Podcasts about Knowledge graph

AI is already telling your prospects what to think about your brand — and most business owners have no idea what it's saying. Jason Barnard has spent over a decade building the frameworks that make brands visible, credible, and recommended by AI systems. What worked in Google's early days is still the foundation, but the stakes are now radically higher. This episode is a masterclass in owning your digital footprint before someone else defines it for you.KEY TAKEAWAYS1. Your entity home website is the hub — use it to link out and prove to AI that your off-site content is genuinely you.2. AI visibility comes down to the same principle as great marketing: stand where your audience is looking and prove you're the most credible solution.3. Protecting your link juice by refusing to link out is a outdated SEO strategy that actively harms how AI understands your brand.4. Only around 5% of ChatGPT usage involves purchase intent, so don't abandon traditional search — the two must work together.5. Search, assistive AI, and agentic AI are three distinct modes living side by side, each requiring a different strategic response from your business.6. AI agents will reshape some business models far more than others — SaaS and digital services are at the sharp end, and smart founders are re-engineering now.GUEST BIOJason Barnard is the founder of Kalicube, a pioneering digital marketing agency specialising in brand SERP optimisation and entity-based AI visibility. With roots in brand knowledge panels dating back to 2012, Jason is widely regarded as one of the world's leading authorities on how AI systems understand and represent brands online. He works with businesses globally to ensure they are seen, understood, and recommended by Google, ChatGPT, Perplexity, and the next generation of AI agents.If this episode made you think differently about how AI sees your brand, hit subscribe so you never miss an episode of Business Growth Talks — and if you got value from it, a five-star review takes 30 seconds and genuinely helps us reach more founders like you. Share it with a fellow business owner who needs to hear this — it could be the most important conversation they have this year. Mark / Business Growth Talks.Support the showIf you want to watch the full video of this episode go to:https://www.youtube.com/@markhayward-BizGrowthTalksDo you want to be a guest on multiple podcasts as a service go to:www.podcastintroduction.comFind more details about the podcast and my coaching business on:www.businessgrowthtalks.comFind me onLinkedIn - https://www.linkedin.com/in/mark-hayw...Tik Tok - https://www.tiktok.com/@mjh169183YouTube Shorts - https://www.youtube.com/@markhayward-BizGrowthTalks/shorts

Vom Bilder-Archiv zum Fundament der Content-Wertschöpfung: Simon Putzer, Co-CEO von Sharedien, erklärt, wie modernes Digital Asset Management heute funktioniert. Am Beispiel von Otto, Tesa und Siemens geht es um multimodales Bild-Scoring, um Build vs. Buy im KI-Zeitalter und darum, warum ohne saubere Stammdaten kein Use-Case fliegt. In dieser Folge erfährst du: → Wie Otto über 23 Mio. Assets multimodal und für unter 1 Cent pro Bild bewertet → Was ein modernes DAM vom klassischen Bilder-Archiv unterscheidet → Warum 'Stammdatenarmut' der eigentliche Engpass im Mittelstand ist → Build vs. Buy: was man selbst baut und was man einkauft → Wohin die Reise geht: vom DAM zur Knowledge Graph Content Value Chain Über den Gast: Simon Putzer ist Co-CEO von Sharedien und verantwortet Marketing, Vertrieb und die globale Go-to-Market-Strategie. Er ist seit über 25 Jahren im SaaS- und MarTech-Umfeld tätig, davon zehn Jahre bei Salesforce. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

Warum empfiehlt eine Plattform genau diese Tour – und nicht eine andere? In dieser Folge spricht Jonas Rashedi mit Stefan Neubig von Outdooractive über Recommender-Systeme, Knowledge Graphen und die technischen Grundlagen moderner Empfehlungstechnologien. Stefan gibt Einblicke in den Aufbau eines Graphen mit rund 92 Millionen Knoten und 140 Millionen Beziehungen. Dabei geht es um Nutzer, Touren, Regionen und Interaktionen – und darum, wie Machine-Learning-Modelle daraus personalisierte Empfehlungen ableiten. Besonders spannend: Warum Erklärbarkeit immer wichtiger wird, welche überraschenden Erkenntnisse Outdooractive bei Experimenten zur Besucherlenkung gewonnen hat und weshalb Knowledge Graphen im Zusammenspiel mit KI-Agenten eine wichtige Rolle spielen könnten. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.

PrimePoint just raised $10M to solve the one problem AI still can't crack in construction: drawings.Lubomir Bourdev built the first computer vision system at Facebook. Sold a neural net startup to Apple. Hamid was employee five at Trello. Now they're betting that drawings are the key to unlocking AI's full potential in construction.Tune in to find out about:✅ Why LLMs fundamentally can't handle technical drawings — it's an architecture issue, not a capability gap✅ How PrimePoint's Knowledge Graph connects drawings, specs, RFIs, submittals, and schedules✅ How AI does the first pass on constructability reviews, RFIs, and submittals — and why humans still make the call✅ Why early users are actually spending more time understanding their projects, not lessWatch the exclusive episode on Bricks & Bytes YouTube Channel now. Link in the comments below. #aec #construction #constructiontech #bricksandbytes #bricksbytes #ai #vcOur Sponsors:BreadCrumb- 50,000+ projects globally. All running safer, faster, with Breadcrumb. - breadcrumb.coAphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk - “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.com

Today, we're joined by Harsha Chintalapani, Co-Founder and CTO of Collate, an AI-native semantic intelligence platform. We talk about:Solving complex data challenges to drive success at UberThe dream of getting LLMs to identify context for improved semanticsThe challenges in applying meaning and semantics at the metadata levelHow open source attracts talentThe value of retaining the ability to model in the new world of AI-generated code

