Podcasts about graphs

The long-awaited, much anticipated return of Tim Heidecker to the Office Hours compound did not disappoint with the exclusive release of the Epstein List, along with some heartfelt tributes to fallen legends Ozzy Osbourne and to a lesser degree Hulk Hogan. Support Office Hours, watch another hour of today's episode including a cringey look at Billy Joel on Billy Maher's Club Random, Doug's Gaze the Graph game and lots more with OFFICE HOURS+. Get a FREE seven-day trial at patreon.com/officehourslive Get tickets to see Tim Heidecker on tour with Vic Berger IV and DJ Douggpound out west this summer at ⁠timheidecker.com/live⁠ Find everything Office Hours including the merch store at ⁠⁠⁠⁠⁠⁠⁠officialofficehours.com⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

We chat with Jennifer Reif about integrating LLMs with data using RAG, vectorized data, and Graph databases.Discuss this episode: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠discord.gg/XVKD2uPKyF⁠⁠

Still struggling to pick the right testing tools? Could no-code automation finally help your whole team contribute to automation? Is a new visual IDE about to change how you debug Playwright tests? Find out in this episode of the Test Guild New Shows for the week of July 13th.  So, grab your favorite cup of coffee or tea, and let's do this. Support the show learn more about AI and our sponsor: https://testguild.me/ZAPTESTNEWS 0:21 ZAPTESTAI https://testguild.me/ZAPTESTNEWS 1:00 Test Tool Matcher https://testguild.me/toolmatch 4:23 Playwright YAML https://testguild.me/z5ds0v 5:13 Empirical Run https://testguild.me/u50amj 6:14 Playcraft https://testguild.me/xjnwsy 7:16 Flame Graph https://testguild.me/c4vyx6 8:09 ParrotOS https://testguild.me/46kpls

Join Collin and Tyler as they take a break from discussing all things Hawks and preview the 2025 Big 10 Football Season. We use Phil Steele's CFB Magazine to dive into the data, examine trends, and analyze predictions. We look at unit position rankings, SOS, trends, and more to preview each team in the Big 10! (Time stamps below might be slightly off due to ads)(0:00) Intro (1:22) Phil Steele's Big 10 Forecast: Ranking 1-18 (5:49) Hardest and Easiest Schedules in the Big 10 (15:54) Position Unit Rankings: Graph (25:39) Experience Chart (27:32) Trends (32:55) “The Graph”: what separates the top teams from the bottom teams in the Big 10 (46:18) Collin's Conference Record Predictions & Final Thoughts

Ready for a better commission tracking solution? Don't miss our episode featuring suggestions on tracking your hard-earned commissions.   Register for your FREE RitterIM.com Account   Contact the Agent Survival Guide Podcast! Email us ASGPodcast@Ritterim.com or call 1-717-562-7211 and leave a voicemail.   Resources: AHIP Test Tips and Tricks for Medicare Certification Building the Foundation for Success ft. Roy Snarr Four Reasons Why Ritter Should Be Your FMO Insurance Agency How to Avoid Using Elderspeak Operating From a People-First Mentality ft. Scott Zimmerman The Best Appointment Schedulers for Insurance Agents The Difference Between Medicare & Medicaid: What That Means for Insurance Agents The Ultimate Agent Resource List Pt. 3: Staying Organized What Are Agents Responsible for Under HIPAA?   References: “Agencybloc Commissions Management.” Agencybloc.Com, AgencyBloc, www.agencybloc.com/commissions-management/. Accessed 10 June 2025. “AgencyComp Commissions Tracker.” Agencycomp.Com, AgencyComp, agencycomp.com/. Accessed 10 June 2025. “Commissionly Sales Commission Software.” Commissionly.Io, Commissionly, www.commissionly.io/. Accessed 10 June 2025. “Excel Help & Learning.” Support.Microsoft.Com, Microsoft Support, support.microsoft.com/en-us/excel. Accessed 23 June 2025. “Excel Quick and Simple Charts Tutorial.” YouTube.Com, YouTube, www.youtube.com/watch?v=TfkNkrKMF5c. Accessed 23 June 2025. “How to Create a Chart in Microsoft Excel: Step-by-Step Guide.” wikiHow.Com, wikiHow, 31 Dec. 2024, www.wikihow.com/Create-a-Graph-in-Excel. “How to Create Excel Charts and Graphs.” Blog.Hubspot.Com, HubSpot Blog, blog.hubspot.com/marketing/how-to-build-excel-graph. Accessed 23 June 2025. “Medicare Marketing Guidelines.” CMS.Gov, Centers for Medicare & Medicaid Services, www.cms.gov/medicare/health-drug-plans/managed-care-marketing/medicare-guidelines. Accessed 23 June 2025. “Medicare PRO CRM.” Medicareproapp.Com, Medicare PRO, www.medicareproapp.com/. Accessed 10 June 2025. “Radius Agency Management System.” Radiusbob.Com, Radius, www.radiusbob.com/. Accessed 10 June 2025.   Follow Us on Social! Ritter on Facebook, https://www.facebook.com/RitterIM Instagram, https://www.instagram.com/ritter.insurance.marketing/ LinkedIn, https://www.linkedin.com/company/ritter-insurance-marketing TikTok, https://www.tiktok.com/@ritterim X, https://x.com/RitterIM and YouTube, https://www.youtube.com/user/RitterInsurance       Sarah on LinkedIn, https://www.linkedin.com/in/sjrueppel/ Instagram, https://www.instagram.com/thesarahjrueppel/ and Threads, https://www.threads.net/@thesarahjrueppel    Tina on LinkedIn, https://www.linkedin.com/in/tina-lamoreux-6384b7199/    Not affiliated with or endorsed by Medicare or any government agency. 

