Podcasts about Information retrieval

Send us a textIn this episode, I'm joined by Dr. Hamid Tizhoosh, professor of biomedical informatics at the Mayo Clinic, to unravel what's truly holding back AI in healthcare, especially pathology. From the myths of general-purpose foundation models to the missing link of data availability, this conversation explores the technical and ethical realities of deploying AI that's accurate, consistent, lean, fast, and robust.

In episode 28 of Recsperts, I sit down with Robin Burke, professor of information science at the University of Colorado Boulder and a leading expert with over 30 years of experience in recommender systems. Together, we explore multistakeholder recommender systems, fairness, transparency, and the role of recommender systems in the age of evolving generative AI.We begin by tracing the origins of recommender systems, traditionally built around user-centric models. However, Robin challenges this perspective, arguing that all recommender systems are inherently multistakeholder—serving not just consumers as the recipients of recommendations, but also content providers, platform operators, and other key players with partially competing interests. He explains why the common “Recommended for You” label is, at best, an oversimplification and how greater transparency is needed to show how stakeholder interests are balanced.Our conversation also delves into practical approaches for handling multiple objectives, including reranking strategies versus integrated optimization. While embedding multistakeholder concerns directly into models may be ideal, reranking offers a more flexible and efficient alternative, reducing the need for frequent retraining.Towards the end of our discussion, we explore post-userism and the impact of generative AI on recommendation systems. With AI-generated content on the rise, Robin raises a critical concern: if recommendation systems remain overly user-centric, generative content could marginalize human creators, diminishing their revenue streams. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:24) - About Robin Burke and First Recommender Systems (26:07) - From Fairness and Advertising to Multistakeholder RecSys (34:10) - Multistakeholder RecSys Terminology (40:16) - Multistakeholder vs. Multiobjective (42:43) - Reciprocal and Value-Aware RecSys (59:14) - Objective Integration vs. Reranking (01:06:31) - Social Choice for Recommendations under Fairness (01:17:40) - Post-Userist Recommender Systems (01:26:34) - Further Challenges and Closing Remarks Links from the Episode:Robin Burke on LinkedInRobin's WebsiteThat Recommender Systems LabReference to Broder's Keynote on Computational Advertising and Recommender Systems from RecSys 2008Multistakeholder Recommender Systems (from Recommender Systems Handbook), chapter by Himan Abdollahpouri & Robin BurkePOPROX: The Platform for OPen Recommendation and Online eXperimentationAltRecSys 2024 (Workshop at RecSys 2024)Papers:Burke et al. (1996): Knowledge-Based Navigation of Complex Information SpacesBurke (2002): Hybrid Recommender Systems: Survey and ExperimentsResnick et al. (1997): Recommender SystemsGoldberg et al. (1992): Using collaborative filtering to weave an information tapestryLinden et al. (2003): Amazon.com Recommendations - Item-to-Item Collaborative FilteringAird et al. (2024): Social Choice for Heterogeneous Fairness in RecommendationAird et al. (2024): Dynamic Fairness-aware Recommendation Through Multi-agent Social ChoiceBurke et al. (2024): Post-Userist Recommender Systems : A ManifestoBaumer et al. (2017): Post-userismBurke et al. (2024): Conducting Recommender Systems User Studies Using POPROXGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

In episode 27 of Recsperts, we meet Alessandro Piscopo, Lead Data Scientist in Personalization and Search, and Duncan Walker, Principal Data Scientist in the iPlayer Recommendations Team, both from the BBC. We discuss how the BBC personalizes recommendations across different offerings like news or video and audio content recommendations. We learn about the core values for the oldest public service media organization and the collaboration with editors in that process.The BBC once started with short video recommendations for BBC+ and nowadays has to consider recommendations across multiple domains: news, the iPlayer, BBC Sounds, BBC Bytesize, and more. With a reach of about 500M+ users who access services every week there is a huge potential. My guests discuss the challenges of aligning recommendations with public service values and the role of editors and constant exchange, alignment, and learning between the algorithmic and editorial lines of recommender systems.We also discuss the potential of cross-domain recommendations to leverage the content across different products as well as the organizational setup of teams working on recommender systems at the BBC. We learn about skews in the data due to the nature of an online service that also has a linear offering with TV and radio services.Towards the end, we also touch a bit on QUARE @ RecSys, which is the Workshop on Measuring the Quality of Explanations in Recommender Systems.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:10) - About Alessandro Piscopo and Duncan Walker (14:53) - RecSys Applications at the BBC (20:22) - Journey of Building Public Service Recommendations (28:02) - Role and Implementation of Public Service Values (36:52) - Algorithmic and Editorial Recommendation (01:01:54) - Further RecSys Challenges at the BBC (01:15:53) - Quare Workshop (01:23:27) - Closing Remarks Links from the Episode:Alessandro Piscopo on LinkedInDuncan Walker on LinkedInBBCQUARE @ RecSys 2023 (2nd Workshop on Measuring the Quality of Explanations in Recommender Systems)Papers:Clarke et al. (2023): Personalised Recommendations for the BBC iPlayer: Initial approach and current challengesBoididou et al. (2021): Building Public Service Recommenders: Logbook of a JourneyPiscopo et al. (2019): Data-Driven Recommendations in a Public Service OrganisationGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

We're kicking off our month of Weird-Ass Dystopian Cities with Terry Gilliam's classic satirical nightmare, Brazil! Join us as we pick apart this strange world of failing systems and bureaucratic ass-covering that holds absolutely no similarity to the real world in which we live. Heh. Heh. CHAPTERS: (00:00:00) - The Nextlander Watchcast Episode 127: Brazil (1985) (00:00:11) - Intro. (00:01:47) - Our movie this week: Terry Gilliam's Brazil! But which version?!? (00:06:11) - A cinematic Rosetta Stone. (00:17:26) - Our respective attachments to various Gilliam and Monty Python things. (00:21:35) - Was this movie successful? Also, digging into the production. (00:28:04) - Our star-studded cast. (00:37:40) - An intro of exploding televisions and ruinous clerical errors. (00:43:40) - The dreams of Sam Lowry. (00:47:31) - Untangling the web of responsibility for the Buttle/Tuttle affair. (00:52:01) - Break! (00:52:28) - We're back, and it's time to stretch some flesh. (00:58:24) - HVAC anarchy. (01:03:46) - Sam decides to make a house call. (01:10:14) - Central Services gets revenge. (01:16:17) - A Christmas party full of demons. (01:19:26) - Welcome to Information Retrieval. (01:22:49) - The Lint reveal. (01:27:07) - Sam can't stop running into his dream woman. (01:35:06) - Sam has had enough. (01:41:50) - A post-coital SWAT raid. (01:50:05) - A jailbreak in the cooling tower, and things go off the rails. (01:55:27) - The funeral of the seeping woman, and all is not as it seems. (01:59:59) - Final thoughts. (02:06:26) - Our film for next week: Walter Hill's The Warriors! (02:09:48) - Outro. 

On this episode of Crazy Wisdom, host Stewart Alsop speaks with Andrew Altschuler, a researcher, educator, and navigator at Tana, Inc., who also founded Tana Stack. Their conversation explores knowledge systems, complexity, and AI, touching on topics like network effects in social media, information warfare, mimetic armor, psychedelics, and the evolution of knowledge management. They also discuss the intersection of cognition, ontologies, and AI's role in redefining how we structure and retrieve information. For more on Andrew's work, check out his course and resources at altshuler.io and his YouTube channel.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction and Guest Background00:33 The Demise of AirChat00:50 Network Effects and Social Media Challenges03:05 The Rise of Digital Warlords03:50 Quora's Golden Age and Information Warfare08:01 Building Limbic Armor16:49 Knowledge Management and Cognitive Armor18:43 Defining Knowledge: Secular vs. Ultimate25:46 The Illusion of Insight31:16 The Illusion of Insight32:06 Philosophers of Science: Popper and Kuhn32:35 Scientific Assumptions and Celestial Bodies34:30 Debate on Non-Scientific Knowledge36:47 Psychedelics and Cultural Context44:45 Knowledge Management: First Brain vs. Second Brain46:05 The Evolution of Knowledge Management54:22 AI and the Future of Knowledge Management58:29 Tana: The Next Step in Knowledge Management59:20 Conclusion and Course InformationKey InsightsNetwork Effects Shape Online Communities – The conversation highlighted how platforms like Twitter, AirChat, and Quora demonstrate the power of network effects, where a critical mass of users is necessary for a platform to thrive. Without enough engaged participants, even well-designed social networks struggle to sustain themselves, and individuals migrate to spaces where meaningful conversations persist. This explains why Twitter remains dominant despite competition and why smaller, curated communities can be more rewarding but difficult to scale.Information Warfare and the Need for Cognitive Armor – In today's digital landscape, engagement-driven algorithms create an arena of information warfare, where narratives are designed to hijack emotions and shape public perception. The only real defense is developing cognitive armor—critical thinking skills, pattern recognition, and the ability to deconstruct media. By analyzing how information is presented, from video editing techniques to linguistic framing, individuals can resist manipulation and maintain autonomy over their perspectives.The Role of Ontologies in AI and Knowledge Management – Traditional knowledge management has long been overlooked as dull and bureaucratic, but AI is transforming the field into something dynamic and powerful. Systems like Tana and Palantir use ontologies—structured representations of concepts and their relationships—to enhance information retrieval and reasoning. AI models perform better when given structured data, making ontologies a crucial component of next-generation AI-assisted thinking.The Danger of Illusions of Insight – Drawing from ideas by Balaji Srinivasan, the episode distinguished between genuine insight and the illusion of insight. While psychedelics, spiritual experiences, and intense emotional states can feel revelatory, they do not always produce knowledge that can be tested, shared, or used constructively. The ability to distinguish between profound realizations and self-deceptive experiences is critical for anyone navigating personal and intellectual growth.AI as an Extension of Human Cognition, Not a Second Brain – While popular frameworks like "second brain" suggest that digital tools can serve as externalized minds, the episode argued that AI and note-taking systems function more as extended cognition rather than true thinking machines. AI can assist with organizing and retrieving knowledge, but it does not replace human reasoning or creativity. Properly integrating AI into workflows requires understanding its strengths and limitations.The Relationship Between Personal and Collective Knowledge Management – Effective knowledge management is not just an individual challenge but also a collective one. While personal knowledge systems (like note-taking and research practices) help individuals retain and process information, organizations struggle with preserving and sharing institutional knowledge at scale. Companies like Tesla exemplify how knowledge isn't just stored in documents but embodied in skilled individuals who can rebuild complex systems from scratch.The Increasing Value of First Principles Thinking – Whether in AI development, philosophy, or practical decision-making, the discussion emphasized the importance of grounding ideas in first principles. Great thinkers and innovators, from AI researchers like Demis Hassabis to physicists like David Deutsch, excel because they focus on fundamental truths rather than assumptions. As AI and digital tools reshape how we interact with knowledge, the ability to think critically and question foundational concepts will become even more essential.

