Podcasts about augmented intelligence

Recent attacks on water utilities have shown how quickly a local cybersecurity incident can become a national concern. The systems that deliver drinking water are operated by thousands of separate organizations with varying resources and cyber capabilities. For more on what that means for protecting critical infrastructure, I'm joined by Ozgur Ozmen, assistant professor in the School of Computing and Augmented Intelligence at Arizona State University.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

How AI Helps Doctors: 7 Ways AI Gives Doctors Back Their Time In this episode, Barbara discusses: Barbara Hales names the “invisible second shift”—the unpaid hours physicians spend at home on charts, portal messages, and paperwork—and how it steals time from family and rest. Barbara Hales shares seven practical ways to use AI as an assistant, not a replacement, including ambient scribing, drafting portal responses, summarizing research, and streamlining insurance and administrative tasks. Barbara Hales contrasts two physicians—one using AI and one doing everything manually—to show how saving just 30–60 minutes a day can compound into 120–240 hours a year. Barbara Hales explains how AI can multiply a physician's impact by turning everyday patient questions and explanations into reusable education materials and marketing content that grow the practice without adding more work hours. Barbara Hales insists that doctors must “delegate the task, never delegate the judgment,” emphasizing privacy, safety, and clinical oversight so AI reduces burnout while preserving trust and the humanity of medicine. Key Takeaways: “AI's real power in medicine isn't replacing physicians—it's reclaiming the invisible hours lost to documentation and administration so doctors can be fully present with their patients and in their own lives again.” Connect with Barbara Hales:  Twitter: @DrBarbaraHales Facebook: facebook.com/theMedicalStrategist Business Website: TheMedicalStrategist.com Email: info@TheMedicalStrategist.com  YouTube:@barbarahales LinkedIn: https://www.LinkedIn.com/in/barbarahalesBooks: Content Copy Made Easy 14 Tactics to Triple Sales TRANSCRIPTION (245) 7 Ways AI Gives Doctors Back Their Time Chapter 1 — Why Physicians Need Their Time Back Dr. Barbara Hales 00:04 Hello, everyone. Welcome back to Marketing Tips for Doctors. When was the last time you finished seeing your last patient, and you were actually finished for the day? Not finished seeing patients, finished. No charts waiting for you. No patient portal messages. No paperwork you promised yourself you’d finished after dinner. No laptop sitting on the kitchen counter staring at you while your family is doing something without you. If you’re laughing right now, I understand because for many physicians, the workday doesn’t end when the office closes; it just changes location. You go home, have dinner, maybe spend some time with your family, and then out comes the laptop, and somewhere along the way, something happened to medicine. We became doctors because we wanted to take care of people. We did not go to medical school because we had a burning desire to become world-class data entry specialists. We didn’t dream about answering portal messages. We didn’t fantasize about prior authorizations, and I’m fairly certain that when you wrote your medical school application essay, you didn’t say, “My lifelong ambition is to spend more evenings completing documentation, yet here we are, and that is why I want to talk about artificial intelligence today. Not because AI is fashionable, not because everybody is talking about ChatGPT, and certainly not because I think technology should replace physicians. Quite the opposite. I think we need to ask a completely different question: Can technology give physicians enough time back that we can actually be physicians again? Apparently, doctors are beginning to think the answer may be yes. According to the American Medical Association’s 2026 Physician Survey on Augmented Intelligence, 81% of physicians now report using AI professionally. That’s more than double the percentage in 2023. Think about that. In only a few years, AI went from something many doctors viewed with suspicion to something being used in medical practices every day, and here’s another number that really caught my attention: 70% of physicians surveyed by the AMA said they see AI as a tool that can automate tasks contributing to work-related burnout. So today, I’m going to show you seven practical ways physicians can use AI to get some of their time back. But before we get to those seven, I want to talk about what we’re really trying to solve, because the problem is not technology. The problem is time. The invisible second shift. Picture a physician we’ll call Dr. Sarah. Sarah has a busy primary care practice. Her last patient leaves at 515. She gets home around Her husband has dinner waiting. Her daughter sits at the table, telling everyone about something that happened at school. Sarah is listening, sort of, because part of her brain is thinking about the unfinished charts. She knows there are 11 of them, so she eats dinner. She asks her daughter a few questions. She helps clear the table. And then she says the sentence that physician families know all too well. I just have to finish a few charts. She opens the laptop. 8 o’clock becomes nine. Nine becomes 10. Her daughter goes to bed. Sarah finally closes the computer at 1037, and here’s the sad part: nothing particularly unusual happened that day. There wasn’t an emergency. Nobody crashed. There wasn’t a complicated admission. It was simply Tuesday. That story is a composite, but I’ll bet many physicians listening recognize themselves in it. This is what I call the invisible second shift of medicine, and this is precisely where I believe physicians should begin thinking about AI, not with robots, not with futuristic diagnostic machines. Start with what I do every day that doesn’t actually require me to be a physician, because that is where your time is hiding. Let’s begin with the obvious one: documentation. AI-powered ambient scribes can listen to the physician-patient conversation with appropriate systems, safeguards, and consent procedures, and generate a draft clinical note. You review it, you correct it, you approve it. The physician remains responsible, but you’re no longer starting with a blank page. And this isn’t just theoretical. In one medical group study involving 79 providers and more than 25,000 AI-generated notes across 23 specialties, high users of an ambient AI scribe had a 21% decrease in time spent on notes per day. Chapter 2 — AI for Documentation, Inbox, and Patient Education Dr. Barbara Hales  07:26 Even more interesting to me was what happened after work. Before the pilot, about 32% of participating providers reported spending eight or more hours per week documenting outside clinic hours during AI use, which dropped to about 8%. Think about it: 32% to 8%. That’s not just an efficiency statistic. That’s dinner. That’s exercise. That’s reading a book. That’s going to your child’s soccer game. That’s sitting on the couch and doing absolutely nothing and not feeling guilty about it-that is life. Imagine Sarah again. Only now her practice introduces a properly vetted ambient AI documentation system. At first, she’s skeptical. She checks every note carefully, and she should, but gradually something changes. Instead of writing the entire note after the visit, she reviews and corrects a draft. One evening, she gets home. She puts her purse down. She walks into the kitchen, and her daughter looks at her and says, “Mom, where’s your computer? Sarah says, “In my bag. Her daughter looks confused. “Aren’t you going to take it out? And Sarah says, “Nope, I’m done. Now that’s an illustrative story, but that is the outcome we’re talking about. We’re not trying to make physicians type faster. We’re trying to make it possible for physicians to stop typing. Next, the inbox. Ah, yes, the inbox-the place where a message that begins with “Quick question, doctor, occasionally contains 14 questions, three attachments, and a medical History going back to 1987. AI can help draft responses to routine patient communications. Notice the word I used: draft, not diagnose, not independently prescribe, not send medical advice without physician review. Draft. You provide the judgment. AI provides the first pass. The 2026 AMA survey found physicians are already using AI to generate draft responses to patient portal messages; think about the difference between writing 20 responses from scratch and reviewing 20 reasonable drafts. Even saving two or three minutes per message adds up, and there’s another benefit: AI can help you turn a rushed answer into a clear, patient-friendly explanation. You still decide what is medically appropriate, but AI can help you communicate it more efficiently. AI use number three: patient education. Here’s an area I think is enormously underused. How many times have you explained the same thing? What does pre-diabetes mean? Why do I need this medication? What should I expect after this procedure? What does this lab result mean? Why do I need to come back in six months? You explain it Monday, then Tuesday, then Wednesday, and Thursday morning. You’re explaining it again. What if you created a physician-approved library of patient education materials? AI can help you take your explanation and turn it into a one-page handout, a frequently asked questions sheet, a patient-friendly email, a short video script, or instructions written at a more accessible reading level. You review it for accuracy. Then you have something reusable. This is where AI becomes more than a time-saving tool; it becomes a way to multiply a physician’s knowledge without multiplying their hours. Doctor using AI versus doctors who aren’t. Now let’s compare two physicians. Same specialty, same number of physicians, same number of patients, same clinical ability. Doctor A is not using AI. Every note begins from scratch. Every patient handout is created manually, or the staff searches for one. Every portal response is typed individually. Every complicated article takes 20 minutes to read before Doctor A determines whether it matters. Every piece of administrative work begins with a blank page; Doctor B uses AI selectively, and I want to emphasize selectively. AI drafts the note. Doctor B reviews it. AI drafts the portal response. Doctor B corrects and approves it. AI turns the doctor’s explanation into patient education. Doctor B verifies it. AI summarizes a long research article. Doctor B goes to the source before making a clinical decision. Doctor B is not surrendering a medical judgment. Doctor B is eliminating unnecessary friction. That distinction matters because the physician who uses AI appropriately isn’t necessarily practicing medicine faster; they’re spending less time on everything surrounding medicine, and the difference compounds. 10 minutes here, 15. Minutes there, 20 minutes somewhere else. Pretty soon, we’re not talking about minutes anymore. We’re talking about hours. AI used number four, keeping up with medical literature. Here’s another struggle physicians know very well: the literature never stops. Studies, guidelines, drug updates, safety alerts, consensus statements, journal articles. Nobody can read everything. Chapter 3 — AI as a Research Assistant, Not a Doctor Dr. Barbara Hales 15:40 The AMA’s 2026 survey actually found that summarizing medical research and standards of care was the most commonly reported physician use of AI among the specific use cases measured. That tells us something important. Doctors aren’t only looking for help with paperwork; they’re using AI to help manage information overload. Suppose there are six recent papers on a subject relevant to your specialty. Instead of reading every word of every paper, AI can help you identify the research questions, study size, primary outcomes, where the findings agreed, and where they conflicted. What limitations should you pay attention to? Then, and this is critical, you go back to the original evidence when the information will affect patient care. AI becomes the map. It does not become the destination, and that brings us to something every physician listening needs to hear. AI is an assistant, not the doctor. AI can be wrong; it can misunderstand context. It can omit information. It can confidently produce something that sounds wonderful and is completely incorrect. And physicians know something that technology does not possess: clinical judgment. We know the difference between the textbook patient and the human being sitting in front of us. We notice hesitation. We notice fear. We notice when someone says, “I’m fine, while everything about them tells us they’re not fine. Medicine is not simply information. Medicine is interpretation. Medicine is judgment. Medicine is trust. So the goal is not how much medicine can I turn over to AI. The better question is, how much non-physician work can I take away from the physician? That is a very different conversation. In part two, I’m going to show you three more ways to use AI, including one that can help you create weeks of marketing content from something you’re already doing every single day, and I’ll tell you another story. This one is about a doctor who realizes the greatest thing AI gave him wasn’t efficiency. It was something much more valuable. It gave him back a Saturday. So here are seven ways doctors can use AI to get their time back. What would you do with the hours AI gave back? At the end of part one, I told you about a doctor who got something unexpected from AI-a Saturday. Let me explain. The Saturday morning that came back, we’ll call him Dr. Michael again. This is a composite story based on the kinds of workflow problems physicians experience. Michael owns a small specialty practice, and every Saturday morning he has a routine: coffee, laptop, kitchen table. He tells some. He tells himself he’ll work for an hour. It’s almost never an hour. Charts to finish, letters to dictate, patient messages, administrative correspondence, and things he didn’t get to during the week. His wife has learned not to ask when he’ll be finished. One Saturday, after his practice has begun using AI to help with documentation and routine administrative drafting, Michael wakes up. He makes coffee. He walks toward the kitchen table, and there’s no laptop sitting there. He had finished his work Friday. His wife says, “Do you want to go get breakfast? And for the first time in longer than he can remember, he does not say, “Maybe later. He says, “Sure. Now you could look at that and say, “AI saved him three hours. I don’t think that’s what happened. AI gave him back Saturday morning with his wife. We have to stop measuring physician efficiency only in minutes. We need to start measuring it in life. AI used number five insurance correspondence and administrative drafting. Now let’s talk about one of everyone’s favorite subjects: insurance. I can practically hear you groaning. Prior authorizations, appeal letters, medical necessity letters, supporting documentation, repetitive administrative correspondence. This is precisely the kind of structured, repetitive work where AI can assist. A physician or staff member can provide the relevant, privacy-safe information in an approved system and have AI help draft a first version. Then someone qualified reviews it. This isn’t about fabricating an argument. It isn’t about inserting facts that aren’t in the chart. Chapter 4 — Administrative Work, Practice Operations, and Content Dr. Barbara Hales 22:44 It is about organizing legitimate clinical information into the format needed for the task, and this matters because physicians did not train for all those years so that their most limited resource, their time, could be consumed fighting with forums; AI may not make the insurance company say yes. Wouldn’t that be nice? But it may reduce the amount of physician time required to ask the question. AI use number six: practical operations. Here’s one physicians often overlook: AI doesn’t have to touch clinical medicine to be useful. Look around your practice. How many repetitive processes exist? Staff training, frequently asked questions, office policies, meeting summaries, job descriptions, phone scripts, patient reminders, internal checklists, responses to online reviews, ideas for improving scheduling, drafting standard operating procedures. You don’t need a medical AI system to ask, “Help me turn this messy process into a checklist. You don’t need AI making clinical decisions to say, “Here are the five questions patients ask my receptionist every day. Help me draft clear answers. And here’s one I particularly like. At the end of your staff meeting, take your approved, non-sensitive meeting notes and ask AI to organize them into: what did we decide? Who is responsible? What’s the deadline? What needs follow-up? Suddenly, a meeting produces action instead of another meeting. Now we’re talking. AI use number seven. Mark. And patient communication. This one is particularly close to my heart. Doctors tell me, Barbara, I know I should create content. I just don’t have time, and I understand. But here’s what I want you to realize: you are already creating content all day long; you’re just not capturing it. Every patient question is potential educational content. Think about today: how many questions did patients ask you? Should I be worried about this? Is this hereditary? When should I get screened? Is there anything I can do to prevent this? What’s the difference between these two treatments? One idea, multiple assets. Those questions can become a short video, a blog post, a patient email, a special media post, a podcast topic, or an FAQ on your website-you provide the expertise. AI helps you repurpose it. For example, you might say, “Patients ask me this question constantly. Here is the explanation I usually give them. Then dictate your explanation and ask AI. Turn this into a 62nd video script written in my conversational style. Then give me three possible titles. Then turn the same information into a patient email. Then create five short social posts from it.  That is where AI can become an extraordinary multiplier for a small medical practice, not because AI becomes the physician’s voice, but because it helps the physician’s real voice travel further. Here’s a story. The patient who finally felt heard. Let me give you one last story. We’ll call the physician Dr. Patel. Dr. Patel has always been the kind of doctor who looks directly at patients when they are speaking. At least she used to be. Then came increasingly complicated documentation requirements, and gradually she found herself doing what so many physicians do: patient talking, doctor typing, patient talking, doctor clicking, patient asking something important, doctor saying, “Give me one second. While finishing a field on the screen, then her practice begins using an ambient documentation tool. She’s still responsible for the note. She still reviews it, but during the encounter, something changes. She turns away from the computer. She looks at her patient, an older woman who has been seeing her for years, and suddenly stops in the middle of the conversation. Doctor Patel asks, “Is there something wrong? The patient smiles and says, “No, I was just thinking how nice it is to see your face again. That story is illustrative, but think about what it represents. We keep talking about AI as though technology will make medicine less human. Used badly, perhaps it could, but what if we use it to do exactly the opposite? What if technology handles more of the machinery of medicine, so physicians have more time for the humanity of medicine? That’s the opportunity that interests me. Now let’s turn to our two physicians, Doctor A and Doctor B. Remember, they started in essentially the same place, same specialty, same patient volume, same clinical competence. Doctor A continues doing everything manually. Doctor B gradually delegates appropriate repetitive work to carefully selected AI tools, not clinical judgment work. Chapter 5 — Reclaiming Time, Using AI Safely, and Staying Human Dr. Barbara Hales 29:57 Doctor B saves a few minutes. Documenting, a few minutes on correspondence, a few minutes researching, a few minutes creating patient materials, a few minutes handling administrative tasks. Let’s be conservative and imagine that adds up to only 30 minutes a day, five days a week. That’s two and a half hours, over roughly 48 working weeks. That’s 120 hours a year. Think of that-a 120 hours, three full 40-hour work weeks, and that’s from saving only 30 minutes per day. If the physician saves an hour, now we’re talking about approximately 240 hours, six work weeks. So ask yourself: What would you do with six weeks of your life? Would you see more patients? Would you leave the office earlier? Would you exercise? Would you finally take the vacation you’ve postponed? Would you have dinner with your family without your laptop? Would you sit with a patient for an extra three minutes instead of watching the clock? Would you grow your practice? Would you create another revenue stream? Would you simply sleep? There isn’t a correct answer. The point is, you get to decide. Start small. If you are listening to this and thinking, Barbara, this sounds great, but I don’t even know where to begin. Don’t begin with seven things. Begin with one. Tomorrow, notice the task that makes you say, “I can’t believe I’m wasting my time doing this again. Write it down. That’s your candidate. Then ask, “Could AI draft this? Could AI summarize this? Could AI organize this? Could AI turn this into a template? Could AI help my staff do this without involving me every single time? You don’t need to transform your entire practice this month. Choose one repetitive task. Get comfortable. Measure whether it actually saves time. Then choose another. That’s how change becomes sustainable. A word about safety. Because we’re physicians, I need to emphasize something very important: convenience does not outrank confidentiality. Don’t copy protected patient information into a public consumer AI tool. Your practice needs to evaluate privacy, security, HIPAA requirements, vendor agreements, accuracy, workflow integration, and appropriate human oversight, and every clinically relevant AI-generated output needs appropriate professional review. Delegate the task, Never delegate the judgment. AI can generate extraordinarily convincing errors. So remember this: delegate the task, never delegate the judgment. That sentence may be the most important thing I say in this entire episode. If today’s episode made you look at your workday differently, do two things. First, choose one task this week that AI might be able to take partially off your plate. Don’t try to revolutionize your entire practice. Just reclaim 10 minutes, and second, subscribe to Marketing Tips for Doctors and share this episode with another physician who is still taking the laptop home every night, because every week on Marketing Tips for Doctors we talk about practical ways to build a stronger, more profitable medical practice without sacrificing the reason you became a physician in the first place. And remember, you’re one tweak away from greatness. AI won’t solve everything that’s wrong with medicine. It won’t eliminate insurance headaches. It won’t fix reimbursement overnight. It won’t replace the judgment that comes from years of training and experience. And I don’t want it to. What I want AI to replace is something very different: the unnecessary hour at the computer, the repetitive letter, the blank page, the administrative task that followed you home, the Saturday morning you were supposed to spend with someone you love, because perhaps the most exciting promise of artificial intelligence in medicine is not artificial intelligence at all. Perhaps it’s giving physicians the time to be More human, and if technology can help us do that, then I think it’s worth paying attention to. I’m Dr. Barbara Hales. Thank you for joining me on Marketing Tips for Doctors. Till next time. The post How AI Helps Doctors first appeared on The Medical Strategist.

