Podcasts about predictive modeling

Machine learning is transforming scientific research across disciplines, but many scientists remain skeptical about using approaches that focus on prediction over causal understanding. That's why we are excited to have Christoph Molnar return to the podcast with Timo Freibusleben. They are co-authors of "Supervised Machine Learning for Science: How to Stop Worrying and Love your Black Box." We will talk about the perceived problems with automation in certain sciences and find out how scientists can use machine learning without losing scientific accuracy.• Different scientific disciplines have varying goals beyond prediction, including control, explanation, and reasoning about phenomena• Traditional scientific approaches build models from simple to complex, while machine learning often starts with complex models• Scientists worry about using ML due to lack of interpretability and causal understanding• ML can both integrate domain knowledge and test existing scientific hypotheses• "Shortcut learning" occurs when models find predictive patterns that aren't meaningful• Machine learning adoption varies widely across scientific fields• Ecology and medical imaging have embraced ML, while other fields remain cautious• Future directions include ML potentially discovering scientific laws humans can understand• Researchers should view machine learning as another tool in their scientific toolkitStay tuned! In part 2, we'll shift the discussion with Christoph and Timo to talk about putting these concepts into practice. What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

In this episode Ed Talks with Dr. Damon Smith of the University of Wisconsin. They discuss Damon's work in disease forecasting and predictive modeling. Additional Resources https://badgercropdoc.com/ https://ipcm.wisc.edu/apps/ How to cite the podcast: Zaworski, E. (Host) and Smith, D. (Interviewee). S4:E12 (Podcast). Prophecy of Plague: Plant Diseases and Predictive Modeling Part 1. 3/19/25. In I See Dead Plants. Crop Protection Network.

Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We hope you enjoy these examples of where the greatest AI and machine learning techniques exist in your routine today.More AI agent disruptors (0:56)Proxy from London start-up Convergence AIAnother hit to OpenAI, this product is available for free, unlike OpenAI's Operator. AI Paris Summit - What's next for regulation? (4:40)[Vice President] Vance tells Europeans that heavy regulation can kill AIUS federal administration withdrawing from the previous trend of sweeping big tech regulation on modeling systems.The EU is pushing to reduce bureaucracy but not regulatory pressureModeling paradigms explained (10:33)As companies look for an edge in high-stakes computations, we've seen best-in-class rediscovering expert system-based techniques that, with modern computing power, are breathing new light into them. Paradigm 1: Agents (11:23)Paradigm 2: Generative (14:26)Paradigm 3: Mathematical optimization (regression) (18:33)Paradigm 4: Predictive (classification) (23:19)Paradigm 5: Control theory (24:37)The right modeling paradigm for the job? (28:05)What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

Predictive Modeling (4:15) Human judgement and processes (14:06)Imperfection in models (21:40)BioClaudia Perlich is Managing Director and Head of Strategic Data Science for Investment Management at Two Sigma, where she has worked for seven years. In this role, Claudia is responsible for developing innovative alpha strategies at the intersection of alternative data, thematic hypotheses and machine learning in public markets. Claudia joined Two Sigma from Dstillery, an AI ad targeting company, where she worked as Chief Scientist. Claudia began her career in data science at the IBM Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications.Since 2011, Claudia has served as an adjunct professor teaching Data Mining in the M.B.A. program at New York University's Stern School of Business. Claudia received a Ph.D. in Information Systems from Stern School of Business, New York University, holds an M.S. of Computer Science from Colorado University and a B.S. in Computer Science from Technical University Darmstadt, Germany. Connect with ClaudiaClaudia Perlich on LinkedinConnect with UsMargot Gerritsen on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

In the Energy News Beat – Conversation in Energy with Stuart Turley, talks with George McMillan delves into the intricate relationship between energy, geopolitics, and global strategy, exploring how energy infrastructure, such as pipelines, shapes regional stability and global alliances. The discussion examines key dynamics, including Russia and China's energy integration, Middle Eastern tensions like the Shia-Sunni divide, and the strategic significance of Iran and Syria. Utilizing frameworks like Mackinder's Heartland Theory, the speakers analyze how regional cleavages are exploited through proxy wars, revolutions, and covert operations, with external powers like the U.S., NATO, and Israel playing pivotal roles. The conversation highlights the economic and military dimensions of energy politics, including battlefield shaping operations and the strategic control of resources, offering a nuanced perspective on the factors driving global power shifts and regional conflicts.George and I have recorded additional updates in production on the German energy policies related to Russian Natural Gas and geopolitics and will be out this week. Thanks, George, for stopping by the podcast. I recommend contacting him on his LinkedIn if you need geopolitical analysis in energy, especially if you are in the new United States administration or an energy company. https://www.linkedin.com/in/george-mcmillan-5665b015/Highlights of the Podcast00:00 - Intro02:17 - Energy Crisis and Global Dynamics04:01 - Sea Power vs. Land Power Strategies06:43 - Middle East Tensions and Regional Analysis08:09 - Energy Politics and Infrastructure12:47 - Historical Context and Regional Power Shifts16:56 - Strategic Models and Global Influence21:10 - Iran's Energy Crisis and Strategic Importance27:20 - Geopolitical Mapping and Future Projections36:27 - Israel, Turkey, and Strategic Alliances45:12 - Energy Economics and Military Strategies53:02 - Predictive Modeling in Geopolitics59:31 - Outro

What are the barriers that keep students from finishing an application or from completing their degree? Emily Coleman joins me today from HAI Analytics to discuss her company's work in building predictive models for higher education institutions. These models help predict student behavior to optimize critical processes like the allocation of financial aid. Emily explains the importance of retaining data, overcoming data silos, and incorporating both human and artificial intelligence in predictive analytics. She also provides insights into how predictive models can aid enrollment forecasting, retention strategies, and resource allocation for better student support. Additionally, real-world examples of schools implementing these models and the difference they made are shared. Emily highlights the benefits of data-driven decision-making in effectively managing enrollment and student success.What you will learn:How humans and AI can work together to handle data in the best way.Why predictive modeling is a great tool to help meet enrollment, headcount, and revenue goals.Examples of predictions that data models from HAI Analytics can make.The impact that comes when a college can understand the problems a student faces before they stop attending. Thanks for listening!Connect with GradComm:Instagram:@gradcommunicationsFacebook:@GradCommunicationsLinkedIn:@gradcommSend us a message: GradComm.com

Stephen Solka, CTO and co-founder of Standd.io, joins Elixir Wizards Owen and Charles to share the journey of building an AI-native deal intelligence and due diligence platform. Designed to streamline document analysis and text generation for venture capital firms, Standd.io leverages large language models and AI tools to address key customer pain points in document workflows. Stephen explains how Elixir and Phoenix LiveView enabled rapid UI iteration and seamless integration between the front-end and back-end. The conversation also explores the human side of startup life. Stephen reflects on balancing tech debt with customer demands, the value of accelerators in building networks and securing funding, and the challenges of pricing in early-stage startups. He emphasizes the importance of validating ideas with potential customers and learning from the hurdles of growing a business. Tune in for insights on leveraging AI in Elixir, solving real-world problems, and navigating the journey from concept to company. Topics discussed in this episode: The journey from self-taught programmer to CTO The perks of Phoenix LiveView for rapid UI development Integrating front-end and back-end technologies AI tools for code generation How early adopters balance functionality with product polish Validating ideas and understanding customer needs The impact of accelerators on networking and fundraising Approaches to managing pricing strategies for startups Balancing technical debt with feature development The role of telemetry and error reporting in product development Creating collaborative and supportive tech communities Educating users on AI's capabilities and limitations The broader implications of AI tools across industries Links Mentioned Contact Stephen & Julie at Standd: founders@standd.io https://www.standd.io/ https://www.digitalocean.com/community/tutorials/gangs-of-four-gof-design-patterns https://www.thriftbooks.com/w/code-completesteve-mcconnell/248753/item/15057346/ https://aws.amazon.com/sagemaker/ https://www.anthropic.com/ https://getoban.pro/ https://kubernetes.io/ https://www.apollographql.com/ https://aws.amazon.com/startups/accelerators https://accelerate.techstars.com/ https://aider.chat/ https://github.com/Aider-AI/aider https://neovim.io/ https://ui.shadcn.com/ https://tailwindui.com/ https://www.ycombinator.com/ https://www.thriftbooks.com/w/close-to-the-machine-technophilia-and-its-discontentsellen-ullman/392556 Special Guest: Stephen Solka.

In this special episode of the Higher Ed Pulse podcast series, recorded live at the American Marketing Association (AMA) conference, Mallory Willsea speaks with Dan Giroux, AVP of Advancement Communications & Stewardship at Drexel University. With a focus on personalized engagement, operational efficiency, and AI-powered predictive analytics, Dan shares insights on leveraging technology to enhance advancement efforts and build meaningful relationships with donors.Key TakeawaysAI's Role in Advancement: AI tools, like ChatGPT, offer efficiency and personalization opportunities in advancement, filling gaps for teams with limited resources.Predictive Analytics Potential: AI can transform donor engagement by analyzing data to inform outreach strategies, predict giving patterns, and improve campaign development.Institutional AI Integration: The higher education sector awaits seamless AI integration within existing platforms, which will likely drive widespread adoption in advancement.Practical Use Cases for AI in Advancement: From drafting content to strategic donor engagement, AI provides practical solutions to ongoing challenges in advancement.Continuous Learning with AI: For advancement professionals, embracing AI through regular exploration and experimentation is essential to staying ahead in the evolving higher ed landscape.Why Does AI Matter in Advancement? The podcast opens with Dan explaining the importance of AI in advancement efforts, especially as these teams face increasing demands and limited resources. AI provides ways to automate tasks, personalize engagement, and support decision-making—all critical in the alumni and donor relations space. By using AI tools, advancement teams can streamline workflows and communicate more effectively with constituents, enhancing overall efficiency without adding headcount. This is especially useful for content creation, such as personalized donor thank-yous, targeted emails, and solicitations, which often require a consistent tone of voice and branding.Addressing Resource Constraints with AI Mallory and Dan discuss a significant challenge in advancement: open positions and budget constraints. Dan notes that Drexel University's advancement team has faced unfilled positions for two years. For teams stretched thin, AI serves as a valuable tool to tackle basic but essential tasks, like generating first drafts of communications or tailoring messages to different donor levels. This capability not only relieves pressure on understaffed teams but also ensures a more personalized experience for constituents. AI allows smaller teams to handle large volumes of content and maintain a cohesive voice, a function once dependent on extensive editorial guidelines.Current AI Tools in Advancement: Are We There Yet? Mallory inquires about AI's integration into existing tech stacks within advancement. Dan explains that while some AI features are starting to appear in niche tools, broader integration into major platforms like Salesforce or Anthology Encompass is still in its early stages. Institutions are beginning to explore AI's potential, with tools like “AI gift officers” gaining attention, though the landscape is still developing. Without comprehensive institutional support, many professionals are experimenting with AI independently, often without clear guidance. As more universities form task forces to evaluate ethical AI use, these technologies will likely become better integrated into the advancement workflow.Predictive Modeling and Donor Engagement The conversation turns to predictive analytics as a promising AI use case in advancement. Dan suggests that AI's potential for donor engagement lies in analyzing donor history to create targeted outreach and even develop customized offers. By understanding constituent patterns, advancement teams can make strategic decisions about where to invest their time and resources. While true AI-driven predictive modeling in advancement may not yet be fully realized, this approach could transform campaign development and improve ROI by identifying the most promising donor segments.Preparing for Strategic Shifts in Advancement Marketing Looking ahead to his AMA session, Dan previews how he plans to address ongoing strategic shifts in advancement marketing. Drawing on his experience at Drexel, he'll discuss how teams can stay adaptable and evolve even as industry changes continue to accelerate. Attendees can expect insights on managing advancement amidst shifting priorities and learning to position advancement as a strategic university partner. These themes resonate across advancement, university marketing, and college-based roles, making them applicable for a broad audience seeking to elevate their strategies.The Value of Continuous Learning and Experimentation with AI Dan closes with a call for professionals to cultivate curiosity and a willingness to learn. He encourages listeners to spend just 15 minutes a day exploring AI tools, which can lead to new ideas and inspire innovative solutions in their work. Regularly testing and experimenting with AI can demystify the technology and enable professionals to discover meaningful ways it can support their advancement efforts. Dan's advice resonates as a reminder that continuous learning is essential to adapt to new technologies and unlock their full potential. - - - -Connect With Our Co-Hosts:Mallory Willsea https://www.linkedin.com/in/mallorywillsea/https://twitter.com/mallorywillseaSeth Odell https://www.linkedin.com/in/sethodell/https://twitter.com/sethodellAbout The Enrollify Podcast Network:The Higher Ed Pulse is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Some of our favorites include Generation AI and Confessions of a Higher Education Social Media Manager.Enrollify is made possible by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.Attend the 2025 Engage Summit! The Engage Summit is the premier conference for forward-thinking leaders and practitioners dedicated to exploring the transformative power of AI in education. Explore the strategies and tools to step into the next generation of student engagement, supercharged by AI. You'll leave ready to deliver the most personalized digital engagement experience every step of the way.Register now to secure your spot in Charlotte, NC, on June 24-25, 2025! Early bird registration ends February 1st -- https://engage.element451.com/register