In episode 26 of Recsperts, I speak with Sanne Vrijenhoek, a PhD candidate at the University of Amsterdam's Institute for Information Law and the AI, Media & Democracy Lab. Sanne's research explores diversity in recommender systems, particularly in the news domain, and its connection to democratic values and goals.We dive into four of her papers, which focus on how diversity is conceptualized in news recommender systems. Sanne introduces us to five rank-aware divergence metrics for measuring normative diversity and explains why diversity evaluation shouldn't be approached blindly—first, we need to clarify the underlying values. She also presents a normative framework for these metrics, linking them to different democratic theory perspectives. Beyond evaluation, we discuss how to optimize diversity in recommender systems and reflect on missed opportunities—such as the RecSys Challenge 2024, which could have gone beyond accuracy-chasing. Sanne also shares her recommendations for improving the challenge by incorporating objectives such as diversity.During our conversation, Sanne shares insights on effectively communicating recommender systems research to non-technical audiences. To wrap up, we explore ideas for fostering a more diverse RecSys research community, integrating perspectives from multiple disciplines.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:24) - About Sanne Vrijenhoek (14:49) - What Does Diversity in RecSys Mean? (26:32) - Assessing Diversity in News Recommendations (34:54) - Rank-Aware Divergence Metrics to Measure Normative Diversity (01:01:37) - RecSys Challenge 2024 - Recommendations for the Recommenders (01:11:23) - RecSys Workshops - NORMalize and AltRecSys (01:15:39) - On the Different Conceptualizations of Diversity in RecSys (01:28:38) - Closing Remarks Links from the Episode:Sanne Vrijenhoek on LinkedInInformfullyMIND: MIcrosoft News DatasetRecSys Challenge 2024NORMalize 2023: The First Workshop on the Normative Design and Evaluation of Recommender SystemsNORMalize 2024: The Second Workshop on the Normative Design and Evaluation of Recommender SystemsAltRecSys 2024: The AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas in RecommendationPapers:Vrijenhoek et al. (2021): Recommenders with a Mission: Assessing Diversity in News RecommendationsVrijenhoek et al. (2022): RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News RecommendationsHeitz et al. (2024): Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys ChallengeVrijenhoek et al. (2024): Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsHelberger (2019): On the Democratic Role of News RecommendersSteck (2018): Calibrated RecommendationsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

On this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, sit down with Brendon Wong, the founder of Unize.org. We explore Brendon's work in knowledge management, touching on his recent talk at Nodes 2024 about using AI to generate knowledge graphs and trends in the field. Our conversation covers the evolution of personal and organizational knowledge management, the future of object-oriented systems, the integration of AI with knowledge graphs, and the challenges of autonomous agents. For more on Brendon's work, check out unize.org and his articles at web10.ai.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:35 Exploring Unise: A Knowledge Management App01:01 The Evolution of Knowledge Management02:32 Personal Knowledge Management Trends03:10 Object-Oriented Knowledge Management05:27 The Future of Knowledge Graphs and AI10:37 Challenges in Simulating the Human Mind22:04 Knowledge Management in Organizations26:57 The Role of Autonomous Agents30:00 Personal Experiences with Sleep Aids30:07 Unique Human Perceptions32:08 Knowledge Management Journey33:31 Personal Knowledge Management Systems34:36 Challenges in Knowledge Management35:26 Future of Knowledge Management with AI36:29 Melatonin and Sleep Patterns37:30 AI and the Future of the Internet43:39 Reasoning and AI Limitations48:33 The Future of AI and Human Reasoning52:43 Conclusion and Contact InformationKey InsightsThe Evolution of Knowledge Management: Brendon Wong highlights how knowledge management has evolved from personal note-taking systems to sophisticated, object-oriented models. He emphasizes the shift from traditional page-based structures, like those in Roam Research and Notion, to systems that treat information as interconnected objects with defined types and properties, enhancing both personal and organizational knowledge workflows.The Future Lies in Object-Oriented Knowledge Systems: Brendon introduces the concept of object-oriented knowledge management, where data is organized as distinct objects (e.g., books, restaurants, ideas) with specific attributes and relationships. This approach enables more dynamic organization, easier data retrieval, and better contextual understanding, setting the stage for future advancements in knowledge-based applications.AI and Knowledge Graphs Are a Powerful Combination: Brendon discusses the synergy between AI and knowledge graphs, explaining how AI can generate, maintain, and interact with complex knowledge structures. This integration enhances memory, reasoning, and information retrieval capabilities, allowing AI systems to support more nuanced and context-aware decision-making processes.The Limitations of Current AI Models: While AI models like LLMs have impressive capabilities, Brendon points out their limitations, particularly in reasoning and long-term memory. He notes that current models excel at pattern recognition but struggle with higher-level reasoning tasks, often producing hallucinations when faced with unfamiliar or niche topics.Challenges in Organizational Knowledge Management: Brendon and Stewart discuss the persistent challenges of implementing knowledge management in organizations. Despite its critical role, knowledge management is often underappreciated and the first to be cut during budget reductions. The conversation highlights the need for systems that are both intuitive and capable of reducing the manual burden on users.The Potential and Pitfalls of Autonomous Agents: The episode explores the growing interest in autonomous and semi-autonomous agents powered by AI. While these agents can perform tasks with minimal human intervention, Brendon notes that the technology is still in its infancy, with limited real-world applications and significant room for improvement, particularly in reliability and task generalization.Reimagining the Future of the Internet with Web 10: Brendon shares his vision for Web 10, an ambitious rethinking of the internet where knowledge is better structured, verified, and interconnected. This future internet would address current issues like misinformation and data fragmentation, creating a more reliable and meaningful digital ecosystem powered by AI-driven knowledge graphs.

Private Information Retrieval (PIR) is a cryptographic primitive that enables a client to retrieve a record from a database hosted by one or more untrusted servers without revealing which record was accessed. It has a wide range of applications, including private web search, private DNS, lightweight cryptocurrency clients, and more. While many existing PIR protocols assume that servers are honest but curious, we explore the scenario where dishonest servers provide incorrect answers to mislead clients into retrieving the wrong results.We begin by presenting a unified classification of protocols that address incorrect server behavior, focusing on the lowest level of resistance—verifiability—which allows the client to detect if the retrieved file is incorrect. Despite this relaxed security notion, verifiability is sufficient for several practical applications, such as private media browsing.Later on, we propose a unified framework for polynomial PIR protocols, encompassing various existing protocols that optimize download rate or total communication cost. We introduce a method to transform a polynomial PIR into a verifiable one without increasing the number of servers. This is achieved by doubling the queries and linking the responses using a secret parameter held by the client. About the speaker: Stanislav Kruglik has been a Research Fellow at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, since April 2022. He earned a Ph.D. in the theoretical foundations of computer science from the Moscow Institute of Physics and Technology, Russia, in February 2022. He is an IEEE Senior Member and a recipient of the Simons Foundation Scholarship. With over 40 scientific publications, his work has appeared in top-tier venues, including IEEE Transactions on Information Forensics and Security and the European Symposium on Research in Computer Security. His research interests focus on information theory and its applications, particularly in data storage and security.

Jonas Hübotter, PhD student at ETH Zurich's Institute for Machine Learning, discusses his groundbreaking research on test-time computation and local learning. He demonstrates how smaller models can outperform larger ones by 30x through strategic test-time computation and introduces a novel paradigm combining inductive and transductive learning approaches. Using Bayesian linear regression as a surrogate model for uncertainty estimation, Jonas explains how models can efficiently adapt to specific tasks without massive pre-training. He draws an analogy to Google Earth's variable resolution system to illustrate dynamic resource allocation based on task complexity. The conversation explores the future of AI architecture, envisioning systems that continuously learn and adapt beyond current monolithic models. Jonas concludes by proposing hybrid deployment strategies combining local and cloud computation, suggesting a future where compute resources are allocated based on task complexity rather than fixed model size. This research represents a significant shift in machine learning, prioritizing intelligent resource allocation and adaptive learning over traditional scaling approaches. SPONSOR MESSAGES: CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/ Transcription, references and show notes PDF download: https://www.dropbox.com/scl/fi/cxg80p388snwt6qbp4m52/JonasFinal.pdf?rlkey=glk9mhpzjvesanlc14rtpvk4r&st=6qwi8n3x&dl=0 Jonas Hübotter https://jonhue.github.io/ https://scholar.google.com/citations?user=pxi_RkwAAAAJ Transductive Active Learning: Theory and Applications (NeurIPS 2024) https://arxiv.org/pdf/2402.15898 EFFICIENTLY LEARNING AT TEST-TIME: ACTIVE FINE-TUNING OF LLMS (SIFT) https://arxiv.org/pdf/2410.08020 TOC: 1. Test-Time Computation Fundamentals [00:00:00] Intro [00:03:10] 1.1 Test-Time Computation and Model Performance Comparison [00:05:52] 1.2 Retrieval Augmentation and Machine Teaching Strategies [00:09:40] 1.3 In-Context Learning vs Fine-Tuning Trade-offs 2. System Architecture and Intelligence [00:15:58] 2.1 System Architecture and Intelligence Emergence [00:23:22] 2.2 Active Inference and Constrained Agency in AI [00:29:52] 2.3 Evolution of Local Learning Methods [00:32:05] 2.4 Vapnik's Contributions to Transductive Learning 3. Resource Optimization and Local Learning [00:34:35] 3.1 Computational Resource Allocation in ML Models [00:35:30] 3.2 Historical Context and Traditional ML Optimization [00:37:55] 3.3 Variable Resolution Processing and Active Inference in ML [00:43:01] 3.4 Local Learning and Base Model Capacity Trade-offs [00:48:04] 3.5 Active Learning vs Local Learning Approaches 4. Information Retrieval and Model Interpretability [00:51:08] 4.1 Information Retrieval and Nearest Neighbor Limitations [01:03:07] 4.2 Model Interpretability and Surrogate Models [01:15:03] 4.3 Bayesian Uncertainty Estimation and Surrogate Models 5. Distributed Systems and Deployment [01:23:56] 5.1 Memory Architecture and Controller Systems [01:28:14] 5.2 Evolution from Static to Distributed Learning Systems [01:38:03] 5.3 Transductive Learning and Model Specialization [01:41:58] 5.4 Hybrid Local-Cloud Deployment Strategies

