Could AI be eroding the unique textures of your cultural identity? In this episode of AI Daily, we delve into a thought-provoking study from Cornell University that suggests AI writing tools may be homogenizing human expression, nudging us toward Western norms. Explore how these tools impact cultural diversity, from Indian to American writing prompts, questioning the potential flattening of humanity into a monoculture. As AI reshapes communication, listeners will gain insights into the subtle risks of losing cultural richness to convenience. Subscribe for more explorations into the implications of AI on our world. 

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

Think about something unique to your culture, perhaps a beloved local holiday, a meal made from childhood recipes, or even the particular rhythms of stories your elders told around a dinner table. Now consider—what happens if artificial intelligence subtly nudges us towards uniformity, smoothing over these textured features of cultural identity, leaving us with flattened, standardized versions?

Yesterday, The Overspill highlighted a fascinating—and troubling—new report from The New Yorker, anchored in research from Cornell University. This groundbreaking study finds that AI writing tools—like ChatGPT—are quietly homogenizing human expression, steering users from diverse backgrounds toward culturally Western norms. In fact, the findings suggest these AI writing assistants could slowly erase the idiosyncratic beauty that sets our world’s cultures apart.

Cornell researchers looked at writing tasks completed by Indian and American participants, all using GPT-4 writing assistants. When asked open-ended prompts about everything from holidays to names to food preferences, the tool overwhelmingly favored responses aligned with Western traditions—pushing pizza over biryani, Christmas over Diwali, "Shaquille O'Neal" over Shah Rukh Khan. For Indian users, the AI’s Western-centric suggestions actually cost them more energy correcting these culturally skewed outputs, resulting in 30% more editing effort than their American counterparts.

This phenomenon isn't merely about algorithmic bias or a creators' quirk. It's driven by underlying incentives and structural realities: creating AI models that privilege streamlined cultural standards over diverse cultural inputs cuts costs and speeds global scalability. Yet beneath this business logic lies a potent political force—a quiet digital translator exporting the power and norms of Silicon Valley.

Sam Altman, founder of OpenAI—maker of ChatGPT—famously coined the optimistic term "gentle singularity," envisioning AI seamlessly amplifying human creativity. But what if "amplification" actually means flattening humanity into a global monoculture? Journalist Kyle Chayka in The New Yorker captures this poignantly, calling AI "a technology of averages, quietly nudging us toward average tastes, experiences and identities."

Critics point out this echoes uncomfortable historical parallels—colonial-era educational models once systematically suppressed local languages and customs. And today, AI acts as a similarly soft yet potent cultural colonizer, incentivizing uniform global expressions at the expense of rich, localized diversity.

But why does this matter? Why worry that future generations see pizza or Christmas as "typical," while biryanis and Diwali become vaguely "ethnic"? Because narratives shape societies. When AI nudges billions of users toward a single cultural lens, diversity doesn't just fade—our connection to individual histories, heritage, and identity erodes as well.

Looking ahead, education and creative industries are rapidly embracing these AI-driven tools. In just a few short years, cultural homogenization can accelerate beyond recognition, turning unique regional narratives into premium, niche commodities, deepening existing digital divides.

But perhaps most haunting is the personal dimension—voices silenced in subtle, daily ways. In Cornell’s study, for instance, one participant remarked, "I stopped describing my mother’s biryani because the AI called it 'generic ethnic food.'"

AI promises a world of efficiency, creativity, and collaboration, but doesn't warning about its darker potential mean engaging with these very human questions—questions of who we are, what we value, and what price we’re willing to pay for efficiency’s sake?

As artificial intelligence continues reshaping how we think, speak, and remember, we need to keep asking: Are we unwittingly trading away our irreplaceable global tapestry for the glossy convenience of algorithmic averages—and if so, which parts of our human story are we quietly sacrificing?

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

AI Monoculture: How Automation Flattens Global Diversity

How is artificial intelligence being used in cancer care, and what do patients think about it?

In this 'Ask Me Anything' episode, Ellie Wolmark, Programme Manager for Integrated Cancer Medicine (ICM) at the CRUK Cambridge Centre, speaks with patient representatives Lorraine, Frankie, and Ann from across our Patient and Public Involvement groups, alongside ICM researchers Professor James Brenton, Dr Ines Machado, and Dr Gabriel Funingana from the University of Cambridge.

Together, they explore how artificial intelligence (AI) is being applied in cancer research, how it could support personalised treatment, and what the future might hold. The patients share thoughtful questions and concerns about the role of AI in healthcare, while the researchers explain how it's currently being used and what challenges remain.

This lively and insightful discussion highlights just how valuable patient involvement is in shaping research that truly meets the needs of those it aims to help.

Ask me anything about cancer

Ask me anything about... the use of artificial intelligence in healthcare

When was the last time you lost yourself in a thrilling novel or a moving memoir? This episode delves into the growing conflict between authors and artificial intelligence, as over seventy prominent authors unite to keep AI away from the art of human storytelling. We explore the deepening fears about AI’s role in publishing, from writing original content to narrating audiobooks, and the legal and economic implications for authors. Discover what this uprising reveals about the future of literature and the fundamental human spirit behind storytelling. Don't forget to subscribe, share, or leave a review! 

