What happens when AI goes rogue in the courtroom? Tune in as we explore a federal lawsuit involving MyPillow CEO Mike Lindell, where generative AI tools like ChatGPT and Google's Gemini produced fictitious legal citations, leading to attorney fines and a national stir. Discover how this alarming incident highlights a systemic issue with AI reliance in the legal realm and the broader implications for our justice system. You'll grasp the essential need for rigorous AI verification and oversight in high-stakes professions. Don't forget to subscribe, share, and leave a review to support the podcast! 

FULL TRANSCRIPT: I'm Harry Kermode, and this is AI Daily.

Imagine for a moment stepping into court—a high-stakes defamation lawsuit against a prominent public figure. Attorneys, impeccably dressed, files neatly organized, and arguments meticulously charted out. Except today, the attorneys have unknowingly brought fiction into the courtroom—not lies told by witnesses, but fabrications from an AI chatbot.

This week, that's precisely what happened in a federal courtroom, drawing national attention. Lawyers representing MyPillow CEO Mike Lindell relied on generative AI tools—common services like ChatGPT and Google's Gemini—to draft their legal documents. But rather than assisting them efficiently and accurately, the AI produced what's known as a "hallucination," inventing nearly thirty completely fictitious case citations and misquoting other legal precedents. When the deception was unveiled, the stunned attorneys were fined $3,000 each.

AI hallucinations aren't new, of course. In 2023, a now-infamous case of "phantom citations" shook the legal community in Mata v. Avianca Airlines. Lawyers there were sanctioned for using AI-generated cases—yet, two years later, these issues persist. The Lindell fiasco is more than an isolated mess-up; it represents a dangerous trend that's unfolding quietly but insistently across courtrooms worldwide, with more than 200 documented incidents of AI-induced legal errors since just this spring.

Why, despite warnings, do professional attorneys persist in gambling their reputations, their client's fates, and their financial well-being on these algorithms? The answer, in short, is efficiency—but efficiency at what price?

For law firms operating under pressure, generative AI promises faster turn-around on tedious research tasks, saving precious hours and billable labor. The American Bar Association promotes these AI solutions boldly, framing it as augmenting productivity. Yet the Lindell case reveals the troubling reality beneath that optimistic narrative: that AI in legal settings is treated far too casually, often delegated to junior staff without verification or adequate oversight—or worse, simply trusted blindly.

It's not just the attorneys who failed here; it's systemic. Regulatory oversight from the ABA mandates attorneys verify any AI-generated materials. Still, enforcement varies dramatically. The courts themselves remain patchy in holding practitioners accountable, occasionally sanctioning attorneys but generally overlooking rampant casual use of these powerful—and deeply flawed—generative technologies.

And here's what's most troubling: the people bearing the brunt of AI's failures may ultimately not be wealthy clients or high-stakes defendants, but rather everyday individuals—pro se litigants or smaller law offices with limited resources to audit software outputs effectively. They are the ones at greatest risk of being misled by these tools, widening inequities in our justice system even further.

So, what can we do? Some experts argue the answer will lie in stricter AI verification tools embedded into legal software. Others suggest the legal profession itself must evolve: train attorneys rigorously not just in law but in technology—embedding AI literacy right into licensing exams and professional standards. But one thing is clear: trust, once lost, is hard to recover. Every incident chips away at confidence—in the courtroom, judges are now skeptical of AI claims, and opposing attorneys will eagerly scrutinize digital fingerprints of AI reliance.

In an ironic twist, Mike Lindell, known widely for spreading conspiracy theories about election fraud, himself became the victim, inadvertently, of a very different sort of misinformation crisis—one created directly by unchecked artificial intelligence.

Ultimately, the Lindell case signals a broader trend: as AI permeates high-stakes professions, ethical guardrails and accuracy checks must become non-negotiable. The law, built upon careful reasoning and factual precision, cannot sustain itself on shortcuts. If our courts, built to seek truths, cannot even trust their own filings, then what hope do we have as society grapples with the promises—and perils—of AI technology?

I'm Harry Kermode, and this has been AI Daily.

AI Hallucinations in Court: The Lindell Case Exposes Dangerous Missteps in Legal Practice

Can artificial intelligence redefine medicine and fast-track new treatments? In this groundbreaking episode of AI Daily, host Harry Kermode explores Isomorphic Labs' announcement of human trials for AI-designed drugs. Backed by DeepMind's AlphaFold breakthroughs, this marks a pivotal moment for AI's role in healthcare and drug discovery. Expect insights into the challenges and promises of such innovations, as well as ethical and workforce implications. Tune in to learn if AI truly signifies a new era for medicine. Don't forget to subscribe, share, and leave a review! 

