocates for writing your own database layers and using industrial-grade orchestration tools. Yes, it's more work upfront. But when you need to debug or scale, you'll thank yourself. In the podcast, we also cover: Why fine-tuning is almost always the wrong choice The "just-in-time" learning approach for staying sane in AI Building writing assistants that actually preserve your voice Why robots, not chatbots, are the real endgame 💡 Core Concepts Agentic Patterns: These patterns seem complex but are actually straightforward to implement once you understand the core loop. React : Agents that Reason, Act, and Observe in a loop Reflection : Agents that review and improve their own outputs Fine-tuning vs Base Model + Prompting: Fine-tuning involves taking a pre-trained model and training it further on your specific data. The alternative is using base models with careful prompting and context engineering. Paul's take : "Fine-tuning adds so much complexity... if you add fine-tuning to create a new feature, it's just from one day to one week." RAG: A technique where you retrieve relevant documents/information and include them in the LLM's context to generate better responses. Paul's approach : "In the beginning I also want to avoid RAG and just introduce a more guided research approach. Like I say, hey, these are the resources that I want to use in this article." 📶 Connect with Paul: LinkedIn X / Twitter Newsletter GitHub Book 📶 Connect with Nicolay: LinkedIn X / Twitter Bluesky Website My Agency Aisbach (for ai implementations / strategy) ⏱️ Important Moments From CUDA to LLMs : [02:20] Paul's journey from writing CUDA kernels and 3D object detection to modern AI applications. AI Content Is Natural Evolution : [11:19] Why AI writing tools are like the internet transition for artists - tools change, creativity remains. The Framework Trap : [36:41] "I see them as no code or low code tools... not good frameworks to build on top of." Fine-Tuning Complexity Bomb : [27:41] How fine-tuning turns 1-day features into 1-week experiments. End-to-End First : [22:44] "I don't focus on accuracy, performance, or latency initially. I just want an end-to-end process that works." The Orchestration Solution : [40:04] Why Temporal, D-Boss, and Restate beat LLM-specific orchestrators. Hype Filtering System : [54:06] Paul's approach: read about new tools, wait 2-3 months, only adopt if still relevant. Just-in-Time vs Just-in-Case : [57:50] The crucial difference between learning for potential needs vs immediate application. Robot Vision : [50:29] Why LLMs are just stepping stones to embodied AI and the unsolved challenges ahead. 🛠️ Tools & Tech Mentioned LangGraph (for prototyping only) Temporal (durable execution) DBOS (simpler orchestration) Restate (developer-friendly orchestration) Ray (distributed compute) UV (Python packaging) Prefect (workflow orchestration) 📚 Recommended Resources The Economist Style Guide (for writing) Brandon Sanderson's Writing Approach (worldbuilding first) LangGraph Academy (free, covers agent patterns) Ray Documentation (Paul's next deep dive) 🔮 What's Next Next week, we will take a detour and go into the networking behind voice AI with Russell D’Sa from Livekit. 💬 Join The Conversation Follow How AI Is Built on YouTube , Bluesky , or Spotify . If you have any suggestions for future guests, feel free to leave it in the comments or write me (Nicolay) directly on LinkedIn , X , or Bluesky . Or at nicolay.gerold@gmail.com . I will be opening a Discord soon to get you guys more involved in the episodes! Stay tuned for that. ♻️ I am trying to build the new platform for engineers to share their experience that they have earned after building and deploying stuff into production. Pay it forward by sharing with one engineer who's facing similar challenges. That's the agreement - I deliver practical value, you help grow this resource for everyone. ♻️…

How AI Is Built

1
#050 TAKEAWAYS Bringing LLMs to Production: Delete Frameworks, Avoid Finetuning, Ship Faster 11:01

۵ weeks پیش11:01

11:01

How AI Is Built

1
#049 BAML: The Programming Language That Turns LLMs into Predictable Functions 1:02:39