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

When was the last time you lost yourself in a thrilling novel or a moving memoir? Chances are, the heart and life flowing through those pages came directly from the brilliant mind of an author. Today, that very brilliance is at the center of a growing conflict—between human creators and the unstoppable forces of artificial intelligence.

Over this past weekend, more than seventy prominent authors—including literary heavyweights Dennis Lehane, Lauren Groff, and Gregory Maguire—published an open letter that instantly resonated across the publishing industry. Within 24 hours, support snowballed to over a thousand writers, artists, and publishing professionals, all rallying behind a single message to the world’s biggest publishers: Keep AI away from the art of human storytelling.

At the heart of this literary uprising are deepening fears about publishers incorporating artificial intelligence into critical creative roles—writing original content, designing book covers, and perhaps most controversial of all, narrating audiobooks. This isn't just theoretical anxiety. A recent federal ruling declared AI training on copyrighted books as legal "fair use," giving tech companies the green light to feed their algorithms on millions of authors' words.

But beneath the surface, there's more than legal semantics. Publishers see financial incentives. Using AI can dramatically cut expenses, replacing entire job categories—from editors to audiobook narrators—with streamlined, algorithm-driven outputs. Meanwhile, tech giants like Amazon and Audible are aggressively promoting synthetic narrations, promising scalable audio production at a fraction of human labor costs.

For many authors, the stakes are existential. Audiobook sales represent nearly a third of some writers' yearly incomes—livelihoods seriously threatened when synthetic AI voices start narrating their stories. Voice actors and translators, essential artists in their own right, risk losing even more ground.

Yet publishers and tech firms confidently frame the shift as progress. Audible has argued that AI narration "democratizes storytelling," broadening access to literature. Publishers speak glowingly about artificial intelligence as an enabler of creativity rather than its replacement.

But in reality, a troubling shadow economy is emerging. Independent authors report rampant plagiarism by poorly regulated platforms filled with AI-generated books masquerading as human-authored originals. Critics liken these works to literary "deepfakes"—algorithmically-assembled text cobbled together without meaning, craft, or authenticity.

And then there's the profound human cost. Artists fear AI-produced content doesn't just take livelihood—it "strips authors of dignity," as the open letter passionately frames it. A powerful statement reads: "AI-generated books feel cheap because they are cheap. The replacement of art and artists isn't societal benefit—it’s cultural loss."

Today's author rebellion reveals profound questions that strike at the heart of what makes us human. Stories have always been our way of understanding ourselves, of reaching across cultures and generations. But as technology threatens to automate even our most cherished forms of human expression, does this disrupt storytelling itself? Or more critically, could it quietly erase the storyteller?

As creative professionals take an unprecedented stand, publishers now face a turning point. Will they defend the irreplaceable human spirit behind every story, or choose efficiency above authenticity? For now, the future of literature—and the vivid human worlds it creates—hangs by a thread, caught between human passion and machine efficiency.

I'm Harry Kermode. That's it for AI Daily, we'll see you tomorrow.

AI vs. Authors: The Battle for the Soul of Storytelling

Welcome to *AI with Shaily*! 🎙️ I'm Shailendra Kumar, your guide to the fascinating world of artificial intelligence, sharing insights drawn from years of hands-on experience in the field. Today, we're exploring a crucial but often overlooked aspect of machine learning: the best techniques for splitting validation sets during model training. Why is this important? Because how you divide your data can make or break your model’s real-world performance. 📊🤖

Let me start with a personal story. Early in my career, I built a classifier that looked amazing on paper but failed miserably in production. The problem? A careless data split that ignored the class distribution. This taught me a vital lesson: the way you split your dataset is just as important as the model itself! 🧠💡

So, what are the top approaches you should know about?

1. **Random Sampling** 🎲  
   This is the simplest method: shuffle your data and split it roughly into 70% training, 20% validation, and 10% testing. It works well if your dataset is balanced—like having equal numbers of cats and dogs. But beware! If your classes are imbalanced, random splits can mislead your model into thinking it’s smarter than it really is.

2. **Stratified Splitting** ⚖️  
   This technique is a lifesaver for imbalanced datasets, such as detecting rare diseases or fraud. Stratified sampling ensures that the class proportions in your splits mirror the real-world distribution, preventing inflated accuracy scores that ignore rare but critical cases.

3. **Cross-Validation (especially Stratified k-Fold)** 🔄  
   When you have limited data and want to maximize reliability, cross-validation is the gold standard. By rotating your validation fold across k different sections of the data, your model is trained and tested multiple times. This gives you a robust estimate of performance and helps avoid overfitting traps.