Winning the AI Trust Race Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this compelling discussion from the Ultimate Partner Winter Retreat, Vince Menzione sits down with Marc Monday of ServiceNow and marketing expert Ashleigh Vogstad to deconstruct the “tectonic shifts” currently hitting the tech industry. As the market moves from AI excitement into a period of “POC fatigue,” the conversation pivots to the essential groundwork required for success: clean data, governed workflows, and the transition from an attention economy to a trust-based machine economy. They explore how Gen Z's massive spending power is reshaping marketplaces and why simply automating a 27-step bad process with AI is a recipe for failure. Whether you are a partner manager or an entrepreneur, this episode provides a roadmap for staying human in a machine-to-machine world. Key Takeaways The market is experiencing “POC fatigue,” making it critical to transition from experimental AI to real-world value driven by central databases and knowledge graphs. ServiceNow is shifting focus toward “Control Tower” solutions to govern and orchestrate how various AI agents interact with mission-critical data. We are moving from a human-centric “attention economy” to a “trust economy” where machines make high-stakes decisions on behalf of users. Automating an existing 27-step approval process without rethinking the workflow first results in an “automated bad process” rather than a solution. By 2030, 75% of B2B buyers will be Gen Z, a demographic that favors authentic voices and direct-to-fan platforms like Substack over traditional channels. Hyperscaler partnerships are becoming essential “third-party validation” layers that allow AI agents to verify a company's win rates and credibility. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags ServiceNow, Marc Monday, Ashleigh Vogstad, Ultimate Partner, AI Fatigue, Agentic AI, Control Tower, Trust Economy, Knowledge Graph, Workflow Engine, Gen Z B2B, Marketplace, Hyperscalers, Machine-to-Machine, Data Governance, POC Fatigue, Substack, LinkedIn, Digital Transformation, Co-Selling, Partner Programs, ERP Intelligence, Uncanny Valley, Marketing Lag, Shared Business Planning. Transcript Ashleigh and Marc Monday Audio Episode [00:00:00] Ashleigh Vogstad: But the reality is, if you’re not using AI in a very meaningful way in your sales and marketing functions of your businesses, I mean you’re just way behind. [00:00:13] Vince Menzione: We just finished Ultimate Partners Winter Retreat here in beautiful Boca to a sold out crowd. Come join me now for a compelling discussion on the impacts of the tectonic shifts we’re all seeing. Maybe just a second about roles and responsibilities. Most of you know Ash from previous, uh, things you’ve been doing with us. [00:00:34] Vince Menzione: But, but maybe for you, Martin, this is your first time. [00:00:36] Marc Monday: Where should I [00:00:37] Vince Menzione: look there? Alternate partner. Their lives [00:00:38] Marc Monday: there? [00:00:39] Vince Menzione: Uh, yeah, over here is good. Either one. [00:00:41] Marc Monday: Look over there. Which would you prefer? [00:00:43] Vince Menzione: Um, this is good. [00:00:44] Marc Monday: Great. It’s, [00:00:45] Vince Menzione: and, but right now I’m just asking you for everybody, tell everybody who you are in your role. [00:00:49] Vince Menzione: ’cause you just shifted roles at ServiceNow. It’s [00:00:51] Marc Monday: true. It’s true. Hello everyone. My name is Mark one day and I lead the America’s partner business, uh, partner sales business at ServiceNow today. And effective Monday I’ll lead the global partner team. Uh, Jen Odes, who’s been on the podcast. Yes. She’s been and I are switching roles. [00:01:07] Marc Monday: Jen’s gonna go run the patch and I’m gonna run the programs, uh, effective next week. [00:01:11] Vince Menzione: That’s fantastic. [00:01:12] Marc Monday: And I live in Seattle. [00:01:15] Vince Menzione: You live in Seattle. Yeah. And you made the trip out here. I really appreciate that. It’s a long journey. And Vancouver or Whistler? So both of you came from the, from the West coast. [00:01:23] Marc Monday: This may be the first snowboarding panel in history of ultimate partner. [00:01:29] Ashleigh Vogstad: I liked the question earlier. Somebody asked, did anyone leave the snow to be here? It was literally a blizzard. I did not know if I would make it driving at 4:00 AM to the airport in a total whiteout. [00:01:41] Marc Monday: You’re getting zero sympathy from me Live in Whistler. [00:01:44] Vince Menzione: So, so Service now has been, uh, I would say on the forefront of this AI thing. I mean, like you were early in and control towers, that I always get the, the nomenclature wrong, but I do feel like we are seeing some, a level of fatigue right now. And I keep seeing, I mean, it feels like every, we’re getting whiplashed at least the last few weeks. [00:02:03] Vince Menzione: Are you seeing that? And what are the two or three biggest blockers you’re seeing now in the market? [00:02:10] Marc Monday: I think there’s, there’s a lot of excitement obviously in the marketplace, but there is a bit of AI fatigue. There’s a POC fatigue, I think that’s going on. I think the reality is we have to make AI real, and the reality is it starts with good data, uh, a, a central, uh, a database, and really making sure that that’s extensible through a knowledge graph. [00:02:31] Marc Monday: And then that provides us the ability to identify that workflow. Then importantly, um, making it real and, and as fast as possible. And I think that’s really important for the customer. One of the value props of ServiceNow, of course, is that we’ll meet the customer where they are with whatever their estate has, [00:02:47] Vince Menzione: right? [00:02:47] Marc Monday: So any hyperscaler, any workload, any core dataset, um, any LLM and, um, our history is as a workflow engine, and so we can bring that level of knowledge to their business. And then importantly, we bring together the governance and orchestration from a control tower perspective. [00:03:08] Vince Menzione: Nice. Ash had perspective on this, on the kind of the whiplash we’ve been feeling. [00:03:13] Vince Menzione: From From the marketing agency side? [00:03:15] Ashleigh Vogstad: Yeah. I mean, what comes to mind is the Miriam Webster dictionary said that LOP is the 2025 Word of the Year lop and Satchin Nadella actually came out with some press immediately following on that, saying that essentially that LOP is an exactly a useful construct to be having a conversation around the future of media. [00:03:37] Ashleigh Vogstad: But I think what this is pointing to is just we’re all navigating. Exactly how much AI is good ai, and maybe we will get into a little bit later, but what is the difference between selling to a human being and selling to a machine? Um, and really when we’re getting into this age agent landscape, it’s much more about that machine to machine conversation. [00:04:01] Ashleigh Vogstad: It’s not necessarily. Human eyeballs on recommendation links that is paid for by advertising. It’s more of a trust economy actually, where machines wanna be able to make decisions on our behalf with high trust so that you continue to enable that machine to make those decisions for you. [00:04:22] Vince Menzione: We talked about the data. [00:04:23] Vince Menzione: I thought we’d double click a little bit on that. In fact, that point it would normally have been here, but because of the snow wasn’t able to, they focus in on this governance and this data element. I was thinking maybe we could talk a little bit about that, because it doesn’t seem like AI will work properly if we don’t have the data to stay governed and clean, right? [00:04:42] Marc Monday: I think this is the amazing opportunity for the partners out there. They do this already. This is one of those assessments that’s so quick and not easy, but clear to deliver a value prop as a partner. Let’s get you ready for ai. Let’s make sure that we’re ensuring that your data’s in a extensible in a way across, uh, some sort of knowledge graph that can be accessed across a number of different, um, use cases. [00:05:09] Marc Monday: And oftentimes that’s multiple data sets. And so how do you get those columns and rows organized in a way that’s