Alex Wiltschko, CEO and Founder of Osmo, returns to Brave New World on Episode 97 to discuss the developments in the field of mapping smell and what the future holds for olfactory AI. Useful Resources: 1. Alex Wiltschko, Founder and CEO of OSMO. 2. Brave New World Episode 81: Alex Wiltschko On The Sense Of Smell. 3. StockX. 4. Gas Chromatography - Mass Spectrometry5. Brave New World Episode 90: Sandeep Robert Datta on Smell and The Brain6. Brave New World Episode 62: Dmitry Rinberg on the Mysteries of Smell7. The Science of Scent, Luca Turin's TED Talk. 8. Graph Neural Network9. Convolutional Networks on Graphs for Learning Molecular Fingerprints; paper by Dougal Maclaurin, David Duvenaud, Alán Aspuru-Guzik10. OSMO AI passes human performance in Odour Turing Test. 11. Giving Computers a Sense of Smell, Alex Wiltschko in conversation with Patrick O'Shaughnessy 12. A principal odour map unifies diverse tasks in olfactory perception, OSMO's paper in Science. 13. First Success, teleporting the scene of a Plum. 14. Generation, OSMO's fragrance company. 15. Industry report by Global Market Insights on the Global Fragrance Market. 16. Glossine, Wizerine and Fractalin; proprietary AI-generated scent molecules 17. An Anthropologist On Mars: Seven Paradoxical Tales - Oliver Sacks   Check out Vasant Dhar's newsletter on Substack. The subscription is free! Pre-order Thinking With Machines: The Brave New World of AI - Vasant Dhar

How a €1 B digital‑engineering firm uses generative AI and agent tech to reinvent retail supply chains and CX.18 000 engineers, €1 B revenue, 50+ patents—Nagarro's Global CTO Rahul Mahajan explains how generative AI, vector databases and knowledge graphs are reshaping demand planning and personalization at scale.⏱️ CHAPTERS00:00 Intro: product‑to‑service mind‑set 00:22 Meet Rahul Mahajan & Nagarro overview 01:17 Missed NRF meetings + digital engineering culture 02:35 Diversified industries & complex problem solving 03:35 Rahul's 50+ patents in retail AI 04:58 CPG use case: multi‑channel demand planning 06:49 SKU‑level AI forecasting & supply chain accuracy 07:32 “Humanizing personalization” patent explained 08:20 Ecosystem shift: partner products & services 09:29 Agent tech & zero‑downtime integration 10:16 From transactions to lifestyle services 12:08 Patenting novel data structures & AI models 13:19 Knowledge graphs + vectorized semantics 14:24 AI governance: tone, privacy, explainability 15:14 LLM interoperability (OpenAI, Anthropic, Gemini) 16:32 Why retailers must move before they're disrupted 17:13 Contact Rahul & closing

our 15 year anniversary celebration, continuing on with a meticulously crafted stellar set from graph. dedicated to tomas. ponty mython - so what did you expect? (bal5000 back to 96 remix) [wewillalwaysbealovesong] jonny miller, kid fonque - afrika is the future! (shur-i-kan slinky mix) [stay true sounds] kllo - walls to build (mall grab remix) [ghostly international] octo octa - loops for healing [technicolour] alton miller - all things good [waella's choice] halo - glory feat. maiya (atjazz galaxy aart instrumental remix) [foliage records] tuxedo - integrate [plastic city suburbia] steve kelley - green daze [sublease music] vincenzo - assassin [sublease music] danny howells - mama loves you [bedrock records] reverse commuter - icarus (doc martin's subwarp mix) [hallucienda] robag whrume - cdv feat. bruno pronsato (remix-edit) [kompakt extra] matthias schaffhäuser, jorge socarras - radical freedom (ryan crosson remix) [hallucienda] laurent garnier, chambray - feelin' good (radio slave remix) [rekids] jim rivers - calycanthus (bushwacka! remix) [poker flat recordings] indoor man - rhythm changes [hallucienda] gab rhome, mark alow - microbioma (esteble remix) [bar25 music] coyu - 1+1 feat. thomas gandey [suara] joe hertz, jasper tygner - equals [shall not fade] paul white - set the tone feat. denai moore [r&s records]

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Once we expand to other star systems, we may begin a self-propagating expansion of human civilisation throughout the galaxy. However, there are existential risks potentially capable of destroying a galactic civilisation, like self-replicating machines, strange matter, and vacuum decay. Without an extremely widespread and effective governance system, the eventual creation of a galaxy-ending x-risk seems almost inevitable due to cumulative chances of initiation over time and across multiple independent actors. So galactic x-risks may severely limit the total potential value that human civilisation can attain in the long-term future. The requirements for a governance system to prevent galactic x-risks are outlined, and updates for space governance and big picture cause prioritisation are discussed. Introduction I recently came across a series of posts from nearly a decade ago, starting with a post by George Dvorsky in io9 called “12 Ways Humanity Could Destroy the Entire Solar System”. It's a [...] ---Outline:(01:00) Introduction(03:07) Existential risks to a Galactic Civilisation(03:58) Threats Limited to a One Planet Civilisation(04:33) Threats to a small Spacefaring Civilisation(07:02) Galactic Existential Risks(07:22) Self-replicating machines(09:27) Strange matter(10:36) Vacuum decay(11:42) Subatomic Particle Decay(12:32) Time travel(13:12) Fundamental Physics Alterations(13:57) Interactions with Other Universes(15:54) Societal Collapse or Loss of Value(16:25) Artificial Superintelligence(18:15) Conflict with alien intelligence(19:06) Unknowns(21:04) What is the probability that galactic x-risks I listed are actually possible?(22:03) What is the probability that an x-risk will occur?(22:07) What are the factors?(23:06) Cumulative Chances(24:49) If aliens exist, there is no long-term future(26:13) The Way Forward(31:34) Some key takeaways and hot takes to disagree with me onThe original text contained 76 footnotes which were omitted from this narration. --- First published: June 18th, 2025 Source: https://forum.effectivealtruism.org/posts/x7YXxDAwqAQJckdkr/galactic-x-risks-obstacles-to-accessing-the-cosmic-endowment --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