Here’s a bonus tip for you savvy listeners: after tuning your hyperparameters using validation sets, retrain your final model on the combined training and validation data before testing. This way, you leverage all labeled data for the best shot at real-world success—just don’t peek at your test set until the very end! 🎯📈

Wondering how these strategies shape the future of ML? As AI spreads into every sector, these splitting techniques are not just academic—they’re essential for building fair, trustworthy, and high-performing models. The buzz on social media—from Twitter to LinkedIn—shows experts debating best practices to avoid data leakage and bias. With new tools making stratified sampling easier, the field is evolving rapidly. 🚀🌍

To wrap up, here’s a gem from Geoffrey Hinton:  
*"The best way to get good at machine learning is to treat data splitting as carefully as model building."* 🧩✨

For more cutting-edge AI news and tips, follow me, Shailendra Kumar, on YouTube, Twitter, LinkedIn, and Medium. Don’t forget to subscribe and join the conversation by sharing your experiences and questions in the comments. 🙌💬

Thanks for tuning in to *AI with Shaily*. Until next time, keep experimenting and happy modeling! 🤖💻🎉

Mastering Data Splits: The Secret Sauce for Reliable Machine Learning Models

The United States Senate has delivered a decisive verdict on AI governance, rejecting a proposal for a ten-year ban on state-level AI regulations by a 99 to 1 vote. This amendment was fiercely supported by Big Tech giants like OpenAI and Google, who feared a regulatory "patchwork," but bipartisan state attorneys general and civil rights groups resisted, citing concerns about unchecked biases and threats to democratic oversight. Listeners will gain insights into the balance of power between tech corporations and grassroots coalitions in shaping AI policy. Don't forget to subscribe, share, or leave a review! 

FULL TRANSCRIPT: It's Wednesday, July 2nd, 2025.

In a late-night session yesterday, as Americans slept, the United States Senate delivered a thunderous verdict on AI governance. By a resounding vote of 99 to 1, lawmakers crushed a proposal that would have imposed a sweeping ten-year ban on state-level AI regulations, a measure backed aggressively by Big Tech.

The amendment had been spearheaded by Senator Marsha Blackburn of Tennessee, originally receiving strong support from industry giants—including OpenAI and Google—who spent millions lobbying for regulatory uniformity. Their argument? That a "patchwork" of state-specific laws would cripple innovation and hand competitive advantages directly to China.

But beneath that narrative of streamlined innovation lay a very different reality—and it was one the Senate simply wasn't buying. Forty-four bipartisan state attorneys general mounted an intense and unified opposition, underscored by influential voices like California's Rob Bonta, who described the measure as a "dangerous giveaway to Big Tech." Civil rights groups warned starkly of unchecked algorithmic bias, deepfake threats, and rampant discrimination if the states' emerging protections were overturned.

So how did we get here? Over the past two years, state's legislatures took advantage of Congress's gridlock and surged ahead. California required unprecedented transparency on training data for major AI models; New York demanded rigorous safety protocols; Colorado set standards against bias in automated hiring tools. The waves of state initiatives prompted tech lobbyists—who feared soaring compliance costs—to lean heavily on the federal government for a lifeline.

The tech industry almost got its wish, until public backlash erupted against the amendment. Senator Thom Tillis—representing North Carolina, home to significant tech investments—emerged as the single voice voting "yes," calling the ban vital for innovation. But his voice stood solitary against an extraordinary bipartisan consensus rarely seen in this era of polarization.

This decisive rebuke highlighted not just uncommon unity, but also the emergence of two powerful camps: on one side, immense corporate wealth and the promise of AI innovation without shackles; on the other, grassroots coalitions, state lawmakers, and civil liberties organizations fighting fiercely for local oversight and accountability.

But even this definitive vote leaves questions floating. While states retain their freedom to govern AI with tailored approaches, industry insiders warn this could lead to regulatory chaos. Will companies face fifty different AI laws? Will longer timelines and massive compliance burdens snuff out American competitiveness globally?

Activists like the Demand Progress coalition strongly disagree. Their statement highlights that the alternative—giving industry a blank check to police themselves—risks far greater harm.

And now, the battle shifts to states' legislatures and courtrooms across America. Companies like OpenAI could pursue legal challenges against restrictive regulations. California and New York, emboldened by this victory, will undoubtedly draft wider, more aggressive measures.

What happens next may determine who truly holds the power in America's AI future: Big Tech, states, or citizens demanding accountability. One thing, though, is certain—yesterday's Senate vote was bigger than policy—it was a warning shot, clarifying that democracy, despite fierce lobbying, is not yet for sale.

For "AI Daily," I'm Harry Kermode. See you tomorrow.

Rejecting Big Tech: Senate Crushes AI Deregulation Proposal

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, a seasoned expert in artificial intelligence, this podcast dives deep into the latest AI breakthroughs and their real-world impacts. Today’s episode focuses on a fascinating topic: vault apps on smartphones. 📱🔒

Shailendra explains how our smartphones are like treasure chests filled with personal data—photos, messages, bank info—and how vault apps create secret compartments inside these devices. These apps encrypt and hide files, messages, and even other apps, often for privacy reasons but sometimes for more concerning purposes like espionage or unauthorized surveillance. 🕵️‍♂️🔐

Detecting these vault apps is notoriously difficult because they disguise themselves as ordinary apps, making traditional detection methods ineffective. Law enforcement agencies have struggled to identify them using existing databases since vault apps act like digital chameleons. 🦎❌

Here’s the exciting part: researchers from Edith Cowan University and the University of Southern Queensland have developed a machine learning approach that doesn’t rely on prior knowledge of vault apps. Instead, it analyzes behavioral and system patterns to detect these hidden apps with up to 98% accuracy! 🤖🐕‍🦺 This breakthrough acts like a digital bloodhound, sniffing out vault apps no matter how cleverly they’re concealed.