۶ weeks پیش1:02:39

1:02:39

Nicolay here, I think by now we are done with marveling at the latest benchmark scores of the models. It doesn’t tell us much anymore that the latest generation outscores the previous by a few basis points. If you don’t know how the LLM performs on your task, you are just duct taping LLMs into your systems. If your LLM-powered app can’t survive a malformed emoji, you’re shipping liability, not software. Today, I sat down with Vaibhav (co-founder of Boundary) to dissect BAML —a DSL that treats every LLM call as a typed function. It’s like swapping duct-taped Python scripts for a purpose-built compiler. Vaibhav advocates for building first principle based primitives. One principle stood out: LLMs are just functions; build like that from day 1. Wrap them, test them, and let a human only where it counts. Once you adopt that frame, reliability patterns fall into place: fallback heuristics, model swaps, classifiers—same playbook we already use for flaky APIs. We also cover: Why JSON constraints are the wrong hammer—and how Schema-Aligned Parsing fixes it Whether “durable” should be a first-class keyword (think async/await for crash-safety) Shipping multi-language AI pipelines without forcing a Python microservice Token-bloat surgery, symbol tuning, and the myth of magic prompts How to keep humans sharp when 98 % of agent outputs are already correct 💡 Core Concepts Schema-Aligned Parsing (SAP) Parse first, panic later. The model can handle Markdown, half-baked YAML, or rogue quotes—SAP puts it into your declared type or raises. No silent corruption. Symbol Tuning Labels eat up tokens and often don’t help with your accuracy (in some cases they even hurt). Rename PasswordReset to C7, keep the description human-readable. Durable Execution Durable execution refers to a computing paradigm where program execution state persists despite failures, interruptions, or crashes. It ensures that operations resume exactly where they left off, maintaining progress even when systems go down. Prompt Compression Every extra token is latency, cost, and entropy. Axe filler words until the prompt reads like assembly. If output degrades, you cut too deep—back off one line. 📶 Connect with Vaibhav: LinkedIn X / Twitter BAML 📶 Connect with Nicolay: Newsletter LinkedIn X / Twitter Bluesky Website My Agency Aisbach (for ai implementations / strategy) ⏱️ Important Moments New DSL vs. Python Glue [00:54] Why bolting yet another microservice onto your stack is cowardice; BAML compiles instead of copies. Three-Nines on Flaky Models [04:27] Designing retries, fallbacks, and human overrides when GPT eats dirt 5 % of the time. Native Go SDK & OpenAPI Fatigue [06:32] Killing thousand-line generated clients; typing go get instead. “LLM = Pure Function” Mental Model [15:58] Replace mysticism with f(input) → output; unit-test like any other function. Tool-Calling as a Switch Statement [18:19] Multi-tool orchestration boils down to switch(action) {…}—no cosmic “agent” needed. Sneak Peek—durable Keyword [24:49] Crash-safe workflows without shoving state into S3 and praying. Symbol Tuning Demo [31:35] Swapping verbose labels for C0,C1 slashes token cost and bias in one shot. Inside SAP Coercion Logic [47:31] Int arrays to ints, scalars to lists, bad casts raise—deterministic, no LLM in the loop. Frameworks vs. Primitives Rant [52:32] Why BAML ships primitives and leaves the “batteries” to you—less magic, more control. 🛠️ Tools & Tech Mentioned BAML DSL & Playground Temporal • Prefect • DBOS outlines • Instructor • LangChain 📚 Recommended Resources BAML Docs Schema-Aligned Parsing (SAP) 🔮 What's Next Next week, we will continue going more into getting generative AI into production talking to Paul Iusztin. 💬 Join The Conversation Follow How AI Is Built on YouTube , Bluesky , or Spotify . If you have any suggestions for future guests, feel free to leave it in the comments or write me (Nicolay) directly on LinkedIn , X , or Bluesky . Or at nicolay.gerold@gmail.com . I will be opening a Discord soon to get you guys more involved in the episodes! Stay tuned for that. ♻️ Here's the deal: I'm committed to bringing you detailed, practical insights about AI development and implementation. In return, I have two simple requests: Hit subscribe right now to help me understand what content resonates with you If you found value in this post, share it with one other developer or tech professional who's working with AI That's our agreement - I deliver actionable AI insights, you help grow this. ♻️…

How AI Is Built

1
#049 TAKEAWAYS BAML: The Programming Language That Turns LLMs into Predictable Functions 1:12:35

