Podcasts about Knowledge graph

In this episode of Cloud Wars Live, Bob Evans speaks with T.K. Anand, Executive Vice President at Oracle, during Oracle AI World in Las Vegas. The discussion centers on Oracle's new AI Data Platform, a major initiative designed to help customers harness their own data to drive AI transformation. Anand outlines how Oracle's open, unified approach to managing enterprise data enables organizations to bring AI directly to their business processes, while also citing breakthrough developments in industry-specific applications.Reinventing with AIThe Big Themes:Data + AI = Business Reinvention: Enterprises cannot simply adopt AI as a bolt‑on; they must combine their private business data — workflows, applications, processes — with advanced models if they hope to reinvent themselves ahead of disruption. Anand notes that many AI/LLMs have been trained on public domain data and thus “know nothing about our customers' private data.” The AI Data Platform is designed to enable that union.Pre‑integrated with SaaS Applications: For customers of Oracle's large SaaS portfolio (e.g., Fusion, NetSuite, industry apps) the AI Data Platform offers tailored variants that are pre‑integrated with the application's data models and semantics. This means organizations get out‑of‑the-box predictive models, analytics, and agents aligned to their workflows, but still have the full platform underneath to extend or customize. This helps reduce time‑to‑value for companies using those applications.Full Stack Advantage: Oracle positions its differentiator as owning and integrating the full stack: cloud infrastructure (OCI including autonomous database), data and analytics platform, applications (SaaS) and now AI/agents. This end‑to‑end control enables closer integration between data assets, applications, and AI use cases. For example, having application workflow knowledge baked into the data platform allows faster mapping from business process to predictive agent.The Big Quote: “The AI Platform is all about helping our customers achieve AI transformation through the power of their own data."More from T.K. Anand and Oracle:Connect with T.K. Anand on LinkedIn or get an overview of Oracle AI Data Platform. Visit Cloud Wars for more.

Send us a textWhat if your site could be read like a map of meaning instead of a pile of keywords? Edd walks through a complete reframe of SEO around entities — the people, organisations, products, places, and ideas that define your niche — and shows how to turn that model into durable authority across search and AI.We start with how modern search reads the web: extracting entities, resolving ambiguity, and linking to public knowledge bases that feed Google's Knowledge Graph. From there, we break down why entities power rich SERP features like knowledge panels, featured snippets, and AI Overviews, and how consistency across your site, social profiles, and trusted publications raises Google's confidence in your facts. You'll also learn how large language models actually represent meaning with vectors, why hallucinations happen, and how grounding with retrieval augmented generation changes the authority game.Then we get practical. Run a four-pillar entity audit (brand/products, people, services/concepts, audience interests), perform entity-based competitor analysis to surface gaps, and build topic clusters that deliver information gain through research, case studies, and expert commentary. Implement schema.org with JSON-LD using @id and sameAs to connect Organisation, Person, Product, and Service entities into a clean graph. Optimise writing for AI citations with clear headings, concise lists, factual claims with sources, and FAQs that mirror People Also Ask. Finally, project authority off-site with digital PR, consistent identities across key platforms, partnerships that create co-occurrence with respected brands, and expert sourcing on journalist platforms.Subscribe, share with a colleague who's still chasing keywords, and leave a review telling us which entity gap you'll tackle first.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

In dieser kontroversen SEOPRESSO-Live-Episode diskutieren Björn Darko und Prof. Dr. Mario Fischer die These: „Das Web, wie wir es kennen, wird sterben.“Mario erklärt, warum HTML und Webseiten als Kommunikationsoberfläche ein Auslaufmodell sind, wie KI-Assistenten, Agenten und APIs die Zukunft des Internets formen und warum Marken sich auf eine maschinenlesbare Weltvorbereiten müssen.Gemeinsam analysieren sie, was das Model Context Protocol, AI-Browser wie Atlas & Komet und der neue Begriff KI-Slop (massenhafter, wertloser KI-Content) für SEO, Publisher und Gesellschaft bedeuten.

C'est quoi le GEO ( Generative Engine Optimization ) ? Les conseils et astuces de Fabien Elharrar Le SEO évolue avec l'arrivée des moteurs génératifs (ChatGPT, Gemini, Perplexity…).  Le GEO consiste à optimiser son contenu pour ces nouveaux moteurs d'IA.   Comment les IA répondent  Elles s'appuient sur deux sources :  Une base de connaissances (mise à jour par “saccades”) Le RAG (récupération d'infos en temps réel depuis Google/Bing) Les réponses sont plus fiables avec le RAG (5 % d'erreurs contre 60 % sans).     Impact sur le trafic   Comme après une mise à jour Google, les IA provoquent une baisse du trafic.  Elles répondent plus vite, sans passage par plusieurs sites.  Résultat : moins de clics, montée du “zero-click” et importance accrue du branding.     Comment s'adapter   Ne pas bloquer les bots IA, sous peine d'être invisible. Créer des contenus difficiles à résumer : vidéos, verbatim, outils, études. Travailler l'intention plutôt que le mot-clé. Structurer son contenu : balises Schema.org, FAQ, JSON-LD, chunks clairs. Actualiser régulièrement pour rester pertinent dans les modèles RAG. Développer sa topical authority : cocons sémantiques, angles originaux.   Les backlinks évoluent   Les liens restent utiles, mais leur valeur dépend du contexte sémantique.  Les IA analysent surtout les co-citations et la récurrence des entités.  Les meilleures sources : YouTube, Reddit, Quora, Medium, Wikipédia.     En résumé : le GEO repose sur trois piliers :   Un contenu riche, structuré et à forte valeur. Une marque identifiable (Knowledge Graph). Une stratégie d'autorité thématique claire et cohérente. Plus d'infos sur  https://fabien.elharrar.com/ https://www.linkedin.com/in/fabienelharrar/

Send us a textForget neat rows of facts—your brand lives inside AI as a point on a vast map of meaning. We unpack how large language models like ChatGPT convert words into vectors, arrange them in a multi‑dimensional latent space, and “reason” by navigating probabilistic paths rather than retrieving certified entries from a knowledge graph. That shift explains both the astonishing creativity of LLMs and the stubborn problem of hallucinations, and it reveals why your content choices directly influence how machines see you.We start by separating Google's Knowledge Graph—built on labelled, verifiable relationships—from the statistical engine that powers LLMs. From there, we walk through tokens, embeddings, and the geometry of meaning: why “king” sits near “queen,” how “bank” splits by context, and how directions in vector space encode relationships like gender or capital cities. Then we explore probabilistic reasoning and chain‑of‑thought prompting, showing how stepwise guidance can reduce errors by constraining the model's path through its internal map.The practical payoff is clear: you can shape your brand's coordinates. Consistent naming, precise definitions, structured internal linking, authoritative citations, and schema markup help AIs place you in the right neighbourhood of concepts. Pillar pages and topical clusters reinforce the connections that matter, while concise fact sheets and retrieval‑ready content give models the anchors they need to avoid plausible-but-wrong continuations. Think of every page as another vector pull toward accuracy; over time, your credibility becomes the shortest path the model can take.If this helped you see how AI really “thinks” about your brand, follow the show, share it with a colleague, and leave a quick review. Got a question you want answered on air? Send a voice message via the link in the show notes and tell us where you want your brand's coordinates to land.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

Send us a textMost brands still try to “tell” Google who they are. We show how Google actually decides: by stitching together a ledger of facts from your site, LinkedIn, Wikipedia, news articles, and structured data—then trusting only what aligns. This is the Knowledge Graph at work, and it's quietly steering whether you earn a knowledge panel, sitelinks, and richer visibility across search.We break down the four streams feeding the graph—public web pages, licensed datasets, human‑edited knowledge bases like Wikidata, and direct owner signals via schema.org—and explain how each contributes to a confidence score for your entity. If your about page says Jane Doe is CEO but LinkedIn shows John Smith, the score drops and your brand becomes ambiguous. If your website, LinkedIn, reputable press, and Wikidata all agree, trust rises and your facts become “truth” in search.From there, we get specific about what you can control. Use schema.org to describe your organisation, people, products, and identifiers in clear, machine‑readable terms. Link out with sameAs to authoritative profiles so Google can triangulate identity. Audit your knowledge panel as a live diagnostic: check logos, dates, roles, and categories, and chase down any mismatch to the original source. Treat digital PR and reputation management as part of technical SEO—because today they are.By the end, you'll have a practical checklist for entity hygiene that helps you earn and keep a clean knowledge panel, avoid costly confusion, and unlock higher‑trust features across the results page. If this helped clarify how entities power modern SEO, subscribe, share with a colleague, and leave a quick review with one takeaway you'll act on next.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

Send us a textKeywords don't tell the whole story—entities do. We take you inside the three-step process machines use to read your content like a detective at a crime scene: highlighting potential entities, using context to resolve ambiguity, and linking each mention to a unique identifier in a global knowledge base. By the end, you'll see why “Jordan” only makes sense when surrounded by the right clues—and how to present those clues so search engines and AIs make the right call every time.We start with named entity recognition, the digital highlighter that picks out people, organisations, products, places, and dates across unstructured text. Then we move to entity disambiguation, where context—co-occurring teams, locations, or concepts—guides the system to the correct meaning. Finally, we close with entity linking, the moment a string becomes a node with a library card in Wikipedia or Wikidata. That linkage is the bridge into Google's Knowledge Graph, powering features like knowledge panels and richer, more confident results.Along the way, we dig into why Wikipedia and Wikidata matter far beyond vanity. Accurate, well-sourced entries create a feedback loop that improves how machines understand your brand, your founders, and your products. If you don't meet notability yet, don't force it; build authority elsewhere with consistent profiles, structured data, and content that names and connects related entities. We also share a simple action: search for your brand, founder, and main product on Wikipedia and Wikidata and assess accuracy. Want more like this? Follow the show, share it with a colleague, and leave a review so we can help more teams make sense of entity-first SEO.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

Network automation has a data problem. Traditional tools may hit limitations when managing complex infrastructure relationships. We explore how OpsMill’s InfraHub uses graph databases and temporal versioning to create what our guest calls “the knowledge graph of infrastructure” – enabling true version control at the database level while maintaining the flexibility to model anything from... Read more »

Video Version About the Podcast In this episode of State of Readiness, host Joseph Paris speaks with Alan Michaels, founder of the Industry Knowledge Graph, a strategic planning tool built on Michael Porter's competitive strategy framework. The discussion traces Alan's multi-decade journey to develop a globally comprehensive, highly granular industry taxonomy and its transformation into a usable, dynamic digital platform. Alan recalls the pivotal moment in 1986 when, while working in IT at Manufacturers Hanover Bank, he was introduced to Porter's Competitive Advantage. The structured, recipe-like nature of Porter's methodology resonated deeply with him, prompting a career pivot toward corporate strategy. Over time, Alan held various strategic roles, including at IBM and in insurance, but ultimately dedicated himself full-time to his ambitious goal: to map the entire global economy by industry, using Porter's definitions of competitive structure and market forces. The result, launched in April 2024, is the Industry Knowledge Graph, a platform that classifies the global economy into over 24,000 distinct industries, based on competitive commonalities such as products, buyers, substitutes, and vendors. This granularity far exceeds traditional classifications like NAICS codes. For instance, while NAICS might group all jet aircraft in one industry, Alan's system separates fighter jets, commercial jets, and blimps into unique segments. Even within food, categories like potato chips, pretzels, and popcorn are treated as different industries based on buyer behavior and competitive factors. The platform supports top-down and bottom-up analysis. A user can examine which industries a company like PepsiCo operates in (156 in total), or conversely, explore a given industry like potato chips and see the top competitors, value chains, channels, and influencing trends. Users can also compare companies by overlapping and unique industry participation—offering a precise view ideal for M&A analysis, competitive benchmarking, strategic expansion, or private equity targeting. Alan emphasizes that his system empowers corporate planners, marketers, and strategists to cut research time dramatically. What previously took months—such as comparing competitors by line of business—can now be done in seconds. A standardized set of industry data fields, inspired by Porter's methodology, makes this possible. Each industry entry includes value chains, buyer segments, substitute threats, supplier dependencies, market trends, and more. To bring this vision to life digitally, Alan partnered with Semantic Arts, a leader in semantic technology and the data-centric revolution. Together, they formed Industry Knowledge Graph LLC, combining Alan's industry content with a modern knowledge graph platform. The system launched with an initial demo and subscription access, and plans are underway to expand its data, integrate public classification codes (e.g., NAICS, UN), and invite partnerships to enrich its content. Alan concludes by emphasizing that the Industry Knowledge Graph offers a strategic lens to view the economy—one grounded in Porter's logic, built with real-world granularity, and powerful enough to revolutionize strategic planning across industries. About Alan Michaels As the Director of Industry Research at Industry Knowledge Graph LLC, I am solely focused on enhancing our industry model of the global economy, which leverages the IBB model of the global economy (covering 25,000 industries) developed by Industry Building Blocks LLC. For the past 24 years, I have been building and maintaining the best available industry segmentation of the global economy by line of business, using Michael E. Porter's five forces industry analysis methodology. My business expertise is in corporate planning, business unit planning, industry analysis, new business development, and aligning and coordinating business and IT and other activities to make the whole greater than the sum of the parts. In 1994, I self-published (a Porter-inspired step-by-step corporate planning workbook) "Structured Strategic Planning" while teaching at Pace University Graduate School. In short, since reading Porter's book "Competitive Advantage" in 1986 I have been passionate about leveraging his five-forces industry framework to provide high-quality, granular, and comprehensive industry data to raise the level of strategic thinking. Executive Contact: Alan Michaels Title: Managing Director of Industry Research LinkedIn Profile: https://www.linkedin.com/in/alansmichaels/ Company: Industry Knowledge Graph Website: https://www.industrykg.com/ Company Type: Private Year Founded: 2021 Practice Areas: Industry Model of the Global Economy, Knowledge Graph Platform, Market Segmentation, Five Forces Industry Analysis, M&A Analysis, Industry Taxonomy, Industry Classification Systems, Industry Ecosystems, Michael Porter Frameworks, Semantics, Ontology, Linked Data, Industry Trends, Macroeconomics, Microeconomics, Industry Classification Systems, Corporate Strategy, Business Unit Strategy, Competitor Analysis, and Market Intelligence