Climate modeler Aditi Sheshadri says that while weather forecasting and climate projection are based on similar science, they are very different disciplines. Forecasting is about looking at next week, while projection is about looking at the next century. Sheshadri tells host Russ Altman how new data and techniques, like low-cost high-altitude balloons and AI, are reshaping the future of climate projection on this episode of Stanford Engineering's The Future of Everything podcast.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your quest. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profile: Aditi SheshadriConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/XChapters:(00:00:00) IntroductionRuss Altman introduces guest Aditi Sheshadri, a professor of Earth systems science at Stanford University.(00:02:58) Climate Projection vs. Weather ForecastingThe differences between climate projection and weather forecasting.(00:04:58) The Window of ChaosThe concept of the "window of chaos" in climate modeling.(00:06:11) Scale of Climate ModelsThe limitations and scale of climate model boxes.(00:08:19) Computational ConstraintsComputational limitations on grid size and time steps in climate modeling.(00:10:56) Parameters in Climate ModelingEssential parameters measured, such as density, temperature, and water vapor.(00:12:18) Oceans in Climate ModelsThe role of oceans in climate modeling and their integration into projections.(00:14:35) Atmospheric Gravity WavesAtmospheric gravity waves and their impact on weather patterns.(00:18:51) Polar Vortex and CyclonesResearch on the polar vortex and on tropical cyclone frequency.(00:21:53) Climate Research and Public AwarenessCommunicating climate model findings to relevant audiences.(00:23:33) New Data SourcesHow unexpected data from a Google project aids climate research,(00:25:09) Geoengineering ConsiderationsGeoengineering and the need for thorough modeling before intervention.(00:28:19) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/X

Live from CPHI Milan 2024, this episode is a roundup of conversations with a number of key exhibitors Recipharm, Tjoapack, Renaissance Lakewood, Ecolab and NovaCina. As you'll hear, it was a noisy event, so written transcripts are all available on PharmaSource. Jon Reed, Head of Strategic Business Planning at Recipharm discussed the CDMO's recent strategic moves, including Predictive Modeling and GLP-1 focus. https://pharmasource.global/content/podcast/recipharms-strategic-moves-embracing-predictive-modeling-and-glp-1-trends/ Dexter Tjoa, CEO of Tjoapack, shared insights into the company's 35 year evolution from a small Dutch contract packaging organisation (CPO) to a global player in pharmaceutical packaging. https://pharmasource.global/content/podcast/tjoapacks-global-expansion-bridging-european-and-us-pharmaceutical-packaging-markets/ Eric Kaneps, Vice President of Sales & Marketing at Renaissance Lakewood, discussed the company's expansion and focus on nasal spray technology. https://pharmasource.global/content/podcast/renaissance-lakewood-drives-cdmo-innovation-in-nasal-spray-technology/ Craig Cox, Vice President of Global Sales, Bioprocessing at Ecolab, shared insights into the company's bioprocessing solutions, and how they help support sustainability. https://pharmasource.global/content/podcast/ecolabs-bioprocessing-solutions-addressing-sustainability-and-complexity-in-drug-manufacturing/ Peter Bullard, Senior Vice President of Manufacturing at NovaCina, shared insights into the new Australian CDMO's unique position in the global pharmaceutical market and 50 year legacy. https://pharmasource.global/content/podcast/novacina-new-australian-cdmo-with-50-year-legacy-in-sterile-manufacturing/

Ever feel like you're navigating the property market blindfolded?  You're not alone.  Claire Westra, our guest on Dashdot Insider, has been there.  She went all-in on a DIY approach, thinking she could handle it solo—until she found herself frustrated with a property in Toowoomba that sat stagnant for a whole year.  Her first property investment in Brisbane brought some early success, but when the stock market crash hit, it made her rethink everything. That's when she realised the real cost of relying on emotions and outdated market data. Claire found Dashdot, and suddenly, everything changed.  Using predictive data and a team of experts, she stopped guessing and started investing. Her goals shifted, too—from dreaming of a forever home to focusing on building a life that gave her freedom. She's now well on track to early retirement with a thriving portfolio. Claire's advice? Don't go it alone. Get yourself a team—solid professionals who can guide you through the ups and downs. And be open to new strategies, like rent-vesting or even Airbnb.  She's learned that with the right support, those big, scary goals start to look a whole lot more achievable. If you love this episode, email us at podcast@dashdot.com.au, and don't forget to subscribe, rate, and share this podcast!  See you on the inside!    In this episode, we cover: 00:00 Coming Up 03:24 Realising When It's Time to Seek Professional Help 11:51 Discovering Dashdot: The Game-Changer in Predictive Modeling 14:47 Shifting Goals: From Dream Homes to Strategic Investing 18:04 Key Lessons and Winning Strategies for Success 28:53 Why Professional Advice and Portfolio Planning Matter   Connect With Us: Free Rentvesting Calculator (https://dashdot.com.au/rentvesting)  Subscribe on Youtube (https://www.youtube.com/@dashdotinsider)  Listen on Spotify (https://spoti.fi/3Np19x8) Dashdot Website (https://www.dashdot.com.au/) Ready to work with us directly? (https://dashdot.com.au/discovery) Get your Property Portfolio Growth Plan (https://dashdot.com.au/portfoliogrowthplan) See omnystudio.com/listener for privacy information.

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Discover the future of real estate with AI:   AI is not just a trend.   It's a game-changer.   This week, Sally Johnstone from Altus Group (from whence the Argus software we all know and love comes) reveals how AI is revolutionizing commercial real estate.   Consider this: AI-Driven Insights for NOI Predictions: AI models now predict net operating income (NOI) with pinpoint accuracy. This helps investors make smarter choices about buying, holding, or selling properties. Gentrification Indicators: AI can identify future gentrification indicators, like the presence of flower shops (of all things!). These indicators signal areas ripe for growth and investment. Tailored Market Insights: Altus Group's Market Insights Premium uses machine learning to provide fair market NOI predictions. This helps you know if your property is underperforming or overperforming based on factors like population growth and home values. Overperforming Properties: Properties near open spaces like parks or trails can overperform in NOI by 20-30%. This offers a hidden gem for savvy investors. Leverage AI to optimize your real estate investments.   Don't miss out on these actionable insights from Sally Johnstone at the Altus Group, one of the true pioneers in real estate data analytics. ***** The only Podcast you need on real estate and AI.   Learn how other real estate pros are using AI to get ahead of their competition.   Get early notice of hot new game-changing AI real estate apps.   Walk away with something you can actually use in every episode.   PLUS, subscribe to my free newsletter and get: • practical guides, • how-to's, and • news updates   All exclusively for real estate investors that make learning AI fun and easy and insanely productive, for free.   EasyWin.AI

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This machine learning algorithm runs nightly and is linked to a smart texting application that goes out to patients every morning for 7 days following chemotherapy, asking about symptoms like diarrhea, fever, nausea, vomiting, and pain. Patients reporting severe or worsening symptoms have the smart text escalated to the oncology clinic where they received chemotherapy. Initial analysis broken down by responders (those that opted in and answered the daily text messages) and non-responders (opted out or opted in but did not answer the texts) found that ED visits were 5.7% for responders compared to 6.7% for non-responders. Across the health system, about 30 responders are added daily to the program. Guest: Michelle Eichelmann Executive Director, Oncology Services and Precision Medicine Mercy, Mercy Oncology Services Saint Louis, Missouri   “It's one thing to mine data out of our EMR, but to actually use it in a proactive approach to patients I think is very unique. That's the key behind this...not just data, but what do we do with the data?” —Michelle Eichelmann Hear more about this innovation at the ACCC 41st National Oncology Conference, October 9-11, 2024, in Minneapolis, Minnesota.   Additional Resources: Smart-Texting High-Risk Patients After Chemotherapy Reduces ED Visits – ACCCBUZZ Blog Reducing ED Visits and Hospital Admissions after Chemotherapy with Predictive Modeling of Risk Factors Utilizing Technology to Identify Patient Co-Morbidities and Reduce Hospital and ED Admissions ACCC 41st National Oncology Conference Registration

Keith Goode is the vice president of products and services at ZeroedIn Technologies - an organization that uses data analytics to make HR decisions more effective. In this episode Paul and Keith discuss the role that data analytics and HR can responsibly play, but also the challenges that emerge when organizations rely too much on data. Resources mentioned in this episode ZeroedIn Technologies - https://www.zeroedin.com Keith's LinkedIn - https://www.linkedin.com/in/keithagoode/Humanity Working is brought to you by BillionMinds - the company that makes employees ready for the Future of Work.BillionMinds helps companies be ready for the future of work by developing adaptable, resilient employees. You can learn more about them on LinkedIn or by visiting billionminds.com.

This machine learning algorithm runs nightly and is linked to a smart texting application that goes out to patients every morning for 7 days following chemotherapy, asking about symptoms like diarrhea, fever, nausea, vomiting, and pain. Patients reporting severe or worsening symptoms have the smart text escalated to the oncology clinic where they received chemotherapy. Initial analysis broken down by responders (those that opted in and answered the daily text messages) and non-responders (opted out or opted in but did not answer the texts) found that ED visits were 5.7% for responders compared to 6.7% for non-responders. Across the health system, about 30 responders are added daily to the program. Guest: Michelle Eichelmann Executive Director, Oncology Services and Precision Medicine Mercy, Mercy Oncology Services Saint Louis, Missouri   “It's one thing to mine data out of our EMR, but to actually use it in a proactive approach to patients I think is very unique. That's the key behind this...not just data, but what do we do with the data?” —Michelle Eichelmann Hear more about this innovation at the ACCC 41st National Oncology Conference, October 9-11, 2024, in Minneapolis, Minnesota.   Additional Resources: Smart-Texting High-Risk Patients After Chemotherapy Reduces ED Visits – ACCCBUZZ Blog Reducing ED Visits and Hospital Admissions after Chemotherapy with Predictive Modeling of Risk Factors Utilizing Technology to Identify Patient Co-Morbidities and Reduce Hospital and ED Admissions ACCC 41st National Oncology Conference Registration

Keith Goode has been the Vice President of Client Services at ZeroedIn for more than 10 years, where he leads the implementation and support efforts of the ZeroedIn platform used to transform HR, talent and business data into actionable intelligence. ‌ Previously, Keith held various management consulting and corporate roles at General Dynamics, AON Hewitt and Saba. With over 20 years delivering human capital management and business intelligence solutions, Keith finds it exciting to deploy tools and techniques such as Data Mining, Collective Listening, Machine Learning and Predictive Modeling in a platform that solves real world HR and business issues. What you will learn Discover how AI is revolutionizing HR by turning data into actionable insights for better decision-making. See how workforce analytics can help organizations uncover valuable insights from historical data to improve their human capital strategies. Learn how analytics can supercharge learning management systems, helping employees get the right training to meet company goals. Find out how AI models can identify organizational trends and predict future changes to stay ahead of the curve. Explore how data-driven insights can help find the perfect balance between remote and in-office work in the post-pandemic world.'

Highlights from this week's conversation include:Clint's Background and Journey in Data (0:51)Starting a Data Career (2:01)Transition to Startup SaaS World (4:27)Clint's Connection to a Federal Reserve Database (5:31)Challenges in Predictive Modeling (10:27)Data Input Challenges (15:50)Marketers' Workflow and Data Integration (18:29)Soft ROI vs. Hard ROI in Data Analysis (00:21:31)Balancing Internal Marketing and Data Team's Value (22:35)Simplifying Data Inputs for Predictive Models (25:09)Data Analysis Workflow and Tech Stack (29:06)Open Data Formats and Impact on Data Platforms (34:40)The S3 and Ecosystem Model (37:08)In-browser SQL Queries with DuckDB (39:24)Data Security Concerns and Solutions (41:47)Clean Rooms and Data Sharing (43:32)Final Thoughts and Takeaways (47:35)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.

