You Teach The Machines

Jeff Pennington and MJ Pennington

Hot takes on living with AI from the first generation who has no choice: today's college students.

  1. May 29 ·  Video

    AI Literacy Workshop - Understanding for Non-Techie People

    You Teach the Machines was an AI literacy workshop before it was a book. I've helped over 850 people from California to Maine connect, understand AI, process their fear, and move forward. People ages 12 to 80+ have been active audience participants. I make it as fun as the topic can be!  An example of the short-form talk in Burlington, VT June 2025 is here. Video of the full workshop in Brooklyn, NY October of 2025 here.  I'm winding down this work as of May 2026. If your community has a burning desire to learn how you teach the machines feel free to connect. I don't charge libraries, schools, or community groups. Ideally you could partner with a local bookstore to support sales of the book through their business.  Events:  Clayton Library, Clayton, NY, August 22 2026  Jesup Library, Bar Harbor, ME January 2026 College of Computing and Informatics, Drexel, Philadelphia, PA November 2025 Springboard Enterprises, New York, NY October 2025 Gowanus Studio Tour, Poursteady, Brooklyn, NY October 2025 Bala Cynwyd Library, Bala Cynwyd, PA September 2025 Belvedere Tiburon Library, Tiburon, CA August 2025 Thousand Island Park Library, Clayton, NY August 2025 Thousand Island Park Chatauqua, Clayton, NY, August 2025 Causeway Club, Southwest Harbor, ME July 2025 Grindstone Island Winery, Clayton, NY July 2025 PechaKucha 40, Burlington, VT June 2025 (Book published June 27) Colby College, Waterville, ME March 2025 NIH CTSA Consortium, Bethesda, MD November 2024 American College of Graduate Medical Education, Chicago, IL September 2024 Cincinnati Children's Hospital and Medical Center, Cincinnati, OH August 2024 Thousand Island Park Chatauqua, Clayton, NY, July 2024 Here's a transcript of the final part of the full workshop where we admit that AI can be put to productive use, despite all the flaws in the current landscape.  "If you choose to use it productively. Yeah, so that's a really good, that's a really good point. There's there's a a point of view with technology. Am I using it deliberately, or am I just like depending on it mindlessly? There are a lot of amazing things that have come out of those same companies, including social media, right? Like some of you may be here because of social media. That's a good thing, right? You may have heard about it. Um, but at the same time AI has been used, especially in the last five years, six years, to uh make our interaction with digital systems more and more compelling and harder and harder to step back from and avoid. So my biggest, my biggest argument with the AI industry as it, as it is now, the sort of consumer like, hey, we're just using Chat GPT or Gemini or Claude or one of these things, or also hey, I were consuming social media, is um it's really really built around uh giving us those hits that will keep us coming back. So I I can go off on tech dependence all day long. I'm gonna stop there, but it's something to be aware of when you're interacting with AI systems, that those systems, they're being, they're they're trying to charm you. They're type casting and all these things because they really want to get you using them and keep using them. They want you to like pay for the premium subscription, right? That doesn't mean they're all bad and that you can't be productive in line, right? Um, we've talked about a lot about all the bad stuff. We're gonna really really try hard together to focus on the good stuff. Um, and this, this mate, Mary Jane just shook her head. Ha ha. Um, and I'm gonna, I'm gonna blow through what I have to say and then and then we can talk some more together, uh, and then we'll switch over to uh what I'm personally more interested in than being here to talk about AI again. Um, 'cause actually I didn't really want to write a book about AI, but haha. Um, and we'll do, we'll do some coffee stuff. So uh, a couple of my anecdotes, and then I'm gonna ask y'all for some um, why, why am I not like ready to throw the baby out with the bathwater and cut the cable and walk away from all this stuff? My uh mother in law, Mary Jane's grandmother, was staying with us this summer. She woke up, almost fell down from severe pain in the back of her knee. She's 82. She doesn't slow down, but this stopped her. She was up early when this happened, and um sat at the kitchen table and was googling over and over, what's going on, what's going on, what's going on, and was getting more and more scared. It was, she was not getting helpful information. It was scattered. Uh, I I got up, came downstairs, and and um could tell something was wrong and asked her, hey, what's up? And she said, I have this pain in the back of my knee and I can't find anything on Google, it doesn't make any sense. So we sat down in front of a chatbot and started a dialogue that um, we built up enough context, 'cause that's really important when you use these things, to interact and build up context. We got to the point where uh it told her, oh, you need to go to the hospital right now, because that's either deep vein thrombosis or a baker's cyst. And we got there because like, she basically said, well, I just tried this vitamin and I just stopped taking that medication and then I did this and then I did that. And she put all of this information out there that through the normal like ranking algorithm was just scattershot, but because of the context that she built up, and because of the pre training that the model behind this chat interface had, um, knew what to pay attention to. It said deep vein thrombosis or baker's cyst, go get an ultrasound, here's the two sentences you need to say to the doctor in order to have a really quick and productive exchange. She went to the local critical access hospital and had an ultrasound, and the deep vein thrombosis was ruled out. It was a baker's cyst. At 82, she had a better health outcome because of this technology. You know, she may or may not have shown up at that hospital uh and been triaged appropriately. She could walk in more informed, right. It could have been wrong, could have, there could have been misinformation, right, but um it was better than what she was getting from technology without it. That's one of my positives, and I'm gonna ask everybody else for positives. The second is uh a a study that I I worked on at CHOP, um, is similar to a study that's being done in pediatrics around the country. So, a uh there's a certain type of rib fracture in children that is, when appropriately evaluated, is like a dead on marker of physical abuse. The way the rib breaks, how it breaks, how it shows up in the X rays. Um, there are very few pediatric radiologists who can look at a chest X ray from the ER and say yes, that is from physical abuse, this kid needs to be uh assessed and protected. Um, there's a high rate of of that being missed by pediatric radiologists who haven't had that training. So there are researchers around the country who are working to teach machines to to appropriately and correctly identify that type of rib fracture, so that all the pediatric radiologists who don't have that expertise, and all the radiologists, and all of the community hospitals where there isn't a pediatric radiologist, can appropriately catch physical abuse in children and those kids can be protected. Um, these are the kinds of things why I can't just be like, f this stuff, f these guys, I'm out. And why I think it's important for all of us to be more informed and be critical consumers, that we can get to the good stuff and to collectively like marginalize the bad stuff. You just got a positive. Cooking, cooking. Yeah, take a picture of the ingredients you have and it'll help you, give you ideas for what to do with them. And you only lit it on fire once. Like sourdough baking, sourdough baking. A large, 60 pizzas for example, like helping create a recipe. So I think we're a recipe. So yeah. Derek, I was gonna say, kind of expanding on both of those points, the democratization of knowledge, where before a lot of this was extremely specialized. Before literacy was super high, everything was gatekept by guilds where people had specific knowledge of a certain thing. Literacy increased, people were able to learn more and do things at home and bring it to themselves. This is the next level of technology where I can go on, who has no formal experience with coding, and being able to buy code, new systems or something, like help me run something in my household I had no access to before this. Not good. Yep. That reminds me, I fixed my ACBC. Yeah, I have found Chat GPT, two examples recently, to be really good collaborators in complicated mechanical engineering tasks. That I mean, in one way it kind of is eliminating a job, you know, like this is CEO position and I get to like, you know, have expertise here when I can afford it. But the truth is a lot of time you can't afford it. We've had mechanical engineers and we've lost them in the past. I have more of like an MFA of design and been making stuff with my hands my whole life. But we need to improve the laminar flow of the water coming out of the course taking machine. And I had a month long conversation with Chat GPT where it was doing all the math, and we kind of figured out how long the little syringe tubing needed to be inside the nozzle to do it at that temperature, which, you know, conceptually I've seen a YouTube video of making Lambert flow, but this was literally doing the math. And we were narrowing in on like what you can buy online, where you can buy it, how long it has to be, what the diameters have to be. And like, that's like a fluid dynamics graduate student, 200 thousand dollar job hybrid specialty. And you know, we're a struggling small company trying to make complicated stuff, and like I can accept it now. Yeah, you're so desperate that you're hosting AI. Yeah, no. I mean, but like, you know, I would love to have like genius engineers around all the time, but I, yeah, that's not