We wallow in the mud with some of the worst gurus of the gurusphere. Join us and lament the guru paradise that we all live in.Supplementary Material 3200:00 Introduction and Banter01:22 Old Squeaky and Daily Life03:53 Matthew McConaughey Episode Recap08:13 The Liver King Controversy16:14 Nazi Propaganda on YouTube21:11 Historical Revisionism: Darryl Cooper and David Irving27:46 Huberman's very public hardcore research32:25 Huberman sells out34:32 Chris Langan: The Bottom (Racist) Tier of Gurudom36:03 Langan on Weinstein42:21 Langan's grievances against Elon Musk and Jordan Peterson49:47 Matt Goodwin visits London55:59 Gary Stevenson hates Graphs and Data01:10:33 Gary compares himself to Russell Brand01:15:12 THEY won't let you talk about the economy01:17:22 Matt invokes Goodwin's Law01:25:08 The All In Podcast Besties launch a Tequila Brand01:28:32 Matt's Modest Utopian Plan01:31:12 Lab Leak Discourse continues at the Guardian01:35:55 Matt attacks the Mainstream Media01:39:11 Dugin's Forum of the Future 2050 and the Guru Horseshoe01:45:57 Extended OutroThe full episode is available for Patreon subscribers (1hr 50 mins).Join us at: https://www.patreon.com/DecodingTheGurusSources2Lazy2Try: The Liver King Gets Arrested For Trying To Hunt Down Joe RoganRob Mohr tweets out an iconic photo of HubermanScott Carney: Documenting Andrew Huberman's LiesChris Langan's thoughts on Eric WeinsteinChris Langan's thoughts on Elon Musk & Jordan PetersonMatt Goodwin's visit to LondonDespolarisa: #89 GARY STEVENSON - Economics, Trading, Inequality, Wealth, Populism, Tax, DepolarizeThe All-In Podcast guys being bastardsTsargard Institute: The Forum for the Future 2050

Born in the 18th century when Leonhard Euler solved the puzzle of the seven bridges of Königsberg, graph theory has become a foundational tool in mathematics. It studies relationships through nodes (vertices) and the links (edges) that connect them, transforming the complexity of systems — from friendship networks to airline routes — into elegant abstractions that reveal underlying structure and interaction. Maria Chudnovsky from Princeton University is a leading mathematician in the field. In this episode of The Joy of Why, Chudnovsky talks with co-host Janna Levin about how she got into graph theory, solved the decades-old perfect graph problem, and used it to plan her wedding seating chart. Chudnovsky also reflects on her appearance in commercials as a “superstar mathematician,” and how her background primed her for a discipline that transcends language, culture and time.

The Unseen World supercut. How big is the universe? What's at the edge? Why does light have a speed limit? Discover our full back catalogue of hundreds of videos on YouTube: ⁠https://www.youtube.com/@astrumspace⁠For early access videos, bonus content, and to support the channel, join us on Patreon: ⁠https://astrumspace.info/4ayJJuZ⁠

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes.These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.Today's guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.These highlights are from episode #217 of The 80,000 Hours Podcast: Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress, and include:Can we see AI scheming in the chain of thought? (00:00:34)We have to test model honesty even before they're used inside AI companies (00:05:48)It's essential to thoroughly test relevant real-world tasks (00:10:13)Recursively self-improving AI might even be here in two years — which is alarming (00:16:09)Do we need external auditors doing AI safety tests, not just the companies themselves? (00:21:55)A case against safety-focused people working at frontier AI companies (00:29:30)Open-weighting models is often good, and Beth has changed her attitude about it (00:34:57)These aren't necessarily the most important or even most entertaining parts of the interview — so if you enjoy this, we strongly recommend checking out the full episode!And if you're finding these highlights episodes valuable, please let us know by emailing podcast@80000hours.org.Highlights put together by Ben Cordell, Milo McGuire, and Dominic Armstrong

The era of making AI smarter just by making it bigger is ending. But that doesn't mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods, and those underlying technical changes force a big rethink of what coming years will look like.Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.Links to learn more, video, highlights, and full transcript: https://80k.info/to25As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that's over.What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.So they pivoted to something radically different: instead of training smarter models, they're giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.The results are impressive but this extra computing time comes at a cost: OpenAI's o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica's worth of reasoning to solve individual problems at a cost of over $1,000 per question.This isn't just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out, starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.Toby and host Rob discuss the implications of all that, plus the return of reinforcement learning (and resulting increase in deception), and Toby's commitment to clarifying the misleading graphs coming out of AI companies — to separate the snake oil and fads from the reality of what's likely a "transformative moment in human history."Recorded on May 23, 2025.Chapters:Cold open (00:00:00)Toby Ord is back — for a 4th time! (00:01:20)Everything has changed (and changed again) since 2020 (00:01:37)Is x-risk up or down? (00:07:47)The new scaling era: compute at inference (00:09:12)Inference scaling means less concentration (00:31:21)Will rich people get access to AGI first? Will the rest of us even know? (00:35:11)The new regime makes 'compute governance' harder (00:41:08)How 'IDA' might let AI blast past human level — or not (00:50:14)Reinforcement learning brings back 'reward hacking' agents (01:04:56)Will we get warning shots? Will they even help? (01:14:41)The scaling paradox (01:22:09)Misleading charts from AI companies (01:30:55)Policy debates should dream much bigger (01:43:04)Scientific moratoriums have worked before (01:56:04)Might AI 'go rogue' early on? (02:13:16)Lamps are regulated much more than AI (02:20:55)Companies made a strategic error shooting down SB 1047 (02:29:57)Companies should build in emergency brakes for their AI (02:35:49)Toby's bottom lines (02:44:32)Tell us what you thought! https://forms.gle/enUSk8HXiCrqSA9J8Video editing: Simon MonsourAudio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongMusic: Ben CordellCamera operator: Jeremy ChevillotteTranscriptions and web: Katy Moore