As a developer, you're trained to think in rows and tables. But what if that's the exact reason you're missing the most powerful connections in your data? There's a fundamental "Graph Problem" hiding in plain sight in almost every application, and once you see it, you'll wonder how you ever missed it.In this episode, we reveal this "obvious" secret and show you how to leverage it to build smarter, more accurate, and context-aware AI.In this episode/video, we cover:​The "Graph Problem" explained: Why you have more graph problems than you think.​Why basic RAG isn't enough, and how Graph RAG provides the context your AI is missing.​How to uncover the hidden relationships in your unstructured data and build a knowledge graph.​Real-world examples (from Amazon to your own notes) that reveal the graph structure all around you.​The #1 reason knowledge graph projects fail and how to avoid it.This conversation is for any developer who feels their projects are hitting a wall. If you're ready for the "aha!" moment that will change how you look at data forever, this episode is for you.Timestamps:00:00:00 - Intro00:00:39 - From Unstructured Data to a Knowledge Graph00:02:00 - The Experiment: What Happens When You Break a Knowledge Graph?00:05:41 - What Are Ontologies in the Graph World?00:07:35 - The Graph Problem You Didn't Know You Had00:09:09 - Why Graphs Are So Good for GenAI Context00:10:10 - The Best Way to Create Vector Embeddings for Graphs00:12:50 - Using Graphs to Solve Extreme Corporate Complexity00:17:14 - Real-World Problems That Are Actually Graph Problems00:19:31 - How to Find The Right Expert in Your Company00:23:33 - The Rise of Federated RAG Agents00:25:31 - The #1 Reason Knowledge Graph Projects Fail00:29:37 - A Standard Query Language for Graphs (GQL)00:32:53 - Why Teams Are Moving From RAG to Graph RAG00:34:34 - Should Your Company Build Its Own AI Assistant?00:38:28 - The "Fear of Missing Out" Driving Bad AI Projects00:40:21 - The Dangers of Chaotic vs. Laser-Focused Company Priorities00:44:05 - Why Gantt Charts Don't Work for Software00:47:08 - How Top Engineers Actually Learn New TechnologiesGuests on this podcast express their own views and do not represent their employers.#GraphDatabase #KnowledgeGraph #SoftwareArchitecture

In this episode of The Digital Marketing Podcast, hosts Daniel Rowles and Ciaran Rogers return with a fresh round of insights and hands-on tools to help digital marketers adapt and experiment in the evolving AI-driven marketing landscape. From Google's AI Max campaigns to the explosion of generative engine optimisation, Daniel and Ciaran unpack the shifts happening across search, paid media, and SEO. This isn't just another roundup of shiny tools, this episode explores how to think strategically about the role of AI in search, the real impact on your organic performance, and the mindset required to stay ahead of the curve. What's Inside This Episode: AI Max Campaigns in Google Ads Discover how Google's new AI-powered campaign structure is changing the game. It's not just another bidding strategy, it's an integrated layer that leverages keywordless signals, context, and user behaviour across Google properties to personalise campaigns. Learn why separating these into siloed budgets might actually hinder performance and what you need to consider before rolling out. The Vanishing Organic Click While impressions are up, clicks are down. Daniel shares Target Internet's own data showing a drop in organic click-through rates and explains why Google's AI-generated answers, Reddit posts, and video content are pushing traditional organic listings further down the page. LLM Refs and the New SEO Frontier With AI overviews, ChatGPT, and other LLMs becoming key discovery engines, how do you make sure your brand shows up? The hosts explore LLMRefs, a tool that shows how different brands are ranking across major AI models, and reveal just how fragmented and competitive this new landscape is. Structured Data, Schema & the Knowledge Graph Daniel and Ciaran highlight the importance of schema markup and entity relationships in Google's Knowledge Graph, using tools like the Knowledge Graph Explorer to demonstrate how Google perceives your brand and its connections. If you're not surfacing in the right contexts, it could just be due to a missing link, literally. Search Engine Optimization ≠ Generative Engine Optimization The hosts challenge the idea that SEO principles remain unchanged in the world of AI. With so many variables, location, devices, user intent, history, and model-specific behavior, your approach needs to be more agile and nuanced than ever. Key Takeaways: Performance Max and AI Max are now core to Google Ads - understanding signals and context is essential Organic traffic is declining, but impressions may still rise - CTR is the new battleground You can't game the system anymore with a few smart keywords; LLMs require strong content, schema, and reputation The tools are out there - you just need to experiment, validate, and iterate Don't fall into the trap of “a little knowledge” - simplifying complex changes leads to bad decisions AI models aren't clairvoyant - you need to structure and declare your content clearly

This episode is part of the AI Summary series, which covers iPullRank's AI Search Manual chapter by chapter. Chapter 1 examines how search has evolved from “10 blue links” to AI-driven answers, altering how audiences discover and evaluate brands.We trace the evolution from Universal Search and the Knowledge Graph to large language models, such as ChatGPT, Gemini, and Claude, which generate conversational results and reduce the need for clicks. The discussion explains the move from SEO to GEO, Generative Engine Optimization, where the focus is on semantic clarity, authority, and multimodal content that machines can interpret and present.The episode also introduces Relevance Engineering and Retrieval-Augmented Generation (RAG), which show how information is retrieved, scored for relevance, and synthesized into answers. These concepts set the stage for building strategies that position content to be included in AI summaries rather than excluded from them.Read the whole chapter at ipullrank.com/ai-search-manual

Bruce Clay is back on the EDGE and he's bringing a fresh arsenal of SEO wisdom—plus his latest tool, Prewriter.AI! Bruce unpacks the ever-evolving landscape of search, where being a subject matter expert isn't just nice to have, it's non-negotiable in the age of large language models. Get ready for a hands-on journey through the world of structured data, website architecture, and the key realm of silos and clusters (yes, we're geeking out on knowledge graphs). Bruce dishes out why schema isn't just a box-ticking exercise—and why too much schema can lead Google down the wrong rabbit hole. There's a hearty debate on how SEOs, content teams, and AI must now dance together to outwit homogenized AI content and truly stand out in the SERPs. Side note: If you're thinking the future is all about stuffing your site with as much schema as humanly (err, robotically?) possible, Bruce is here to beg you—please don't overdo it. Ambiguity is the enemy, but “over-schematizing” is hardly the answer! To wrap things up, Bruce gives us the lowdown on his new-and-improved Prewriter AI toolset and why Conversion Rate Optimization is now the SEO sidekick we all need. Stay tuned, stay sharp, and remember: in the battle of humans versus bots, a little wit (and a lot of structured data) goes a long way! Key Segments [00:03:14] "SEO, AI, and Structured Data" [00:08:26] "Structured Data's Role in SEO" [00:14:52] "Optimize Schemas for Search Clarity" [00:17:57]  EDGE of the Web Title Sponsor: Site Strategics [00:21:09]  What do you think the future actually holds for SEO professionals? [00:24:32] AI-Driven SEO Strategy Analysis [00:28:45] "Experience-Driven SEO Strategy" [00:31:02] "Leveraging Client Expertise Effectively" [00:35:37] EDGE of The Web Sponsor: Inlinks (WAIKAY) [00:37:55] AI SEO Localization Techniques Thanks to Our Sponsors! Site Strategics: http://edgeofthewebradio.com/site Inlinks/WAIKAY: https://edgeofthewebradio.com/waikay  Follow Our Guest Twitter / X: @BruceClay LinkedIn: https://www.linkedin.com/in/bruce-clay/ Resources Bruce Clay's Prewriter: https://www.prewriter.ai/

In this episode of Crazy Wisdom, host Stewart Alsop speaks with Moritz Bierling, community lead at Daylight Computer, about reimagining our relationship to technology through intentional hardware and software design. The conversation traverses the roots of Daylight Computer—born from a desire to mitigate the mental and physiological toll of blue light and digital distraction—into explorations of AI integration, environmental design, open-source ethos, and alternative models for startup funding. Moritz discusses the vision behind Daylight's “Outdoor Computing Club,” a movement to reclaim nature as a workspace, and the broader philosophical inquiry into a “third timeline” that balances techno-optimism and primitivism. You can explore more about the project at daylightcomputer.com and connect through their primary social channels on X (Twitter) and Instagram.Check out this GPT we trained on the conversationTimestamps00:00 – Introduction to Daylight Computer, critique of mainstream tech as a distraction machine, and inspiration from Apple's software limitations.05:00 – Origin story of Daylight, impact of blue light, and how display technology influences wellbeing.10:00 – Exploration of e-ink vs. RLCD, Kindle as a sanctuary, and Anjan's experiments with the Remarkable tablet.15:00 – Development of Solo OS, the role of spaces in digital environments, and distinctions between hardware and software.20:00 – Vision for AI-assisted computing, voice interaction, and creating a context-aware interface.25:00 – Emphasis on environmental design, using devices outdoors, and the evolutionary mismatch of current computing.30:00 – Reflections on solar punk, right relationship with technology, and rejecting accelerationism.35:00 – Introduction of the third timeline, rhizomatic organizational structure, and critique of VC funding models.40:00 – Discussions on alternative economics, open-source dynamics, and long-term sustainability.45:00 – Outdoor Computing Club, future launches, on-device AI, and the ambition to reclaim embodied computing.Key InsightsTechnology as Both Lifeline and HindranceMoritz Bierling frames modern computing as a paradox: it connects us to society and productivity while simultaneously compromising our well-being through overstimulation and poor design. The Daylight Computer aims to resolve this by introducing hardware that reduces digital fatigue and invites outdoor use.Inspiration from E-Ink and Purposeful ToolsThe initial concept for Daylight Computer was inspired by the calm, focused experience of using a Kindle. Its reflective screen and limited functionality helped Anjan, the founder, realize the power of devices built for singular, meaningful purposes rather than general distraction.Designing for Contextual IntentWith the introduction of Sol OS, Daylight enables users to define digital “spaces” aligned with different modes of being—such as waking, deep work, or relaxation. This modular approach supports intentional interaction and reduces the friction of context-switching common in modern OS designs.Respectful Integration of AIRather than chasing full automation, the Daylight team is exploring AI in a measured way. They're developing features like screen-aware AI queries through physical buttons, creating a contextual assistant that enhances cognition without overpowering it or promoting dependency.Alternative Economic ModelsRejecting venture capital and the short-term incentives of traditional tech funding, Daylight pursues a community-backed model similar to Costco's membership. This aligns financial sustainability with shared values, rather than extracting maximum profit.Third Timeline VisionMoritz discusses a conceptual “third timeline”—a balanced future distinct from both primitivism and techno-solutionism. This alternative future integrates technology into life harmoniously, fostering right relationship between humans, nature, and machines.Environmental Computing and Cultural RegenerationDaylight is not just a hardware company but a movement in environmental design. Through initiatives like the Outdoor Computing Club, they aim to restore sunlight as a central influence in human life and work, hinting at a cultural shift toward solar punk aesthetics and embodied digital living.

