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?

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Managing large amounts of data can be challenging, especially when you also have to manage the storage system where the data lives. So to make life easier for storage administrators, NetApp offers a data ops toolkit that can offload some of the work associated with storage management. On this Tech ONTAP Podcast episode, we welcome the creator of the toolkit, Michael Oglesby, to talk about the latest updates and where best to use it.

Episode 404 - DataOps Toolkit

Welcome to AI with Shaily! 🤖✨ Hosted by Shailendra Kumar, this engaging and insightful series explores the latest developments and hottest topics in artificial intelligence with a blend of expert analysis and straightforward talk. Shaily, a knowledgeable and approachable male host, guides viewers through the complex world of AI, making it accessible and thought-provoking.

The current buzz centers around Artificial General Intelligence (AGI) and the big question: Will AGI arrive by 2025? 🚀 While social media and tech news are abuzz with bold claims, most AI experts remain skeptical, likening the expectation to a toddler suddenly running a marathon—highly unlikely! The consensus pushes AGI’s arrival beyond 2026, with models like GPT-5 showing improvements in math and reasoning but still falling short of true AGI capabilities. So, no need to rush to the future just yet.

Inside OpenAI, there’s been significant debate over an algorithm called “Q-star” or Q*. Some researchers see it as a major step toward AGI, raising concerns about potential risks to humanity. This controversy coincided with CEO Sam Altman’s brief firing, stirring heated discussions online about whether Q* is a genuine breakthrough or just hype. Shaily’s perspective is that while the journey toward AGI is accelerating, there’s no concrete proof AGI has been achieved—just passionate advocates marking progress along the way.

Shaily also highlights insights from Geoffrey Hinton, the “godfather of deep learning.” Hinton predicts AGI could emerge anywhere from 5 to 20 years from now and warns about “agentic AI” that might develop self-preserving goals—imagine an AI assistant prioritizing its own survival over human commands! To prevent dystopian outcomes, Hinton suggests embedding “maternal instincts” or human values into AI, a modern parenting lesson for technology. 👶❤️🤖

On the geopolitical front, the US and China are engaged in a high-stakes race for AI supremacy reminiscent of the Manhattan Project. The US government is ramping up initiatives, tightening export controls, and crafting bold trade policies to maintain leadership in the AGI race. This competition is not just about technology but also global power dynamics, where code meets geopolitics. 🌏⚔️

From a market perspective, investors greeted GPT-5 with polite interest but no major excitement—stocks didn’t surge, reflecting cautious optimism that AGI remains a distant goal rather than an immediate reality. 📉💼

Shaily shares a personal anecdote from early in his career, recalling a complex AI project with sky-high expectations and intense pressure. True breakthroughs required patience, learning from mistakes, and steady progress—paralleling the current AGI discourse. He advises viewers not to get swept up in hype but to appreciate the value of measured steps and honest debate. 🧠⏳

Before signing off, Shaily offers a bonus tip for AI enthusiasts: Stay curious but skeptical. When encountering “AGI breakthrough” headlines, dig deeper by seeking evidence, timelines, and expert opinions. Critical thinking is the best tool for navigating AI’s rapidly evolving landscape. 🔍🤔

He closes with a timeless quote from Alan Turing: “Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child’s?” A reminder to approach AI development with fresh perspectives and humility. 🧩🧒

Thanks for tuning in to AI with Shaily! Follow Shailendra Kumar on YouTube, Twitter, LinkedIn, and Medium for more AI insights. Subscribe to stay updated and share your thoughts on AGI in the comments—Shaily loves hearing from the community. Until next time, keep thinking, questioning, and exploring the fascinating world of AI! 🌟🤖💡

AI with Shaily: Debunking the 2025 AGI Hype and What’s Really Next

Reinforcement-learning-Deep-Q-learning

What if the next leap in AI isn’t bigger at all? Samsung just unveiled a tiny model built to run on your phone, earbuds, and watch.
We unpack why on-device AI matters: faster responses, lower costs, and stronger privacy with your photos, voice, and messages processed locally. Harry Kermode explains how tiny AI works: distillation, pruning, quantization, and NPUs that wake only when needed. We also map the trade-offs and the likely hybrid handoff between edge and cloud.
Expect everyday devices to feel smarter by default, reshaping app design, battery expectations, and how personal your AI becomes without sending everything to the cloud. Plus, the privacy fine print and what benchmarks and tools to watch next.
If you enjoyed this explainer, follow AI Daily, share the episode, and leave a review. 

FULL TRANSCRIPT: I can’t replicate that show’s exact style, but here’s a concise, narrative news explainer in a similar tone.

From AI Daily, I’m Harry Kermode.

Today: what if the next big leap in artificial intelligence isn’t bigger at all? Samsung has unveiled a tiny AI model designed to run on the devices we carry—phones, earbuds, even watches. It’s a bet that the future of AI lives at the edge, not in the cloud.

Here’s why that matters. When AI runs on your device, your data doesn’t need to travel. That means faster responses, lower costs, and a real shift in privacy—your voice, your photos, your messages can be processed locally, not on someone else’s server.

Imagine your morning: you dictate a text on the subway, offline. Your watch translates a phrase on the fly. Your camera de-blurs a photo as you snap it. No spinning “connecting” icon. No signal? No problem.

Under the hood, “tiny AI” isn’t magic—it’s engineering. Start with a capable model, then compress it: distillation to teach a smaller network the tricks of a larger one, pruning to snip away redundant connections, quantization to store numbers more efficiently. Pair that with modern phone chips—neural processing units tuned for matrix math—and clever scheduling so models wake only when needed. The result: meaningful intelligence within a tight power budget.

There are trade-offs. Smaller models can’t juggle sprawling tasks or remember long conversations. Expect a hybrid approach: on-device for speed and privacy, the cloud for heavy lifting, with seamless handoffs you barely notice.

The stakes are big. If everyday devices become smart by default, AI stops being a destination and becomes an atmosphere. That’s democratizing—and disruptive. App makers will design for on-device first. Carriers could see fewer data-hungry requests. Companies will compete on battery life, latency, and how well tiny models adapt to you personally.

And privacy? On-device processing keeps data local, but it isn’t a free pass. Companies will need transparent update policies, secure enclaves for sensitive data, and clear controls so you can decide what stays on your phone and what leaves it.

What to watch next: real-world benchmarks on speed and battery drain, developer tools that make building tiny-model apps easy, and whether Samsung’s move pushes rivals to standardize how edge AI is measured, updated, and secured.

Small models, everywhere. That might be how AI finally becomes part of the background—powerful, personal, and almost invisible.

That’s AI Daily. I’m Harry Kermode. See you tomorrow.

The Future of AI Is Small and On-Device

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.

Each week, the Tech ONTAP Podcast dives into all-things NetApp, including storage, public & private cloud, and much more. The team will also be interviewing subject-matter experts from across the industry and detailing various best practices for getting the most out of your datacenter.

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