extensible for an agent, that we’re basically asking to do something that is an unique opportunity for partners right now. And I, I think that we maybe missed that step. So I see what I see happening right now is we’ve gotta come back to that as a starting point for the partners. [00:05:31] Vince Menzione: Let’s talk about agent ai or you also have orchestration AI as well. I wanna talk about their, your new service platform specifically, but maybe if you could double click with this on that. [00:05:42] Marc Monday: Well, I think that, you know, everyone is kind of trying to figure out how do we get there and who’s gonna orchestrate and govern what AI agent is calling on, what data set at what time, and what sequence. [00:05:54] Marc Monday: You may have a mission critical application that needs to have immediate access, and you may have other agents that have casual access. How do you control that in a meaningful way is gonna be become increasingly important. So we have the idea of this product that we call control tower. The control tower gives you the ability to manage that orchestration as well as the governance. [00:06:14] Vince Menzione: Any perspective on this? [00:06:17] Ashleigh Vogstad: I think I’ll share the perspective. As an entrepreneur, I know many people here represent. Companies that are our clients and are, are massive in scale and, and hyperscalers. But I think there are some people in the room who are running their own organizations. I think when I came out, Vince asked, you know, Ash growth mindset, how are you actually living this? [00:06:36] Ashleigh Vogstad: And we’re going through a journey in my business right now around what are all of the data sources that we have and how can we get that into an enterprise resource planning type system so that we can then overlay more intelligence. And that’s kind of where we’re at in the, it’s funny ’cause when you look at those maturity curves, they try and fit you in a box. [00:06:57] Ashleigh Vogstad: Nobody here likes being in a box. Um, and we’re in a corner. Yeah. In some ways it’s like we’re in that agentic box. I built an agent last week, funny enough for Microsoft actually, um, an executive comms agent, but in one hand we’re on that end and on the other, our data’s a mess and we really can’t apply a lot of intelligence to the majority of the data sources within our organization. [00:07:20] Ashleigh Vogstad: So we’re getting that all together right now. [00:07:22] Vince Menzione: When you came out, we talked a little bit, you were, you were mentioning having an advertising agency, marketing agency. The changes that are going on right now. Right? The attention economy and the trust economy. And I thought maybe you could double click with us on that. [00:07:35] Vince Menzione: ’cause that’s, uh, very interesting to see this shift. [00:07:39] Ashleigh Vogstad: It’s a huge shift. So, uh, 1964 Canadian philosopher, Marshall McCluen, he comes out and he says The medium is the message. [00:07:49] Audience Question: Yeah. [00:07:49] Ashleigh Vogstad: And so you wanna think about how is agenta a different medium and what are the biases that this medium inherently has? So in my media world, you know, you get these storytelling tools rolling out at Speed Chat, GBT, soa, and in the beginning they’re really at that low end of the curve. [00:08:08] Ashleigh Vogstad: You know, they can produce a shitty first draft, uh, but the content that they’re creating is really low emotional resonance. If you take kind of a neuroscientist perspective on this, and I’m definitely not a neuroscientist, but the part of your brain that’s responsible for that pattern recognition, your cortical sal circuit, that’s what’s kicking in. [00:08:29] Ashleigh Vogstad: And when you’re looking at, say, an advertisement, you’re starting to think, you know, is what I’m looking at actually commensurate with what I expect to see? And when it’s not, you can trigger that what psychologists call your uncanny valley. Now some will argue that on County Valley is really diminishing these days because AI generated media is getting better and better. [00:08:52] Ashleigh Vogstad: And I do think that it’s something you want to lean in, but you also wanna think intelligently around how you’re using this new medium and exactly what its, what its biases are. [00:09:03] Vince Menzione: Is that the gut syndrome? Like when you feel something in your gut? Is that what you described? [00:09:07] Ashleigh Vogstad: Yeah. Yeah. I mean, the classic example is Coca-Cola. [00:09:10] Ashleigh Vogstad: So 2024 Coca-Cola rolled out their very nostalgic for many of us holiday campaign, and they decided to use tools like Luma Dream Machine to make this whole Santa Claus North Pole, but AI generated universe. And it had that classic stuff around, you know, six fingered people and it gave you this. Kind of creepy post-apocalyptic vibe and the campaign completely tanked in market. [00:09:37] Ashleigh Vogstad: Or more recently, last year, mango rolled out a new fashion line Mango’s a huge global fashion retailer. They rolled out a new fashion line, and in their advertisements they had AI generated models and AI generated clothing. Like to sell a real line. So, you know, you, you have to really be thinking about, again, when we come to an attention economy based on human beings or a machine economy based on trust, many of these companies are still selling to us human beings. [00:10:09] Ashleigh Vogstad: And I, I think they can forget that at times. [00:10:12] Vince Menzione: So what’s your guidance to customers today and to this audience and viewers watching us today from a go-to market motion? In this world of ai, like what? What are you telling? What? How are you counseling these organizations? [00:10:25] Ashleigh Vogstad: You need to have an authentic voice. [00:10:27] Ashleigh Vogstad: We, we’ve heard this a million times, so I’ll try and put a bit of a, a different spin on it at platforms direct to fan platforms, things like Substack. Substack grew 48% last month. I mean, we are seeing this skyrocket, and that’s a new channel where you can have an authentic voice. Many people in this room, myself included, we live on LinkedIn as the business to business platform. [00:10:50] Ashleigh Vogstad: Consider expanding out into, into a new channel, um, would be one of my recommendations. Interesting. [00:10:57] Vince Menzione: Any, anything else from, uh, what you developed or what you use and ai and what do you, what, what tools do you recommend they use and what. [00:11:06] Ashleigh Vogstad: There. [00:11:06] Vince Menzione: Yeah. [00:11:06] Ashleigh Vogstad: What are we seeing with our, so I can give this example of this executive comms agent that we built. [00:11:12] Ashleigh Vogstad: Or even part, yeah, we’re building agents all the time, so what we try to do is think about what is our customer seeking to solve. We heard a lot today about outcomes, and then we challenge an AI first lens, which is how can we build something with AI to make this easier, better, faster, more creative? We’ll even do things, we’re a marketing agency, so we’ll even do things like beat the bot, pitch competitions. [00:11:37] Ashleigh Vogstad: So this is where you’re inviting your agent into the room and you’re asking it to put the pitch together, say for ServiceNow and Microsoft, and what can it come up with? And then we put it in a room of human beings and see who can out pitch. Bot, um, and come up with a more novel, creative idea. But the reality is, if you’re not using AI in a very meaningful way in your sales and marketing functions of your businesses, I mean, you’re just way behind. [00:12:07] Ashleigh Vogstad: And I see it a bit more advanced in all honesty and sales because I think some of your large. Organizations push the AI down to the sellers. Mm-hmm. Um, so they’re somewhat forced to use it, but in marketing, I’m still seeing a real lack, which is funny since generative AI came out in 2022 and everybody thought the marketing function was the one to really be disrupted and displaced. [00:12:30] Ashleigh Vogstad: I do think your marketing teams need to be leaning in more. [00:12:35] Vince Menzione: We were talking about trust earlier. I wanna weave this into the conversation. Right. How do, how do you. How do you think through trust and applying trust in