Explore God's purpose in trials: growing faith, not seeking shortcuts like wealth or false spiritual paths. Learn to surrender unmet desires and resist temptation. This message, rooted in James 1, guides you to find true freedom and fulfillment in Jesus alone, embracing His process for a life transformed.   Pastor: Jordan Hansen Series: James: Faith That Works (2) Title: Shortcuts Date: 2025.06.21+22   CHAPTERS: 00:00 - Teaser 00:34 - Series 01:04 - Sermon (1) 01:43 - Thunderbirds 02:11 - Sermon (2) 04:58 - Question 08:42 - Point 1a 16:52 - Crown of Life 17:36 - Point 1b 28:08 - Point 2 30:10 - Point 3a 32:51 - Graph 33:59 - Point 3b 38:42 - Big Idea 39:40 - Closing   SERVICE TIMES:

Send us a textSEO 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/

Formosa: Fulcrum of the Future?An invasion of Taiwan is uncomfortably likely and potentially catastrophic. We should research better ways to avoid it. TLDR: I forecast that an invasion of Taiwan increases all the anthropogenic risks by ~1.5% (percentage points) of a catastrophe killing 10% or more of the population by 2100 (nuclear risk by 0.9%, AI + Biorisk by 0.6%). This would imply it constitutes a sizable share of the total catastrophic risk burden expected over the rest of this century by skilled and knowledgeable forecasters (8% of the total risk of 20% according to domain experts and 17% of the total risk of 9% according to superforecasters). I think this means that we should research ways to cost-effectively decrease the likelihood that China invades Taiwan. This could mean exploring the prospect of advocating that Taiwan increase its deterrence by investing in cheap but lethal weapons platforms [...] ---Outline:(00:13) Formosa: Fulcrum of the Future?(02:04) Part 0: Background(03:44) Part 1: Invasion -- uncomfortably possible.(08:33) Part 2: Why an invasion would be bad(10:27) 2.1 War and nuclear war(19:20) 2.2. The end of cooperation: AI and Bio-risk(22:44) 2.3 Appeasement or capitulation and the end of the liberal-led order: Value risk(26:04) Part 3: How to prevent a war(29:39) 3.1. Diplomacy: speaking softly(31:21) 3.2. Deterrence: carrying a big stick(34:16) Toy model of deterrence(37:58) Toy cost-effectiveness of deterrence(41:13) How to cost-effectively increase deterrence(43:30) Risks of a deterrence strategy(44:12) 3.3. What can be done?(44:42) How tractable is it to increase deterrence?(45:43) A theory of change for philanthropy increasing Taiwan's military deterrence(45:56) en-US-AvaMultilingualNeural__ Flow chart showing policy influence between think tanks and Taiwan security outcomes.(48:55) 4. Conclusion and further work(50:53) With more time(52:00) Bonus thoughts(52:09) 1. Reminder: a catastrophe killing 10% or more of humanity is pretty unprecedented(53:06) 2. Where's the Effective Altruist think tank for preventing global conflict?(54:11) 3. Does forecasting risks based on scenarios change our view on the likelihood of catastrophe?The original text contained 16 footnotes which were omitted from this narration. --- First published: June 15th, 2025 Source: https://forum.effectivealtruism.org/posts/qvzcmzPcR5mDEhqkz/an-invasion-of-taiwan-is-uncomfortably-likely-potentially --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is a link post. Thank you to Arepo and Eli Lifland for looking over this article for errors. I am sorry that this article is so long. Every time I thought I was done with it I ran into more issues with the model, and I wanted to be as thorough as I could. I'm not going to blame anyone for skimming parts of this article. Note that the majority of this article was written before Eli's updated model was released (the site was updated june 8th). His new model improves on some of my objections, but the majority still stand. Introduction: AI 2027 is an article written by the “AI futures team”. The primary piece is a short story penned by Scott Alexander, depicting a month by month scenario of a near-future where AI becomes superintelligent in 2027,proceeding to automate the entire economy in only [...] ---Outline:(00:45) Introduction:(05:27) Part 1: Time horizons extension model(05:33) Overview of their forecast(10:23) The exponential curve(13:25) The superexponential curve(20:20) Conceptual reasons:(28:38) Intermediate speedups(36:00) Have AI 2027 been sending out a false graph?(41:50) Some skepticism about projection(46:13) Part 2: Benchmarks and gaps and beyond(46:19) The benchmark part of benchmark and gaps:(52:53) The time horizon part of the model(58:02) The gap model(01:00:58) What about Eli's recent update?(01:05:19) Six stories that fit the data(01:10:46) ConclusionThe original text contained 11 footnotes which were omitted from this narration. --- First published: June 19th, 2025 Source: https://forum.effectivealtruism.org/posts/KgejNns3ojrvCfFbi/a-deep-critique-of-ai-2027-s-bad-timeline-models Linkpost URL:https://titotal.substack.com/p/a-deep-critique-of-ai-2027s-bad-timeline --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Today on this edition of the Math Lab Shorts. This is going to be talking about the Polar Equations using for software development.Recorded on 6/14/2025

In this podcast, Nikolaos Vasiloglou from @RelationalAI team discusses how knowledge graph based applications are leveraging Generative AI technologies and how Graph RAG techniques can be used to enhance data analytics and insights. Read a transcript of this interview: https://bit.ly/43IYWGN Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: InfoQ Dev Summit Boston (June 9-10, 2025) Actionable insights on today's critical dev priorities. devsummit.infoq.com/conference/boston2025 InfoQ Dev Summit Munich (October 15-16, 2025) Essential insights on critical software development priorities. https://devsummit.infoq.com/conference/munich2025 QCon San Francisco 2025 (November 17-21, 2025) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon AI New York 2025 (December 16-17, 2025) https://ai.qconferences.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - Twitter: twitter.com/InfoQ - LinkedIn: www.linkedin.com/company/infoq - Facebook: bit.ly/2jmlyG8 - Instagram: @infoqdotcom - Youtube: www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

Episode: 2467 Graph Theory and the Königsberg Bridge Problem.  Today, the bridges of Königsberg.