This innovation is a game-changer for digital forensics, especially considering there are over 5 billion smartphone users worldwide. It means law enforcement can uncover hidden illegal activities quickly and less invasively, enhancing digital security without guesswork or broad searches. 🌍🚓

Shailendra reflects on how this breakthrough reminds him of the early 2000s when AI first decoded human handwriting—a moment showing AI’s power to reveal the invisible. ✍️🤯

For privacy-conscious listeners, Shailendra offers a bonus tip: always monitor app permissions and watch for unusual battery or data usage as a first defense against suspicious apps before AI tools are even needed. 🔍📊

He closes with a thought-provoking question about balancing privacy rights with security needs as AI becomes more adept at uncovering hidden data. He also shares a quote from Tim Berners-Lee: “The Web does not just connect machines, it connects people,” emphasizing AI’s role in connecting and protecting us while also challenging our values. 🌐❤️⚖️

Shailendra invites listeners to follow him on YouTube, Twitter, LinkedIn, and Medium for more AI insights and encourages sharing thoughts in the comments. He signs off reminding everyone to stay curious and safe in the evolving AI era. 🚀✨

In summary, this episode highlights the cleverness of vault apps, the challenges in detecting them, and a cutting-edge AI solution that promises to revolutionize digital security and forensics—all shared by Shailendra Kumar, your trusted AI guide! 🎧🤖🔐

AI Detectives: Unmasking Hidden Vault Apps with Machine Learning

What makes us human? In today's episode of AI Daily, we delve into the unveiling of "Centaur," the pioneering AI that simulates human cognitive processes across various psychological scenarios. We explore its cutting-edge capabilities, including its ability to mimic human decision-making and reaction times, as well as its potential impact on both AI development and psychology. Listeners will gain insights into the ethical dilemmas and transformative possibilities posed by AI that can think like us. Don't miss this profound discussion—subscribe now and leave us a review! 

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

What makes us human? For centuries, we've asked this question, defining ourselves by our cognitive uniqueness—our ability to reason, decide, to think human thoughts. But imagine for a moment an AI capable of mirroring human decision-making with startling precision, one not only mimicking behavior, but emulating the very threads that weave our minds. What then remains of our cherished human uniqueness?

Yesterday, researchers at Helmholtz Munich unveiled "Centaur," the first AI model capable of simulating human cognitive processes across diverse psychological scenarios. Published in Nature, this groundbreaking research described how Centaur, trained using the massive "Psych-101" dataset—ten million human psychological decisions carefully aggregated from decades of experiments—managed not only to predict human choices in familiar settings, but crucially in completely novel situations too.

Centaur is profound because it's the first AI that seems genuinely capable of emulating human thought across tasks like gambling behavior, memory challenges, ethical dilemmas—even problem-solving puzzles. But it's the depth of its mimicry that's especially jolting. The model doesn't just replicate final decisions; it aligns uncannily well with human reaction times, hesitations, and even neural activation patterns. As lead researcher Marcel Binz described it, Centaur represents a "virtual laboratory," opening unprecedented windows into our cognitive processes without ever needing to peer inside a human brain.

Yet behind this remarkable breakthrough lies a more complex narrative. For Marcel Binz and his colleagues, Centaur intentionally positions publicly funded European science against corporate-driven AI dominance. Using Meta's Llama 3.1—a robust open-source language model—Helmholtz Munich showcases the strategic shift towards sustainable and ethical AI development in the EU. But skeptics say it's too soon to celebrate.

Critics, like Jeffrey Bowers from the University of Bristol, warn that Centaur isn't genuinely replicating human cognition but merely sophisticated statistical fitting: a powerful illusion of intelligence without true understanding. And beyond scholarly critique, larger ethical and labor questions loom: could a model like Centaur eventually replace living humans as primary participants in psychological research? Might we soon trust AI more than therapists or diagnosticians to predict behaviors, make diagnoses, or even prescribe interventions?

Psychology itself faces a turning point. Historically reliant on human volunteers and painstaking experiments, the field now confronts a future where vast datasets and algorithms might supplant traditional methods. The Psych-101 dataset's sheer scale, synthesizing sixty years of studies, raises another concern—its data, exclusively drawn from Western, educated populations, might inadvertently universalize a narrow worldview, exporting bias worldwide in the invisible fabric of AI cognition.

Centaur also challenges us philosophically. Eric Schulz, director at Helmholtz's Institute, proudly says we’re finally "doing machine psychology"—but do we risk losing sight of the fundamentally mysterious and irreducible qualities of human experience?

These tensions—ethical dilemmas versus transformative scientific potential, wondrous capabilities versus concealed limitations—are the heart of today's AI breakthrough. Centaur reveals as much about us as it does about technology: our hopes, our fears, and the uncertain path that lies ahead.

Because when our creations begin thinking like us, acting and hesitating and deciding as we do, it's not just our machines but ourselves that we must reconsider.

I'm Harry Kermode—Join us tomorrow on AI Daily.