Summary In this episode, Dr. Noah St. John discusses how understanding the caveman brain can unlock success in sales, leadership, and personal growth. He shares practical tools like the caveman calculator and quiz, and emphasizes the importance of taming the caveman in the age of AI. Key Topics The caveman brain and its impact on decision-making AI's role in transforming customer experience Tools like the caveman calculator and quiz The importance of taming the caveman, not fighting it Inner game vs. outer game in success The role of mindset in leveraging AI effectively Case studies of clients who increased revenue by taming their caveman brain The concept of invisible costs and how to measure them Practical steps to improve conversion rates and revenue The future of AI and human collaboration in business Takeaways Most people are unaware of how their caveman brain sabotages their success. AI is a magnifier; it can amplify both good and bad habits. Taming the caveman brain is essential for maximizing AI's potential. Understanding your caveman type can provide insights into your behavior. Focusing on conversions rather than just leads drives revenue growth. The 'invisible brake' often prevents people from reaching their goals. Practical tools like the caveman calculator can reveal hidden costs. Success involves mastering both inner and outer game. AI is a powerful tool, but human insight and mindset are crucial. Changing core beliefs can lead to significant financial and personal breakthroughs. Chapters 00:00 Introduction to Augmented Intelligence 01:34 The Impact of AI on Customer Experience 04:10 Understanding the Caveman Brain 11:31 Taming the Caveman for Success 15:04 The Importance of Completing Your Past 17:53 Inner Game vs. Outer Game in AI 21:50 The Shift in Mindset for Value Creation 27:25 Conversions: The Key to Revenue 30:37 Next Steps with Dr. Noah St. John The AI for Sales Podcast is brought to you by BDR.ai, Nooks.ai, and ZoomInfo—the go-to-market intelligence platform that accelerates revenue growth.Skip the forms and website hunting—Chad will connect you directly with the right person at any of these companies.