the area I world, I’ll ask you both this question under service. Now think about it. How do you think about it or transcend? [00:12:54] Marc Monday: Maybe I’ll take a step back. I, I think just to kind of go back to the previous question, I think we’re in this age of massive complexity. Incredible complexity. Nina said it earlier, the customers kind of want us to tell them what to do. What are the steps? We’re at this dichotomy of this level of complexity that’s almost unimaginable and we have to make it simple. [00:13:18] Marc Monday: I think that’s the first one. And then that, that is put up against this notion of we have to go incredibly fast ’cause the market’s moving faster than we can even understand it. [00:13:28] Vince Menzione: Yeah. [00:13:29] Marc Monday: And then we have to add on this veneer, and this is where the partner community becomes so important of how do we scale? [00:13:35] Marc Monday: So how do you take simplicity, speed, and scale and bring it to market? It starts with the data, of course it starts with the workflow, but I might just take a giant step back and say one of the things that another partner opportunity you might run to really consider is automating a bad process, even with AI is still a bad process. [00:13:58] Marc Monday: So again, a partner opportunity is, let’s zoom back out and say if your approval. Takes 13 steps in 27 days, building an AI process around that. Without rethinking it might not be the right solution. So I think part of it is also like rather than just dictating all of the steps, part of it, to the point of telling the customer the steps is getting them to participate in that conversation. [00:14:29] Marc Monday: Why do you have 27 approval layers? Well. It’s the most dangerous thing in the language. It’s because we’ve always done it that way. Well, what if we did it differently? Yeah. And so I think that’s an area where the trust is a two-way street and you can’t just the part, the customer shouldn’t just outsource all of their decision making to you. [00:14:50] Marc Monday: At the same time, you have to bring them into that discussion of what are you trying to accomplish and what is your, um, risk appetite relative to that. [00:15:02] Ashleigh Vogstad: Yeah, that, that’s great, mark. I mean, trust is a really important conversation. I think about the Amazon versus Perplexity lawsuit right now that some are headlining the end of commerce. [00:15:14] Ashleigh Vogstad: Um, and so really this precedent setting case, what this is about is perplexity. Essentially is disintermediating the Amazon platform. So you know it’s making purchase decisions on your behalf, so, so this idea of trust in the agent world is something I think about a lot. And how do you optimize trust for this agentic world? [00:15:36] Ashleigh Vogstad: The professor I was mentioning, Eric Zow, who has this attention economy and the trust economy for agents where my research is leaning in is really around what is the hyperscaler layer on top of that. My working theory is that hyperscaler partnerships are just gonna become more important because the machines need to verify via trusted third party data sources what it is that you’re up to. [00:16:02] Ashleigh Vogstad: So how many deals have you done? Uh, what is your win rate percentage? This kind of information is incredibly valuable to the agent world, and so I think we’re gonna see an. Increasing lean in to these third party validation co-selling systems like partner center. [00:16:22] Marc Monday: I mean, just to add onto yeah. This idea, I mean, we do talk a lot about trust, but attention is probably underserved if I think about the role of a partner manager or an alliance director, it’s all about the trade-offs of what am I gonna spend my time on today? [00:16:37] Marc Monday: And you’re being pulled in a million directions, and I dunno about you, but it’s probably 900 to 10,000 unread emails and maybe you’ll respond to your immediate messages and if something happens, you’ll respond in in text. Part of it is also delineating between the busyness and the impact, and I think a lot of that’s also part of this discussion of how do we get focused on the outputs that matter. [00:17:02] Marc Monday: Really helping the customer get there through that discussion, which again goes back to it has to be a dialogue with the customer rather than just, this is the solution. Here’s our SOW. We’ll see you in six months. [00:17:14] Vince Menzione: Agree. We have a couple extra minutes. I was thinking of maybe opening it up for you. Any questions? [00:17:19] Vince Menzione: We have a mic in the back and I’m sure people have questions about this topic is, is fascinating to me and I wanna make sure that we’ve covered any of the questions we have. We have one right in the front from Shannon. [00:17:30] Marc Monday: Send the hard [00:17:31] Vince Menzione: questions over there. Not Yes. I’ll take the Easy books. Yeah. [00:17:36] Audience Question: You referenced marketing lag. [00:17:38] Audience Question: I think all of us would love to see marketing leading. [00:17:41] Ashleigh Vogstad: Yes. [00:17:42] Audience Question: Um, so how are you infusing within your marketing team at different levels around content creation? Um, there’s so much, uh, ego right on being a graphic designer or an editor, a copy editor that they. The human inflation in that conversation is a, is a hard thing to get them over. [00:18:02] Audience Question: And now AI can help this. How are you? [00:18:04] Ashleigh Vogstad: Yeah, let’s have a conversation after. But you just brought up a funny No, I’m gonna answer as well, but you brought up, brought up a funny, uh, conversation we had internally, just in the last 24 hours we’re interviewing for a new creative director and one of our candidates said, yes, but I don’t do Figma. [00:18:20] Ashleigh Vogstad: I’m not a UX person. I just laughed and I said, you know, the day is coming where It’s a designer, it’s a UX person, it’s a project manager, a program manager, a copywriter. You know, AI is condensing a lot of roles in that way. So I think being multidisciplinary in your skillset is, um. Is quite valuable, but I’ll also take this into a hyperscaler direction and say, no. [00:18:46] Ashleigh Vogstad: Here audiences, 75% of it buyers are going to be Gen Z by 2030. They have 12 trillion in spending power. I was in Silicon Valley yesterday, uh, helping a customer with a wind story. They did a $12 million transaction through Marketplace. Now that’s very impressive, but it would’ve been more impressive two years ago. [00:19:06] Ashleigh Vogstad: There are more and more, 10 million plus. Deals happening through marketplace. And so if you look at that Gen Z and start to understand them and their buying behavior, like another example is, I think it’s 80%, no, no half, sorry, half of Gen Z last month made a purchase via Instagram, TikTok, or YouTube. They are used to making these online transactions and average purchase price is going up. [00:19:35] Ashleigh Vogstad: You know, $500,000 plus is starting to be the average in some of these enterprise selling platforms. So as a marketing team, how are we kind of going in and leading the marketplace? Conversation I think is really critical and there’s technical elements to that. [00:19:52] Marc Monday: Maybe the caveman view of that would be, um, the other side, which is I think someone earlier said, we have to know where our customer is at. [00:20:00] Marc Monday: And a lot of our, we are very lucky. We live in this very insular tech bubble and we’re thinking about, you know, where we are 10 years from now and the customer’s gonna are gonna get there eventually, and it’s gonna happen faster. But I would say in marketing, I mean the two easiest use cases right now are around localization. [00:20:16] Marc Monday: Language localization and then specific market localization, like we don’t have to solve world hunger right now. There are some steps and those steps are some of the easy things. Localization probably is a big component of your marketing budget. That’s something that you can get really good, really fast language localization, addition market localization. [00:20:35] Marc Monday: This market is a healthcare market. This market is an SMB market. Those are two areas where that through partner marketing motion can to