In this episode, Seth interviews Alex Hall, CEO of Concrete AI, to discuss the impact of AI on the concrete industry. They explore the use of AI in concrete design, material optimization, and the challenges of carbon reduction. Alex shares insights on the future of concrete technology and its global impact. Takeaways AI is revolutionizing the concrete industry by enabling material optimization and carbon reduction. The use of AI in concrete design allows for predictive modeling of concrete performance based on raw material data. The future of concrete technology involves addressing the global impact of concrete production and consumption. The challenges of carbon reduction in the concrete industry require innovative solutions and a shift towards low-carbon concrete products. The history and evolution of concrete pumping have significantly influenced construction practices, especially in high-rise and infrastructure projects. Chapters 00:00 Revolutionizing the Concrete Industry with AI 07:30 Predictive Modeling and Material Optimization in Concrete Design 33:49 Challenges and Solutions for Carbon Reduction in the Concrete Industry *** Did you learn something from this episode? If so, please consider donating to the show to help us continue to provide high-quality content for the concrete industry. Donate here: https://www.concretelogicpodcast.com/support/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ***  Episode References Guest: Alex Hall | Concrete AI |  ⁠⁠ concreteai@vsc.co Guest Website:⁠ https://www.concrete.ai/   Producers: Jodi Tandett Donate & Become a Producer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.concretelogicpodcast.com/support/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   Music: Mike Dunton | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.mikeduntonmusic.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mikeduntonmusic@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Mike_Dunton⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Host: Seth Tandett, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠seth@concretelogicpodcast.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Host LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/seth-tandett/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.concretelogicpodcast.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/concrete-logic-podcast