FULL TRANSCRIPT: It's Monday, July 14th, 2025. I'm Harry Kermode, and this is AI Daily.

For decades, we've imagined artificial intelligence reshaping medicine, promising faster cures, cheaper treatments, and untold leaps in human health. Today, we're stepping closer to that promise becoming reality.

Yesterday, Isomorphic Labs, a spinoff of Google's famous AI research division DeepMind, announced that human trials for drugs exclusively designed by artificial intelligence will soon begin. This news marks an unprecedented milestone: for the first time in history, we will witness human clinical testing of medicines whose molecular structures have been discovered purely by AI algorithms.

To grasp the significance of this moment, let's step back briefly to look at its foundations. Remember, just two years ago DeepMind's AlphaFold won the Nobel Prize in Chemistry for solving one of biology's oldest and toughest challenges—accurately predicting protein structures. That discovery fundamentally transformed how scientists approach disease and treatment. Proteins and their shapes determine nearly every biological process, and knowing those structures enables scientists to precisely design molecules to target diseases.

At the intersection of biology and AI, Isomorphic Labs was born—named for the symmetry between information science and biological systems. The company set out to prove that AI could shorten the slow, costly traditional process of drug discovery, a system notorious for taking up to 15 years and billions of dollars per drug.

Fast forward to July 2025, and the world is about to see if AI can truly deliver. With over $600 million in funding and high-stakes partnerships totaling billions—including pharmaceutical giants like Novartis and Eli Lilly—Isomorphic Labs carries immense expectations on its shoulders. Investors are banking not only on revolutionary medicine, but also on transforming their financial returns. For Alphabet, Google's parent company, success means validation for their massive strategic bet on biotechnology.

Behind the press releases and optimism, there's an important conversation about risk and realism. AlphaFold's capabilities, while groundbreaking, do not yet model the full complexities and uncertainties of human biology. It's one thing to design a molecule in a digital lab; it's another entirely to ensure its safety and effectiveness in real patients. Already, respected voices in the medical community caution against seeing AI as a cure-all shortcut. Margaret Foti, CEO of the American Association for Cancer Research, reminds us that "AI accelerates hypotheses, but it will never replace clinical rigor."

There are ethical questions too: who controls this powerful technology? As breakthrough treatments created by algorithms and big data might one day flood the pharmaceutical market, will the benefits reach the broader global community, or remain out of reach, patented and prohibitively expensive?

Critics also highlight an understated human consequence—labor displacement. If AI designs drugs faster than pharmaceutical chemists, what becomes of their careers? Will the future workforce adapt, or will this technological disruption leave skilled professionals behind?

These trials, set to start within months, are indeed historic. Success might trigger regulatory shifts, new investment waves, and massive transformations in science and healthcare. Failure could lead to immense scrutiny, skepticism, and cautionary tales.

Behind every technological revolution lies a tension between its potential and its unintended consequences. Today, we find ourselves at the dawn of such a moment in medical history—hope tempered by questions, excitement balanced by caution.

AI has promised a revolution in medicine. Now, we hold our breath to see if it can deliver.

This has been AI Daily. We'll see you tomorrow.

The Unstoppable Rise of AI in Medicine: Promise or Peril?

This is episode 298 recorded on July 9th, 2025, where John & Jason talk the Microsoft Fabric June 2025 Feature Summary including lots of Notebook updates in Data Engineering, lower cost for AI functions in Data Science, Copilot for RTI dashboards, and more.

For show notes please visit www.bifocal.show

Episode 298 - Microsoft Fabric June 2025 Feature Summary

DAX in my veins (Vocal mix)