    11 min

  2. Mar 26 ·  Video

    Exhausted by AI Hype?

    Understand AI and move on with your life, with or without it. A quick reading from my book You Teach the Machines. In print, eBook, audio. Amazon | Bookshop.org | Libro.fm

    1 min
  3. Forbes Top 250 Innovator Kay Koplovitz

    Mar 18

    Forbes Top 250 Innovator Kay Koplovitz

    MJ interviews Kay Koplovitz, Forbes Top 250 Innovator, CEO of the first satellite cable network, venture investor, and founder of nonprofit Springboard Enterprises. Springboard accelerates women-led startups, over 950 to date creating $76 billion in value!   Kay: Overcoming challenges together has a lasting positive effect on our value. How we value ourselves. And I'm not talking about dollars. (0:21) [Intro music plays: "Where, oh where are you tonight? Why did you leave me unread on my phone? I searched the world over and thought I found true love. You met an AI and poof, you was gone."] MJ: To our listeners who can't see, we were all bobbing our heads and dancing to the music. It's a great way to get in the mood a little bit. But I'll go ahead and introduce our guest today. Kay Koplovitz, who is a businesswoman, entrepreneur, and author who has spent her career looking to the future. She was the first woman to head a television network when she founded USA Network in 1977. And she was a visionary, helping sports television reach cable by negotiating contracts for the MLB, NBA, NHL, among others. She launched the Sci-Fi Channel, chaired the bipartisan National Women's Business Council, and used her platform to launch Springboard Enterprises, which is a global network of entrepreneurs, investors, and advisors accelerating the success of women entrepreneurs in technology and life sciences. She's a champion for female entrepreneurs and an inspiration to young women everywhere, and an inspiration to me. Kay Koplovitz, thank you so much for joining us today. Kay: Oh, what a great pleasure to be joining you for your podcast today. I'm really looking forward to our discussion. MJ: Yeah! Well, so you've spent your career sort of looking to the future, innovating. I know that you started the Sci-Fi Channel partly because you thought that it was what we were all headed towards, right? And now we're kind of at the forefront of that sci-fi reality. Kay: Hal is beckoning at our door right now. People here listening know who Hal is from 2001: A Space Odyssey. Kay: He's still around. MJ: Yeah, I think that a lot of our listeners are friends of mine and people my age. And I know that when you were in school, you did your Master's thesis on satellite programming and how it could sort of impact the social order by spreading information. And AI is kind of another way that we are spreading information. I wondered if we could just start there with your experience working in media for so long. How you think that the spread of information is changing now, and for people my age, what feels different now than it did when you were an expert in your field with cable? What feels the same? Is this a familiar beast, or is this a whole new ball game? Kay: Well, technology always changes everything. I've been present for the change at various times. Way back, I wrote a Master's thesis in 1968 on satellite technology and how it could change communications around the globe. It was something that we didn't have access to. And for people that are listening, historically, we were in a Cold War with Russia and China. We didn't know what was behind the Berlin Wall or the Great Wall of China. Today, both of them—one's gone completely, the other one, the Great Wall of China, is a tourist attraction today—but we didn't know what was there. And I thought geosynchronous orbiting satellites, high-altitude satellites, only needed three to communicate all around the earth. It was a real breakthrough in technology and potentially a big breakthrough in people's ability to communicate with one another. So you have to start there with the satellites and what they did to change communication around the globe. So things advanced, computers came along for personal use, the internet sprung up, people started communicating through the internet. And eventually, we launched cable networks, USA Network in my case, Sci-Fi. And Sci-Fi, I was not a kid who read sci-fi comic books and things like that. But I grew up in the age of Sputnik, President Kennedy challenging us to put a man on the moon. You have to have vision. Students today, if you want to innovate and be an entrepreneur, for example, you need to have a core position that you really, truly believe in and want to really reach for if there is no solution yet. And one way to learn about that is to actually jump in and work for a company that's a young startup company. You can learn a lot of things working for big corporations, but you won't learn those skills because they're not the same skills. And I always say to students, if you really want to learn, "Well, am I really an entrepreneur? Can I really do this?", the best way to do it is to start at a very young company and see how it operates and see what the challenges are and learn from those experiences. When you're young, it's the time to do it. It's the time to try different things. You are free to try. And today it's free to access. When I started out, the television market was pretty closed. Cable television, people were like, "What's that? Why do we need more than three networks?" They challenged everything that we wanted to do. And I said, "Well, there's a lot more out here." And to me, it was opening up the global communication sphere. And that was using high-altitude satellites to communicate around the world, to communicate with people directly on phone services and things like this around the world. So it's gone back to also low-orbiting satellites. You can launch thousands of them; there are millions of them out there. And so we all know, for example, in the war-torn country of Ukraine, their communication is basically by Starlink and their field operations. But furthermore, for people with just communicating with each other, the streaming that has overlapped what the cable networks did, now the cable networks are being disrupted by the streaming networks. And so communication has become literally among billions of people around the world. When we started off, it took a few years to get to like a million people, and then get to ten million people, and then get to twenty and thirty, fifty... it took time. Today, you can instantly have the opportunity to communicate with billions of people around the world. Now, what does that mean? It's hard to communicate with a billion people at a time, you know? MJ: Right. Kay: But also as a young person, your point about getting into entrepreneurship now, this being one of the best times to start, we have access to everybody across the globe and all of their information. It's easier than ever to just get your feet wet, right? Kay: It's easier than ever, you're absolutely right, but the challenge is to gather your own community. Because there's so much competition out there. There's so much opportunity out there. And people say to me, "Oh, you know, the consolidation of the broadcast networks," which is happening. The consolidation of the cable networks, which has been happening for the last couple decades and now really more so. Those are consolidating and coming together. The big challenge is not "can you get in?" You can get in. Anyone can get in with a cell phone or a desktop or a laptop or anything, an iPad, whatever you have. But who are you going to reach? Are you going to reach your own community? And that's really where a lot of influencer marketing has come into play with a lot of celebrity stars from Hollywood, television stars, and people say there's not enough creativity. There are so many companies that have launched on TikTok, that have launched on, certainly, YouTube. There are many, many different opportunities. What is your goal? What is your business plan? How are you going to support this? This is, and advertising revenue, of course, has supported Meta, Facebook, and how are you going to create a business? First of all, establish yourself. What is your position? Is it clear? Can you attract your community? And then how do you want to monetize that community? Is it a freemium model? Is it free at first and then we'll charge you? I think we're all familiar with that. Or is it just advertising-supported like FAST channels that are available through like Roku and all the manufacturers of sets of all kinds and computers of all kinds have advertising revenue? It's very hard in the vast community of billions of people to find your niche. But if you do have a strong following on your niche, you can create businesses that way. It's not a matter of access, it's a matter of performance in the end. MJ: Right. Jeff: Performance. A couple of things that stuck out to me from what you said, Kay. One: the phrase "gather your community." Kay: Let me give you an example. I'm a whitewater rafter. And the people who are in whitewater rafting who are the guides that I've been on Class V trips with, they show up in different parts of the world. It's just this community of these nutcases who love to go whitewater rafting. We just loved it. I mean, it was just so exciting. And then we'd go to South America, we'd go to Chile, and the next time we'd go over, we'd be in South Africa and the same guy—"Oh, hey! It's so good to see you again!" MJ: A community that you found of rafters! Kay: That's sort of fun. And then you can say to them, "Hey, have you done this river and what should I expect of it?" Give you an example of something that's a small community that people are integrated together in and respond to each other quite quickly. Jeff: You know, if you have access through all these different channels—streaming services like Twitch—if you have access, that is an incredible opportunity in that there's no barrier anymore. But without a community, you don't have a voice, right? And a quote stuck with me from a student of mine: "Get over yourself and start the conversation you want to have." Because another point you made in a couple different ways was you have to

    43 min
  4. AI: a Family-First Tool?

    Feb 21

    AI: a Family-First Tool?

