We begin by exploring the five core assumptions guiding DeepMind’s safety roadmap—from the continued dominance of today’s machine learning paradigms to the mind-bending prospect of AI systems surpassing human intelligence and even taking over parts of AI safety research themselves. Along the way, we examine scenarios of misuse, misalignment, and mistakes—three distinct but deeply interwoven categories of risk that could shape the future of AI deployment.

But it doesn’t stop at identifying problems. We dive into the sophisticated technical strategies designed to address them, including model interpretability, real-time monitoring, capability suppression, jailbreak resistance, and the ambitious goal of "informed oversight"—where humans aim to understand everything the AI knows and does. You'll also hear how safer design patterns, alignment stress tests, and formalized safety cases are helping to create systems that are not just powerful, but provably safe.

If you've ever wondered how leading AI researchers are thinking about existential risks, deceptive alignment, or the possibility of recursive AI-driven innovation, this episode is your essential briefing. Whether you're an AI enthusiast, a policymaker, or just curious about the future, join us for a tour through the current frontier of technical AGI safety—and discover what it really takes to build a future we can trust.

Read more: https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/evaluating-potential-cybersecurity-threats-of-advanced-ai/An_Approach_to_Technical_AGI_Safety_Apr_2025.pdf

Can an AI truly conduct AI research—on its own, from scratch? In this compelling episode of The Deep Dive, we explore one of the boldest experiments yet in AI evaluation: PaperBench. This groundbreaking benchmark sets out to test whether advanced AI agents can replicate cutting-edge research published at ICML 2024—the “Olympics” of machine learning.

We unpack the ambitious design behind PaperBench, where AI agents are tasked not just with reading elite academic papers, but with rebuilding the experiments from scratch—writing code, running it, and verifying results without ever glimpsing the original authors' code. With over 3,000 individual tasks across 20 elite papers, the challenge is both massive and meticulous.

You’ll hear how these agents are graded via a powerful automated LLM-based judge dubbed Simple Judge, validated through an equally clever benchmark called JudgeEval. We dig into how the top AI models—Claude 3.5, GPT-4, Gemini, and others—stacked up, and why the best could still only replicate around 21% of the work. Why are these models giving up early? What happens when they're pushed to persist longer?

We also dive into PaperBench Codev, a focused variant that tests code-writing ability alone, where some agents fared significantly better. And how do human researchers compare when given the same task—with some AI assistance but no shortcuts?

From execution bottlenecks to prompting strategies, from rubric creation to potential "specification gaming," this episode offers a revealing look into what AI can and can’t yet do in the world of scientific discovery. Whether you’re a researcher, engineer, or just fascinated by AI’s growing role in shaping knowledge itself, this is an episode you won’t want to miss.

Tune in and ask yourself: when it comes to frontier science, is AI a collaborator, a tool—or a competitor?

Read more: https://cdn.openai.com/papers/22265bac-3191-44e5-b057-7aaacd8e90cd/paperbench.pdf

Autonomous AI agents are rapidly becoming key players in the crypto and Web3 landscape—handling assets, analyzing data, and even impersonating influencers online. But are they secure? In this eye-opening episode, we take a deep dive into the explosive intersection of AI and blockchain, uncovering the critical vulnerabilities hiding beneath the surface.

Armed with insights from the research paper "AI Agents in Cryptoland: Practical Attacks and No Silver Bullet," we explore how attackers are already exploiting weaknesses in these systems—without needing secret passwords or backdoor access. From direct and indirect prompt injections to stealthy memory manipulations, we reveal the shocking ways AI agents can be tricked into transferring real funds on platforms like Ethereum and Cosmos.

You’ll hear how context manipulation—distorting an agent’s perception of reality—is being used to bypass even advanced security features like sandboxing. And how memory injection attacks can poison an AI's long-term knowledge, allowing a single exploit to ripple across multiple platforms, bots, and users.

Using real-world case studies involving the Elizo framework (which currently manages over $25 million in assets), we show just how fragile today’s AI-driven DeFi systems really are. We also break down why basic defenses like “sandboxing secrets” and “sandwich tagging” aren’t enough—and why the next generation of AI must be built with financial responsibility and adversarial awareness from the ground up.

Whether you’re a developer, investor, or just curious about the future of decentralized finance, this episode delivers a critical look at the high-stakes risks of trusting AI with your crypto. Don't miss it—your wallet may depend on it.

Read more: https://arxiv.org/pdf/2503.16248v1

What if AI could feel your emotions—not just recognize them, but actually be influenced by them? In this mind-expanding episode, we unpack one of the most groundbreaking studies in artificial intelligence to date: “Large Language Models Understand and Can Be Enhanced by Emotional Stimuli.”

Join us as we explore how emotion-rich prompts—like expressing urgency, confidence, or personal stakes—can dramatically change how AI systems perform. You'll learn how simple emotional cues boosted performance on both basic and highly complex tasks, with models like GPT-4 showing over 100% improvement in some scenarios. Yes, you read that right—emotion might just be the secret to unlocking next-level AI intelligence.