In episode 25, we talk about the upcoming ACM Conference on Recommender Systems 2024 (RecSys) and welcome a former guest to geek about the conference. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (01:56) - Overview RecSys 2024 (07:01) - Contribution Stats (09:37) - Interview Links from the Episode:RecSys 2024 Conference WebsitePapers:RecSys '24: Proceedings of the 18th ACM Conference on Recommender SystemsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Manage your data access effectively while using Microsoft 365 Copilot. Navigate the SharePoint admin center to adjust site privacy settings, ensuring only authorized members can access sensitive content. Set up test accounts to identify potential oversharing and take corrective actions. By refining permissions, protect valuable information and enhance the relevance of AI-generated responses. Jeremy Chapman, Director of Microsoft 365, shares how to find and control oversharing, so you can confidently utilize Microsoft 365 Copilot for your small business needs.   ► QUICK LINKS: 00:00 - Prepare data for search 01:22 - Search hygiene 02:04 - Test to see who has access 02:33 - How to set up a test account 03:32 - Search for items 05:08 - Information retrieval process 05:45 - Shared items by invitation link 06:19 - Oversharing 07:33 - How to reduce oversharing 08:35 - Check permissions 11:07 - Confirm permissions are in place 11:52 - Wrap up   ► Link References Get to the SharePoint admin center from Microsoft 365's admin center at https://admin.microsoft.com   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

In episode 24 of Recsperts, I sit down with Amey Dharwadker, Machine Learning Engineering Manager at Facebook, to dive into the complexities of large-scale video recommendations. Amey, who leads the Video Recommendations Quality Ranking team at Facebook, sheds light on the intricate challenges of delivering personalized video feeds at scale. Our conversation covers content understanding, user interaction data, real-time signals, exploration, and evaluation techniques.We kick off the episode by reflecting on the inaugural VideoRecSys workshop at RecSys 2023, setting the stage for a deeper discussion on Facebook's approach to video recommendations. Amey walks us through the critical challenges they face, such as gathering reliable user feedback signals to avoid pitfalls like watchbait. With a vast and ever-growing corpus of billions of videos—millions of which are added each month—the cold start problem looms large. We explore how content understanding, user feedback aggregation, and exploration techniques help address this issue. Amey explains how engagement metrics like watch time, comments, and reactions are used to rank content, ensuring users receive meaningful and diverse video feeds.A key highlight of the conversation is the importance of real-time personalization in fast-paced environments, such as short-form video platforms, where user preferences change quickly. Amey also emphasizes the value of cross-domain data in enriching user profiles and improving recommendations.Towards the end, Amey shares his insights on leadership in machine learning teams, pointing out the characteristics of a great ML team.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (02:32) - About Amey Dharwadker (08:39) - Video Recommendation Use Cases on Facebook (16:18) - Recommendation Teams and Collaboration (25:04) - Challenges of Video Recommendations (31:07) - Video Content Understanding and Metadata (33:18) - Multi-Stage RecSys and Models (42:42) - Goals and Objectives (49:04) - User Behavior Signals (59:38) - Evaluation (01:06:33) - Cross-Domain User Representation (01:08:49) - Leadership and What Makes a Great Recommendation Team (01:13:01) - Closing Remarks Links from the Episode:Amey Dharwadker on LinkedInAmey's WebsiteRecSys Challenge 2021VideoRecSys Workshop 2023VideoRecSys + LargeRecSys 2024Papers:Mahajan et al. (2023): CAViaR: Context Aware Video RecommendationsMahajan et al. (2023): PIE: Personalized Interest Exploration for Large-Scale Recommender SystemsRaul et al. (2023): CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender SystemsZhai et al. (2024): Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative RecommendationsSaket et al. (2023): Formulating Video Watch Success Signals for Recommendations on Short Video PlatformsWang et al. (2022): Surrogate for Long-Term User Experience in Recommender SystemsSu et al. (2024): Long-Term Value of Exploration: Measurements, Findings and AlgorithmsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

In episode 23 of Recsperts, we welcome Yashar Deldjoo, Assistant Professor at the Polytechnic University of Bari, Italy. Yashar's research on recommender systems includes multimodal approaches, multimedia recommender systems as well as trustworthiness and adversarial robustness, where he has published a lot of work. We discuss the evolution of generative models for recommender systems, modeling paradigms, scenarios as well as their evaluation, risks and harms.We begin our interview with a reflection of Yashar's areas of recommender systems research so far. Starting with multimedia recsys, particularly video recommendations, Yashar covers his work around adversarial robustness and trustworthiness leading to the main topic for this episode: generative models for recommender systems. We learn about their aspects for improving beyond the (partially saturated) state of traditional recommender systems: improve effectiveness and efficiency for top-n recommendations, introduce interactivity beyond classical conversational recsys, provide personalized zero- or few-shot recommendations.We learn about the modeling paradigms and as well about the scenarios for generative models which mainly differ by input and modelling approach: ID-based, text-based, and multimodal generative models. This is how we navigate the large field of acronyms leading us from VAEs and GANs to LLMs.Towards the end of the episode, we also touch on the evaluation, opportunities, risks and harms of generative models for recommender systems. Yashar also provides us with an ample amount of references and upcoming events where people get the chance to know more about GenRecSys.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:58) - About Yashar Deldjoo (09:34) - Motivation for RecSys (13:05) - Intro to Generative Models for Recommender Systems (44:27) - Modeling Paradigms for Generative Models (51:33) - Scenario 1: Interaction-Driven Recommendation (57:59) - Scenario 2: Text-based Recommendation (01:10:39) - Scenario 3: Multimodal Recommendation (01:24:59) - Evaluation of Impact and Harm (01:38:07) - Further Research Challenges (01:45:03) - References and Research Advice (01:49:39) - Closing Remarks Links from the Episode:Yashar Deldjoo on LinkedInYashar's WebsiteKDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and OpportunitiesRecSys 2024 Workshop: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RECSYS'24)Papers:Deldjoo et al. (2024): A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)Deldjoo et al. (2020): Recommender Systems Leveraging Multimedia ContentDeldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial NetworksDeldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation ModelsLiang et al. (2018): Variational Autoencoders for Collaborative FilteringHe et al. (2016): Visual Bayesian Personalized Ranking from Implicit FeedbackGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

What are the built-in Python modules that can work as useful command-line tools? How can these tools add more functionality to Windows machines? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

Daniel & Chris sit down with Denis Yarats, Co-founder & CTO at Perplexity, to discuss Perplexity's sophisticated AI-driven answer engine. Denis outlines some of the deficiencies in search engines, and how Perplexity's approach to information retrieval improves on traditional search engine systems, with a focus on accuracy and validation of the information provided.

Daniel & Chris sit down with Denis Yarats, Co-founder & CTO at Perplexity, to discuss Perplexity's sophisticated AI-driven answer engine. Denis outlines some of the deficiencies in search engines, and how Perplexity's approach to information retrieval improves on traditional search engine systems, with a focus on accuracy and validation of the information provided.

This season, we'll be alternating episodes between learning about AI's potential in the Kitchen & Bath design businesses and taking concrete steps to harness that potential. Today, let's get into a practical first step: how to begin using AI in your daily business operations.GET IN TOUCH

