Podcasts about Unstructured data

You think using AI is your moat? Nope. Just using LLMs isn't enough to power your company's AI success. But do you know the real fuel? Having your data right is the ACTUAL key. So how do you do it? And how does your company's data strategy change with agentic AI? Find out from Deloitte's US Chief Data Analytics Officer, Ashish Verma.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Have a question? Join the convo here.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Transformative Data Strategy for AI SuccessImportance of Data Strategy in AIDeloitte's Data Marketplace ApproachMulti-Agent Orchestration ChallengesStructured vs. Unstructured Data in AISynthetic Data and AI TransformationAgentic AI and Data Labeling EssentialsAI's Impact on Business Value ChainTimestamps:00:00 "AI Success Requires Data Strategy"05:27 Data Integration and Utilization Insights10:31 Contextual Data Marketplace Evolution13:06 Structuring Unstructured AI Insights17:02 Agent Reasoning and Orchestration Insights20:37 Data Annotation Challenges23:39 AI's Impact on Industry Evolution26:09 "Data Strategy: Begin with the End"Keywords:transformative data strategy, AI success, generative AI, non-technical people, data teams, data strategy, business leaders, companies, careers, unedited podcast, livestream, Deloitte, US chief data and analytics officer, data analytics, GenAI, data experiments, third-party data, synthetic data, data marketplace, data concierge, chief data officer, compute environment, deterministic, probabilistic, AI transformation, digital transformation, data minder, CFO, CMO, public domain data, business partner data, metadata, business glossary, technical catalog, agentic AI, multi-agent orchestration, agent registry, agent orchestration, open standard protocols, economic AI, digital transformation strategy, data advantages, structured data, unstructured data, hybrid data, PowerPoint, staffing optimization, resource management, query engine, relevance-ranked search, annotation, data regulation, governance, data procurement, data curation, data feeds, data platforms, information indexing, future predictions.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Try Google Veo 3 today! Sign up at gemini.google to get started. Try Google Veo 3 today! Sign up at gemini.google to get started.

June 6, 2025: Timothy Boettcher, SVP, Head of GTM Strategy North America for AvePoint, discusses the often-overlooked sensitive information spread across Microsoft 365 environments, particularly as departments independently adopt AI tools. Drawing from his experience across Australia, Japan, and the US, Timothy examines why information governance challenges continue to be the primary barrier to AI implementation and explores the balance between clinical innovation and maintaining necessary security standards. What does effective data governance look like when anyone can now create automated workflows through simple AI interactions, and how do organizations turn governance from an overwhelming challenge into a strategic foundation for their AI initiatives? The conversation is just beginning! Join the “Building Responsible AI in Healthcare” Webinar on June 12th for a deep dive into better implementation: https://carahevents.carahsoft.com/Event/Details/618554-thisweekhealth Key Points: 02:49 Introducing AvePoint 05:07 Chronic Data Problems 16:23 Change Management and Adoption X: This Week Health LinkedIn: This Week Health Donate: Alex's Lemonade Stand: Foundation for Childhood Cancer

Instabase founder and CEO Anant Bhardwaj joins a16z Infra partner Guido Appenzeller to discuss the revolutionary impact of LLMs on analyzing unstructured data and documents (like letting banks verify identity and approve loans via WhatsApp) and shares his vision for how AI agents could take things even further (by automating actions based on those documents). In more detail, they discuss:Why legacy robotic process automation (RPA) struggles with unstructured inputs.How Instabase developed layout-aware models to extract insights from PDFs and complex documents.Why predictability, not perfection, is the key metric for generative AI in the enterprise.The growing role of AI agents at compile time (not runtime).A vision for decentralized, federated AI systems that scale automation across complex workflows.Follow everyone on X:Anant BhardwajGuido Appenzeller Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

It's wild how much information businesses collect, only to let it sit there gathering digital dust. Reports, feedback, emails—tons of it just float around, never really touched or understood. And while the numbers get all the attention, the actual words people use—their thoughts, complaints, ideas—stay locked away in messy files and forgotten databases. It's like having a map to hidden treasure but refusing to read it because it's not in the format you're used to. Meanwhile, big decisions get made with only half the picture in view. Bill Inmon, once a pro golfer, is now a legend in the tech world for his work in data and AI. He created Datavox, a platform that turns messy, unstructured text into structured, usable insights—solving a problem that plagues nearly every industry. From healthcare to fraud detection, his tech helps uncover what's hidden in the 80% of data companies usually ignore. Today, Bill breaks down how this innovation is already reshaping medical research and business intelligence. His shift from golf to groundbreaking tech proves that real innovation often comes from unexpected places. Stay tuned! Resources: What is your data telling you? Datavox allows you to measure and monetize this remaining 80%. Follow Bill Inmon on Facebook Connect with Bill Inmon on LinkedIn

In this episode of Alter Everything, we chat with Eric Soden and JT Morris from Alteryx partner Capitalize about the practical applications and limitations of generative AI. They discuss ideal use cases for large language models, the importance of balancing generative AI with traditional analytics techniques, and strategies for scaling AI capabilities in enterprise environments. Eric and JT also share real-world examples and insights into achieving productivity gains and ROI with generative AI, along with the importance of maintaining data quality and explicability in AI processes.Panelists: JT Morris, Senior Manager, Advanced Analytics Practice Lead @ Capitalize, @JTMorris, LinkedInEric Soden, Co-founder and Managing Partner @ Capitalize, @esoden, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Capitalize AnalyticsAlteryx Partners - Solution ProvidersEric's LinkedIn posts on Gen AI + AlteryxCapitalize Webinar: Alteryx +GenAI: 5 Real-World Use Cases Explained Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

In this episode of Alter Everything, we chat with Patrick Leung, CTO of Faro Health, about the innovative ways his company is using generative AI to revolutionize the clinical trial process. They discuss handling unstructured data, insights generated by AI to optimize trials, and significant cost savings in the pharmaceutical space! Not sure what a clinical trial is? Fear not! Patrick explains the structure of clinical trials, Faro Health's AI-driven enhancements in medical writing, and the importance of data extraction and analysis.Panelists: Patrick Leung, Chief Technical Officer @ Faro Health - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Faro HealthAlteryx's Generative AI for AnalyticsClinicaltrials.gov Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

In this episode of The Future of ERP, Gaye Bingul, SAP Insurance Industry Lead, and Javier Gil, Head of Global Insurance & Reinsurance at SAP, discuss how the future of insurance relies on AI-powered ERP systems that unify all types of data-structured, unstructured, SAP, and non-SAP-to unlock deeper insights into customers and climate risks. They highlight key challenges insurers face, including complex global data privacy regulations, the growing aging population (“silver segment”), and a widening skills gap due to retiring experts and difficulty attracting new tech talent. Gaye and Javier emphasize how insurers can leverage connected devices, wearables, and AI-driven analytics to offer personalized health and wellness services that help the aging population stay healthy and independent while driving innovation and growth. Listeners gain insight into why digital transformation and advanced ERP solutions are critical for navigating regulatory demands and demographic shifts in the insurance industry.

