Podcasts about predictive

For the Good of the Public brings you news and weekly conversations at the intersection of faith and civic life. Monday through Thursday, The Morning Five starts your day off with scripture and prayer, as we also catch up on the news together. Throughout the year, we air limited series on Fridays to dive deeper into conversations with civic leaders, thinkers, and public servants reimagining public life for the good of the public. Today's host was Michael Wear, Founder, President and CEO of the Center for Christianity and Public Life.  Thanks for listening to The Morning Five! Please subscribe to and rate The Morning Five on your favorite podcast platform. Learn more about the work of the Center for Christianity and Public Life at www.ccpubliclife.org. Today's scripture: Matthew 11:2-11 (ESV) News sources:  https://apnews.com/live/donald-trump-news-updates-2-25-2026#0000019c-95c4-d3e7-a5fe-b5f62faa0000  https://www.nytimes.com/2026/02/25/world/europe/pope-africa-visit.html  https://www.npr.org/2026/02/24/nx-s1-5724999/house-rejects-aviation-safety-bill-rotor-act  https://www.wsj.com/business/media/kalshi-fines-former-gubernatorial-candidate-mrbeast-employee-on-prediction-wagers-208b6b5a?mod=hp_lead_pos11  https://www.pbs.org/newshour/nation/a-400000-payout-after-maduros-capture-put-prediction-markets-in-the-spotlight-heres-how-they-work  Join the conversation and follow us at: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@michaelwear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@MichaelRWear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife and check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@tsfnetwork⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music by: King Sis #politics #faith #prayer #scripture #IranTalks #PresidentTrump #PopeLeo #Africa #Congress #Aviation #MrBeast #PredictionMarkets Learn more about your ad choices. Visit megaphone.fm/adchoices

TL;DR We describe the persona selection model (PSM): the idea that LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona. Interactions with an AI assistant are then well-understood as being interactions with the Assistant—something roughly like a character in an LLM-generated story. We survey empirical behavioral, generalization, and interpretability-based evidence for PSM. PSM has consequences for AI development, such as recommending anthropomorphic reasoning about AI psychology and introduction of positive AI archetypes into training data. An important open question is how exhaustive PSM is, especially whether there might be sources of agency external to the Assistant persona, and how this might change in the future. Introduction What sort of thing is a modern AI assistant? One perspective holds that they are shallow, rigid systems that narrowly pattern-match user inputs to training data. Another perspective regards AI systems as alien creatures with learned goals, behaviors, and patterns of thought that are fundamentally inscrutable to us. A third option is to anthropomorphize AIs and regard them as something like a digital human. Developing good mental models for AI systems is important for predicting and controlling their behaviors. If our goal is to [...] ---Outline:(00:10) TL;DR(01:02) Introduction(06:18) The persona selection model(07:09) Predictive models and personas(09:54) From predictive models to AI assistants(12:43) Statement of the persona selection model(16:25) Empirical evidence for PSM(16:58) Evidence from generalization(22:48) Behavioral evidence(28:42) Evidence from interpretability(35:42) Complicating evidence(42:21) Consequences for AI development(42:45) AI assistants are human-like(43:23) Anthropomorphic reasoning about AI assistants is productive(49:17) AI welfare(51:35) The importance of good AI role models(53:49) Interpretability-based alignment auditing will be tractable(56:43) How exhaustive is PSM?(59:46) Shoggoths, actors, operating systems, and authors(01:00:46) Degrees of non-persona LLM agency en-US-AvaMultilingualNeural__ Green leaf or plant with yellow smiley face character attached.(01:06:52) Other sources of persona-like agency(01:11:17) Why might we expect PSM to be exhaustive?(01:12:21) Post-training as elicitation(01:14:54) Personas provide a simple way to fit the post-training data(01:17:55) How might these considerations change?(01:20:01) Empirical observations(01:27:07) Conclusion(01:30:30) Acknowledgements(01:31:15) Appendix A: Breaking character(01:32:52) Appendix B: An example of non-persona deception The original text contained 5 footnotes which were omitted from this narration. --- First published: February 23rd, 2026 Source: https://www.lesswrong.com/posts/dfoty34sT7CSKeJNn/the-persona-selection-model --- Narrated by TYPE III AUDIO. ---

The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This podcast reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The discussion includes the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows.  1. Transforming recovery paradigms Traditional post‑injury rehab relies on periodic in‑person assessments, therapist intuition, and standardized protocols that only partially account for individual variability. AI is shifting this model toward: Continuous, data‑driven care: Instead of snapshots in clinic, rehab can be informed by near real‑time streams of kinematic, physiological, and behavioral data from wearables, smart devices, and robot interfaces. Dynamic adaptation: Therapy intensity, task difficulty, and exercise selection can be automatically adjusted based on ongoing performance, fatigue, and recovery trends, rather than fixed schedules. Precision rehabilitation: Algorithms can identify which patients are likely to respond to specific interventions (e.g., constraint‑induced movement therapy vs robotics) and tailor plans accordingly. This moves rehabilitation from a "one‑size‑fits‑many" paradigm toward precision, context‑aware therapy, analogous to precision oncology but focused on function and participation. 2. Assessment, monitoring, and optimization AI for assessment Sensor‑based movement analysis: Machine learning models process accelerometer, IMU, EMG, and pressure data to quantify gait symmetry, joint kinematics, balance, and fine motor control with higher resolution than visual observation alone. Automated scoring: AI can approximate or support standardized scales (e.g., Fugl‑Meyer, Berg Balance Scale) by mapping sensor features or video-derived pose estimates to clinical scores, reducing inter‑rater variability and saving clinician time. Continuous monitoring Home and community tracking: Wearable and ambient sensors enable monitoring of daily steps, walking speed, arm use, posture, and adherence to exercises outside the clinic, feeding rich longitudinal datasets into AI models. Real‑time alerts: Algorithms can detect abnormal patterns—such as increased fall risk, reduced limb use, or signs of over‑exertion—and flag the clinician or adjust digital therapy content automatically. Optimization and decision support Predictive models: Using historical data, AI can forecast functional gains, plateau points, or risk of complications (e.g., falls, readmission), supporting individualized goal‑setting and resource allocation. Reinforcement learning and "digital twins": Emerging work in neurorehabilitation treats rehab as a sequential decision problem, using model‑based reinforcement learning and patient "digital twins" to recommend optimal timing, dosing, and progression of interventions over weeks to months.​ 3. Technologies: ML, wearables, analytics Machine learning algorithms: Supervised ML classifies movement quality (normal vs compensatory), detects exercise type from sensor streams, and estimates clinical scores. Unsupervised learning clusters patients into phenotypes (e.g., gait patterns after stroke), revealing subgroups that respond differently to certain therapies. Reinforcement learning and contextual bandits explore which therapy adjustments yield the best long‑term functional outcomes for a given individual.​ Wearable sensors and robotics: Inertial sensors, EMG, pressure insoles, and exoskeleton sensors capture high‑frequency movement and muscle activity data during training. Robotic devices (upper‑limb exoskeletons, gait trainers) coupled with AI can modulate assistance, resistance, or task difficulty in real time based on performance and predicted fatigue. Predictive and prescriptive analytics: Predictive analytics estimate trajectories (e.g., time to independent walking, expected upper‑limb function) to inform shared decisions with patients and families. Prescriptive analytics recommend therapy intensity, modality mix, and scheduling to maximize functional gains under resource constraints. 4. Benefits: outcomes, efficiency, engagement Improved functional outcomes: Studies report better motor recovery, gait quality, and ADL performance when AI‑assisted training is used—especially when robotics and intelligent feedback are involved. Reduced recovery time and resource use: More precise dosing and earlier identification of non‑responders can reduce ineffective sessions, shorten time to key milestones, and support safe earlier discharge with robust remote follow‑up. Increased adherence and engagement: AI‑driven digital rehab platforms use gamification, adaptive difficulty, and personalized feedback to keep patients engaged in home programs, improving adherence compared to static paper instructions. Support for clinicians: Instead of replacing therapists, AI can offload repetitive measurement tasks, highlight concerning trends, and offer data‑driven suggestions, allowing clinicians to focus on relational, motivational, and complex decision‑making aspects of care. 5. Challenges and ethical considerations Data privacy and security: Rehab AI often relies on continuous collection of sensitive motion, physiological, and sometimes audio/video data, raising questions about consent, storage, secondary use, and breach risk. Approaches like federated learning and on‑device processing are being explored to reduce centralization of identifiable data while still enabling model training. Algorithmic bias and fairness: If training data under‑represent older adults, women, certain racial/ethnic groups, or people with severe disability, AI models may misestimate performance or risk for those groups, potentially widening disparities in rehab access and outcomes. Ongoing auditing, diverse datasets, and participatory design with patients and clinicians are needed to ensure equitable performance. Integration with clinical workflows: Many AI tools are developed in research settings and are not yet seamlessly integrated into EHRs, scheduling systems, or therapist documentation workflows. Poorly integrated tools risk adding documentation burden or "alert fatigue," reducing adoption. Successful implementations co‑design interfaces with frontline therapists and physicians. Regulation, liability, and trust: It remains unclear in many jurisdictions how to regulate adaptive rehab algorithms (as medical devices, clinical decision support, or wellness tools) and who is liable when AI‑informed plans cause harm.​ Transparent, explainable models and clear communication to patients about the role of AI are critical for maintaining trust. 6. Case studies and emerging trends Remote and hybrid digital rehabilitation: AI‑driven platforms providing home‑based stroke, orthopedic, or Parkinson's rehab with clinician dashboards are improving adherence and extending care beyond brick‑and‑mortar clinics. Collaborative AI for precision neurorehabilitation: Frameworks combining patient‑clinician goal setting, digital twins, and reinforcement learning exemplify "collaborative AI" that augments rather than replaces therapists.​ Multimodal personalization: Integration of movement data, EMG, heart rate, sleep, and self‑reported pain/fatigue is enabling more nuanced adaptation to daily fluctuations in capacity. Conversational AI for education and coaching: Early work is assessing tools like ChatGPT as low‑risk supports for exercise education and motivation, though they are not yet precise enough to replace professional plan design AI is moving rehab toward patient‑centered, continuously adapting, and data‑rich care, but realizing this promise depends on addressing privacy, bias, workflow, and regulatory challenges in partnership with clinicians and patients.