Wenn du als Online-Marketer die digitale Sichtbarkeit deines Unternehmens auf ein neues Level heben willst, dann solltest du dir diese Podcast-Folge nicht entgehen lassen! Mario Jung (OMT GmbH) und Benny Windolph (HECHT INS GEFECHT) nehmen dich mit in die Welt des Google Knowledge Graph und zeigen dir, warum er für deine Marke unverzichtbar ist. Sie erklären, wie sich der Knowledge Graph von klassischen Suchergebnissen unterscheidet und welche Vorteile du als Unternehmer daraus ziehen kannst. Außerdem bekommst du praxisnahe Tipps, wie du aktiv daran arbeiten kannst, im Knowledge Graph sichtbar zu werden – inklusive der Rolle von strukturierten Daten (Schema Markup), Wikipedia und anderen relevanten Quellen. Doch Achtung: Wer hier Fehler macht, kann sich selbst schaden! Die Experten decken die häufigsten Fallstricke auf und zeigen dir, wie du veraltete oder falsche Informationen korrigieren kannst. Und natürlich werfen sie einen Blick in die Zukunft: Welche Rolle wird Künstliche Intelligenz (KI) spielen? Welche neuen Google-Produkte und Features sind für dich relevant? Abgerundet wird das Ganze mit echten Erfolgsbeispielen und wertvollen Tools, die dir helfen, deinen Platz im Knowledge Graph zu sichern. Diese Episode ist ein Must-Listen für alle, die ihr SEO-Game auf das nächste Level bringen und bei Google ganz oben mitspielen wollen!

Over the last year or so, Klarna has been on a rampage to automate away. They've sliced tools, costs and staff. Begging the question...Is this just a VC narrative or is there more than meets the eye?(00:00) - Introduction (02:44) - Klarna's Journey and Financials (05:49) - AI's Role in Cost Reduction (07:51) - Customer Service Innovations (10:28) - Internal Knowledge Management (13:47) - Introduction to Data Consolidation (14:29) - Enterprise Search and Internal Tools (15:20) - Klarna's Knowledge Graph and AI Integration (16:38) - Deprecating Salesforce and Workflow Changes (20:05) - HR and Employee Feedback Innovations (25:51) - Build vs. Buy Debate Never miss a new episode, join our newsletter on revenueformula.substack.com

In this new episode Niklas Siemer, Product Specialist for SAP Business Technology Platform, is talking to Shabana Samsudheen, Senior Product Manager for SAP HANA Cloud. We're making a deep dive into the new Knowledge Graph engine of SAP HANA Cloud. Talking about what graphs are and what they're used for. Typical uses cases of graphs and how to use them in SAP HANA Cloud.

ServiceNow, the AI platform for business transformation, has announced the Yokohama platform release, unleashing new AI agents across CRM, HR, IT, and more, for faster, smarter workflows and maximum, end-to-end business impact. These latest innovations include teams of preconfigured AI agents that deliver productivity and predictable outcomes from day one, on a single platform, as well as capabilities to build, onboard, and manage the entire AI agent lifecycle. Because data fuels AI, the company also announced expansion of its Knowledge Graph with advancements to its Common Service Data Model (CSDM) to break down barriers among data sources for more connected AI agents. According to Gartner, "By 2028, 40% of CIOs will demand "Guardian Agents" be available to autonomously track, oversee, or contain the results of AI agent actions," underscoring the growing need for a coordinated, enterprise-wide approach to AI deployment and management. As businesses race to unlock the full potential of agentic AI, ServiceNow serves as the AI agent control tower for enterprises, with solutions that remove common roadblocks like data fragmentation, governance gaps, and real-time performance challenges. Unlike other AI providers that operate in silos or require complex integrations, ServiceNow AI Agents are built on a single, enterprise-wide platform, helping ensure seamless data connectivity with Workflow Data Fabric. By providing a single view of all workflows, AI, and automation needs, ServiceNow enables companies to seamlessly coordinate thousands of AI agents across CRM, IT, HR, finance, and more, enabling total enterprise-wide visibility and control. "Agentic AI is the new frontier. Enterprise leaders are no longer just experimenting with AI agents; they're demanding AI solutions that can help them achieve productivity at scale," said Amit Zavery, president, chief product officer, and chief operating officer at ServiceNow. "ServiceNow's industry-leading agentic AI framework meets this need by delivering predictability and efficiency from the start. With the combination of agentic AI, data fabric, and workflow automation all on one platform, we're making it easier for organisations to embed connected AI where work happens and both measure and drive business outcomes faster, smarter, and at scale." ServiceNow AI Agents are now available to radically accelerate productivity at scale Enterprise leaders are moving beyond experimentation, demanding AI solutions that drive real outcomes. ServiceNow's AI capabilities generate insights that power AI agent reasoning, planning, learning, and orchestration, equipping businesses to more rapidly achieve impactful goals. New ServiceNow AI Agents are available today and ready to help businesses accelerate productivity, streamline operations, and drive real outcomes for enterprise-wide use cases. For example: Security Operations (SecOps) expert AI agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks and empowering SecOps teams to focus on quickly stopping real threats. Autonomous change management AI agents act like a seasoned change manager, instantly generating custom implementation, test, and backout plans by analyzing impact, historical data, and similar changes - ensuring seamless execution with minimal risk. Proactive network test & repair AI agents operate as AI-powered troubleshooters that automatically detect, diagnose, and resolve network issues before they impact performance. Simplify AI agent management for a more streamlined lifecycle ServiceNow AI Agent Orchestrator and AI Agent Studio are also now generally available with expanded capabilities to govern the complete AI agent lifecycle - from building AI agents, to onboarding and monitoring their performance, to ensuring enterprises realize the value they need. This includes: Enhanced onboarding capabilities through AI Agent Studio to streamline the setup process with guided instru...

Organizations are facing a critical challenge to AI adoption: how to leverage their domain-specific knowledge to use AI in a way that delivers trustworthy results. Knowledge graphs provide the missing "truth layer" that transforms probabilistic AI outputs into real world business acceleration. Knowledge graphs are powering products for the likes of Amazon and Samsung. The Knowledge graph market is expected to grow to $6.93 Billion by 2030, at a CAGR of 36.6%. Gartner has been advocating for the role of knowledge graphs in AI and the downstream effects in organizations going forward for the last few years. Neither the technology nor the vision are new. Knowledge graph technology has been around for decades, and people like Tony Seale were early to identify its potential for AI. Seale, also known as "The Knowledge Graph Guy", is the founder of the eponymous consulting firm. In this extensive conversation, we covered everything from knowledge graph first principles to application patterns for safe, verifiable AI, real-world experience, trends, predictions, and the way forward. Read the article published on Orchestrate all the Things here: https://linkeddataorchestration.com/2025/03/11/knowledge-graphs-as-the-essential-truth-layer-for-pragmatic-ai/

Marcel Henriquez – Red Data Solutions. We specialize in combining (unstructured) data from multiple sources to provide insights. With the recent developments in AI, this capability is increasingly the base on which these new KM developments are built. We are mainly focused on getting as much data as possible and structuring it to make it work for the required application. On several occasions we team up with other companies specializing in Knowledge Graph systems and conversational chatbots. I am our companies first contact for clients, new and existing, and I am the first to work out requirements with the client team. This is very deliberate, because I do not want technology to limit the question when the client does not yet know what they want exactly. When we have a solution on paper, that is the moment I shift to my technical experience and start working out how the solution on paper can be transformed into working software. #knowledgemanagement #KMsystems #dataprocessing #searchengines #onedatastoremultipleapplications #conversational_intelligence #natural_language_querying https://www.researchgate.net/publication/373707915_CLARK_Building_Conversational_Intelligence_for_Knowledge_Management_in_the_Space_Domain https://reddata.nl/cases/eglossary/ (dutch) https://reddata.nl/cases/esa-taxonomy-tool/ (dutch) Andrew Herd has 25 years of experience in the space domain, with the past decade as Senior Engineer for Corporate Knowledge Management at the European Space Agency (ESA). He has led over 50 lesson capture and learning initiatives, managed knowledge for ESA's largest Directorate, and developed innovative Lessons Learned web and mobile applications. Andrew is a recognized thought leader with over 30 publications and has chaired international panels on lessons learned. He founded BraveLLAMA, dedicated to advancing knowledge management through enabling others to learn from experience, and launched #ExperienceXChange blog for this same purpose. His energies are currently dedicated towards leading a KMGN hosted project: KM Landscape@2025 – and for this he is grateful to all the co-creators, together with whom he is walking, talking and working. Screen Shot of the CLARK System. Like Superman and CLARK Kent: CLARK allows you to transform your data into a super-power.

Unlocking the Power of Business Research with GraphIQIn a recent episode of "The Thoughtful Entrepreneur," host Josh engages in an insightful discussion with Malcolm DeLeo, co-founder of GraphIQ. The conversation explores the complexities of business research, the hurdles organizations encounter in sourcing reliable information, and how GraphIQ is transforming data gathering and utilization for businesses. This episode distills key insights and practical advice, offering a guide for listeners aiming to enhance their business research strategies.Josh begins by emphasizing the significance of business connections, noting that strong relationships with partners, investors, influencers, and clients are crucial for success. Drawing from his extensive experience, he points out that the best business opportunities often stem from these connections. He encourages listeners to access a free video that outlines strategies for achieving 100% inbound business growth without relying on spam, ads, or sales tactics.Malcolm DeLeo shares insights from interviews with over 40 executives, revealing a common challenge: finding reliable companies to do business with. Many organizations rely on Google, personal networks, or consultants for research, leading to the creation of GraphIQ, a search engine tailored for business research. GraphIQ leverages natural language processing (NLP) to compile and organize data from billions of websites, allowing users to search for companies based on specific capabilities and attributes. Malcolm highlights the importance of accurate information in today's AI-driven age and encourages companies to invest in innovative solutions like GraphIQ, which has already transformed workflows and improved efficiency for its users.About Malcolm De Leo:Malcolm De Leo is an innovation expert who has successfully built and developed new markets for both Fortune 500 companies and Silicon Valley Startups. Over his 25 year career, Malcolm's leadership as an evangelist for new ideas, new technologies and for developing innovative cultures is what drives his work. Most recently, as Orbital Insight's Chief of Solution Strategy he was the customer facing person from the product team tasked with understanding the marketplace to provide strategic guidance on how the company delivered scalable and repeatable customer value. Prior to his time at Orbital insight; he was Chief Evangelist at both Quantifind and Netbase. At both companies, he helped pioneer the usage of social media data to drive business decisions across the Fortune 500 Landscape. Before entering Silicon Valley, Malcolm was Global Vice President of Innovation at Daymon Worldwide, the world's largest private brand product broker and also worked developing innovation partnerships and new products for the Clorox Company where he started his career. He holds a Ph.D. in Inorganic Chemistry from University of California at Santa Barbara and an MBA in Technology Management from the University of Phoenix.About GraphIQ:GraphIQ is a massive Knowledge Graph of business information containing trillions of organizational facts and relationships, sourced and continuously updated from billions of websites.Apply to be a Guest on The Thoughtful Entrepreneur: https://go.upmyinfluence.com/podcast-guestLinks Mentioned in this Episode:Want to learn more? Check out GraphIQ website athttps://graphiq.ai/Check out GraphIQ on LinkedIn athttps://www.linkedin.com/company/graphiq-ai/Check out Malcolm De Leo on LinkedIn