Subscribe at Thisnewway.com to get the step-by-step AI workflows.In episode 3 of This New Way, Aydin sits down with Philippe Dame, CPO of Recollective, to unpack how AI is transforming qualitative market-research, sales operations, internal knowledge-sharing, and day-to-day productivity.Phil walks through the product pivot that turned Recollective's vast unstructured data into an “AI-first” insights engine, then demos a stack of no-code automations — from AI-powered lead scoring in Google Sheets to a self-serve Vertex AI search that makes every doc, deck, and Gong call instantly searchable.You'll hear concrete play-by-plays, see cost breakdowns , and learn how to start small, scale fast, and turn AI into a force-multiplier across your org.Timestamps:00:35 Phil's background and early web-tool days01:44 Pivot to Recollective and focus on market-research tech03:18 Why hybrid qual + quant research matters03:58 Unstructured data → perfect AI playground04:50 “Roadmap reset” after ChatGPT pressure06:00 How the team up-skilled on LLMs & vector DBs08:12 First AI features: summarization → theme extraction → comparisons09:05 Making space for AI work without derailing commitments10:10 Company-wide AI wins: sales data mining & lead scoring13:08 Relay + Google Sheets workflow for automated enrichment16:18 Running internal “AI office hours” to drive adoption17:05 Staying current: newsletters, trials, and cost control20:25 Seat-based vs. usage-based pricing—Phil's take23:04 Perplexity as Phil's go-to research sidekick24:50 Cutting the “collaboration tax” with self-serve AI answers27:24 Live demo: Recollective's Ask-AI tab & verbatim citations31:28 Segment @mentions for instant comparative analysis37:04 Emotion tagging and drilling into negative feedback38:30 Building an internal Vertex-AI search in one afternoon42:23 Agent Builder setup walkthrough44:34 Relay use-cases: lead workflows, news scraping, stand-up bot53:23 n8n migrations: 4,000 Gong calls plus on-the-fly analysis56:30 OpenAI Playground & Notebook LM for ad-hoc knowledgebases1:02:20 Google AI Studio multimodal experiments (free)1:04:04 Start simple—one use-case, one stakeholder, iterateTools & Resources Mentioned:AI / LLMs & Model-PlaygroundsChatGPT (OpenAI)OpenAI Platform & PlaygroundClaude (Anthropic)Perplexity Pro- Google Gemini (Workspace)Google AI StudioKnowledgeBase & Search Vertex AI Agent Builder / Search (Google Cloud) Notebook LM (Google) Internal Vertex-powered “ask-AI” portalAutomation & WorkflowsRelay (no-code workflow tool with AI steps)Zapier (reference point)n8n (open-source automation + AI agents)Data, CRM & Sales EnablementGoogle SheetsSalesforceOutreachGong (call recordings)Collaboration & ProductivitySlack (AI channel, human-in-the-loop flows)Notion (company intranet)Google Drive & Google CalendarRSS feeds + custom “Article Extractor” scraperCore ProductRecollective (Phil's qualitative-research platform with built-in AI features)Developer / Engineering UtilitiesCursor IDE (AI-assisted coding, briefly cited)

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #RAG #SemanticSearch #Langchain #LlamaIndex #Developer #BigData

In this episode of Product by Design, Kyle is joined by Kirk Marple, founder and CEO of Graphlit, to explore the world of unstructured data and how it's transforming with the rise of LLMs and AI-native tools. Kirk shares his journey from working at Microsoft and General Motors to building Graphlit—a platform designed to make unstructured data as usable as structured data.We dive into:The difference between structured and unstructured dataReal-world use cases across industries like healthcare, real estate, and constructionBuilding long-term knowledge graphs and their role in enabling better search and RAG applicationsLessons learned from starting a developer-focused platformBalancing horizontal scale with vertical specificityThe importance of product vision, scalability, and listening to customersHow AI is reshaping engineering and product development from the ground upWhether you're a founder, developer, product manager, or just curious about the future of AI-powered data platforms, this episode is packed with insights you won't want to miss.Links from the Show:Kirk MarpleGraphlitMore by Kyle:Follow Prodity on Twitter and TikTokFollow Kyle on Twitter and TikTokSign up for the Prodity Newsletter for more updates.Kyle's writing on MediumProdity on MediumLike our podcast, consider Buying Us a Coffee or supporting us on Patreon

The rule that your AI is only as good as your data is now well understood, with businesses all over the world looking to modernize their data sources and infrastructure to make the most of AI products and services. But something that can be easily lost in these discussions is consideration of the exact format your data exists in at rest. Unstructured data is especially important for AI and knowing where it fits into your AI strategy is essential. This doesn't just mean knowing where it resides and capturing it, but also storing it in architecture that makes it searchable, secure, and accessible whenever you need it.In this special edition of the ITPro Podcast, in association with Nasuni, Jane and Rory are joined by Jim Liddle, chief innovation officer of data intelligence and AI at Nasuni, to discuss using unstructured data in the most effective way possible.