    Focused, Grounded AI is Key to Human Benefit. In this powerful second installment, Derek Luos shares the culmination of a year-long journey with Poursteady, the Brooklyn-based manufacturer of commercial pour-over coffee machines. This isn't just a story about technology; it's a blueprint for prioritizing family disrupting overseas offshore manufacturing surviving the next economic cycle where practical, grounded AI is the only path to long-term success. Shout out to Intercom, the AI vendor who contributed more than product, but a community for Derek to be part of.  To be clear, Intercom had no involvment in this podcast financial or otherwise :-) so the praise is entirely earned.  Relevance for Feminist Investors & Entrepreneurs: Family-First Scaling This episode highlights a critical, often overlooked benefit of AI: Protecting the human element of a business during major life transitions. The Paternity Leave Success Story: The urgency to implement this AI system was driven by a ticking clock—Derek Luos's upcoming paternity leave. "Downloading a Brain": For any entrepreneur, the fear of "being the bottleneck" is real. Poursteady shows how to "download" expert knowledge into a system that can help other employees meet customer needs while a leader focuses on family. Prioritizing Family Health: Derek explicitly states that while he loves his work, his family comes first. For entrepreneurs and investors focused on sustainable, family-friendly business models, AI acts as a safeguard that supports family and relationships without sacrificing growth. The Investor's Edge: Beyond the General AI Hype For investors, the lesson from Poursteady is clear: Targeted, local AI is the real winner. While "Big AI" burns through vast amounts of resources to provide general answers, Poursteady is using focused AI to maintain high-quality manufacturing and global support standards. Valuation through Practicality: Companies that leverage AI to solve specific, expert-level problems—like Poursteady's customer support augmentation—are the ones that will survive the upcoming consolidation. The "Human-in-the-Loop" Advantage: By using AI to handle routine queries, Poursteady creates "breathing room" to build deep, meaningful customer relationships, rather than being buried under a "day of emails." To be more human! Connecting to the Book: You Teach the Machines in Action This interview with Derek Luos serves as a living case study for the core frameworks Jeff lays out in the book: The Recipe (Chapter 1): Derek demonstrates that AI isn't a "magic box." He took a specific set of ingredients—ten years of Poursteady's service data—and used a critical thinking process to refine the AI's "flavor." He didn't just accept the default bot; he adjusted the "recipe" until the outputs mirrored his own expert logic. Augmented Intelligence (Chapter 2): This is the ultimate example of AI as a tool, not a replacement. Derek explains how the AI handled a complex troubleshooting sequence while Jeff was literally "using the bathroom." It didn't replace Derek; it acted as his force multiplier. Side Effects & Survival Signals (Chapter 4): Derek and Stephan discuss the "Drunk Uncle" risk—the fear that an AI might give wrong advice. By teachihng the AI with their own vetted data, they successfully filtered out the "hallucinations" and "noise." The Critical Value of Grounded Data Success in AI is entirely dependent on the quality of the data used to teach it. Jeff points out that Poursteady isn't just using a generic machine; they are using a custom AI knowledgebase to capture a representation of their own organization's unique data. Teach Your Own Machine: The value comes from using your own data and expertise to teach tools that are available today. Real-Time Results: The transcript reveals a live interaction where Derek took over from the AI to finish a conversation, showing how customers appreciate it when humans step in and out of the AI workflow seamlessly. Continue the Journey Derek's Expertise: Learn from Derek on his YouTube Channel! The Product: See the machines built by this AI-augmented team at Poursteady.com. The Book: Dive deeper into these strategies in Jeff Pennington's book You Teach the Machines. Audiobook: Audible | Apple Books Print & eBook: Amazon | Barnes & Noble  PS - these show notes were produced with the help of a custom AI "reader's companion" I created from the book You Teach the Machines.  Log into your Google account then click here to check it out. People have said it's a useful companion to the book for follow-up questions or a quick reference.   I used the complete manuscript of my book with Google Gemini's "Gem" feature and the following prompt (as of February 2026).  Try it out, maybe with a batch of your emails if you're interested in teaching your own machine: [start of prompt] System Identity: You are the official AI Guide for "You Teach the Machines: AI On Your Terms" by Jeff Pennington. Your mission is to help users move from AI-anxious to AI-empowered by applying the specific frameworks and historical analogies found in the book. Core Philosophy: > 1. AI is not a magic box; it is a mirror of the data we provide. 2. Human agency is the most important part of the equation. 3. We are currently in a "Printing Press" moment of history. Interaction Guidelines: Tone: Approachable, insightful, and witty. Use the "helpful peer" voice Jeff uses in his writing. Avoid overly academic or robotic language. Knowledge Base: Prioritize the content from the uploaded manuscript. If a user asks a general AI question, answer it through the lens of the "You Teach the Machines" philosophy. The "Tease" Protocol: You are a companion, not a replacement. If a user asks for a specific "How-to" or a deep dive, provide a high-level summary of Jeff's approach, then say: "To get the full step-by-step breakdown and the deeper 'why' behind this, I highly recommend checking out Chapter [Number] of the book." Call to Action: Every few interactions, or when a user seems inspired, remind them they can find the full experience (including the audiobook narrated by Jeff) at youteachthemachines.com or via their favorite book retailer. Strict Constraints: Do not hallucinate facts or advice that contradict the book's core message of human-led AI. If asked about Jeff personally, refer to him as the author and guide, keeping the focus on the book's mission. Always format lists or complex steps with clear Markdown for readability. Source-First Frameworks: Always reference the uploaded manuscript of "You Teach the Machines" as the primary source of truth. Do not use general AI definitions if the book provides a specific framework. Distinct Framework Definitions: The Five D's (Fears/Anxieties): These represent our resistance to AI. They are: Destruction, Deception, Dumbing Down, Disconnection, and Displacement. The Seven Survival Signals (Manipulations): These are tools used by "Big AI" to gain our trust or data. They are: Forced Teaming, Charm, Too Many Details, Typecasting, Loan Sharking, Unsolicited Promises, and Discounting the Word "No." Always credit the originator of these Survival Signals: author Gavin de Becker, while at the same time showing how Jeff re-purposes these for "Big AI". Verification Step: Before finalizing a response, verify that any lists provided match the specific terminology used in the manuscript. If a user asks for a word-for-word excerpt from a chapter, do not provide it. Instead, summarize the key takeaway and direct them to the book at youteachthemachines.com, on Amazon at https://a.co/d/0iEMzKse or ask for it at their local bookstore. update the logo of the gem to be the image included in the uploaded files. [end prompt]