Most enterprise AI failures aren't model problems—they're data architecture problems. Vivek Vaidya, serial entrepreneur with 25+ years building enterprise software and current CTO/Co-founder of super{set}, explains why vector databases alone can't solve enterprise AI and why knowledge graphs are foundational for production systems. He breaks down the critical difference between augmented intelligence (AI proposes, human approves) versus full automation, details how governance layers must respect existing enterprise data policies, and reveals why non-deterministic LLM outputs create compliance nightmares that kill enterprise adoption.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In the past week, law enforcement in Florida has issued urgent warnings about a dangerous new prank involving artificial intelligence-generated fake crime videos. According to the Orange County Sheriffs Office, these deepfake videos depict realistic scenes of crimes in progress, tricking officers into real responses. In one case, a person showed a deputy a video appearing to show someone breaking into the officers squad car, prompting an immediate reaction with the deputy placing a hand on his holster. The video was later revealed to be fabricated using artificial intelligence from a simple photo of the vehicle. The Seminole County Sheriffs Office highlighted a similar incident in a popular Tik Tok video, emphasizing that such pranks waste valuable resources and divert attention from actual emergencies.Authorities stress that this trend is not harmless entertainment. The Orange County Sheriffs Office stated clearly that using artificial intelligence to spread misinformation can lead to criminal charges for filing false reports. Officials report at least two confirmed incidents in recent days, and while not yet widespread, they are taking the issue seriously to prevent escalation. Deputies are now advising the public to verify information before contacting law enforcement and to report anyone engaging in these deceptive acts.Meanwhile, researchers at Arizona State University are pushing for global standards to combat the growing challenge of artificial intelligence-generated media. Yang, from the School of Computing and Augmented Intelligence, leads efforts to embed detectable signals, like digital watermarks, into all artificial intelligence-created content. His team notes that people can distinguish fake media from real only about fifty-one percent of the time, akin to random guessing, as reported in a study from the Communications of the Association for Computing Machinery. Projects like Robust Adversarial Concept Erasure and Erase Flow aim to remove harmful or sensitive elements from artificial intelligence models without retraining them entirely, preserving quality while enhancing safety.Google security team has also warned this week about indirect prompt injection attacks on artificial intelligence platforms. These exploits poison data sources that large language models rely on, subtly altering outputs without direct user input, as detailed by Adam Gavish of the Google Generative Artificial Intelligence Security Team. Open Artificial Intelligence introduced Lockdown Mode and elevated risk warnings in Chat GPT to counter prompt injection and data exfiltration risks, according to e Week reports.These developments underscore the urgent need for better detection and regulation as artificial intelligence blurs the line between reality and fabrication.Thanks for tuning in, listeners, please subscribe, and remember this episode was brought to you by Quiet Please podcast networks. For more content like this, please go to Quiet Please dot Ai. Come back next week for more.Some great Deals https://amzn.to/49SJ3QsFor more check out http://www.quietplease.aiThis content was created in partnership and with the help of Artificial Intelligence AI

In this episode of Future Finance, hosts Glenn Hopper and Paul Barnhurst are joined by John Thomas Foxworthy, the founder and CEO of the Global Institute of Data Science (GIDS). John discusses the role of augmented intelligence in the future of AI, particularly in finance, and shares his insights from his book The Augmented Intelligence Revolution: How Leaders Win in the AI Century.John Thomas is the Founder and CEO of the Global Institute of Data Science (GIDS), a consulting and professional development organization focused on helping organizations successfully implement AI and data science initiatives. He serves as a Fractional Chief AI Officer for Fortune 500 companies and teaches AI and machine learning courses at Caltech CTME and UC San Diego Extended Studies.In this episode, you will discover:The role of augmented intelligence in financeHow GIDS helps companies implement AIOvercoming AI adoption challenges and cognitive biasesThe evolution of machine learning and AI terminologyHow AI enhances decision-making and operations in financeJohn highlights how the rapid evolution of AI can create challenges in its adoption, especially within complex industries like finance. The Global Institute of Data Science addresses these issues by focusing on feasibility studies, AI strategy, and tackling cognitive biases, ensuring the successful integration of AI in business operations.Follow John:GIDS: https://gidsco.substack.comLinkedIn: https://www.linkedin.com/in/john-thomas-foxworthy-m-s-data-science-1718073/Future Alpha Event: https://www.alphaevents.com/events-futurealphaglobal/agenda-page/filter?_gl=1*1j0347f*[…]ovIhoCWOYQAvD_BwE&gbraid=0AAAAAomEzrlLzh-epjUJjbfXNnASlChgaFollow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn - https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[01:50] – John's Background and Expertise[03:11] – The Augmented Intelligence Revolution[05:40] – Machine Learning vs. Augmented Intelligence[08:45] – The Global Institute of Data Science[11:04] – Feasibility Studies in AI Projects[15:17] – Managing AI Expectations[17:18] – Closing Thoughts and Book Release

In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper explore how leaders can cut through the hype around artificial intelligence and focus on real-world impact. The conversation dives into why so many AI initiatives fail, how cognitive biases affect AI adoption, and why finance professionals must learn to ask better questions before deploying models. John Thomas is the Founder and CEO of the Global Institute of Data Science (GIDS), a consulting and professional development organization focused on helping organizations successfully implement AI and data science initiatives. He serves as a Fractional Chief AI Officer for Fortune 500 companies and teaches AI and machine learning courses at Caltech CTME and UC San Diego Extended Studies.In this episode, you will discover:Why 85% of AI projects fail and how to avoid The difference between AI hype and real implementationHow augmented intelligence improves human decision-makingWhy asking the right questions about AI models matters mostHow AI can help with risk analysis and financial decision-makingThis episode highlights that successful AI adoption is not about chasing the latest technology trends but about asking better questions, understanding assumptions, and focusing on real business problems. As AI continues to evolve, finance leaders who combine human judgment with intelligent systems will be best positioned to turn AI from hype into measurable results.Follow John:GIDS: https://gidsco.substack.com LinkedIn: https://www.linkedin.com/in/john-thomas-foxworthy-m-s-data-science-1718073/Future Alpha Event: https://www.alphaevents.com/events-futurealphaglobal/agenda-page/filter?_gl=1*1j0347f*[…]ovIhoCWOYQAvD_BwE&gbraid=0AAAAAomEzrlLzh-epjUJjbfXNnASlChgaFollow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn - https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[03:05] – Meet John Thomas[04:11] – Augmented intelligence explained[11:21] – Global Institute of Data Science[15:18] – Why AI projects fail[21:29] – Understanding AI models[24:45] – AI in portfolio risk analysis[30:16] – Best advice for finance leaders[32:15] – Rapid-fire questions & wrap-up

Cosmic LOVE with Dr Christopher Rudy The Aquarian Transition - Geopolitics, Free Energy, and the Disclosure of Cosmic Consciousness This broadcast of Cosmic Love explores the intersection of the 2026 Iran conflict, the hidden history of suppressed Tesla technologies, and the metaphysical shift into the Age of Aquarius. The "Cosmic Cowboy" posits that humanity is at a choice point between a surveillance-driven "digital gulag" and a global "economics of abundance" triggered by full disclosure. The Geopolitical "Disclosure Bomb" and the Iran Conflict The current escalation in the Middle East is framed not merely as a regional conflict, but as a final struggle over oil-based scarcity. The speaker argues that the Western hegemony is facing a "reality report" where traditional military dominance is being challenged by hypersonic missile technology—reportedly supplied to Iran by China and Russia—which the West currently lacks the means to intercept. This "fog of war" is exacerbated by media suppression regarding the extent of the damage, particularly concerning the failure of existing defense systems. Suppressed Technology and the Tesla Legacy A central theme of the discussion is the existence of "Promethean technologies," including anti-gravity and zero-point energy, which have allegedly been held in secret since Nikola Tesla's death in 1943. The speaker highlights the role of Dr. John G. Trump (Donald Trump's uncle), who was tasked with reviewing Tesla's confiscated papers, suggesting a direct familial link to this suppressed knowledge. The emergence of wireless power systems in Finland is cited as a modern "unveiling" of these century-old concepts, signaling that the era of energy monopolies is nearing its end. Cosmic Weather and the Aquarian Shift The metaphysical context of these events is defined by a "Grand Trine" in water signs (Sun in Pisces, Moon in Scorpio, Jupiter in Cancer) and a potent "Stellium" in Aries involving Neptune, Venus, and Saturn. This alignment represents a volatile blending of fire and water—emotional depth meeting a "firing of the spirit". This astrological pressure is driving the transition from the 3D density of the Piscean Age (characterized by patriarchal control and win/lose dynamics) into the 5D consciousness of the Aquarian Age, which emphasizes win/win cooperation and the "United Sovereigns of Earth". The broadcast concludes that the "Apocalypse" is not an end-of-the-world scenario, but a literal "unveiling" of suppressed truths. By embracing "Augmented Intelligence" and the "currency of conscience," humanity can navigate the current geopolitical steam and transition into a global society defined by abundance and cosmic love.

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* AI is “Augmented Intelligence” and a Catalyst for Transformation: AI should be viewed as an augmenting tool to enhance legal work, not a “magic box” for perfect results. The systemic and responsible deployment of AI/legal tech is essential for a business transformation that shifts from a reactive to a proactive service delivery model, ultimately freeing up time from mundane tasks.* Focus on Strategy First, Tools Second (Stage One vs. Stage Two): The mistake many make is focusing on the sheer number of AI tools (Stage Two) before defining a strategy (Stage One). The first step should be to map current operations, identify pain points, define a target operating model, and understand what to do with the surplus capacity created by efficiency. Deploying tech without a defined structure is “piecemeal.”* The Human Element and Risk Allocation Remain Critical: Despite advancements in AI, people still seek lawyers for human interaction, to assign and share accountability, and to feel a sense of confidence and comfort in resolving legal problems. The human lawyer acts as a “guardian” and “shield,” and human judgment is necessary for interpreting legal dilemmas, especially since a 1% uncertainty will always exist, regardless of machine precision.* The Need for Purpose-Built Technology: Using a single, ultimate generalist AI tool for everything in law is like “trying to saw a two by four with a power drill.” Lawyers must select technology that is purpose-built for specific use cases (e.g., contracts, litigation, research) to achieve true efficiency and higher-quality work.* A Shift in Business Model is Essential for AI Success: The full potential of AI cannot be realized if the fundamental incentive structure remains the billable hour. If all incentives are to “bill more time,” AI's 4% time saving is seen as lost revenue. However, by adopting models like subscriptions or value-based billing, AI helps with problem avoidance and speed to deal, making the more efficient model more profitable.__________________________Want your question to be answered on a future show? Fill out this short survey.Check out G.O.L.T.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lawsubscribed.com/subscribe