Host Victoria Guido welcomes Wendell Adams, CEO of PrimeLab.io, as he talks about his lifelong passion for technology and entrepreneurship. Wendell shares his experiences, from hacking electronics as a child to studying various fields in college and eventually starting his own business. He emphasizes the importance of understanding market needs and leveraging language to make technology accessible. Wendell's drive to improve encryption and data security led to the formation of PrimeLab; a company focused on making encryption functional and accessible without compromising performance. Wendell discusses PrimeLab's strategic direction and market fit. He outlines the challenges and opportunities in the entertainment industry, emphasizing the need for innovative solutions that respect user control and privacy. Wendell also shares insights into how PrimeLab's technology can democratize data access and enhance business processes. The episode concludes with a reflection on the future of AI and encryption technologies and Wendell's advice for aspiring entrepreneurs to think critically and creatively about their ventures. PrimeLab.io (https://primelab.io/) Follow PrimeLab.io on LinkedIn (https://www.linkedin.com/company/primelab-io/), or X (https://x.com/PrimeLab4). Follow Wendell Adams on LinkedIn (https://www.linkedin.com/in/wendell-a-83317895/). Follow thoughtbot on X (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Transcript:  AD: We're excited to announce a new workshop series for helping you get that startup idea you have out of your head and into the world. It's called Vision to Value. Over a series of 90-minute working sessions, you'll work with a thoughtbot product strategist and a handful of other founders to start testing your idea in the market and make a plan for building an MVP. Join for all seven of the weekly sessions, or pick and choose the ones that address your biggest challenge right now. Learn more and sign up at tbot.io/visionvalue.  VICTORIA: This is the Giant Robots Smashing Into Other Giant Robots podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido. And with us today is Wendell Adams, CEO at PrimeLab io. Wendell, thank you for joining us. WENDELL: Thanks for having me. So, question, actually, where'd you guys come up with the name? VICTORIA: You know, I have asked this before, and I think I remember the answer. I might have to go back to the 500th episode to get it, but I think it was just robots was already kind of a theme at thoughtbot. I mean, thoughtbot, obviously, has robot in the name. Joe might have the best answer. And we have our special co-host, Joe Ferris. Who better to answer? JOE: [chuckles] Yes, I'm not sure who better to answer, probably Chad. I don't remember the answer either, but happy to be here to speculate with the two of you. It comes from the blog. We named the blog Giant Robots Smashing Into Other Giant Robots and then used it for our podcast. But I don't remember where the blog name came from. WENDELL: It kind of reminds me of the Robot Wars thing, like, where they would have competitors driving around the robots and then smashing into each other, trying to flip them over and disable them. JOE: That was excellent. I also watched that. WENDELL: [laughs] VICTORIA: Yeah, it's a pretty great name. I really enjoy being a host. And, you know, I go out to local San Diego events and meet people and introduce myself as a co-host of Giant Robots Smashing Into Other Giant Robots. It's usually pretty funny [laughter], which is where I met you, Wendell; we met at a San Diego CTO Lunches, which was super fun. WENDELL: Yeah, I always enjoy any type of tech conversation or anything else. I thought that was a lot of fun to sit down and just talk with people and talk about what they're working on. VICTORIA: I love that, yeah. And before we dive into the tech and get to hear more about PrimeLab, I just want to start a little more socially question. What did you do last weekend, Wendell? WENDELL: It was my father-in-law's birthday party at Legoland. We took my daughters my mother-in-law, and we all went to Legoland. It was a lot of fun. Although, honestly, I prefer the San Diego Zoo over Legoland, so... VICTORIA: Can you please describe what Legoland is to people who may not know? WENDELL: Okay. Legoland is based in Carlsbad, and it's really ideal for, like, four to nine-year-olds. And they have, like, miniatures of all the different cities. Actually, the SF miniature that they have is crazy detailed with Chinatown and everything else. They did an amazing job there. They actually...I think they just redid the San Diego part of it. But the miniatures are really cool, seeing all this stuff. They have different rides performers, but it's definitely, like, one of those things that it's more for kids to go and kind of experience. If you're an adult, you're going to love a lot of the processes that go into place, like how they built things, but mostly, yeah, it's very much kid rides and stuff like that. VICTORIA: I imagined it to be, like, life-size Lego buildings, but maybe I'm...that's very interesting all those other things you could do there. WENDELL: Well, like, they have the One World Trade Center, and I think it's, like, 25 feet tall. It is, like, the replica of it. It's kind of interesting, too, because not all the Legos that they build, they're huge, are solid Legos. So, it's like, they'll do where it's like, on the outside, they'll do a base, and then they'll build it. There's a replica of a Lamborghini. That one's life-size. But it's heavy. It's, like, 2,000 pounds, something like that. VICTORIA: Is that as much as a regular Lamborghini weighs, too, 2,000 pounds? It can't be that far up. WENDELL: I don't know. No, I don't think it...no, it couldn't be. VICTORIA: I have no idea how much cars [laughs] weigh. What about you, Joe? Did you do anything fun this weekend? JOE: Not a lot. It was supposed to be my son's first soccer game ever, but it rained here in Boston, so they postponed it. Sunday he went to my parents' house for a grandma day, and so I did nothing. I ate cookies. WENDELL: [laughs] VICTORIA: Wait, what kind of cookies were they, though? JOE: They were chocolate chip cookies. VICTORIA: That's so good. JOE: They were good. They were brown butter chocolate chip cookies, I should say. VICTORIA: Were they homemade, or did you get them somewhere? JOE: They were. We made them in this home. VICTORIA: Oh, that's the best. Yeah, love that. I got some fancy cookies that someone else made, and they were also [laughs] very good. And then, yeah, I've just been having cookies pretty much every day. So, that's been my time. WENDELL: My mother-in-law recently made me peanut butter cookies, and those are my favorite kind of homemade cookies. VICTORIA: Okay. Noted. You'll get a post-podcast gift of peanut butter cookies [laughter]. I love that. It's so great to hear a little bit more about each of you as, like, in a personal way before we dive into AI. And tell me a little bit more about your background and what led you to PrimeLab. WENDELL: I've always kind of, like, been a hacker, so to speak, just from a technical standpoint. My one grandfather was an engineer. He worked for GM designing, like, assembly arms and stuff like that. And then my other grandfather was a master electrician. So, I've always been the person that, like, just worked on things, got stuff together. You know, there's a lot of stories. Like, there's the story about when I broke my grandmother's workbench, rocking bench out front, and it was all aluminum. I remember telling my grandfather, and he's like, "Oh, what are you going to do?" And I was like, "Buy a new one?" He's like, "You got money?" I said, "No." And he said, "Well, you better figure how to make it then." So, ironically, it's half aluminum, half wood. We took wood, sanded it down, and stuff. So, it's just like I've always been an entrepreneur. I've always been interested in this kind of stuff. I used to hack VCRs, and PlayStations, and all kinds of stuff. I always liked parts and components and rewiring things. And as I got older, I also really liked math and all those things. And I wanted to understand more about how the world works, so to speak, like why it works the way it does, not just from a technology standpoint. But why do people think the way that they do? Why do things behave the certain way they do? So, initially, I started going to college. I thought I might be a math professor, and then decided to get degrees in business, economics, finance, marketing, consumer product goods, and comparative religions. So, while I was in college, I started working on, like, hacking, different video games, writing JavaScript, writing Java, all kinds of stuff. And then, eventually, even writing mobile applications early on, and then just analyzing because I always liked to build phones, too. I would take apart phones. And I really was curious about, like, how to make things faster, more efficient, and better. So, now to bring it down, like, how to make things accessible, where it benefits some of the smallest people and make it where it's a greater opportunity for someone to come out ahead of something. Like, one thing that I learned from my marketing degree is language matters. So, it's like, all the marketing it's not anything special. It's just they intentionally create language barriers that cause people not to feel as accessible with it. And then, like, you hire a consultant or something to just basically teach you about those language barriers. And I think every industry has, like, SAT, or LTM, or something like these abbreviations that mean a lot of different things. And it causes bottlenecks if you don't speak the language. So, understanding the language but also learning about how was very helpful from a standpoint on the marketing side. And I always try to figure out how do I make this accessible to people who don't understand that language? VICTORIA: And what was the turning point where you decided to start PrimeLab, and what made you realize there was a company there? WENDELL: It was a project I've been working on since at least 2011, honestly. And just as a heads up, PrimeLab as a whole works with encrypted data for AI models and to speed that up and everything else. So, early on, I was very obsessed with how advertising works through, like, stealing user data, which stealing is different, here or there, the sense of privacy, the sense of, like, how things could run, and the sense of messaging. And initially, a lot of it was using encryption as an overlay in, like, the pixel application space, which is always a way to hack or get into it. And it slows everything down. So, I had always been working on trying to figure out how do you speed up and embed security so it's actually functional? And it took a while to figure out, like, give encryption functionality, like, make the encryption something that you could actually execute on. And, actually, one of the things that really helped is the blockchain space there's a lot of, like, hash trees and everything else, like, where people are innovating in that. That's really helped innovate encryption as a whole from understanding, like, Merkle trees, hash graphs, and everything else to make it more functional and faster. Because people are trying to speed up distributed networks and stuff, but the actual technology that they built, like Hedera is...What Hedera has done with Hashgraphs and everything else—really amazing. I'm glad that they open-source stuff like that. But it's also really interesting just to see how things push forward. So, like, when I first started, like, RAM was, like, 256 in a phone. So now, you know, you can get multiple gigabytes, which makes it a lot more capable to do encryption, decryption, and work more in the functional space of things. The bigger problem that you have on the data part is how an application communicates because there's so many levels of abstraction. Like, you have the Swift language that communicates into something else that then communicates into something else. Like, right now, we're talking on a system that's recording us over the internet through a browser, all those different things. And it's an approximation of what the data is and what we sound like. It's not an absolute. So, I was really interested in when you have absolutes, and you can verify those absolutes, what can you do with that? A few years ago, I felt like we got to a point where we could actually execute those things and actually deliver on that. So, therefore, I decided to start PrimeLab with my co-founder, who I really liked and enjoyed. And we've had a lot of really great advisors, where people have helped us continuously. Over, you know, the decade-plus of working on this, I've gotten a lot of input from some of the smartest people I know, from people who have designed full server racks for AWS to literally a good friend of mine that built cloud storage. His name's on the patent for it. So, that kind of stuff has really helped me understand and build this where it can communicate the lowest possible level. VICTORIA: Yeah, and to just recap and reflect that back a little bit, it sounds like you were always interested in how to make encryption faster and lighter weight, and so you could build it in and build in security without impacting the performance of the applications. And then meeting your co-founder and the advancement of technology, this time a couple of years ago, led you to think, okay, let's really go forward with this. WENDELL: Kind of rephrasing, I was always interested in control. So, like, one of the things that really interested me...so, I started a video game store buying and selling, like, video games and trading cards and stuff when I was roughly ten and a half or so, and then sold it roughly when I was 17, which is how I paid for quite a bit of college and likewise. But the things that really interested me about that is it went out of business three to four months afterwards because the person who basically bought the rest of it bought too much of Madden. And Madden, at this time, the margins were, like, a buck, as you go all the way through, and the price drops immensely. So, I wanted to really understand why that happened. What you kind of get to is, like, they didn't have control over it, just, like, the bulk orders methodology, where they would buy the whole entire supply. And what I've seen over the years, be it Apple, Google, or anything else, is, like, that was...in that example, that's a game publisher, EA, flexing control, right? But more and more companies are flexing control on a platform like now with Facebook or advertising. If you think about what Google used to do, Google used to provide a lot more insights when you had your own website. You used to know your own keywords. You used to know a lot of things about your users who come through. More and more, Facebook and Google try to stop that. And they're really the ones determining your own user personas for you. So, you become dependent upon them. So, I wanted to say, okay, from a business standpoint, how do you implement control and privacy where it's permissioned? And encryption was one of the answers that I came to. But then it was, how do you make encryption functional then to actually execute on control? Because unless the system is secure, faster, cheaper, better, it's never going to get adopted. VICTORIA: That makes sense. Thank you for sharing that. And you mentioned your founder. I'm curious, how does your founder kind of complete what you needed to be able to get the business up and running and off the ground? WENDELL: He has a robotics degree, so he had launched several products that had failed. And he wanted to learn marketing after they had failed. So, we have a similar like mindset about, like, control and functionality for how something may or may not work, and that allowed us to communicate well. So, like, I have a lot of friends and stuff. But the thing that allows me and my co-founder to work really well is that we come from things in different angles, but we have the same language that we speak. So, like, that's what I was talking about before, like, LTMs or otherwise, like, language really matters from how you can move something forward when you're talking in different industries. And just with him, there's a lot of stuff that you don't have to say. You can skip a lot of filler and then go straight to what something might be or a solution or something. Or if we have to jump to a tech abbreviation, to a market abbreviation, to a financial abbreviation, he's one that can follow along with me really quickly and then teach me a lot of things about operational execution because he's great at operations. I am not great at operations. VICTORIA: That's really interesting. And I think you're making a good point about, like, a shared language. And it reminds me of any product that you're building; if you want to sell it to a company and you want them to adopt it, you have to consider their language, their belief system, how to influence change within the organization. And I wonder if you could talk a little bit more about that with your experience at PrimeLab. WENDELL: I'll give you an example of a market that we decided to go after. So, instead of just working at, like, healthcare markets where you have, like, GDPR...for people who don't know GDPR or HIPAA, HIPAA is for the United States. GDPR is the EU privacy requirements, right? For the right to be forgotten and everything else. So, these are vernaculars that you need to know. But the requirements of each one is very different, and these are markets that we've learned being in tech and likewise. But we wanted to change it up. So, I wanted to go after the entertainment market as a whole, namely because after meeting with some select people, including a stunt man, this is going back a few years ago, I started to realize that the entertainment market was getting kind of screwed over quite a bit from a tech standpoint. Basically, tech goes through this thing where...someone wrote a great article about this. It's called Enshittification. But, basically, where they go they try to take over a whole entire market, where first they're providing great value to your users. And then, gradually, you enshittify your product to provide greater value to your investors. And then, gradually, you suck all of the value out of the room for both. Right now, if you look at Sora, what OpenAI is trying to do in entertainment, [inaudible 16:08], you kind of can see that happening. They're going, "Hey, here's a great value for it." And they're really pushing that stuff off. But the thing about the entertainment market that I think is really interesting is it's basically thousands and thousands of small businesses that are constantly going, it's so chaotic. It's not like tech and startups. There's a lot of overlay of, like, you know, people are looking for that top quartile film that's going to make the money back, and then long-term royalties that they can earn off of it, right? Whereas in tech, they're looking for those huge markups as well. So, I was really fascinated by it, but it was something that, like, we had to learn. Like it was something that I didn't know otherwise. So, it was literally...how we learned it was we took our tech stuff, and we would walk SAG-AFTRA strike lines. We would walk strike lines. We would go to entertainment events, and we would demo what we were trying to do, and we would show them. And then, oftentimes, we got really negative feedback right off the bat. And we're like, "No, no, no, so, you know, this is for you. Like, you could control. Like, this is going to help you." And then, after doing that enough times, talking to the SAG-AFTRA lawyers, and everything else from there, and all of the creatives, the creatives were coming to us and giving us ideas how to explain it because there's, like, three different formats. You have tech, business, creatives in the entertainment industry. And it's like, we could talk to the tech people. We could talk to the business people. But you really need the creatives. And, like, the wording of each one, like, each group of those is vastly different. So, having the creatives be able to explain something in 90 seconds that used to take me a couple of hours to dive into became really valuable. And also, in tech, like, you have this thing where it's feature creep, where you're like, oh, I'll add this, this, and this. Just to hear very coldly and bluntly, like, "If it does X, I'm interested. If it does Y, I'm not interested." That was very interesting or refreshing of, like, "Yes, you're going to solve these problems. But I need sign-off for everything in there." And it's kind of weird in the entertainment part, too. Like, you want to solve a problem without being a competitor to another vendor because you need so many different sign-offs. And if you're a competitor to another vendor, to a certain point, maybe that's going to cause a hiccup with sign-offs because there's 18 different cooks in the kitchen, so to speak, just so many different people that need to say, "Yes," all the way through with it. VICTORIA: Thank you. Yeah, that's really interesting. I'm curious, Joe, if you have an answer for that question as well, like, any experiences about navigating change and putting new products in place at different clients, different industries? JOE: I don't think I've had the same kind of resistance. Like, I haven't been on the front lines the way you described, like, literally in the, you know, going and talking to people on strike. I think I have more indirect experience talking to the people who are doing that. And certainly, like, I think there's generally a resistance to bringing in new technology without eliminating the old way of doing things if that makes sense. Like, people want the old ways of backup. Like they want to be able to go back to paper, which I empathize with. But that's frequently been a challenge for the people I've worked with is that they don't fully embrace the new process, which significantly reduces the value they would get from using it. I don't know if that's something you've encountered with PrimeLab. WENDELL: So, we were building another company of mine many, many, many years ago. I was building a website for this lumber company, and I remember showing up, and the owner was there. But it was his son that had commissioned it, and the owner didn't know about the website. And I was like, "Oh yeah, we'll get the website going." He goes, "Oh, this web thing it's a fad. It's never going to happen. You don't need websites. It's faxes." That's how everything would happen. But secretly, what was happening is they would get an order. They would print it off, and then they would fax it. So [laughs], I always thought that was crazy. VICTORIA: I mean, one of my local bars still just writes the order on a ticket and sends it on a clothesline down to the grill. So [laughs], sometimes old is good. But I think that you know, I want to hear more about where you found or how you found a product-market fit for PrimeLab and where that AI really becomes useful and ethical in the industry you're focusing on WENDELL: How I look at PMF (product-market fit)...and if you hear me just say PMF, that's what that means. So, how I look at PMF is I'm a little different in the fact that when I look at a product, or a technology, I don't just look at, like, so you have foundational tech. Like, okay, this is encryption. This is control, right? Now, where's the market that has the biggest problems with it? So, I like to go out and actually talk to those people. Because, like, when you're implementing tech, or you're implementing the product itself, it's different. So, you're like, you have the underlying infrastructure, but whether that's a button or a simple API that you need to build so it works different to hit that PMF...are you familiar with the term build a better mousetrap? VICTORIA: I don't think so. JOE: I'm familiar, but I'd still love to hear you describe it. WENDELL: So, in business school, and likewise, they will tell you "If you build a better mousetrap, people will come, and they will buy your product." So, like, it's a common thing where they're like, "Build a better mousetrap. People will come. They'll be there." And the thing that you learn with consumer product goods and marketing, though, is they actually built a better mousetrap, and it failed. And the reason why it failed is you had a mousetrap that was roughly a cent versus another mousetrap that was three cents. And I think this is in the '60s or so. The other mousetrap was reusable, so it executed a lot better, and everything else is more humane. But what they didn't understand is that it was wives most of the time that would have to actually handle this. And they didn't want the mouse alive, and they didn't want to reuse the trap. They wanted them to actually be disposed of right away. So, by not understanding the market, even though they built a better mousetrap, they'd missed the point. Like, the main problem to solve wasn't killing the mouse or having it be reusable. The main problem to solve was, like, getting rid of the mouse. So like, if you have a solution for getting rid of the mouse, the next thing is your execution for it. Like, does it hit the actual market, which is the fit aspect? Like, every product is a little bit different where you look at, like, how does this fit in? So, in this case, fit is very important for, like, disposing of the mouse, which is why you also have, like, you know, mouse poisons are popular, even though they're terrible because they die somewhere and, hopefully, you don't see them. And it's like sight unseen, right? Now, I'm glad, like, that's changing and stuff. But it's understanding even if you have a solution to something, you need to understand what your market wants out of your solution, and it's not going to be an abstract. It's going to be an emotional, like, execution-based process. So, you kind of have to go, all right, this is my market. This is kind of my fit. But the actual product I'm building is going to change to make sure it works all the way through with this. I was advising a startup many, many years ago, and they were building this CRM software on Android for South America. And I think they were building it for Android 6 or 7 at the time. But the market that they were targeting, they all ran Android 4.1. So, they spent a little over a million dollars building for the wrong version of Android that wouldn't even work on that version of the system. Like, it was one of those things where they were required to build it for that. But they didn't understand the actual market, and they didn't spend enough time researching it. So, it's like you get the Bay Area groupthink. If they had actually spent the time to analyze that market and go, "Oh, they run, you know, an inexpensive phone. It's 4.1. It's low RAM," now you can design a product. If you want it to be a CRM, you're going to, like, chunk up the system more. Like, you're going to change all that instead of just wasting a million dollars building something that now you basically have to start over again from scratch. VICTORIA: That seems like he got off cheap, too. People make way bigger mistakes that cost way more money [laughs] because they [inaudible 24:13] WENDELL: Well, that wasn't me. That was an investor that -- VICTORIA: Oh no. I mean, yeah, not just them. Yeah. WENDELL: He's like, "What would you do?" And I was like, "You should sell this company or sell your stake ASAP because that's a really bad sign." JOE: I have found that the answer nobody ever wants when you're doing product validation or testing product fit is, "You should not build this product." The idea that the software just shouldn't be written is universally unpopular. WENDELL: Yes [laughs]. That's, you know, that's part of the reason why it took me so long to do PrimeLab is because, like, it took a long enough for the software to actually need to be written, if that makes sense. Mid-Roll Ad: When starting a new project, we understand that you want to make the right choices in technology, features, and investment but that you don't have all year to do extended research. In just a few weeks, thoughtbot's Discovery Sprints deliver a user-centered product journey, a clickable prototype or Proof of Concept, and key market insights from focused user research. We'll help you to identify the primary user flow, decide which framework should be used to bring it to life, and set a firm estimate on future development efforts. Maximize impact and minimize risk with a validated roadmap for your new product. Get started at: tbot.io/sprint. VICTORIA: What does success look like now versus six months or even five years from now? WENDELL: I take a different approach to this because I have so many friends that have sold their businesses. They raise and everything else. I look at success as instead of an exit or another large thing, like, literally, we turned down a billion-dollar term sheet offer. I didn't like the terms. I didn't like what it would do from the control standpoint of the technology. What I care about is go-to-market and, like, adoption and actually getting the tech out there in a way that has market penetration but, like, that adds value to every person's life. VICTORIA: Yeah, maybe say more about that. Like how do you see AI and this technology you have with PrimeLab benefiting people and benefiting the industry that you're working within? WENDELL: So, the current AI models are kind of weird. They're basically just filter systems because they communicate in pixel space and then go down to functional space. It's the GPU. GPUs are actually terrible to use for AI. This is why you have dedicated AI chips getting built. Hopefully, the RISC-V chipset does actually do something because that's a chipset that I think it's an open-source chipset, but you can actually especially build models on it. So, I think that we're going to see a lot more in the RISC-V chipset where it's like, this is just for one particular image, or this is just for explosions, or this is just for touching up all these different points in the actual individual, like, microcontroller module data that ends up compiling to move forward with it. But the AI models now it's like you took the internet, and you're trying to ask it a probability question, what I was talking about before, where it's not an absolute. So, it's like, if I want to do an OCR system or anything, I take an image. It's got to say, "This is..." letters; it's going to recognize that. So, there's, like, multiple models and algorithms that need to run on that whole entire process. You even have artificial data, but all of that information is an approximation. It's not an absolute. If you want absolute, you can get a lot of absolute data from the actual hardware devices themselves. You know, take a Sony camera. You could see the lighting. You could see the raw information, everything else there. But because of how expensive it is, people compress it. Like, take YouTube where it's compressed, and now you're training off of it. You're trying to compress it more and then run an algorithm so that you don't have to actually process those large, raw files all the way through. That's just a bad infrastructure for compute. You're trying to reduce, but you're also trying to utilize what you own for rights, same thing, contextual, or anything else there. There's no value in a model. Once a model is out there, it's just weights moving it back and forth. The value is in the data and the applications. So, the actual data itself that's going in. So, if you have just lava scenes, like, having all that data for lava, and I want to put it in a background, now I can do that, but more importantly, it's not about just adding it into the background. The thing that is often missed is contextually the output. So, like, say I want to do a financial report. Rather than having the data of all financial reports out there, what I want as the input is my financial data. And what I want as, like, a fine-tuning output is an example of the reports that were generated. And I don't want those reports as the input to inform the output because that's where you get a hallucination. Maybe it starts grabbing financial data from someone else. And I also think we're in store for a lot more hacks because with not just poisoning data, which we do in the functional space, if someone tries to access it. But, I mean, literally, there's the story...I think the guy was in Hong Kong, where they faked his board all the way through with it. Because you have agents acting and executing on people's behalf, you're going to have systems where people go onto the hardware and start generating fake financial numbers. And now that's going to get reported. Or you pay an invoice that you weren't supposed to pay because someone manipulated your AI agent. And a lot of the stuff that we're seeing now from Microsoft and everything else that's not really where the models will go. It's great to do it, but it's kind of like we're in the dial-up stage of AI. Like [chuckles], dial-up has its use cases and stuff, but it's nowhere near what the tech will look like in the future, and it's nowhere near how it will function. And one of the big pushbacks that you see, like, from Google, from all these different places, like, they want your attention. But at the end of the day, Google's an ad company. Facebook's an ad company. It's not in their best interest to have hyper-localized data that you control for your models and likewise. They want it in the cloud. They want it used there, where they can control that data, and they can monetize and advertise for you. But at the same time, like AI models work the best, and AI applications work the best when the data set is limited, so it can't hallucinate, and when the outputs are actually controlled to what it should be from an informed standpoint. So, where we're at this is just in the beginning stages of stuff. VICTORIA: That's really interesting. Thank you so much for sharing. I think if you could go back in time when you first started PrimeLab and give yourself some advice, what would you say? WENDELL: You know, I lived through the Great Recession. The Great Recession informed me a lot more. The things that I didn't understand this time...like the Great Recession, was market contributors doing stuff that impacted everyone with their spend and their adoption, and how those things were. But the Fed raising interest rates, which is, you know, Silicon Valley Bank failed and stuff like that, that dynamic of those startups and, like, how much startups power everything, like, I would have advised myself to pay more attention to the Fed and those market dynamics going forward. Because what changed is it's not just the Silicon Valley Bank failed it, you know, Rippling went down, for instance, which would pay therapists in Florida and all kinds of stuff. Like, it broke so many different things. It caused bottlenecks in business that we're still going through. Like, everyone's like, "Oh, we're getting back to normal." Really not. It's still, like, delayed all the way through it. The AI aspect is really getting back to normal, where people are really pushing AI. But if you look at SaaS and other industries, it really, really slowed down. And the reason why that matters is, like, in my field, production and timelines matter. So, when you have that plus, you know, the entertainment strike and everything else, you have things where the actual production of things starts slowing down immensely. Whereas AI is one of the few things that you still have innovations because that never really slowed down, same thing with the models. But all the rest of the industries and stuff have really slowed down. And understanding what that means from an operational execution standpoint...it's a good thing I have my co-founder [inaudible 32:24]. It matters quite a bit because it means your team sizes have to change, how you handle certain clients has to change. Because once those companies start downsizing or laying off people for whatever reason like, that's going to change how you're working with them, and their requirements are going to change as well. VICTORIA: And what do you see on the horizon as a challenge or a big hurdle that you face as a company or as an industry? WENDELL: You know, the entertainment market's really interesting from all the different sign-offs. The challenge is more execution of timeline. So, like, if you're doing something with, like, Nvidia and the healthcare thing, it could take years. If you're doing something in, like, the IoT space, you know, also years. If you do something in the entertainment space, it could take weeks to months, except the large studios. The larger studios, it could take a couple of years as well. But going to market, I think, is a very big challenge, not just for us but the whole entire industry. I mean, there's a reason why Sam Altman came down to LA to meet with studios, to try and get stuff moving forward. And I think one of the things that he's forgetting is like, you think of Netflix. Netflix is streaming. In order for that to work, they needed Roku, and they needed Kevin Spacey because [chuckles]...it's crazy to say that, but House of Cards is kind of what made it, right? And Hollywood was mostly boxing them out quite a bit. Same thing with Blockbuster otherwise. They had to drop a hundred million dollars, a large enough bankable star at the time that would really push something forward. And they had to basically really push Roku out there so that they had PMF across the board. What that means, though, is, like, Netflix is paying for content like crazy, right? So, this is kind of enshittification in a process. So, they're paying for content like crazy. So, now Hollywood's making money. They like it. At the studios, they don't love it when their stuff's going there because maybe it's less money, but now they start cutting the seasons short. They start cutting...it's a lot more algorithmic-driven. You have the ad systems that sort of come out. So, now, like, Netflix is not just doing ads where the customer experience is getting worse, but now, also, the business experience for those partners selling stuff is also getting worse, and all that value is getting driven to Netflix. Like, that's the tech system and Hollywood's learned that. But, like, when you're looking at the next adoption, like, they're hesitant for that. Just like a lot of stuff with AI, they're hesitant because they're thinking about all the power and control that they gave up. But you have to show how they're going to make money. You can't just cut costs, right? If you can't show how they're going to make money, you're not going to get adopted. That's kind of what I like there because so much of tech is about saving costs and being more efficient. In the entertainment industry, it's not just those two things. It's how can I make more money? And it's going to, like, ooh, you can monetize your content through training samples and stuff like that. So, our model goes exactly against what the large tech companies have where they want to take content, train on it, like the search engine does, suck the value off Sam Altman's Sora. Ours goes, all right, this is your content. Only you own this. You can take your own content, train it, and then perform this operation on it that is more efficient likewise. And if you choose to monetize it in any way, shape, or form, we can just take the functional space, not all the images and no one will ever see it, and take that functional space for training so that you can actually monetize from that as well. VICTORIA: I love that. Super interesting. Thank you so much for sharing. And do you have any questions for me or for Joe? WENDELL: I've noticed a lot of differences on, like, applications and how systems are built. So, I'm kind of curious about you guys' standpoint about applications, you know, the Apple Vision Pro. Facebook just said they'd start licensing out their AI system, or Meta, whatever. So, you have the comparisons to Android versus iOS that's happening, stuff like that. So, I'm really curious about, like, you guys' thoughts on the Vision Pro and that ecosystem. JOE: Well, I can't speak for all of thoughtbot, but I can say that, to me, it was interesting to see that get released. And it's been interesting to see how aggressively Meta and Apple have been pursuing the various VR markets. Like it reminds me of when television companies and studios worked really hard to get 3D movies to be a thing. WENDELL: [laughs]. JOE: Because I think they just ran out of things that people are asking for. Like, people were interested in getting better resolutions up to a point. Like, they wanted better packaging. But it got to a point where it was like, they didn't want to give anybody anything they were asking for. So, they were like, what if it's in 3D? And, like, for years, it seemed like Apple was really on top of seeing what people really wanted, and being able to present a very well-prepared version of that product before other companies were able to. And, personally, it's not what I saw with the Apple Vision Pro. Like, it wasn't the obvious missing space that was there when the iPhone or the iPad showed up. WENDELL: Yeah, I always go back to, like, the "Why?" question. You know, previously when...even just before we had talked, I was talking about comparative religions, and why that's so valuable is because it really teaches you...again, I've had this conversation before, but the comparative religions, if you think about religion as a tech company, they're always trying to solve why. Like, why did the sun come up? Why did this happen, right? And you always have to do that. So, apply that to technology, Google or Apple, why does this product exist? And when you get to, like, it just existed to make money, I think that's really the 3D thing. Whereas, like, why did the iPhone exist? It existed to solve this problem of being portable on the go and getting information in the way that we communicated, too. VICTORIA: Yeah. I think the Apple Vision Pro appeals to a very specific market segment and that that segment is not me [laughter]. I, actually, during COVID...after...it was, like...yeah, we're still in COVID. But during the pandemic, I moved from DC to California. And to connect with some old friends, I bought a VR headset and decided to go to virtual coffee with them. And it just makes me nauseous. And it actually affects...quite a lot of women get nauseous in VR. For some people, the look—the capability is really exciting. They have the extra money to spend on gadgets, and that's what they like. And it's very appealing, and the, like potential, is really interesting. I just find it for myself. Personally, I'm more drawn to tech that's not maybe cutting edge but solves problems for actual people. And kind of why I'm interested in PrimeLab, what you were mentioning is just how artists can use this technology to protect their creative work. To give that power back to people and that control over their content, I think, is really interesting rather than...I'm not really sure what I would do with the Apple Vision Pro [laughs]. Like, the early ones, I mean, it's cool. It's fun. I definitely enjoy it. Like, I sometimes like to learn about it, but it's not my passionate genre of tech that I normally go for. WENDELL: Going back to what you just said about, like, control, like, part of the thing is because of the hash IDs that we put into place, like, you don't need analytics. You don't need cookies or anything else, like the content holder. Basically, like, if you have a TV set or something and you want to stream content to it, you can actually see that information directly yourself. So, it takes the person generating it and the person viewing it. It forms...we call them function access keys. It forms a one-to-one relationship, basically, where you guys know if you want to know what you want to know, but then you choose to give access to the platform if you want to, which changes the dynamic of control quite a bit. And it's interesting because when you look at platforms like the Apple Vision Pro, and you look at Apple's whole entire system as a whole, just trying to lock in people, I think it's interesting because something like what I just described, Apple can't really stop. It's how compute works. So, if people want to use it, there's nothing they could do to stop it from being used. So, I'm really interested in the product stuff and just more about, like, how...and I'm curious what you guys think on this, too. Especially as you see phones and processors and everything else, I'm really interested in, like, how these things come about, like, how things are actually built and developed and the why for that, like, in the everyday use. So, like, the Apple Watch it started off as a fashion thing, which looked like a money grab, and then the why was, oh yeah, fitness. So, just curious if you guys have seen any other products out there that you're like, oh, this really resonates with me and the why. JOE: Yeah, I'm not really a gadget person, but I think the idea of taking some of the capabilities that we've gotten with the internet and with phones and making them hands-free was interesting. And that, to me, was what I think started pushing the development of products like the Apple Watch or Google Glass. Like, I think that hands-free capability, the trade-off became rewarding in the fitness field, but I think it's more generically applicable. I think that technology it's too obtrusive in other scenarios and too bad at its job to do some of the things it could do. And people got creeped out by Google Glass. But it doesn't really seem like the Vision Pro fits in there. Something being successful hands-free means it becomes less obtrusive, whereas the Vision Pro is like you become a cyborg. VICTORIA: Do you have anything else you would like to promote? WENDELL: I wouldn't say necessarily promote as much as like people with ideas or aspirations, like, I think it's important that you think counter to what everyone else is doing. There's that line of, like, when everyone else is running in one direction, run the other. And it's like, if you have a business or startup idea, really think about your market. Like, think about why you're doing what you're doing, and don't be afraid to just go out there and talk to people. You will get value no matter who you talk to. So, like, I'm a hugely tech-based person. My wife is a therapist, and I learn from her everyday things about emotional intelligence and all kinds of things that I would be an idiot otherwise. But also, learn, like, you can always learn something from someone. Like, take the time to listen to them. Take the time to actually, like, try and figure out what's one thing I can learn from someone, even if, you know, I learn stuff from my daughters even. Like, don't put things in boxes. Like, try to think outside of like, how can I ask a question to learn? VICTORIA: I love that advice. That's great. WENDELL: Have you guys used Suno before? VICTORIA: That's music, right? Music AI. WENDELL: All right, I got to show you guys this. We're going to create you a quick theme song. Like, this is what I mean by, like, it's an interesting solution for why. VICTORIA: That does sound fun. I like the ones...like my friend's a doctor, and she uses AI to take her conversation she's having with patients and automatically fill out her notes. And it saves her, like, 20 hours of documentation every week. Like, I like that kind of app. I'm like, oh, that makes a lot of sense. WENDELL: What's a style of music that you guys really like? JOE: Swedish pop VICTORIA: Like ABBA [laughs]? I'm down for an ABBA Giant Robots theme song. Sounds great. WENDELL: I think you're going to like this. [Music Playing] VICTORIA: These are awesome. They're super fun. Thank you so much. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, you can email us at hosts@giantrobots.fm. And you can find me on X @victori_ousg. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening. See you next time.  AD: Did you know thoughtbot has a referral program? If you introduce us to someone looking for a design or development partner, we will compensate you if they decide to work with us. More info on our website at: tbot.io/referral. Or you can email us at: referrals@thoughtbot.com with any questions. Special Guest: Joe Ferris.