Anant Bhardwaj is the founder and CEO of Instabase, an AI-native unstructured data platform. They've raised $322M in funding to date from NEA, Andreessen Horowitz, Greylock, and Index Ventures. He did his masters from Stanford and PhD from MIT. Anant's favorite book: The Singularity Is Near (Author: Ray Kurzweil)(00:07) Defining Unstructured Data(01:18) The Growth of Unstructured Data and Its Challenges(02:05) Evolution of Tools for Analyzing Unstructured Data(04:25) How Large Language Models (LLMs) Changed Data Processing(05:27) Do We Still Need ETL in the LLM Era?(06:05) Structured Queries vs. Direct Unstructured Querying(08:22) Applying LLMs in Enterprise Settings(09:34) Ensuring Accuracy in AI-Driven Data Analysis(11:29) SQL vs. AI-Driven Queries in Business Use Cases(13:48) Retrieval-Augmented Generation (RAG) for Enterprise AI(15:02) The Founding of Instabase and Its Early Vision(19:03) Building the MVP of Instabase(22:52) First 10 Customers: Lessons from Early Sales(26:01) Scaling Customer Acquisition: Experiments and Failures(30:35) When to Hire a Sales Team: Key Lessons(33:52) AI Adoption at Instabase for Internal Productivity(37:48) The Technology Stack Behind Instabase(42:36) Transition from OS-Based Architecture to LLM-Based System(43:45) Rapid Fire Questions--------Where to find Anant Bhardwaj: LinkedIn: https://www.linkedin.com/in/anantpb/--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 X: https://x.com/prateekvjoshi 

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

Your AI is only as good as the data its trained on. In this episode we talk to Krishna Subramaniam, Co-Founder and President of Komprise, a provider of intelligent Data Management. We discuss both the importance and challenge of unlocking the value unstructured data for AI pipelines. For more info on Komprise's Intelligent Data Management Platform:www.komprise.comAWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: rethinkpodcast@amazon.com

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

Unstructured data is growing at an unprecedented rate, now making up 90% of enterprise data—but how do businesses harness its full potential?  In this episode, I sit down with Krishna Subramanian, co-founder and COO of Komprise, to explore how companies can optimize, manage, and extract value from their unstructured data across hybrid and multi-cloud environments. Krishna shares insights from over a decade in the industry, explaining how data has outgrown traditional storage solutions and why independent data management is critical for organizations to avoid vendor lock-in. We discuss how AI is revolutionizing data strategies, from optimizing storage costs to preparing unstructured data for AI-driven workflows—a challenge every enterprise is now facing. We also dive into the hidden costs of cold data, how Komprise's Global File Index brings structure to unstructured data, and the role of transparent data tiering in reducing storage expenses while maintaining easy access. Krishna highlights why data governance and security are more important than ever, especially as enterprises explore AI adoption and face increasing ransomware threats. As AI becomes more deeply embedded in business processes, how can organizations ensure their data is AI-ready? What steps should they take today to future-proof their data strategies? Join the conversation and discover why unstructured data management is the next big frontier in enterprise IT.

Wole Moses, chief AI officer for civilian at Microsoft Federal, said as agencies test AI use cases, leadership support remains a leading success factor. Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Wole Moses, chief AI officer for civilian at Microsoft Federal, said as agencies test AI use cases, leadership support remains a leading success factor. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This episode is sponsored by Oracle.   Oracle Cloud Infrastructure, or OCI is a blazing fast and secure platform for your infrastructure, database, application development, plus all your AI and machine learning workloads. OCI costs 50% less for compute and 80% less for networking. So you're saving a pile of money. Thousands of businesses have already upgraded to OCI, including MGM Resorts, Specialized Bikes, and Fireworks AI.   Cut your current cloud bill in HALF if you move to OCI now:  https://oracle.com/eyeonai In this episode of the Eye on AI podcast, Jamie Lerner, CEO of Quantum, joins Craig Smith to discuss the future of data storage, unstructured data management, and AI's transformative role in modern workflows.   Jamie shares his journey leading Quantum, a company revolutionizing the storage and management of unstructured data for industries like healthcare, media, and AI research. With decades of expertise in creating innovative data solutions, Quantum is at the forefront of enabling efficient, secure, and scalable data workflows.   We dive into Quantum's cutting-edge technologies, from high-speed flash storage systems like the Myriad file system to cost-effective, long-term archival solutions such as tape systems. Jamie unpacks how Quantum supports AI-powered workflows, enabling seamless data movement, metadata tagging, and policy-driven automation for unstructured data like medical imaging, genomics, and video archives.   Jamie also explores the critical role of data sovereignty in today's global landscape, the growing importance of "forever archives," and how Quantum's tools help organizations balance exponential data growth with flat budgets. He sheds light on innovations like synthetic DNA and compressed storage mediums, providing a glimpse into the future of data storage.   Don't forget to like, subscribe, and hit the notification bell for more engaging discussions on AI, technology, and innovation! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Jamie Lerner and Quantum (02:21) Quantum's Focus on Unstructured Data Storage (05:19) Structured vs. Unstructured Data: Key Differences (07:52) Managing Data Workflows with AI and Automation (10:55) Quantum's Role in Long-Term Data Archives (13:32) Data Sovereignty and Security (16:18) How Data is Stored and Protected Across Mediums (19:54) Metadata in AI and Data Management (21:29) Quantum's Role in Building Forever Archives (24:16) Tape Storage: Efficiency and Longevity (29:11) Innovations in Data Storage (34:39) Competing in the Evolving Data Storage Industry (37:56) Innovations in Flash Storage (40:55) Balancing Cost and Efficiency in Data Storage (44:28) The Future of Data Storage and AI Integration (50:07) Quantum's Vision for the Future