Coming to you live from the Oregon HFMA Winter Conference, we will discuss observations drawn from Office Hours, the Infinx thought leadership forum where healthcare leaders regularly explore the realities of change across revenue cycle operations.Across these conversations, artificial intelligence consistently emerges as the anchor that connects common themes involving workflows, staffing models, compliance pressures, and technology adoption, while accelerating both progress and tension.The discussion will focus on how change is experienced in practice, why some areas of healthcare evolve rapidly while others remain resistant, and how teams are affected operationally and emotionally along the way. Grounded in real conversations and lived experience, the goal is to offer a realistic perspective on navigating change while helping organizations and their teams adapt with confidence and clarity. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

Geschätzte Lesedauer: 11 Minuten Hallo und herzlich willkommen! Hier ist Christopher Funk. Das Thema Sprachen lernen mit KI beschäftigt derzeit viele von uns im Vertrieb sehr intensiv. Denn wir leben in einer Zeit, in der Technologie fast alles möglich macht. Deshalb stellst du dir vielleicht die berechtigte Frage: Müssen wir überhaupt noch mühsam Vokabeln pauken? Schließlich gibt es Apps, die Texte in Sekunden übersetzen, und kleine Gadgets, die fast in Echtzeit dolmetschen. Doch bedeutet das automatisch, dass klassische Sprachschulen überflüssig sind? Oder ist Sprachen lernen mit KI vielleicht nur ein Teil der Lösung? Genau darüber habe ich ausführlich mit Teila Klemp gesprochen. Sie ist Head of Marketing bei Berlitz, einer Traditionsmarke, die seit vielen Jahren am Markt ist. Allerdings befindet sich das Unternehmen gerade in einem spannenden Wandel hin zu einer modernen Bildungsplattform. Ihre Antworten auf die Frage "Mensch oder Maschine?" sind überraschend und für deinen B2B-Vertrieb extrem wichtig. Warum Sprachen lernen mit KI allein im B2B nicht reicht Wenn Teila erzählt, dass sie bei Berlitz arbeitet, wird sie oft gefragt: "Braucht man das heute noch? Ich habe doch mein Smartphone." Ihre Antwort ist jedoch ganz klar: Ja, unbedingt. Denn Sprache ist weit mehr als nur die reine Übersetzung von Wörtern. Vielmehr transportiert Sprache auch Bedeutung, Beziehungen, feine Nuancen sowie Vertrauen und Respekt. Zwar kann eine KI Informationen schnell übersetzen, aber sie kann bisher keine echte menschliche Verbindung aufbauen. Gerade im Vertrieb wissen wir genau: Geschäfte werden immer noch zwischen Menschen gemacht. Wenn du also deine Kommunikation nur auf den Austausch von Daten reduzierst, verlierst du die wichtige Beziehungsebene. Dennoch solltest du die Technik nicht ignorieren, sondern sie klug für dich nutzen. Wie Sprachen lernen mit KI deine Effizienz steigert Berlitz verschließt sich der Technologie keineswegs. Im Gegenteil: Das Unternehmen nutzt Sprachen lernen mit KI als massiven Beschleuniger für deinen Lernerfolg. Die Daten sprechen hier nämlich eine deutliche Sprache. Wer sein klassisches Training mit digitalen KI-Lösungen kombiniert, lernt im Schnitt 40 Prozent schneller. Doch warum ist diese Kombination eigentlich so effektiv? Einerseits erkennt die KI in Echtzeit deinen aktuellen Wissensstand. Sie sieht sofort, wo Fehler passieren, und passt dein Training direkt an. Andererseits kannst du so ohne Hemmungen üben. Denn ein KI-Avatar verurteilt niemanden, auch wenn du eine Vokabel zum fünften Mal falsch aussprichst. Das gibt dir Sicherheit und ein hohes Tempo. Schließlich bleibt der echte Trainer für deine Motivation, die Empathie und den kulturellen Kontext zuständig. Augmented Intelligence als dein neues Vertriebs-Werkzeug Ein weiterer Begriff, den wir im Gespräch vertieft haben, ist Augmented Intelligence. Viele Menschen haben Sorge, dass KI sie komplett ersetzt. Aber die Realität in deinem Arbeitsalltag sieht anders aus: KI übernimmt vor allem die lästige Fleißarbeit. Wenn du heute beispielsweise eine komplexe Excel-Tabelle brauchst, lässt du dir die Formel einfach von einer KI schreiben. Du musst die Formel also nicht mehr auswendig können, sondern nur noch verstehen, was du erreichen willst. Das schafft dir wertvollen Freiraum. Genau hier setzt Augmented Intelligence an. Es bedeutet, dass du deine menschlichen Fähigkeiten stärkst, weil die KI dir den Rücken freihält. Im Kontext von Sprachen lernen mit KI heißt das konkret: Die Technologie liefert dir das Vokabular und die Grammatik. Du nutzt diese Basis anschließend, um komplexe Verhandlungen zu führen, Konflikte zu lösen oder dein Team souverän zu leiten. Somit ergänzen sich beide Welten perfekt. Business Englisch lernen und interkulturelle Kompetenz stärken Hier wird es für dich als Vertriebler oder Führungskraft besonders interessant. Neben der reinen Sprache ist nämlich die interkulturelle Kompetenz ein oft unterschätzter Erfolgsfaktor. Stell dir folgendes Beispiel aus der Praxis vor: Du verhandelst mit einem potenziellen Geschäftspartner in Asien. Dein Gegenüber nickt freundlich und lächelt dich an. Daraufhin interpretierst du das als Zustimmung und denkst, der Deal steht. Doch kulturell betrachtet war das Nicken vielleicht nur ein Zeichen von Höflichkeit, keinesfalls aber ein klares "Ja" zum Vertrag. Wer hier zu direkt auftritt, kann ungewollt das Geschäft ruinieren. Interkulturelle Kompetenz ist also kein weicher Faktor für die Wohlfühlatmosphäre. Vielmehr ist es ein hartes Business Asset. Es entscheidet oft über Missverständnisse, Reibungsverluste und am Ende über deinen Abschluss. Return on Learning: Warum sich deine Investitionen lohnen Früher wurde Weiterbildung oft nur als netter Vorteil für Mitarbeiter gesehen – also als reines "nice to have". Heute hingegen, wo Budgets strenger geprüft werden, zählt vor allem der Return on Learning. Unternehmen investieren gezielt in Sprachtraining für Unternehmen, weil es sich unter dem Strich rechnet. Erstens sind internationale Fachkräfte durch ein schnelleres Onboarding früher produktiv. Zweitens sorgen weniger Missverständnisse in Produktion und Logistik dafür, dass weniger Fehler passieren und Kosten gespart werden. Drittens stärkt eine kultursensible Kommunikation dein Kundenerlebnis und die langfristige Bindung. Deshalb sitzen heute oft auch Finanz- oder Operations-Manager mit am Tisch, wenn über Trainingsbudgets entschieden wird. Denn Sprache und Kultur sind ein fester Teil deiner Wertschöpfungskette. Fazit: Die Mischung macht deinen Vertrieb erfolgreich Mein Fazit aus dem Gespräch mit Teila Klemp ist eindeutig: Die Diskussion "Mensch gegen Maschine" führt in die Irre. Die Zukunft gehört vielmehr der intelligenten Verbindung aus beidem. Wenn du administrative Aufgaben an die KI abgibst, bleibt für dich das, was du am besten kannst: Empathie zeigen, echte Beziehungen aufbauen und kreativ Probleme lösen. Wer Sprachen lernen mit KI also als starkes Werkzeug begreift und gleichzeitig in seine sozialen Kompetenzen investiert, sichert sich den entscheidenden Vorteil im Wettbewerb. Nutze die Technik, aber vergiss nie den Menschen dahinter. Die wichtigsten Erkenntnisse auf einen Blick KI als Turbo: Die Kombination aus KI-Tools und menschlichem Training steigert deine Lerngeschwindigkeit um bis zu 40%. Mehr als Worte: Sprache transportiert Vertrauen und Beziehung – das kann reine Übersetzungssoftware bisher nicht leisten. Augmented Intelligence: Nutze KI für die Basisarbeit, damit du dich auf komplexe zwischenmenschliche Aufgaben fokussieren kannst. Kultur entscheidet Deals: Interkulturelle Kompetenz verhindert teure Missverständnisse in deinem internationalen Geschäft. Return on Learning: Weiterbildung ist eine Investition mit messbarem Erfolg durch weniger Fehler und höhere Produktivität. Wie handhabst du das in deinem Unternehmen? Nutzt du schon digitale Tools zur Weiterbildung oder setzt du noch auf klassische Methoden? Vernetze dich gerne mit mir auf LinkedIn und lass uns darüber diskutieren!

Dr. Amel Havkic, founder of EvoMed Consulting and a lung and critical care specialist, hospital clinical lead. Amel works at the intersection of bedside medicine and MedTech strategy, helping innovators build clinician-approved solutions that scale safely across real healthcare systems.In this episode, we unpack why “clinicians love it” is rarely enough to win adoption, and what hospital purchasing actually looks like when procurement, IT, finance, compliance and workflows all have a seat at the table. Amel breaks down why switching away from legacy tools is painful, how integrations can break care pathways, and why solutions that feel like a natural part of the hospital ecosystem win faster. He also shares a practical lens for building frictionless implementation by aligning with standards like HL7, FHIR and DICOM, while proving measurable value for patients and payers.We also go deep on decentralising healthcare. Amel explains how the Dutch model centralises high-end expertise while decentralising access through remote monitoring and home-based onboarding, and why this becomes a winning approach as staffing pressures rise. On AI, he makes the case for reframing it as augmented intelligence, not autonomy, and shows where decision support can raise the baseline of care by supporting clinicians in time-critical situations, as well as offloading admin burden that drains capacity.Finally, Amel shares the thinking behind the EMC StarMap framework, a navigation tool built from real-world patterns of what makes MedTech succeed or fail. His core message is simple: regulatory approval is a milestone, but clinical adoption is the real finish line.Timestamps[00:00:05] Clinician + Consultant Lens: Seeing Adoption and Safety Risks[00:01:22] Why “Physicians Love It” Does Not Mean Hospitals Will Buy[00:03:27] What Hospitals Consider Beyond Cost: Workflow, Integration and Training[00:05:09] Frictionless Implementation: Standards, EHR Fit and “Team Player” Products[00:06:24] Real Clinical Workflow: ICU Reality and Why UI Clicks Matter[00:07:31] Decentralising Care: Centralised Expertise With Home-Based Delivery[00:10:37] AI in Healthcare: Reframing as Augmented Intelligence[00:12:55] Staffing Shortages: Where AI Can Remove Waste and Scale Expertise[00:14:38] If You Could Change One Thing: Put the Patient Back at the Center[00:16:59] StarMap: Measuring What Drives Clinical AdoptionConnect with Rick - ⁠⁠⁠⁠https://www.linkedin.com/in/a-havkic/Learn more about Evomed Consulting - https://evomed-consulting.eu/Get in touch with Karandeep Badwal - ⁠⁠⁠⁠⁠https://www.linkedin.com/in/karandeepbadwal/ ⁠⁠⁠⁠⁠Follow Karandeep on YouTube - ⁠⁠⁠⁠⁠https://www.youtube.com/@KarandeepBadwal⁠⁠⁠⁠⁠Subscribe to the Podcast