Session 3 ‘Data, AI, and Predictive Modeling in Sepsis' from the 2024 WSC Spotlight. Featuring Paul Turner, Laura Merson, Paul Elbers, Maria del Pilar, Chris Paton, and Peiling Yap as your moderator.

LaToya Bowlah  is a leader in digital transformation and customer lifecycle marketing. Her background also includes developing go-to-market strategies at prominent tech/high-growth start-ups including Nasdaq and Bloomberg Digital, as well as leadership roles at AnthologyAI, Bloomberg News, Ellevest, and others.   LaToya and Matt discuss:   *   Striking the right balance between revenue growth and customer value is essential *   Investing in tech should be proportionate to your current growth and operational needs *   Lifecycle marketing sits at the intersection of data science, content, and automation *   Adapting to industry changes is crucial for any marketing leader   Chapters: (0:00) Balancing Customer Value and Revenue in Lifecycle Marketing   (4:10) Innovating Digital Media with Dynamic Paywalls at Bloomberg   (5:40) LaToya Bowlah's Journey Through Marketing and Product Strategy   (8:47) Ethical Data Sourcing and Consumer Control in Predictive Modeling   (10:14) Strategizing Marketing Channels and Automation in Business   (14:58) The Misconception of SEO as a Silver Bullet   (17:01) Strategies for Effective Go-to-Market Execution   (18:12) Strategies for a Successful Go-To-Market Approach   (21:46) Defining Market Segments for Communication Platforms   (22:47) Optimizing Customer Lifecycle for Growth and Activation   (24:44) Strategizing Sales and Marketing for Scalable Business Growth   (26:39) Lifecycle Marketing's Impact on Customer Experience and Revenue   (28:17) Email's Enduring Intimacy and Importance in Digital Marketing   (29:47) The Underestimated Power of Email Marketing   (31:19) Maximizing Efficiency in User Acquisition and Lifecycle Marketing   (34:11) Misconceptions in Market Strategy and Investment   (34:52) Striking Balance in Customer Engagement and Revenue Growth   (38:43) Understanding Customer Relationships and Communication Strategies   (40:07) Strategizing Customer Engagement and Growth Loops   (43:28) Optimizing Fintech User Engagement Through Iterative Testing   (45:52) Strategies for Early Stage Founders to Adapt in Marketing   (49:22) Sales Success Without Technology: A Practical Approach   (51:05) Debunking the Silver Bullet Myth in Lifecycle Marketing   Link to Transcript