Centaur: The AI Breaking Down and Challenging Human Uniqueness

Welcome to AI with Shaily! 🎙️ Shailendra Kumar, also known as Shaily, takes you on an exciting journey through the latest breakthroughs in artificial intelligence, especially where it intersects with biology and medicine. Today’s episode dives deep into the fascinating world of RNA splicing and how machine learning is revolutionizing this crucial biological process. 🧬✨

RNA splicing is nature’s way of editing genetic information inside our cells—deciding which parts of RNA to keep and which to cut out, much like a molecular editor. This process is vital because it influences how genes express themselves and plays a key role in understanding diseases and developing targeted therapies. Getting RNA splicing predictions right is like having the blueprint for personalized medicine! 🧩🔬

Shaily highlights how researchers are now using advanced 1D convolutional neural networks (CNNs) to detect splice sites in RNA sequences. These models are specially designed to handle the noisy, complex data that comes from real-world RNA sequencing—imagine trying to catch whispered secrets in a noisy crowd! These CNNs have become better listeners, improving accuracy even across different species. 🎧🧠

Taking it a step further, Shaily introduces futuristic generative AI models like TrASPr+BOS, which use multi-transformer architectures. These models don’t just predict RNA splicing—they actually design RNA sequences tailored for specific tissues. It’s like giving scientists a colorful palette to paint precise RNA behaviors, opening doors to groundbreaking personalized treatments. 🎨🤖💉

One of the most inspiring parts of this AI evolution is the rise of interpretable machine learning. Unlike traditional “black box” models that just give answers, these interpretable tools explain *why* a certain RNA sequence is spliced in a particular way. They provide intuitive visualizations that act like a GPS, guiding researchers through the complex landscape of RNA splicing with clarity and confidence. 🗺️🔍💡

Shaily shares a personal reflection on his early AI journey, where he often struggled with the frustration of black box models. Seeing how interpretability is now making AI more transparent and trustworthy is a game changer—transforming AI from a mysterious magic trick into a reliable partner in science and medicine. 🤝✨

He also sparks curiosity by asking why imaging data hasn’t yet taken center stage in RNA splicing prediction. Could integrating cellular imaging with sequence data reveal even deeper insights? This open question invites the community to think creatively and explore new frontiers. 🧫📸❓

For those eager to dive into RNA splicing and AI, Shaily offers a valuable tip: prioritize models that provide interpretable outputs. These not only boost confidence in predictions but also unlock innovative possibilities in RNA design and therapeutic development. Transparency isn’t just nice to have—it’s essential in life sciences. 🔑🧬💡

Quoting the legendary Alan Turing, Shaily reminds us that while we can only see a short distance ahead, there’s plenty of work to be done. Machine learning is lighting the path forward in RNA splicing—one splice at a time. 🌟🚀

Don’t miss out on more insightful AI stories with Shailendra Kumar! Follow him on YouTube, Twitter, LinkedIn, and Medium to stay updated and join the conversation. Drop your thoughts and questions in the comments because AI is a journey we’re all on together. 🤗💬

Until next time, keep questioning, keep learning, and keep innovating. This is AI with Shaily, signing off! 👋🤖📚

AI with Shaily: How Machine Learning is Revolutionizing RNA Splicing Prediction and Design

Imagine AI as a beacon of progress, transforming societies and economies—but at what hidden cost? In this eye-opening episode, Harry Kermode delves into an escalating crisis: the dramatic rise in energy consumption driven by the AI revolution. Explore how post-2022 advancements, particularly in GPU-intensive computing, have shifted energy dependencies and sparked a fossil fuel revival. Discover the geopolitical and economic repercussions as data centers may consume up to 9% of US electricity by 2030. Gain insights into the emerging energy burden and its implications for a sustainable future. Subscribe to AI Daily for more critical discussions and share your thoughts in a review! 

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

For years, our focus on artificial intelligence has centered around its dazzling potential: economies transformed, problems solved, humanity elevated. But today, we look at a quiet crisis emerging from the AI revolution—not one of unchecked intelligence, but of unchecked energy consumption.

Over the past few hours, a story surfaced revealing that the United States is witnessing the largest spike in energy demand in 30 years. And this surge isn't caused by cars, factories, or traditional industries—it's powered almost entirely by new AI infrastructure and data centers designed for intensive computation.

Just ten years ago, the growth of data centers was largely tempered by efficiency gains. Cloud computing consolidated servers, renewable power expanded, and even as internet use skyrocketed, overall energy consumption remained stable. But everything changed after 2022. That's when OpenAI launched ChatGPT, kicking the industry into high gear and initiating an AI arms race that shifted workloads dramatically—away from energy-efficient CPUs toward power-devouring GPUs. Energy density skyrocketed, efficiency gains evaporated, and now, it seems, the bill is finally coming due.

Companies building AI infrastructure have quietly turned toward fossil fuels—especially natural gas—to supply uninterrupted, cheap, readily available energy. New gas plants are rapidly popping up, often bypassing traditional environmental regulations under emergency permits. The public grid, meanwhile, struggles under massive new burdens—and consumers across America will soon feel those burdens, as utilities pass the costs of infrastructure upgrades onto household electricity bills.

