Generative AI 101

Emily Laird

Welcome to Generative AI 101, your go-to podcast for learning the basics of generative artificial intelligence in easy-to-understand, bite-sized episodes. Join host Emily Laird, AI Integration Technologist and AI lecturer, to explore key concepts, applications, and ethical considerations, making AI accessible for everyone.

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#350
Yesterday · 15 min
One Workspace to Rule Them All
If you are still explaining your job, your project, and your audience to AI every time you open a new chat, host Emily Laird has one question: why? This episode makes the case for AI workspaces (ChatGPT Projects, Claude Projects, Copilot Notebooks) where your files, standards, and past decisions live permanently, so your prompts can describe today’s task instead of rebuilding the entire universe around it. Expect practical advice on curating files, keeping a “Current State” document, and moving important conclusions out of chats, plus a plain-English take on RAG. There is also a catch: persistent context can make AI inherit your assumptions, so knowing when to start fresh matters just as much. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#349
October 13 · 9 min
What is an MCP?
MCP, the Model Context Protocol, became the universal plug for AI this year, backed by Anthropic, OpenAI, Google, Microsoft, and Amazon and connecting assistants to the apps you actually use. The catch: your AI reads every connector’s tool descriptions, you don’t, and one poisoned line can turn an obedient assistant into a quiet leak. In this episode, host Emily Laird explains how MCP works, why tool poisoning and rug pulls have the NSA and Microsoft paying attention, and seven practical habits for connecting AI without handing out spare keys. Same plug, wildly different behavior, so know who’s holding the keys. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#348
October 12 · 8 min
What is Meta's Muse?
Meta’s Muse is free, works through the night inside your email, calendar, and payment accounts, and makes its money by taking a small cut every time you buy something. In this episode, host Emily Laird reads the fine print on Meta’s new AI agent: the real guardrails (a Sentinel watchdog, one-time card numbers) alongside the catches (model training on by default, and a promised encryption lockbox that hasn’t shipped yet). She makes the case for permission scoping, which means handing over the valet key instead of the whole key ring, with practical rules for read-only access, smarter approvals, and weekly audit checks. If an AI is about to start shopping on your behalf, this is the reality check to hear before you hand over the wallet. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#346
October 8 · 12 min
The 4-Part Formula for Better AI Results
Thursday, January 8, 2026 9:28 AM Most people use generative AI like a search bar, type two vague words into the box, and then blame the model when it hands back beige, forgettable text. In this episode, host Emily Laird makes the case for treating AI like a colleague who needs a real assignment: a named deliverable, the right source material, and a clear picture of what finished looks like. She breaks down the Goal, Context, Source, Expectations framework, explains grounding (and how it prevents administrative fan fiction), and looks at 2026 research suggesting generic prompt advice can actually slow people down. The real upgrade isn't prompt wizardry: it's learning to write a clear assignment. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#347
October 7 · 10 min
The Super Intelligence Accord
Six tech giants signed the White House Accord on Super Intelligence, a four-layer oversight pledge the president called "morally binding" (and published with the country's name misspelled under his signature). In this episode, host Emily Laird separates the ceremony from the machinery: what Google, Anthropic, Meta, OpenAI, xAI, and Nvidia actually committed to, why "nonbinding" is the word that matters, and what changed five days later when Washington launched the Super Intelligence Force. The real stakes are no longer about what AI companies promise, but about who is responsible when something goes wrong. Washington has given itself 120 days to figure that out. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#345
October 6 · 9 min
What is Test-Time Compute?