Tomaž Levak is the Co-founder and CEO of Trace Labs – OriginTrail core developers. OriginTrail is a web3 infrastructure project combining a decentralized knowledge graph (DKG) and blockchain technologies to create a neutral, inclusive ecosystem. Collective Memory for AI on Decentralized Knowledge Graph // MLOps Podcast #285 with Tomaz Levak, Founder of Trace Labs, Core Developers of OriginTrail. // Abstract The talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail's symbolic AI fundamentals (i.e. knowledge graphs) and go over details how decentralization improves data integrity, provenance, and user control. We'll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We'll explore practical use cases from the enterprise sector as well as latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS and the general convergence of AI, Internet and Crypto. // Bio Tomaz Levak, founder of OriginTrail, is active at the intersection of Cryptocurrency, the Internet, and Artificial Intelligence (AI). At the core of OriginTrail is a pursuit of Verifiable Internet for AI, an inclusive framework addressing critical challenges of the world in an AI era. To achieve the goal of Verifiable Internet for AI, OriginTrail's trusted knowledge foundation ensures the provenance and verifiability of information while incentivizing the creation of high-quality knowledge. These advancements are pivotal to unlock the full potential of AI as they minimize the technology's shortfalls such as hallucinations, bias, issues of data ownership, and model collapse. Tomaz's contributions to OriginTrail span over a decade and across multiple fields. He is involved in strategic technical innovations for OriginTrail Decentralized Knowledge Graph (DKG) and NeuroWeb blockchain and was among the authors of all three foundational White Paper documents that defined how OriginTrail technology addresses global challenges. Tomaz contributed to the design of OriginTrail token economies and is driving adoption with global brands such as British Standards Institution, Swiss Federal Railways and World Federation of Haemophilia, among others. Committed to the ongoing expansion of the OriginTrail ecosystem, Tomaz is a regular speaker at key industry events. In his appearances, he highlights the significant value that the OriginTrail DKG brings to diverse sectors, including supply chains, life sciences, healthcare, and scientific research. In a rapidly evolving digital landscape, Tomaz and the OriginTrail ecosystem as a whole are playing an important role in ensuring a more inclusive, transparent and decentralized AI. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://origintrail.io Song recommendation: https://open.spotify.com/track/5GGHmGNZYnVSdRERLUSB4w?si=ae744c3ad528424b --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Tomaz on LinkedIn: https://www.linkedin.com/in/tomazlevak/

Wikipedia is one of the top the most-visited websites in the world. Additionally, it has influence. The information on Wikipedia is also disseminated across the web on Google's Knowledge Graph and associated search page components, quick summaries of topics on Siri and Alexa, and even into news articles, podcast discussions, and government websites. Wikipedia is also of interest for SEO practitioners, heavily influencing Google search results and now impacting outputs from generative AI software like Chat-GPT.Everyone from journalists, investors, and your customers are using Wikipedia to understand your company. Do you understand how it works and what you can do about it?Guest: Rhiannon RuffEmail | LinkedIn | Lumino Digital's website | Rhiannon's book Managing a Crisis on WikipediaRate this podcast https://ratethispodcast.com/storiesstrategiesConnect with usLinkedIn | X | Instagram | You Tube | Facebook Request a transcript of this episodeSupport the show

In this episode, we dive into the world of generative AI with May Habib, co-founder of Writer, a platform transforming enterprise AI use. May shares her journey from Qordoba to Writer, emphasizing the impact of transformers in AI. We explore Writer's graph-based RAG approach, and their AI Studio for building custom applications. We also discuss Writer's Autonomous Action functionality, set to revolutionize AI workflows by enabling systems to act autonomously, highlighting AI's potential to accelerate product development and market entry with significant increases in capacity and capability. Writer Website - https://writer.com X/Twitter - https://x.com/get_writer  May Habib LinkedIn - https://www.linkedin.com/in/may-habib X/Twitter - https://x.com/may_habib FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series, hosted at Ramp's beautiful HQ. If you are ever in New York, you can join the upcoming events here: https://www.eventbrite.com/o/firstmark-capital-2215570183 (00:00) Intro (01:47) What is Writer? (02:52) Writer's founding story (06:54) Writer is a full-stack company. Why? (07:57) Writer's enterprise use cases (10:51) Knowledge Graph (17:59) Guardrails (20:17) AI Studio (23:16) Palmyra X 004 (27:18) Current state of the AI adoption in enterprises (28:57) Writer's sales approach (31:25) What May Habib is excited about in AI (33:14) Autonomous Action use cases

Speaker Resources:Neo4j+Senzing Tutorial: https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/#neo4jWhen GraphRAG Goes Bad: A Study in Why you Cannot Afford to Ignore Entity Resolution (Dr. Clair Sullivan): https://www.linkedin.com/pulse/when-graphrag-goesbad-study-why-you-cannot-afford-ignore-sullivan-7ymnc/Paco's NODES 2024 session: https://neo4j.com/nodes2024/agenda/entity-resolved-knowledge-graphs/Graph Power Hour: https://www.youtube.com/playlist?list=PL9-tchmsp1WMnZKYti-tMnt_wyk4nwcbHTomaz Bratanic on GraphReader: https://towardsdatascience.com/implementing-graphreader-with-neo4j-and-langgraph-e4c73826a8b7Tools of the Month:Neo4j GraphRAG Python package:  https://pypi.org/project/neo4j-graphrag/Spring Data Neo4j: https://spring.io/projects/spring-data-neo4jEntity Linking based on Entity Resolution tutorial: https://github.com/louisguitton/spacy-lancedb-linkerhttps://github.com/DerwenAI/strwythuraAskNews (build news datasets) https://asknews.app/The Sentry https://atlas.thesentry.org/azerbaijan-aliyev-empire/Announcements / News:Articles:GraphRAG – The Card Game https://neo4j.com/developer-blog/graphrag-card-game/Turn Your CSVs Into Graphs Using LLMs https://neo4j.com/developer-blog/csv-into-graph-using-llm/Detecting Bank Fraud With Neo4j: The Power of Graph Databases https://neo4j.com/developer-blog/detect-bank-fraud-neo4j-graph-database/Cypher Performance Improvements in Neo4j 5 https://neo4j.com/developer-blog/cypher-performance-neo4j-5/New GraphAcademy Course: Building Knowledge Graphs With LLMs https://neo4j.com/developer-blog/new-building-knowledge-graphs-llms/Efficiently Monitor Neo4j and Identify Problematic Queries https://neo4j.com/developer-blog/monitor-and-id-problem-queries/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEventsAll Neo4j events: https://neo4j.com/events/(Nov 5) Conference (virtual): XtremeJ https://xtremej.dev/2024/schedule/(Nov 7) Conference (virtual): NODES 2024 https://dev.neo4j.com/nodes24(Nov 8) Conference (Austin, TX, USA): MLOps World https://mlopsworld.com/(Nov 12) Conference (Baltimore, MD, USA): ISWC https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/summary(Nov 13) Meetup (Seattle, WA, USA): Puget Sound Programming Python (PuPPY) - Talk night Rover https://www.meetup.com/psppython/events/303896335/?eventOrigin=group_events_list(Nov 14) Meetup (Seattle, WA, USA): AI Workflow Essentials (with Pinecone, Neo4J, Boundary, Union) https://lu.ma/75nv6dd3(Nov 14) Conference (Reston, VA, USA): Senzing User Conference https://senzing.com/senzing-event-calendar/(Nov 18) Meetup (Cleveland, OH, USA): Cleveland Big Data mega-meetup https://www.meetup.com/Cleveland-Hadoop/(Nov 19) Chicago Java User Group (Chicago, IL, USA): https://cjug.org/cjug-meeting-intro/#/(Dec 3) Conference (London, UK): Linkurious Days https://resources.linkurious.com/linkurious-days-london(Dec 10) Meetup (London, UK): ESR meetup in London by Neural Alpha(Dec 11-13) Conference (London, UK): Connected Data London https://2024.connected-data.london/

Many events have passed since our post-Sapphire clean core review. Time to hash out AI, clean core and more - in the context of ASUG's fall events, and fresh research data. Analyst Josh Greenbaum, ASUG CEO Geoff Scott and your host Jon Reed rejoin for another unscripted review of event lessons learned - from SAP shows and beyond. Along with Geoff's new ASUG data, Jon shares his top event gripes and learnings; Josh shares his clean core/BTP findings. Then we look ahead to what we want to learn from ASUG Tech Connect, and why Enterprise Architects are central to the changes afoot. Show notes: 1 - 31:00 - fall event reviews, ASUG research findings, clean core, and AI hype vs customer reality 31:00 - SAP TechEd news review 51:00 - ASUG Tech Connect preview, why Enterprise Architects matter, and community as a learning framework Jon recommends listeners look at SAP's Knowledge Graph pursuits, a notable TechEd topic not covered in this podcast.

Many events have passed since our post-Sapphire clean core review. Time to hash out AI, clean core and more - in the context of ASUG's fall events, and fresh research data. Analyst Josh Greenbaum, ASUG CEO Geoff Scott and your host Jon Reed rejoin for another unscripted review of event lessons learned - from SAP shows and beyond. Along with Geoff's new ASUG data, Jon shares his top event gripes and learnings; Josh shares his clean core/BTP findings. Then we look ahead to what we want to learn from ASUG Tech Connect, and why Enterprise Architects are central to the changes afoot. Show notes: 1 - 31:00 - fall event reviews, ASUG research findings, clean core, and AI hype vs customer reality 31:00 - SAP TechEd news review 51:00 - ASUG Tech Connect preview, why Enterprise Architects matter, and community as a learning framework Jon recommends listeners look at SAP's Knowledge Graph pursuits, a notable TechEd topic not covered in this podcast. Here is a link to the smartShift research mentioned in the podcast.

⚠️ SMART, advanced episode!

Speaker Resources:Eastridge Analytics: https://www.eastridge-analytics.com/Graph Data Science with Python and Neo4j book: https://a.co/d/hkfkxPrLinkedIn profile: https://www.linkedin.com/in/timeastridge/NODES 2024 (look for more info on Tim's talk soon!): https://dev.neo4j.com/nodes24Neo4j GraphAcademy: https://graphacademy.neo4j.com/Graph Algorithms for Data Science (Tomaž Bratanic): https://a.co/d/7WhibUkTools of the Month:Jennifer: VS Code https://code.visualstudio.com/Jason: Cursor AI https://www.cursor.com/Tim: Neo4j LLM Graph Builder https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/Announcements / News:Articles:Graph Databases Offer a Deeper Understanding of Organizational Risk https://neo4j.com/developer-blog/graph-database-organizational-risk/Using Embeddings to Represent String Edit Distance in Neo4j https://neo4j.com/developer-blog/embeddings-string-edit-distance/Build a Knowledge Graph-based Agent with Llama 3.1, NVIDIA NIM, and LangChain https://neo4j.com/developer-blog/knowledge-graph-llama-nvidia-langchain/Entity Linking and Relationship Extraction With Relik in LlamaIndex https://neo4j.com/developer-blog/entity-linking-relationship-extraction-relik-llamaindex/Integrating Microsoft GraphRAG into Neo4j https://neo4j.com/developer-blog/microsoft-graphrag-neo4j/Ingesting Documents Simultaneously to Neo4j & Milvus https://neo4j.com/developer-blog/ingest-documents-neo4j-milvus/Enriching Vector Search With Graph Traversal Using the Neo4j GenAI Package https://neo4j.com/developer-blog/graph-traversal-neo4j-genai-package/Create a Neo4j GraphRAG Workflow Using LangChain and LangGraph https://neo4j.com/developer-blog/neo4j-graphrag-workflow-langchain-langgraph/Introducing Concurrent Writes to Cypher Subqueries https://neo4j.com/developer-blog/concurrent-writes-cypher-subqueries/Running Neo4j on a Commodore 64 https://neo4j.com/developer-blog/neo4j-commodore-64/Change Data Capture and Neo4j Connector for Confluent and Apache Kafka Go GA https://neo4j.com/developer-blog/change-data-capture-cdc-ga/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEventsAll Neo4j events: https://neo4j.com/events/(Sep 9) Conference (San Francisco, CA, USA): Pre-AI Conference Hack Day:  https://lu.ma/bsype6t6?tk=1dgMCa(Sep 9-11) Conference (San Francisco, CA, USA): AI Conference: https://aiconference.com/(Sep 10) Meetup (San Francisco, CA, USA): AI Tools HackNight: https://lu.ma/ozt7jtq5(Sep 12) Meetup (San Jose, CA, USA): AI & Tech Talks:  https://lu.ma/jjgnoqik?tk=sMOLyE(Sep 24-26) Conference (Dallas, TX, USA): JConf https://2024.jconf.dev/(Sep 30-Oct 3) Conference (Denver, CO, USA): dev2next https://www.dev2next.com/(Oct - TBD) Meetup (Charlotte, NC, USA): Data Science Meetup https://www.meetup.com/Data-Science-Charlotte/