Send us a text*Causal Inference From Human Behavior, Reproducibility Crisis & The Power of Causal Graphs*Is Jonathan Heidt right that social media causes the mental health crisis in young people?If so, how can we be sure?Can other disciplines learn something from the reproducibility crisis in Psychology, and what is multiverse analysis?Join us for a conversation on causal inference from human behavior, the reproducibility crisis in sciences, and the power of causal graphs!------------------------------------------------------------------------------------------------------Audio version available on YouTube: https://youtu.be/YQetmI-y5gMRecorded on May 16, 2025, in Leipzig, Germany.------------------------------------------------------------------------------------------------------*About The Guest*Julia Rohrer, PhD, is a researcher and personality psychologist at the University of Leipzig. She's interested in the effects of birth order, age patterns in personality, human well-being, and causal inference. Her works have been published in top journals, including Nature Human Behavior. She has been an active advocate for increased research transparency, and she continues this mission as a senior editor of Psychological Science. Julia frequently gives talks about good practices in science and causal inference. You can read Julia's blog at https://www.the100.ci/*Links*Papers- Rohrer, J. (2024) "Causal inference for psychologists who think that causal inference is not for them" (https://compass.onlinelibrary.wiley.com/doi/10.1111/spc3.12948)- Bailey, D., ..., Rohrer, J. et al (2024) "Causal inference on human behaviour" (https://www.nature.com/articles/s41562-024-01939-z.epdf)- Rohrer, J. et al (2024) "The Effects of Satisfaction with Different Domains of Life on GenInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Around 1 month ago, I wrote a similar Forum post on the Easterlin Paradox. I decided to take it down because: 1) after useful comments, the method looked a little half-baked; 2) I got in touch with two academics – Profs. Caspar Kaiser and Andrew Oswald – and we are now working on a paper together using a related method. That blog post actually came to the opposite conclusion, but, as mentioned, I don't think the method was fully thought through. I'm a little more confident about this work. It essentially summarises my Undergraduate dissertation. You can read a full version here. I'm hoping to publish this somewhere, over the Summer. So all feedback is welcome. TLDR Life satisfaction (LS) appears flat over time, despite massive economic growth — the “Easterlin Paradox.” Some argue that happiness is rising, but we're reporting it more conservatively — [...] ---Outline:(00:57) TLDR(02:11) 1. Background: A Happiness Paradox(04:02) 2. What is Rescaling?(06:23) 3. My Approach: Life Events would look smaller on stretched out rulers(08:10) 4. Results: Effects Are Shrinking(10:46) 5. How much might we be underestimating life satisfaction?(12:42) 6. Implications--- First published: May 26th, 2025 Source: https://forum.effectivealtruism.org/posts/wSySeNZ6C7hfDfBSx/rescaling-and-the-easterlin-paradox-2-0 --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes. (See graph.)These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.Today's guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.Links to learn more, video, highlights, and full transcript: https://80k.info/bbBeth's team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work. The resulting paper “Measuring AI ability to complete long tasks” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It's regarded by many as the most useful AI forecasting work in years.Beth has found models can already do “meaningful work” improving themselves, and she wouldn't be surprised if AI models were able to autonomously self-improve as little as two years from now — in fact, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”Beth adds:The sense I really want to dispel is, “But the experts must be on top of this. The experts would be telling us if it really was time to freak out.” The experts are not on top of this. Inasmuch as there are experts, they are saying that this is a concerning risk. … And to the extent that I am an expert, I am an expert telling you you should freak out.Chapters:Cold open (00:00:00)Who is Beth Barnes? (00:01:19)Can we see AI scheming in the chain of thought? (00:01:52)The chain of thought is essential for safety checking (00:08:58)Alignment faking in large language models (00:12:24)We have to test model honesty even before they're used inside AI companies (00:16:48)We have to test models when unruly and unconstrained (00:25:57)Each 7 months models can do tasks twice as long (00:30:40)METR's research finds AIs are solid at AI research already (00:49:33)AI may turn out to be strong at novel and creative research (00:55:53)When can we expect an algorithmic 'intelligence explosion'? (00:59:11)Recursively self-improving AI might even be here in two years — which is alarming (01:05:02)Could evaluations backfire by increasing AI hype and racing? (01:11:36)Governments first ignore new risks, but can overreact once they arrive (01:26:38)Do we need external auditors doing AI safety tests, not just the companies themselves? (01:35:10)A case against safety-focused people working at frontier AI companies (01:48:44)The new, more dire situation has forced changes to METR's strategy (02:02:29)AI companies are being locally reasonable, but globally reckless (02:10:31)Overrated: Interpretability research (02:15:11)Underrated: Developing more narrow AIs (02:17:01)Underrated: Helping humans judge confusing model outputs (02:23:36)Overrated: Major AI companies' contributions to safety research (02:25:52)Could we have a science of translating AI models' nonhuman language or neuralese? (02:29:24)Could we ban using AI to enhance AI, or is that just naive? (02:31:47)Open-weighting models is often good, and Beth has changed her attitude to it (02:37:52)What we can learn about AGI from the nuclear arms race (02:42:25)Infosec is so bad that no models are truly closed-weight models (02:57:24)AI is more like bioweapons because it undermines the leading power (03:02:02)What METR can do best that others can't (03:12:09)What METR isn't doing that other people have to step up and do (03:27:07)What research METR plans to do next (03:32:09)This episode was originally recorded on February 17, 2025.Video editing: Luke Monsour and Simon MonsourAudio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongMusic: Ben CordellTranscriptions and web: Katy Moore