get accelerated very quickly and has a tremendous ROI. [00:20:47] Vince Menzione: Yeah. Great one. Nina, you had a question [00:20:50] Audience Question: three Mark. You, you just, you just hit on part of it is that value proposition message is, it’s really easy in AI to, to fine tune that. [00:20:59] Audience Question: The other thing that I’ll be very transparent about, um, at least in my organization and America’s partner, we only work with um, third party. Marketing vendors now that are AI first period. [00:21:12] Audience Question: Nice. We [00:21:12] Audience Question: completely cleaned out who the vendors are that we will approve to work with. Wow. Um, so because we can also see the cost reduction, but it is a mindset change. [00:21:22] Audience Question: They have to, they, if, if they’re gonna be positioning this, it has to be inherent. It has to be part of their culture of, at. [00:21:29] Marc Monday: Ashley made a really wonderful point. I mean, this bad first draft is so key and so, you know, in the past we would’ve spent. A couple days or maybe even a week on a really bad first draft. [00:21:40] Marc Monday: And the bad first draft is just to generate feedback. You can generate a bad, a good, bad first draft in a couple of minutes with the right prompts. [00:21:48] Vince Menzione: Yeah, good. Point. Point questions to the back, Steven. [00:21:55] Audience Question: Mark, as you guys are building out agents, the orchestration to manage them, is that taking you into workflows outside of ServiceNow? [00:22:05] Audience Question: Yes. [00:22:07] Vince Menzione: Repeat the question, sorry. Yeah. Just in case people aren’t getting [00:22:09] Marc Monday: Yes. The question is, um, for ServiceNow specifically, um, is that taking you out of your traditional business? And I think he, he means it’s probably business in it, and the answer is yes. So our value promise is that we can go north, south, east, west, across the estate. [00:22:24] Marc Monday: Regardless of the workflow. So there are scenarios where we are expanding. Of course, we have a commitment to driving the CRM business, moving beyond just customer service management, but all the way through the process to CPQ and we’ll productize many of those things. But the reality is, if the workflow touches, let’s say. [00:22:42] Marc Monday: Uh, a, a big database, you know, from one of your known providers, uh, an HCM system, your our traditional IT system. This is maybe around service delivery of a particular set of kit to a new employee for onboarding or offboarding across a number of those systems of record. Yes, we’ll continue to do that, and honestly, it’s the value promise for us that because we are capable of working with. [00:23:06] Marc Monday: Every hyperscaler, every application, every data set, we can go up and down and across the state. [00:23:12] Audience Question: Hi everyone. I’m Jen Pauls. Hey, Jen. I have a um, I have a question for you. So when you’re incorporating AI, and also you mentioned trust, how do you make sure that the offerings that you’re coating on are feasible specifically for that whole individual partner and client? [00:23:34] Audience Question: And you’re not repeating. Something. Does that make sense to you? Yeah. Like how do you make sure that there is an individualized component that is original in thought, even though you’re feeding this pipeline, all these combined thoughts? [00:23:51] Marc Monday: I, I don’t wanna push back on the premise, but I do think in some instances, partners, implementers will have competing solutions that do effectively the same thing. [00:23:59] Marc Monday: Ideally they’re differentiated, but I do think publishing a, a standard. Particularly from a security and a reliability perspective, what that traditionally we would’ve called that API standard, and then a level of validation, either via human validation or systemic AI validation is really key. Um, the solution that gets marketed, let’s say, in our marketplace should work and it should be secure and it should be reliable. [00:24:25] Marc Monday: So we processes to manage that, if that’s the question. [00:24:29] Audience Question: Right? Well, it would, you know, yes. Yes. But. Um, when you’re trying to create a dispute or an offering, right, that’s specific to that particular partner, this is where I’m going. How do you make sure that the thoughts that are coming in are specifically, I guess, individualized for that one partner and what they’re doing and how they’re going to make a new, um, new, uh, track or a new journey in what you’re selling? [00:24:57] Ashleigh Vogstad: I mean, I would answer that I think with differentiation is still really important. And if anything, if we had an 80 20 rule for 80% of the lift is coming from ai, we’re all still here and employed because there is a rule for the, the human, at least currently in that 20%. And I would say. Running teams who are often building new offers and products, both on the ISV and SI side of things. [00:25:25] Ashleigh Vogstad: Getting that unique differentiation is critically important. Like that’s where a lot of value is created. Or you could look at, I mean Nabil probably has stories about this all day in the MSP world is it’s really challenging for MSPs to differentiate on top of their core offering, but that is where value creation happens. [00:25:43] Ashleigh Vogstad: Yeah. Nina more, I’ll [00:25:44] Audience Question: just piggyback on that. My recommendation to a lot of, of our partners today is build out agents at that 80% watermark. Right? And that’s a little bit what you were talking about, the 80, 20, 80% of that functionality. Quite honestly, if you’re looking at an call center or something, is something that can be ported. [00:26:05] Audience Question: The, the magic is working with the partner on what X 20 is that differentiates their business, their experience, how, uh, the applicability to. So I, I will, I, to your point about ology, the premise, I mean it, to me, I think repeatability is, is awesome. It’s a superpower. It’s gonna get us there faster. It’s in that 20%. [00:26:31] Audience Question: Yeah. [00:26:34] Vince Menzione: Thank, perfect, thank you. [00:26:36] Marc Monday: Maybe I’ll close with with one really simple use case just for all of us that are in the partner profession and we work in alliances or partner management. The easiest and best, most effective use case for us as power users today is a shared business plan. Here are the goals and objectives of us as a vendor or a platform provider. [00:26:57] Marc Monday: Here are the goals and objectives of us as the implementer or a resell partner. Um, and in the past I used to describe this as a really complicated bow tie. On one side, you’d have our goals, and on the other side you’d have the, the, the implementer’s goals. And you’d spend all this time weaving together a knot and try to tie it together. [00:27:16] Marc Monday: That activity can happen in about five seconds with the right prompt. And you can very quickly say, oh, you guys think about a CV. We think about a RR Oh, your fiscal year is, is offset. Your fiscal year isn’t, oh, you call this product something different. Um, we care about platform revenue. We care about services revenue. [00:27:35] Marc Monday: You can reconcile that into a pretty darn good shared scorecard and business plan in a matter of seconds. Yeah, and that is a huge time saver. I [00:27:45] Vince Menzione: love that. [00:27:47] Ashleigh Vogstad: It’s just an ama uh, it just thumbs up for me because that joint business planning just doesn’t happen enough. I, I’m in some of the biggest alliances on, on the planet really, and it’s shocking to me how little joint business planning is done. [00:28:00] Ashleigh Vogstad: And for the marketing question, Shannon, like how can marketers lean in? I mean, market development funds are made available based on things like joint business plugs. [00:28:09] Vince Menzione: That’s right. Yeah, really great point. Great voice. Thank you so much. So good to have you finally have you here. Thank you, mark and Ash. [00:28:17] Vince Menzione: Thank you so much [00:28:18] Audience Question: Owens. [00:28:19] Vince Menzione: Don’t forget, ultimate Partner Live is coming soon, May 11th through the 13th in beautiful Bellevue, Washington. I hope to see you there.