There’s saddening irony here. Prominent AI industry figures publicly champion the technology as a force for good—even a solution to climate change. Yet, behind the scenes, they're forging partnerships deeply tethered to fossil fuel interests. OpenAI itself recently inked a $30 billion deal with Oracle to build data centers dependent upon natural gas. Chevron's CEO recently claimed bluntly that "AI depends on Permian gas, not Silicon Valley code."

But this reliance on fossil fuels extends beyond costs and grids. It’s already reshaping geopolitics. America now diverts gas exports once headed to Europe—placing allies under pressure—all to feed energy-hungry data centers domestically. Meanwhile, China moves toward nuclear-powered AI campuses, aiming to sidestep the western dependency on limited fossil fuel supply.

Critics argue that AI's enormous power demands aren't negligible or temporary. Some experts warn data centers could account for nearly 9% of US electricity consumption by 2030, surpassing entire industries. Others point out that a single sophisticated AI model training process requires as much energy as tens of thousands of homes use in a year. Yet, the conversation around artificial intelligence rarely highlights this hidden energy emergency.

This unchecked boom carries harsh economic consequences. Utility customers in some regions face imminent rate hikes of up to 20%, absorbing the hidden costs of supplying AI's unlimited appetite. Labor markets, too, face disruption—data centers offer jobs, but often precarious, hazardous, and union-resistant.

We now stand at a crossroads. Can our grids sustain endless intelligence growth without destabilizing entire economies—and without sacrificing our goals for a sustainable planet? Must we trade a livable climate for convenience and computing?

As AI reshapes every aspect of society, perhaps it's finally time to ask ourselves: What price are we really willing to pay for artificial intelligence—and can we afford to keep miscalculating its true cost?

I'm Harry Kermode, and that's your AI Daily.

AI's Dirty Secret: The Energy Crisis Fueling Artificial Intelligence

**Christopher W. White's "The AI Music Problem"** explores the complex relationship between machine learning and musical creativity, arguing that despite advancements, AI faces fundamental challenges in generating music comparable to human artistry. The book highlights **five key factors** affecting musical AI development: **motivations and potential payoffs**, **data availability**, **data representation**, **musical structure detection**, and **human interpretation**. White suggests that **limited resources**, **data scarcity** due to music's repetitive nature and specialized creation, and the **inherent complexity of musical elements** like harmony and rhythm pose significant hurdles for AI. Ultimately, the book posits that while AI can create impressive "craftwork," it cannot achieve true "art" because it lacks the **experiential knowledge and intentionality** that define human creativity and are crucial for an audience's emotional connection and interpretation of music.

The AI Music Problem | Global Bills, Laws, Fingerprint Technology (Audiobook Summary)

Can you trust what you hear? This episode dives into a startling incident involving an AI-powered deepfake of Secretary Marco Rubio's voice reaching high-level government officials through Signal. We explore the growing threat of AI-driven deception in diplomacy, highlighting how vulnerabilities exposed by recent incidents could impact international trust and security. With alarming statistics on human susceptibility to fake voices and past electoral interference, we uncover who might benefit from this chaos. Listeners will gain insight into the urgent need for systemic reform in digital communication to protect against AI impersonations. Don't forget to subscribe, share, and leave a review! 

FULL TRANSCRIPT: From The New York Times, this is AI Daily. I'm Harry Kermode.

Imagine picking up your phone and seeing an encrypted message from the U.S. Secretary of State. It's urgent, collaborative, and entirely believable—marked with familiar speech patterns and diplomatic language. You trust it, act upon it. Only later do you discover you've been fooled: not by traditional hackers, but by something new and more disturbing—an AI-powered deepfake voice impersonating Secretary Marco Rubio himself.

Just weeks ago, this astonishing scenario played out in reality. An unknown attacker crafted an AI-generated replica of Marco Rubio's voice, created a fake account on the encrypted messaging app Signal, and proactively reached out to at least three foreign ministers, a U.S. governor, and a sitting member of Congress. The U.S. State Department confirmed the incident, leaving government officials grappling with uncomfortable questions: how could AI-driven deception breach the highest levels of diplomatic communication so easily?

To understand why this incident is more than just alarming, consider this: experts now estimate that humans fail to detect fake voices roughly seventy-five percent of the time. Today's leading software requires mere seconds of audio to create voice duplicates nearly indistinguishable from genuine speech. Even more troubling, there’s no technology currently strong enough to reliably shield diplomats and policymakers from an AI spear-phishing assault of this caliber.

The Rubio incident isn't an isolated outlier, but only the latest chapter in a troubling history. Think back to the 2024 New Hampshire primary, where voters received AI-generated robocalls of President Biden's voice urging them to stay home. The FCC responded firmly, fining the perpetrator six million dollars. Or recall the 2023 elections in Slovakia, where carefully timed deepfake audio swayed public confidence, potentially influencing the outcome. Now, tactics once confined to election meddling have invaded the realm of direct diplomacy, shaking trust among global leaders while exposing vulnerabilities at the heart of democratic systems.