“Limitless.” That's how Kishan Chetan, the Executive VP and GM of Salesforce Service Cloud, describes the future of AI in customer service. Kishan Chetan explains why customer experience has evolved from deflecting customer interaction and how state-of-the-art tools like Agentforce are the key to providing proactive customer engagement, meaningful connection for employees and customers alike, and equitable accessibility for every type of customer. Whether you're searching for that hidden, game-changing data that's currently free-floating in an untitled spreadsheet, or you need to centralize your customer's feedback so every department offers impeccable service, or you simply want to know how to choose, pilot, and customize the right AI tool for your business… this episode is for you.Key Moments:00:00 Introduction to Customer Efficiency00:41 Transforming Customer Service with AI02:06 The Limitless Future of AI03:48 Proactive and Reactive AI Service05:47 Introducing Agentforce07:49 AI Agents vs. Chatbots09:43 Human and AI Collaboration17:15 Real-World Examples of AI in Action22:58 Leveraging Unstructured Data for Better Operations23:20 Unified Knowledge: Powering AI with Comprehensive Data24:03 Challenges in Centralizing Data for AI25:55 Importance of Quality Data and Human Curation26:47 Practical Tips for Implementing AI in Customer Service28:16 Choosing the Right Channels for Customer Interaction29:21 Balancing AI and Human Interaction31:37 Piloting AI Solutions for Maximum Impact32:23 Creating Exceptional Customer Experiences with AI36:04 Future Trends in AI and Customer Service38:25 Potential Pitfalls and Considerations41:34 Optimizing Customer Experience: Real-World Examples44:04 Advice for Customer Experience Leaders –Are your teams facing growing demands? Join CX leaders transforming their strategies with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org

Cody Coleman is the cofounder and CEO of Coactive AI, a multimodal AI platform to accelerate metadata generation. They recently raised their $30M Series B co-led by Cherryrock Capital and Emerson Collective along with participation from Greycroft, Andreessen Horowitz, and Bessemer Venture Partners. He has a masters degree from MIT and a PhD from Stanford. Cody's favorite books: - The Inner Game of Tennis (Author: W. Timonthy Gallwey) - Gettine More (Author: Stuart Diamond)(00:01) Introduction: Setting the Stage for Metadata(00:21) What is Metadata? Structure in Unstructured Data(01:37) Metadata in Real-World Visual Data Analysis(03:01) Automating Metadata Generation: Challenges and Approaches(06:57) Introduction to Multimodal AI: What and Why(11:25) Managing Trade-Offs in Multimodal AI Systems(13:31) Labeling Challenges in Multimodal Datasets(16:23) Characteristics of an Ideal Metadata Language(18:22) Linking Metadata Quality to Model Effectiveness(20:56) Measuring Efficiency of Metadata Extraction Engines(22:55) Role of Synthetic Data in Metadata and AI(25:30) Evolution of Data Labeling and Future of Metadata(27:27) Exciting Technological Advancements in AI(29:29) Rapid Fire Round--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 Twitter: https://twitter.com/prateekvjoshi 

In our second episode from Insuretech Connect we bring C-suite execs from Neuralmetrics, Optalitix, Carpe Data, & Novidea to show the route between data usage from risk and product through to distribution and delivering insights on the products sold.In this episode:Why the deployment of automated data assistants will help improve underwritingHow to take unstructured data and make it connect with other sources in the cloud for better insightsHow to find balance between internal underwriting orgs vs pricing orgs within insurersWhat can be done to make the data you have more predictiveThe importance of building services across the distribution modelWhy partnerships matter to create effective product distributionHow brokers and agents are thriving after being declared an endangered species 5 years agoThe journey to create a product, underwrite it, price it, and then distribute it has never seen a dots connected like this before with the perspectives from these leaders and companies.Do you want to share or view each interview on its own? Check out our Youtube page here: The Insurance PodcastSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, I'm joined by Jitesh Ghai, CEO of Hyland, to discuss the pressing yet often overlooked issue: unstructured data and its potential to redefine AI strategies. As enterprises increasingly prioritize AI, unstructured data—like documents, videos, and images—remains a largely untapped resource, despite making up about 80-85% of enterprise data. We explore why this data holds untold potential and how Hyland aims to unlock this through their newly launched Content Innovation Cloud. Jitesh shares insights on why organizations should look beyond conventional data lakes and warehouses, shifting focus towards content repositories. This approach enables companies to harness large language models to extract insights and accelerate innovation. We also discuss Hyland's role as a long-standing leader in enterprise content management, trusted by over 14,000 customers worldwide. With a vision to drive AI-driven transformation, Hyland's platform goes beyond simple automation to offer a unique mix of content intelligence, process intelligence, and application intelligence. Jitesh provides a preview of Hyland's AI strategy, highlighting their rapid development cycle, where new capabilities are delivered in months rather than years. We explore how leveraging customers' data assets can serve as a powerful tool in developing advanced AI features, while balancing the pace of innovation with real-world practicality. If you've been considering how to effectively integrate AI into your enterprise and harness the power of your existing content, this episode offers a fresh perspective on the opportunities and strategies that can make it possible. Are you ready to rethink your approach to unstructured data and unlock its full potential? Tune in, and let's see where it takes us. What do you think? Could unstructured data be the missing piece in your AI strategy? Share your thoughts with us after listening!

Kevin Werbach speaks with Krishna Gade, founder and CEO of Fiddler AI, on the the state of explainability for AI models. One of the big challenges of contemporary AI is understanding just why a system generated a certain output. Fiddler is one of the startups offering tools that help developers and deployers of AI understand what exactly is going on.  In the conversation, Kevin and Krishna explore the importance of explainability in building trust with consumers, companies, and developers, and then dive into the mechanics of Fiddler's approach to the problem. The conversation covers current and potential regulations that mandate or incentivize explainability, and the prospects for AI explainability standards as AI models grow in complexity. Krishna distinguishes explainability from the broader process of observability, including the necessity of maintaining model accuracy through different times and contexts. Finally, Kevin and Krishna discuss the need for proactive AI model monitoring to mitigate business risks and engage stakeholders.  Krishna Gade is the founder and CEO of Fiddler AI, an AI Observability startup, which focuses on monitoring, explainability, fairness, and governance for predictive and generative models. An entrepreneur and engineering leader with strong technical experience in creating scalable platforms and delightful products,Krishna previously held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft. At Facebook, Krishna led the News Feed Ranking Platform that created the infrastructure for ranking content in News Feed and powered use-cases like Facebook Stories and user recommendations.   Fiddler.Ai How Explainable AI Keeps Decision-Making Algorithms Understandable, Efficient, and Trustworthy - Krishna Gade x Intelligent Automation Radio    