Forwarded this email? Subscribe here for more The rEVOLUTION of Sovereignty CELEBRATING 250 YEARS OF rEVOLUTION Christopher Rudy Feb 17 Check out my newsletter for the Solar Eclipse tomorrow: The Tragedy and the Triumph of �Augmented Intelligence� (A.I.) My archive of articles and podcasts for 2026 is at: 2026 Global rEVOLUTION Series (emphasizing 90% of the word) My personal bio - a Wikipedia preliminary - is at: https://www.heartcom.org/Wiki-Rudy.htm

This episode, recorded live from the Oregon HFMA Winter Conference, reflects on how change shows up across healthcare revenue cycle operations and how augmented intelligence has become a recurring thread connecting those experiences. We'll explore why some parts of healthcare evolve quickly while others resist change, and what that means for the people navigating it every day.Brought to you by www.infinx.com

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

מה קורה כשיזם ישראלי בונה חברה של מיליארד דולר במכירות שנתיות בארה"ב ואז עוזב הכל כדי להתחיל מחדש? אלון מטס, מייסד Better Help שהגיעה להכנסות של 1.1 מיליארד דולר, חוזר לספר על המעבר המפתיע שלו לעולם הקואצ'ינג עם Strawberry - והפעם הוא משלב בינה מלאכותית בתהליכי קואוצ'ינג. שוחחנו כמובן גם על "הדעות הלא פופולריות" שלו ביחס להתאגדות בישראל, גיוס, הערכת שווי, ועוד. פתיחה והכרות עם אלון מטס - 0:00הסיפור האישי של אלון ומעבר לארצות הברית - 0:57הקמת Better Help ופיתוח פלטפורמת הטיפול המקוונת - 2:01המעבר מ-Better Help לסטראברי והחזון החדש - 3:25גיוס הכסף לסטראברי ושיתוף עם אורן זאב - 5:49הסיפור האישי של ירידה במשקל והשימוש בזריקות GLP1 - 11:00הפילוסופיה של שיתוף אישי ויתרונותיו - 12:20המעבר לעולם הקואוצ'ינג והבדלים מתרפיה - 18:28הגישה של מרקטפלייס ותעדוף הקונסומר על הפרובייד - 22:36השינוי שחולל קוביד על תעשיית הטיפול המקוון - 25:15הטכנולוגיה של AI בקואוצ'ינג וה-Augmented Intelligence - 32:40דעות לא פופולריות על התאגדות בישראל מול ארצות הברית - 42:00גיוס כסף - למה כדאי לגייס כמה שיותר בסיד - 52:40הבעיה עם פאונדר פיינדרס והאם זה לגיטימי - 58:04הטעות הגדולה של יזמים - התאהבות בבעיה אישית - 1:02:25

In this episode of the Shift AI Podcast, Alex Waddell, Chief Information Officer at Adobe Population Health, joins host Boaz Ashkenazy live from Dreamforce in San Francisco for a deep dive into AI adoption in one of the most highly regulated—and most impactful—industries: healthcare.Alex shares his unconventional journey from Salesforce administrator to CIO, and how Adobe Population Health built a custom electronic medical record (EMR) on the Salesforce platform to support population health case management long before it became an industry buzzword. The conversation explores why traditional EMRs often get in the way of care—and how AI can help remove friction so clinicians can focus on patients, not paperwork.Together, Boaz and Alex unpack how AI is being applied today to reduce clinician burnout, automate documentation, improve quality assurance, and deliver the right data at the right time. Alex also explains why “augmented intelligence,” not full automation, is the future of healthcare—and why humans will always remain at the center of care delivery.The episode closes with a thoughtful discussion on AI adoption, clinician trust, and why involving end users directly in building AI workflows is essential for success.This episode is a must-listen for healthcare leaders, technologists, and operators who want to understand how AI can drive real-world outcomes—not just efficiency metrics.Key Themes & TakeawaysWhy population health required building a custom EMR from scratchThe hidden cost of documentation and clinician burnoutHow AI can get “the system out of the way” of patient careUsing AI for chart summarization, note generation, and QA auditsOvercoming fear and resistance to AI in regulated environmentsWhy adoption—not technology—is the real challengeThe future of healthcare as augmented intelligenceChapters[00:00] Welcome & Live from Dreamforce[01:30] Alex Waddell's Journey: From Admin to CIO[03:39] Building a Custom EMR for Population Health[05:45] Data, Interoperability, and MuleSoft[06:45] Reducing Clinician Burnout with AI[08:24] Voice, Automation, and the Future of Admin Work[09:30] Using AI for Quality Assurance at Scale[10:49] AI's Real Impact on Patient Outcomes[12:20] “Augmented Intelligence” and the Future of Work[14:00] Adoption, Trust, and Bringing Clinicians Along[16:00] Learning More & Closing ThoughtsEpisode Quote“An EMR doesn't change lives. The human interaction does. AI's job is to get out of the way so clinicians can actually care.”Connect with the GuestsAlex WaddellChief Information Officer, Adobe Population HealthWebsite: https://www.adobepophealth.comLinkedIn: https://www.linkedin.com/in/alexander-waddell-066bb914a/Boaz AshkenazyHost, Shift AI PodcastLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Mo Gawdat, former Chief of Business at Google, emphasized the need for a reorientation of both students and teachers in the age of augmented intelligence. He highlighted the importance of asking better questions of AI tools to improve learning outcomes.

As BCBAs, we all know how challenging it can be to keep up with the latest research while managing the demands of daily practice. In this conversation with Dr. Adam Ventura, CEO of Intraverbal AI, we explore how technology can make evidence-based care more accessible for behavior analysts everywhere.We talk about “the big binder problem,” how AI can help replace piles of research papers with instant, research-backed answers, and what it really means to practice augmented intelligence—not artificial intelligence. Adam shares how Intraverbal AI is designed to enhance, not replace, human judgment by supporting BCBAs in focusing more on client care and less on paperwork.If you've ever wished for more time to focus on what matters most—your learners and your team—this episode will inspire you to see how technology can actually make your job more human. You can also join us on our upcoming CEU, AI in Supervision: Time-Saving Tools for Busy BCBAs, led by Dr. Adam Ventura himself.What's Inside:How Intraverbal AI bridges the gap between research and practiceThe difference between artificial and augmented intelligenceWays technology can support, not replace, clinical judgmentMentioned in This Episode:AI in Supervision: Time-Saving Tools for Busy BCBAsIntraverbal AIHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

Iain Thomas is a poet, author, and the Chief Innovation Officer at Sounds Fun—an advertising and creative agency that he co-founded with the belief that human creativity could be enhanced, rather than diminished, with the help of AI. It's a realization that actually began to dawn on Iain a few years prior, after his mother died. He wasn't sure how to explain death to his children, so he turned to an early version of ChatGPT for help—and was so impressed by the poetry of its responses that he came away convinced of AI's immense potential as a thought partner for his creative work. On this episode, Iain talks about using AI to make more space for the creative parts of your work, and why, in a world where everyone has access to the same tools, it's never been more important to lean into the skills, context, and experiences that make each of us most unique—and most human.Learn more about Sounds Fun soundsfun.co~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

Send us a textExploring the intersection of creativity and innovation, Geoff Thatcher, Founder and CCO Creative Principals, shares his insights on how AI is revolutionizing live experiences. From personalized exhibits to universal storytelling, Geoff delves into the possibilities and pitfalls of harnessing AI to elevate our connections with others. 01:39 Introducing Geoff Thatcher 02:48 Thinking about AI Differently 13:47 What Does it Mean to "Create" Now? 19:32 Augmented Intelligence 22:27 Death By PowerPoint 23:57 Five Rules for Using AI 24:47 Difference between Better and Easier? 30:16 Don't Let AI Steal Moments of Inspiration 33:18 Always Use the Most Reliable Source 34:57 Use AI to Tell Stories 37:36 The Worry of Getting Lazy 39:48 Humanizing AI! LinkedIn: https://www.linkedin.com/in/geoffthatcher/ Website: https://www.creativeprincipals.com/ Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a textExploring the intersection of creativity and innovation, Geoff Thatcher, Founder and CCO Creative Principals, shares his insights on how AI is revolutionizing live experiences. From personalized exhibits to universal storytelling, Geoff delves into the possibilities and pitfalls of harnessing AI to elevate our connections with others. 01:39 Introducing Geoff Thatcher 02:48 Thinking about AI Differently 13:47 What Does it Mean to "Create" Now? 19:32 Augmented Intelligence 22:27 Death By PowerPoint 23:57 Five Rules for Using AI 24:47 Difference between Better and Easier? 30:16 Don't Let AI Steal Moments of Inspiration 33:18 Always Use the Most Reliable Source 34:57 Use AI to Tell Stories 37:36 The Worry of Getting Lazy 39:48 Humanizing AI! LinkedIn: https://www.linkedin.com/in/geoffthatcher/ Website: https://www.creativeprincipals.com/ Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary In this episode of the AI for Sales podcast, Chad Burmeister speaks with Tamara Jackson, a marketing strategist and AI early adopter, about the transformative role of AI in sales. They discuss how AI can enhance customer experiences, dispel misconceptions about job security, and highlight the importance of education in leveraging AI effectively. Tamara shares real-world examples of AI applications in various industries and emphasizes the need for businesses to adapt to the evolving landscape of sales and technology. Takeaways AI allows conversations to happen anytime, regardless of time zone. AI can provide a personal and consistent customer experience. AI can help businesses recover lost revenue and clarify their positioning. Misconceptions about AI replacing jobs can hinder progress. AI can enhance efficiency and allow salespeople to focus on higher-level tasks. The evolution of AI will challenge everyone to upskill and adapt. AI can help businesses connect with larger clients and opportunities. Education and access to technology are crucial for leveraging AI. AI can free up time for individuals to engage in meaningful activities. The future of AI holds potential for greater equality and opportunity. Chapters 00:00 Introduction to AI in Sales 02:51 The Customer-Centric Approach of AI 05:55 Real-World Applications of AI in Sales 08:46 Misconceptions About AI in Sales 11:48 Augmented Intelligence vs. Artificial Intelligence 14:30 The Future of AI Technologies 17:20 AI as an Equalizer in Society 19:58 The Importance of Education in AI 22:56 Closing Thoughts and Future Outlook The AI for Sales Podcast is brought to you by BDR.ai, Nooks.ai, and ZoomInfo—the go-to-market intelligence platform that accelerates revenue growth. Skip the forms and website hunting—Chad will connect you directly with the right person at any of these companies.