Summary In dieser Folge spricht Björn mit Olaf Kopp über Google Patente. Olaf ist Experte für Google-Patente und erklärt, welche Auswirkungen sie auf die Suche haben. Sie diskutieren die Leaks der Google Search API und die darin enthaltenen Rankingsysteme. Olaf teilt seine Datenbank mit 135 wichtigen Patenten und Papieren, die SEOs lesen sollten. Sie sprechen auch über die Bedeutung von Crawling, Indexing, Brand Authority und Links. Olaf erklärt, dass Patente keine konkreten Beweise liefern, aber sie helfen, theoretische Grundlagen des modernen Information Retrieval zu verstehen. Sie diskutieren auch die Bedeutung von Content für die Zukunft des SEO und die Rolle von KI-Systemen wie LLMs. In diesem Teil des Gesprächs diskutieren Björn und Olaf die Bedeutung von AI-Overviews und Updates in Bezug auf die Produktivität. Olaf betont, dass es wichtiger ist, die Grundlagen zu verstehen und sich mit den Funktionsweisen von Systemen auseinanderzusetzen, anstatt sich über aktuelle Ergebnisse aufzuregen. Sie sprechen auch über die Zusammenarbeit von Google mit Reddit und Medium in Bezug auf das Training von LLMs. Olaf empfiehlt, sich mit Retrieval Augmented Generation (RRG) auseinanderzusetzen, da dies die Zukunft der LLMs und des Information Retrievals darstellt. Sie diskutieren auch die Rolle von Google Scholar und die Nutzung von Patenten zur Weiterentwicklung der Suche. Olaf erwähnt Patente zu Phrase-Based Indexing, BERT und Information Gain. Sie schließen das Gespräch mit einem Hinweis auf Olafs Patentdatenbank und die Möglichkeit, seine Zusammenfassungen und Analysen gegen Entgelt zu nutzen. Gutscheincode: patentlovers-seopresso um 99 Euro/Jahr zu sparen Eingeben auf folgender Seite: https://www.kopp-online-marketing.com/patents-papers Takeaways Google-Patente haben Auswirkungen auf die Suche und sind wichtig für SEOs, um die theoretischen Grundlagen des modernen Information Retrieval zu verstehen. Die Leaks der Google Search API und Rankingsysteme haben einige überrascht, während andere bereits mit ähnlichen Konzepten vertraut waren. Es gibt 135 wichtige Patente und Papiere, die SEOs lesen sollten, um ihr Wissen zu erweitern. Crawling, Indexing, Brand Authority und Links sind weiterhin wichtige Faktoren für das Ranking in den Suchergebnissen. Content ist entscheidend für die Zukunft des SEO, da KI-Systeme wie LLMs Content benötigen, um trainiert zu werden. Es ist wichtiger, die Grundlagen zu verstehen und sich mit den Funktionsweisen von Systemen auseinanderzusetzen, anstatt sich über aktuelle Ergebnisse aufzuregen. Die Zusammenarbeit von Google mit Reddit und Medium in Bezug auf das Training von LLMs ist interessant, aber ihre Motivation ist nicht immer klar. Retrieval Augmented Generation (RRG) ist ein spannendes Thema, das die Welt des Information Retrievals mit LLMs verbindet und die Zukunft der Suche beeinflussen könnte. Google Scholar und Patente können wertvolle Informationen liefern und zur Weiterentwicklung der Suche beitragen. Patente zu Phrase-Based Indexing, BERT und Information Gain sind bahnbrechend für die Suche und bieten Einblicke in die Funktionsweise von Google. Olafs Patentdatenbank bietet eine umfangreiche Sammlung von Patenten und Analysen, die für SEO-Fachleute von Interesse sein können. Chapters 00:00 Einführung und Vorstellung von Olaf Kopp 03:18 Wichtige Patente und Papiere für SEOs 06:00 Die Bedeutung von Crawling, Indexing, Brand Authority und Links 09:07 Das Patent 'Website Representation Vector' und EAT 16:20 Content als Schlüsselfaktor für die Zukunft des SEO 21:31 Retrieval Augmented Generation (RRG) als Zukunft der Suche 24:00 Die Rolle von Google Scholar und Patenten in der Suche 26:01 Bahnbrechende Patente zu Phrase-Based Indexing, BERT und Information Gain 30:04 Olafs Patentdatenbank als Ressource für SEO-Fachleute

Ensure high-accuracy, efficient vector search at massive scale with Azure Cosmos DB. Leveraging Microsoft's DiskANN, more IO traffic moves to disk to maximize storage capacity and enable high-speed similarity searches across all data, reducing memory dependency. This technology, powering global services like Microsoft 365, is now integrated into Azure Cosmos DB, enabling developers to build scalable, high-performance applications with built-in vector search, real-time fraud detection, and robust multi-tenancy support. Join Kirill Gavrylyuk, VP for Azure Cosmos DB, as he shares how Azure Cosmos DB with DiskANN offers unparalleled speed, efficiency, and accuracy, making it the ideal solution for modern AI-driven applications.   ► QUICK LINKS: 00:00 - Latest Cosmos DB optimizations with DiskANN 02:09 - Where DiskANN approach is beneficial 04:07 - Efficient querying 06:02 - DiskANN compared to HNSW 07:41 - Integrate DiskANN into a new or existing app 08:39 - Real-time transactional AI scenario 09:29 - Building a fraud detection sample app 10:59 - Vectorize transactions for anomaly detection 12:49 - Scaling to address high levels of traffic 14:05 - Manage multi-tenancy 15:35 - Wrap up   ► Link References Check out https://aka.ms/DiskANNCosmosDB Try out apps at https://aka.ms/DiskANNCosmosDBSamples   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

In episode 22 of Recsperts, we welcome Prabhat Agarwal, Senior ML Engineer, and Aayush Mudgal, Staff ML Engineer, both from Pinterest, to the show. Prabhat works on recommendations and search systems at Pinterest, leading representation learning efforts. Aayush is responsible for ads ranking and privacy-aware conversion modeling. We discuss user and content modeling, short- vs. long-term objectives, evaluation as well as multi-task learning and touch on counterfactual evaluation as well.In our interview, Prabhat guides us through the journey of continuous improvements of Pinterest's Homefeed personalization starting with techniques such as gradient boosting over two-tower models to DCN and transformers. We discuss how to capture users' short- and long-term preferences through multiple embeddings and the role of candidate generators for content diversification. Prabhat shares some details about position debiasing and the challenges to facilitate exploration.With Aayush we get the chance to dive into the specifics of ads ranking at Pinterest and he helps us to better understand how multifaceted ads can be. We learn more about the pain of having too many models and the Pinterest's efforts to consolidate the model landscape to improve infrastructural costs, maintainability, and efficiency. Aayush also shares some insights about exploration and corresponding randomization in the context of ads and how user behavior is very different between different kinds of ads.Both guests highlight the role of counterfactual evaluation and its impact for faster experimentation.Towards the end of the episode, we also touch a bit on learnings from last year's RecSys challenge.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:51) - Guest Introductions (09:57) - Pinterest Introduction (21:57) - Homefeed Personalization (47:27) - Ads Ranking (01:14:58) - RecSys Challenge 2023 (01:20:26) - Closing Remarks Links from the Episode:Prabhat Agarwal on LinkedInAayush Mudgal on LinkedInRecSys Challenge 2023Pinterest Engineering BlogPinterest LabsPrabhat's Talk at GTC 2022: Evolution of web-scale engagement modeling at PinterestBlogpost: How we use AutoML, Multi-task learning and Multi-tower models for Pinterest AdsBlogpost: Pinterest Home Feed Unified Lightweight Scoring: A Two-tower ApproachBlogpost: Experiment without the wait: Speeding up the iteration cycle with Offline Replay ExperimentationBlogpost: MLEnv: Standardizing ML at Pinterest Under One ML Engine to Accelerate InnovationPapers:Eksombatchai et al. (2018): Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-TimeYing et al. (2018): Graph Convolutional Neural Networks for Web-Scale Recommender SystemsPal et al. (2020): PinnerSage: Multi-Modal User Embedding Framework for Recommendations at PinterestPancha et al. (2022): PinnerFormer: Sequence Modeling for User Representation at PinterestZhao et al. (2019): Recommending what video to watch next: a multitask ranking systemGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Explain my like i am five: Die Grundlagen moderner SuchenWir, als User, erwarten heutzutage ziemlich viel von einer Suchmaschine. Es soll “magisch” verstehen, was wir eigentlich finden möchten. Egal ob wir das richtige Wort dafür nutzen (aka Synonym-Suche) oder ob der Begriff einen Tippfehler hat (aka “Meinten Sie …?”).Oft werden Tools wie Elastic- oder OpenSearch, Solr, Algolia und Co. für sowas eingesetzt, denn eine einfache Volltext-Suche mittels eines Wildcard-SQL-SELECT Statement reicht dafür nicht mehr aus. Doch was steckt eigentlich dahinter? Wie funktionieren all diese modernen Suchen eigentlich im Inneren? In dieser Episode geht es um die Grundlagen moderner Suchmaschinen. Wir schmeißen mit Begriffen wie Stemming, Homonyme, BERT, Stopwords, Inverted Index, Suffixbäume, N-Grams, Term Frequency-Inverse Document Frequency, Vector Space Model und Co um uns und erklären das ganze im “Explain me Like I am five”-Stil.Bonus: Wie Konzepte des Information Retrieval mit Bälle-Bädern erklärt werden.**** Diese Episode wird von der HANDELSBLATT MEDIA GROUP gesponsert.Wirtschaft ist nicht immer einfach. Deswegen lautet die Mission der HANDELSBLATT MEDIA GROUP: „Wir möchten Menschen befähigen, die Wirtschaft zu verstehen.“ Mit ihren Kernprodukten, dem Handelsblatt und der WirtschaftsWoche, sowie 160.000 Abonnements, 15 Millionen Besuchern und 3 Milliarden Anfragen in einem Monat leisten sie einen wichtigen Beitrag zur Orientierung und Meinungsbildung in den Bereichen Wirtschaft und Politik und machen damit einen ausgezeichneten Job.Wenn du Teil dieser Mission sein möchtest, schau auf https://engineeringkiosk.dev/handelsblatt vorbei und werde ein Teil der HANDELSBLATT MEDIA GROUP.********Das schnelle Feedback zur Episode:

In episode 21 of Recsperts, we welcome Martijn Willemsen, Associate Professor at the Jheronimus Academy of Data Science and Eindhoven University of Technology. Martijn's researches on interactive recommender systems which includes aspects of decision psychology and user-centric evaluation. We discuss how users gain control over recommendations, how to support their goals and needs as well as how the user-centric evaluation framework fits into all of this.In our interview, Martijn outlines the reasons for providing users control over recommendations and how to holistically evaluate the satisfaction and usefulness of recommendations for users goals and needs. We discuss the psychology of decision making with respect to how well or not recommender systems support it. We also dive into music recommender systems and discuss how nudging users to explore new genres can work as well as how longitudinal studies in recommender systems research can advance insights. Towards the end of the episode, Martijn and I also discuss some examples and the usefulness of enabling users to provide negative explicit feedback to the system.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:03) - About Martijn Willemsen (15:14) - Waves of User-Centric Evaluation in RecSys (19:35) - Behaviorism is not Enough (46:21) - User-Centric Evaluation Framework (01:05:38) - Genre Exploration and Longitudinal Studies in Music RecSys (01:20:59) - User Control and Negative Explicit Feedback (01:31:50) - Closing Remarks Links from the Episode:Martijn Willemsen on LinkedInMartijn Willemsen's WebsiteUser-centric Evaluation FrameworkBehaviorism is not Enough (Talk at RecSys 2016)Neil Hunt: Quantifying the Value of Better Recommendations (Keynote at RecSys 2014)What recommender systems can learn from decision psychology about preference elicitation and behavioral change (Talk at Boise State (Idaho) and Grouplens at University of Minnesota)Eric J. Johnson: The Elements of ChoiceRasch ModelSpotify Web APIPapers:Ekstrand et al. (2016): Behaviorism is not Enough: Better Recommendations Through Listening to UsersKnijenburg et al. (2012): Explaining the user experience of recommender systemsEkstrand et al. (2014): User perception of differences in recommender algorithmsLiang et al. (2022): Exploring the longitudinal effects of nudging on users' music genre exploration behavior and listening preferencesMcNee et al. (2006): Being accurate is not enough: how accuracy metrics have hurt recommender systemsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Nikolay and Michael have a high-level discussion on all things search — touching on full-text search, semantic search, and faceted search. They discuss what comes in Postgres core, what is possible via extensions, and some thoughts on performance vs implementation complexity vs user experience. Here are some links to things they mentioned:Simon Riggs https://www.linkedin.com/feed/update/urn:li:activity:7178702287740022784/Companion databases episode https://postgres.fm/episodes/companion-databasespgvector episode https://postgres.fm/episodes/pgvectorFull Text Search https://www.postgresql.org/docs/current/textsearch.htmlSemantic search https://en.wikipedia.org/wiki/Semantic_searchFaceted search https://en.wikipedia.org/wiki/Faceted_searchFaceting large result sets in PostgreSQL https://www.cybertec-postgresql.com/en/faceting-large-result-sets/RUM index https://github.com/postgrespro/rum Hybrid search (Supabase guide) https://supabase.com/docs/guides/ai/hybrid-search Elastic https://www.elastic.co/ GiST indexes https://www.postgresql.org/docs/current/gist.html GIN indexes https://www.postgresql.org/docs/current/gin.html btree_gist https://www.postgresql.org/docs/current/btree-gist.html btree_gin https://www.postgresql.org/docs/current/btree-gin.html pg_trgrm https://www.postgresql.org/docs/current/pgtrgm.html Text Search Types (tsvector and tsquery) https://www.postgresql.org/docs/current/datatype-textsearch.html Postgres full text search with the “websearch” syntax (blog post by Adam Johnson) https://adamj.eu/tech/2024/01/03/postgresql-full-text-search-websearch/Understanding Postgres GIN Indexes: The Good and the Bad (blog post by Lukas Fittl) https://pganalyze.com/blog/gin-index ParadeDB https://www.paradedb.com/ ZomboDB https://www.zombodb.com/ Introduction to Information Retrieval (book by Manning, Raghavan, and Schütze) https://www.amazon.co.uk/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719 How to build a search engine with Ruby on Rails (blog post by Justin Searls) https://blog.testdouble.com/posts/2021-09-09-how-to-build-a-search-engine-with-ruby-on-rails/~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is brought to you by:Nikolay Samokhvalov, founder of Postgres.aiMichael Christofides, founder of pgMustardWith special thanks to:Jessie Draws for the amazing artwork 

OpenAI is set to release GPT-5, a language model that could revolutionize the AI world by calling on other AI agents to help with tasks. The European Conference on Information Retrieval is taking place in Glasgow, with a focus on ethical issues in information retrieval technologies. Three AI research papers were discussed, including a new approach to generating high-quality videos more efficiently, a framework for simplifying video editing, and a novel mesh-based representation for real-time rendering and editing of complex 3D effects. The episode also included some fun banter and jokes, making for an entertaining and informative listen. Contact:  sergi@earkind.com Timestamps: 00:34 Introduction 01:25 OpenAI Might Launch GPT-5 Soon 03:00 European Conference on Information Retrieval This Week in Glasgow 05:03 Scaling vector search using Cohere binary embeddings and Vespa 06:20 Fake sponsor 08:42 Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition 10:14 AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks 11:49 Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering 13:28 Outro

Daniel Svonava is the Co-Founder of Superlinked. Daniel Svonava attended the Faculty of Informatics and Information Technologies, Slovak University of Technology. MLOps podcast #214 with Daniel Svonava, CEO & Co-founder at Superlinked, Information Retrieval & Relevance: Vector Embeddings for Semantic Search // Abstract In today's information-rich world, the ability to retrieve relevant information effectively is essential. This lecture explores the transformative power of vector embeddings, revolutionizing information retrieval by capturing semantic meaning and context. We'll delve into: - The fundamental concepts of vector embeddings and their role in semantic search - Techniques for creating meaningful vector representations of text and data - Algorithmic approaches for efficient vector similarity search and retrieval - Practical strategies for applying vector embeddings in information retrieval systems // Bio Daniel is an entrepreneurial technologist with a 20 year career starting with competitive programming and web development in highschool, algorithm research and Google & IBM Research internships during university, first entrepreneurial steps with his own computational photography startup and a 6 year tenure as a tech lead for ML infrastructure at YouTube Ads, where his ad performance forecasting engine powers the purchase of $10B of ads per year. Presently, Daniel is a co-founder of Superlinked.com - a ML infrastructure startup that makes it easier to build information-retrieval heavy systems - from Recommender Engines to Enterprise-focused LLM apps. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️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 Daniel on LinkedIn: https://www.linkedin.com/in/svonava/?originalSubdomain=ch

This is an interview for the Crazy Wisdom Podcast, where Stewart Alsop interviews AI strategist, Christian Ulstrup. Ullstrup shares his perspective on the progression and usage of Artificial Intelligence in businesses. He talks about the power of goal-setting in an organizational structure and highlights his belief towards goals being discovered rather than set. He further discloses his method to compile and make sense of huge amounts of proprietary information through Large Language Models (LLMs). They discuss the potential of AI memory, handling misinformation, and the rise of open-source AI models. The two also briefly touch upon the ideas of 'alignment' within a biological lens and its potential connection to AI, touching upon the philosophies of Pierre Teilhard de Chardin. Timestamps 00:00 Introduction to the Crazy Wisdom Podcast 00:39 Guest Introduction: Christian Ulstrup, AI Strategist 00:54 Exploring the Latest in AI and its Impact 01:13 The Role of Social Media in Information Dissemination 02:07 Deep Dive into AI Alignment and its Challenges 03:21 Exploring the Concept of Stress and its Contagious Nature 04:17 The Future of AI and its Potential Impact 05:06 The Role of Internet and Social Networks in Our Lives 05:57 The Fear Surrounding AI and its Future 06:20 The Concept of Effective Accelerationism and Deceleration 07:07 The Relationship Between Technology and Adolescents 08:02 The Importance of Goal Setting and its Impact 09:33 The Role of AI in the Future of Consciousness 16:33 The Emergence of AI and its Implications 24:51 The Role of Memory in AI and Human Interaction 27:24 The Importance of Reflection and Accurate Facts 28:20 Using Transcripts for Real-Time Analysis 28:54 The Power of AI in Real-Time Discussions 29:03 The Role of AI in Goal Setting and Decision Making 30:42 The Challenge of Misinformation in AI 32:13 The Role of AI in Knowledge Management 34:18 The Impact of AI on Memory and Information Retrieval 35:04 The Potential Dangers of AI and Disinformation 36:49 The Future of AI: Centralization vs Decentralization 47:58 The Role of Open Source in AI Development 54:04 How to Connect and Learn More 55:39 Closing Remarks Key Insights AI Strategy and Innovation: Christian emphasizes the importance of a thoughtful AI strategy that aligns with the broader goals of an organization. He discusses how AI can drive innovation by automating tasks, enhancing decision-making processes, and creating new opportunities for business growth. Ullstrup highlights the need for companies to stay abreast of AI advancements to maintain a competitive edge. Productivity and AI Tools: The discussion covers how AI tools can significantly boost productivity by streamlining workflows and reducing the cognitive load on individuals. Ullstrup shares insights into how AI can assist in goal setting and knowledge management, enabling people to focus on more creative and high-level tasks. Philosophy and AI Alignment: A significant part of the conversation is dedicated to the philosophical aspects of AI, particularly the ethical considerations of AI development and its alignment with human values. Christian talks about the challenges of ensuring AI systems act in ways that are beneficial to humanity and the complexities involved in defining and programming these values. Individual Freedom and Data Centralization: Ullstrup expresses concerns about data centralization and its implications for individual freedom in the digital age. He advocates for a more decentralized approach to data management, where individuals have greater control over their personal information. Limits of Computational Advancements: The episode touches upon the potential limits of computational advancements, questioning the inevitability of the singularity—a point where AI surpasses human intelligence in all aspects. Christian suggests a more nuanced view of technological progress, emphasizing the importance of understanding the limitations and ensuring responsible development. Enhancing Human Capabilities: A recurring theme is the potential for AI to not only automate tasks but also to enhance human capabilities. Christian discusses how AI can complement human intelligence, fostering a deeper understanding of complex systems and enabling us to tackle problems beyond our current capabilities. Skepticism Towards the Singularity: Ullstrup shares a healthy skepticism towards the concept of the singularity, cautioning against overestimating the pace of AI development and underestimating the complexities involved in creating truly autonomous, superintelligent systems. Societal and Philosophical Implications: Finally, the episode explores the broader societal and philosophical implications of AI. It discusses how AI can transform our understanding of ourselves and the world, posing both opportunities and challenges that require thoughtful consideration and dialogue.