Send us a textIn this week's episode of Sidecar Sync, hosts Amith and Mallory dive into two exciting AI innovations: ChatGPT Canvas and Microsoft Copilot's Wave 2 updates. They break down how these tools can enhance productivity and transform workflows in associations and beyond. From the advanced features of ChatGPT Canvas, enabling real-time edits and content creation, to Microsoft Copilot's newest collaborative capabilities, including Excel's Python integration and Copilot Pages, this episode provides actionable insights for AI-powered productivity. Whether you're exploring AI for content transformation or team collaboration, this episode covers it all!digitalNow Conference 2024

By next year, 70 to 80% of all the data we receive will be unstructured. Is your organization prepared?In this episode, we are joined by Victoria Gnatoka, Customer Experience Product Manager at Expedia. Drawing on over 10 years of experience, she digs into how adopting an omnichannel approach can help obtain unstructured data that can be turned into quality, actionable insights through AI, data engineering and other technologies.We also discuss:Strategies to unlock the full potential of unstructured data.How to focus on actionable, high-impact insights rather than getting overwhelmed by data overload.Why integrating data across channels is crucial for comprehensive customer understanding.

Send us a textIn this special episode of Sidecar Sync, Amith and Mallory dive into the transformative power of AI in analyzing unstructured data, especially for associations. They explore how AI can help organizations unlock insights from sources like emails, community discussions, event recordings, and more, all of which have been traditionally challenging to analyze. Amith shares real-world examples of how unstructured data can reveal hidden trends and opportunities, offering valuable insights for future decision-making. Tune in to discover how your association can stay ahead of the curve with AI-driven data strategies.

Markus Stoll is the Co-Founder of Renumics and the developer behind the open-source interactive ML dataset exploration tool, Spotlight. He shares insights on: AI in Engineering and ManufacturingInteractive ML Data VisualizationML Data Exploration Follow Markus for hands-on articles about leveraging ML while keeping a strong focus on data. Visualize - Bringing Structure to Unstructured Data // MLOps Podcast #258 with Markus Stoll, CTO of Renumics. A huge thank you to SAS for their generous support! // Abstract This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models. // Bio Markus Stoll began his career in the industry at Siemens Healthineers, developing software for the Heavy Ion Therapy Center in Heidelberg. He learned about software quality while developing a treatment machine weighing over 600 tons. He earned a Ph.D., focusing on combining biomechanical models with statistical models, through which he learned how challenging it is to bridge the gap between research and practical application in the healthcare domain. Since co-founding Renumics, he has been active in the field of AI for Engineering, e.g., AI for Computer Aided Engineering (CAE), implementing projects, contributing to their open-source library for data exploration for ML datasets (Renumics Spotlight) and writing articles about data visualization. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://renumics.com/ MLSecOps Community: https://community.mlsecops.com/ Blogs: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 : https://medium.com/itnext/how-to-explore-and-visualize-ml-data-for-object-detection-in-images-88e074f46361 --------------- ✌️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 Markus on LinkedIn: https://www.linkedin.com/in/markus-stoll-b39a42138/

Connect with Kirk: https://www.linkedin.com/in/kirkmarple/ Be A Better YOU with AI: Join The Community: https://10xyou.us Get AIDAILY every weekday. Subscribe at https://aidaily.us --- In this episode, Kirk Marple, CEO of GraphFlit, dives into the fascinating world of unstructured data and how his company uses knowledge graphs to make sense of it. Unstructured data isn't just about spreadsheets—it's the massive collection of text, audio, video, and geospatial data that traditional databases struggle to organize. Kirk explains how GraphFlit ingests and indexes this unstructured data, then uses knowledge graphs to connect the dots between entities like people, organizations, and locations. By doing so, they enrich the information provided to large language models (LLMs) like GPT-3, which significantly improves the quality of AI-generated responses. GraphFlit's platform primarily targets developers creating AI-powered applications across various industries. Kirk envisions a future where AI doesn't just answer questions but proactively surfaces relevant insights, although he's cautious about the hype around artificial general intelligence (AGI), expecting progress to be more gradual. --- Support this podcast: https://podcasters.spotify.com/pod/show/thinkfuture/support

Welcome to another episode of SaaS Fuel! In this episode, we'll explore the world of SaaS scaling and innovation with Kirk Marple, the CEO of Graphlit.Kirk discusses his journey from working at Microsoft and General Motors to becoming a successful SaaS founder. The conversation delves into Graphlit's strategies for platform growth and scaling revenue, emphasizing the crucial role of SDK releases, free tier quotas, and pricing adjustments. Kirk also shares insights into the pivotal shift from a B2B sales outbound motion to product-led growth (PLG) and marketing-led growth, shedding light on how the company is reshaping its development team for better productivity. From tackling platform risks and pricing models to addressing challenges in AI model obsolescence and infrastructure evolution, this episode is packed with valuable insights for SaaS founders striving to scale their businesses. Get ready to dive into the world of SaaS scaling and innovative strategies with Jeff and Kirk on SaaS Fuel!Key Takeaways00:00 Encountering mysterious creature in a bungalow at night.05:09 Surprised by the large land crab, dramatic yet humorous.09:24 Debated between API platform and app route.10:10 User resistance to new software implementation explained.15:33 Improving user experience with SDK and platform.18:40 Enhancing data with external sources for analysis.21:03 Hackathons benefit developers and the company's exposure.25:28 Visual and audible learning for platform usage.28:23 SDKs and sample apps are open source, platform is closed.30:47 Addressing platform risks, including data migration options.33:56 Allowing users to bring their own API keys.37:57 Improving developer portal and addressing user needs.41:27 Reverse UI as a service for chatbot.43:38 AI infrastructure adapts to new models rapidly.45:47 GPT-5 beats GPT, new leader emerging.Tweetable QuotesRemoving Friction in Product Development: "I just got a customer feedback of somebody being like, hey. This is great, but it's still like, I want more demo concept of, like, more just direct access to try it out without, like, reading the documentation." — Kirk Marple 00:15:49 Quote: "I would love to have video series on each feature area that instead of having to read the docs, we can just have a a video to watch. And I think I think that'll help with conversion and just, I mean, making people happier Yeah. For for using the platform." — Kirk Marple 00:25:28 Developer Portal Redesign: "And so when you create a project, we have a much better starting page that links properly to documentation and sample apps and kinda gives them a like, okay, start here, do this, concept, and then we have the API keys that they can just copy right out of there." — Kirk Marple 00:38:06Expanding Understanding before Solutions: "You know, at this stage, our goal is to expand our understanding of the problem space, not just jump to solutions and that's really hard for me because I wanna jump straight into solve mode but when we get really really curious, we gain new insights" — Jeff Mains 00:03:14Scaling SaaS Businesses: "We learn, we adapt, we grow,...