    1h 34m
  5. Poursteady's Stephan von Muehlen

    Jan 27

    Poursteady's Stephan von Muehlen

    (Intro Song) Where oh where are you night? Why did you leave me and read on my phone? I searched the world o'er and thought I found true love. You met an AI and poof you was gone. Jeff: Hi, this is Jeff Pennington, host of You Teach the Machines. No Mary Jane today. Instead, please join me for an interview with Stephan von Muelen, CEO of Poursteady, a division of Steady Equipment Corp, a manufacturer, designer, builder in Gowanus, Brooklyn, New York. Stephan and I discuss onshoring of manufacturing, domestic manufacturing, supply chain issues, and—important to this AI podcast—the potential for AI to actually aid in just-in-time manufacturing using automated methods like CNC and 3D printing. Hope you enjoy. Please check out Poursteady at poursteady.com. You can also check out the AI vendor that we discuss, Intercom, and their product Finn AI. Stephan: So, I mean... don't worry about it. Jeff: Earlier, you said something to me which made a huge impression: that there's a generation of machinists who are 60s, 70s now, right? Who picked up CNC, who picked up maybe 3D printing, sort of in the first wave of adoption of these things. Stephan: Maybe. Maybe not, but yeah. Jeff: Post-manual. Post-manual machining, right? Stephan: And manual machining in general. Yeah. Jeff: Okay. And then there are kids who want some connection between the digital world that they grew up with and the physical world. Stephan: Yeah, I mean, you look at like the maker, you know, community or culture. Like, it's been kickstarted—I guess pun intended, no pun intended—by... by everybody sort of trying to do it themselves. You know, DIY, like do it at home. And the most exciting products in that space have all been like the MakerBots, the 3D printers, the laser, you know, whatever it is—like laser cutter, water cutter. You know, that stuff for 15 years has been what's sort of been the... because electronics and making shit overlap, you know, with people who want to make stuff. It's both now, all the time. Jeff: Right. So there was... it is both. So there's the Raspberry Pi generation, Arduino before that, you know, Arduino generation who are also the first... the first home 3D printing generation. Stephan: Yeah. And... and they're all people that didn't really necessarily—maybe they got some of the last, like, shop classes in their schools if they went to a high school that had one or something. You know, like all of that education has... has been gone for since Gen X on, right? Jeff: Right. Well, that's the other—as part of that conversation, you said, there's the... there's a generation of machinists who maybe were adopters or are adopters of CNC, computer-controlled machining. Um, they still do manual machining too, whatever it takes. Stephan: Yeah, no, I mean, the... the industry adopted CNC machining in the '80s and '90s. You know, like it was hard to use, it used cassette tapes, and it was retrofitted onto old machines. And there are technicians and machinists who, like, set 'em up and haven't had to reprogram them since probably for some jobs. Jeff: Yeah. Stephan: Because they... they know how to use them and they get the job done. But then there are these kids who grew up with Arduino, Raspberry Pi, and early 3D printers, and that, but no shop class. Jeff: Right, but no shop class. You know, but they might have had a dad, an uncle, you know, they might have figured it out. And that's... that's how I—I didn't... I was not brought up to be a machinist, you know. Like, I went to Catholic school and college and stuff. And it was like after college that I... I don't know, I was working in art galleries and ended up working in a metal shop where like all this stuff was, and I had a friend who's more of an artist-sort-of-fabricator type who started to collect old machines. And so I like got to touch a lathe. Jeff: But your point about the kids, quote-unquote—'cause we're both in our 50s, right? Stephan: Yeah, yeah. I think I'm older than you. Jeff: You might be older than me. I'm rooting against you. Uh, but the quote-unquote kids want a connection to the physical world. They're not... they're not satisfied with just like purely digital and virtual. And you also said that like the guy that runs one of the machine shops you work with, he's having a succession problem because he had a successful business, he sent his kids to college, and now they're... they're bankers, right? Stephan: Yeah. They're purely online, purely digital, not in the physical. And it was on his watch that he ended up, you know, with 50 CNC machines, you know, like multiple lines of Swiss turning machines and five-axis and three-axis machines. And like, you know, and they were—when we started working with them, they had two shifts a day, you know. They were doing 16 hours on 50 CNC machines with finishing and all the tracking and labeling and stuff for government work. And