Integration and the Intersection of Ancient Wisdom and Future Technology: A Deep Dive In this interactive session, Kole delves into the fascinating world of ancient technology and AI, referring to this fusion as 'sacred as plant medicine'. Kole shares personal experiences and 'aha' moments utilizing AI, specifically ChatGPT, and emphasizes its potential when approached thoughtfully. The discussion examines how AI can enhance the overall experience of personal growth, coaching, data retrieval, and more. Further, Kole demonstrates how to integrate human design coaching and AI to provide personalized and intuitive growth strategies for clients. The session then outlines an intake process and emphasizes the need for setting an intention before any particular activity using AI. The episode also explores the fears associated with AI, providing reassurance that AI is a tool, not a replacement, and highlights the importance of embracing this technology for the efficient functioning of society in the future.   00:00 Introduction to the Intersection of Ancient Wisdom and AI 00:58 Understanding AI and Its Role in Our Lives 01:27 Personal Experiences with AI and Its Impact 04:18 The Transformative Encounter with AI 04:54 The Four Ins Process: Intake, Intention, In Space, and Integration 06:19 AI as a Valuable Tool for Data Analysis and Information Retrieval 07:32 Addressing Common Concerns about AI 09:46 Set and Setting: The Importance in AI and Psychedelic Spaces 19:22 Practical Application of AI: Creating an E-book 28:00 AI in Coaching: Client Intake and Agreement 35:36 Creating a Daily Journaling Process with AI 36:20 Understanding Authentic Self Through Signs 36:25 Customizing Approach to Individual Clients 36:41 Quickly Crafting a Plan with ChachiPT 37:08 Creating New Offers and Emails with Chat GPT 37:40 Authentic Connection vs Digital Prompt 38:15 Using AI for Affirmations and Meditations 38:47 The Importance of Learning AI for Coaches 39:03 Avoiding Financial Losses by Articulating Needs 39:52 The Benefits of Becoming a Condor Master Coach 40:11 Building Your Chat GPT to Be You 40:53 Managing Your Physical Health and Writing Letters 41:15 The Future of Condor Master Coach Program 42:08 The Importance of Learning Technology 42:31 The Value of Being a Condor Coach 42:52 Closing Remarks and Transition to Q&A

The 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2023) is set to make waves as it returns for another year, bringing AI to the forefront of the scientific community. Hosted by Atlantic Technological University (ATU) in collaboration with Ulster University, this year's conference will be held in person at the Letterkenny campus of ATU on the 7th & 8th of December 2023. The conference will explore how AI is developing and how it is used in an array of industries, including health and wellness in sport. It will also examine the ethical concerns often raised with Artificial Intelligence. Established in 1988, the AICS Conference has been a cornerstone of the Irish scientific landscape, uniting researchers in the fields of Artificial Intelligence and Cognitive Science. These fields have evolved to encompass diverse areas such as Data Analytics, Natural Language Processing, Information Retrieval, and Machine Learning, all of which play pivotal roles in shaping modern computing research and industry in Ireland. The AICS 2023 programme, supported by technical sponsorship from the Institute of Electrical and Electronics Engineers (IEEE) the UK and Ireland Computational Intelligence Chapter, promises to be a beacon of innovation, featuring presentations of high-quality theoretical and applied scientific papers. Keynote speakers include Salvatore Tedesco, a Senior Researcher and Team Leader at the Wireless Sensor Networks Group, Tyndall National Institute, UCC, Toju Duke, a passionate advocate for Responsible AI and the founder of Diverse AI, and Dr Róisín Loughran, Manager of the Regulated Software Research Centre at Dundalk Institute of Technology Salvatore will explore the realm of AI-based Smart Wearable Systems for Health and Wellness in Sports, Ageing, and Rehabilitation, while Toju will shed light on the crucial topic of "The Responsible AI Framework: Developing Impactful AI Systems." Meanwhile, Dr Loughran will examine the ethical side of AI in her address: "The Regularity and Ethical Concerns for AI in Critical and Non-Critical Domains". Registration for AICS2023 is now open, and the public can register to attend by visiting www.aics.ie

In episode 20 of Recsperts, we welcome Bram van den Akker, Senior Machine Learning Scientist at Booking.com. Bram's work focuses on bandit algorithms and counterfactual learning. He was one of the creators of the Practical Bandits tutorial at the World Wide Web conference. We talk about the role of bandit feedback in decision making systems and in specific for recommendations in the travel industry.In our interview, Bram elaborates on bandit feedback and how it is used in practice. We discuss off-policy- and on-policy-bandits, and we learn that counterfactual evaluation is right for selecting the best model candidates for downstream A/B-testing, but not a replacement. We hear more about the practical challenges of bandit feedback, for example the difference between model scores and propensities, the role of stochasticity or the nitty-gritty details of reward signals. Bram also shares with us the challenges of recommendations in the travel domain, where he points out the sparsity of signals or the feedback delay.At the end of the episode, we can both agree on a good example for a clickbait-heavy news service in our phones. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review (00:00) - Introduction (02:58) - About Bram van den Akker (09:16) - Motivation for Practical Bandits Tutorial (16:53) - Specifics and Challenges of Travel Recommendations (26:19) - Role of Bandit Feedback in Practice (49:13) - Motivation for Bandit Feedback (01:00:54) - Practical Start for Counterfactual Evaluation (01:06:33) - Role of Business Rules (01:11:26) - better cut this section coherently (01:17:48) - Rewards and More (01:32:45) - Closing Remarks Links from the Episode: Bram van den Akker on LinkedIn Practical Bandits: An Industry Perspective (Website) Practical Bandits: An Industry Perspective (Recording) Tutorial at The Web Conference 2020: Unbiased Learning to Rank: Counterfactual and Online Approaches Tutorial at RecSys 2021: Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances GitHub: Open Bandit Pipeline Papers: van den Akker et al. (2023): Practical Bandits: An Industry Perspective van den Akker et al. (2022): Extending Open Bandit Pipeline to Simulate Industry Challenges van den Akker et al. (2019): ViTOR: Learning to Rank Webpages Based on Visual Features General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

Summary Large language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Piero Molino about the application of declarative ML in a world being dominated by large language models Interview Introduction How did you get involved in machine learning? Can you start by summarizing your perspective on the effect that LLMs are having on the AI/ML industry? In a world where LLMs are being applied to a growing variety of use cases, what are the capabilities that they still lack? How does declarative ML help to address those shortcomings? The majority of current hype is about commercial models (e.g. GPT-4). Can you summarize the current state of the ecosystem for open source LLMs? For teams who are investing in ML/AI capabilities, what are the sources of platform risk for LLMs? What are the comparative benefits of using a declarative ML approach? What are the most interesting, innovative, or unexpected ways that you have seen LLMs used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on declarative ML in the age of LLMs? When is an LLM the wrong choice? What do you have planned for the future of declarative ML and Predibase? Contact Info LinkedIn (https://www.linkedin.com/in/pieromolino/?locale=en_US) Website (https://w4nderlu.st/) Closing Announcements Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast (https://www.dataengineeringpodcast.com) covers the latest on modern data management. Podcast.__init__ () covers the Python language, its community, and the innovative ways it is being used. Visit the site (https://www.themachinelearningpodcast.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@themachinelearningpodcast.com (mailto:hosts@themachinelearningpodcast.com)) with your story. To help other people find the show please leave a review on iTunes (https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243) and tell your friends and co-workers Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Links Predibase (https://predibase.com/) Podcast Episode (https://www.themachinelearningpodcast.com/predibase-declarative-machine-learning-episode-4) Ludwig (https://ludwig.ai/latest/) Podcast.__init__ Episode (https://www.pythonpodcast.com/ludwig-horovod-distributed-declarative-deep-learning-episode-341/) Recommender Systems (https://en.wikipedia.org/wiki/Recommender_system) Information Retrieval (https://en.wikipedia.org/wiki/Information_retrieval) Vector Database (https://thenewstack.io/what-is-a-real-vector-database/) Transformer Model (https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)) BERT (https://en.wikipedia.org/wiki/BERT_(language_model)) Context Windows (https://www.linkedin.com/pulse/whats-context-window-anyway-caitie-doogan-phd/) LLAMA (https://en.wikipedia.org/wiki/LLaMA) 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/)

Ever wonder why the world of SEO feels like shifting sands beneath your feet? One moment, you think you’ve cracked the code, and the next, you’re back to square one. That’s precisely where I often find myself. Staring at my screen, bewildered by fluctuating website rankings, I usually think: “There has to be more to…

Guest: Eric Olson, Co-Founder & CEO at Consensus.app [@ConsensusNLP]On LinkedIn | https://www.linkedin.com/in/eric-olson-1822a7a6/On Twitter | https://twitter.com/IPlayedD1_____________________________Host: Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli_____________________________This Episode's SponsorsBlackCloak

Discover how to boost your social skills and achieve social fluency using the Checkers and Browsers method, a game-changing approach based on the study by Thomas, McDuff, Czerwinski, and Craswell. Learn actionable strategies to enhance your communication skills and transform every conversation into a meaningful connection. ✨Join Explearning Academy for only $10/month : https://rb.gy/z4l9i

Discover how to boost your social skills and achieve social fluency using the Checkers and Browsers method, a game-changing approach based on the study by Thomas, McDuff, Czerwinski, and Craswell. Learn actionable strategies to enhance your communication skills and transform every conversation into a meaningful connection. ✨Join Explearning Academy for only $10/month : ⁠https://rb.gy/z4l9i ⁠