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise.  Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbolic AI to represent and reason about complex relationships. This involves creating causal ontologies, incorporating the "weight" or strength of causal relationships and hyperrelations. This field has many practical applications such as for AI explainability, healthcare and autonomous driving. Follow our guest Utkarshani Jaimini's Webpage Linkedin Papers in focus CausalLP: Learning causal relations with weighted knowledge graph link prediction, 2024 HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph, 2024  

Well nerds, buckle up for this one. My buddy Ryan Burge has returned with his latest graphs about religion and the 2024 election, and let me tell you - it was zesty. We started talking about minor league baseball, chicken raising, and somehow ended up dissecting why 83% of white evangelicals voted for Trump (spoiler: it's not shocking). Ryan breaks down the real story of the 2024 election - how non-white evangelicals are now 50/50, why mainline Protestants aren't actually that liberal, and the fascinating shifts happening in the Catholic vote. We dive into the data that shows education and church attendance create some pretty stark political divides, and why Democrats might want to rethink their approach to people of faith. But this is us, so we also talked about LeBron's hair transplants, whether 100 men could take down a silverback gorilla, why online gambling is destroying America, and Ryan's ongoing campaign to get academics to eat at steakhouses instead of Sweet Green. Plus, Ryan explains why Mark Driscoll might be the godfather of the manosphere, and we debate whether Joe Scarborough and Mika have the worst work schedule in television. Oh, and we somehow got into a deep discussion about Mayor Pete's beard and why Democrats need to learn how to talk about their faith without sounding like they're apologizing for it. Because apparently that's where our brains go. Want the full conversation? This is just a taste of what we covered in over two hours of completely unhinged discussion. If you're a member of either Graphs About Religion (Ryan's substack) or Process This (mine), you get access to the entire unedited conversation, plus invitations to join us live for future streams where things get even more zesty - and yes, I'm using that word in the Whitehead sense, not the Gen Z sense.   Previous Visits from Ryan Burge Distrust & Denominations Trust, Religion, & a Functioning Democracy What it's like to close a church The Future of Christian Education & Ministry in Charts The Sky is Falling & the Charts are Popping! Graphs about Religion & Politics w/ Spicy Banter a Year in Religion (in Graphs) Evangelical Jews, Educated Church-Goers, & other bits of dizzying data 5 Religion Graphs w/ a side of Hot Takes Myths about Religion & Politics Ryan P. Burge is an assistant professor of political science at Eastern Illinois University. Author of numerous journal articles, he is the co-founder of and a frequent contributor to Religion in Public, a forum for scholars of religion and politics to make their work accessible to a general audience. Burge is a pastor in the American Baptist Church. Upcoming Online Class:⁠⁠⁠⁠ Rediscovering the Spirit: Hand-Raisers, Han, & the Holy Ghost⁠⁠⁠⁠ is an open-online course exploring the dynamic, often overlooked third person of the Trinity. Based on Grace Ji-Sun Kim's groundbreaking work on the Holy Spirit this class takes participants on a journey through biblical foundations, historical developments, diverse cultural perspectives, and practical applications of Spirit theology. ⁠⁠⁠⁠As always, this class is donation-based, including 0. To get class info and sign up, head over here. ⁠⁠⁠⁠ _____________________ ⁠⁠⁠⁠⁠⁠Hang with 40+ Scholars & Podcasts and 600 people at Theology Beer Camp 2025 (Oct. 16-18) in St. Paul, MN. ⁠⁠⁠⁠⁠⁠ This podcast is a ⁠⁠⁠⁠⁠⁠Homebrewed Christianity⁠⁠⁠⁠⁠⁠ production. Follow ⁠⁠⁠⁠⁠⁠the Homebrewed Christianity⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠Theology Nerd Throwdown⁠⁠⁠⁠⁠⁠, & ⁠⁠⁠⁠⁠⁠The Rise of Bonhoeffer⁠⁠⁠⁠⁠⁠ podcasts for more theological goodness for your earbuds. Join over 80,000 other people by joining our⁠⁠⁠⁠⁠⁠ Substack - Process This!⁠⁠⁠⁠⁠⁠ Get instant access to over 45 classes at ⁠⁠⁠⁠⁠⁠www.TheologyClass.com⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Follow the podcast, drop a review⁠⁠⁠⁠⁠⁠, send ⁠⁠⁠⁠⁠⁠feedback/questions⁠⁠⁠⁠⁠⁠ or become a ⁠⁠⁠⁠⁠⁠member of the HBC Community⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this special edition of the Washington AI Network Podcast, recorded live at NVIDIA 2025 GTC conference, host Tammy Haddad takes listeners inside the future of AI innovation with startups. NVIDIA's Startup Guru, Howard Wright, shares how the Inception program supports over 27,000 startups worldwide, unlocking capital, customers, and compute to fuel AI breakthroughs. Fay Arjomandi, CEO of mimik, introduces her edge-first, privacy-focused approach to agent-based AI systems. ArangoDB's Corey Sommers explains why graph databases are the foundation of intelligent, responsive generative AI platforms. And nTop's Alec Guay and Todd McDevitt demonstrate how their GPU-powered design tools are slashing engineering cycles across aerospace, energy, and manufacturing. From defense to design, this episode showcases the real-world power of AI startups—and how the NVIDIA ecosystem helps them scale.

On this week's episode, Kyle recaps his playoff weekend in Indianapolis — from attending Games 3 and 4 of Pacers/Cavs to exploring a massive 200-table card show. He shares game-day impressions, autograph stories, and some key card pickups along the way.