On today's episode, Joe, Prez and Bryan Power discuss the upcoming college basketball schedule and its massive implications, predictive market betting and more!00:00 INTRODUCTION00:11 USA WINS GOLD & PREZ IS NOT HAPPY06:26 PREZ DOESN'T UNDERSTAND TACO BELL11:08 DIFFERENCE BETWEEN SWEATER AND JERSEY13:00 NBA PREVIEW: SPURS VS PISTONS18:17 CBB PREVIEW: HOUSTON AT KANSAS27:06 PARLAY OF THE DAY28:34 PREDICTIVE MARKETS: SOCCER + BITCOIN43:28 TODAY'S BEST BETS45:41 NBA + CBB EXTRA GAMES47:58 NHL FUTURE BETS

News of the Bogus: 0:43 – Before We Blame AI For Suicide, We Should Admit How Little We Know About Suicide https://www.techdirt.com/2026/02/19/before-we-blame-ai-for-suicide-we-should-admit-how-little-we-know-about-suicide/ Prediction of suicidal behavior in high risk psychiatric patients using an assessment of acute suicidal state: The suicide crisis inventory https://pubmed.ncbi.nlm.nih.gov/27712028/ Predictive validity and symptom configuration of proposed diagnostic criteria for the Suicide Crisis Syndrome: A replication study https://www.sciencedirect.com/science/article/abs/pii/S0022395622005702 7:12 – Trump said tariffs would reduce the trade deficit. It hit a record high in 2025. https://reason.com/2026/02/19/trump-said-tariffs-would-reduce-the-trade-deficit-instead-it-hit-a-record-high-in-2025/ 11:14 – NSA and IETF, part 5: One battle after another. #pqcrypto #hybrids #nsa #ietf #lastcall https://blog.cr.yp.to/20260219-obaa.html 20:45 – Biggest Bogon Emitter: California Tesla Takes Corrective Action to Avoid DMV Suspension – California DMV https://www.dmv.ca.gov/portal/news-and-media/tesla-takes-corrective-action-to-avoid-dmv-suspension/ Now We Know Why Tesla Killed Autopilot https://insideevs.com/news/787656/tesla-autopilot-california-dmv-marketing/ 25:50 – Idiot Extraordinaire: Democrats Democrats revive a once-taboo idea: Capping grocery prices https://www.yahoo.com/news/articles/democrats-revive-once-taboo-idea-145715732.html This Week’s Quote: “Despite the disastrous history of price controls, politicians never manage to resist tampering with prices—that’s not a flattering observation of their learning abilities.” —Walter E. Williams 🔊Pᴏᴅᴄᴀꜱᴛ: https://podcast.bogosity.tv/💬Dɪꜱᴄᴏʀᴅ: https://discord.bogosity.tv/▶️YᴏᴜTᴜʙᴇ: https://www.youtube.com/shanedk▶️Oᴅʏsᴇᴇ: https://odysee.com/%24/invite/@shanedk:4▶️Rᴜᴍʙʟᴇ https://rumble.com/c/shanedk💰Dᴏɴᴀᴛᴇ ᴏʀ ꜱᴜʙꜱᴄʀɪʙᴇ: https://donate.bogosity.tv

Fertility care is undergoing a significant shift as new diagnostic technologies offer deeper insight into reproductive health. OTO Fertility focuses on predicting the likelihood of IVF success before treatment begins, giving couples clarity at a stage where uncertainty has traditionally dominated the process. The system uses a wearable device that captures physiological signals and converts them into predictive metrics, offering a noninvasive method for understanding fertility readiness.The challenge addressed by this technology is substantial. IVF remains expensive, time‑consuming, and emotionally demanding, yet the success rate for a single transfer remains low. Many couples undergo multiple cycles without clear guidance on their likelihood of success. By providing predictive insight before treatment, the system aims to reduce unnecessary cycles, lower financial burden, and support more informed decision‑making.Physiological Monitoring and Predictive ModelingThe OTO Fertility device performs medical‑grade ECG and EEG measurements, capturing data from multiple physiological systems. These include cardiac activity, central and autonomic nervous system responses, hormonal regulation patterns, and energy supply indicators. Dozens of body signals are synthesized into a set of metrics that correlate with fertility outcomes. These metrics are then combined into a single index that reflects the couple's overall fertility readiness.Both partners participate in the process, allowing the system to evaluate male and female factors independently and together. This dual‑side approach supports a more complete understanding of fertility challenges, including those that may not be detected through traditional testing. The system also supports natural conception by identifying physiological patterns that can be optimized without clinical intervention.Personalized Guidance Through AIOnce the fertility index is generated, the system provides personalized recommendations designed to improve physiological readiness. These recommendations are based on lifestyle factors that influence fertility, including activity levels, rest, recovery, nutrition, sleep, and stress management. The guidance is tailored to each individual and delivered through the accompanying application, creating a structured pathway for improvement.The use of AI allows the system to adapt recommendations as new data is collected. Daily measurements support continuous monitoring, enabling couples to track progress and understand how their bodies respond to changes. This dynamic approach contrasts with traditional fertility diagnostics, which often rely on static snapshots taken at a single point in time.Broader Fertility Insights and Lifecycle SupportThe technology also provides insight into previously unexplained fertility challenges. A significant portion of infertility cases fall into the unexplained category, where standard tests show no clear cause. By analyzing physiological patterns across multiple systems, the device offers a new layer of understanding that can help clarify these cases.The system extends beyond conception, offering support during pregnancy and postpartum. This continuity allows couples to remain engaged with their physiological health throughout the entire fertility journey. The solution is delivered in partnership with treating physicians and virtual clinics, integrating seamlessly into existing care pathways.ConclusionOTO Fertility introduces a predictive diagnostic system designed to improve IVF outcomes and support natural conception through physiological monitoring and AI‑driven guidance. By providing insight into fertility readiness, offering personalized recommendations, and addressing both partners' health, the system creates a more informed and supportive pathway for couples seeking to build their families. As fertility challenges continue to rise globally, technologies that deliver clarity, personalization, and noninvasive insight are becoming essential components of modern reproductive care.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Fertility care is undergoing a significant shift as new diagnostic technologies offer deeper insight into reproductive health. OTO Fertility focuses on predicting the likelihood of IVF success before treatment begins, giving couples clarity at a stage where uncertainty has traditionally dominated the process. The system uses a wearable device that captures physiological signals and converts them into predictive metrics, offering a noninvasive method for understanding fertility readiness.The challenge addressed by this technology is substantial. IVF remains expensive, time‑consuming, and emotionally demanding, yet the success rate for a single transfer remains low. Many couples undergo multiple cycles without clear guidance on their likelihood of success. By providing predictive insight before treatment, the system aims to reduce unnecessary cycles, lower financial burden, and support more informed decision‑making.Physiological Monitoring and Predictive ModelingThe OTO Fertility device performs medical‑grade ECG and EEG measurements, capturing data from multiple physiological systems. These include cardiac activity, central and autonomic nervous system responses, hormonal regulation patterns, and energy supply indicators. Dozens of body signals are synthesized into a set of metrics that correlate with fertility outcomes. These metrics are then combined into a single index that reflects the couple's overall fertility readiness.Both partners participate in the process, allowing the system to evaluate male and female factors independently and together. This dual‑side approach supports a more complete understanding of fertility challenges, including those that may not be detected through traditional testing. The system also supports natural conception by identifying physiological patterns that can be optimized without clinical intervention.Personalized Guidance Through AIOnce the fertility index is generated, the system provides personalized recommendations designed to improve physiological readiness. These recommendations are based on lifestyle factors that influence fertility, including activity levels, rest, recovery, nutrition, sleep, and stress management. The guidance is tailored to each individual and delivered through the accompanying application, creating a structured pathway for improvement.The use of AI allows the system to adapt recommendations as new data is collected. Daily measurements support continuous monitoring, enabling couples to track progress and understand how their bodies respond to changes. This dynamic approach contrasts with traditional fertility diagnostics, which often rely on static snapshots taken at a single point in time.Broader Fertility Insights and Lifecycle SupportThe technology also provides insight into previously unexplained fertility challenges. A significant portion of infertility cases fall into the unexplained category, where standard tests show no clear cause. By analyzing physiological patterns across multiple systems, the device offers a new layer of understanding that can help clarify these cases.The system extends beyond conception, offering support during pregnancy and postpartum. This continuity allows couples to remain engaged with their physiological health throughout the entire fertility journey. The solution is delivered in partnership with treating physicians and virtual clinics, integrating seamlessly into existing care pathways.ConclusionOTO Fertility introduces a predictive diagnostic system designed to improve IVF outcomes and support natural conception through physiological monitoring and AI‑driven guidance. By providing insight into fertility readiness, offering personalized recommendations, and addressing both partners' health, the system creates a more informed and supportive pathway for couples seeking to build their families. As fertility challenges continue to rise globally, technologies that deliver clarity, personalization, and noninvasive insight are becoming essential components of modern reproductive care.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

In this episode, Daniel Metcalf and Mike Stromsoe discuss the challenges and opportunities faced by independent insurance agencies as they scale. They emphasize the importance of visibility in understanding progress, the current landscape of AI in the industry, and the need for predictive analysis to drive future growth. The discussion also highlights the significance of leadership, structure, and technology in overcoming fragmentation and achieving sustainable growth.Key takeaways:You're not behind, you just can't see it.Visibility is crucial for understanding agency progress.83% of agencies have used AI, but only 10% see ROI.Clarity of vision is essential for agency growth.Revenue growth without system growth feels like falling behind.Leadership must evolve as agencies scale.Fragmentation can hinder agency efficiency and growth.Predictive analysis can help agencies plan for the future.Technology should complement human efforts, not replace them.Agencies need to identify and address workflow gaps. Chapters:00:00 Unlocking Potential in Insurance Agencies03:11 Visibility: The Key to Progress05:51 AI in Insurance: The Current Landscape08:51 Predictive Analysis: Looking Ahead12:03 Overcoming Fragmentation in Growth15:12 Leadership and Structure in Scaling Agencies17:59 The Role of Technology in Agency Growth21:01 Final Thoughts and Future Opportunities

Most health insurance brokers rely on reactive cost-containment strategies. They wait for a catastrophic claim to hit and then scramble to manage it through cheaper drug sourcing or traditional case management. But looking in the rearview mirror won't solve the reality of 20% stop-loss increases. To win in a brutal renewal market, brokers must shift from managing claims after the fact to avoiding them entirely.My guest, Ryan Chapman, VP of Sales at HealthCare Strategies, joins me to share the predictive care playbook. We discuss how AI algorithms can identify emerging health risks, like missed screenings and trending A1C levels, years before a member ever reaches the hospital. We break down how to position predictive intervention to self-funded employers, the strategy behind running low-risk pilot programs, and why stop-loss carriers are actively rewarding this proactive approach. This is the blueprint for delivering long-term savings and proving your value before you ever win the Agent of Record.▶▶ Sign Up For Your Free Discovery Callcompletegameu.com/agaCONNECT WITH ANDY NEARY