Wie oft sucht man etwas am Tag im Internet oder in Datenbanken? Auch wenn es da keine genauen Zahlen gibt, ist die Antwort auf jeden Fall: oft. Information Retrieval, also die Informationsrückgewinnung, sorgt dafür, dass wir die Informationen in großen, komplexen Datensammlungen überhaupt wiederfinden. Aber wie funktioniert das?In dieser Folge spricht Jacqueline Klusik-Eckert mit Dr. Lisa Dieckmann und Dr. Jürgen Hermes über das interdisziplinäre Fachgebiet Information Retrieval. Ist es nicht ein Problem, dass wir in einer Bildwissenschaft wie der Kunstgeschichte mit Texten suchen? Und woher weiß ich, ob ich alles gefunden habe, was in der Datensammlung drin steckt? Dr. Lisa Diekmann ist promovierte Kunsthistorikerin und Softwareentwicklerin und sorgt mit dem prometheus-Team dafür, dass wir nun schon seit 20 Jahren im prometheus-bildarchiv mit einer Suchanfrage in aktuell 121 Datensammunlungen suchen können. Dr. Jürgen Hermes ist Geschäftsführer des Instituts für Digital Humanities der Universität zu Köln und kennt sich mit sprachlicher Informationsverarbeitung und Suchmaschinen wie Google aus.Gemeinsam möchten wir herausfinden, was hinter dem Suchfeld passiert, warum man manchmal enttäuscht von den Ergebnissen ist und wie Suchmaschinen bei der Suche helfen, ohne dass man es merkt. Wir stellen auch die Frage, was eigentlich Relevanz ist und wie den unterschiedlichen Erwartungshaltungen von Benutzer*innen entgegenkommt.Begleitmaterial zu den Folgen findest du auf der Homepage unter https://www.arthistoricum.net/themen/podcasts/arthistocastAlle Folgen des Podcasts werden bei heidICON mit Metadaten und persistentem Identifier gespeichert. Die Folgen haben die Creative-Commons-Lizenz CC BY 4.0 und können heruntergeladen werden. Du findest sie unterhttps://doi.org/10.11588/heidicon/1747388

Jim Carter is a technology coach with over 25 years of experience advising social impact organizations, brands, and experts on maximizing growth using content and technology. We discuss the myths surrounding AI and how it can be harnessed to save time and make a difference in one's life and business. He emphasizes that what we do with AI is crucial and warns against copying and pasting chunks of information and republishing it. We explore the limitless possibilities of AI and its potential to do good in various fields, including medicine. Jim encourages entrepreneurs to lean in and remove the biggest barriers that keep them from taking action. In this episode [00:02:11] Social good and creative use of technology [00:05:13] Jim's journey to finding authentic impact [00:09:41] Myths about AI [00:15:16] Ethics and Jobs [00:16:14] AI and Information Retrieval [00:19:43] AI and Creative Production [00:24:14] AI in Medicine [00:28:19] AI and Job Security [00:31:47] Starting a Business with AI [00:32:34] The Power of AI [00:36:10] Success Stories [00:39:13] Ways to Learn More [00:40:52] Jim's offer to help entrepreneurs [00:42:36] Be willing to lean in Related episodes The Most Underrated Skill For Navigating Challenges in Business and Life Unlocking Limitless Potential: The Power of the Subconscious Mind in Business These 5 Lies Are Holding You Back from Success Connect https://jimcarter.me/call - free call with Jim https://fastfoundations.com - fast foundations mastermind https://fastfoundations.com/chatgpt - free chatgpt training with prompts Connect with Corene on IG @corene.phelps *join ResilienceRx starting May 23 *Rather work one on, one Book a consultation call

Anthropic's Claude introduces 100k tokens context windows for businesses to analyze complex documents quickly, while Huggingface releases Transformers Agents, an experimental API for natural language processing and task completion. Stability AI also releases Stable Animation SDK, a powerful text-to-animation tool for artists and developers. Additionally, researchers propose new AI models and frameworks, including Federated Instruction Tuning, Evaluating Embedding APIs for Information Retrieval, and Pretraining Without Attention. Contact: sergi@earkind.com Timestamps: 00:34 Introduction 01:26 Anthropic's Claude introduces 100k tokens context Windows 02:54 Huggingface Releases Transformers Agents 04:10 Stability AI releases Stable Animation SDK, a powerful text-to-animation tool for developers 05:39 Microsoft makes strategic investment into Builder.ai, integrates its services into Teams 07:16 Fake sponsor: SlimDown 09:19 Towards Building the Federated GPT: Federated Instruction Tuning 10:52 Evaluating Embedding APIs for Information Retrieval 12:24 Pretraining Without Attention 14:00 Outro

In this podcast episode, you will learn:how Meinard Müller became a professor for Music Information Retrieval at AudioLabs in Erlangen,what are AudioLabs and how they relate to Fraunhofer IIS and the University of Erlangen-Nürnberg,how professor Müller approaches doing teaching and research in his research group,how to learn doing research and how to collaborate with your supervisor (for master thesis, PhD thesis, or other research work),how to mentor your students,what is the book “Fundamentals of Music Processing” about and how did the process of writing it look,how to tackle huge projects,what is a professor's day-to-day life like,what is music information retrieval and how did the AI/deep learning revolution influence it.

In this YouTube podcast, we explore three essential apps that every Macbook user should be utilizing for optimal productivity. We dive into the functionalities of Sticky Notes, Spotlight, and Siri, and how they can revolutionize your Macbook experience. With Sticky Notes, you can quickly jot down ideas, reminders, and to-do lists to keep yourself organized. Spotlight, the powerful search tool, allows you to find files, apps, and information on your Macbook with lightning speed. And Siri, the virtual assistant, can perform tasks, answer questions, and save you time with voice commands. Join us as we discuss these must-use apps and uncover how they can enhance your Macbook workflow to new heights. Don't miss out on these game-changing apps for Macbook users!

In this episode of Neural Search Talks, Andrew Yates (Assistant Prof at the University of Amsterdam) Sergi Castella (Analyst at Zeta Alpha), and Gabriel Bénédict (PhD student at the University of Amsterdam) discuss the prospect of using GPT-like models as a replacement for conventional search engines. Generative Information Retrieval (Gen IR) SIGIR Workshop Workshop organized by Gabriel Bénédict, Ruqing Zhang, and Donald Metzler https://coda.io/@sigir/gen-ir Resources on Gen IR: https://github.com/gabriben/awesome-generative-information-retrieval References Rethinking Search: https://arxiv.org/abs/2105.02274 Survey on Augmented Language Models: https://arxiv.org/abs/2302.07842 Differentiable Search Index: https://arxiv.org/abs/2202.06991 Recommender Systems with Generative Retrieval: https://shashankrajput.github.io/Generative.pdf Timestamps: 00:00 Introduction, ChatGPT Plugins 02:01 ChatGPT plugins, LangChain 04:37 What is even Information Retrieval? 06:14 Index-centric vs. model-centric Retrieval 12:22 Generative Information Retrieval (Gen IR) 21:34 Gen IR emerging applications 24:19 How Retrieval Augmented LMs incorporate external knowledge 29:19 What is hallucination? 35:04 Factuality and Faithfulness 41:04 Evaluating generation of Language Models 47:44 Do we even need to "measure" performance? 54:07 How would you evaluate Bing's Sydney? 57:22 Will language models take over commercial search? 1:01:44 NLP academic research in the times of GPT-4 1:06:59 Outro

In episode 103, Neeva Co-Founder & CEO, Sridhar Ramaswamy sits down with Mike King to explain the inner workings of search engines fueled by AI technology. Sridhar shares thoughts on SEO in relation to LLM search as well as the recent petition from Elon Musk and others to halt AI development. Mike and Sridhar also discuss the difference in Neeva's approach to AI and why it may be difficult for others to replicate.(0:00) Intro(1:42) How Can Neeva Compete with Google?(5:53) Will Answer Based Search Replace Traditional Search?(12:25) How Neeva is leveraging AI to build a better search engine(17:01) Did Dense Embeddings Change Everything for Information Retrieval?(19:09) Google's Techniques for Information Retrieval(20:45) Neeva's Difference in Approach to AI - Retrieval Augmented Generation(24:34) Why is Closed Loop So Difficult?(28:00) What Place Does SEO Have in the LLM Search Environment(31:16) Creating a Feedback Loop for Users(32:40) The Petition to Halt AI Development (35:35) What is Gist? (38:15) How Neeva makes your life better

Marzieh Fadaee — NLP Research Lead at Zeta Alpha — joins Andrew Yates and Sergi Castella to chat about her work in using large Language Models like GPT-3 to generate domain-specific training data for retrieval models with little-to-no human input. The two papers discussed are "InPars: Data Augmentation for Information Retrieval using Large Language Models" and "Promptagator: Few-shot Dense Retrieval From 8 Examples". InPars: https://arxiv.org/abs/2202.05144 Promptagator: https://arxiv.org/abs/2209.11755 Timestamps: 00:00 Introduction 02:00 Background and journey of Marzieh Fadaee 03:10 Challenges of leveraging Large LMs in Information Retrieval 05:20 InPars, motivation and method 14:30 Vanilla vs GBQ prompting 24:40 Evaluation and Benchmark 26:30 Baselines 27:40 Main results and takeaways (Table 1, InPars) 35:40 Ablations: prompting, in-domain vs. MSMARCO input documents 40:40 Promptagator overview and main differences with InPars 48:40 Retriever training and filtering in Promptagator 54:37 Main Results (Table 2, Promptagator) 1:02:30 Ablations on consistency filtering (Figure 2, Promptagator) 1:07:39 Is this the magic black-box pipeline for neural retrieval on any documents 1:11:14 Limitations of using LMs for synthetic data 1:13:00 Future directions for this line of research