Who stands to gain from this carefully executed chaos? Cybersecurity experts point fingers at state-linked hacking teams—Russian operatives known for using sophisticated AI tools to destabilize geopolitical relationships, aiming to erode trust and weaken Western alliances. But there are quieter beneficiaries, too: tech firms positioning themselves as essential gatekeepers promising protection even as their innovations inadvertently empower attacks like these.

Ironically, Marco Rubio himself foresaw these threats. In 2023, as a senator, he co-sponsored groundbreaking legislation aimed at criminalizing AI-based identity theft. Yet now, as Secretary of State, Rubio finds himself targeted by precisely those technologies he once warned about.

In response to this crisis, the Biden administration has reaffirmed their commitment to "strengthening cybersecurity" without clear new measures. However, critics warn that these reassurances do little to confront the crumbling foundations of digital trust. Without systemic reform—secure and verifiable communication infrastructures specifically—the floodgates remain open.

And so diplomats, elected officials, and even ordinary citizens face today’s most pressing question: if anyone's voice can be flawlessly cloned, how do we distinguish allies from impostors? Our institutions from infiltrators?

If we're going to survive deepfake diplomacy, it's a question we'll need answered—and soon.

From The New York Times, I'm Harry Kermode. This was AI Daily.

Deepfake Diplomacy: The AI Threat to Global Trust

Author: Wes C | Date: 10th July 2025

The Strategic Takeaway

The future of business isn't about analyzing "what happened," but predicting "what will happen" and intelligently deciding "what to do." This pivotal shift is driven by AI and ML.

AI is the overarching vision, while Machine Learning (ML) is the engine that learns from data to make predictions. ML excels at replacing complex rule-based systems, automating processes, understanding unstructured data, and powering personalization. However, its success hinges on high-quality, well-scrutinized data.

Google Cloud offers a comprehensive AI/ML arsenal, from BigQuery ML for data analysts and Pre-trained APIs for quick wins, to AutoML for custom models without code, and Vertex AI for deep specialization. Bespoke solutions like Contact Center AI are also available. Choosing the right path involves balancing speed, differentiation, and team expertise. Always remember the critical importance of Responsible AI and Explainable AI for trust and transparency.

With Google Cloud, businesses can stop reacting to the past and proactively shape their future.

W&I Group - All rights reserved

10th July 2025, What I Learned Today - Innovating with Google Cloud Artificial Intelligence

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?

Dr. Joy Buolamwini is a computer scientist and a poet of code who uses art and research to illuminate the social implications of artificial intelligence. She joins to discuss her career as the founder of the Algorithmic Justice League, her best-selling book Unmasking AI: My Mission to Protect What is Human in a World of Machines, and her featured role in the acclaimed Netflix documentary Coded Bias.

Dr. Joy Buolamwini

In episode 10 of Platform Builders, Christine Spang and Isaac Nassimi chat with Seth Besmertnik about Conductor's journey through the WeWork acquisition and its incredible comeback from near-disaster, offering a powerful lesson in resilience and leadership. The conversation then pivots to the future, exploring how AI is about to turn the web upside down. Seth provides a clear framework for how brands must adapt their content and SEO strategies to survive and thrive in this new AI-driven world.

Subscribe

Platform Builders - Ep. #10, Building Leverage, Not Features with Seth Besmertnik

Machine learning The Bay Area

Welcome to *AI with Shaily*! I’m Shailendra Kumar, your host, here to explore the exciting world of artificial intelligence 🤖✨.

Today’s episode dives deep into the evolving landscape of fraud detection and how data science is set to transform it by 2026. Fraud detection used to be simple—basic rules like flagging transactions over a certain amount or from unusual locations. But fraudsters are always one step ahead, adapting faster than software updates. That’s where advanced machine learning comes in 🧠💡. These intelligent algorithms learn from vast amounts of historical data and continuously adapt to new fraud patterns, much like training a clever dog to anticipate ever-changing squirrel antics 🐕🐿️.

One major breakthrough expected by 2026 is real-time fraud analysis. Imagine your credit card company detecting suspicious purchases the very moment they happen, sending instant alerts so action can be taken immediately 🚨💳. It’s like having a fraud detective on call 24/7, minimizing losses and boosting security.

Another exciting innovation is privacy-preserving technology. This might sound like a paradox—sharing insights without sharing actual data—but techniques like federated learning make it possible. Banks and financial institutions can collaborate securely without exposing sensitive customer information 🔒🤝. Think of it as a secret team effort against fraud, working behind the scenes.

Looking further ahead, the combination of blockchain and AI promises to create an almost unbreakable defense against fraud. Blockchain’s transparent ledger paired with AI’s analytical prowess will make tracking transactions as easy as following a GPS tracker on every move 🛡️📊📍.

Behind all these advancements, companies will focus on building AI systems that are scalable, customizable, and capable of continuous learning—self-updating models that improve with every new fraud attempt. Strong data infrastructure and governance will be essential, because poor data management is often the Achilles’ heel of AI projects ⚙️📈.

Here’s a bonus tip for business owners: start integrating AI-driven fraud detection today. Early adoption can give you a serious competitive advantage and save you from future headaches 🚀💼.