۶ weeks پیش1:12:35

1:12:35

How AI Is Built

1
#048 TAKEAWAYS Why Your AI Agents Need Permission to Act, Not Just Read 7:07

۷ weeks پیش7:07

7:07

Nicolay here, most AI conversations obsess over capabilities. This one focuses on constraints - the right ones that make AI actually useful rather than just impressive demos. Today I have the chance to talk to Dexter Horthy, who recently put out a long piece called the “12-factor agents”. It’s like the 10 commandments, but for building agents. One of it is “Contact human with tool calls”: the LLM can call humans for high-stakes decisions or “writes”. The key insight is brutally simple. AI can get to 90% accuracy on most tasks - good enough for spam-like activities but disastrous for anything that requires trust. The solution isn't to wait for models to get smarter; it's to add a human approval layer for critical actions. Imagine you are writing to a database or sending an email. Each “write” has to be approved by a human. So you post the email in a Slack channel and in most cases, your sales people will approve. In the 10%, it’s stopped in its tracks and the human can take over. You stop the slop and get good training data in the mean time. Dexter’s company is building exactly this: an approval mechanism that lets AI agents send requests to humans before executing. In the podcast, we also touch on a bunch of other things: MCP and that they are (atm) just a thin client Are we training LLMs toward mediocrity? What infrastructure do we need for human in the loop (e.g. DBOS)? and more 💡 Core Concepts Context Engineering : Crafting the information representation for LLMs - selecting optimal data structures, metadata, and formats to ensure models receive precisely what they need to perform effectively. Token Bloat Prevention : Ruthlessly eliminating irrelevant information from context windows to maintain agent focus during complex tasks, preventing the pattern of repeating failed approaches. Human-in-the-loop Approval Flows : Achieving 99% reliability through a "90% AI + 10% human oversight" framework where agents analyze data and suggest actions but request explicit permission before execution. Rubric Engineering : Systematically evaluating AI outputs through dimension-specific scoring criteria to provide precise feedback and identify exceptional results, helping escape the trap of models converging toward mediocrity. 📶 Connect with Dexter: LinkedIn X / Twitter Company 📶 Connect with Nicolay: LinkedIn X / Twitter Bluesky Website My Agency Aisbach (for ai implementations / strategy) ⏱️ Important Moments MCP Servers as Clients : [03:07] Dexter explains why what many call "MCP servers" actually function more like clients when examining the underlying code. Authentication Challenges : [04:45] The discussion shifts to how authentication should be handled in MCP implementations and whether it belongs in the protocol. Asynchronous Agent Execution : [08:18] Exploring how to handle agents that need to pause for human input without wasting tokens on continuous polling. Token Bloat Prevention : [14:41] Strategies for keeping context windows focused and efficient, moving beyond standard chat formats. Context Engineering : [29:06] The concept that everything in AI agent development ultimately comes down to effective context engineering. Fine-tuning vs. RAG for Writing Style : [20:05] Contrasting personal writing style fine-tuning versus context window examples. Generating Options vs. Deterministic Outputs : [19:44] The unexplored potential of having AI generate diverse creative options for human selection. The "Mediocrity Convergence" Question : [37:11] The philosophical concern that popular LLMs may inevitably trend toward average quality. Data Labeling Interfaces : [35:25] Discussion about the need for better, lower-friction interfaces to collect human feedback on AI outputs. Human-in-the-loop Approval Flows : [42:46] The core approach of HumanLayer, allowing agents to ask permission before taking action. 🛠️ Tools & Tech Mentioned MCP OpenControl DBOS Temporal Cursor 📚 Recommended Resources 12 Factor Agents BAML Docs Rubric Engineering 🔮 What's Next Next week, we will continue going more into getting generative AI into production talking to Vibhav from BAML. 💬 Join The Conversation Follow How AI Is Built on YouTube , Bluesky , or Spotify . If you have any suggestions for future guests, feel free to leave it in the comments or write me (Nicolay) directly on LinkedIn , X , or Bluesky . Or at nicolay.gerold@gmail.com . I will be opening a Discord soon to get you guys more involved in the episodes! Stay tuned for that. ♻️ I am trying to build the new platform for engineers to share their experience that they have earned after building and deploying stuff into production. I am trying to produce the best content possible - informative, actionable, and engaging. I'm asking for two things: hit subscribe now to show me what content you like (so I can do more of it), and if this episode helped you, pay it forward by sharing with one engineer who's facing similar challenges. That's the agreement - I deliver practical value, you help grow this resource for everyone. ♻️…

How AI Is Built

38 weeks پیش58:40

58:40

Today we have Jessica Talisman with us, who is working as an Information Architect at Adobe. She is (in my opinion) the expert on taxonomies and ontologies. That’s what you will learn today in this episode of How AI Is Built. Taxonomies, ontologies, knowledge graphs. Everyone is talking about them no-one knows how to build them. But before we look into that, what are they good for in search? Imagine a large corpus of academic papers. When a user searches for "machine learning in healthcare", the system can: Recognize "machine learning" as a subcategory of "artificial intelligence" Identify "healthcare" as a broad field with subfields like "diagnostics" and "patient care" We can use these to expand the query or narrow it down. We can return results that include papers on "neural networks for medical imaging" or "predictive analytics in patient outcomes", even if these exact phrases weren't in the search query We can also filter down and remove papers not tagged with AI that might just mention it in a side not. So we are building the plumbing, the necessary infrastructure for tagging, categorization, query expansion and relexation, filtering. So how can we build them? 1️⃣ Start with Industry Standards • Leverage established taxonomies (e.g., Google, GS1, IAB) • Audit them for relevance to your project • Use as a foundation, not a final solution 2️⃣ Customize and Fill Gaps • Adapt industry taxonomies to your specific domain • Create a "coverage model" for your unique needs • Mine internal docs to identify domain-specific concepts 3️⃣ Follow Ontology Best Practices • Use clear, unique primary labels for each concept • Include definitions to avoid ambiguity • Provide context for each taxonomy node Jessica Talisman: LinkedIn Nicolay Gerold: ⁠LinkedIn⁠ ⁠X (Twitter) 00:00 Introduction to Taxonomies and Knowledge Graphs 02:03 Building the Foundation: Metadata to Knowledge Graphs 04:35 Industry Taxonomies and Coverage Models 06:32 Clustering and Labeling Techniques 11:00 Evaluating and Maintaining Taxonomies 31:41 Exploring Taxonomy Granularity 32:18 Differentiating Taxonomies for Experts and Users 33:35 Mapping and Equivalency in Taxonomies 34:02 Best Practices and Examples of Taxonomies 40:50 Building Multilingual Taxonomies 44:33 Creative Applications of Taxonomies 48:54 Overrated and Underappreciated Technologies 53:00 The Importance of Human Involvement in AI 53:57 Connecting with the Speaker 55:05 Final Thoughts and Takeaways…

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