I'm talking with veteran SEO expert Erin Sparks, host of the "Edge of the Web" podcast. Erin shares insider knowledge on building trust with Google and mastering SEO foundations.Key Topics:The Evolution of SEO: Discover how SEO has transformed since 2004, emphasizing accountability and digital strategy. Erin explains the shift from traditional marketing to a data-driven approach, highlighting the importance of staying current in the fast-paced digital landscape.Google's Knowledge Graph: Learn how to leverage Google's Knowledge Graph to enhance your online visibility. Erin discusses how businesses can curate detailed information about their services and products to establish authority and trust with Google.Effective Content Strategy: Erin delves into the importance of content clustering and internal linking. He provides practical tips on organizing your content to improve user experience and increase your website's relevance in Google's eyes.Maximizing Multimedia in SEO: Understand the role of podcasts, videos, and structured data in boosting your SEO. Erin highlights the benefits of hosting content on your website and using structured data to make your content more discoverable.Practical SEO Tools and Techniques: Erin introduces tools like AlsoAsked.com for discovering user queries and content gaps. He also explains the use of structured data and schema to communicate effectively with search engines, enhancing your site's visibility.What you can do:Targeting Non-Branded Keywords: Learn why focusing on non-branded keywords can attract a broader audience and enhance your site's discoverability.Optimizing Internal Links: Find out how to use internal linking to guide users and improve your site's navigability, helping Google understand your content structure.Implementing Structured Data: Erin breaks down the process of using structured data to provide search engines with a clear understanding of your site's content, leading to better rankings.Featured Highlights:ErinSend me a text!The Growth GearExplore business growth and success strategies with Tim Jordan on 'The Growth Gear.Listen on: Apple Podcasts Spotify This Is PropagandaChallenging marketers' delusions about the cultural impact of our work. A WEBBY winner!Listen on: Apple Podcasts SpotifySupport the Show.Search the Simple and Smart SEO Show podcast for something you heard! It's free!Apply to be my podcast guest!

Speaker Resources:Testcontainers https://testcontainers.com/NODES 2024 https://dev.neo4j.com/nodes24Tools of the Month:Neo4j Kubernetes documentation https://neo4j.com/docs/operations-manual/current/kubernetes/ragas framework https://ragas.io/Haiper.ai https://haiper.ai/home (Neo4j GenAI Package + DreamStudio.ai)Announcements / News:Neo4j GenAI Ecosystem Tools https://neo4j.com/labs/genai-ecosystem/Neo4j Knowledge Graph Builder https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/Neoconverse (text-to-cypher) https://neo4j.com/labs/genai-ecosystem/neoconverse/LLM framework integrations: LlamaIndex, LangChain, Spring AI, Haystack, Langchain4j, Semantik Kernel, DSPy Project RunwayRepository https://github.com/a-s-g93/neo4j-runwayArticles:GenAI Starter Kit: Everything You Need to Build an Application with Spring AI in Java https://neo4j.com/developer-blog/genai-starter-kit-spring-java/Knowledge Graph vs. Vector RAG: Benchmarking, Optimization Levers, and a Financial Analysis Example https://neo4j.com/developer-blog/knowledge-graph-vs-vector-rag/From Ancient Epic to Modern Marvel: Demystifying the Mahabharata Chatbot with GraphRAG (Part 3) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-3/Unleashing the Power of NLP with LlamaIndex and Neo4j https://neo4j.com/developer-blog/nlp-llamaindex-neo4j/Rags to Reqs: Making ASVS Accessible Through the Power of Graphs and Chatbots https://neo4j.com/developer-blog/asvs-security-graph-chatbot/Data Exploration With the Neo4j Runway Python Library https://neo4j.com/developer-blog/neo4j-runway-python-exploration/Easy Data Ingestion With Neo4j Runway and arrows.app https://neo4j.com/developer-blog/neo4j-runway-python-ingestion/A Tale of LLMs and Graphs: The GenAI Graph Gathering https://neo4j.com/developer-blog/genai-graph-gathering/Get Started With GraphRAG: Neo4j's Ecosystem Tools https://neo4j.com/developer-blog/graphrag-ecosystem-tools/LLM Knowledge Graph Builder: From Zero to GraphRAG in Five Minutes https://neo4j.com/developer-blog/graphrag-llm-knowledge-graph-builder/A Brief History of SQL and the Rise of Graph Queries https://neo4j.com/developer-blog/gql-sql-history/Customizing Property Graph Index in LlamaIndex https://neo4j.com/developer-blog/property-graph-index-llamaindex/Graph Exploration By All MEANS With mongo2neo4j and SemSpect https://neo4j.com/developer-blog/mean-stack-mongo2neo4j-semspect/Mix and Batch: A Technique for Fast, Parallel Relationship Loading in Neo4j https://neo4j.com/developer-blog/mix-and-batch-relationship-load/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jul 4) Livestream (virtual): GraphAcademy Live: Cypher Fundamentals https://www.youtube.com/@neo4j/live(Jul 8) Workshop (Bengaluru, India): Neo4j and GCP Generative AI Workshop(Jul 9) GenAI + Graph Meetup (Osaka, Japan) https://jp-neo4j-usersgroup.connpass.com/event/322658/(Jul 17-19) Conference (Berlin, Germany): WeAreDevelopers World Congress 2024 https://www.wearedevelopers.com/world-congress(Jul 18) Meetup (Berlin, Germany): Ollama & Friends Coming to AI Tinkerers Berlin https://berlin.aitinkerers.org/p/ollama-friends-coming-to-ai-tinkerers-berlin(Jul 19) Meetup (Bengaluru, India): Graphing the Future: How Generative AI, RAGs and Knowledge Graphs are Shaping AI https://www.meetup.com/graph-database-bengaluru/events/301273119/?isFirstPublish=true(Jul 28-30) Conference (Sydney, Australia): Gartner Data & Analytics Summit Sydney https://neo4j.com/event/gartner-data-analytics-summit-sydney-2/(Jul 28 - Aug 2) Conference (Wisconsin Dells, Wisconsin, USA): THAT Conference https://thatconference.com/activities/4AlNeqK2OogWQFdhkfuc(Jul 31) Meetup (Richmond, Virginia, USA): Connecting the future: Integrating Neo4j with GenAI, LLMs and RAGs https://www.meetup.com/graphdb-melbourne/events/301618964/?isFirstPublish=true(Jul 31) Meetup (Sydney, Australia): Decoding the Generative AI Landscape: A Deep Dive into RAGs and Graphs https://www.meetup.com/graphdb-sydney/events/301635885/?isFirstPublish=true

In today's episode of the SEOLeverage Podcast, Gert and his guest, Tim Warren discussed the future of AI technology and its impact on SEO. They emphasize the need to understand and embrace these changes to remain competitive while acknowledging the limitations of machine learning and AI in capturing human emotions.  They also discuss the potential impact of AI on the legal industry, proposing specialist AI engines and a shared platform for the entire industry. Finally, Tim Warren tells Gert about his hopes that Knowledge Graph will make it possible for Google to have personalized conversations that understand who people are. Podcast Highlights: 00:00 Prologue 01:06 Introduction to the podcast episode topic and the guest 02:15 Tim Warren's background and his role as a Chief Provocation Officer 03:46 The importance of asking questions in the face of change 04:39 AI impact on white-collar jobs 08:06 The Gartner Hype Cycle and AI's evolution 13:26 Rise and fall of AI companies 16:06 The Importance of Human Expertise in SEO 23:12 Why more and more people are utilizing AI for their online search 32:13 AI in Personal Finance 36:35 The importance of trust and personalization in AI 43:35 The role of digital companions 50:41 Where to connect with Tim Warren? 51:09 End   Resources:  ChatGPT - https://chatgpt.com/ Anthropic - https://www.anthropic.com/ OpenAI - https://openai.com/ Claude - https://claude.ai CopilotAI - https://www.copilotai.com/ Perplexity - https://www.perplexity.ai/ Connect with Tim Warren: LinkedIn - https://www.linkedin.com/in/tswarren/   Connect with Gert Mellak: Website: https://seoleverage.com/ Email: info@seoleverage.com  

Speaker Resources:Diffbot https://www.diffbot.com/Tomaz Bratanic's Medium blog: https://bratanic-tomaz.medium.com/What is DSP/DSPy? https://github.com/stanfordnlp/dspyTools of the Month:cypher-shell command line tool https://neo4j.com/docs/operations-manual/current/tools/cypher-shell/Langchain/Diffbot graph transformer https://python.langchain.com/v0.1/docs/integrations/graphs/diffbot/st-cytoscape https://github.com/vivien000/st-cytoscapeAnnouncements / News:NODES 2024 CfP resources:GraphStuff episode https://graphstuff.fm/episodes/navigating-a-technical-conference-talk-from-submission-to-deliveryNODES submission tips: https://neo4j.com/blog/nodes-talk-submission-tips/How to Submit a Technical Presentation https://jmhreif.com/blog/nodes-2024-cfp/Articles:Topic Extraction with Neo4j GDS for Better Semantic Search in RAG applications https://neo4j.com/developer-blog/topic-extraction-semantic-search-rag/Using LlamaParse to Create Knowledge Graphs from Documents https://neo4j.com/developer-blog/llamaparse-knowledge-graph-documents/Going Meta: Wrapping Up GraphRAG, Vectors, and Knowledge Graphs https://neo4j.com/developer-blog/going-meta-knowledge-graph-rag-vector/Unveiling the Mahabharata's Web: Analyzing Epic Relationships with Neo4j Graph Database (Part 1) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-1/Bringing the Mahabharata Epic to Life: A Neo4j-Powered Chatbot with Google Gemini (Part 2) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-2/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jun 4) Meetup (virtual): Tuesday Tech Talks: Graph Based RAG w/ Demo https://lu.ma/tys2a4zt?tk=ax2gtz(Jun 4) Workshop (virtual): Discover Neo4j Aura: The Future of Graph Database-as-a-Service https://go.neo4j.com/DE-240604-Discover-Aura-Workshop_Registration.html(Jun 5) Conference (Paris, France): GraphSummit Paris https://neo4j.com/graphsummit/paris24/(Jun 5) Workshop (Sydney, Australia): Neo4j and GCP Generative AI Workshop https://go.neo4j.com/LE240606Neo4jandGCPGenerativeAIWorkshop-Sydney_Registration.html(Jun 7) Conference (Athens, Greece): Generative AI for Front-end Developers https://athens.cityjsconf.org/talk/3b9XHj1HBahP8KJ13uWVui(Jun 10) Conference (San Francisco, California, USA): Data & AI Summit https://neo4j.com/event/data-ai-summit-2/(Jun 11) Meetup (San Francisco, California, USA): HackNight at GitHub with Graphs and Vectors https://www.meetup.com/graphdb-sf/events/301026060/?isFirstPublish=true(Jun 10) Workshop (Jakarta, Indonesia): Neo4j and GCP Generative AI Workshop https://go.neo4j.com/LE240423Neo4jandGCPGenerativeAIWorkshopJakarta_Registration.html(Jun 11) Conference (Oslo, Norway): NDC Oslo - Beyond Vectors: Evolving GenAI through Transformative Tools and Methods https://ndcoslo.com/agenda/beyond-vectors-evolving-genai-through-transformative-tools-and-methods-0x1u/011ha54g6jp(Jun 12) Conference (Munich, Germany): GraphTalk: Pharma https://go.neo4j.com/LE240612GraphTalkPharmaMunich_Registration.html(Jun 12) Conference (Frankfurt, Germany): Google Summit https://cloudonair.withgoogle.com/events/summit-mitte-2024(Jun 12) Livestream (virtual+München, Germany): LifeScience Hybrid Event 2024 https://go.neo4j.com/LE240612LifeScienceWorkshop2024_01Registration.html(Jun 12) Meetup (Brisbane, Australia): Graph Database Brisbane https://www.meetup.com/graph-database-brisbane/events/300367474/?isFirstPublish=true(Jun 12) Meetup (San Francisco, California, USA): Introduction to RAG https://lu.ma/u4uhtfqz(Jun 18) Meetup (London, UK): ISO GQL - The ISO Standard for Graph Has Arrived https://www.meetup.com/graphdb-uk/events/300712991/(Jun 20) Meetup (Stuttgart, Germany): Uniting Large Language Models and Knowledge Graphs https://neo4j.com/event/genai-breakfast-session-stuttgart-uniting-large-language-models-and-knowledge-graphs/(Jun 20) Meetup (Reston, Virginia, USA): LLMs, Vectors, Graph Databases and RAG in the Cloud https://lu.ma/mctijpjm(Jun 25) Conference (San Francisco, California, USA): AI Engineer World's Fair https://www.ai.engineer/worldsfair(Jun 26) Conference (virtual): Neo4j Connections GenAI https://neo4j.com/connections/go-from-genai-pilot-to-production-faster-with-a-knowledge-graph-june-26/(Jun 27) Conference (Kansas City, Missouri, USA): KCDC 2024 https://www.kcdc.info/(Jun 26) Conference (virtual): Neo4j Connections GenAI (Asia Pacific) https://neo4j.com/connections/go-from-genai-pilot-to-production-faster-with-a-knowledge-graph-asia-pacific-june-27/