Shuveb Hussain is co-founder of Unstract, a no-code platform that uses large language models to extract structured data from unstructured documents, allowing users to build API endpoints and ETL pipelines to automate document processing workflows. Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Pocket Casts • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes - with links to many references - can be found on The Data Exchange web site.

On our final episode this season of Working Smarter we talk to Sophia Wang, an assistant professor of ophthalmology at Stanford University. Wang leads the school's ophthalmic informatics and artificial intelligence group, which uses the latest machine learning techniques to analyze electronic health records. In practice, that means looking at disparate sources of data—from doctors' notes and eye exam data to diagnostic imagery and billing codes—and finding the sorts of patterns that can be difficult for humans to spot.Hear Wang talk about using AI to extract useful information from a sea of unstructured data, and how to make better decisions with the data you already have—which, in Wang's case, means improving outcomes for glaucoma patients and providing a better quality of care.Show notes:Learn more about Sophia and her researchVisit Stanford University's Ophthalmic Informatics and Artificial Intelligence Group~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.

Highlights from this week's conversation include:Current State of LLMs (1:12)Historical Analogy to the iPhone (3:32)Limitations of Early iPhones (5:02)Comparing LLMs to Historical Technologies (6:08)Skepticism About LLM Capabilities (9:11)Broad Nature of AI Innovations (10:12)User Input Challenges (14:32)Transcription and Unstructured Data (16:19)Single Player vs. Multiplayer Experiences with LLMs (18:50)Revenue Insights from ChatGPT (20:27)Contextual Use of LLMs in Development (23:43)Implications of Human Involvement (26:15)The Role of Human Feedback (29:19)Customer Data Management and LLMs (31:25)Streamlining Data Engineering Processes (34:24)Prototyping Content Recommendations (37:42)Summarizing Content for LLMs (39:51)Challenges with Output Quality (41:18)Data Formatting for Marketing Use (43:20)Efficient Workflow Integration (46:20)Exploring New Prototyping Techniques (50:56)Distance Metrics for Improved Relevance (53:00)Improving Search Techniques (56:46)Utilizing LLMs in Customer Data (59:15)Challenges in Customer Data Processing (1:01:10)Final thoughts and takeaways (1:02:12)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Welcome to another episode of Category Visionaries — the show that explores GTM stories from tech's most innovative B2B founders. In today's episode, we're speaking with Joe Witt, CEO & Co-Founder of Datavolo, a pioneering company in the AI data ingestion space that has raised $21 Million in funding. Here are the most interesting points from our conversation: NSA to Commercial World: Joe transitioned from working at the NSA, where he focused on data collection and security, to founding Datavolo, leveraging his expertise in large-scale data ingestion. From Open Source to Enterprise: The journey of Apache NiFi, an open-source project initiated at the NSA, and how it became a cornerstone for Datavolo's technology in managing unstructured data. Generative AI and Data Ingestion: Joe discusses the crucial role of data ingestion for generative AI, emphasizing the need to capture and process unstructured data for effective AI training and application. Target Audience: Datavolo primarily targets data engineers who interact with AI engineers, providing tools to automate and optimize data pipelines. Marketing Strategy: The importance of educating the market about the steps involved in integrating company-specific data with large language models (LLMs), and the benefits of automated data ingestion. Funding and Growth: Insights into Datavolo's successful fundraising journey, emphasizing the importance of a strong founding team and a clear vision in securing investment. //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.  www.GlobalTalent.co

In this episode of The Digital Executive, host Brian Thomas sits down with Kirk Marple, the visionary CEO and founder of Graphlit. Kirk shares his impressive journey from key roles at Microsoft, General Motors, and STATS to the founding of Graphlit, a company revolutionizing the way unstructured data is handled. Graphlit's serverless, cloud-native platform automates data ingestion, knowledge extraction, and LLM conversations, bringing unprecedented efficiency to data processing and retrieval.Kirk delves into the technology behind Graphlit, explaining how the platform's innovative use of metadata indexing and AI-powered analysis transforms various data formats into actionable insights. He also discusses the integration of knowledge graphs with large language models (LLMs) and their practical applications, such as enhancing data processing and retrieval through a web of interconnected information.Looking ahead, Kirk envisions Graphlit as an essential middleware layer that simplifies the handling of unstructured data for developers, allowing them to create powerful applications with ease. Tune in to discover how Graphlit is setting new standards in data management and what the future holds for this groundbreaking company.

Chang She is CEO and co-founder of LanceDB, an open-source database designed for multimodal AI applications, offering scalable vector search, streaming training data, and interactive exploration of large AI datasets. In this episode we discuss Lance, an open-source columnar data format that tackles the unique challenges posed by modern AI and machine learning workloads.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Pocket Casts • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

In this episode, Jesse, Anaiya, and Mike engage in a riveting discussion with Jerry Liu, the CEO of LlamaIndex.Dive into the world of unstructured data and learn how LlamaIndex is revolutionizing the way developers build applications over their data. Discover the integration with MongoDB and the challenges faced when transitioning from prototype to production.Jerry shares insights into the future of AI, chatbots, and autonomous task assistants. Don't miss out on the latest updates and innovations in data management and AI development!