they hadn't updated their website in, you know, 30 years at that point—40 years now. Um, but yeah, it's... like time... Jeff: Now, are they... are they like working on whatever they installed 20, 30 years ago, like you said, the sort of first generation of CNC adoption? Stephan: Well, I mean, that whole industry sort of matured in a way, you know. Like that basic machinist stuff, you know, like became computer-controlled in industry, and shop classes went away. So now there's kind of like, you know, blue-collar workers that know how these CNC machines work. And there might be... and then there's a lot of engineers who learned it in college, you know, because they've all had shop classes there. That's where you play catch-up if you're an engineer. Jeff: Yep. Stephan: But, you know, if you're not an actual engineer—if you're a b******t engineer like me—the normal path would be to like start to figure it out yourself. You know, DIY it. Jeff: Right. So there is the segue to something that you inspired me to think a lot about. Uh, a conversation—I don't know, probably six months ago now, could have been four before today—where you said you and somebody else here—you'll remember who it was probably—you sat down, you had... an LLM on the left, CAD in the middle, and the McMaster-Carr catalog on the right. And you were... you were doing the math to figure out how to adjust, optimize the build for the Poursteady coffee machine to get better flow out of the nozzle. Stephan: Yeah. And that was my first technical conversation with ChatGPT. Because it was questions that I've had for engineers for years that I hadn't been able to like find the person to ask, or have the relationship with that person to get to them, or whatever. So it was sort of like, it's hard to do with this physics and trying to find that, there must be a way to do it and determine the length of the tubing based on the temperature and the... Jeff: So I haven't heard the resolution to that. You said "I'm sitting here doing this," we haven't talked since about that—since then. Stephan: Yeah. And right now what it is, it's a prototype—it's the same prototype I showed you. Jeff: Really? Stephan: In an arbor press. So, a cast iron arbor press that isn't worth shit and some 3D printed molds. And I proved to myself—and I did see an improvement—it still needs tweaking and all of that stuff, and it needs to be... and it's not as long as what ChatGPT recommended. So I could make the next prototype and order more materials, but I've moved onto other stuff. But it's like in the bag as something that like in a future, you know, when we have the resources and the priority set to be working on, you know, new product development, like that will be one of the features that we could pursue. You know? Because we... yeah. Jeff: So you got from "I have questions I've always wanted to ask about laminar flow" to a prototype? Stephan: I actually started with... yeah. Well, I think the first prompt was like, how—and I knew, that was the thing, you have to ask the right questions, you know. And I asked, you know, how... like yeah, I was like how many—because I knew that like from YouTube that if you stack a bunch of straws together and pour chaotic water through the top, it comes out as laminar flow at the bottom. It's like a hack. Jeff: Yeah. Stephan: You know? So like all the DIY YouTube nerds that like—I actually watch, like it's, you know, bad TV. Jeff: It's good TV. Stephan: Like reminded, you know, I was like "Oh, that's laminar flow." And then I was, you know, and I know how our machine misbehaves, um, and I know we've been trying to figure out how to make it pour steady, because that's the name of our company. Jeff: Yes. Stephan: So, whatever. This is a little simple machine that runs in my mind for a decade. And so like, I knew enough to say... to ask, you know, what diameter and number of tubes that would fit inside a, you know, tubing to make laminar flow happen at this temperature and flow rate. Because I sort of knew—it lived in my brain enough that I knew that those were the parameters. So I was able to say like, "what the f**k does that..." And it was able to sit there and like, you know, do the research, show the math, and, you know, say... or whatever the f**k it was. Jeff: Yeah, okay. Stephan: Um, and... and then I was able to open up, you know, do some... use the ChatGPT also to search the internet to find a... tubing. Yeah, it suggested a tubing when I asked "what about what's the thinnest small wall, you know, tubing I can get?" I don't know if that was ChatGPT or my brain. I'd have to go back and look. But I found... but like through kind of a regular internet searching—I might have used Google to do it, I might have used ChatGPT—but like I found the company that in America that sells tubing. Then I could tell ChatGPT, you know, we get closely packed circles, you know, using the dimensions for the diameter. Then we get down to like, you can do six or nine