Send us a textGet the vidIQ plugin for FREE: https://vidiq.ink/3yvoc7rJoin Discord: https://www.vidiq.com/discordWant a 1 on 1 coach? https://vidiq.ink/theboost1on1Check out the video version here: https://youtu.be/0HPucaCMwrQAudience engagement metrics on YouTube aren't always what they seem, and retention graphs can be misleading if viewed in isolation. We explore why "good" retention varies drastically based on content length, traffic sources, and viewer behavior across different platforms.• TikTok has reportedly surpassed Twitch to become the second most streamed platform after YouTube• Retention graphs look different depending on video length, with shorts typically exceeding 100% while long-form content rarely does• High-view videos often have lower retention percentages than creators expect• Search traffic typically has shorter view duration than browse or suggested traffic• Breaking down retention by traffic source and subscriber status reveals more useful insights than aggregate data• YouTube doesn't currently track "hover behavior" when viewers preview but don't click• Platform differences significantly impact engagement behaviors (TV vs. mobile vs. desktop)• Mr. Beast has set unrealistic retention expectations for many creators• Viewer disclaimers and overexplaining opinions have become increasingly common in contentIf you enjoyed this episode, please leave us a five-star review and join us next time when we'll be sharing more about ourselves as creators.

Graph Therapeutics combines AI-driven perturbation modeling with multi-omics data to develop targeted therapies for patients with complex immune-mediated diseases who currently lack effective treatment options.

This show has been flagged as Explicit by the host. Research Tools Harvard Referencing - https://en.wikipedia.org/wiki/Parenthetical_referencing#Author%E2%80%93date_(Harvard_referencing) Google Notebook LM - https://notebooklm.google/ Google Scholar - https://scholar.google.co.uk/ Connected Papers - https://www.connectedpapers.com/ Zotero - https://www.zotero.org/ Databases SQL Databases - https://en.wikipedia.org/wiki/Relational_database NoSQL Databases - https://en.wikipedia.org/wiki/NoSQL Graph Databases - https://en.wikipedia.org/wiki/Graph_database Misc Borland Graphics Interface - https://en.wikipedia.org/wiki/Borland_Graphics_Interface Hough Transform - https://en.wikipedia.org/wiki/Hough_transform Joplin - https://joplinapp.org/ Provide feedback on this episode.

Or on the types of prioritization, their strengths, pitfalls, and how EA should balance them The cause prioritization landscape in EA is changing. Prominent groups have shut down, others have been founded, and everyone is trying to figure out how to prepare for AI. This is the first in a series of posts examining the state of cause prioritization and proposing strategies for moving forward. Executive Summary Performing prioritization work has been one of the main tasks, and arguably achievements, of EA. We highlight three types of prioritization: Cause Prioritization, Within-Cause (Intervention) Prioritization, and Cross-Cause (Intervention) Prioritization. We ask how much of EA prioritization work falls in each of these categories: Our estimates suggest that, for the organizations we investigated, the current split is 89% within-cause work, 2% cross-cause, and 9% cause prioritization. We then explore strengths and potential pitfalls of each level: Cause [...] ---Outline:(00:37) Executive Summary(03:09) Introduction: Why prioritize? Have we got it right?(05:18) The types of prioritization(06:54) A snapshot of EA(16:45) The Types of Prioritization Evaluated(16:57) Cause Prioritization(20:56) Within-Cause Prioritization(25:12) Cross-Cause Prioritization(30:07) Summary Table(30:53) What factors should push us towards one or another?(37:27) Possible Next Steps(39:44) Conclusion(40:58) Acknowledgements(41:01) en-US-AvaMultilingualNeural__ Modern geometric logo design with text RETHINK PRIORITIES(41:55) Appendix: Strengths and Pitfalls of Each Type(42:07) Within-Cause Prioritization Strengths(42:12) Decision-Making Support(42:37) Comparability of Outputs(44:18) Disciplinarity Advantages(45:45) Responsiveness to Evidence(46:48) Movement Building(48:06) Within-Cause Prioritization Weaknesses and Potential Pitfalls(48:12) Responsiveness to Evidence(50:54) Decision-Making Support(52:45) Cross-Cause Prioritization Strengths:(53:06) Decision-Making Support(54:49) Responsiveness to Evidence(56:08) Movement Building(56:22) Comparability of Outputs(56:45) Decision-Making Support(57:14) Cross-Cause Prioritization Weaknesses and Potential Pitfalls(57:20) Comparability of Outputs(58:01) Disciplinarity Advantages(58:41) Movement Building(59:09) Decision-Making Support(01:00:27) Cause Prioritization Strengths(01:00:32) Decision-Making Support(01:02:01) Responsiveness to Evidence(01:02:52) Movement Building(01:03:28) Cause Prioritization Weaknesses and Potential Pitfalls(01:04:28) Decision-Making Support(01:06:08) Responsiveness to EvidenceThe original text contained 23 footnotes which were omitted from this narration. --- First published: April 16th, 2025 Source: https://forum.effectivealtruism.org/posts/ZPdZv8sHuYndD8xhJ/doing-prioritization-better-2 --- Narrated by TYPE III AUDIO. ---Images from the article:

Are you looking for some projects where you can practice your Python skills? Would you like to experiment with building a generative AI app or an automated knowledge graph sentiment analysis tool? This week on the show, we speak with Raymond Camden about his journey into Python, his work in developer relations, and the Python projects featured on his blog.