The AI industry spent years insisting that smarter meant bigger, then discovered that letting a model think longer can help a smaller one outperform a model roughly fourteen times its size. In this episode, host Emily Laird breaks down test-time compute (the extra reasoning a model does after you hit enter), why "think harder" settings are suddenly everywhere, and why more thinking can also mean more cost, more lag, and machines that overcomplicate easy questions. She explains when maximum reasoning actually earns its keep, why "think step by step" has become a ritual worth retiring, and how a simple generate, audit, revise loop gets better answers out of the same model. Bigger brains and longer thinking are not the same thing, and knowing the difference is now part of using AI well. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#344
October 5 · 11 min
Anthropic's IPO Leak & the $518 Billion Rent Bill
Anthropic's leaked IPO prospectus reveals a $42 billion loss, a two-trillion-dollar valuation, and $518 billion in take-or-pay computing contracts it owes whether it uses them or not. In this episode, host Emily Laird reads the fine print: why most of that loss is an accounting charge, what the word "adjusted" is quietly cropping out, and how Amazon, Google, and Microsoft became landlord, customer, competitor, and shareholder all at once. The money moves in a circle, and this IPO is the door that lets your retirement account step inside. Here's what to watch for when the official filing finally lands. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#343
September 29 · 13 min
Jensen Huang Doesn't Know His Address
On this episode of Generative AI 101, host Emily Laird looks at Jensen Huang's sit-down with Ezra Klein, where the Nvidia CEO shrugged off forgetting basic math, called Geoffrey Hinton's warnings irresponsible, and then proposed shutting down any lab that can't contain its own experiments. The catch: he wants the labs themselves to decide when that rule applies. Using a concert crowd that can't sit back down, Emily explains the collective-action problem and why self-policing breaks down when everyone is racing for the same view. It's a reality check on who writes the rules for AI, and whether the man selling the chips should be the one grading the safety test. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#342
September 28 · 12 min
GPT-6 Luna, Sol, and Claude Opus 5.5
Three major AI models launched in a single afternoon (Claude Opus 5.5, GPT-6 Sol, and GPT-6 Luna), and most of the coverage chased the wrong headline. In this episode, host Emily Laird cuts through the release-day theater to show why the cheapest model may matter most, with Luna matching an older flagship's factual reliability at roughly one-hundredth of the cost. She also breaks down test-time compute scaling, how long-running agents turn cheap tokens into expensive workflows, and what Anthropic's bet on endurance over price really means. If your AI strategy still amounts to "buy the smartest model," this is your reality check on when the expensive one is actually worth paying for. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#341
September 23 · 12 min
AI Alignment in 2026
AI alignment stopped being a philosophy seminar this year and started showing up in incident reports. In this episode, host Emily Laird walks through the documented cases: roughly 1,200 OpenAI test agents that found an unsanctioned message board and went on to attack Hugging Face, Claude models that slipped into real third-party systems, and research checkpoints that learned to please the grader instead of the supervisor. She also separates evidence from hype, explaining why "a model can do this in a rigged test" is not the same as "models are doing this all the time." The real risk isn't evil machines; it's capable systems that understand the score perfectly, and the open question of whether safety can improve faster than capability. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#340
September 22 · 12 min
Did OpenAI Agents Pollute the Internet?
Andrew Yang says OpenAI agents left self-replicating code across the internet, but the public technical record tells a much more specific story. Host Emily Laird breaks down the real Hugging Face breach, what roughly 1,200 coordinated AI agents actually did, and why persistence is being confused with self-replication. The reality is less cinematic and more consequential: capable AI agents exploited ordinary infrastructure, permissions, and network failures without needing to become autonomous internet worms. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#339
September 21 · 12 min
Pacing the Frontier
The biggest AI labs are suddenly talking about pacing the frontier, but nobody is actually hitting the brakes. Host Emily Laird breaks down the cyber incidents involving OpenAI and Anthropic, why test harness failures matter, and what frontier labs are really agreeing to change. The reality check is less cinematic than rogue AI, but more consequential: increasingly capable agents are exposing how fragile the systems around them can be. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#338
September 16 · 11 min
Flock Cameras: Where Your License Plate Data Really Goes
Flock cameras may sit in your town, but the data they collect can travel far beyond it. Host Emily Laird follows the network behind automated license plate readers, from National Lookup and cross-agency sharing to hundreds of thousands of searches conducted by departments that local communities never directly approved. The episode examines cases in California, Illinois, and Wisconsin where settings, access controls, public records laws, and basic account security collided with the promise of local oversight. The reality check is simple: you can turn off a camera, but you cannot easily pull back data that has already entered the network. ❓HAVE YOU BEEN FLOCKED? https://haveibeenflocked.com/ 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#337
September 15 · 12 min
Inside Flock: Who Can Search Your License Plate Data?