In this episode of the ODSC Podcast, Alex Landa, the head of content at ODSC, interviews Nick Walton, CEO and co-founder of Latitude and creator of the groundbreaking AI-powered storytelling game, AI Dungeon. Nick shares his journey from machine learning for self-driving cars to reimagining interactive fiction through generative models. They dive into the challenges and magic of building persistent, emergent game worlds with AI, and discuss how AI enables new modes of player agency and storytelling that go far beyond traditional game mechanics.
🔥 Key Topics Covered:
-The origin story of AI Dungeon and how GPT-2 sparked its creation
-How AI Dungeon enables open-ended, player-driven storytelling
-Technical and creative challenges in AI gaming: model curation, memory, and narrative tension
-What makes AI-generated characters compelling and emotionally resonant
-The upcoming game engine Latitude is building to power rich, persistent AI-driven worlds
-How AI can unlock new creative possibilities for indie and non-technical game creators
-Multiplayer and long-term visions for dynamic, socially evolving AI worlds
-Ethical considerations in using AI for gaming and storytelling
Memorable Outtakes:
💬 Nick Walton: “If every dwarf you meet is a gruff miner whose clan got eaten by goblins, it gets a little old after a while... you need higher variation.”
💬 Nick Walton: “You’re not just playing a game. You're building a story, and when loss happens, it becomes meaningful. That's the magic of AI Dungeon.”
💬 Nick Walton: “We’re building not just a game, but a living world—one where your choices shape everything, and the AI evolves with you.”
💬  Alex (Host): “It’s not about replacing anyone—it’s about creating something entirely new that couldn't exist without AI.”
🖥 References & Resources:
- AI Dungeon: https://aidungeon.com
-Latitude (Nick’s company): https://latitude.io
-Whispers from the Star (AI game mentioned): https://store.steampowered.com/app/3730100/Whispers_from_the_Star/
-Nick Walton’s X (Twitter) profile: https://twitter.com/nickwalton00
-Nick Walton’s LinkedIn: https://www.linkedin.com/in/waltonnick/
-Nick Walton's profile on Crunchbase: https://www.crunchbase.com/person/nick-walton
-AI Dungeon Dev Blog: https://latitude.io/blog


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Creating Unique Narrative Experiences with Generative AI in Gaming with Nick Walton

In this episode of the ODSC Podcast, host Sheamus McGovern is joined by Veronika Durgin, Vice President of Data at Saks. Veronika brings over two decades of experience in data engineering, platform architecture, data modeling, and analytics. She joins us to expand on her ODSC presentation, The 10 Most Neglected Data Engineering Tasks, offering practical insights into often-overlooked practices that can make or break modern data systems. From defining what “done” really means to preparing for seasonality and building self-recovering pipelines, Veronika shares hard-won lessons, entertaining stories, and a deep well of practical advice.

💻 Key Topics Covered:
- Veronika’s unconventional career journey from biology to data leadership
- The “bridge strategy” in build vs. buy decisions and how it improves agility
- Why “definition of done” must include more than just working code
- The value of empathy: walking a mile in the business team’s shoes
- The dangers of ignoring business seasonality in data pipelines
- What makes a truly self-healing data pipeline
- Testing with production data vs. data in production
- Handling date/time data with precision and naming conventions
- The forgotten bucket of work: tech debt, improvements, and innovation
- The environmental impact of data systems and responsible engineering
- The rise of “vibe coding” and the challenges it poses for data engineering

💬 Memorable Outtakes:
- “If you don't know why you're working on this and who needs it, you shouldn't be working on it.” 
- “Everything we build today is tomorrow's legacy. Build things to be replaceable.”  
- “We automate, not because we’re lazy—but because we care about sleeping at night.” 

🖥 References & Resources:
- Veronika Durgin on LinkedIn: https://www.linkedin.com/in/vdurgin/
- Veronika Durgin on Medium: https://veronikadurgin.medium.com
- The Phoenix Project (pdf of book): https://shorturl.at/Ce5gA
- “Definition of Done”: https://medium.com/@durginv/definition-of-done-eea52c472cc3
- Tsedal Neeley Digital Mindset (book referenced): https://www.tsedal.com/book/the-digital-mindset/
- Stargate AI data center project (referenced during energy discussion): https://openai.com/index/announcing-the-stargate-project/

Sponsored by:
🔥 ODSC West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.com/california

The Most Neglected Tasks in Data Engineering with Veronika Durgin

We're joined by Brett Ferrin (https://www.linkedin.com/in/brett-ferrin-8504b8346/) VP of Sales at Traffic Logix. Together we have a conversation about how technology and data can be used to proactively improve traffic safety. If you're into data, urban planning, or transportation, this is an episode for you.