Artificial intelligence isn’t here to replace radio — but it is coming for the audience’s attention, and it’s doing so on radio’s most important turf: deep personal connection. Dan McQuillin, Managing Director at Broadcast Bionics, joins Kirk to explore how AI and large language models can augment radio production rather than compete with it. Dan shares the fascinating “DanGPT” experiment — an AI version of himself so convincing it won over his wife of 34 years — as proof that AI can be just as personal and engaging as traditional radio. Together, they discuss how radio’s greatest strengths — shared experience, belonging, and community — remain central, and how embracing AI as “Augmented Intelligence” can amplify those qualities. With a growth mindset, AI becomes more than a cost-cutting tool; it’s a creative partner that makes the once-impossible possible. As Dan puts it, we used to have more ideas than time — now, thanks to AI, ideas are the only limit. Guest:Dan McQuillin - Managing Director at Broadcast Bionics Host:Kirk Harnack, The Telos Alliance, Delta Radio, Star94.3, South Seas, & Akamai BroadcastingFollow TWiRT on Twitter and on Facebook - and see all the videos on YouTube.TWiRT is brought to you by:Broadcasters General Store, with outstanding service, saving, and support. Online at BGS.cc. Broadcast Bionics - making radio smarter with Bionic Studio, visual radio, and social media tools at Bionic.radio.Aiir, providing PlayoutONE radio automation, and other advanced solutions for audience engagement.Angry Audio and the new Rave analog audio mixing console. The new MaxxKonnect Broadcast U.192 MPX USB Soundcard - The first purpose-built broadcast-quality USB sound card with native MPX output. Subscribe to Audio:iTunesRSSStitcherTuneInSubscribe to Video:iTunesRSSYouTube

In a special Future of Everything podcast episode recorded live before a studio audience in New York, host Russ Altman talks to three authorities on the innovation economy. His guests – Fei-Fei Li, professor of computer science and co-director of the Stanford Institute for Human-Centered AI (HAI); Susan Athey, professor and authority on the economics of technology; and Neale Mahoney, Trione Director of the Stanford Institute for Economic Policy Research – bring their distinct-but-complementary perspectives to a discussion on how artificial intelligence is reshaping our economy.Athey emphasizes that both AI broadly and AI-based coding tools specifically are general-purpose technologies, like electricity or the personal computer, whose impact may be felt quickly in certain sectors but much more slowly in aggregate. She tells how solving one bottleneck to implementation often reveals others – whether in digitization, adoption costs, or the need to restructure work and organizations. Mahoney draws on economic history to say we are in a “veil of ignorance” moment with regard to societal impacts. We cannot know whose jobs will be disrupted, he says, but we can invest in safety nets now to ease the transition. Li cautions against assuming AI will replace people. Instead, she speaks of AI as a “horizontal technology” that could supercharge human creativity – but only if it is properly rooted in science, not science fiction.Collectively, the panel calls on policymakers, educators, researchers, and entrepreneurs to steer AI toward what they call “human-centered goals” – protecting workers, growing opportunities, and supercharging education and medicine – to deliver broad and shared prosperity. It's the future of the innovation economy on this episode of Stanford Engineering's The Future of Everything podcast.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profile: Fei-Fei LiStanford Profile: Susan AtheyStanford Profile: Neale MahoneyConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces live guests Fei-Fei Li, Susan Athey, and Neale Mahoney, professors from Stanford University.(00:02:37) Lessons from Past TechnologyComparing AI with past technologies and the bottlenecks to their adoption.(00:06:29) Jobs & Safety NetsThe uncertainty of AI's labor impact and investing in social protections.(00:08:29) Augmentation vs. ReplacementUsing AI as a tool to enhance, not replace, human work and creativity.(00:11:41) Human-Centered AI & PolicyShaping AI through universities, government, and global collaboration.(00:15:58) Education RevolutionThe potential for AI to revolutionize education by focusing on human capital.(00:18:58) Balancing Regulation & InnovationBalancing pragmatic, evidence-based AI policy with entrepreneurship.(00:22:22) Competition & Market PowerThe risks of monopolies and the role of open models in fair pricing.(00:25:22) America's Economic FunkHow social media and innovation are shaping America's declining optimism.(00:27:05) Future in a MinuteThe panel shares what gives them hope and what they'd study today.(00:30:49) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Artificial intelligence isn't here to replace radio — but it is coming for the audience's attention, and it's doing so on radio's most important turf: deep personal connection. Dan McQuillin, Managing Director at Broadcast Bionics, joins Kirk to explore how AI and large language models can augment radio production rather than compete with it. Dan shares the fascinating “DanGPT” experiment — an AI version of himself so convincing it won over his wife of 34 years — as proof that AI can be just as personal and engaging as traditional radio. Together, they discuss how radio's greatest strengths — shared experience, belonging, and community — remain central, and how embracing AI as “Augmented Intelligence” can amplify those qualities. With a growth mindset, AI becomes more than a cost-cutting tool; it's a creative partner that makes the once-impossible possible. As Dan puts it, we used to have more ideas than time — now, thanks to AI, ideas are the only limit.

Are brokers being replaced by AI? Not quite. In this episode, we unpack how Augmented Intelligence is transforming the aviation insurance industry—and how brokers can lead the charge. From faster submissions to predictive analytics and scalable tools, AI isn't taking jobs—it's making them better. Our guests share how to prepare for the shift, serve clients better, and stay relevant in a rapidly digitizing world.

Pankaj Singh is a leadership strategist and founder of Singh Leadership, where he helps high-achieving professionals transform burnout into clarity and purpose using neuroscience-informed frameworks like the Purpose FactorIf Leading Human is where emotional intelligence meets organizational impact, then Pankaj Singh is your kind of guest. A former C-suite leader who began his journey training under a lama, Pankaj now empowers leaders to transform reactive pressures into purpose-driven presence.In this episode of 'Leading Human', Chad welcomes Pankaj Singh, a leadership strategist and founder of Singh Leadership, to discuss turning stress into strategic clarity, the role of purpose in leadership, and how mindfulness can unlock better decision-making. Pankaj shares his journey from a stressful professional experience to developing neuroscience-informed frameworks like the Purpose Factor and the Inner Compass Journey. The conversation covers how leaders can reframe chronic stress, the importance of being present, and specific practices such as micro journaling and mindfulness exercises. Pankaj also discusses the PROGRESS and PIVOT frameworks, and the significant impacts of aligning personal purpose with organizational goals on productivity and employee retention.01:09 Pankaj Singh's Stressful Turning Point02:49 Understanding Chronic Stress and the Brain04:50 Mindful Micro Practices08:07 The Purpose Factor Framework10:12 Micro Journaling and Reflection15:01 The PIVOT Framework for Mindfulness17:37 Impact of Mindfulness on Productivity20:36 Understanding Your Audience21:07 Organizing Your Life: Emotional and Relational Aspects21:26 The Importance of Relationships22:32 Aligning with Your Purpose23:01 The Role of Fulfillment in Coaching23:11 The 14-Week Journey23:34 Practicing Mindfulness and Meditation25:21 Balancing Technology and Humanity26:58 Augmented Intelligence and AI Tools27:15 Conscious Data Framework29:43 Micro-Journaling and Presence33:23 Final Thoughts and ResourcesConnect with PankajWant a communication and wellbeing workshop that actually sticks? Whether you're building trust or leveling up team accountability, we've got you. Book a call to ask questions and learn more about improving how your team communicates here.

What if your entire team's experience, every customer interaction, and the hard-won lessons you've gathered over the years could be turned into actionable, revenue-driving intelligence? In this episode of Predictable B2B Success, Vinay Koshy sits down with Mehdi Tehranchi, serial entrepreneur and CEO of KnowledgeNet AI, to explore the untapped power of AI in B2B sales and organizational knowledge. Mehdi shares candid stories from his entrepreneurial journey—including building tech companies from scratch and guiding them to successful exits—and reveals why most AI pilots fail to deliver real business value. Together, they uncover what it truly takes to leverage AI for meaningful relationship-building, sales productivity, and capturing knowledge that too often leaves with your top performers. You'll discover the unique advantage of organizational AI, why security matters more than ever, and how even startups can harness machine learning for smarter, faster sales without drowning in data complexity. If you're curious about how AI can transform the way your business sells, learns, and grows, this is an episode you won't want to miss. Some areas we explore in this episode include: Leveraging Corporate Relationships – How AI uncovers and utilizes previously untapped relationship networks within organizations.Challenges in B2B AI Adoption – Data limitations, integration issues, costs, and lack of AI talent.KnowledgeNet AI's Role – Positioning the platform as an enterprise “knowledge brain” integrating data across sources.Augmented Intelligence for Sales – Driving productivity and personalized engagement through actionable AI insights.Security & Privacy Concerns – The need for organization-specific, secure AI versus open tools like ChatGPT.Talent and Experience in Startups – Importance of learning from experienced hires at critical growth stages.Organizational Mindset Shifts – Encouraging leaders and teams to adopt and adapt to AI-driven change.Channel Partnerships & CRM Integrations – Go-to-market strategies emphasizing trusted ecosystem partners.Measuring AI Impact – Setting benchmarks, tracking improvement, and iteratively optimizing results.AI Skills for the Future – Emphasis on adaptability and practical AI application over technical prompt engineering.And much, much more...

This week's Faculty Factory Podcast is about building the skillset needed to keep pace with the many ways Artificial Intelligence (AI) can augment your productivity as an academic medicine professional. Leading this discussion for us are Stacey Pylman, PhD, and John Lowry, PhD.  Drs. Pylman and Lowry have been featured in an ongoing series through the American Association of Medical Colleges (AAMC) on AI education. You can learn more about it here: https://www.aamc.org/about-us/mission-areas/medical-education/artificial-intelligence-and-academic-medicine   

AI systems can do amazing things, but they can sometimes suffer from a drawback called “catastrophic forgetting”. Researchers at Arizona State University hope to learn how to solve the problem by probing the brains of sleeping bees. The pay-off could be more reliable, more memory-efficient artificial intelligence. When AI systems learn one task — say, how to recognize dogs — and are later trained on a new task — like identifying cars — they often forget the first thing they learned. This is called ctastrophic forgetting. Ted Pavlic is an associate professor of computer science and engineering in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at ASU, with a joint appointment in the School of Life Sciences. He leads a unique interdisciplinary research project that blends biology and computer science.