For the Good of the Public brings you news and weekly conversations at the intersection of faith and civic life. Monday through Thursday, The Morning Five starts your day off with scripture and prayer, as we also catch up on the news together. Throughout the year, we air limited series on Fridays to dive deeper into conversations with civic leaders, thinkers, and public servants reimagining public life for the good of the public. Today's host was Michael Wear, Founder, President and CEO of the Center for Christianity and Public Life.  Thanks for listening to The Morning Five! Please subscribe to and rate The Morning Five on your favorite podcast platform. Learn more about the work of the Center for Christianity and Public Life at www.ccpubliclife.org. Today's scripture: Matthew 7:7-11 News sources:  https://www.washingtonpost.com/world/2026/02/18/russia-ukraine-geneva-negotiations-impasse/  https://theconversation.com/air-pollution-may-directly-contribute-to-alzheimers-disease-new-study-275873  https://www.deseret.com/utah/2026/02/17/governor-cox-vows-legal-battle-over-prediction-markets/  https://www.nytimes.com/2026/02/18/technology/meta-65-million-election-ai.html  https://thehill.com/policy/technology/5742396-ai-regulation-midterm-ads/  https://ai-law-center.orrick.com/us-ai-law-tracker-see-all-states/  Join the conversation and follow us at: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@michaelwear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@MichaelRWear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife and check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@tsfnetwork⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music by: King Sis #politics #faith #prayer #scripture #Russia #Ukraine #airpollution #Alzheimers #predictionmarkets #SpencerCox #BenSasse #AI #Meta #midterms Learn more about your ad choices. Visit megaphone.fm/adchoices

February 17th, 2026 EP: 081 AI or Oracle? The Rise of Predictive Personal Intelligence Can artificial intelligence move beyond algorithms and statistics to predict your future? In this one-off special, we explore whether AI could evolve into a modern-day Tech Oracle—capable of foreseeing personal destinies—or whether its insights remain bound to probabilities and pattern recognition. From predictive policing and life forecasting to eerie AI “coincidences,” we push past data science and into the uncanny, asking where technology ends… and the unknown begins.

AI is moving faster than most service businesses can keep up with. Voice agents. Text automation. Follow-up systems. AI sales emails. Predictive messaging. But here's the real question: How do you use AI to convert more leads without losing the human touch? In this episode of Service Business Mastery, Tersh Blissett and Joshua Crouch sit down with Ryan Fenn, CEO at CHIIRP, to break down what's actually working in AI-driven lead conversion right now. They dive into: Why speed to lead still wins (and why seconds matter) How one word in your follow-up could be killing 90 percent of your conversions Why most contractors misuse AI for sales emails How to "download your brain" into systems using AI Why tools are becoming commodities and experience is becoming king How AI agents are replacing static drip campaigns If you're a service business owner trying to figure out how to adopt AI without sounding robotic, this episode will give you clarity, strategy, and practical application. What You Will Learn in This Episode • Why speed to lead still dramatically impacts booking rates • The difference between static drip campaigns and dynamic AI follow-up • How AI can analyze hundreds of millions of messages to improve conversion • Why empathy in messaging increases response rates • How to use AI as an editor instead of a ghostwriter • How to turn 20 years of experience into systems and training using AI • Why the future of software is outcome-based, not tool-based • How to evaluate tech partners based on deployment experience, not features • Why humans will still matter even if AI becomes indistinguishable Timestamps 00:00 The AI arms race and what's changed 03:00 Why speed to lead still dominates 08:00 The future of AI in sales and follow-up 12:20 Why humans will still matter in the age of AI 21:20 Download your brain using AI 22:11 How to use AI to write better sales emails 27:24 Why experience matters more than software features 33:02 The single word that could be killing your sales 36:00 Is there such thing as responding too fast? 40:57 AI agents replacing static drip campaigns 49:00 What contractors should stop doing immediately Follow the Host and Guest Tersh Blissett: https://www.linkedin.com/in/tershblissett/ Josh Crouch: https://www.linkedin.com/in/josh-crouch/ Ryan Fenn: https://www.linkedin.com/in/ryan-fenn-76568b165/ CHIIRP: https://www.linkedin.com/company/chiirp/  Connect with Us • LinkedIn - https://www.linkedin.com/company/service-business-mastery • TikTok - https://www.tiktok.com/@servicebusinessmastery • Facebook Group - https://www.facebook.com/groups/servicebusinessmasterypodcast • Instagram - https://www.instagram.com/servicebusinessmasterypodcast  

Your team is sitting on closed loans… and doesn't even know it.In this episode, Josh Friend (CEO & Founder of Insellerate) explains how Aithena listens to 100% of your calls, surfaces the moments that win or lose deals, and predicts who's likely to close in the next 60 days—so your loan officers stop chasing dead ends and start closing more loans.If you lead mortgage sales, lending ops, call centers, or revenue teams, this is the AI episode that cuts through the hype—because it's not a shiny tool. It's real work getting done: call reviews, coaching, prioritization, and smarter follow-up.Automates call reviews by jumping straight to the critical moment (objection, missed step, buying signal)Gives managers 10 coachable moments in 20 minutes instead of wasting 20 minutes on one full callApplies a deal likelihood score so reps know who to call today vs. who to nurtureDetects borrower intent using patterns learned from 1M+ conversationsHelps reps stay confident and effective by prioritizing higher-probability borrowers firstTurns scattered conversations into a repeatable, coachable system that scalesWhen call volume spikes and rates shift, most teams can't tell the difference between:“Not ready”and“Ready to close—just not with you yet.”Aithena helps you find the borrowers who are most likely to act soon—before they close somewhere else.FAQ'sWhat is Aithena?Aithena is an AI platform from Insellerate that reviews calls, scores conversations, and delivers coaching + lead prioritization based on borrower intent.How does Aithena help close more loans?It identifies high-intent borrowers, surfaces objections and missed moments, improves coaching speed, and focuses reps on the leads most likely to close soon.Who should use Aithena?Mortgage and lending organizations, call centers, and revenue teams that want better call performance, faster coaching, and smarter follow-up.Email Josh: josh@insellerate.com0:00 Welcome + Josh Friend returns0:40 Why most AI projects fail ROI2:10 Aithena: AI call reviews + coaching moments4:00 100% call coverage and better insights5:20 Predictive lead scoring + borrower intent9:00 Sales confidence and performance11:10 Vibe coding + building faster with AI19:20 Events + how to connect21:30 Wrap-up###Michael Hammond, Founder of NexLevel Advisors, is the leading fractional CMO in mortgage and mortgage technology, specializing in AI-powered growth strategy and audience development.

Predictive, generative, agentic. These might be fairly new words, yet they already resonate and are immediately recognisable. Artificial Intelligence is turning the media landscape on its head, from powerful prediction planning tools to cutting-edge creative tech and innovative solutions to buy and optimise ad campaigns. AI is fundamentally reshaping the industry. A good example is Values Media, an independent media agency based in France, which recently completed a full TV campaign buying via agentic AI. We're only scratching the surface, but the implications are huge and profound. In this episode, we talked to Emmanuel Crego, Values Media Group Managing Director to talk about his perspectives and what the future potentially holds for the media industry. 

In this episode of Darnley's Cyber Café, we explore a deeper question about modern privacy: what happens when systems don't need your words to understand you?From behavioural research to predictive algorithms, studies show that digital traces: clicks, pauses, search queries, and browsing patterns all can reveal personality traits, emotional states, and future behaviour with surprising accuracy. As artificial intelligence and data modelling improve, privacy may no longer end when we speak. It may narrow before we decide.This episode examines the documented research behind predictive systems, how they shape outcomes through ranking and nudging, and why awareness matters in a world where thought leaves a shadow.If you care about AI, digital privacy, algorithmic influence, or the future of human autonomy, this conversation is for you.Click here to send future episode recommendationSupport the showSubscribe now to Darnley's Cyber Cafe and stay informed on the latest developments in the ever-evolving digital landscape.

On today's episode, Joe, Prez and Bryan Power discuss the upcoming college basketball schedule and its massive implications, predictive market betting and more!00:00 INTRODUCTION00:31 WEEKEND RECAP06:03 HUGE CBB WEEK STARTS TONIGHT WITH HOUSTON VS IOWA STATE09:24 OLYMPIC HOCKEY PICKS17:13 PREDICTIVE MARKETS: NHL-CBB-NBA-BITCOIN29:40 PARLAY OF THE DAY36:00 TODAY'S BEST BETS38:43 CBB RUNDOWN: HOUSTON AT IOWA STATE40:30 MAILBAG! VIEWER QUESTIONS

 For one week in university, his dreams stopped feeling symbolic.They became precise. Predictive. Details appeared before reality caught up—conversations, locations, moments that unfolded exactly as he'd already seen them. It was unsettling, but still easy to rationalize.Until one dream went further.Sleep dropped him into a familiar bar and slowed time to a crawl. He watched a bullet spin toward him, felt the heat, the impact, the certainty of dying. When he woke up, the fear didn't fade. It lingered—enough to change his plans. So he stayed home.His friends didn't. Hours later, his roommate returned shaken with news that transformed the dream from coincidence into warning. After that, the dreams stopped—almost as if they'd said everything they needed to say.#RealGhostStoriesOnline #Premonition #PredictiveDreams #ParanormalPodcast #TrueStory #DreamWarning #DéjàVu #Unexplained #Intuition #CloseCall Love real ghost stories? Want even more?Become a supporter and unlock exclusive extras, ad-free episodes, and advanced access:

Did you know that there are predictable patterns of behavior that drive the success and failure of relationships? The Bible and the social sciences provide clear pathways to relational success, but the world system clouds them with chaos and confusion. We see the fallout in the trail of broken relationships, the ghosting phenomenon, the attachment crisis, and the tragic stats on anxiety and depression in the youngest generations. Join Dr. Lisa Dunne for today's show as we talk about the secrets of relational success, from friendship to marriage. With a few simple and strategic changes, you can activate patterns of predictive behavior that will strengthen your interpersonal relationships. K to 12 Rescue Mission: https://www.academicrescuemission.com Christian Community College: https://www.veritascc.us CVCU degree programs: https://www.cvcu.us Book Dr. Lisa to speak: https://www.DrLisaDunne.com @DrLisaDunne

Enterprise IT is drowning in repeat incidents, slow triage, and reactive firefighting—burning teams out while costs rise and service quality slips. In this episode, Sandy and Umesh Shiknis of Publicis Sapient explore how Sapient Sustain uses AI-driven automation, predictive insights, and self-healing workflows to break the cycle, turning IT operations from constant crisis mode into a resilient, proactive engine that sustains the business. They also discuss how Publicis Sapient is leveraging AI to address challenges in the healthcare sector. They put an importance on modernizing legacy systems while also emphasizing the concept of agentic AI.Check out more about Sapient Sustain here: https://www.publicissapient.com/sapient-ai/sustainIn this episode, they talk about:Publicis Sapient focuses on human-centered digital transformation in healthcareAI can accelerate product development and modernize legacy systemsIt's easy to confuse automation with simple elements of machine learning, which are progressively more deterministicOrganizations must establish guardrails for AI implementation because of how powerful agentic AI can beSapient Sustain helps healthcare companies manage and stabilize their applicationsThe end-user experience is crucial in technology deploymentAI can significantly reduce technical debt in healthcare organizationsHealthcare leaders should look at the boring stuff and focus on practical AI applicationsEducate your workforce to embrace the future instead of fearing itA Little About Umesh:Umesh Shiknis is Executive Vice President and Global Chief Growth Officer at Publicis Sapient, a human-centered, product-led digital business transformation firm. He leads global growth and go-to-market strategy, scaling new buying centers, accelerating client impact, and driving transformational revenue across industries. Previously, Umesh held senior leadership roles at Capgemini, Infosys, and ISG. His current focus is on taking the Publicis Sapient AI product suite—Sapient Slingshot, Bodhi, and Sapient Sustain—to market, turning AI innovation into measurable, enterprise-wide outcomes.