Summary Using machine learning in production requires a sophisticated set of cooperating technologies. A majority of resources that are available for understanding how to design and operate these platforms are focused on either simple examples that don’t scale, or over-engineered technologies designed for the massive scale of big tech companies. In this episode Jacopo Tagliabue shares his vision for "ML at reasonable scale" and how you can adopt these patterns for building your own platforms. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Your host is Tobias Macey and today I’m interviewing Jacopo Tagliabue about building "reasonable scale" ML systems Interview Introduction How did you get involved in machine learning? How would you describe the current state of the ecosystem for ML practitioners? (e.g. tool selection, availability of information/tutorials, etc.) What are some of the notable changes that you have seen over the past 2 – 5 years? How have the evolutions in the data engineering space been reflected in/influenced the way that ML is being done? What are the challenges/points of friction that ML practitioners have to contend with when trying to get a model into production that isn’t just a toy? You wrote a set of tutorials and accompanying code about performing ML at "reasonable scale". What are you aiming to represent with that phrasing? There is a paradox of choice for any newcomer to ML. What are some of the key capabilities that practitioners should use in their decision rubric when designing a "reasonable scale" system? What are some of the common bottlenecks that crop up when moving from an initial test implementation to a scalable deployment that is serving customer traffic? How much of an impact does the type of ML problem being addressed have on the deployment and scalability elements of the system design? (e.g. NLP vs. computer vision vs. recommender system, etc.) What are some of the misleading pieces of advice that you have seen from "big tech" tutorials about how to do ML that are unnecessary when running at smaller scales? You also spend some time discussing the benefits of a "NoOps" approach to ML deployment. At what point do operations/infrastructure engineers need to get involved? What are the operational aspects of ML applications that infrastructure engineers working in product teams might be unprepared for? What are the most interesting, innovative, or unexpected system designs that you have seen for moderate scale MLOps? What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML system design and implementation? What are the aspects of ML systems design that you are paying attention to in the current ecosystem? What advice do you have for additional references or research that ML practitioners would benefit from when designing their own production systems? Contact Info jacopotagliabue on GitHub Website LinkedIn 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. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site 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@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links The Post-Modern Stack: ML At Reasonable Scale Coveo NLP == Natural Language Processing RecList Part of speech tagging Markov Model YDNABB (You Don’t Need A Bigger Boat) dbt Data Engineering Podcast Episode Seldon Metaflow Podcast.__init__ Episode Snowflake Information Retrieval Modern Data Stack SQLite Spark SQL AWS Athena Keras PyTorch Luigi Airflow Flask AWS Fargate AWS Sagemaker Recommendations At Reasonable Scale Pinecone Data Engineering Podcast Episode Redis KNN == K-Nearest Neighbors Pinterest Engineering Blog Materialize OpenAI The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

How much of the training and test sets in TREC or MS Marco overlap? Can we evaluate on different splits of the data to isolate the extrapolation performance? In this episode of Neural Information Retrieval Talks, Andrew Yates and Sergi Castella i Sapé discuss the paper "Evaluating Extrapolation Performance of Dense Retrieval" byJingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma.

Topics:00:00 Kick-off by Judy Zhu01:33 Introduction by Dmitry Kan and his bio!03:03 Daniel's background04:46 “Science is the difference between instinct and strategy” 07:41 Search as a personal learning experience11:53 Why do we need Machine Learning in Search, or can we use manually curated features?16:47 Swimming up-stream from relevancy: query / content understanding and where to start?23:49 Rule-based vs Machine Learning approaches to Query Understanding: Pareto principle29:05 How content understanding can significantly improve your search engine experience32:02 Available datasets, tools and algorithms to train models for content understanding38:20 Daniel's take on the role of vector search in modern search engine design as the path to language of users45:17 Mystical question of WHY: what drives Daniel in the search space today49:50 Announcements from Daniel51:15 Questions from the audienceShow notes:[What is Content Understanding?. Content understanding is the foundation… | by Daniel Tunkelang | Content Understanding | Medium](https://medium.com/content-understanding/what-is-content-understanding-4da20e925974)[Query Understanding: An Introduction | by Daniel Tunkelang | Query Understanding](https://queryunderstanding.com/introduction-c98740502103)Science as Strategy [YouTube](https://www.youtube.com/watch?v=dftt6Yqgnuw)Search Fundamentals course - https://corise.com/course/search-fundamentalsSearch with ML course - https://corise.com/course/search-with-machine-learningBooks:Faceted Search, by Daniel Tunkelang: https://www.amazon.com/Synthesis-Lectures-Information-Concepts-Retrieval/dp/1598299999Modern Information Retrieval: The Concepts and Technology Behind Search, by Ricardo Baeza-Yates: https://www.amazon.com/Modern-Information-Retrieval-Concepts-Technology/dp/0321416910/ref=sr11?qid=1653144684&refinements=p_27%3ARicardo+Baeza-Yates&s=books&sr=1-1Introduction to Information Retrieval, by Chris Manning: https://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr1fkmr0_1?crid=2GIR19OTZ8QFJ&keywords=chris+manning+information+retrieval&qid=1653144967&s=books&sprefix=chris+manning+information+retrieval%2Cstripbooks-intl-ship%2C141&sr=1-1-fkmr0Query Understanding for Search Engines, by Yi Chang and Hongbo Deng: https://www.amazon.com/Understanding-Search-Engines-Information-Retrieval/dp/3030583333

Episode 35: Interview with Ricardo Baeza-Yates, Professor at Northeastern University (Silicon Valley). ACM Fellow and IEEE Fellow. Best-selling textbook author. Startup founder, Former Lead of Multiple Yahoo! Labs, and Faculty in Chile. Has lived and worked on 4 continents (S and N America, Europe, Asia). Grew up in Chile.

MLOps Coffee Sessions #95 with Ciro Greco, MLOps as Tool to Shape Team and Culture. // Abstract Good MLOps practices are a way to operationalize a more “vertical” practice and blur the boundaries between different stages of “production-ready”. Sometimes you have this idea that production-ready means global availability but with ML products that need to be constantly tested against real-world data, we believe production-ready should be a continuum and that the key person that drives that needs to be the data scientist or the ML engineer. // Bio Ciro Greco, VP of AI at Coveo. Ph.D. in Linguistics and Cognitive Neuroscience at Milano-Bicocca. Ciro worked as visiting scholar at MIT and as a post-doctoral fellow at Ghent University. In 2017, Ciro founded Tooso.ai, a San Francisco-based startup specializing in Information Retrieval and Natural Language Processing. Tooso was acquired by Coveo in 2019. Since then Ciro has been helping Coveo with DataOps and MLOps throughout the turbulent road to IPO. // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links Company Website psicologia.unimib.it/03_persone/scheda_personale.php?personId=518 gist.ugent.be/members --------------- ✌️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 Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Ciro on LinkedIn: https://www.linkedin.com/in/cirogreco/en Timestamps: [00:00] Introduction to Ciro Greco [02:32] Ciro's bridge to Coveo [07:15] Coveo in a nutshell [11:30] Confronting disorganization and challenges [16:08] Fundamentals of use cases [18:09] Immutable data in the data warehouse [21:36] Data management in Coveo [24:48] Pain for advancement [29:56] Rational process and Stack [32:24] Habits of high-performing ML Engineers [35:46] Sharpening the sword [37:50] Attracting talents vs firing people [42:18] Wrap up

On this week’s episode of Would You Data Scientist, Wendy is joined by guest Shuai Yuan, Director of Data Science at FreeWheel. Shuai manages a data science team working on optimization problems from the side of the buyer. Join the discussion on why you should get into data science, how you can get into it, and what different avenues the field offers. As always there will be chat on ethics and what you can do to impress recruiters. KEY TAKEAWAYS Shuai’s career got going while studying a PHD in Information Retrieval, studying sponsored searches, display advertising, and real time bidding for search engines. Shuai believes there are three avenues within data science that can cover a lot of roles. They are machinery researcher, machinery engineer, and data analyst. FreeWheel recently hired someone without any full-time work experience in data science. She was able to demonstrate her skills by showing a personal work portfolio she had built in her own time. Even though the applicant hadn’t had any actual experience, she had ticked that box off her own back. There is a war online for the user's attention, websites are bombarding people with ads to try and get noticed. The problem with this is that the user's attention is finite and they get tired of seeing large numbers of ads. The best way to be seen is to realise that it can be about quality, rather than quantity. BEST MOMENTS ‘Python is such a lovely langue and enables so many things’ ‘Everyone is trying to fight for the user's attention, the user's attention is a limited resource’ ‘We need to encourage people from different backgrounds to apply so we have more choice’ EPISODE RESOURCES www.freewheel.com ABOUT THE GUESTShuai Yuan is the Director of Data Science at FreeWheel. You can connect with him on LinkedIn using the link below. https://www.linkedin.com/in/yuanshuai VALUABLE RESOURCES linkedin.com/in/ethicalrecruiter qwerkrecruitment.com @qwerkrec on socials ABOUT THE HOST Wendy Gannon started Qwerk Recruitment in the middle of the Covid Pandemic to disrupt the recruitment sector with ETHICAL data recruitment and treat everyone with love, dignity and respect. Avid Music photographer of 15 years. PODCAST DESCRIPTION Interviewing successful Data Scientists to find out about their journey into Data Science and any struggles they’ve faced to help next generation Data Scientists from all walks of life to access their dream career. See omnystudio.com/listener for privacy information.

In this week's episode, we talk with Dawn Anderson, Managing Director at Bertey, International Technical SEO Consultant and Speaker.We talk about how she went from owning a construction company to transitioning into web development, digital marketing, and then becoming a self-taught SEO. We talk about what it's like to go through the challenge of initially learning SEO when there are so many grey areas and opinions, and how she learned SEO from an Information Architect,.Additionally, Dawn shares her thoughts on traditional education as it relates to digital marketing preparedness, why she started her own company, and her passion for information retrieval and indexing which has led her to get her Masters in the field of Computer Science, AI and Machine Learning.For our core topic, we discuss Google's BERT and how it incorporates natural language understanding, AI, machine learning, and just what – if anything – you can do to optimize for it (spoiler: it's not simple to optimize for).Finally, we answer Twitter questions of the week and award some more Page 2 Podcast swag.So get your popcorn ready as we tell Dawn's SEO story and have another great roundtable discussion.

In this episode of the Data Exchange I speak with Edo Liberty, founder of Hypercube, a startup building tools for deploying deep learning models in search and information retrieval involving large collections. When I spoke at AI Week in Tel Aviv last November several friends encouraged me to learn more about Hypercube - I'm glad I took their advice!Our conversation covered several topics including:Edo's experience applying machine learning and building tools for ML at places like Yale, Yahoo's Research Lab in New York, and Amazon's AI Lab.How deep learning is being used in search and information retrieval.Challenges one faces in building search and information retrieval applications when the size of collections are large.Deep learning based search and information retrieval and what Edo describes as “enterprise end-to-end deep search platforms”.Detailed show notes can be found on The Data Exchange web site.