AI patient summaries: Who writes after-visit summaries? How reliable is AI in health care? Can AI help write clinical notes? How is AI used in hospitals? Our guest today is Veena Jones, MD, vice president and chief medical information officer at Sutter Health. In this episode, Dr. Jones talks about how Sutter Health is turning clinical notes from the care team into patient-friendly summaries using AI. American Medical Association CXO Todd Unger hosts.

Can AI diagnose medical problems? How does AI help doctors? Can AI be used in medical diagnostics? How is AI impacting the practice of medicine? Our guest is Jason Wiesner, MD, chair of the imaging service line at Sutter Health. American Medical Association CXO Todd Unger hosts.

In this impromptu livestream conversation, Scott Perry sits down with Solopreneur Success Circle member Anna Kohler Smith to tackle one of today's most pressing questions: How do we harness AI without sacrificing the messy, imperfect humanity that builds trust and sparks real change?Together, they explore:* The real problem: Why sheer information overload leaves us hungry for authentic connection* Defining intelligence: Moving from “collecting facts” to “closing the gap” between where we are and where we want to be* AI as a tool: Why “augmented intelligence” is a more useful lens than “artificial intelligence”* When to lean in—and when to step back: Practical guidance on purpose-first creation* The wisdom factor: How lived experience, empathy, and human judgment outpace any algorithmWhether you're a creator, coach, or purpose-driven solopreneur, you'll walk away with fresh questions to clarify your mission before you pick up any tool—and actionable insights on using AI to amplify your unique voice, not replace it.Join the Conversation:Become part of our community and get on-demand coaching, group calls, and principle-driven resources in the Solopreneur Success Circle.Prefer to watch?Here's the video replay.↓↓↓ This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit creativeonpurpose.substack.com/subscribe

How is AI currently used in health care? How will AI impact health care in the future? Can AI be used to predict cancer risk? What is ambient AI in health care? Our guest is Jeremy Cauwels, MD, chief medical officer at Sanford Health. American Medical Association CXO Todd Unger hosts.

Are you spending countless hours trying to figure out how to make AI work for your business, while bigger companies seem to have it all figured out? What if you could learn how one of the world's most innovative companies actually implements AI, and discover how to scale those same strategies in your business - without the enterprise budget? The truth is, most small businesses are drowning in AI tools and possibilities, unsure which strategies actually drive results and which are just burning time and money. They're stuck watching larger companies pull ahead while trying to piece together a working AI strategy. But today, we're going to change that. I'm thrilled to welcome Michael Todasco, former Director of Innovation at PayPal, where he managed over 100 patents and drove innovation initiatives that transformed how one of fintech's biggest players leverages AI. Now, Michael spends his time helping businesses of all sizes implement practical AI solutions that drive real results. He's going to share the strategies that actually work - no fluff, no complexity, just proven approaches you can implement starting today. The AI Hat Podcast host Mike Allton asked Mike Todasco about: ✨ Start Small, Think Big: Implement AI strategically by starting with one high-impact area while planning for scalable growth. ✨ Focus on Core Problems: Target AI implementation at solving specific business challenges rather than adopting technology for its own sake. ✨ Measure What Matters: Establish clear metrics and ROI goals before implementing any AI solution to ensure meaningful business impact. Learn more about Mike Todasco Connect with Mike Todasco on LinkedIn Resources & Brands mentioned in this episode PayPal James Silberrad Brown Center for Artificial Intelligence at SDSU Hanging up on ChatGPT's Operator AI Primer: A Comprehensive Guide Explore past episodes of the The AI Hat Podcast podcast Chapters 00:00 Introduction to AI in Business 02:08 Welcome to The AI Hat Podcast 02:32 Challenges and Opportunities for Small Businesses 03:06 Interview with Michael Todasco 04:51 AI Implementation at PayPal 06:17 Practical AI Solutions for Small Businesses 11:12 Creating an AI-First Culture 18:04 Augmented Intelligence vs. Artificial Intelligence 21:10 Debunking the Myth: AI as a Cheating Tool 21:41 Sponsor Break: Introducing Magai 23:41 Biggest Mistakes in AI Implementation 25:59 Success Stories and Real-World Applications 29:48 Measuring AI ROI for Businesses 32:59 Emerging AI Trends and Future Outlook 38:17 Conclusion and Final Thoughts SHOW TRANSCRIPT & NOTES: https://theaihat.com/scale-like-paypal-simple-ai-strategies-that-actually-work-for-small-business/ Start your AI journey with the AI Marketing Primer. Brought to you by The AI Hat - Get Your AI On. Interesting in sponsoring an episode? Learn more here. AI Training for Business Leaders & Teams: https://theaihat.com/ai-training-for-business/ Powered by Magai - why choose one AI tool when you can have them all? And Descript, the magic wand for podcasters. Produced and Hosted by Mike Allton, AI Consultant & Trainer at The AI Hat, where he's tirelessly helping businesses and marketers get ahead of the AI Revolution and apply advanced technologies to their roles. He's spent over a decade in digital marketing, bringing an unparalleled level of experience and excitement to the fore, whether he's delivering a presentation or leading a workshop. If you're interested in helping business owners with AI in an upcoming episode, reach out to Mike. Powered by the Marketing Podcast Network. Learn more about your ad choices. Visit megaphone.fm/adchoices

Live from the DHL Legal Innovation Summit, David Cowen sits down with Omar Haroun, co-founder of Eudia, to break down the shift from artificial intelligence to augmented intelligence. Together they unpack what's actually happening in legal innovation: smarter workflows, real cost transformation, and human-machine collaboration at scale. This conversation goes deep from M&A disruption with DHL to the messy middle of change, to the opportunities ahead for those willing to live in the future. If you're trying to figure out how to adapt, evolve, and lead with AI at your side, start here. What You'll Learn: Why AI isn't replacing lawyers but the ones who use it might The real reason behind layoffs (hint: it's not just tech, it's operational innovation) How DHL and Eudia are unbundling legal services and redefining M&A speed What it means to be a Pattern Breaker and why “messy middle” is where the magic happens Don't wait for the dust to settle. If you're a seeker, striver, or top guard thinking out loud about AI, talent, and transformation, this one's for you.

In episode 75 of Venture Everywhere, Scott Hartlery, co-founder of Everywhere Ventures and MP of Everywhere Ventures, talks with Omar Haroun, co-founder and CEO of Eudia — an augmented intelligence platform transforming legal departments from cost centers into strategic drivers. Omar shares how he built Eudia to provide legal teams with portfolio-level insights and adaptive AI agents that go beyond automation to drive meaningful business outcomes. Omar also discusses Eudia's approach to rethinking the economics of legal work—addressing systemic inefficiencies, moving beyond billable hours, and expanding access to quality legal support.In this episode, you will hear:Viewing legal risk and decisions through a portfolio management lensLeveraging proprietary enterprise data to customize AI agent behaviorPositioning legal departments as drivers of revenue, not just cost centersAutomating high-volume tasks like contracts and NDAsReducing hallucination risk with deeper knowledge system integrationConfronting outdated legal incentives to expand access to legal servicesLearn more about Omar Haroun | EudiaLinkedIn: https://www.linkedin.com/in/omarharoun Website: https://www.eudia.com/ Learn more about Scott Hartley | Everywhere VCLinkedin: https://www.linkedin.com/in/scotthartleyWebsite: https://everywhere.vc/

What can you do with an MD? Are there non-clinical careers for doctors? What is the next big thing in health care technology? What do investors look for in healthcare startups? Discussing health care startups and innovative ideas for health care businesses with Chris Stock, MD, a managing director for Health2047. American Medical Association CXO Todd Unger hosts.

Is AI good for health care? How are doctors using augmented intelligence? What is AI used for in medicine? What percentage of doctors are using AI? Do doctors trust AI? Our guest Margaret Lozovatsky, MD, vice president of Digital Health Innovations at the American Medical Association, discusses key findings from the latest survey on physician attitudes on AI in healthcare. Highlights include AI tools for administrative tasks, the importance of feedback loops, and AMA programs supporting physician AI education, artificial intelligence governance, and AI collaborative initiatives. AMA CXO Todd Unger hosts.

If you ask Jason Hoss what AI stands for, he will say “Augmented Intelligence”, a tool used for building human capabilities, rather than replacing them. This is the start of a two-part series about hands on AI within your resilience program. Hello everyone, and welcome to episode 184 of the Resilient Journey podcast, presented by Anesis Consulting Group! This week, our dear friend Jason Hoss returns to elaborate on his massively successful presentation at DRJ Spring on using AI to augment your resilience program. In this episode Jason will explain why he calls AI a “bold face liar” and encourages us to distrust > trust > and verify whatever AI tells us. And he provides us with tips to mitigate risks, and ideas that you can do right now to start using AI in a meaningful way.    Be sure to follow The Resilient Journey!  We sure do appreciate it! Want to learn more about Mark? Click here or on LinkedIn or Twitter. Special thanks to Bensound for the music.   

In this week's episode of the ArtTactic Podcast, host Adam Green is joined by Nicole Sales Giles, Vice President and Director of Digital Art at Christie's, to explore the rapidly evolving but still niche sector of the art world, AI-generated art. As Christie's prepares to launch Augmented Intelligence, its first dedicated AI art auction (running from February 20-March 5), we take a deep dive into how artificial intelligence is reshaping the art market. We discuss the fundamentals of AI-generated art, its evolution since Christie's historic 2018 sale of an AI portrait, and the significance of this auction in positioning AI art within the fine art landscape. Nicole shares insights into collector interest, the mix of digital and physical works in the sale, and how AI art is being received by traditional collectors vs. NFT buyers. The conversation also addresses some of the pressing ethical debates surrounding AI-generated art, including a recent open letter raising concerns about the use of copyrighted material in AI models. Plus, Nicole highlights some of the most exciting artists in the sale and where she sees AI art heading in the coming years.