In Part 2 of our focus this month on ‘Understanding Pain in 2026', we take a look at Predictive Processing (the brain guessing what is happening and outputting pain when it predicts danger) and Active Inference (the brain acting to confirm or update these guesses, shaping how you move, feel, and interpret sensations).  Joining host Matt Phillips of Runchatlive.com are special guests Professor Dave Newell, Head of The Centre for Pain & Active Inference Research at Health Sciences University, and Anna Maria Mazzieri, a PhD student at Health Sciences University, and Director of The ST School.   The episode discusses how the brain builds models, predicts sensory input, and acts to minimize prediction error -  shaping movement, sensation and chronic pain. The conversation covers embodiment, precision, therapeutic alliance, and practical implications for clinicians, including sense-making, creating safe liminal spaces, graded exposure and using touch as a communication tool. Listeners are invited & encouraged to re-listen for deeper understanding and to use the episode's insights to rethink clinical reasoning and patient-centred approaches to pain management. Chapter Markers 00:00:00 - Welcome to the STA Clinician Podcast 00:01:38 - Meet Professor Dave Newell and Anna Maria Mazzieri 00:03:25 - Anna-Maria's PhD Journey 00:05:22 - The Centre for Pain & Active Inference Research 00:06:58 - Understanding Active Inference 00:15:46 - The Role of Active Inference 00:21:35 - Clinical Applications of Active Inference 00:23:17 - Contextual Factors in Pain 00:29:46 - The Concept of Self-Evidencing 00:37:38 - Chronic Pain and Active Inference 00:38:35 - Listener Questions 00:42:15 - Therapeutic Alliance in Pain Management 00:45:50 - Co-Creation in Therapy 00:48:30 - The Complexity of Pain 00:53:42 - Conclusion and Next Steps Special Guest Links Website: The Centre for Pain and Active Inference (PAIn) Research at HSU Professor Dave Newell on Facebook Website: The ST School  The ST School on Facebook Further Reading An Embodied Predictive Processing Theory of Pain Experience Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Synchrony MASSAG model: Towards an integrative neuroscience framework linking emotional trauma, pain, and mechanisms of force-based manipulations Mark Miller on Predictive Processing - Todd Hargrove on Substack Other Episodes  Part 1 - Ep.272 with Dr. Bronnie Lennox Thompson and Ben Whybrow - available on the Sports Therapy Association YouTube channel and all popular podcast apps. Part 3 Tues Feb 17th with special guest Dr. Rachel Dewar-Haggart, Postdoctoral Qualitative Researcher within the Medical Sociology & Health Experiences Research Group at the Nuffield Department of Primary Care Health Sciences, University of Oxford. Part 4 Wed Feb 25th with special guest Dr. Rachel Zoffness, Pain scientist, International Speaker, Science Writer, with her new book “Tell Me Where It Hurts” coming out March 26th  Want to join the live recordings? Episodes of the STA Clinician Podcast are recorded live every TUESDAY at 8pm on the Sports Therapy Association YOUTUBE CHANNEL and FACEBOOK page. Everyone is welcome - you do not have to be an STA member! If you cannot join us live, be sure to subscribe to the 'Sports Therapy Association Podcast' on all popular podcast apps to be notified when new episodes are available. Interested in joining the STA? Use the code PODCAST25 to get 3 MONTHS EXTRA when you join for a single year! In other words, £75 will get you 15 months instead of 12!  Only valid for NEW members. If you are Level 3 (qualified after 2014) make sure you choose the ‘associate member' option.

Im aktuellen Predictive AI Quarterly sprechen wir über zentrale Entwicklungen im Bereich Predictive AI und teilen Erfahrungen aus einem konkreten LLM-Projekt. Thema sind unter anderem TabPFN 2.5, neue Ansätze für Explainability sowie der wachsende Einfluss von AI-Agents auf Softwareentwicklung. Im Praxisteil berichten wir über ein mehrsprachiges Textanalyse-Projekt für den gemeinnützigen Verein Monda Futura. Dabei geht es um die strukturierte Auswertung von rund 850 Zukunftsvisionen mithilfe von LLMs. Abschließend diskutieren wir Learnings zu Modellwahl, Kosten und dem sinnvollen Zusammenspiel von Mensch und KI. **Zusammenfassung** TabPFN 2.5: Skalierung, Distillation für produktive Nutzung und höhere Inferenzgeschwindigkeit ExplainerPFN als Alternative zu SHAP für Feature Importance ohne Zugriff auf das Originalmodell Trend zu AI-Agents, die große Teile der Softwareentwicklung übernehmen Use Case Monda Futura: Analyse von 850 mehrsprachigen Zukunftsvisionen (DE/FR/IT) Pipeline: Fragmentierung, Themenextraktion, Klassifikation und Szenarienerstellung Effektiver Einsatz von GPT-5-Mini vs. GPT-5.2-Pro je nach Aufgabentyp Zentrales Learning: Beste Ergebnisse durch Human-in-the-Loop statt Vollautomatisierung **Links** Prior Labs TabPFN-2.5 Model Report https://priorlabs.ai/technical-reports/tabpfn-2-5-model-report ExplainerPFN Forschungs-Paper (zero-shot Feature Importance) https://arxiv.org/abs/2601.23068 OpenCode – Open Source AI Coding Agent https://opencode.ai/ Monda Futura https://mondafutura.org/ OpenAI API & GPT-Modelle Überblick https://platform.openai.com/docs/models OpenAI Structured Output Guide https://platform.openai.com/docs/guides/structured-outputs

Prediction markets are exploding in popularity—but most people still don't understand how they actually work. In today's episode, we break down the surge in predictive markets, what they are, how they function, and the big questions traders are asking: Are prediction markets legal? How do they differ from traditional markets? And what risks do they carry for you as a participant? We'll cut through the hype and explain the mechanics behind event-based trading, how pricing reflects probability, and why regulators are paying closer attention. Whether you're looking at political prediction markets, economic-event contracts, or blockchain-based platforms, you need to understand the structure before you risk capital. I'll also answer several viewer questions—including a (slightly delayed!) response to Ed in San Jose about my recent Tesla trade and what was going through my mind during that position. If you're curious about alternative markets and evolving financial structures, this episode is a must-listen. Listen now:

HEADLINE: The Predictive Brain and Auditory Hallucinations. GUEST: Professor Andy Clark. SUMMARY: Clark explains how brains predict reality, using "White Christmas" auditory hallucination experiments and a deer-spotting anecdote to illustrate that expectation strongly shapes perception. 1917

On today's episode, Joe, Prez and Bryan Power discuss Super Bowl bad beats and bad bets, predictive market betting, NBA, college basketball and more!00:00 INTRODUCTION00:44 SUPER BOWL RECAP | BAD BEATS & BETS02:01 PREZ LEARNS THE NAME OF A BAD BUNNY SONG04:56 MAKING MONEY IN THE PREDICTIVE MARKETS06:21 NEW ENGLAND'S COACHING14:16 PREZ SHOUTS OUT MUSICIANS19:45 BE CAREFUL BETTING THE NBA MOVING FORWARD22:55 PARLAY OF THE DAY30:05 TODAY'S BEST BETS33:29 NBA RUNDOWN35:36 CBB RUNDOWN

In the rapidly evolving landscape of data science, the need for efficient and effective predictive modeling is more critical than ever. As organizations strive to leverage data for informed decision-making, the traditional methods of creating predictive models often prove to be cumbersome and time-consuming. However, the advent of autonomous data science agents, such as ELAI, is revolutionizing this process by automating predictive model creation and democratizing access to predictive analytics.The Challenge of Traditional Predictive ModelingHistorically, the creation of predictive models has been a labor-intensive endeavor, often requiring extensive manual effort. Organizations typically face significant roadblocks in data readiness, including issues related to data integration, cleaning, and enrichment. The process can take anywhere from six to nine months to develop a single predictive model, which is a considerable investment of time and resources. This lengthy timeline not only hinders the agility of businesses but also limits their ability to respond to market changes and customer needs effectively.Introducing ELAI: The Autonomous Data Science AgentELAI, described by its CEO Antonio Sciuto, is an autonomous data science agent designed to streamline the end-to-end workflow of predictive modeling. By combining a robust reasoning engine with machine learning capabilities, ELAI can automate the tasks associated with data integration, cleaning, enrichment, model deployment, backtesting, and maintenance. This innovative approach significantly reduces the time required to create predictive models, allowing organizations to transition from a six-to-nine-month timeline to just a few hours.ELAI's architecture is built to handle complex data environments. It can automatically integrate existing customer data with over 650 social and economic indicators, enriching the dataset and enhancing the predictive capabilities of the model. For instance, in the case of a company like QNX, which provides infotainment systems in vehicles, ELAI can analyze customer data to identify the best products for cross-selling, thereby optimizing sales strategies and improving customer satisfaction.Democratizing Predictive AIOne of the most significant contributions of ELAI is its potential to democratize predictive AI across various business functions. Traditionally, predictive modeling has been confined to specialized teams within organizations, limiting its accessibility. However, by automating the intricate processes involved in model creation, ELAI enables a broader range of stakeholders to engage with predictive analytics.Predictive models can now be applied to diverse areas such as human resources, where organizations can assess employee attrition risks and development needs; supply chain management, which benefits from enhanced forecasting and predictive maintenance; and customer relationship management, where businesses can optimize their offerings based on customer behavior and preferences. This democratization of predictive AI empowers organizations to make data-driven decisions across all functions, enhancing overall operational efficiency.The Future of Predictive ModelingAs the demand for data-driven insights continues to grow, the automation of predictive model creation represents a significant advancement in the field of data science. By reducing the time and complexity associated with traditional methods, autonomous agents like ELAI are enabling organizations to harness the power of predictive analytics more effectively. The implications of this shift are profound, as businesses can now respond rapidly to changing market dynamics and customer needs, ultimately leading to improved outcomes and competitive advantages.ConclusionIn conclusion, automating predictive model creation is not just a technological innovation; it is a transformative approach that redefines how organizations leverage data. By streamlining the modeling process and making predictive analytics accessible to a wider audience, tools like ELAI are paving the way for a future where data-driven decision-making is the norm, rather than the exception. As we embrace this new era of data science, the potential for enhanced insights and improved business performance is limitless.Interview by Scott Ertz of F5 Live: Refreshing Technology.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