Thanks to our Partner, NAPA TRACS and AutoFix Auto Shop Coaching Industry leaders John Hanighen, CEO of Cloyes, Matt Buchholz, CEO of Motorrad, and Andy Fiffick, CEO of a 10 store franchise chain in Cleveland, dive into the current state of the supply chain within the automotive aftermarket, addressing issues of product availability, quality concerns, and the impact of unpredictable demand. Insights into data-driven forecasting, manufacturer-distributor collaboration, and the importance of communication between suppliers and auto repair shops are also explored to give a holistic view of managing challenges in the aftermarket industry. Andy Fiffick, CEO Rad Air, 10-locations, franchise. Listen to Andy's other episodes HERE John Hanighen, CEO at Cloyes Gear and Products. Listen to John's other episodes HERE Matt Buchholz, CEO MotoRad Watch Full Video Episode Supply Chain Resilience and Whiplash (00:02:41) Discussion on the unique demand contraction and subsequent supply chain adjustments due to the COVID-19 pandemic. Quality and Fill Rate Issues in the Industry (00:05:44) Reports of decreased quality and high defect rates in automotive parts, as well as challenges related to supply chain disruptions and limited delivery services. Steps to Ensure Proper Installation and Quality Control (00:09:20) Use of QR codes and training videos to ensure proper installation, labor claim reimbursement programs, and the importance of minimizing comebacks through quality control measures. Challenges in Addressing Quality Issues (00:14:29) Discussion on the complexity of the supply chain, the need for quality parts, and the challenges in identifying and addressing part failures and defects. Cataloging and Product Quality (00:15:51) Discussion on the importance of cataloging products correctly and the impact of catalog errors on product performance. Supply Chain Logistics (00:19:26) Challenges and opportunities related to cataloging systems, data accuracy, and supply chain management. Predictive Modeling and Assortments (00:28:29) The use of data for predictive modeling and assortment recommendations at the store and distribution center levels. Data Sharing and Tracking (00:30:41) The challenges and variations in tracking and sharing data across different systems and trading partners. The supply chain management challenge (00:31:38) Discussion on using cross-referencing to identify supply chain gaps and the need for distributor approval. Impact of demand fluctuations (00:32:44) The impact of erratic demand changes and consumer confidence on the automotive industry. Adapting to market changes (00:34:46) Discussion on adapting to demand changes due to weather, election cycles, and marketing strategies. Predictive analytics and supply chain adjustments (00:35:36) Exploring the use of predictive tools and data, including weather, election cycles, and global conflicts, to adjust supply templates. Challenges in supply chain disruptions (00:36:35) The ripple effect of global conflicts and...

What kinds of problems are organizations solving with Machine Learning? In this episode, we explore a situation where a public works department was looking for more accurate information to predict future water levels based on rainfall to maintain water tank storage for balancing pressure and to prevent overflow flooding. Marathon data solutions consultants Brian Knox and Andy Yao, built a custom machine learning model and made the results available through Power BI reporting. We talk through some of the data hurdles the project presented, the tools they used, and how their work provided results the client could rely on. We touch on Azure ML environment and future integrations that will come with Power BI and ML.  Have you done any work in ML or predictive modeling? Did you get any good take-aways from today's podcast? Leave us some love ❤️ on LinkedIn, Twitter/X, Facebook, or Instagram.  The show notes for today's episode can be found at Episode 275: Machine Learning and Power BI. Have fun on the SQL Trail!

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How can home health and hospice organizations apply data to provide better patient care? What if data-driven predictive modeling could define with a high degree of certainty that a patient was on the verge of hospitalization … or pinpoint when a patient would die?What do future opportunities look like as artificial intelligence and machine learning make predictive modeling even more accurate and able to adjust predictions in real time as risk factors change?In this episode, Elliott Wood – president and CEO of Medalogix – and Stan Massey discuss these impactful topics and more.With Medalogix since 2013, Elliott leads the company in providing data outcomes that empower home health and hospice to make better patient care decisions. Based in Nashville, Medalogix is dedicated to empowering individualized patient care with innovative, data-driven solutions that enable a shift to value-based care. Including previous roles at HealthStream Inc in Nashville and AirStrip Technology in San Antonio, Elliott holds over 15 years of healthcare technology experience.

Ready to unlock the secrets to smart investing and entrepreneurial success? Dive deep into the world of identifying winning investment opportunities with our latest Compassionate Capitalist Show Podcast episode featuring special guest Andrew Einhorn, co-founder and CEO of LevelFields! Join our knowledgeable host, Karen Rands, as she and Andrew explore the pivotal strategies behind finding companies that stand out through grit and are primed for significant growth. Whether you're an investor or an aspiring entrepreneur, this episode is loaded with insights! ✨ Key Highlights: *  An in-depth look at Andrew's entrepreneurial journey from "ohmygov" to the creation of LevelFields - AI * Insights on the high-stress process of company acquisition as Andrew discusses the Synoptos' merger, outlining how they capitalized on corporate partnerships and the value of focusing on customers' needs in the midst of industry skepticism. * Andrew's methodology for selecting successful companies, based his impressive portfolio of 53 investments. Learn his secret to investing success in various stages of companies. * The impact of capital efficiency and potential for market disruption with truly novel innovation, that is wanted by the target market.  * Andrew and Karen discuss the significance of strategic value propositions and the the power of pivot in business.  * They discuss the essence of product-market fit in successful entrepreneurship to lead to sustainable growth strategies.   * Lastly the creation of Levelfields and developing an AI and Machine Learning approach to monitor and predict market movement that will prove to revolutionize investment research and decision making.   This episode is a must-listen for anyone interested in leveraging technology for smarter investments.   Plus we have a special offer for our Subscribers:  Get a special glimpse into the future of investing with a FREE TRIAL to see the predictive power of their platform firsthand.  https://www.levelfields.ai/ PROMO CODE -- Podcast23 About Andrew Einhorn:  Co-Founder and CEO of Levelfields. an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. His mission is to create AI tools that make advanced financial strategies effortless and accessible for all. Former founder of OhMyGov and avid angel investor and cultivator of entrepreneurs. 

In Episode 3 of the "Active Inference Insights" series, host Darius Parvizi-Wayne welcomes John Vervaeke for an insightful discussion bridging cognitive science and philosophy. The episode delves into topics like relevance realization, evolutionary processes in cognition, and understanding cultural variations in self-modeling. Verveke articulates the dynamic nature of cognition and its relationship with the environment, challenging traditional views on consciousness and the subjective-objective divide. Listeners will better understand how computational models and philosophical frameworks can synergistically enhance our comprehension of the mind and its processes. This episode is a thought-provoking journey that connects cognitive science theories with philosophical inquiries, offering listeners nuanced perspectives on the complexity of human cognition and its implications for meaning in life.   Glossary of Terms   4E Cognitive Science: A view of cognition as embodied, embedded, enacted, and extended. Relevance Realization: The ability to focus on salient information in a complex environment. Predictive Processing: A framework in cognitive science that describes how the brain makes predictions about incoming sensory information. Opponent Processing: A concept in biology where two subsystems work in opposition to regulate functions like arousal.   Resources and References:   Dr. John Vervaeke: Website | YouTube | Patreon | X | Facebook Darius Parvizi: X | Active Inference Institute | Active Inference Insights   The Vervaeke Foundation Awaken to Meaning John Vervaeke YouTube Awakening from the Meaning Crisis After Socrates The Crossroads of Predictive Processing and Relevance Realization | Leiden Symposium Books, Articles, Publications, and Videos Heidegger, Neoplatonism, and the History of Being: Relation as Ontological Ground - James Filler Predictive processing and relevance realization: exploring convergent solutions to the frame problem. Phenomenology and the Cognitive Sciences. Andersen, B. P., Miller, M., & Vervaeke, J. (2022) The Self‐Evidencing Brain. Noûs Hohwy, Jakob (2016). Attenuating oneself. Philosophy and the Mind Sciences. Limanowski, Jakub & Friston, Karl (2020). 'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference. Frontiers in psychology. Limanowski, J., & Friston, K. (2018). Deeply Felt Affect: The Emergence of Valence in Deep Active Inference. Neural computation.  Forgetting Ourselves in Flow: An Active Inference Account of Flow States. Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021). Parvizi-Wayne, D., Sandved-Smith, L., Pitliya, R. J., Limanowski, J., Tufft, M. R. A., & Friston, K. (2023, December 7). Cognitive effort and active inference. Neuropsychologia. Parr, T., Holmes, E., Friston, K. J., & Pezzulo, G. (2023). "The Theory of Affordances" The Ecological Approach to Visual Perception. Boston: Houghton Mifflin, Gibson, James J. (1979). Karl Friston ~ Active Inference Insights 001 ~ Free Energy, Time, Consciousness    Quotes   "Relevance realization inverts the way common sense works." - John Verveke  "The deeper your temporal model, the more critical relevance realization becomes." - Darius Parvizi Wayne Chapters with Timestamps   Introduction and Overview [00:00:00] Evolution and Function in Cognition [00:06:17] Opponent Processing in Biology [00:09:42] Problem-Solving and Anticipation [00:14:22] Relevance Realization and Evolution [00:31:34] Consciousness and Subject-Object Distinction [00:53:00] Cultural and Historical Perspectives on Cognition [00:56:35] Ontological Self and Phenomenal Self Modeling [01:11:19] Self-Modeling and Cultural Perspectives [01:14:00] Agency and Selfhood in Cognitive Processes [01:18:16] Self-Modeling Under flow States [01:22:01] Arousal and Metamotivational Theory [01:35:54] Predictive Processing Symposium and Relevance Realization [01:46:26] Episode Conclusion and Future Plans [01:48:20] Timestamped Highlights   [00:00:00] - Darius Parvizi Wayne introduces the episode and guest John Verveke, highlighting John's expertise in psychology, cognitive science, and Buddhist philosophy​​. [00:06:17] - John Verveke discusses the evolution of cognitive functions and the role of evolution in shaping cognition​​. [00:11:40] - Explanation of the autonomic nervous system, detailing how its two subsystems with opposite biases work together to regulate bodily functions. [00:14:43] - The conversation delves into the nature of problem-solving, exploring how organisms predict and prepare for future states.  [00:22:23] - The concept of hyperbolic discounting in cognition is examined, analyzing its impact on decision-making and goal pursuit. [00:26:20] - Discussion on the role of affordances in predictive processing, exploring how environments offer action possibilities to organisms. [00:31:34] - Conversation on the analogy between relevance realization and evolutionary processes, highlighting the dynamic nature of cognitive adaptation​​. [00:38:00] - The existential imperative is clarified in the context of the free energy principle, exploring its implications in cognitive science​​. [00:53:00] - Consciousness and the subject-object distinction are addressed, challenging traditional cognitive models and exploring interrelational perspectives​​. [00:56:35] - Cultural and historical influences on cognitive processes are explored, examining how these factors shape our understanding of cognition​​. [00:57:13] - John Verveke discusses the hermeneutics of suspicion in cognitive science, questioning the distinction between appearance and reality​​.   [01:04:49] - The role of perception and its function in cognitive processes are discussed, emphasizing the interconnectedness of perception and cognition​​. [01:11:19] - The concepts of ontological and phenomenal self-modeling are delved into, discussing how these models influence cognitive processes​​. [01:14:00] - Self-modeling and its cultural variations are discussed, highlighting the diversity in conceptualizing the self across different cultures​​. [01:18:16] - Agency and selfhood in cognitive processes are examined, focusing on how these concepts enhance predictive agency in the world​​. [01:22:01] - Exploration of self-modeling under flow states and their impact on cognitive processes​​. [01:35:54] - Analysis of arousal in the context of meta motivational theory, discussing how arousal is framed differently based on goals and motivations​​. [01:38:04] - Discussion of the intersection of philosophical concepts and computational models in cognitive science, emphasizing the importance of integrating these approaches to enhance understanding without oversimplifying complex phenomena. [01:46:26] - Overview of a talk integrating predictive processing and relevance realization theory, offering insights into their combined impact on cognitive science​​.  