Resources and Shoutouts:
-   Traffic Logix: https://trafficlogix.com/
-   Vision Zero: https://www.cdc.gov/transportation-safety/vision-zero/index.html

Music Credit: Tumbleweeds by Monkey Warhol

98 Proactive Traffic Safety

This is episode 300 recorded on July 31st, 2025, where John & Jason talk the Microsoft Fabric July 2025 Feature Summary including tags in Fabric Domains, Fabric Data Agent integration with Microsoft Copilot Studio, enhancements to Activator, manual control of Auto-Refresh in pipelines, and CosmosDB in preview now in Fabric.

For show notes please visit www.bifocal.show

Episode 300 - Microsoft Fabric July 2025 Feature Summary

In episode 41 of Generationship, Brighthive CEO Suzanne El-Moursi joins Rachel Chalmers to unpack the “three-layer cake” of modern data architecture: composable stacks, agentic AI, and governance-first design. From integrating hundreds of data sources to enforcing real-time compliance, Suzanne shares how her team is tackling the grunt work of data so humans can focus on innovation. Plus, her vision for turning every organization—big or small—into a data powerhouse.

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Generationship - Ep. #41, Covenant with Suzanne EL-Moursi

In this special episode of the ODSC AI+ Podcast, host Sheamus McGovern dives into the real-world impact of GPT-5—from routing and hallucination issues to cost savings and open-weight models.

Joining him are two expert guests:
- Ivan Lee: Founder and CEO of Datasaur, who helps enterprises build private LLM stacks and has deep experience evaluating model upgrades.
-Nir Gazit: Co-founder and CEO of Traceloop, and co-creator of the OpenTelemetry Generative AI SIG, who brings insight into model routing, evaluation strategies, and observability tooling.
Together, they unpack what GPT-5 actually changed—and what teams should do next.

Key Topics Covered:
-Why GPT-5’s biggest shift is routing, not reasoning
-What casual vs. power users gained (or lost) with the rollout
-Hallucination benchmarks vs. real-world results
-Evaluation strategies using open-source tools like Phoenix and LangChain
-OpenAI’s OSS model release and its enterprise implications
-Why developers worry about black-box routing and the lack of traceability
-How to migrate safely: pinning snapshots, running evals, shadow testing
-Whether GPT-5 gets us closer to AGI, or just better infrastructure
-What to expect from agent workflows, tool selection, and model specialization

Memorable Outtakes:
- Ivan Lee: “GPT-5 is an upgrade for 98% of users—but for the power users, the loss of model choice felt like control was taken away.”
- Nir Gazit: “Of course, every new model crushes it on benchmarks—they’re optimizing for the benchmarks. That doesn’t mean it works for your use case.”
- Ivan Lee: “OpenAI’s OSS release might be the bigger story than GPT-5. Suddenly, enterprises are back at the table.”

References & Resources:
Guests
- Ivan Lee – CEO of Datasaur
Website: https://www.datasaur.ai
LinkedIn: https://www.linkedin.com/in/iylee/
- Nir Gazit – CEO of Traceloop
Website: https://www.traceloop.com
Blog: https://www.traceloop.com/blog
LinkedIn: https://www.linkedin.com/in/nirga/

Resources Mentioned
- OpenAI GPT-5  https://openai.com/gpt-5
- OpenTelemetry Project: https://opentelemetry.io
- Traceloop OpenLLMetry: https://www.traceloop.com/openllmetry
- Phoenix (Arize AI open-source evals): https://github.com/Arize-ai/phoenix
- LangChain Evals: https://python.langchain.com/api_reference/langchain/evaluation.html
- GPT-OSS Open Weight Models by OpenAI: https://platform.openai.com/docs/models/gpt-oss
- Claude + Model Context Protocol (Anthropic): https://docs.anthropic.com/en/docs/tool-use
- ARC-AGI Leaderboard: https://arcprize.org/leaderboard

Sponsored by:
🔥 ODSC AI West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.ai 


GPT-5 Unboxed: What Changed, What Broke, and What’s Next with Ivan Lee and Nir Gazit

Heavybit is the leading early-stage investor in enterprise infrastructure. Since 2013, we've helped launch and scale visionary technical startups—from DevSecOps and feature flagging to AI code generation and beyond. We go hands-on from day 0, backing founders as they turn code into companies and build the future of software from the bottom up to the top down.

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community.

Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/  
Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

Civic Tech Chat is a podcast about the civic technology movement.  We seek to harness the power technology has to  improve the delivery of public services to people everywhere.

Having started DJing at 15, music has always been a passion. Now well into my 40s I spend any spare time I can get creating music solely on the iPad. Please leave feedback and don't forget to click like and repost if you do…

New tracks tagged #Data Science