In the rapidly evolving landscape of data science, the need for efficient and effective predictive modeling is more critical than ever. As organizations strive to leverage data for informed decision-making, the traditional methods of creating predictive models often prove to be cumbersome and time-consuming. However, the advent of autonomous data science agents, such as ELAI, is revolutionizing this process by automating predictive model creation and democratizing access to predictive analytics.The Challenge of Traditional Predictive ModelingHistorically, the creation of predictive models has been a labor-intensive endeavor, often requiring extensive manual effort. Organizations typically face significant roadblocks in data readiness, including issues related to data integration, cleaning, and enrichment. The process can take anywhere from six to nine months to develop a single predictive model, which is a considerable investment of time and resources. This lengthy timeline not only hinders the agility of businesses but also limits their ability to respond to market changes and customer needs effectively.Introducing ELAI: The Autonomous Data Science AgentELAI, described by its CEO Antonio Sciuto, is an autonomous data science agent designed to streamline the end-to-end workflow of predictive modeling. By combining a robust reasoning engine with machine learning capabilities, ELAI can automate the tasks associated with data integration, cleaning, enrichment, model deployment, backtesting, and maintenance. This innovative approach significantly reduces the time required to create predictive models, allowing organizations to transition from a six-to-nine-month timeline to just a few hours.ELAI's architecture is built to handle complex data environments. It can automatically integrate existing customer data with over 650 social and economic indicators, enriching the dataset and enhancing the predictive capabilities of the model. For instance, in the case of a company like QNX, which provides infotainment systems in vehicles, ELAI can analyze customer data to identify the best products for cross-selling, thereby optimizing sales strategies and improving customer satisfaction.Democratizing Predictive AIOne of the most significant contributions of ELAI is its potential to democratize predictive AI across various business functions. Traditionally, predictive modeling has been confined to specialized teams within organizations, limiting its accessibility. However, by automating the intricate processes involved in model creation, ELAI enables a broader range of stakeholders to engage with predictive analytics.Predictive models can now be applied to diverse areas such as human resources, where organizations can assess employee attrition risks and development needs; supply chain management, which benefits from enhanced forecasting and predictive maintenance; and customer relationship management, where businesses can optimize their offerings based on customer behavior and preferences. This democratization of predictive AI empowers organizations to make data-driven decisions across all functions, enhancing overall operational efficiency.The Future of Predictive ModelingAs the demand for data-driven insights continues to grow, the automation of predictive model creation represents a significant advancement in the field of data science. By reducing the time and complexity associated with traditional methods, autonomous agents like ELAI are enabling organizations to harness the power of predictive analytics more effectively. The implications of this shift are profound, as businesses can now respond rapidly to changing market dynamics and customer needs, ultimately leading to improved outcomes and competitive advantages.ConclusionIn conclusion, automating predictive model creation is not just a technological innovation; it is a transformative approach that redefines how organizations leverage data. By streamlining the modeling process and making predictive analytics accessible to a wider audience, tools like ELAI are paving the way for a future where data-driven decision-making is the norm, rather than the exception. As we embrace this new era of data science, the potential for enhanced insights and improved business performance is limitless.Interview by Scott Ertz of F5 Live: Refreshing Technology.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Guest IntroductionRick Griffin is the founder and CEO of the Neuro Leadership Academy, an organization dedicated to demystifying neuroscience and making it actionable for everyday life, leadership, and healing. With a master's degree in education, Rick is known for translating complex brain science into engaging, usable concepts.Before launching Neuro Leadership Academy, he spent decades as the executive director of a trauma-informed therapeutic residential program for teens, witnessing firsthand how past struggles shape present behavior. This experience ignited his passion to understand the brain's role in trauma and resilience.Rick is a renowned speaker and developer of programs used by schools, businesses, and community organizations, including the Certified Trauma-Informed Specialist micro-credential. He now focuses on writing, teaching, and designing training that applies neuroscience to leadership, learning, relationship-building, and culture, helping people move from being trauma-informed to what he calls "neuro-informed."Summary / Key Takeaways:In this enlightening conversation, Rick Griffin explains the core concept of the brain as a prediction machine. Our experiences aren't direct recordings of reality, but constructions created by our brain based on sensory input and past experiences. Its primary job is to keep us safe by asking: "Is this a threat, or is this a resource?"Key Insights:Prediction Over Perception: We don't just react to the world; our brain constantly predicts what will happen next based on past patterns to conserve energy and ensure survival.The Threat Bias: The brain's default setting leans toward predicting potential threats (e.g., "Is that something that will eat me?"). This is the root of the stress/trauma response.From Trauma-Informed to Neuro-Informed: Understanding behavior shifts from "What happened to you?" to "How is your brain preparing you for what's happening right now?" This removes blame and focuses on the brain's protective, biological function.Healing Through Prediction: Recovery—from trauma or concussion—involves helping the brain make new, safer predictions. By intentionally introducing safe, positive sensory experiences (sights, smells, sounds, connection), we can create predictions that update the brain's model of the world.Agency & The "Sensory Buffet": With 11 million sensory inputs processed every second, we have immense power to influence our state. We can "stamp" resilience into simple cues (a mint, a coin, a song) and use them to ground ourselves or others.Application to Concussion Recovery: Symptoms like brain fog, fatigue, and anxiety are not signs of a "broken" brain, but of a brain in protection mode, reallocating energy to heal. Recognizing this allows for self-compassion and intentional practices (like rest and calibrated sensory input) to support the healing process.Resources Mentioned by Rick:Book: How Emotions Are Made: The Secret Life of the Brain by Dr. Lisa Feldman Barrett. This book was Rick's gateway into understanding the predictive brain.Website: Neuro-LA.com (Home of the NeuroLeadership Academy)Newsletter/Substack: Neuro Notes – Weekly articles on neuro-informed practices. Subscribe at: https://rickgriffin894.substack.comLinkedIn: linkedin.com/in/rick-griffin-nlaEmail: Rick@neuro-LA.com (He welcomes engagement and questions)Services: Rick offers keynote speaking, virtual and in-person training, workshops, and consulting for organizations. He also has a series of eBooks available. Contact him via email or his website for more information.Bethany Lewis & The Concussion Coach:Free Guide: "5 Best Ways to Support Your Loved One Dealing with a Concussion" - Download at www.theconcussioncoach.comConcussion Coaching Program: For personalized mentorship in recovery. Sign up for a free consultation HERE 

“Predictive coding offers a powerful lens for understanding psychosis…”Dr. Marta Garrido is a professor at the Melbourne School of Psychological Sciences, where she leads the Cognitive Neuroscience and Computational Psychiatry Laboratory and directs the Cognitive Neuroscience Hub. She is also a research program lead at the Graeme Clark Institute. With a background in engineering physics from the University of Lisbon and a PhD in neuroscience from University College London under the mentorship of Professor Karl Friston, Marta has become a leading figure in understanding how the brain processes predictions and surprise. Her research spans mismatch negativity, predictive coding theory, dynamic causal modeling, and the development of cutting-edge neuroimaging technologies, including Australia's first optically pumped MEG system.In this episode, Peter and Marta explore the elegant framework of predictive coding and its implications for understanding psychiatric conditions like psychosis. They discuss how the brain generates predictions about sensory input and how disruptions in these mechanisms may contribute to symptoms of mental illness. Marta shares her journey from engineering to neuroscience, her transformative PhD experience, and the challenges of building a new MEG system from the ground up. The conversation covers fascinating topics including mismatch negativity as a prediction error signal, subcortical shortcuts for processing threatening stimuli, the phenomenon of blindsight, and the critical importance of mentorship in academic careers. Marta also offers candid reflections on being a woman in neuroscience and her vision for the future of computational psychiatry.We hope you enjoy this episode!Chapters:00:00 - Introduction to Dr. Marta Guerrero04:46 - Journey from Engineering to Neuroscience10:39 - Understanding Predictive Coding and Bayesian Inference18:34 - Implications of Predictive Coding in Schizophrenia27:08 - Advancements in Brain Imaging Techniques36:31 - Exploring Blindsight and Subcortical Shortcuts44:14 - Reverse Engineering the Brain: Challenges and Ambitions51:23 - The Journey of Developing Optically Pumped Magnetometers01:00:29 - Promoting Women in Neuroscience and Leadership ChallengesWorks mentioned:15:59 - Randeniya et al. (2018). Sensory prediction errors in the continuum of psychosis. https://doi.org/10.1016/j.schres.2017.04.01918:36 - Goodwin et al. (2026). Predictive processing accounts of psychosis: Bottom-up or top-down disruptions. https://doi.org/10.1038/s44220-025-00558-526:02 - Larsen et al. (2019). 22q11.2 deletion syndrome: intact prediction but reduced adaptation. https://doi.org/10.1016/j.nicl.2019.10172129:40 - Garvert et al. (2014). Subcortical amygdala pathways enable rapid face processing. https://doi.org/10.1016/j.neuroimage.2014.07.04729:40 - McFadyen et al. (2017). A rapid subcortical amygdala route for faces. https://doi.org/10.1523/JNEUROSCI.3525-16.2017Episode producers:Karthik Sama, Xuqian Michelle Li

Unlock real ROI with predictive AI in this episode of Future Tech featuring Pecan AI CEO Zohar Bronfman. While GenAI dominates headlines, Zohar breaks down how predictive models are quietly transforming decision-making and operations across industries. From data democratization to actionable use cases, this episode is a must-listen for leaders ready to use AI to solve real business problems — not just chase trends.