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company. Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai. A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/ // Abstract RAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // Bio Technical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need — Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/ Timestamps: [00:00] Tom's preferred coffee [00:33] Takeaways [03:04] Please like, share, leave a review, and subscribe to our MLOps channels! [03:23] Academic Curiosity and Knowledge Graphs [05:07] Logician [05:53] Knowledge graphs incorporated into RAGs [07:53] Graphs & Vectors Integration [10:49] "Exactly wrong" [12:14] Data Integration for Robust Knowledge Graph [14:53] Structured and Dynamic Data [21:44] Scoped Knowledge Retrieval Strategies [28:01 - 29:32] LatticeFlow Ad [29:33] RAG Limitations and Solutions [36:10] Working on multi agents, questioning agent definition [40:01] Concerns about performance of agent information transfer [43:45] Anticipating agent-based systems with modular processes [52:04] Balancing risk tolerance in company operations and control [54:11] Using AI to generate high-quality, efficient content [01:03:50] Wrap up

Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human" Interview Introduction How did you get involved in machine learning? Can you start by unpacking the idea of "human-like" AI? How does that contrast with the conception of "AGI"? The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment? The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models? As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems? How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications? What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems? When is cognitive AI the wrong choice? What do you have planned for the future of cognitive AI applications at Aigo? Contact Info LinkedIn (https://www.linkedin.com/in/vosspeter/) Website (http://optimal.org/voss.html) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Aigo.ai (https://aigo.ai/) Artificial General Intelligence (https://aigo.ai/what-is-real-agi/) Cognitive AI (https://aigo.ai/cognitive-ai/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Causal Modeling (https://en.wikipedia.org/wiki/Causal_model) Bayesian Statistics (https://en.wikipedia.org/wiki/Bayesian_statistics) Thinking Fast & Slow (https://amzn.to/3UJKsmK) by Daniel Kahneman (affiliate link) Agent-Based Modeling (https://en.wikipedia.org/wiki/Agent-based_model) Reinforcement Learning (https://en.wikipedia.org/wiki/Reinforcement_learning) DARPA 3 Waves of AI (https://www.darpa.mil/about-us/darpa-perspective-on-ai) presentation Why Don't We Have AGI Yet? (https://arxiv.org/abs/2308.03598) whitepaper Concepts Is All You Need (https://arxiv.org/abs/2309.01622) Whitepaper Hellen Keller (https://en.wikipedia.org/wiki/Helen_Keller) Stephen Hawking (https://en.wikipedia.org/wiki/Stephen_Hawking) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

Tools of the Month:Descript https://www.descript.com/LLM Graph Builder by Neo4j https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/LangSmith https://www.langchain.com/langsmithObsidian https://obsidian.md/ Announcements / News:NODES 2024: Submit Your Talk https://neo4j.com/blog/nodes-by-neo4j/ISO GQL Standard:Press release: https://neo4j.com/press-releases/gql-standard/Blog post: https://neo4j.com/blog/gql-international-standard/Blog post: https://neo4j.com/blog/cypher-path-gql/Articles:Enhancing RAG with Neo4j Cypher and Vector Templates Using LangChain Agents https://neo4j.com/developer-blog/rag-cypher-vector-templates-langchain-agent/Graph Data Models for RAG Applications https://neo4j.com/developer-blog/graph-data-models-rag-applications/Maximizing Your Neo4j Project's Potential: An In-depth Guide to Solution Assessment https://neo4j.com/developer-blog/neo4j-project-solution-assessment-guide/LangChain Library Adds Full Support for Neo4j Vector Index https://neo4j.com/developer-blog/langchain-library-full-support-neo4j-vector-index/Constructing Knowledge Graphs From Unstructured Text Using LLMs https://neo4j.com/developer-blog/construct-knowledge-graphs-unstructured-text/Entity Resolved Knowledge Graphs: A Tutorial https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/The Future of Knowledge Graph: Will Structured and Semantic Search Become One? https://neo4j.com/developer-blog/knowledge-graph-structured-semantic-search/Building RAG Applications With the Neo4j GenAI Stack: A Comprehensive Guide https://neo4j.com/developer-blog/rag-genai-stack-guide/Adding Retrieval-Augmented Generation (RAG) to Your GraphQL API https://neo4j.com/developer-blog/rag-graphql-api/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(May 1) Fireside chat (virtual): Advanced RAG Techniques with Graph Databases for LLMs https://www.eventbrite.com/e/advanced-rag-techniques-with-graph-databases-for-llms-jason-koo-neo4j-tickets-878275013207(May 2) Hands-On Lab (Herndon, VA, USA): AWS and Neo4j Generative AI https://go.neo4j.com/LE-240502-AWS-GenAI-Workshop-Herndon_Registration.html(May 2) YT series (virtual): Knowledge Graph Builder App https://youtube.com/live/NbyxWAC2TLc(May 5) Conference (New York City, NY, USA): The Knowledge Graph Conference https://go.neo4j.com/TS-240506-The-Knowledge-Graph-Conference_Registration-Page.html(May 6) Conference (Singapore): AWS Summit 2024 https://neo4j.com/event/aws-summit-2024-singapore/(May 7) Meetup (virtual): Knowledge Graph-Based Chatbot https://www.meetup.com/nycneo4j/events/299160585/(May 7) Conference (virtual): WeAreDevelopers - Security Day https://www.wearedevelopers.com/event/security-day-may-2024(May 8) Webinar (virtual): Unveiling the Power of Knowledge Graphs and AWS Bedrock in Advancing Generative AI https://go.neo4j.com/WBR240508AWSBedrock_Registration.html(May 8) Meetup (Sydney, Australia): Graph Database Sydney Meetup https://www.meetup.com/graphdb-sydney/events/300446306/?isFirstPublish=true(May 9) Webinar (virtual): Building Powerful Fraud Detection Applications with Neo4j https://go.neo4j.com/WBR-240509-Fraud-Detection_Registration.html(May 11) Meetup (Bangalore, India): GenAI Meetup: Deep diving into RAG & Multi agent framework https://www.meetup.com/graph-database-bengaluru/events/300324064/?isFirstPublish=true(May 12) Conference (London, UK): Gartner Data & Analytics Summit https://www.gartner.com/en/conferences/emea/data-analytics-uk(May 13) Conference (Milan, Italy): GraphSummit Milan https://neo4j.com/graphsummit/milan24/(May 14) Hands-On Lab (Santa Monica, CA, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240514-AWS-Bedrock-Workshop-Santa-Monica_Registration.html(May 14) Conference (Kraków, Poland): Geecon 2024 https://2024.geecon.org/speakers/info.html?id=900(May 14) Conference (Bengaluru, India): AWS Summit https://neo4j.com/event/aws-summit-2024-bengaluru/(May 15) Conference (Berlin, Germany): AWS Summit Berlin https://pages.awscloud.com/aws-summit-berlin-2024-registration.html?Languages=German(May 15) Conference (Sydney, Australia): Google Cloud Summit https://neo4j.com/event/google-cloud-summit-sydney/(May 15) Hands-On Lab (Palo Alto, CA, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240507-AWS-Bedrock-Workshop-Palo-Alto_Registration.html(May 15) Meetup (Dresden, Germany): Dresdner Datenbankforum Graph Data Science with Neo4j https://www.dresdner-datenbankforum.de/anstehende-vortr%C3%A4ge#h.y2x2aesouh7p(May 15) Meetup (Mainz, Germany): Neo4j for Java Developers https://www.meetup.com/jug-mainz/events/299232685(May 16) Hands-on Lab (San Francisco, CA, USA): GenAI Hands-on Lab with AWS and Deloitte https://go.neo4j.com/LE-----240509-Neo4j-Deloitte--AWS-Generative-AI-Workshop_Registration.html(May 20) Conference (Stockholm, Sweden): GraphSummit Stockholm https://neo4j.com/graphsummit/stockholm24/(May 21) Hands-on Lab (Chicago, IL, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240521-AWS-GenAI-Workshop-Chicago_Registration.html(May 22) Conference (Milan, Italy): AWS Summit Milan https://aws.amazon.com/it/events/summits/emea/milano/(May 27) Conference (Jakarta, Indonesia): Google Cloud Summit https://neo4j.com/event/google-cloud-summit-jakarta/(May 27) Conference (Sofia, Bulgaria): jPrime 2024 https://jprime.io/

Want to learn how to use the Knowledge Graph for SEO benefits? I got you covered. I just had an incredible conversation with the one and only Jason Barnard from Kalicube about leveraging the power of Google's Knowledge Graph for SEO benefits. Jason shared his invaluable insights and strategies on how you can harness the Knowledge Graph to: ✅ Boost your online visibility ✅ Establish your brand authority ✅ Dominate search engine results pages If you missed the live show, don't worry! You can watch the full interview on my YouTube channel right now: https://youtube.com/live/eOOq8qOc4OY Follow SEO Consultant Olga Zarr or hire Olga to help you with SEO Follow Olga Zarr X/Twitter Follow Olga Zarr on LinkedIn The best SEO newsletter The best SEO podcast SEO consultant Olga Zarr

Tools of the Month:apoc.create.vRelationship https://neo4j.com/docs/apoc/current/overview/apoc.create/apoc.create.vRelationship/GenAI Starter Kits for Langchain, LlamaIndex, Spring.AI and Semantic Kernel, covering the most popular orchestration frameworks in Python, Java, and dotnet. https://neo4j.com/labs/genai-ecosystem/Vish: Vector support in Neo4j https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/Articles:Implementing RAG: How to Write a Graph Retrieval Query in LangChain https://neo4j.com/developer-blog/rag-graph-retrieval-query-langchain/Implementing Advanced Retrieval RAG Strategies with Neo4j https://neo4j.com/developer-blog/advanced-rag-strategies-neo4j/Using a Knowledge Graph to Implement a RAG Application https://neo4j.com/developer-blog/knowledge-graph-rag-application/Generative Transformation from ER Diagram to Graph Model Using Google's Gemini Pro https://neo4j.com/developer-blog/genai-graph-model-google-gemini-pro/Cypher Workbench as a Neo4j Labs Project https://neo4j.com/developer-blog/cypher-workbench-neo4j-labs-project/Accelerate Neo4j App Development with Low-Code Keymaker Framework https://neo4j.com/developer-blog/keymaker-low-code-neo4j-framework/Needle StarterKit 2.0: Templates, Chatbot, and More! https://neo4j.com/developer-blog/needle-starterkit-2-0-templates-chatbot/Announcing Neo4j JDBC Driver Version 6 https://neo4j.com/developer-blog/neo4j-jdbc-driver-v6/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Apr 2) YouTube series: Going Meta - A Series on Graph, Semantics, and Knowledge Episode 27 https://www.youtube.com/@neo4j/live(Apr 2) Conference (Paris, France): AWS Summit Paris https://aws.amazon.com/fr/events/summits/emea/paris/(Apr 8) Conference (London, UK): QCon London https://qconlondon.com/(Apr 8) Conference (Madrid, Spain): GraphSummit Madrid https://neo4j.com/graphsummit/madrid24/(Apr 8) Conference (Nürburgring, Germany): Javaland 2024 https://www.javaland.eu/en/home/(Apr 9) Conference (Las Vegas, NV, USA): Google Cloud Next https://cloud.withgoogle.com/next(Apr 9) Conference (Atlanta, GA, USA): DevNexus 2024 https://devnexus.com/(Apr 9) Workshop (Munich, Germany): Amazon Bedrock & Neo4j https://go.neo4j.com/LE240409AWSBedrockWorkshopMunich_Registration.html(Apr 9) Conference (Sydney, Australia): AWS Summit Sydney https://neo4j.com/event/aws-summit-sydney/(Apr 13) Workshop (San Francisco, CA, USA): GenAI Beyond Chat with RAG, Knowledge Graphs and Python https://www.meetup.com/graphdb-sf/events/299339190/(Apr 16) Conference (Paris, France): Devoxx France https://www.devoxx.fr/(Apr 17) Workshop (Toronto, ON, Canada): Neo4j & AWS Generative AI https://go.neo4j.com/LE240417AWSandNeo4jGenerativeAIHands-onLabToronto_Registration.html(Apr 18) Meetup (San Francisco, CA, USA): Cloud-Native Geospatial Analytics Combining Spatial SQL & Graph Data Science https://www.meetup.com/graphdb-sf/events/297525658/(Apr 23) Conference (Bengaluru, India): GIDS India 2024 https://www.meetup.com/graphdb-sf/events/297525658/(Apr 23) Workshop (Chicago, IL, USA): Neo4j and Google Cloud GenAI Hands-On https://go.neo4j.com/LE240423-Neo4j-GCP-GenAI-Workshop---Chicago_Registration.html(Apr 23) Conference (Stockholm, Sweden): Penningtvattsdagarna https://penningtvattsdagarna.se/anmalan/(Apr 24) Conference (Stockholm, Sweden): Data Innovation Summit https://datainnovationsummit.com/(Apr 24) Conference (London, UK): AWS Summit London https://aws.amazon.com/events/summits/emea/london/(Apr 24) Conference (Munich, Germany): GraphSummit Munich https://neo4j.com/graphsummit/munich-apr-24/(Apr 25) Hands-On Lab (New York City, NY, USA): AWS and Neo4j Generative AI https://go.neo4j.com/LE-240425-LE-240425-AWS-GenAI-Workshop-NYC_Registration.html(Apr 25) Meetup (London, UK): Modern Java Ecosystems: Advancing Connectivity and Cloud Deployment https://www.meetup.com/graphdb-uk/events/299949029/