AI is reshaping not just what software can do, but how it gets built and by whom. JB Brown, VP of Engineering at Smartsheet, shares how his team is navigating that shift from both sides of the equation, developing AI-powered capabilities for customers while fundamentally rethinking how engineers do their work.In conversation with Chris McNabney, Director of Solution Architecture at AWS, JB explains how Smartsheet's intelligent work management vision, powered by a Knowledge Graph built on Amazon Neptune, is giving customers digital teammates that help drive strategic outcomes at a speed and scale previously out of reach. He also shares the concrete productivity gains his engineering teams are seeing with tools like Roo Code and Amazon Bedrock, including an 80% reduction in CI pipeline time and around a 60% drop in average token costs.From managing agentic workflows to building AI that earns enterprise trust, this episode offers engineering and product leaders a grounded look at what it takes to lead AI transformation from the front.

The adoption rate of Agentic AIs appears staggering. Making, deploying, and managing AI driven agents is easier than ever. This, of course, introduces a myriad of security concerns, many of which will become apparent faster than we think. Hosts Kristine Schachinger and Jim Hedger talk about security and privacy concerns with Clawdbot and Copilot. Google's AI Mode is operable in 53 new languages. Search Console's new AI powered configuration tools went live this week. Users can trick out and customize GSC reporting. Bugs reported in Google's reviews system with local reviews disappearing randomly. Another bug is reported in Google AdSense anchor and vignette ads with the close option not resolving properly. The annual CIA World Fact Book was one of the factual foundations of Google's Knowledge Graph and for most LLMs. Due to Trump cuts, the CIA no longer publishes it. Meta CEO Mark Zuckerberg was questioned in a LA Superior Court about potential harms Facebook and Instagram might pose to children including a risk of social media addiction. As it turns out, a 2015 email sent by Zuckerberg called on Facebook engineers to find ways to increase user's time spent on FB by 12%. Illinois Governor JB Pritzker is proposing a tax on social media platforms he says could raise as much as $200million a year for education. Google is showing its overall dominance as its size, ability to invent and innovate, and its financial independence offer it enormous advantages over rivals. According to a study by Kevin Indig, 44% of AI citations come from the first 30% of content. 53% of citations come from the middle of paragraphs. We talk about the scope and implications of Kevin's study. We also have several shorter SEO technique stories covering advisories on anchor text, the impact of JavaScript "unavailable" files, new GSC features, and Google's grip on titles. Support this podcast at — https://redcircle.com/webcology/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

he podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

he podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

he podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

he podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

The podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

The podcast dives into the explosive advances in agentic AI, where developers and even Fortune 100 companies are racing to use powerful tools like Gastown, despite their unfinished and sometimes dangerous edges. If you thought ChatGPT was a revolution, wait until you hear how developers are orchestrating armies of AIs with real-world impact. Anthropic's Move Into Legal Is Sinking Data Services Stocks Data centers in space makes no sense The hitchhiker's guide to Musk's SpaceX memo Two kinds of AI users are emerging. The gap between them is astonishing. Does AI already have human-level intelligence? The evidence is clear - Nature OpenAI will retire several models, including GPT-4o, from ChatGPT next month Jensen Huang says Nvidia would love to back an OpenAI IPO, and there's 'no drama' with Sam Altman Firefox will soon let you block all of its generative AI features Salesforce signs $5.6B deal to inject agentic AI into the US Army HHS Is Making an AI Tool to Create Hypotheses About Vaccine Injury Claims French office of Elon Musk's X raided by Paris prosecutor's cybercrime unit An AI Toy Exposed 50K Logs of Its Chats With Kids To Anyone With a Gmail Account Darren Aronofsky's AI Studio Used Artificial Intelligence Tools for Revolutionary War Animated Series — but Hired Human Actors to Voice Founding Fathers Forget Hinge or Bumble. This App Promises a Personal AI Matchmaker Scientists Launch AI DinoTracker App That Identifies Dinosaur Footprints Project Genie: Experimenting with infinite, interactive worlds Anthropic Takes Aim at OpenAI's ChatGPT in Super Bowl Ad Debut Move to Ban Social Media for Kids Gains Traction in Europe The Matrix Resurrections Is a Messy, Imperfect Triumph The Thatcher Effect and other Optical Toys Fascinating Research: AIs are highly inconsistent [i.e., random] when recommending brands or products Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Steve Yegge Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: joindeleteme.com/twit promo code TWIT monarch.com with code IM zscaler.com/security helixsleep.com/machines

A story about choosing what others avoid—and creating competitive advantage no one can copy.This episode is for sales-led SaaS founders wondering why their AI product investments are not creating the competitive edge they expected.Most SaaS companies race to add AI features and wonder why nothing changes.Tal Peretz, CEO of Onfire, took the opposite path. Before writing a single line of code, he interviewed 275 revenue leaders. Then he spent months building a proprietary data layer from the public web—Reddit, Stack Overflow, Discord—tracking 50 million engineers. Only after that foundation was solid did he add AI on top.The result: customers generating 4x more pipeline with the same headcount, $50 million in closed deals since beta launch, and a $20 million funding round.And this inspired me to invite Tal to my podcast. We explore how mastering curiosity—reading signals competitors ignore—creates competitive moats that compound over time. Tal shares how 275 customer interviews revealed one critical pattern everyone else missed, and why choosing the hardest buyers simplified everything else. You'll discover why he spent months building invisible infrastructure before writing features, and how that decision alone separated Onfire from hundreds of AI tools fighting for attention.We also zoom in on three of the 10 traits that define remarkable software companies:Master the art of curiosityAim to be different, not just betterSell the idea, not the productTal's journey proves that remarkable companies don't chase the obvious path—they build the hard thing first, creating advantages no competitor can copy.Here's one of Tal's quotes that captures his contrarian thesis:"AI basically makes sales much harder, not easier, because the noise-to-ratio right now goes up. When we started the company, we said the main advantage is to find the needle in the haystack in your context. Building what we call our Knowledge Graph—this is probably the main IP of the company."By listening to this episode, you'll learn:Why building infrastructure before features creates advantages competitors cannot replicateWhat customer discovery reveals when you interview hundreds before building anythingWhy focusing on the hardest segment often creates easier sales than targeting everyoneWhy adding intelligence to strong foundations beats bolting features onto weak dataFor more information about the guest from this week:Guest: Tal Peretz, Co-founder and CEO at Onfire Website: onfire.ai