John Vervaeke and guest Sam Tideman delve into the intricate world of artificial general intelligence (AGI) and its intersection with healthcare. Sam, an expert in biostatistics, machine learning, and AI, shares valuable insights from his professional experiences, particularly in healthcare system optimization. The conversation navigates the ethical and moral challenges of applying AI in complex environments like emergency departments, the intricacies of predictive modeling, and the broader societal implications of AI, including its energy consumption and public perception. This episode is essential listening for anyone interested in understanding the nuanced interplay between technology, healthcare, and ethics, offering a comprehensive perspective on the current and future potential of AI to transform lives and systems.   Sam Tideman, an accomplished healthcare data scientist with an MS in Biostatistics, blends his analytical acumen with a passion for theology in his podcast, "Transfigured." The podcast features long-form discussions exploring the identity of Jesus, reflecting Sam's unique intersection of scientific expertise and spiritual inquiry.   Glossary of Terms   AGI (Artificial General Intelligence): An AI that has the ability to understand, learn, and apply its intelligence to a wide range of problems, much like human intelligence. Biostatistics: The application of statistics to a wide range of topics in biology.   Resources and References:   Dr. John Vervaeke: Website | YouTube | Patreon | X | Facebook Sam Tideman: YouTube   The Vervaeke Foundation John Vervaeke YouTube Awakening from the Meaning Crisis - series Artificial Intelligence - series The Crossroads of Predictive Processing and Relevance Realization | Leiden Symposium   Books, Articles, Publications, and Videos Mentoring the Machines: Orientation - Part One: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne  Mentoring the Machines: Origins - Part 2: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne  Predictive processing and relevance realization: Exploring convergent solutions to the frame problem. Phenomenology and the Cognitive Sciences. Andersen, B., Miller, M., & Vervaeke, J. (2022).   Related Resources Chicagoland Bridges of Meaning Meetup   Chapters with Timestamps   [00:00:00] Introduction of Sam Tiedemann and Episode Overview  [00:01:15] Sam's Background and Intersection with AI  [00:04:11] The Role of AI in Healthcare and Emergency Departments  [00:14:26] The Limitations of AI in Morally Complex Environments  [00:24:34] Discussion on AI's Capability to Predict vs. Normative Decision-Making  [00:53:06] The Energy Consumption and Environmental Impact of Training AI Models    Timestamped Highlights   [00:00:00] John opens the discussion by welcoming Sam and introducing the topic of artificial general intelligence (AGI). [00:01:15] Sam shares his diverse background, which spans theology, philosophy, and artificial intelligence. [00:06:15] The conversation focuses on AI's potential and dangers, setting the stage for the day's discussion. [00:09:28] Sam reflects on the complexities he faced while trying to implement AI in emergency department forecasting. [00:14:53] Sam points out the practical limitations of AI in real-world applications. [00:21:38] Sam criticizes the inflated expectations surrounding AI in healthcare projects. [00:26:26] John and Sam discuss how predictive processing and relevance realization can be integrated into AI. [00:29:37] They delve into the potential of AI to emulate human qualities like intentionality and care. [00:34:11] John emphasizes the need to recognize the limitations of AI in solving complex real-world problems. [00:38:30] Sam's parable features an AI model in healthcare that prescribes drugs probabilistically and learns from outcomes, hinting at AI's emerging agency. [00:42:10] The feasibility of AI replicating human intuition and judgment in complex scenarios is questioned. [00:46:15] John highlights the importance of a multidisciplinary approach to understanding and developing AI. [00:49:57] Philosophical aspects of AI, such as intentionality and consciousness, are explored in-depth. [00:53:30] Sustainability concerns in AI development, especially compared to the human brain's efficiency, are discussed. [01:06:40] The episode concludes with a discussion on AI's inability to align with human normativity and the limitations of its social, cultural, and biological understanding.  

In part 3 of a 3-part series, Dr. Sam Terman discusses his paper, "Back to the Basics in Predictive Modeling—Predicting Surgical Success". Show references: https://journals.sagepub.com/doi/full/10.1177/15357597231205437

In part 2 of a 3-part series, Dr. Sam Terman discusses his paper, "Back to the Basics in Predictive Modeling—Predicting Surgical Success". Show references: https://journals.sagepub.com/doi/full/10.1177/15357597231205437 https://riskcalc.org/  

In part 1 of a 3-part series, Dr. Sam Terman discusses his paper, "Back to the Basics in Predictive Modeling—Predicting Surgical Success". Show references: https://journals.sagepub.com/doi/full/10.1177/15357597231205437

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs. The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively. Topics discussed: Using pre-trained models in Bumblebee for Elixir projects Training models using Python and SQL The importance of data preprocessing before building models Popular tools used for machine learning in different languages Getting started with ML by picking a personal project topic of interest Resources for ML aspirants, such as online courses, tutorials, and books The potential for Elixir to train more customized models in the future Similarities between ML approaches in different languages Collaboration opportunities across programming communities Choosing the right ML approach for the problem you're trying to solve Productionalizing models like fine-tuned LLM's The need for hands-on practice for learning ML skills Continued maturation of tools like Bumblebee in Elixir Katelynn's upcoming CodeBeam talk on advanced motion tracking Links mentioned in this episode https://launchscout.com/ https://www.cars.com/ Genetic Algorithms in Elixir (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) by Sean Moriarity Machine Learning in Elixir (https://pragprog.com/titles/smelixir/machine-learning-in-elixir/) by Sean Moriarity https://github.com/elixir-nx/bumblebee https://github.com/huggingface https://www.docker.com/products/docker-hub/ Programming with MATLAB (https://www.mathworks.com/products/matlab/programming-with-matlab.html) https://elixirforum.com/ https://pypi.org/project/pyspark/  Machine Learning Course (https://online.stanford.edu/courses/cs229-machine-learning) from Stanford School of Engineering Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) by Aurélien Géron Data Science for Business (https://data-science-for-biz.com/) by Foster Provost & Tom Fawcett https://medium.com/@carscomtech  https://github.com/k-burns  Code Beam America (https://codebeamamerica.com/) March, 2024 Special Guests: Alexis Carpenter and Katelynn Burns.

The oil and gas industry are increasingly relying on chemometrics and Raman spectroscopy to expedite quality assurance, processing and distribution, with predictive modeling playing an important role. This podcast looks at how predictive modeling is helping companies achieve faster turnaround, higher throughput and real-time results for the processes that they are looking to monitor, quantify or blend.

In this special episode of AbbottTalks, recorded live from DeviceTalks West, Julie Tyler, president of Abbott's Vascular business, discusses the company's strategic evolution and commitment to comprehensive patient care, including a new technology, the Esprit BTK, intended to be released next year. Additionally, Tyler provides an insider's perspective on Abbott's integration of Cardiovascular Systems Inc., revealing how this move enhances Abbott's portfolio and positions the company at the forefront of vascular health. Tyler's vision extends beyond current interventions, aiming to leverage data and predictive modeling to improve patient outcomes and potentially reduce the need for later-stage interventions. With a patient-centric approach, Tyler also discusses the challenges and successes in diversifying clinical trial demographics and expanding global reach. This episode is a must-listen for those interested in how strategic acquisitions and a holistic health model can redefine patient care. Thank you to Resonetics for sponsoring this episode. To learn more about how Resonetics works with medical device companies, visit https://resonetics.com/. Thank you for listening to the AbbottTalks Podcast. Subscribe to this podcast on every major podcast platform.

In our second Critical Point episode about AI applications in insurance, we drill down into the topic of machine learning and particularly its evolving uses in healthcare. Milliman Principal and Consulting Actuary Robert Eaton leads a conversation with fellow data science leaders about the models they use, the challenges of data accessibility and quality, and working with regulators to ensure fairness. They also pick sides in the great debate of Team Stochastic Parrot versus Team Sparks AGI. You can read the episode transcript on our website.

This episode features an interview with Dan Visnick, Chief Marketing Officer at HoneyBook, the leading business and financial management platform for solopreneurs and freelancers. Dan has over two decades of technology and internet industry experience, from start-ups to established brands. Prior to HoneyBook, he led global marketing for Change.org, was head of consumer marketing for Google Shopping, and held various marketing leadership roles at Yahoo!In this episode, Kailey and Dan discuss leading marketing efforts with an authentic voice, applying B2C tactics in a B2B world, and using AI to build customer experiences.-------------------Key Takeaways:Customers have grown tired of the corporate spiel in marketing. Approaching your marketing with an authentic and unmanicured tone of voice makes your company resonate with customers in a more human way.Just because you're a B2B company doesn't mean you can integrate B2C practices and tactics. As Dan learned, building an inbound funnel, referral programs, and providing premium assistance can actually help you reach higher levels of efficiency in the long run.If you're not using AI to build customer experience, you're already behind the curve. For example, chatbots can help reduce support tickets and enable your support team to have faster response times. AI can also improve conversion rates by providing instant gratification to customers.-------------------“We've also been using predictive modeling to target who is relevant for our business. We started off with an internal algorithm to identify what segment someone is once they started a trial. Then we could speak to them and personalize how we address their onboarding journey. But now, we're using that externally well to identify who are the best product market fit in advance so that we can have higher efficiency in our own business and acquisition.” – Dan Visnick-------------------Episode Timestamps:*(02:56) - Dan's career journey‍*(07:05) - Trends in the customer experience journey at HoneyBook ‍*(08:10) - How HoneyBook uses AI to build customer experience‍*(16:46) - How Dan applies B2C tactics in a B2B environment ‍*(23:42) - How Dan defines “good data” *(32:32) - An example of another company doing it right with customer engagement (hint: it's Duolingo and USAA)‍*(36:57) - Changes in customer experience in the next 6-12 months*(38:25) - Dan's recommendations for upleveling customer experience strategies-------------------Links:Connect with Dan on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorGood Data, Better Marketing is brought to you by Twilio Segment. In today's digital-first economy, being data-driven is no longer aspirational. It's necessary. Find out why over 20,000 businesses trust Segment to enable personalized, consistent, real-time customer experiences by visiting Segment.com

This cancer program used a business intelligence-enabled dashboard to collect and analyze data on emergency department (ED) visits, admits, and discharges. These data were then used to improve patient triage and evaluation through development of an Express Symptom Management program. After targeted in-service training and education to clinical teams and patients on utilization of the new program, only 2% of patients needed to be seen in the ED, with the rest receiving symptom management by phone, participating in a virtual clinic visit, and/or coming into the infusion suite for in-person assessment and treatment. Improvement efforts around patient self-management and triage to the Express Symptom Management program also included development of a pre- and post-initial infusion visit via Epic MyChart.   Guest: Dana Salcedo, MSN, APRN, NP-C Oncology Nurse Practitioner, Express Symptom Management & Outpatient Infusion Orlando Health Cancer Institute Orlando, Florida   “Ultimately our role was to prevent ED admissions, but also to help reduce stress, manage common side effects, and to let patients know they had a resource available to them at a moment's notice.”   This podcast is part of a special series with the 2023 ACCC Innovator Award winners. For a deeper dive into this and other content that will help your team reimagine how care is delivered at your cancer program or practice, register today for the ACCC 40th National Oncology Conference, Oct. 4-6, in Austin Texas.   Resources: Deploying Technology Across an Interdisciplinary Team to Improve Oral Oncolytic Compliance Expediting Cancer Treatment Through a Rapid Access APP-Led Diagnostic Clinic An APP-Physician Model Improves Risk Stratification and Palliative Care Reducing ED Visits and Hospital Admissions after Chemotherapy with Predictive Modeling of Risk Factors Utilizing Technology to Identify Patient Co-Morbidities and Reduce Hospital and ED Admissions Right Place, Right Provider, Right Time: Implementing Our 24-Hour Cancer Clinic

In this episode I pull back the curtain and help you understand how our brains and consciousness operates to produce our experience of reality. When you begin to understand how experiences, memories, internal representations and emotions are all connected, you gain elite, life-changing power to transform your life. This is just the TIP of the iceberg. The assets I'm building behind the scenes that help you tangibly understand and apply all this information to experience real lasting change is going to transform millions of lives. If you want to better understand how trauma impacts you and leads to a problematic relationship with alcohol, this episode is for you. This will help you understand how your past is still impacting you today, leading you to consciously or unconsciously choose strategies and behaviors that no longer serve your growth and evolution. The Stop Drinking Coach is a proactive coaching system to help you stop drinking alcohol through a neuroscientific and trauma informed lens. If you're ready to follow a proven system to help you stop and finally step into the next chapter of your life visit www.thestopdrinkingcoach.com, fill out an application and join my private community where you'll get access to a proven system, accountability, community, tools and resources to transform your life. -- If you received value from my podcast, please subscribe and leave a 5 star review. There are millions of people suffering in silence and your small gesture will help this reach the person who needs it. If you think my podcast would help anyone you know, please share it with them. Thank you for listening. #stopdrinking #quitdrinking #sober #alcoholism #sobercurious -- To work with me directly to quit drinking and transform your life, visit: www.thestopdrinkingcoach.com and fill out an application for coaching. Connect with me: TikTok: @stopdrinkingcoach Instagram: @thestopdrinkingcoach Website: www.thestopdrinkingcoach.com Email: Support@thestopdrinkingcoach.com --- Support this podcast: https://podcasters.spotify.com/pod/show/bardia-rezaei5/support

