Graph Databases and AI

Data Skeptic

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اضافه شده در eight سال پیش

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

How to Be a Better Human

1
Throwing good parties and building community (w/ Priya Parker) 38:16

24 weeks پیش38:16

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

38:16

Many of us are entering the new year with a similar goal — to build community and connect more with others. To kick off season five, Priya Parker shares ideas on how to be the host with the most. An expert on building connection, Priya is the author of “The Art of Gathering: How We Meet and Why It Matters.” Whether it's a book club, wedding, birthday or niche-and-obscurely themed party, Priya and Chris talk about how to create meaningful and fun experiences for all of your guests — including yourself. For the full text transcript, visit go.ted.com/BHTranscripts . For the full text transcript, visit go.ted.com/BHTranscripts Want to help shape TED’s shows going forward? Fill out our survey here ! Learn more about TED Next at ted.com/futureyou Hosted on Acast. See acast.com/privacy for more information.…

حدود یک سال پیش 35:58

MP3•خانه قسمت

In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detection in finance and insurance, security, healthcare, and supply chain optimization.

579 قسمت

#Data Science #Datamining #Datascience #Machinelearning #Statistics #Kyle Polich #Science #Math #Tech #Skeptic

Graph Databases and AI

Data Skeptic

281 subscribers

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

579 قسمت

#Data Science #Datamining #Datascience #Machinelearning #Statistics #Kyle Polich #Science #Math #Tech #Skeptic

همه قسمت ها

Data Skeptic

1
Complex Dynamic in Networks 56:00

1 day پیش56:00

56:00

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads. Our guest, Professor Baruch Barzel of Bar-Ilan University, is a leading researcher in network dynamics and complex systems ranging from biology to infrastructure and beyond. BarzelLab BarzelLab on Youtube Paper in focus: Universality in network dynamics, 2013…

Data Skeptic

1
Github Network Analysis 36:46

۸ روز پیش36:46

36:46

In this episode we'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his engineering team by analyzing GitHub metadata - such as pull requests, issues, and discussions - as a bipartite graph of people and projects. Some insights we'll discuss are how network centrality measures (like eigenvector and betweenness centrality) reveal organizational dynamics, how vacation patterns influence team connectivity, and how decentralizing communication hubs can foster healthier collaboration. Gabriel’s open-source project, GH Graph Explorer, enables other managers and engineers to extract, visualize, and analyze their own GitHub activity using tools like Python, Neo4j, Gephi and LLMs for insight generation, but always remember – don't take the results on face value. Instead, use the results to guide your qualitative investigation.…

Data Skeptic

1
Networks and Complexity 17:49

16 روز پیش17:49

17:49

In this episode, Kyle does an overview of the intersection of graph theory and computational complexity theory. In complexity theory, we are about the runtime of an algorithm based on its input size. For many graph problems, the interesting questions we want to ask take longer and longer to answer! This episode provides the fundamental vocabulary and signposts along the path of exploring the intersection of graph theory and computational complexity theory.…

Data Skeptic

1
Actantial Networks 37:34

29 روز پیش37:34

37:34

In this episode, listeners will learn about Actantial Networks—graph-based representations of narratives where nodes are actors (such as people, institutions, or abstract entities) and edges represent the actions or relationships between them. The one who will present these networks is our guest Armin Pournaki, a joint PhD candidate at the Max Planck Institute for Mathematics in the Sciences and the Laboratoire Lattice (ENS-PSL), who specializes in computational social science, where he develops methods to extract and analyze political narratives using natural language processing and network science. Armin explains how these methods can expose conflicting narratives around the same events, as seen in debates on COVID-19, climate change, or the war in Ukraine. Listeners will also discover how this approach helps make large-scale discourse—from millions of tweets or political speeches—more transparent and interpretable, offering tools for studying polarization, issue alignment, and narrative-driven persuasion in digital societies. Follow our guest Armin Pournaki's Webpage Twitter/X Bluesky Papers in focus How influencers and multipliers drive polarization and issue alignment on Twitter/X, 2025 A graph-based approach to extracting narrative signals from public discourse, 2024…

Data Skeptic

1
Graphs for Causal AI 41:00

۵ weeks پیش41:00

41:00

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…

Data Skeptic

1
Power Networks 41:50

۶ weeks پیش41:50

41:50

Data Skeptic

18 weeks پیش27:48

27:48

Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and director of the Center for Complex Systems at Binghamton University. Hiroki delves into the applications of network science in organizational structures and innovation dynamics by showing his recent work of extracting network structures from organizational charts to enable insights into decision-making and performance, He'll also cover how network connectivity impacts team creativity and innovation. Key insights include how the structure of organizational networks—such as the depth of hierarchy or proximity to leadership—can influence corporate performance and how sparse network connectivity fosters more diverse and innovative ideas than fully connected networks.…

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