Andrea Rosi, Head of Operations and Marketing at StatSocial, shares her career journey and offers insights into how StatSocial helps brands understand audience interests, media preferences, and influencer relationships to drive more effective marketing strategies. She highlights the importance of an audience-first approach, especially in B2B marketing, where looking beyond job titles to understand people as individuals leads to more authentic and meaningful connections. Andrea also breaks down StatSocial's marketing mix, spanning paid search, social media, and event marketing, and discusses the ongoing challenge of balancing long-term brand building with short-term lead generation.   About StatSocial StatSocial is a people-based intelligence platform that delivers identity-resolved, AI-ready audience data built from public social behaviour across major platforms. Powered by StatSocial's Identity Graph and Knowledge Graph, the platform enables audience insights, influencer strategy, targeting, and exposure-based measurement. Leading brands and agencies use StatSocial to understand real audiences, improve marketing decisions, and quantify impact across paid, earned, and owned channels. Learn more at StatSocial.com   About Andrea Rosi Andrea Rosi is a leading marketing and operations expert with over 10+ years experience working with Fortune 500 companies in the marcom technology space. Her background includes expertise in go-to-market strategies, product and content marketing, product management and sales. Time Stamps 00:00:18 - Guest Introduction: Andrea Rossi 00:01:46 - Overview of StatSocial's Product 00:02:18 - Understanding Audience Insights 00:06:01 - Benefits for B2B Companies 00:10:12 - Risk Aversion in B2B Marketing 00:14:19 - Balancing Data and Creativity 00:14:33 - StatSocial's Marketing Strategy 00:16:08 - Measuring Event Marketing Success 00:18:00 - Budgeting for Branding vs. Lead Gen 00:19:06 - Future of Marketing and AI   Quotes "I think one of the biggest challenges in B2B is that engaging, analyzing and engaging audiences has been fairly limited to people's title." Andrea Rosi, Head of Operations and Marketing at StatSocial. "It's been a really critical gap is being able to enable clients to take an audience first approach to their influencer programs. I can't tell you how many times we've spoken to clients that previously would choose influencers based on reach and engagement metrics." Andrea Rosi, Head of Operations and Marketing at StatSocial. "It's hard to find a balance sometimes... you don't necessarily know what movement is going to go viral. So you use data to the best of your ability." Andrea Rosi, Head of Operations and Marketing at StatSocial.   Follow Andrea Rosi: Andrea Rosi on LinkedIn: https://www.linkedin.com/in/andrea-rosi-343b8158/ StatSocial website: https://www.statsocial.com/ StatSocial on LinkedIn: https://www.linkedin.com/company/statsocial/   Follow Mike: Mike Maynard on LinkedIn: https://www.linkedin.com/in/mikemaynard/ Napier website: https://www.napierb2b.com/ Napier LinkedIn: https://www.linkedin.com/company/napier-partnership-limited/   If you enjoyed this episode, be sure to subscribe to our podcast for more discussions about the latest in Marketing B2B Tech and connect with us on social media to stay updated on upcoming episodes. We'd also appreciate it if you could leave us a review on your favourite podcast platform. Want more? Check out Napier's other podcast - The Marketing Automation Moment: https://podcasts.apple.com/ua/podcast/the-marketing-automation-moment-podcast/id1659211547

In this special Cloud Wars report, Bob Evans sits down with Michael Ameling, President and Chief Product Officer of SAP Business Technology Platform, for a deep dive into how SAP is helping customers navigate the fast-moving AI Era. Ameling and Evans discuss how SAP's Business Data Cloud, partnerships with Snowflake and Databricks, HANA Cloud innovations, and new AI-powered tools and agents are helping SAP evolve from an applications powerhouse into a data-and-AI-driven business platform for the next generation.SAP's AI Data FutureThe Big Themes:SAP HANA Cloud Becomes an AI-Optimized Database: SAP HANA Cloud is evolving into “the database AI was looking for." As a multi-model system supporting spatial, graph, vector, and document storage, HANA Cloud enables AI workloads to run more efficiently and contextually. Recent additions, like vector engines and Knowledge Graph capabilities, give customers powerful tools for retrieval-augmented generation (RAG), contextual reasoning, and advanced analytics.Developers Are 'The AI Revolution': Developers aren't observing the AI Revolution, they are the revolution. With modern AI tools, developers can innovate faster, solve bigger problems, and directly influence business outcomes. SAP is investing heavily in meeting developers where they are by enhancing IDEs, building business-aware development tools, and providing context-rich assets such as APIs, business objects, and process insights. AI acts as a teammate, not a replacement.SAP: An Applications and a Data Company: SAP must be both an applications and a data company. Customer value emerges when applications, data, and AI converge seamlessly. SAP's decades of industry expertise give it unparalleled business context, which becomes even more powerful when embedded into AI agents and data platforms. With more than 34,000 SAP HANA Cloud customers and rapidly expanding AI adoption, SAP is positioning itself as the platform where business process knowledge meets modern AI capability.The Big Quote: " . . what we need to understand that AI is our teammate. It's like asking your best friend who has a lot of knowledge, but you can ask multiple friends at the same time. Not everything is always right, but you can ask questions, you can continuously improve. If we understand that pattern, we understand that AI helps us to solve much bigger problems as a developer, and then, of course, having much more impact on real business."More from Michael Ameling and SAP:Connect with Michael Ameling on LinkedIn, or get more insights from SAP TechEd.  Visit Cloud Wars for more.

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.

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

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/

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/)