To leave you with a thought: as AI grows smarter, do fraudsters stand a chance? Or is it only a matter of time before AI wins this digital cat-and-mouse game for good? 🐱🐭🤔

And a wise quote from AI pioneer Andrew Ng to close: “AI is the new electricity.” Just as electricity revolutionized industries, AI is poised to transform how we fight fraud at its very core ⚡🌍.

If you enjoyed this episode, find me on YouTube, Twitter, LinkedIn, and Medium. Subscribe, follow, and join the conversation by sharing your thoughts in the comments 💬📲.

Thanks for listening to *AI with Shaily*. Until next time, keep your data safe and your AI smarter! 🔐🤖💡

AI vs. Fraudsters: How Machine Learning Will Revolutionize Fraud Detection by 2026

यह स्रोत **मशीन लर्निंग में नाइफ बेज़ एल्गोरिथम** का एक विस्तृत अवलोकन प्रदान करता है, जिसमें इसके मौलिक सिद्धांतों और अनुप्रयोगों पर प्रकाश डाला गया है। इसमें **बेज़ प्रमेय** की व्याख्या की गई है, जो इस वर्गीकरण एल्गोरिथम का आधार है, और इसकी "नाइफ" धारणा को स्पष्ट किया गया है। पाठ में **विभिन्न प्रकार के नाइफ बेज़ क्लासिफायर** जैसे गॉसियन, मल्टीनॉमिअल और बर्नौली शामिल हैं, और प्रत्येक के लिए उपयुक्त डेटा प्रकारों पर चर्चा की गई है। यह **एल्गोरिथम के कार्यप्रणाली, फायदे, नुकसान** और पाठ वर्गीकरण जैसे व्यावहारिक अनुप्रयोगों की भी पड़ताल करता है, साथ ही इसे अन्य मशीन लर्निंग मॉडल जैसे डिसीजन ट्री और GBMs से भी तुलना करता है। स्रोत **स्किकिट-लर्न में नाइफ बेज़ के कार्यान्वयन** का एक कोड उदाहरण भी प्रस्तुत करता है, लेकिन यह स्वीकार करता है कि संदर्भित डॉ. चिनमोय पाल का विशिष्ट लेख उपलब्ध नहीं है।

ML-EP-6_ Naive Bayes_ मशीन लर्निंग में नाइफ बेज़ का अवलोकन

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.

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

Building a better future through technology

Tired of marketing podcasts that feel like a non-stop sales funnel? Welcome to your new obsession! I diving deep into the ever-evolving worlds of SEO and digital marketing, bringing you the latest strategies, actionable tips, and insider insights – all without a single commercial break or hidden agenda. Get ready to boost your online presence and conquer the digital landscape, purely for the love of learning. No fluff, just powerful knowledge from me.

I genuinely believe in the potential of music to facilitate personal growth, foster meaningful connections, and cultivate shared experiences. My mission centers on producing dynamic musical works, both in the booth and studio, as an effective medium for storytelling, spiritual exploration, and the creation of deep emotional bonds.


+ Life Long Musical Professional & Enthusiast | Creative Entrepreneur | Music Producer | Club DJ | Event Production, Management, and Design Specialist | Electrician | Graphic Designer | Barber & Comedian 
+ Academic Background Master of Arts in Arts and Media, Audio Production, Media Management, Psychology, Mathematics, Sciences, and Business.   

**UNIFI MUSIC LLC**
NYC-Based Record Label, Music Production & Artist Management
At UNIFI MUSIC LLC, we aim to cultivate a creative and collaborative ecosystem for emerging musicians, artists, and producers. We are dedicated to educating up-and-coming artists to help them reach their highest potential while providing them the means to release music on all essential platforms. Our goal is to create an inspiring and lasting impact on the music industry. Music, dance, and storytelling are fundamental parts of the human experience. Art inspires, helps us find purpose, and fosters meaningful, lifelong connections..

Website - https://bio.site/anelo
Label - https://UnifiMusicRecords.com
Email - Promo@UnifiMusic.info

Design Matters by Debbie Millman features interviews with designers, artists and cultural leaders, including Lawrence Weiner, Barbara Kruger, Malcolm Gladwell, Eric Kandel, Stefan Sagmeister, John Maeda, Jonathan Hoefler and Tobias Frere-Jones, Michael Arad, Milton Glaser, Massimo Vignelli, Paula Scher, Steven Heller, Jonah Lehrer, among others.

In 2011, Design Matters received the People’s Design Award from the prestigious Cooper-Hewitt National Design Awards.

Subscribe to Design Matters for free on iTunes, where new episodes are uploaded weekly: http://itunes.apple.com/us/podcast/design-matters-debbie-millman/id328074695

The Cancer Research UK Cambridge Centre unites over 1,000 world-leading biologists, chemists, physicists, engineers, mathematicians, computer scientists, clinicians, nurses and allied healthcare professionals from across Cambridge, UK to tackle cancer from every angle.

Our mission is to end death and disease caused by cancer through research, treatment and education. We are detecting cancer at its earliest stage and are developing personalised treatments for every patient through facilitating new collaborations and driving the translation of new scientific discoveries into clinical applications to improve patient care. By working together across a range of different disciplines, our members are breaking down the barriers between the laboratory and the clinic, enabling patients to benefit from the latest innovations in cancer science.

New tracks tagged #Machine Learning