In this episode of The Lectern, host Ethan Hsieh sits down with philosopher and cognitive scientist Mark Miller to explore the science of predictive processing and its implications for happiness, meaning, and wellbeing. They unpack how the brain is not a passive receiver of reality, but an active prediction engine—constantly generating its best guesses about the world and updating them through experience. From belief formation and perception to resilience, virtue, play, and mindfulness, the conversation bridges cutting-edge cognitive science with ancient contemplative wisdom. Together, Ethan and Mark discuss how understanding the predictive nature of the mind can transform how we relate to uncertainty, cultivate agency, and develop a deeper, more participatory sense of happiness—both individually and collectively. This episode also introduces Mark Miller's upcoming course, Generations of Joy, which explores these ideas through neuroscience, philosophy, and contemplative practice. Sign up for the course: https://lectern.johnvervaeke.com/courses/generations-of-joy   00:00 Welcome back to The Lectern 02:30 Mark Miller's background and research focus 06:00 Predictive processing and cognitive science 09:00 Belief, perception, and meaning-making 10:18 "You're not seeing the world—you're seeing your best guess about the world." 13:00 Course overview and key themes 27:00 Honesty, virtue, and transformation 39:30 Practical applications and course dynamics 41:30 Real-world implications of science 43:00 Emptiness, neuroscience, and insight 43:30 The frame problem in cognitive science 45:30 Optimism vs. pessimism: locking onto the world 46:30 Training the mind to discern 47:30 The interpretive nature of reality 52:00 The role of play in cognitive development 56:00 Managing uncertainty through play 01:12:30 Mindfulness and emerging evidence 01:22:00 The Transformational Neuroscience course   Mark Miller is a philosopher and cognitive scientist whose work bridges philosophy, neuroscience, and contemplative science. His research explores how the predictive brain shapes happiness, wellbeing, and meaning in a technologically saturated world. He is a Senior Research Fellow at Monash University's Centre for Consciousness and Contemplative Studies (Australia), cross-affiliated with the Psychology Department at the University of Toronto (Canada), and a visiting researcher at Hokkaido University's Centre for Human Nature, Artificial Intelligence, and Neuroscience (Japan). Website: https://www.markdmiller.live/   Ethan Hsieh is a facilitator, educator, and philosophical practitioner working at the intersection of performance, cognition, and transformative pedagogy. He is the creator of TIAMAT, a three-tier developmental framework integrating cognitive science, dialogical philosophy, and embodied practice. Through immersive learning environments and collaborative inquiry, Ethan helps individuals cultivate virtuosity as a way of life—emphasizing participatory sense-making, metacognitive mapping, and shared agency. His work with the 5toMidnight collective focuses on building deliberately developmental communities grounded in relational ontology and lived philosophical transformation.

NEW 2026 PODCAST SCHEDULEGuest Episodes every MondayAstrology Episodes with Vanessa every ThursdayWatch this episode on Youtube: https://youtu.be/jUVWEnvI5lAReady to work with astrology in a grounded, practical way?Book your 2026 Astrology Session or Package here: https://vanessasoul.com/AstrologyEpisode 139: Astrology Is a Cheat Code for Conscious Entrepreneurs with Vanessa Soul Astrology doesn't have to be confusing, overwhelming, or something you “believe in” to be useful.In this episode of the Power and Purpose Podcast, Vanessa Soul shares why astrology is officially becoming a regular part of the show and how to use it as a functional, real-world tool for navigating life, business, relationships, and major decisions with more clarity and confidence.This conversation sets the foundation for the Thursday astrology episodes, so whether you're astrology-curious or already familiar with the language, you'll walk away with enough context to actually apply what you hear without feeling overloaded or intimidated.Astrology is not about labels or identity alone. It's about understanding the energetic weather so you can prepare, respond intentionally, and stop feeling blindsided by change.What You'll Learn in This Episode• Why astrology works best as a practical, functional tool• The difference between identity astrology and usable astrology• How to use astrology without studying charts or memorizing meanings• Why knowing your Sun, Rising, and Moon really matters• How transits impact your emotions, decisions, and business timing• Why astrology is like checking the weather, not predicting fate• When astrology helps you prepare instead of react• How to integrate astrology with intuition and modern toolsTimestamps00:00 – Welcome and why attention matters01:00 – The new 2026 podcast schedule explained02:35 – Why astrology is returning to the podcast03:30 – What functional astrology actually is05:10 – Identity astrology vs practical application06:30 – Who these astrology episodes are for09:30 – Using astrology in business, career, and daily life11:00 – Timing awareness and common astrology “do nots”13:00 – Astrology styles, house systems, and philosophy15:20 – Understanding the Big Three: Sun, Rising, Moon17:00 – The Rising sign, identity, and the first house18:15 – The Moon, emotions, and fulfillment19:10 – Why houses matter more than memorization22:30 – Using astrology updates without full chart readings26:20 – Astrology as energetic weather forecasting28:30 – Predictive astrology vs thematic astrology31:30 – Building trust instead of dependency with astrology33:00 – Closing thoughts and what's coming next

Gamble on anything & everything.

Rheumatoid arthritis is often seen as “just” joint pain, but Mayo Clinic rheumatologist Dr. John Davis and University of Colorado researcher Dr. Kevin Deane reveal a far more complex and promising story. In this episode of Tomorrow's Cure, host and journalist Cathy Wurzer explores how autoimmune disease can quietly develop for years before the first swollen joint, and how new blood tests, gut microbiome insights, and the exposome, our lifetime of environmental exposures such as cigarette smoke and wildfire haze, are helping clinicians see risk much earlier. The conversation dives into emerging tools that use artificial intelligence to sift through genetics, autoantibodies, microbiome data, and real-world exposures to predict who is most likely to develop rheumatoid arthritis and who will respond to specific treatments. Hear how prevention trials, lessons from type 1 diabetes, and more virtual models of care could change what it means to live with, or even avoid, rheumatoid arthritis in the future. How to listen and stay connected: Subscribe to Tomorrow's Cure on your favorite podcast app and follow the show so you never miss an episode. Get the latest health information from Mayo Clinic's experts—subscribe to Mayo Clinic's newsletter for free today: https://mayocl.in/3EcNPNc Connect with Mayo Clinic: Like Mayo Clinic on Facebook: https://www.facebook.com/mayoclinic/ Follow Mayo Clinic on Instagram: https://www.instagram.com/mayoclinic/ Follow Mayo Clinic on X (formerly Twitter): https://x.com/MayoClinic Follow Mayo Clinic on Threads: https://www.threads.net/@mayoclinic

Nonprofit financial leaders are being asked to do more with less, manage rising risk, and still advance mission with confidence. Dr. Stephanie Rose-Belcher, COO of JMT Consulting, provides a forward-looking conversation on FinTech forecasts and what nonprofit executives must be preparing for now.Dr. Rose-Belcher reframes finance as the operational backbone of nonprofit leadership, not a barrier. She opens by addressing cybersecurity, urging leaders to view it as a governance, compliance, and financial responsibility rather than an IT task. “This is not an IT issue. This is an everybody issue,” she explains, placing accountability squarely with executive leadership and boards. With donor data, financial systems, and integrated platforms increasingly interconnected, leaders must treat cyber readiness as an enterprise-wide business risk.From there, the discussion moves to platform strategy. Rather than promoting a one-size-fits-all solution, Dr. Rose-Belcher challenges nonprofit leaders to take ownership of their technology ecosystem. The responsibility, she explains, is not simply to buy tools, but to understand workflows, integrations, return on investment, and the operational change required to realize value. Finance leaders, often serving as what she calls “accidental technologists,” must either develop this fluency or intentionally bring in expert guidance.Predictive analytics emerges as a defining leadership capability for 2026. Dr. Rose-Belcher encourages nonprofit financial teams to move beyond static annual budgets toward rolling forecasts and scenario planning. By modeling funding shifts, cost changes, and operational risks throughout the year, finance teams can elevate their role from record-keeping to strategic decision support.Talent constraints also surface as a pressing leadership challenge. With fewer trained accountants entering the field and nonprofit compensation pressures persisting, leaders must focus on skill development, retention, and creative workforce pipelines. Dr. Rose-Belcher shares how JMT is investing in long-term intern programs to strengthen the sector as a whole.The episode closes with a powerful leadership message: finance teams must act as strategic partners and educators across the organization. “We consider finance the enabler of the mission, not the blocker,” Dr. Rose-Belcher notes—a mindset shift that can transform silos into shared stewardship.Find us Live daily on YouTube!Find us Live daily on LinkedIn!Find us Live daily on X: @Nonprofit_ShowOur national co-hosts and amazing guests discuss management, money and missions of nonprofits! 12:30pm ET 11:30am CT 10:30am MT 9:30am PTSend us your ideas for Show Guests or Topics: HelpDesk@AmericanNonprofitAcademy.comVisit us on the web:The Nonprofit Show