How do you uncover misinformation and financial fraud hidden in plain sight across thousands of digital platforms during a global election cycle? In this episode, I spoke with Jim Webber, Chief Scientist at Neo4j, to explore how graph database technology is being used to expose coordinated disinformation campaigns, empower AI systems, and help enterprises manage the complexity of modern data. At the heart of our conversation is the story of the ElectionGraph Project, where Syracuse University used Neo4j's graph technology to investigate political ad spend on Meta platforms. What they discovered was not just political messaging, but sophisticated scams disguised as legitimate campaigns. These efforts, targeting civically engaged users, used merchandise giveaways as a front to harvest credit card details and enroll victims in recurring billing traps. Traditional analytics would have struggled to trace these relationships, but graph databases allowed researchers to map and understand the deeper connections between thousands of entities. We also unpack how graph technology goes far beyond fraud detection. Jim explains why graph databases are now foundational for businesses building AI systems, particularly those using Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve decision making. Whether it's helping enterprises respond to customer needs or enabling AI agents to take action in real time, graphs provide the structure and context needed for reliable outcomes. Jim also shares the backstory behind Klarna's data transformation, where the company embraced knowledge graphs at the core of its operations and replaced major systems, including parts of Salesforce. It's a striking example of what becomes possible when a business commits to connected data as a strategic asset. From misinformation to intelligent automation, this episode dives into the real-world value of graph technology in 2025. Are you thinking critically about how your data infrastructure supports your AI ambitions?

People immersed in chaos try to solve for what it all adds up to. Visit thisamericanlife.org/lifepartners to sign up for our premium subscription.Prologue: A scientist who is used to organizing data starts tracking scientific meetings that seem to exist only on paper—meetings that might decide the fate of years of research. The NIH website shows one reality; the empty conference rooms tell another story. She graphs the chaos. (9 minutes)Act One: American doctors returning from Gaza compare notes and start to see a pattern. (28 minutes)Act Two: A woman watches her partner get taken in handcuffs with no explanation. Days later, she spots him in the most unexpected place. The coordinates of her life suddenly don't make sense as she navigates the bewildering map of the US immigration system. (23 minutes)Transcripts are available at thisamericanlife.orgThis American Life privacy policy.Learn more about sponsor message choices.

The host of CNN's "Searching for Spain" shares why Americans should try to live life like the Spaniards do. A rare record collector reunites a woman with a Voice-o-Graph she recorded 70 years ago. How this record-setting rodent is saving lives with his sense of smell. From deception to acceptance, a female magician's decades-long journey into the world's most prestigious magic club. Plus, scientists may have found the first signs of life on a planet outside our solar system. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Welcome back to another episode of School Counseling Simplified! All April long, I'm sitting down with amazing guest experts to bring you insight, encouragement, and practical tools for your school counseling practice. Today's guest is the incredible Patti Hoelzle from Rooted Well, and we're talking all about something many counselors shy away from… data. But don't worry—Patti breaks it down in a way that's simple, empowering, and exciting! Patti Hoelzle is the owner of Rooted WELL and a National Board Certified School Counselor with a passion for building proactive, equitable systems of student support. She trains and consults on mindfulness in schools, trauma-informed practices, tiered interventions, and PBIS, working with educators and families nationwide. A sought-after speaker, Patti has presented at local and national conferences and teaches as an adjunct professor in a school counseling graduate program. Previously, she led social-emotional learning and MTSS efforts in a school district and has spent 18 years dedicated to being a professional school counselor. Recognized as Washington's 2021 School Counselor Advocate of the Year, Patti is dedicated to ensuring every student gets the whole-child support they deserve. Why Data Matters in School Counseling School counselors are in a unique position—we have to do the job, prove our impact, and often justify our position for the following school year. The good news? Data can do all three. Using data allows you to: Advocate for your role and time Communicate impact to stakeholders, families, and administration Support budget decisions and staffing Build confidence in your work Time Tracking as a Starting Point Patti recommends starting with one of the simplest tools: a time tracker. She's created an Excel spreadsheet workbook that allows counselors to track: Time spent on individual students Tasks completed throughout the day Graphs and charts that automatically populate from your entries This is perfect for sharing with admin, staying accountable, and noticing patterns in how your time is spent. You can find this resource in Patti's Teachers Pay Teachers store (linked in the show notes below). Using Google Tools for Easy Data Collection Another strategy Patti loves: Google Forms + the Google Suite. These tools are powerful for: Progress monitoring Sending surveys to students, teachers, and caregivers Collecting ongoing data during small groups Tracking changes in student behavior or academic progress And bonus—sending forms to caregivers via email often leads to higher participation rates than paper handouts. Advice for New Counselors Start small. Patti suggests: Begin with tracking your time, since it's something you're already doing Add in pre/post assessments once you're in the groove Use tools that already exist—no need to reinvent the wheel A Mindset Shift: The Slow Cooker Analogy “Our work is like a slow cooker, not a microwave.” Counselors often wish for a quick fix, but real change takes time. Don't be discouraged if you don't see growth right away. If your data isn't showing growth: Don't take it personally—there are many factors at play Use it as a learning opportunity Be willing to adapt and try new approaches Track student growth over time, especially with Tier 2 or Tier 3 students This conversation was such a great reminder that data doesn't have to be intimidating—it can actually empower us to better serve our students and advocate for ourselves. You can connect with Patti and find her time tracker and other amazing resources linked below in the show notes. Thanks for listening, and I'll see you next week on School Counseling Simplified!  Resources mentioned: Join my school counselor membership IMPACT here! If you are enjoying School Counseling Simplified please follow and leave us a review on Apple Podcasts! Connect with Rachel: TpT Store Blog Instagram Facebook Page Facebook Group Pinterest  Youtube Connect with Patti: rootedwellcoaching.com TpT Store TikTok Instagram More About School Counseling Simplified: School Counseling Simplified is a podcast offering easy to implement strategies for busy school counselors. The host, Rachel Davis from Bright Futures Counseling, shares tips and tricks she has learned from her years of experience as a school counselor both in the US and at an international school in Costa Rica. You can listen to School Counseling Simplified on Apple Podcasts, Spotify, Google Podcasts, and more!  

Contextual memory in AI is a major challenge because current models struggle to retain and recall relevant information over time. While humans can build long-term semantic relationships, AI systems often rely on fixed context windows, leading to loss of important past interactions. Zep is a startup that's developing a memory layer for AI agents using The post Knowledge Graphs as Agentic Memory with Daniel Chalef appeared first on Software Engineering Daily.