Speaker Resources:Johannes Jolkkonen: https://www.linkedin.com/in/johannesjolkkonen/Johannes's YouTube channel: https://www.youtube.com/@johannesjolkkonenMar 12 Neo4j Live session: https://www.meetup.com/neo4j-online-meetup/events/299526466/Tools of the Month:PDF-bot chunker (GenAI stack): https://github.com/docker/genai-stack/blob/main/pdf_bot.pySpring AI: https://spring.io/projects/spring-aiInstructor (library): https://jxnl.github.io/instructor/Community Projects:Knowledge Graph for Social Science https://youtube.com/live/wBHgTheV08QArticles:Langchain v0.1 - Updating GraphAcademy Neo4j & LLM Courses https://neo4j.com/developer-blog/langchain-graphacademy-llm-courses/A GenAI-Powered Song Finder in Four Lines of Code https://neo4j.com/developer-blog/genai-powered-song-finder/Object Mapping in the Neo4j Driver for .NET https://neo4j.com/developer-blog/object-mapping-neo4j-driver-net/Slow Cypher Statements and How to Fix Them https://neo4j.com/developer-blog/slow-cypher-statements-fix/Using LangChain in Combination with Neo4j to Process YouTube Playlists and Perform Q&A Flow https://medium.com/neo4j/using-langchain-in-combination-with-neo4j-to-process-youtube-playlists-and-perform-q-a-flow-5d245d51a735PyNeoInstance: A User-Friendly Python Library for Neo4j https://neo4j.com/developer-blog/pyneoinstance-python-library-neo4j/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Mar 5) YouTube Series (virtual): Going Meta Episode 26 https://neo4j.com/event/going-meta-a-series-on-graphs-semantics-and-knowledge-episode-26/(Mar 6) Meetup (virtual): Exploring Graphs and Generative AI: Unlocking New Possibilities https://neo4j.com/event/exploring-graphs-and-generative-ai-unlocking-new-possibilities/(Mar 6) Meetup (virtual): Pass or Play: What Does GenAI Mean for the Java Developer? https://neo4j.com/event/pass-or-play-what-does-genai-mean-for-the-java-developer/(Mar 7) Meetup (Bangkok, Thailand): GraphDB Bangkok meetup w/ GraphQL BKK https://neo4j.com/event/graphdb-bangkok-meetup-w-graphql-bkk/(Mar 8) Conference (virtual): WeAreDevelopers Women In Tech Day https://neo4j.com/event/wearedevelopers-women-in-tech-day/(Mar 10) Conference (Orlando, Florida, USA): Gartner Data & Analytics Summit Orlando https://neo4j.com/event/gartner-data-analytics-summit-orlando/(Mar 11) Training (virtual): Knowledge Graphs & Large Language Models Bootcamp https://neo4j.com/event/knowledge-graphs-large-language-models-bootcamp/2024-03-11/(Mar 11) Workshop (Bengaluru, India): Neo4j and GCP Generative AI Workshop https://neo4j.com/event/neo4j-and-gcp-generative-ai-workshop-bengaluru/(Mar 12) Conference (Brussels, Belgium): AWS Public Sector Symposium https://neo4j.com/event/aws-public-sector-symposium-brussels/(Mar 13) Workshop (San Francisco, CA, USA): Google Gen AI Workshop https://neo4j.com/event/google-gen-ai-workshop-san-francisco/(Mar 13) Conference (Singapore): Singapore Data Innovation Summit 2024 https://neo4j.com/event/data-innovation-summit/(Mar 14) Conference (virtual): Data Next Engineering Summit https://neo4j.com/event/data-next-engineering-summit/(Mar 14) Training (virtual): Intro to Neo4j https://neo4j.com/event/training-series-intro-to-neo4j-2/(Mar 14) Workshop (Mountain View, CA, USA): Google Gen AI Workshop https://neo4j.com/event/google-gen-ai-workshop-mountain-view/(Mar 15) Meetup (Delhi, India): Pythonistas and Graphistas: Navigating the World of Graph Databases with Python https://neo4j.com/event/pythonistas-and-graphistas-navigating-the-world-of-graph-databases-with-python/(Mar 15) Meetup (Bengaluru, India): Graph Genesis: Building Tomorrow's Insights Today https://neo4j.com/event/graph-genesis-building-tomorrows-insights-today/(Mar 18) Training (virtual): Knowledge Graphs & Large Language Models Bootcamp https://neo4j.com/event/knowledge-graphs-large-language-models-bootcamp/2024-03-18/(Mar 18) Conference (Paris, France): KubeCon 2024 https://neo4j.com/event/kubecon2024/(Mar 18) Workshop (Singapore): Neo4j and GCP Generative AI https://neo4j.com/event/neo4j-and-gcp-generative-ai-workshop-singapore/(Mar 19) Conference (virtual): AI42 Conference https://neo4j.com/event/ai42-conference/(Mar 19) Workshop (virtual): Tame Your Graph with Liquibase for Neo4j https://neo4j.com/event/training-series-tame-your-graph-with-liquibase-for-neo4j/(Mar 20) Meetup (Melbourne, Australia): GraphDB Melbourne March Madness https://neo4j.com/event/graphdb-melbourne-march-madness/(Mar 20) Meetup (London, UK): The Perfect Couple: Uniting Large Language Models and Knowledge Graphs for Enhanced Knowledge Representation https://neo4j.com/event/the-perfect-couple-uniting-large-language-models-and-knowledge-graphs-for-enhanced-knowledge-representation/(Mar 21) Training (virtual): Mastering Neo4j Deployment for High-Performance RAG Applications https://neo4j.com/event/training-series-mastering-neo4j-deployment-for-high-performance-rag-applications/(Mar 21) Meetup (virtual): Neo4j & Haystack: Graph Databases for LLM Applications https://neo4j.com/event/neo4j-haystack-graph-databases-for-llm-applications/(Mar 21) Workshop (Los Angeles, CA, USA): Google Gen AI https://neo4j.com/event/google-gen-ai-workshop-los-angeles/(Mar 26) Meetup (Sydney, Australia): GraphSyd March Meetup: Unraveling Connections https://neo4j.com/event/graphsyd-march-meetup-unraveling-connections/(Mar 26) Conference (Las Vegas, NV, USA): Microsoft Fabric Community Conference https://neo4j.com/event/microsoft-fabric-community-conference/(Mar 26) Workshop (virtual): Large-Scale Geospatial Analytics with Graphs and the PyData Ecosystem https://neo4j.com/event/training-series-large-scale-geospatial-analytics-with-graphs-and-the-pydata-ecosystem/(Mar 27) Meetup: Graphs & Vectors: Navigating the Future with Neo4j and Vector Search https://neo4j.com/event/graphs-vectors-navigating-the-future-with-neo4j-and-vector-search/

Founder of data.world on using LLMs to explore your structured databases.

Years ago, I got to be an advisor for this company called data.world, and at the time, they were just getting started on helping figuring out how do you converge all the data sets that are in the world and help people work with them and combine them and share them. They built this thing that was kind of like GitHub for data. I was interested in it because I could see at the time where the world was going and we're going to need these much more advanced tools for being able to manage data. I tried to contribute my small way, but my favorite thing about it is that I got to know Bryon Jacob, who's the CTO of data.world. Brian is delightful guy. This is one of the guys who's been thinking about the the nature of data, the structure of data, how we work with that in computers for his entire career. And he got onto a track that you could consider a little bit fringe, of using graph databases decades ago, the semantic models that we use to understand data from the thinking around RDF and the early semantic web. And now what he's built is the system that when ingests any kind of data, it parses that out, takes it in a graph database and makes it accessible through a query language called SPARQL, which you'll hear us refer to. This is a kind of "advanced mode episode" and I know we're going to lose some people We refer to a lot of technical stuff that probably only data nerds are really going to be interested in. I won't be offended if you check out. But, if you have any interest in data or the future of analyzing data and using data in AIS, you need to listen and understand this conversation. Brian is an expert. He's built one of the most important king pin tools for using all the data in large-scale organizations or projects within the new generative AI context. If you are trying to use something like ChatGPT or another LLM as an interface to structured data, you're doing it wrong, and I think you'll be convinced about that as you start to understand what we're discussing today. So, hang in there. I promise this is a really REALLY valuable conversation for anybody who is trying to work at the forefront of using AIS for data analytics. I'm thrilled that we get to share this conversation with Bryon with you today. Important Links SPARQL data.world HomeAway About Bryon Jacob Bryon Jacob is the CTO and co-founder of data.world - on a mission to build the world's most meaningful, collaborative, and abundant data resource. Bryon is a recognized leader in building large-scale consumer internet systems and an expert in data integration solutions. Bryon's twenty years of academic and professional experience spans AI research at Case Western Reserve University, enterprise configuration software at Trilogy, and consumer web experience at Amazon and most recently in ten years building HomeAway.com. At HomeAway, Bryon oversaw platform development and the integration of thirty acquisitions while building the world's largest online marketplace for vacation rentals.