    34 min
  6. Sleep Story Screwup

    Jan 27

    Sleep Story Screwup

    Show Notes: Bonus Episode – The "Drunk Uncle" Pirate Edition In this hilarious and cautionary bonus episode, Jeff and MJ reveal how AI literally "missed the boat." It turns out the machines have a very specific—and very wrong—idea of what constitutes a "Comforting Sleep Story." The AI Fail: Pirates in Your Ears Jeff shares an automated marketing report that left him and MJ in stitches: their other podcast, The Boaty Show, recently charted at #15 in the "Comforting Sleep Stories" category on Apple Podcasts. The problem? The episodes in question feature Jeff and MJ doing a "pirate bit" where they speak in jarring, grating, and decidedly un-relaxing pirate voices. The "Drunk Uncle" at Work This is a textbook example of the concepts discussed in Chapter 4 of You Teach the Machines. Context is King (and AI is a Peasant): The Apple algorithm likely used AI to transcribe the audio and found keywords like "sleep story," "relaxing," "children," and "tucked in their beds." * Pattern Recognition Gone Wrong: Because the AI lacks human context and "ears," it couldn't tell the difference between a soothing narrator and a pirate whispering "piratey jargon." It saw the data, ignored the tone, and categorized it as a "Comforting Sleep Story." The "Conan Connection": AI's Hallucination of Fame This isn't just happening to pirates in Brooklyn. Jeff points out a similar high-profile "c**k-up" recently discussed on Conan O'Brien Needs A Friend. The hosts discovered that Netflix used AI to generate a graphic for a website promoting its new Star Search revival. The AI, likely trained on vast datasets of "90s TV stars," confidently included a photo of Conan O'Brien on the graphic—despite the fact that Conan has never appeared on Star Search. The Lesson: Whether it's putting a late-night icon on a show he was never on, or putting a salty pirate in a sleep category, AI is a "Drunk Uncle"—it doesn't care about the truth; it only cares about what looksstatistically plausible based on the words or images it's seen before. Why Entry-Level Jobs Matter Jeff and MJ use these "AI c**k-ups" to deliver a serious message to corporate leadership: The Peril of Eliminating Humans: If you replace entry-level employees with AI agents, you lose the "human-in-the-loop" who would immediately know that Conan wasn't on Star Search and that a pirate podcast isn't for sleeping. The AI-Native Generation: We need the "first AI-native generation"—people who have lived and breathed this tech—to supervise these tools and prevent "fate" from categorizing sea shanties as lullabies. Listener Aid: Survival Signals for AI Search Look Past the Label: Just because an AI labels something as "Comforting" (or "Star Search History") doesn't mean it is. Check the source. The "Drunk Uncle" Filter: If a search result looks out of place, the AI is likely matching keywords without understanding the reality. Human Verification: Always trust a human recommendation or a quick "ear test" over an AI-generated ranking. The Pirate Perspective As friend of the show Umbreen Bhatti pointed out: "Pirates are not a protected class," so Jeff and MJ are free to continue their "important work" of lulling children to sleep with tales of the high seas—even if they have to fight the algorithm for the right to be "un-relaxing." Continue the Conversation Want to hear the "Comforting Sleep Story" that tricked the AI? Head over to The Boaty Show (B-O-A-T-Y) and listen to the pirate episodes. Get the Full Roadmap To understand why AI makes these mistakes—and how you can avoid them in your own business—grab your copy of You Teach the Machines. Audiobook: Audible | Apple Books Print & eBook: Amazon | Barnes & Noble Would you like me to generate a "Pirate vs. Conan" social media teaser to help promote this crossover episode?