Iaaaago Jorge convida Raphael Barreto e Lucas Brandão para discutir sobre neutropenia febril, em 5 clinicagens:1. Neutropenia febril é emergência oncológica2. Como escolher o antibiótico?3. Quando escalonar o antibiótico?4. Quando suspender o antibiótico?5. Quando prescrever filgrastim?Referências:1. Klastersky J, de Naurois J, Rolston K, et al. Management of febrile neutropaenia: ESMO Clinical Practice Guidelines. Ann Oncol. 2016;27(suppl 5):v111-v118. doi:10.1093/annonc/mdw3252. Taplitz RA, Kennedy EB, Bow EJ, et al. Outpatient Management of Fever and Neutropenia in Adults Treated for Malignancy: American Society of Clinical Oncology and Infectious Diseases Society of America Clinical Practice Guideline Update. J Clin Oncol. 2018;36(14):1443-1453. doi:10.1200/JCO.2017.77.62113. Zhang H, Wu Y, Lin Z, et al. Naproxen for the treatment of neoplastic fever: A PRISMA-compliant systematic review and meta-analysis. Medicine (Baltimore). 2019;98(22):e15840. doi:10.1097/MD.00000000000158404. Zheng B, Huang Z, Huang Y, Hong L, Li J, Wu J. Predictive value of monocytes and lymphocytes for short-term neutrophil changes in chemotherapy-induced severe neutropenia in solid tumors. Support Care Cancer. 2020;28(3):1289-1294. doi:10.1007/s00520-019-04946-35. Douglas C, Morse JD, Anderson BJ. Mucositis Pain and Its Temporal Relationship to White Cell Count. Paediatr Anaesth. 2025;35(4):302-309. doi:10.1111/pan.150636. Vassallo M, Michelangeli C, Fabre R, et al. Procalcitonin and C-Reactive Protein/Procalcitonin Ratio as Markers of Infection in Patients With Solid Tumors. Front Med (Lausanne). 2021;8:627967. Published 2021 Mar 12. doi:10.3389/fmed.2021.6279677. Smith TJ, Bohlke K, Lyman GH, et al. Recommendations for the Use of WBC Growth Factors: American Society of Clinical Oncology Clinical Practice Guideline Update. J Clin Oncol. 2015;33(28):3199-3212. doi:10.1200/JCO.2015.62.34888. Heil G, Hoelzer D, Sanz MA, et al. A randomized, double-blind, placebo-controlled, phase III study of filgrastim in remission induction and consolidation therapy for adults with de novo acute myeloid leukemia. The International Acute Myeloid Leukemia Study Group. Blood. 1997;90(12):4710-4718.9. Weiss JM, Csoszi T, Maglakelidze M, et al. Myelopreservation with the CDK4/6 inhibitor trilaciclib in patients with small-cell lung cancer receiving first-line chemotherapy: a phase Ib/randomized phase II trial. Ann Oncol. 2019;30(10):1613-1621. doi:10.1093/annonc/mdz27810. Bodey GP, Buckley M, Sathe YS, Freireich EJ. Quantitative relationships between circulating leukocytes and infection in patients with acute leukemia. Ann Intern Med. 1966;64(2):328-340. doi:10.7326/0003-4819-64-2-32811. Nucci M, Arrais-Rodrigues C, Bergamasco MD, et al. Management of febrile neutropenia: consensus of the Brazilian Association of Hematology, Blood Transfusion and Cell Therapy - ABHH. Hematol Transfus Cell Ther. 2024;46 Suppl 6(Suppl 6):S346-S361. doi:10.1016/j.htct.2024.11.11912. Guarana M, Nucci M, Nouér SA. Shock and Early Death in Hematologic Patients with Febrile Neutropenia. Antimicrob Agents Chemother. 2019;63(11):e01250-19. Published 2019 Oct 22. doi:10.1128/AAC.01250-1913. Rosa RG, Goldani LZ. Cohort study of the impact of time to antibiotic administration on mortality in patients with febrile neutropenia. Antimicrob Agents Chemother. 2014;58(7):3799-3803. doi:10.1128/AAC.02561-1414. Averbuch D, Orasch C, Cordonnier C, et al. European guidelines for empirical antibacterial therapy for febrile neutropenic patients in the era of growing resistance: summary of the 2011 4th European Conference on Infections in Leukemia. Haematologica. 2013;98(12):1826-1835. doi:10.3324/haematol.2013.09102515. Beyar-Katz O, Dickstein Y, Borok S, Vidal L, Leibovici L, Paul M. Empirical antibiotics targeting gram-positive bacteria for the treatment of febrile neutropenic patients with cancer. Cochrane Database Syst Rev. 2017;6(6):CD003914. Published 2017 Jun 3. doi:10.1002/14651858.CD003914.pub416. Puerta-Alcalde P, Cardozo C, Suárez-Lledó M, et al. Current time-to-positivity of blood cultures in febrile neutropenia: a tool to be used in stewardship de-escalation strategies. Clin Microbiol Infect. 2019;25(4):447-453. doi:10.1016/j.cmi.2018.07.02617. Ljungman P, Alain S, Chemaly RF, et al. Recommendations from the 10th European Conference on Infections in Leukaemia for the management of cytomegalovirus in patients after allogeneic haematopoietic cell transplantation and other T-cell-engaging therapies. Lancet Infect Dis. 2025;25(8):e451-e462. doi:10.1016/S1473-3099(25)00069-618. Maertens J, Lodewyck T, Donnelly JP, et al. Empiric vs Preemptive Antifungal Strategy in High-Risk Neutropenic Patients on Fluconazole Prophylaxis: A Randomized Trial of the European Organization for Research and Treatment of Cancer. Clin Infect Dis. 2023;76(4):674-682. doi:10.1093/cid/ciac62319. Aguilar-Guisado M, Espigado I, Martín-Peña A, et al. Optimisation of empirical antimicrobial therapy in patients with haematological malignancies and febrile neutropenia (How Long study): an open-label, randomised, controlled phase 4 trial. Lancet Haematol. 2017;4(12):e573-e583. doi:10.1016/S2352-3026(17)30211-9

Voicemail: 951-292-4377;  Predictive market platforms;  Hard 16 vs. Ace in blackjack;  Las Vegas buffets;  What's better in casinos now vs. forty years ago;  Trip reports:  Aruba;  Black Hawk;  Hard Rock Casino Tejon

Noble Predictive Insights has released its power rankings of Arizona and national politicians. Mike Noble, CEO of Noble Predictive Insights, talks about the poll and the shocking places some politicians got.

Mike Noble joined Outspoken to discuss his Arizona Power Rankings poll where Arizonans chose their favorite and least favorite politicians in the state.

In this stream I am joined by professor Jiang of the YouTube channel Predictive History to discuss his game theory in light of Orthodox prophecy and tradition concerning the direction of the world. Make sure to leave a comment and let me know what you think. God Bless

Michael discusses the potential future of predictive markets in political forecasting and their comparison to traditional polling.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Deep Dive in DDH is a three-part limited series where experts in the field of DDH have been invited to discuss the controversies in the management of hip dysplasia. Episode 1 was published in August and discussed management of DDH in infants under 6 months of age. In Episode 2, we are joined by Eduardo Novais at Nemour Children's Health in Jacksonville and Salil Upasani of Rady Children's Hospital and discuss the controversies in the management of developmental hip dislocations in the operating room including the process to decide between closed and open reduction, the use of concomitant osteotomies, adjunctive imaging, and casting protocols. Hosted by Will Morris (Scottish Rite for Children). Music by A. A. Aalto. Referenced Publications: Novais EN, Hill MK, Carry PM, Heyn PC. Is Age or Surgical Approach Associated With Osteonecrosis in Patients With Developmental Dysplasia of the Hip? A Meta-analysis. Clin Orthop Relat Res. 2016 May;474(5):1166-77. doi: 10.1007/s11999-015-4590-5. PMID: 26472583; PMCID: PMC4814411. Schmaranzer F, Justo P, Kallini JR, Ferrer MG, Miller P, Bixby SD, Novais EN. Hip Morphology on Post-Reduction MRI Predicts Residual Dysplasia 10 Years After Open or Closed Reduction. J Bone Joint Surg Am. 2024 Jan 17;106(2):110-119. doi: 10.2106/JBJS.23.00333. Epub 2023 Nov 22. PMID: 37992184; PMCID: PMC12205695. Morris WZ, Chilakapati S, Hinds SA, Herring JA, Kim HKW. The Clinical Significance of Infolded Limbus on Postreduction Arthrogram in Developmental Dysplasia of the Hip. J Pediatr Orthop. 2022 Apr 1;42(4):e309-e314. doi: 10.1097/BPO.0000000000002070. PMID: 35132011. Morris WZ, Hinds S, Worrall H, Jo CH, Kim HKW. Secondary Surgery and Residual Dysplasia Following Late Closed or Open Reduction of Developmental Dysplasia of the Hip. J Bone Joint Surg Am. 2021 Feb 3;103(3):235-242. doi: 10.2106/JBJS.20.00562. PMID: 33252590. Gans I, Sankar WN. The medial dye pool revisited: correlation between arthrography and MRI In closed reductions for DDH. J Pediatr Orthop. 2014 Dec;34(8):787-90. doi: 10.1097/BPO.0000000000000187. PMID: 24787303. Novais EN, Hollnagel KF, Bixby SD, Ferrer MG, Williams DN, Kim YJ, Schmaranzer F. Predictive value of post-reduction gadolinium-enhanced magnetic resonance imaging in detecting avascular necrosis after closed and open reduction for developmental dysplasia: A minimum 5-year follow-up study. J Child Orthop. 2025 Jul 6;19(4):329-338. doi: 10.1177/18632521251350524. PMID: 40630930; PMCID: PMC12230044. Paez C, Badrinath R, Holt J, Bomar JD, Mubarak SJ, Upasani VV, Wenger DR. Ligamentum Teres Transfer During Medial Open Reduction in Patients with Developmental Dysplasia of the Hip. Iowa Orthop J. 2021;41(1):47-53. PMID: 34552403; PMCID: PMC8259203.

As 2025 closed, small business remained resilient with job growth showing very little variation throughout the year, while hourly wage growth finished close to inflation, according to Paychex Small Business Employment Watch. Manufacturing didn't fare well last year and is in contraction, according to the Purchasing Manager's Index. Some industries such as computer and electronic products have seen expansion, but eyes are on what impact tariffs will continue to have. Gene Marks offers insights on these topics, as well as details on a lawsuit in NYC on predictive scheduling that cost Starbucks $39 million to settle. Businesses need to be aware of similar laws in their states.  Additional Resources Meet Paychex: https://bit.ly/3VtM6bs On-demand webinar on top regulatory issues: https://bit.ly/2026-top-regs-webinar Top Regulatory Issues of 2026 article: https://bit.ly/top-regs-2026 No Tax on Tips and OT webinar registration: https://bit.ly/no-tax-on-tips-ot No Tax on Tips article: https://bit.ly/no-tax-tips-ot DISCLAIMER: The information presented in this podcast, and that is further provided by the presenter, should not be considered legal or accounting advice, and should not substitute for legal, accounting, or other professional advice in which the facts and circumstances may warrant. We encourage you to consult legal counsel as it pertains to your own unique situation(s) and/or with any specific legal questions you may have.

  2025 predictions — graded AI-powered knowledge Bob's 2025 prediction: AI would dramatically improve knowledge in contact centers. Result: Early but mostly wrong. The technology moved, but the data did not. Knowledge bases were too fragmented, too dirty, and too poorly governed for AI to meaningfully improve frontline work. The industry instead spent another year chasing bots, automation, and surface-level "AI assistants." Grade: C+ The failure was not AI. It was the state of enterprise knowledge. Remote work reversal Bob's 2025 prediction: Work-from-home would shrink and revert toward pre-COVID norms. Result: Correct. Remote and hybrid work has fallen to within five percentage points of pre-COVID levels. Companies quietly reversed course not because it helped customers or employees, but because leadership never learned how to manage distributed teams. Hybrid was the worst of both worlds: frontline leaders juggling physical rooms, video calls, and dashboards without the training or structure to do any of it well. Grade: A Why remote work collapsed The reversal was not ideological. It was operational. Executives defaulted back to what felt controllable: physical presence. Organizations refused to do the hard work of re-engineering leadership, coaching, quality management, and accountability for a distributed workforce. They solved a people problem with proximity. Amas' prediction for 2026 Voice comes back. Digital channels absorbed most of the AI hype: chat, bots, messaging, and self-service. But customers never stopped calling. Voice is where frustration spikes, where trust is tested, and where automation breaks down. Amas' call: 2026 will be the year voice reasserts itself as the center of the customer relationship — and the CCaaS market will look radically different by 2027 because of it. Bob's prediction for 2026 Data becomes the bottleneck. AI will only become useful where it has access to clean, structured, reliable data. The industry rushed into AI before fixing the foundations: knowledge, case data, call logs, customer history, and operational context. 2026 will be the year contact centers slow down, audit their data, and rebuild the plumbing that AI actually runs on. No data. No intelligence. What the industry is claiming Analysts and vendors are promising three things for 2026: • Predictive and proactive service • Agent empowerment through AI • Fewer humans in contact centers Bob and Amas reject the third and remain skeptical of the first two without structural change. The hype assumes AI will replace labor. Reality says AI will expose how broken the systems around labor really are. Amas' 2026 wish Stop calling software "agents." For twenty years, "agent" meant a human being doing emotional, cognitive, and relational labor. Rebranding bots as agents erases the workforce and confuses accountability. Language shapes power. That battle matters. Bob's 2026 wish Focus on the employee. AI should not be used to replace people. It should be used to remove friction from their work: searching, documenting, switching systems, hunting for answers. Knowledge was always the real use case. The industry just skipped the hard part. Core takeaway 2025 proved that AI without data, governance, and human-centered design does not transform anything. It only adds noise. 2026 will reward the companies that stop chasing demos and start rebuilding the foundations: voice, knowledge, data, and frontline enablement. That is where the real disruption will come from.