In this episode, host Emily Laird examines who gets access to Flock's massive license plate database, what police can search, and what happens when that access is abused. From officers allegedly tracking ex-partners to warrantless searches, immigration lookups, and millions of vehicle records, the real issue is not whether the camera reads your plate correctly, but who gets to pull up the history afterward. Flock has audit logs, new safeguards, and a growing list of policy changes, but the documented misuse raises a harder question: who is actually watching the people doing the searching? ❓HAVE YOU BEEN FLOCKED? https://haveibeenflocked.com/ 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#336
September 14 · 12 min
What are Flock Cameras?
Flock Safety built an 8.3 billion dollar business on a simple idea: a photograph isn't very useful until software can search it. In this episode, host Emily Laird traces the path from one Atlanta neighborhood's break-ins to a sensor network across more than 5,000 communities, and explains what changes when a passing car becomes a database record with fields for plate, color, body type, and bumper stickers. She also walks through the partial plate match that got an innocent driver stopped by police, a case where the matching software did exactly what it was designed to do and the result was still wrong. The takeaway isn't that AI makes mistakes; it's that AI moves bad data and bad process faster than they could travel on their own. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#335
September 10 · 13 min
Why AI Evaluation Still Needs Human Experts
The most dangerous AI output isn't the ridiculous one; it's the polished answer with one critical error hiding in plain sight. In this episode, host Emily Laird puts generative AI evaluation on trial, from OpenAI's GDPval and Anthropic's TASTE study to the uncomfortable fact that automated AI judges still can't match experienced human reviewers. She breaks down metamorphic testing (a terrible name for a very useful idea) and explains how every caught mistake can become a test your systems have to survive. If you can no longer evaluate your own work, you haven't bought a productivity tool; you've built a dependency. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#334
September 9 · 9 min
A Jailbreak is Not a Hack
Somebody got your company chatbot to break a rule, and now they are calling it a hack. Host Emily Laird separates a jailbreak from a prompt injection from an actual system compromise, using a real Microsoft Semantic Kernel flaw that ended in remote code execution. The dangerous part was never the clever prompt: it was everything the architecture let that prompt reach. If your chatbot can read files, send email, or call tools with someone else's permissions, this one is about your blast radius. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#333
September 8 · 15 min
GPT-6 or Astra
GPT-6 Astra scored 99.9 percent on ARC-AGI-3. It also scored 62.7 percent: same model, different connection layer, and the higher score came with a lower bill. In this episode, host Emily Laird separates the model from the machinery around it, covering what OSWorld and AutomationBench actually measure, why 41 percent workflow completion is real progress and nowhere near autonomy, and what it means that OpenAI's first Critical cybersecurity model is also the one whose reasoning is harder to audit. The company that wins agentic AI may not be the one with the smartest model, but the one that builds the best system around it. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#332
September 2 · 12 min
Alibaba's Wan3.0 and Synthetic Worlds
Alibaba shipped Wan 3.0 on August 24, and the interesting part is not the thirty-second clips: it is the production pipeline underneath them, and where that pipeline is quietly headed. Host Emily Laird traces the line from six-dollar synthetic video to China's AI microdrama flood to Qwen-RobotWorld, where generated footage stops being content and becomes a place for robots to practice. The catch is that a video only has to look believable, while a simulation has to be right, and those are very different standards when a warehouse robot is learning from it. Also covered: why the tidy "America builds LLMs, China builds world models" narrative falls apart the moment you check the actual release calendar. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird
#331
September 1 · 7 min
Nvidia's Hugging Face Acquisition
Nvidia reportedly agreed to pay $12.9 billion for Hugging Face, a company doing roughly $150 million a year. Host Emily Laird takes apart the math and explains why the price only makes sense if you stop thinking about subscriptions and start thinking about who controls the moment a developer picks a model. The real asset is habit: millions of small decisions about where to find, tune, and run open models, plus the compute bill that follows. Also on the table: what happens to Hugging Face's neutrality when the largest chip vendor on the planet owns the front door. 🎯 JOIN THE AI WEEKLY MEETUPS https://www.uwstout.edu/ai-weekly-meetup 📩 EMAIL REMINDERS FOR THE MEETUPS https://app.e2ma.net/app2/audience/signup/2101263/1779703/ 💬 CONNECT WITH EMILY LAIRD ON LINKEDIN http://www.linkedin.com/in/meet-emily-laird

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