In this episode you will hear:   (02:20) Mark is joined by the Director of Colorado College, Matt Bonser. Matt discussed Early Decision, Demonstrated Interest, and Predictive Modelling. Mark and Matt discuss 17 ways students attempt to demonstrate interest and Matt gives his feedback whether these 17 attempts to demonstrate interest are effective at communicating interest. Matt rates the different options Mark suggests on a 1-10 scale, with the caveat that this is only his opinion. Matt does a great job of communicating the nuance and how there are always exceptions. This final part focuses a little more on predictive modeling. Part 2 of 2   (36:35) Mark and Lisa will answer a Speakpipe question from Kristen from California and she wants advice for a center right student that is hoping to find a college that is not intolerant of her political views.    (56:28) We start a brand new four part interview as Mark interviews Akil Bello on the topic of, “Tough Questions about SAT and ACT test scores” Preview of Part 1 v  Akil gives his backstory, walking us up to what he is doing now v  Akil tells us what the mission of Fairtest is and he explains why the mission appeals to him v  Akil explains the new Digital SAT; he shares the pros and the cons of the new test v  Akil explains his thoughts on whether it is a good thing that the Digital SAT is an adaptive test v  Akil explains the problem that he has with the College Board v  Akil shares his perspective on schools like MIT and Purdue and Georgetown requiring test scores     (01:09:00) The recommended resource is the Common App mobile app, a great way to have access to your application on your phone.   (01:20:58) Mark and Lisa discuss her visit in June to McGill University in Montreal. McGill is our College Spotlight for the week.      You can also use this for many other purposes: 1) Send us constructive criticism about how we can improve our podcast 2) Share an encouraging word about something you like about an episode or the podcast in general 3) Share a topic or an article you would like us to address 4) Share a speaker you want us to interview 5) Leave positive feedback for one of our interviewees. We will send your verbal feedback directly to them and I can almost assure you, your positive feedback will make their day. Speakpipe.com/YCBK is our preferred method for you to ask a question and we will be prioritizing all questions sent in via Speakpipe. If you have a question for one of our upcoming interviews with admissions professionals, here is a list of admissions professionals who we will interview in 2023 or 2024 Confirmed interviews not yet completedBard-Mackie Siebens Rice University-Tamara Siler American University-Andrea Felder Pitzer College-Yvonne Berumen Chapman University-Marcela Meija-Martinez Connecticut College-Andy Strickler* Trinity College-Anthony Berry* College of the Atlantic-Heather Albert* Spelman College-Chelsea Holley* Scripps College-Victoria Romero* Saint Louis University-Daniel Wood-(Interview is about transfer admissions, Daniel is a transfer counselor) Colby College-Randi Arsenault* University of Georgia-David Graves* University of Minnesota-Keri Risic Cornell University-Jonathon Burdick Oberlin College-Manuel Carballo Carleton College-Art Rodriguez Swarthmore-Jim Bok Joy St. Johns-Harvard Duke-Christoph Guttentag Florida State-John Barnhill Southern Methodist University-Elena Hicks Johns Hopkins-Calvin Wise Cornell University-Shawn Felton Haverford College-Jess Lord UAspire-Brendan Williams Yale University-Moira Poe Bard College Baylor University Butler University California Institute of Technology-Ashley Pallie Colorado School of Mines Creighton University University of Puget Sound- Robin Aijian   To sign up to receive Your College-Bound Kid PLUS, our new monthly admissions newsletter, delivered directly to your email once a month, just go to yourcollegeboundkid.com, and you will see the sign-up popup.   Check out our new blog. We write timely and insightful articles on college admissions:   Follow Mark Stucker on Twitter to get breaking college admission news, and updates about the podcast before they go live. You can ask questions on Twitter that he will answer on the podcast. Mark will also share additional hot topics in the news and breaking news on this Twitter feed. Twitter message is also the preferred way to ask questions for our podcast:   https://twitter.com/YCBKpodcast   1. To access our transcripts, click: https://yourcollegeboundkid.com/category/transcripts/ 2. Find the specific episode transcripts for the one you want to search and click the link 3. Find the magnifying glass icon in blue (search feature) and click it 4. Enter whatever word you want to search. I.e. Loans 5. Every word in that episode when the words loans are used, will be highlighted in yellow with a timestamps 6. Click the word highlighted in yellow and the player will play the episode from that starting point 7. You can also download the entire podcast as a transcript   We would be honored if you will pass this podcast episode on to others who you feel will benefit from the content in YCBK.   Please subscribe to our podcast. It really helps us move up in Apple's search feature so others can find our podcast.   If you enjoy our podcast, would you please do us a favor and share our podcast both verbally and on social media? We would be most grateful!   If you want to help more people find Your College-Bound Kid, please make sure you follow our podcast. You will also get instant notifications as soon as each episode goes live.   Check out the college admissions books Mark recommends:   Check out the college websites Mark recommends:   If you want to have some input about what you like and what you recommend, we change about our podcast, please complete our Podcast survey; here is the link:     If you want a college consultation with Mark or Lisa or Lynda, just text Mark at 404-664-4340 or email Lisa at or Lynda at Lynda@schoolmatch4u.com. All they ask is that you review their services and pricing on their website before the complimentary session. Their counseling website is: https://schoolmatch4u.com/

The need for commercial marketing teams to understand and better be prepared for their future market conditions is ever more pressing with budgets tightening and competition growing. Those who are able to maximize AI and predictive modeling to shape their journeys and drive towards the answers needed can accelerate growth and ultimate market penetration.

Episode brought to you by Trend & Finaloop.On this episode of DTC POD, Frank joins blaine to chat about how e-commerce companies can use AI models to personalize the shopping experience for each individual shopper. They talk about how ML automation and AI can enrich the product side by considering factors such as color, category, and weight to predict the behavior of shoppers in real time. They discuss the importance of having good data management and collecting as much non-PII customer event data as possible which will make them more agile and competitive in leveraging AI components later on, and thinking deeply about product catalog attributes and categories. Key themes discussed include:1. Personalized shopping experiences2. Advancements in AI and ML3. Making AI accessible to all4. Language models for consumer use cases5. AI for e-commerce conversion optimization6. Importance of human oversight in AI7. Importance of data management for AITimestamps[00:05:04] Recent surge in AI due to generative models; open AI major player.[00:09:38] Prediction and automation are ultimate goals with countless use cases.[00:14:26] XGen AI enables user-controlled ML systems for transparency and democratization.[00:20:50] E-commerce requires AI-powered tools to optimize customer experience for product recommendations.[00:28:51] User prediction pipelines create personalized shopping experiences that adapt to activities.[00:34:04] Data organization for Shopify Plus crucial for ML; deep analysis important.[00:41:05] Focus on partnerships for ML education; customize services; AI should automate repetitive interactions.[00:46:36] Need for accessible education on AI tools for the average person. Shownotes powered by CastmagicP.S. Get our pod highlights delivered directly to your inbox with the DTC Pod Newsletter! Episode brought to you by Finaloop, the real-time accounting service trusted by hundreds of DTC Brands. Try Finaloop free - no credit card required. Visit finaloop.com/dtcpod and get 14 days free and a 2-month P&L within 24 hours.Past guests & brands on DTC Pod include Gilt, PopSugar, Glossier, MadeIN, Prose, Bala, P.volve, Ritual, Bite, Oura, Levels, General Mills, Mid Day Squares, Prose, Arrae, Olipop, Ghia, Rosaluna, Form, Uncle Studios & many more.Additional episodes you might like:• #175 Ariel Vaisbort - How OLIPOP Runs Influencer, Community, & Affiliate Growth• #184 Jake Karls, Midday Squares - Turning Your Brand Into The Influencer With Content• #205 Kasey Stewart: Suckerz- - Powering Your Launch With 300 Million Organic Views• #219 JT Barnett: The TikTok Masterclass For Brands• #223 Lauren Kleinman: The PR & Affiliate Marketing Playbook• ​​​​#243 Kian Golzari - Source & Develop Products Like The World's Best Brands-----Have any questions about the show or topics you'd like us to explore further?Shoot us a DM; we'd love to hear from you.Want the weekly TL;DR of tips delivered to your mailbox?Check out our newsletter hereFollow us for content, clips, giveaways, & updates!DTCPod InstagramDTCPod TwitterDTCPod TikTokFrank Faricy - Founder and CEO of XGenRamon Berrios - CEO of Trend.ioBlaine Bolus - Co-Founder of Seated

Dr. Subbarao Kambhampati is a Professor of Computer Science at Arizona State University and the director of the Yochan lab where his research focuses on decision-making and planning, specifically in the context of human-aware AI systems. He has been named a fellow of AAAI, AAAS, and ACM in recognition of his research contributions and also received a distinguished alumnus award from the University of Maryland and IIT Madras.Check out Rora: https://teamrora.com/jayshahGuide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023Rora's negotiation philosophy:https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lies00:00:00 Highlights and Intro00:02:16 What is chatgpt doing?00:10:27 Does it really learn anything?00:17:28 Chatgpt hallucinations & getting facts wrong00:23:29 Generative vs Predictive Modeling in AI00:41:51 Learning common patterns from Language00:57:00 Implications in society01:03:28 Can we fix chatgpt hallucinations? 01:26:24 RLHF is not enough01:32:47 Existential risk of AI (or chatgpt) 01:49:04 Open sourcing in AI02:04:32 OpenAI is not "open" anymore02:08:51 Can AI program itself in the future?02:25:08 Deep & Narrow AI to Broad & Shallow AI02:30:03 AI as assistive technology - understanding its strengths & limitations02:44:14 SummaryArticles referred to in the conversationhttps://thehill.com/opinion/technology/3861182-beauty-lies-chatgpt-welcome-to-the-post-truth-world/More about Prof. RaoHomepage: https://rakaposhi.eas.asu.edu/Twitter: https://twitter.com/rao2zAlso check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.comAbout the Host:Jay is a Ph.D. student at Arizona State University.Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahmlAbout the author: https://www.public.asu.edu/~jgshah1/

In order for underwriters to mitigate risks accurately and efficiently, they need accurate knowledge and data to work with. When dealing with multiple complex claims, there are many different steps where mistakes can happen, and things can go wrong. Join Christine Byun and Justen Nestico for this informative podcast discussing the key benefits of predictive modeling. Discover how data-driven results can help underwriters make educated decisions to better understand risks and improve loss ratio and profitability. The podcast covers: • The value predictive modeling provides as well as the challenges facing underwriters • The importance of accurate data and the need for transparency and trust during the underwriting process • How professionals can use predictive modeling to ensure ethical and non-bias results when applied to underwriting – and so much more Don't miss this highly informative podcast and discover what makes predictive modeling such a powerful tool in the modern insurance industry.

(2:33) - Using Machine Learning to Detect Rare DiseasesThis episode was brought to you by Mouser, our favorite place to get electronics parts for any project, whether it be a hobby at home or a prototype for work. Click HERE to learn about how a) AI is being leveraged in healthcare and b) the tools available from vendors to empower development in this area.

Brett Furst, President of HHS Technology Group, is carrying on the mission of efforts started at the beginning of the COVID outbreak to collaboratively share data to provide insights to those doing research, drug development, and addressing the impact of the pandemic on vulnerable populations. Maintaining this world's largest COVID database, HTG allows research on complex social determinants of health and how to address health equity.  Brett explains, "So we were proud to take it on and be chosen by the consortium. Today we have the largest COVID research database in the world, with over 65 billion records. It touches almost 100% of our population in the United States. We're looking at all encounters from claims and clinical data, but also other more important for predictive modeling like social determinant data. We're trying to be more thoughtful about what's the next thing that's going to hit us out of the blue. We cannot be caught so flat-footed as we were in 2020." "There are concerns about long-term effects on those with Alzheimer's or other types of dementia, COPD, and breathing. I think what we all learned during this collaborative is that it didn't deflate everybody's competitive advantage and that sometimes regulations and rules can keep the market from actually advancing." @HHSTechnologyGroup #HHSTechGroup #HealthTech #HealthCare #COVID #LongCOVID #Pandemic #HealthData #HealthEquity #SDOH  hhstechgroup.com Listen to the podcast here

Brett Furst, President of HHS Technology Group, is carrying on the mission of efforts started at the beginning of the COVID outbreak to collaboratively share data to provide insights to those doing research, drug development, and addressing the impact of the pandemic on vulnerable populations. Maintaining this world's largest COVID database, HTG allows research on complex social determinants of health and how to address health equity.  Brett explains, "So we were proud to take it on and be chosen by the consortium. Today we have the largest COVID research database in the world, with over 65 billion records. It touches almost 100% of our population in the United States. We're looking at all encounters from claims and clinical data, but also other more important for predictive modeling like social determinant data. We're trying to be more thoughtful about what's the next thing that's going to hit us out of the blue. We cannot be caught so flat-footed as we were in 2020." "There are concerns about long-term effects on those with Alzheimer's or other types of dementia, COPD, and breathing. I think what we all learned during this collaborative is that it didn't deflate everybody's competitive advantage and that sometimes regulations and rules can keep the market from actually advancing." @HHSTechnologyGroup #HHSTechGroup #HealthTech #HealthCare #COVID #LongCOVID #Pandemic #HealthData #HealthEquity #SDOH  hhstechgroup.com Download the transcript here

H. James Blum, Chief Medical Information Officer at the University of Iowa, about "Practical implementation of AI and Predictive Modeling for Personalized Care, Improving Patient Access to Care, Focusing Less on Tech Specs and More on Outcomes, and more." Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen/

Today's guest is Titiaan Palazzi, COO and Co-Founder at Myst. Myst is a machine learning platform that leverages AI technology to improve demand and supply forecasting for an increasingly renewable power grid. Their platform helps companies accelerate the deployment of expert forecasting solutions that not only increase profitability but also reduce risk. We have a great discussion about the different energy use cases that more accurate predictive modeling can help, the customer types that use Myst today, and the transition of data science talent into the climate space.Enjoy the show!You can find me on Twitter @codysimms (me), @mcjpod (podcast) or @mcjcollective (company). You can reach us via email at info@mcjcollective.com, where we encourage you to share your feedback on episodes and suggestions for future topics or guests.Episode recorded June 28, 2022.