On this episode of the Crazy Wisdom Podcast, host Stewart Alsop welcomes Reuben Bailon, an expert in AI training and technology innovation. Together, they explore the rapidly evolving field of AI, touching on topics like large language models, the promise and limits of general artificial intelligence, the integration of AI into industries, and the future of work in a world increasingly shaped by intelligent systems. They also discuss decentralization, the potential for personalized AI tools, and the societal shifts likely to emerge from these transformations. For more insights and to connect with Reuben, check out his LinkedIn.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:12 Exploring AI Training Methods00:54 Evaluating AI Intelligence02:04 The Future of Large Action Models02:37 AI in Financial Decisions and Crypto07:03 AI's Role in Eliminating Monotonous Work09:42 Impact of AI on Bureaucracies and Businesses16:56 AI in Management and Individual Contribution23:11 The Future of Work with AI25:22 Exploring Equity in Startups26:00 AI's Role in Equity and Investment28:22 The Future of Data Ownership29:28 Decentralized Web and Blockchain34:22 AI's Impact on Industries41:12 Personal AI and Customization46:59 Concluding Thoughts on AI and AGIKey InsightsThe Current State of AI Training and Intelligence: Reuben Bailon emphasized that while large language models are a breakthrough in AI technology, they do not represent general artificial intelligence (AGI). AGI will require the convergence of various types of intelligence, such as vision, sensory input, and probabilistic reasoning, which are still under development. Current AI efforts focus more on building domain-specific competencies rather than generalized intelligence.AI as an Augmentative Tool: The discussion highlighted that AI is primarily being developed to augment human intelligence rather than replace it. Whether through improving productivity in monotonous tasks or enabling greater precision in areas like medical imaging, AI's role is to empower individuals and organizations by enhancing existing processes and uncovering new efficiencies.The Role of Large Action Models: Large action models represent an exciting frontier in AI, moving beyond planning and recommendations to executing tasks autonomously, with human authorization. This capability holds potential to revolutionize industries by handling complex workflows end-to-end, drastically reducing manual intervention.The Future of Personal AI Assistants: Personal AI tools have the potential to act as highly capable assistants by leveraging vast amounts of contextual and personal data. However, the technology is in its early stages, and significant progress is needed to make these assistants truly seamless and impactful in day-to-day tasks like managing schedules, filling out forms, or making informed recommendations.Decentralization and Data Ownership: Reuben highlighted the importance of a decentralized web where individuals retain ownership of their data, as opposed to the centralized platforms that dominate today. This shift could empower users, reduce reliance on large tech companies, and unlock new opportunities for personalized and secure interactions online.Impact on Work and Productivity: AI is set to reshape the workforce by automating repetitive tasks, freeing up time for more creative and fulfilling work. The rise of AI-augmented roles could lead to smaller, more efficient teams in businesses, while creating new opportunities for freelancers and independent contractors to thrive in a liquid labor market.Challenges and Opportunities in Industry Disruption: Certain industries, like software, which are less regulated, are likely to experience rapid transformation due to AI. However, heavily regulated sectors, such as legal and finance, may take longer to adapt. The discussion also touched on how startups and agile companies can pressure larger organizations to adopt AI-driven solutions, ultimately redefining competitive landscapes.

In this episode of CLOC Talk, Jenn sits down with Nancy Rademaker, Co-Founder of Drawify & Speakers Club, and our brilliant opening keynote at our EMEA 2024 Summit in London. They discuss how AI can solve business challenges by improving efficiency and reducing manual tasks, as well as the importance of identifying the right problems before deploying AI solutions. Nancy touches on the role of AI in enhancing employee engagement and well-being by pointing it at preventing productivity loss and solving knowledge management issues. Tune in as Jenn and Nancy share insights on generational differences in adapting AI tools, the potential of augmented intelligence, and how emerging technologies like brain-computer interfaces could revolutionize various fields! Special thanks to our sponsor Docusign for this episode!

Welcome back to the Win Rate Podcast. Today Andy welcomes Mehdi Tehranchi, CEO of KnowledgeNet.ai, to discuss the transformative role of AI in sales, emphasizing the shift from transactional selling to a model focused on helping the buyer make a decision and creating relationships with a little help from AI, or as Mehdi likes to call it, 'augmented intelligence.' Mehdi highlights how AI can enhance decision-making, improve sales preparation, and foster better relationships between sellers and buyers. He and Andy also talk about the importance of differentiating in competitive markets and the need for companies to adopt AI strategically to maximize efficiency and effectiveness in their sales processes.Host Andy Paul is the expert on modern B2B selling and author of three best-selling, award-winning sales books, including his latest Sell Without Selling Out. Visit andypaul.com to subscribe to his newsletter for even more strategies and tips to accelerate your win rate.

Send Everyday AI and Jordan a text messageWhy is no one talking about this ONE feature of Canvas? OpenAI announced Canvas, a new ChatGPT mode and way to code and write. Everyone's trying to compare this to Anthropic's popular Artifacts feature inside of its Claude chatbot. But almost everyone's missing the point by simply comparing it to Artifacts. We'll break it down and tell you 5 things you need to know about OpenAI's new Canvas mode.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on ChatGPT's CanvasUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Overview of Canvas Mode2. Technical Details of Canvas Mode3. Using Canvas Mode for Coding4. Live demo of Canvas mode5. Canvas Mode Feature ComparisonTimestamps:02:00 Daily AI News05:50 ChatGPT's Canvas feature08:20 Canvas is based on OpenAI's newest model.13:20 Use GPT-4 Canvas, integrates with other tools.16:19 OpenAI first with split interface, November 2022.17:49 AI feels smart but requires constant correction.22:18 Stop comparing; similar interfaces aren't identical.26:13 Ensure GPT-4o with Canvas is selected first.28:30 Large language models give varied responses often.32:11 Basic features of a text editor demonstrated.36:07 Demo of inline editor for live podcast.38:11 New interface enhances large language model experience.41:51 Button polishes writing for clarity and consistency.47:30 Curious about ChatGPT's bug-fixing process.50:20 Prompt engineering is easier with Canvas mode.54:17 Rendered output reveals coding errors effectively.56:01 Sizable step toward future AI-human collaboration.Keywords:OpenAI, Canvas Mode, ChatGPT, Anthropic's Claude Artifacts, human-AI collaboration, GPT-4o, AI News Updates, Gemini's AI, Google, NVIDIA, Taiwan's largest supercomputer, Geoffrey Hinton, John Hopfield, Nobel Prize in Physics, Microsoft WorkLab podcast, Internet-connected GPT, browsing with Bing, Replit, coding, Augmented Intelligence, inline editor, Large Language models, real-time collaboration, language models, GPT-4, Claude 3, Llama 3.1, Gemini 1.5, AI-generated content, Augmented intelligence concept Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

Send Everyday AI and Jordan a text messageWhy is no one talking about this ONE feature of Canvas? OpenAI announced Canvas, a new ChatGPT mode and way to code and write. Everyone's trying to compare this to Anthropic's popular Artifacts feature inside of its Claude chatbot. But almost everyone's missing the point by simply comparing it to Artifacts. We'll break it down and tell you 5 things you need to know about OpenAI's new Canvas mode. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on ChatGPT's CanvasUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Overview of Canvas Mode2. Technical Details of Canvas Mode3. Using Canvas Mode for Coding4. Live demo of Canvas mode5. Canvas Mode Feature ComparisonTimestamps:02:00 Daily AI News05:50 ChatGPT's Canvas feature08:20 Canvas is based on OpenAI's newest model.13:20 Use GPT-4 Canvas, integrates with other tools.16:19 OpenAI first with split interface, November 2022.17:49 AI feels smart but requires constant correction.22:18 Stop comparing; similar interfaces aren't identical.26:13 Ensure GPT-4o with Canvas is selected first.28:30 Large language models give varied responses often.32:11 Basic features of a text editor demonstrated.36:07 Demo of inline editor for live podcast.38:11 New interface enhances large language model experience.41:51 Button polishes writing for clarity and consistency.47:30 Curious about ChatGPT's bug-fixing process.50:20 Prompt engineering is easier with Canvas mode.54:17 Rendered output reveals coding errors effectively.56:01 Sizable step toward future AI-human collaboration.Keywords:OpenAI, Canvas Mode, ChatGPT, Anthropic's Claude Artifacts, human-AI collaboration, GPT-4o, AI News Updates, Gemini's AI, Google, NVIDIA, Taiwan's largest supercomputer, Geoffrey Hinton, John Hopfield, Nobel Prize in Physics, Microsoft WorkLab podcast, Internet-connected GPT, browsing with Bing, Replit, coding, Augmented Intelligence, inline editor, Large Language models, real-time collaboration, language models, GPT-4, Claude 3, Llama 3.1, Gemini 1.5, AI-generated content, Augmented intelligence concept.

Today we'll chat with Carl Tannenbaum, chief economist for Northern Trust. In his role, Carl prepares the bank's official economic outlook and participates in forecast surveys. He is a member of Northern Trust's investment policy committee, its capital committee, and its asset/liability management committee. Prior to joining Northern Trust, Carl spent four years leading the Federal Reserve's risk section. He was deeply involved in the central bank's response to the 2008 financial crisis. Carl began his career in banking at LaSalle Bank/ABN AMRO, serving for more than 20 years as the organization's chief economist and head of balance sheet management. Carl holds an MBA and a BA in finance and economics from the University of Chicago.BackgroundBioEconomic Outlook, Inflation, and Tariffs“The Final Descent,” by Carl Tannenbaum, Ryan James Boyle, and Vaibhav Tandon, Northerntrust.com, July 16, 2024.“Trust the Process,” by Carl Tannenbaum, Ryan James Boyle, and Vaibhav Tandon, Northerntrust.com, June 13, 2024.“The Value of Economic Data,” by Carl Tannenbaum, Northerntrust.com, July 19, 2024.“The Truth About Tariffs,” by Carl Tannenbaum, Northerntrust.com, July 3, 2024.“A New Round of Tariffs,” by Carl Tannenbaum, Northerntrust.com, May 24, 2024.“Inflation Has a Perception Problem,” by Carl Tannenbaum, Northerntrust.com, June 21, 2024.“Aftershocks,” The View From Here With Carl Tannenbaum, Northerntrust.com, March 11, 2024.The Fed and National DebtThe Fed's FunctionsFederal Reserve Bank of Chicago's Spotlight on Childcare and the Labor Market“Debt Matters,” The View From Here With Carl Tannenbaum, Northerntrust.com, Jan. 9, 2024.Real Estate, Banking, and AI“Out of Office,” The View From Here With Carl Tannenbaum, Northerntrust.com, April 1, 2024.“Banking: Back in the News,” by Carl Tannenbaum, Northerntrust.com, Feb. 16, 2024.“Shedding Light on Private Credit,” by Carl Tannenbaum, Northerntrust.com, May 17, 2024.“Empowering AI,” The View From Here With Carl Tannenbaum, Northerntrust.com, July 24, 2024.“Automation and Anxiety,” by Carl Tannenbaum, Northerntrust.com, July 12, 2024.“Augmented Intelligence,” The View From Here With Carl Tannenbaum, Northerntrust.com, June 27, 2023.Other“Last Mile: What It Means in Reaching Customers,” by Adam Hayes, Investopedia.com, Sept. 11, 2023.Committee for a Responsible Federal Budget

Guest Host Lisa Garr and Chris Howard take calls from listeners on how to re-program your mind to free it of negative thoughts.See omnystudio.com/listener for privacy information.