(rev one)  According to TechCrunch, 2024 has already seen one billion stolen records and is still rising. In today's discussion, we learn ways state and local organizations can secure unstructured data. KINDS OF DATA Enterprise architects like to sit in front of a whiteboard and design a network with data all nicely arranged in columns. This is great for optimizing for retrieval from large databases. According to Jimmy Rogers, up to 90% of the data he sees is unstructured. This “unstructured” data comes from sensors, CAD design, satellites, and body cameras. One way to view this data is it is not in a column but in a workflow. Beginning with knowing how to manage unstructured data is key to keeping it secure. STRATEGY Terry Berttinger from Ohio has a simple, but effective strategy to protect data. Make sure a person owns that information. When that happens, people develop pride in working on efforts to make sure that data is not arbitrarily assigned and is stored in the appropriate place to secure it. SMALL BUDGET All participants understand the limitations of a state budget. However, Todd Holler suggests that a systems administrator can make simple changes like hardening Active Directory which can have a major impact. Further, CISA is gaining a well-won reputation for assisting state and local organizations by providing free information, checklists, and advice to secure unstructured data. Finally, one of the participants was willing to share that his system was compromised. Transparency is the only way to help other organizations see what they can do to secure systems.

Today we join Maria Khalusova, Staff Developer Advocate with Unstructured.IO, to discuss how companies can unlock their Unstructured Data to deliver better results from their Large Language Models. We talk about how Unstructured Data can enhance the performance of RAG applications, RAG vs Fine Tuning, data Chunking, Multi-Modal models and more. AWS Hosts: Nolan Chen & Malini ChatterjeeUnstructured Enterprise Platform beta signup: https://unstructured.io/platformEmbedding models MTEB Leaderboard: https://huggingface.co/spaces/mteb/leaderboard2019 Deloitte report (source of the statistics that only 18% of organizations were using unstructured data):https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html80% of data is unstructured, source: https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-dataPapers showing RAG outperforming fine-tuning: https://arxiv.org/abs/2312.05934https://arxiv.org/abs/2401.08406Email Your Feedback: rethinkpodcast@amazon.com

Robbie Moon from the Georgia Tech Scheller College of Business joins us to discuss the analysis of unstructured data and the application of NLP methodologies towards financial data.

In this episode, we're introducing a new format that we hope to revisit every few months. We're joined by HPE's CTO, Fidelma Russo, to discuss the rapid advancements in AI and the broader enterprise tech landscape for 2024. Fidelma shares her insights on the staggering growth in AI, the importance of data management, and the potential for AI to democratize technology and foster inclusivity. She also touches on the ethical considerations and the need for responsible AI implementation.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it.Do you have a question for the expert? Ask it here using this Google form: https://forms.gle/8vzFNnPa94awARHMAAbout this week's guest: https://www.hpe.com/uk/en/leadership-bios/fidelma-russo.htmlSources and statistics cited in this episode:- AI industry growth: Statista - https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide#:~:text=Artificial%20intelligence%20(AI)%20worldwide%20%2D%20statistics%20%26%20facts&text=The%20market%20for%20AI%20technologies%20is%20vast%2C%20amounting%20to%20around,trillion%20U.S.%20dollars%20by%202030 - Connectivity and networking industry size: Markets and Markets - https://www.marketsandmarkets.com/Market-Reports/wireless-connectivity-market-192605963.html - Security industry size: Grandview Research - https://www.grandviewresearch.com/industry-analysis/cyber-security-market - Storage industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/data-storage-market-102991- Green tech and Sustainability industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/green-technology-and-sustainability-market-102221Smartphone ‘X Ray' chip - https://ieeexplore.ieee.org/abstract/document/10381731

In this episode, we're introducing a new format that we hope to revisit every few months. We're joined by HPE's CTO, Fidelma Russo, to discuss the rapid advancements in AI and the broader enterprise tech landscape for 2024. Fidelma shares her insights on the staggering growth in AI, the importance of data management, and the potential for AI to democratize technology and foster inclusivity. She also touches on the ethical considerations and the need for responsible AI implementation.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it.Do you have a question for the expert? Ask it here using this Google form: https://forms.gle/8vzFNnPa94awARHMAAbout this week's guest: https://www.hpe.com/uk/en/leadership-bios/fidelma-russo.htmlSources and statistics cited in this episode:- AI industry growth: Statista - https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide#:~:text=Artificial%20intelligence%20(AI)%20worldwide%20%2D%20statistics%20%26%20facts&text=The%20market%20for%20AI%20technologies%20is%20vast%2C%20amounting%20to%20around,trillion%20U.S.%20dollars%20by%202030 - Connectivity and networking industry size: Markets and Markets - https://www.marketsandmarkets.com/Market-Reports/wireless-connectivity-market-192605963.html - Security industry size: Grandview Research - https://www.grandviewresearch.com/industry-analysis/cyber-security-market - Storage industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/data-storage-market-102991- Green tech and Sustainability industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/green-technology-and-sustainability-market-102221Smartphone ‘X Ray' chip - https://ieeexplore.ieee.org/abstract/document/10381731

The majority of enterprise data exists in heterogenous formats such as HTML, PDF, PNG, and PowerPoint. However, large language models do best when trained with clean, curated data. This presents a major data cleaning challenge. Unstructured is focused on extracting and transforming complex data to prepare it for vector databases and LLM frameworks. Crag Wolfe The post Unstructured Data and LLMs with Crag Wolfe and Matt Robinson appeared first on Software Engineering Daily.

Key Takeaways from H1 2024: Reflecting on Hardware, Unstructured Data, and the Future of Product Management In the season 2 finale of 'Unsolicited Feedback,' co-hosts Brian Balfour, Ravi Mehta, and Joff Redfern join Fareed Mosavat to reflect on the most compelling topics of the season. They start with discussions on how trends diverge between hardware and software markets, and the distinctions between luxury and premium products. The episode takes a deep dive into AI's role in revolutionizing unstructured data and its far-reaching effects on product management. Touching on methodology changes, the hosts contemplate how AI might transform the software development life cycle and what it means for the future roles of product managers, designers, and engineers, giving some Unsolicited Feedback to our friend Lenny Rachitsky along the way. Show Notes: Fareed mentioned that Reforge has just launched a brand new AI product, the Reforge Extension, now in public beta. Ever wish you could get expert feedback from leaders like Fareed, Brian Balfour, Andrew Chen, and Elena Verna while working on your documents? Now you can! He also mentioned our free AI festival, ref:AI, a full-day virtual conference delving into all things AI, featuring a special episode of Unsolicited Feedback with the legendary Andrew Chen. Secure your spot now as spaces are limited. Register here today. And, last but not least, Rupa Chaturvedi & Polly Allen (renowned industry experts with expertise in Generative AI from Amazon's Alexa, Google and Uber) are teaching Generative AI Products: How to get from Idea to MVP for the 3rd time starting on June 14. It's a 3-week course designed to get you started on your journey of leading AI initiatives and projects that involve Generative AI. Check out a full summary of the takeaways and lessons from this episode at ➡️ https://www.unsolicitedfeedback.co/ 00:00 Season Finale Introduction 03:50 Luxury in Software and Physical Goods 18:16 AI's Impact on Product Management 30:29 AI vs Human Intuition in Strategy 38:11 The Role of AI in Communication and Empathy 41:19 Revolutionizing the Software Development Life Cycle 52:25 The Rise of Full-Stack Product Leaders Check out a full summary of the takeaways and lessons from this episode at ➡️ https://www.unsolicitedfeedback.co/