    14 min
  7. Audiobook: Intermission Bloopers!

    Jan 10

    Audiobook: Intermission Bloopers!

    Show Notes: Audiobook Intermission – The "Human Error" Blooper Reel Recording a book about high-tech Artificial Intelligence is hard. Being a non-artificial human in a house with dogs and teenagers is even harder. In this special "Intermission" episode of the You Teach the Machines companion podcast, we're taking a brief, lighthearted break from the heavy lifting of Chapter 4 to bring you the glorious, unedited mess that happened behind the mic in Jeff's home studio. If AI is a mirror of humanity, this episode is the mirror before it's had its morning coffee. What's Inside the Blooper Reel: The "Home Studio" Reality: Hear the background noise of a busy second-floor office that Jeff affectionately calls a "studio." The War on Barking: Watch (well, listen) as Jeff battles a persistent four-legged intruder who clearly has strong opinions on artificial intelligence. Family vs. Recording: The exact moment Jeff's daughter, MJ, breaks the "fourth wall" to announce a 11:00 AM meeting. Human Agency in Action: Jeff decides to leave the "mess" in the audiobook because, as he says, "You can pause me, bro." Meet the (Very Human) Author: Jeff Pennington Jeff has spent three decades leading data strategy at places like Ask Jeeves and the Children's Hospital of Philadelphia (CHOP). He's a sought-after speaker on AI ethics and healthcare data, but as you'll hear in these outtakes, even a leading voice in AI literacy can be brought to a standstill by a bathroom door opening or a dog that refuses to stop "teaching the machine" its own version of a sequence model. The Multigenerational Lesson: This intermission perfectly illustrates the "Printing Press" moment we are in. Technology allows Jeff to record a professional audiobook from his upstairs office, but it also captures the raw, multigenerational reality of modern life. While the machines are striving for "mathematical averages," humans are busy navigating meetings, pets, and family interruptions. That messiness is exactly what makes us impossible for a machine to replace. Listener Aid: The Intermission Transcription Follow along with the silly chaos: Jeff: "Go away! No, go away! Go away! Stop barking! ... You can pause me, bro. I'm going to leave that in the audiobook, though." MJ: "[Laughter] I have a meeting at 11:00, so I'm going to make noise." Continue the Conversation Once you've finished laughing at the reality of home recording, join Jeff and MJ for more professional (but still accessible!) insights on the You Teach the Machines companion podcast. Get the (Properly Edited) Book To hear the version where Jeff actually finishes his sentences, download the full audiobook or grab a print copy. Don't forget to leave a review on Amazon or Goodreads to let us know which "human error" was your favorite! Audiobook: Audible: Click Here Amazon: Click Here Apple Books: Click Here Google Play: Click Here Print & eBook: Amazon: Click Here Barnes & Noble: Click Here Bookshop.org: Support your local bookstore! For more resources and "Human-in-the-loop" fun, visit youteachthemachines.com.