AI has the potential to revolutionize healthcare in areas that range from drug discover to the patient experience. In this podcast, Heather Lane from athenahealth shares the challenges and opportunities of using AI to improve the patient and clinician experience.Heather's Bio:Heather has a  PhD  from Purdue, where she focused on developing machine learning methods for the computer security problem of anomaly detection. She's worked at the   MIT AI Lab (now CSAIL) working with Leslie Kaelbling on reinforcement learning and decision-theoretic planning, Markov decision processes, and the tradeoff between stochastic and deterministic planning.In 2002, she moved to the University of New Mexico as an assistant professor in the Department of Computer Science. There she worked on a number of application areas of ML, including the bioinformatics of RNA interference, genomics, and computational neuroscience (inference of brain activity networks from neuroimaging data). Much of that work involved Bayesian networks and dynamic belief networks.In 2008, she was promoted to associate professor at UNM and was granted tenure. In 2012, she moved from academia to industry, joining Google in Cambridge, MA. working  on Knowledge Graph, Google Books, Project Sunroof, and Ads Latency.In 2017, she joined athenahealth to  lead a Data Science team working to use athena's immense store of healthcare data to improve healthcare experiences for clinicians and patients.Social LinksYou can follow Heather at: https://www.linkedin.com/in/terranlane/You can follow Maribel  at: X/Twitter: https://twitter.com/maribellopezLinkedIn: https://www.linkedin.com/in/maribellopezYouTube: https://www.youtube.com/c/MaribelLopezResearchHashtags: #AI, #Healthcare #PatientExperience

Tools of the Month:Remix for data-driven websites https://remix.run/HTTPie: https://httpie.io/cliPypeteer https://github.com/pyppeteer/pyppeteerRectangle https://rectangleapp.com/Fireflies.ai https://fireflies.ai/Video Speed Controller https://chromewebstore.google.com/detail/video-speed-controller/gioehmkjkeamcinbdelehlpnpdcdjpdp?pli=1Product updates:Neo4j release (5.15) https://neo4j.com/release-notes/database/neo4j-5/Neo4j Driver updatesAPOC Core https://github.com/neo4j/apoc/releases/tag/5.15.0GraphQL release (4.4.4) https://github.com/neo4j/graphql/releasesHelm chart update (5.14.0) https://github.com/neo4j/helm-charts/releases/tag/5.14.0Several Neo4j Connectors updatedArticles:Try Neo4j's Next-Gen Graph-Native Store Format https://neo4j.com/developer-blog/neo4j-graph-native-store-format/Implementing Advanced Retrieval RAG Strategies with Neo4j https://neo4j.com/developer-blog/advanced-rag-strategies-neo4j/Introducing Deno Runtime to the Neo4j Driver for Javascript https://neo4j.com/developer-blog/deno-runtime-neo4j-driver-javascript/Using a Knowledge Graph to Implement a DevOps RAG Application https://neo4j.com/developer-blog/knowledge-graph-devops-rag-application/Convenient Neo4j Integration Tests in Github Actions Using the Aura CLI https://neo4j.com/developer-blog/neo4j-integration-tests-github-actions-aura-cli/Neo4j x LangChain: Deep Dive Into the New Vector Index Implementation https://neo4j.com/developer-blog/neo4j-langchain-vector-index-implementation/Videos:RAG with a Neo4j Knowledge Graph: How it Works and How to Set It Up https://www.youtube.com/watch?v=ftlZ0oeXYRENODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jan 4) YouTube series (virtual): Going Meta Ep 24 https://neo4j.com/event/going-meta-a-series-on-graphs-semantics-and-knowledge-episode-24/(Jan 10) Meetup (Austin, TX and virtual): Airplane Route Optimization Using Neo4j's Graph Database https://neo4j.com/event/airplane-route-optimization-using-neo4js-graph-database/(Jan 10) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Asia https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-asia-pacific-jan-11/(Jan 11) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Europe https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-europe-jan-11/(Jan 11) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Americas https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-jan-11/(Jan 17) Webinar (virtual): O'Reilly Media: Generative AI for Healthcare https://neo4j.com/event/oreilly-media-generative-ai-for-healthcare-jan-17/(Jan 22) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Asia https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-asia-pacific-jan-23/(Jan 23) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Europe https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-europe-jan-23/(Jan 23) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Americas https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-jan-23/(Jan 25) YouTube series (virtual): Neo4j Live: Building a Semantics-Based Recommender System for ESG Documents https://neo4j.com/event/neo4j-live-building-a-semantics-based-recommender-system-for-esg-documents/(Jan 25) Conference (Bristol, UK): GraphTalk Government https://neo4j.com/event/graphtalk-government/(Jan 31) Meetup (London, UK): LLM + Knowledge Graph FTW https://neo4j.com/event/llm-knowledge-graph-ftw/(Jan 31) Meetup: Cloud-Native Geospatial Analytics Combining Spatial SQL & Graph Data Science https://neo4j.com/event/cloud-native-geospatial-analytics-combining-spatial-sql-graph-data-science/

Like many digital practices, search engine optimization is becoming more conversational.  Not long ago, SEOs had to make their best educated guesses about what was working to get their websites to rank better. Now, by focusing on both feeding information to and gleaning feedback from Google's knowledge graph, Jason Barnard helps companies craft content strategies and messaging architectures that keep their brand prominent in Google's search results. https://ellessmedia.com/csi/jason-barnard/

Callum Scott talks with Jason Barnard about understanding and recovering from Google traffic drops. Callum Scott specializes in conducting data-driven and qualitative SEO analysis, focusing primarily on traffic drop analysis, technical SEO and content quality. Callum is expert on Google's Knowledge Graph and Google's use of Entity Understanding for information retrieval and the entire search ecosystem. With over 5 years of experience in complex SEO environments, Callum has conducted nearly 100 technical and content-focused SEO audits, helped many websites achieve consistent growth and integrated well with an organisation's existing team and framework. Imagine you're navigating through a busy city and suddenly your GPS goes off. That's the same kind of confusion and vexation you feel when Google traffic drops affect your website. It's unsettling, irritating, and can definitely impact your profits. But once you understand why it happens and how to recover from it, you'll be back on track in no time. This essential knowledge not only protects your website's performance, but also gives you the ability to navigate the ever-changing world of search engine optimization. In this incredibly awesome episode, Callum (Callie) Scott reveals great nuggets and some real-life examples about traffic drops, their causes and how a website can recover from them. There are also three categories of traffic drops due to core updates - Broad Site Quality Reassessments, Searcher Intent Shift and Relevance, which Callum insightfully explains and suggests alternative strategies to help websites with ranking issues. Callum also highlights how to deal with Google's changing understanding of intent and the shift in SERPs. As always, the show ends with passing the baton… Callum passes the virtual baton to next week's super groovy guest, Alex Sanfilippo. What you'll learn from Callum Scott 00:00 Callum Scott and Jason Barnard 01:08 Callum Scott's Generative AI Result on Google 01:30 Kalicube Support Group 01:38 Blue Orchid Digital Ltd Brand SERP 03:22 When Did Google Start Relying More on Quality Signals Than on Pagerank or Word Count? 04:51 How Has AI Affected Google's Categorization of Website Quality? 05:53 What Significant Algorithm Changes Did Google Make in 2017? 06:26 How Did the Shift Towards Machine Learning Unfold Within the Google Search Team Between 2014 and 2017? 07:34 Understanding the Role of Features in Machine Learning for E-E-A-T 08:25 How Does Google's Confidence in Displaying a Knowledge Panel Affect Users' Trust in Their Search Results? 09:51 What are Some Examples of Traffic Drops, Causes and Recoveries? 10:55 Three Categories of Traffic Drops Due to Core Updates 11:04 First Category: Broad Site Quality Reassessments 12:16 How Does the Persistence of Low-quality Content Affect Google's Focus and Resource Allocation for a Website? 12:48 Second Category: Searcher Intent Shift 14:29 How Feasible is it for a Single Website to be Ranked for Both Informational and Transactional Intents? 16:01 How to Deal with the Change in Google's Understanding of Intent and the Shift in SERPs 18:12 Third Category: Relevance 19:30 What is the Best Alternative Strategy for Websites with Ranking Issues? 21:25 What are the Challenges of Convincing Clients to Address Traffic Drops by Individually Prioritizing Pages and Queries? 23:18 Traffic Drop Following a Core Update: Wait or Act Immediately 24:28 How to Convince Clients Not to Panic When Traffic Drops? 27:36 How Can Branded Search Help to Mitigate a Traffic Drop 29:35 Padding the Baton: Callum (Callie) Scott to Alex Sanfilippo This episode was recorded live on video August 29th 2023

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. That's the conclusion of the latest research that Juan Sequeda, Dean Allemang and Bryon Jacob have recently presented. In this episode, we will dive into the details of this research and understand why to succeed in this AI world, enterprises must treat the business context and semantics as a first-class citizen.

The EDGE of the WEB team ventured across the country to attend the inaugural BrightonSEO U.S conference in beautiful San Diego! This special podcast was filmed LIVE in front of an audience with 5 of the industry's best as panelists. Witness industry experts collaborate in forecasting the unpredictable future of SEO. The panel evaluates the industry's most disruptive topics, including Content at Scale, E-E-A-T, AI Generated Content, SGE, Google's Knowledge Graph, and beyond, offering insights that light the path ahead. Do not miss this very special feature of The EDGE of the Web as we discover the true trajectory of our industry, and SEO's unite to scale the expansive future ahead!   *Thanks to our panelists!* Mordy Oberstein Cindy Krum Julie McCoy Ola King JR Oakes Key Segments: [00:07:20] Panel Segments [00:06:24] Title Sponsor: SE Ranking [00:07:44] How Can We Maintain Creative Control When Using AI To Operate At Scale?  [00:14:52] The Journey Ahead For SEO's As In Relation To Content [00:27:56] How Ca n We Ensure The Accuracy And Reliability Of AI Generated Content?  [00:35:35] The Expanding Google Knowledge Graph [00:45:34] How Is Search Generative AI Going To Transform The Way We Search For Information? [00:55:34] How Will SGE Change Organic Links On The SERP? [01:00:00] EDGE of The Web Sponsor: SE Ranking [01:04:27] How Can SEO Tools Gauge The Success Of SEO Campaigns In The Context Of SGE? [01:11:52] The Future Of SEO In The Next 18 Months   Thanks to Our Sponsor! SE Ranking: edgeofthewebradio.com/seranking  Follow Our Panelists  Cindy Krum Julie McCoy Ola King JR Oakes Mordy Oberstein

RAG, Retrieval Augemented Generation, is the term you now constantly hear in conjunction with LLM that provides context. But how does it actually work? And what's the relationship with Vector Databases and Knowledge Graphs? This will be a geeky AI episode with Mike Dillinger.

Beatrice Gamba, Senior SEO Strategist at WordLift, discusses the Knowledge Graph. The Knowledge Graph is a powerful tool for search engine optimization and can help you improve the relevance of your content by adding more meaning to it. In fact, it's so powerful that simply submitting a Knowledge Graph can be considered an SEO strategy. Today, Beatrice talks about Knowledge Graph SEO. Show NotesConnect With: Beatrice Gamba: Website // LinkedInThe Voices of Search Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex's magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It's like having an analytics co-pilot built right into where you're already doing your work. Then, when you're ready to share, you can use Hex's drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex (https://www.dataengineeringpodcast.com/hex) to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products Interview Introduction How did you get involved in the area of data management? Can you describe what the term "linked data product" means and some examples of when you might build one? What is the overlap between knowledge graphs and "linked data products"? What is JSON-LD? What are the domains in which it is typically used? How does it assist in developing linked data products? what are the characteristics that distinguish a knowledge graph from What are the layers/stages of applications and data that can/should incorporate JSON-LD as the representation for records and events? What is the level of native support/compatibiliity that you see for JSON-LD in data systems? What are the modeling exercises that are necessary to ensure useful and appropriate linkages of different records within and between products and organizations? Can you describe the workflow for building autonomous linkages across data assets that are modelled as JSON-LD? What are the most interesting, innovative, or unexpected ways that you have seen JSON-LD used for data workflows? What are the most interesting, unexpected, or challenging lessons that you have learned while working on linked data products? When is JSON-LD the wrong choice? What are the future directions that you would like to see for JSON-LD and linked data in the data ecosystem? Contact Info LinkedIn (https://www.linkedin.com/in/brianplatz/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Fluree (https://flur.ee/) JSON-LD (https://json-ld.org/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Adjacency List (https://en.wikipedia.org/wiki/Adjacency_list) RDF == Resource Description Framework (https://www.w3.org/RDF/) Semantic Web (https://en.wikipedia.org/wiki/Semantic_Web) Open Graph (https://ogp.me/) Schema.org (https://schema.org/) RDF Triple (https://en.wikipedia.org/wiki/Semantic_triple) IDMP == Identification of Medicinal Products (https://www.fda.gov/industry/fda-data-standards-advisory-board/identification-medicinal-products-idmp) FIBO == Financial Industry Business Ontology (https://spec.edmcouncil.org/fibo/) OWL Standard (https://www.w3.org/OWL/) NP-Hard (https://en.wikipedia.org/wiki/NP-hardness) Forward-Chaining Rules (https://en.wikipedia.org/wiki/Forward_chaining) SHACL == Shapes Constraint Language) (https://www.w3.org/TR/shacl/) Zero Knowledge Cryptography (https://en.wikipedia.org/wiki/Zero-knowledge_proof) Turtle Serialization (https://www.w3.org/TR/turtle/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)