Most people think goal failure is a mindset issue, but the real constraint is often physiological. This episode reframes why we don't rise to our goals, we return to the baseline our nervous system has been conditioned to expect. I break down how predictive physiology, stress exposure, and environmental consistency shape metabolism, hormones, and behavior over time. Topics discussed: - Physiological baselines explained- Predictive nervous system- Stress conditioning patterns- Homeostasis versus optimization- Familiar stress responses- Environment over intensity

Are your tech stacks getting too tall to stand on their own?Hey, it's Mike Brewer—and in this episode of the Multifamily Collective, I'm unpacking two compelling headlines shaping our industry right now.Donald Davidoff's “AI Acceleration and the New Reality for Multifamily Operators.” It's not just about flashy tools. It's about the fatigue we're all feeling. From tangled tech stacks to vendor overload, Donald lays out what every owner and operator needs to hear. And guess what? We're tackling this head-on at the 2025 Cultivate Conference—my talk is all about tech fatigue.Then we pivot to the 2026 Property Maintenance Forecast from PropertyMeld. No fluff here—just pure insight. Want to know what separates the operational winners from the laggards? It's not just speed. It's early maintenance quality, intake accuracy, and vendor performance. Predictive maintenance is evolving, and if you're not evolving with it—you're falling behind.But here's the kicker: None of this matters without intentional organizational design. You can't automate your way out of chaos. You need alignment. You need clarity. You need the right people in the right seats.So, if you're leading in the Multifamily space—or want to—this one's for you.Like what you hear? Smash that like button, subscribe to the Multifamily Collective, and let's grow smarter, together.

A multi-topic MacVoices Live! discussion touches on the growing pains of predictive markets after disputes over Time's Person of the Year, raising questions about gambling, rules, and manipulation. Chuck Joiner, Brian Flanigan-Arthurs, Marty Jencius, Eric Bolden, David Ginsburg, Web Bixby, and Jim Rea also debates proposed age-verification requirements for online content, concerns over data retention and privacy, new calls for social-media disclosures from travelers, and what Alan Dye's departure could mean for Apple's interface direction.  "The Antigravity A1 is the world's first 8K 360 drone, it's genuinely a game-changer. You get full immersive flight with the goggles, insanely intuitive controls, and endless creative freedom in editing.If you're thinking about buying a drone, make it this one. Check out the link in our show notes and get a free landing pad with your order!"https://www.antigravity.tech/drone/antigravity-a1/buy?utm_term=macvoices Show Notes: Chapters: [0:00] Predictive markets and AI controversy[6:42] Gambling, manipulation, and rule disputes[7:50] Age verification and online privacy risks[19:47] Social media data demands for travelers[28:10] Encrypted alternatives and Proton tools[29:51] Alan Dye's departure and Apple design impact[36:53] Interface consistency across Apple platforms Links: Kalshi users are in an uproar over their Time 'Person of the Year' betshttps://www.fastcompany.com/91459584/kalshi-polymarket-time-magazine-artificial-intelligence-sam-altman Apple and Google will be asked to block nude photos unless user age is verifiedhttps://9to5mac.com/2025/12/15/apple-and-google-will-be-asked-to-block-nude-photos-unless-user-age-is-verified/ App age verification in action: What you share and who gets your informationhttps://9to5mac.com/2025/12/11/app-age-verification-in-action-what-you-share-and-who-gets-your-information/ U.S. will require some tourists to hand over 5 years of social mediahttps://www.fastcompany.com/91458104/u-s-will-require-some-tourists-to-hand-over-5-years-of-social-media Why Travel to the U.S. Feels Harder Than It Should: The Real Story Behind Social Media Screening and TSA Delayshttps://johnnyjet.com/why-travel-to-the-u-s-feels-harder-than-it-should-the-real-story-behind-social-media-screening-and-tsa-delays/ Guests: Web Bixby has been in the insurance business for 40 years and has been an Apple user for longer than that.You can catch up with him on Facebook, Twitter, and LinkedIn, but prefers Bluesky. Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him on Twitter, by email at embolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast. Brian Flanigan-Arthurs is an educator with a passion for providing results-driven, innovative learning strategies for all students, but particularly those who are at-risk. He is also a tech enthusiast who has a particular affinity for Apple since he first used the Apple IIGS as a student. You can contact Brian on twitter as @brian8944. He also recently opened a Mastodon account at @brian8944@mastodon.cloud. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession ‘firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

315 | Jess Lytle (Head of Marketing at Exit Five) hosts a live roundtable with Morgan Cole (VP of Demand Gen at Red Canary), Lisa Cole (CMO at 2X), and Jean Cameron (Sr. Director of Field & Partner Marketing at Demandbase) on how B2B teams are using AI to drive pipeline and revenue. They share real examples of how marketers are identifying in-market buyers earlier, moving deals faster, replacing outdated lead scoring, and keeping marketing, sales, and ops aligned around revenue. The conversation goes deep on intent signals, buying groups, predictive analytics, brand vs demand, and what's changing in the new era of pipeline accountability. Timestamps(00:00) - AI hype vs real revenue impact (06:16) - Panel intros and GTM perspectives (08:46) - The real pipeline problem: growth without more headcount (11:16) - How teams use AI to identify in-market buyers earlier (16:46) - Buying groups, not leads: why account signals matter (20:46) - Predictive analytics, pipeline forecasting, and deal analysis (27:36) - Why traditional lead scoring is breaking (37:28) - How teams “swarm” accounts with marketing + sales (43:48) - Brand and demand together: building future pipeline Join 50,0000 people who get our Exit Five Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Today's episode is brought to you by Knak.Email (in my humble opinion) is the still the greatest marketing channel of all-time.It's the only way you can truly “own” your audience.But when it comes to building the emails - if you've ever tried building an email in an enterprise marketing automation platform, you know how painful it can be. Templates are too rigid, editing code can break things and the whole process just takes forever. That's why we love Knak here at Exit Five. Knak a no-code email platform that makes it easy to create on-brand, high-performing emails - without the bottlenecks.Frustrated by clunky email builders? You need Knak.Tired of ‘hoping' the email you sent looks good across all devices? Just test in Knak first.Big team making it hard to collaborate and get approvals? Definitely Knak.And the best part? Everything takes a fraction of the time.See Knak in action at knak.com/exit-five. Or just let them know you heard about Knak on Exit Five.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Links James on LinkedIn (https://www.linkedin.com/in/jahuang/) Mike on LinkedIn (https://www.linkedin.com/in/dominucco/) Mike's Blog (https://dominickm.com) Show on Discord (https://discord.com/invite/k8e7gKUpEp) Alice Promo (https://go.alice.dev/data-migration-offer-hands-on) AI on Red Hat Enterprise Linux (RHEL) Trust and Stability: RHEL provides the mission-critical foundation needed for workloads where security and reliability cannot be compromised. Predictive vs. Generative: Acknowledging the hype of GenAI while maintaining support for traditional machine learning algorithms. Determinism: The challenge of bringing consistency and security to emerging AI technologies in production environments. Rama-Llama & Containerization Developer Simplicity: Rama-Llama helps developers run local LLMs easily without being "locked in" to specific engines; it supports Podman, Docker, and various inference engines like Llama.cpp and Whisper.cpp. Production Path: The tool is designed to "fade away" after helping package the model and stack into a container that can be deployed directly to Kubernetes. Behind the Firewall: Addressing the needs of industries (like aircraft maintenance) that require AI to stay strictly on-premises. Enterprise AI Infrastructure Red Hat AI: A commercial product offering tools for model customization, including pre-training, fine-tuning, and RAG (Retrieval-Augmented Generation). Inference Engines: James highlights the difference between Llama.cpp (for smaller/edge hardware) and vLLM, which has become the enterprise standard for multi-GPU data center inferencing.

In this episode, Sasha Orloff speaks with Usman Gul, founder and CEO of Metal, about building an AI-driven operating system for fundraising backed by Y Combinator and Andreessen Horowitz that helps founders avoid wasting 80% of their investor meetings by using data-driven insights to identify the most likely investors based on historical funding patterns, industry benchmarks, and relationship intelligence across over 500,000 venture rounds. -- SPONSORS: Notion Boost your startup with Notion—the ultimate connected workspace trusted by thousands worldwide! From engineering specs to onboarding and fundraising, Notion keeps your team organized and efficient. For a limited time, get 6 months of Notion AI FREE to supercharge your workflow. Claim your offer now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://notion.com/startups/puzzle⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Puzzle

Quick flip and pivot – Fed in focus. Stagflation alert – inflation remains sticky. Unemployment – ADP show massive losses in one category. Looking into the Prediction Markets with our guest, Andrew Wilkinson – Director of Trading Education at Interactive Brokers.  NEW! DOWNLOAD THIS EPISODE’S AI GENERATED SHOW NOTES (Guest Segment) Andrew Wilkinson, Director of Trading Education at Interactive Brokers. Andrew joined Interactive Brokers in 2007 with a background in interest rate and derivative trading in the city of London during the 1990s. Andrew joined IBKR to create market commentary about stocks, options, forex and bonds for the website before helping create the IBKR Campus, which covers Traders' Insight, Traders' Academy, webinars, podcasts and a variety of other financial training for investors of all levels. Andrew has an MBA from Rollins College FL. Learn More at http://www.ibkr.com/funds Follow @andrewhorowitz Looking for style diversification? More information on the TDI Managed Growth Strategy – https://thedisciplinedinvestor.com/blog/tdi-strategy/ eNVESTOLOGY Info – https://envestology.com/ Stocks mentioned in this episode: (BTCUSD), (DIA), (SPY)