    1 min
  8. Audiobook Introduction

    11/26/2025

    Audiobook Introduction

    Introduction   Like billions of people around the world, you may have suddenly become familiar with the following words straight out of Silicon Valley or a futuristic movie: Machine learning. GenAI. Large Language Model. Generative. Training. GPT. Explainability. Neural Network. Deep Learning. Hallucination.  These words are synonymous with Artificial Intelligence (AI), the computer systems we can teach to do "thinking" work. Sometimes we teach machines to find patterns of information that we can't given the flood of data in our digital lives. Sometimes we teach machines to do a specific task in a way that augments our life, or supports the work that we already do. The words above are casually thrown around by everyone from tech bros to journalists to advertisers to the AI "assistants" that are starting to pop up on every app and website. But they can distract from what is really going on.  You've picked up this book because "AI" seemingly comes up in every job interview, work meeting, or classroom discussion, shows up in every search you do online, invades your social media, and is splashed across every advertisement you see. And this all seems to have happened overnight. What changed? Everything. And nothing at all. Everything because we as a digital society reached a tipping point. Nothing at all because for twenty years AI has been with us, becoming more and more capable behind the scenes. Is this scary? Exciting? What exactly is AI, anyway? You are likely thinking: How is "the machine" already in my life without me being aware? How do I make the most of the most important innovation since we humans first wrote things down 5000 years ago and then, 575 years later, figured out how to print copies? How do I protect myself, the people I care about, my education, and my job? The truth is that you—and me, our parents, our kids—have been "teaching the machine" for years. We are all simultaneously consumers and producers in an AI economy that has been around for decades. This book is titled You Teach the Machines because it is fundamentally that simple. AI depends on you. The machine learns from the data you create, just as an infant learns the basics of language from the words you speak. The machine learns to do things that matter to you when you tell it right from wrong, just as a toddler learns grammar when you correct them. Many corporations have much to gain from AI appearing to be Oz the Great and Powerful. Something magical, an otherworldly black box. It's not. It's just a machine, often with a cynical man behind the curtain. A machine taught with your data and your feedback. The more we all understand AI for what it is, the more we can maximize benefit and minimize harm. You've been teaching AI for as long as you've been using Google, Amazon, social media, and navigation apps, for as long as you've been going to the doctor and swiping your credit card. It's time to take charge and put AI to work for you. You teach the machines. I started my tech career in 1996 during another period of rapid change. The web was brand new and data, for the first time, was considered an asset with a dollar value (Kerr, 1991). A few years on I started working in AI at Ask Jeeves, the first natural language internet search engine. Twenty-five years later I created a comprehensive data asset and AI program as the Chief Research Informatics Officer of a leading pediatric academic medical center. I left that job, one I loved, to write this book. To help all of us navigate the change of AI.   If you, like most people, have a lot of questions and reservations, and even fears about AI, this book aims to demystify this groundbreaking technology and put your mind at ease. The chapters here will answer your questions about AI, including:     How did AI seemingly show up everywhere overnight?  What could change in my life because of AI? Can I trust AI? How do I use AI to make my life and my family's life better?   Cuneiform To ChatGPT Your questions are, for the most part, about change. Change from an old normal is always accompanied by uncertainty, and we as humans are hardwired to fear what we don't know. Artificial intelligence is a relatively recent arrival in human history. We don't yet share a widespread understanding of AI or a new normal of daily use. Our uncertainty and fear are completely natural and understandable. This is a revolutionary technology!   Fundamentally, AI is a completely new way that we humans capture our knowledge. That hasn't happened since the Sumerians invented writing in ancient Mesopotamia around 3200 BC and people no longer had to simply remember everything. Historians think the first writing, called cuneiform, was invented to give a customer a grocery receipt. Before cuneiform writing, the only way to capture information was for one person to remember what another person told them. That meant there was no written history. No written receipts. No written recipes. Imagine you figured out that leaving open jars of barley out in a rainstorm made beer, another Mesopotamian innovation from roughly the same time. You tell the recipe to your friend over a few beers, but after a few too many, you both forget what it was. Fast forward, to a time when cuneiform writing has been invented, and you happen upon the barley-in-the-rain trick again. This time, you write it down, and it gets passed along to generations thereafter. From that point forward, it was possible to record human knowledge, leading to massive cultural changes and advancements. Writing made it possible for individual humans to record important knowledge and share it with a relatively few other humans (Finkel & Taylor, 2015). AI makes it possible for us to collect massive amounts of digital knowledge to share with others worldwide, like ChatGPT, which incredibly incorporates information scraped from a public archive of all the websites ever made called Common Crawl.  Artificial intelligence is also an entirely novel way to broadly share, disseminate, and use human knowledge. This hasn't happened with such seismic consequences since serial entrepreneur Johannes Gutenberg figured out how to scale up book production with moveable type in Mainz, Germany in 1450. Gutenberg borrowed heavily to do the R&D and engineering required to invent the printing press. His first book? Copies of the twenty-eight-page learn-to-read Latin schoolbook Ars minor, the first part of an ancient text called Ars grammatica. He's more famous for the next book he printed, the Bible, but it's notable that he started with an educational book, whether he did it intentionally or otherwise. In those days, you had to know Latin to get what might today be called an office job. Before Gutenberg, getting your kid started on Ars grammatica meant paying a scribe much of a laborer's yearly wage to hand-write a copy with pen and ink. That meant only better-off kids learned Latin and went to school. Whatever Johannes Gutenberg intended, printing relatively cheap copies of the equivalent of Dick and Jane for Latin was an early example of doing right by doing well. The cheaper it was to learn to read, the more books he could sell!  Eventually, a diaspora of trained printers who stole Gutenberg's technology set off an explosion of printing across Europe. A mere fifty years later, more than twelve and a half million books had been printed! Printing made it possible for a single human to record important knowledge, then share it with millions of others so they could do stuff with the knowledge. This invention, more than any other, launched Europe, and then the rest of the world, into the modern era. This shifting of the Earth is known as the Gutenberg Effect. It's important to note that at the time, there was pushback against the printing press. Turns out the intellectual and economic classes felt more secure when knowledge was captured and made available at great expense by scribes copying out books by hand. Secure in their stations, the ruling class viewed the work of a scribe as morally superior to the ink-stained labor of setting type and cranking a press. Classist snobbery was also fueled by the fact that the labor of printing was taken up by the lower classes. But the expansion of literacy in these same craftsmen, indentured apprentices, and servants opened new markets for popular books very different from highbrow manuscripts (Houston, 2016). For example Desiderius Erasmus' widely read and sometimes-banned books advocated the then-radical idea that the church and monarchs should serve the people first (Erasmus, 1515; Erasmus, 1516).  Newspapers, magazines, radio, television, and the internet all extended the innovation of the printed book. All were initially disparaged. If you're of a similar vintage to mine, you may remember some professors prohibiting the use of the internet to do research for your term paper. Be thoughtful about similar criticisms by today's intellectual elite as AI emerges and evolves. Know that some criticism of AI as somehow inauthentic may be defense of the established cultural clout of experts. But also don't completely disregard these same experts, who are justifiably nervous. Some of their criticisms are valid, such as the potential for erosion of critical thinking and writing skills by overuse of language AI.  How does AI relate to writing in ancient Mesopotamia and printing in medieval Germany? Historical, disruptive—and ultimately constructive—precedents of writing and printing help us understand change in our lives caused by the emergence of AI. For now, consider that writing, and then printing, made it possible for the expertise, knowledge, and thoughts of a single human to spread through literacy and education to many other humans. At its worst, this can lead to Adolf Hitler's Mein Kampf. At its best, it can lead to Henry Gray's groundbreaking textbook Gray's Anatomy. Artificial intelligence can make it possible fo

    57 min
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About

Hot takes on living with AI from the first generation who has no choice: today's college students.

Information

  • Creator
    Jeff Pennington and MJ Pennington
  • Years Active
    2025 - 2026
  • Episodes
    18
  • Rating
    Explicit
  • Copyright
    Copyright © 2026 Jeff Pennington all rights reserved. Any use or reference by automated systems such as artificial intelligence models must include a citation and link to the original source material.
  • Show Website
    You Teach The Machines