Evaluating AI Models, Unlocking Unstructured Data, and Achieving Reliability w/ Ben Kus Today on "Unsolicited Feedback," Fareed Mosavat dives deep into the evolving landscape of artificial intelligence with tech expert and Box CTO, Ben Kus. As AI continues to reshape industries, understanding its core mechanisms and potential applications has never been more critical. Ben sheds light on how AI can enhance productivity and decision-making within organizations while also sharing technical strategies he's implemented to ensure reliability for Box's enterprise customers. Check out a full summary of the takeaways and lessons at ➡️ https://www.unsolicitedfeedback.co/ 00:00 Introduction 02:19 Rapid Fire Advice for AI Product and Technology Teams 05:25 Understanding and Navigating the World of AI Models 08:21 The Challenges of AI Change Management and Model Selection 14:29 Evaluating AI Quality and Performance in the Enterprise 25:23 Unstructured Data: The Next Frontier for AI 29:15 The Future of AI in Structuring Unstructured Data 43:55 Exploring the Potential of AI Agents and User Experience Thank you for listening to Unsolicited Feedback! Don't forget to hit subscribe on the podcast network of your choice: Apple: https://podcasts.apple.com/us/podcast/unsolicited-feedback/id1706701340 Spotify: https://open.spotify.com/show/7FzVEIBamWnExyJpJ2LCwF And sign up for our mailing list at ➡️ https://www.unsolicitedfeedback.co/ If you love this show, please leave us a 5-Star Review and tell a friend about the podcast!

This episode's Community Champion Sponsor is Ossur. To learn more about their ‘Responsible for Tomorrow' Sustainability Campaign, and how you can get involved: CLICK HERE---Episode Overview: Clinicians are drowning in a deluge of unstructured medical data, struggling to efficiently extract critical insights that could improve patient care and outcomes. Our next guest, Dr. Tim O'Connell, is taking on this monumental challenge as the co-founder and CEO of emtelligent.With a unique background spanning IT, radiology, and medical informatics, Tim experienced firsthand the limitations of existing natural language processing tools in healthcare settings. Driven to develop a powerful solution, he founded emtelligent to harness cutting-edge AI and language models.While together, Tim shares his pioneering journey building emtelligent's innovative technology that structures overwhelming volumes of unstructured patient data and how he and his colleagues are empowering healthcare organizations to operate more efficiently, reduce physician burnout, and unlock data-driven insights to transform care quality.Join us to learn from Tim and how the emtelligent team is unlocking healthcare's unstructured data troves. Let's go! Episode Highlights:Tim's advice: Get out and make a change, find a business partner with complementary skills ("your Anoop", his co-founder)Origin story: Seeing limitations of existing NLP tools, partnering with NLP expert Dr. Anoop SarkarCutting-edge technology: Structuring massive volumes of unstructured medical data with advanced language AIPowerful use cases: Transforming needle-in-a-haystack data challenges, drastically reducing manual chart review workloadsFuture vision: AI-augmented healthcare teams over next 2-5 years, reducing burnout by automating drudgery tasksAbout our Guest: Dr. Tim O'Connell is a practicing radiologist in Vancouver and cofounded emtelligent in 2016. He has served as vice chair of medical informatics in the University of British Columbia's Department of Radiology since 2017. Prior to his clinical and entrepreneurial careers, Dr. O'Connell worked as an IT professional for Nortel Networks and Bell, where he was director of engineering for the Bank of Montreal account.Early in his career, Dr. O'Connell realized that radiologists were often tasked with a volume of work that didn't afford them the time needed to carefully review and understand each patient's clinical history. In 2015, he met Dr. Anoop Sarkar, a widely published Ph.D. and full professor of natural language processing (NLP) and artificial intelligence. Dr. Sarkar shared Tim's interest in medical language processing and desire to develop meaningful solutions that would have a positive impact on the lives of patients and care providers. It quickly became obvious to both that they were perfectly matched to solve the problem Tim had identified, together bringing a powerful combination of deep clinical and data science expertise. emtelligent was officially formed when Drs. O'Connell and Sarkar joined forces with Jeff O'Connell, an experienced technology operations management leader; and Dan Carriere, a seasoned expert in building and scaling biomedical startup ventures, adding experienced entrepreneurship and business leadership to the team. Collectively, the team successfully built and launched emtelliPro®, one of the industry's first deep-learning-based Medical Language Understanding engines.Links Supporting This Episode:emtelligent Website: CLICK HEREDr. Tim O'Connell...

In this episode, Joice John, Senior Product Manager at Skyflow, joins the show to discuss the complexities of managing privacy and security with unstructured data. Joice explains what unstructured data is and its distinction from structured data, and then dives into the technologies that tackle these challenges. Joice discusses the unique privacy concerns and significant security risks unstructured data poses, highlighting why they're especially tough to mitigate. Sean and Joice also discuss the support modern data lakes offer for secure unstructured data management, alongside Skyflow's solutions for overcoming analytics challenges and protecting sensitive customer information.

In a world buzzing with discussions about artificial intelligence (AI) and capital raising, Jeff Denworth, co-founder of VAST Data, sits down for an insightful interview. From his humble beginnings as a Jersey boy to helping spearhead a company with a staggering $9.1 billion valuation, Jeff shares his journey and insights into the dynamic realms of technology, business development, sales, and marketing.