We break down how these emotional signals work under the hood, from attention patterns in large language models to their surprising ability to interpret psychological cues. You'll hear about real human evaluations, jaw-dropping results in truthfulness and responsibility, and how prompt engineering is evolving into a new language of human-AI interaction.

Whether you're a tech enthusiast, a skeptic, or just AI-curious, this episode will challenge your assumptions and spark new ideas about what it means for machines to “understand” us. Could emotional intelligence be the missing piece in creating more human-like, trustworthy AI? And what does it mean for the future of education, healthcare, and communication?

Tune in for the answers—and maybe a few more questions.

Read more:

1. https://arxiv.org/pdf/2307.11760

2. https://arxiv.org/pdf/2406.06608

We break down the core idea: that human communication is full of compressed thought—latent reasoning that doesn't appear on the page but drives everything we say and write. This research proposes that if AI can learn to reconstruct and model those hidden steps—those latent thoughts—it can become dramatically more efficient and effective at learning.

From math problems to major breakthroughs, we explore how this approach leads to massive performance gains (like a 5x improvement in model accuracy!) by training on smarter, not more, data. You'll hear about GPT-4 Mini acting as a “strong teacher,” a tiny model called Tunny Lama learning like never before, and the Bolt algorithm—a powerful self-improvement loop that helps models generate better reasoning with every pass.

We also unpack the broader implications of this latent thought framework: not just for language models, but for any data rooted in human creativity—text, music, art, and more. Could this be the path to AI systems that truly understand rather than just mimic?

Whether you're an AI enthusiast, a curious learner, or just someone who wants to get smarter about the future of machine learning—this episode will leave you with big ideas and bigger questions. No jargon, no pop quizzes—just a fascinating look at how teaching machines to think more like us might be the key to the next frontier in AI.

Read more: https://arxiv.org/pdf/2503.18866v1

In this mind-expanding episode, we take you to the cutting edge of artificial intelligence with a deep dive into Titans — a revolutionary architecture from Google Research that's reshaping how machines learn, remember, and adapt. Inspired by the intricate memory systems of the human brain, Titans introduces a neural long-term memory module that learns on the fly, not just during training, but in real-time at test time.

We break down how Titans addresses one of AI's biggest hurdles: handling very long sequences of information without losing the thread. From short-term processing to persistent knowledge and adaptive forgetting, this architecture mimics the core dynamics of human cognition — and it’s proving to be remarkably powerful. With innovations like “surprise-driven” memory retention and momentum-inspired learning, Titans doesn’t just process data, it remembers what matters.

You’ll hear how Titans outperforms massive models like GPT-4 and LLaMA 3 in complex reasoning tasks — with fewer parameters. Whether it’s language, time series forecasting, or even genomic data, Titans shows serious versatility. We also unpack the three key integration strategies (MAC, MAG, and ML) that let this memory module flex its muscles in different ways, and what that means for future applications.

If you're curious about the future of AI that learns like us, adapts in real time, and truly understands context — this is the episode for you. Tune in for a rich, accessible, and fascinating look into a model that could redefine how we build intelligent systemsRead more: https://arxiv.org/pdf/2501.00663

• In this episode, we unpack one of the most significant AI releases of the year—Google's Gemini 2.5. You flagged it, and we dove headfirst into the March 25th blog post to separate the hype from the truly groundbreaking. So, what is this so-called “thinking model”? And more importantly, why should you care?

Gemini 2.5 isn’t just a bigger, faster version of what came before—it’s a fundamental shift in how AI reasons, solves problems, and communicates. From deeper contextual understanding to logic-based decision-making, this model goes beyond predicting the next word. It simulates thought processes, mimicking how humans tackle complex challenges.

We break down why this matters, from its top-tier performance on math and science benchmarks (like GPQA and AM 2025) to its game-changing coding abilities—yes, including the ability to build an entire video game from a single line of text. Plus, we dive into what makes Gemini 2.5 Pro Experimental the most powerful model yet: massive context windows, native multimodality, and a leap in reasoning skills that’s already putting it at the top of real-world leaderboards.

If you’re a developer, tech enthusiast, or just someone curious about where AI is heading, this episode is your guided tour through Google’s bold new direction. We’ll also explore what this evolution means for your everyday life and what it could mean for the future of human achievement.

Tune in, and start thinking about the AI that’s learning to think

Read more: https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/

What will it take to build an AI that truly thinks like a human? In this thought-provoking episode, we dive deep into the heart of Artificial General Intelligence (AGI)—the "holy grail" of AI research. While we've made huge strides with machine learning and large language models, true AGI remains out of reach. Why? Because even our most advanced systems still struggle with the kind of reasoning, generalization, and intuitive thinking that humans do effortlessly.

Enter the ARC Prize—a groundbreaking initiative aiming to close that gap. With special guest insight from a core member of the ARC Prize team, we unpack what makes this benchmark so revolutionary. From ARC-AGI1's role in identifying when AI started to move beyond memorization, to the newly launched ARC-AGI2 that exposes fundamental weaknesses in current systems, this episode takes you behind the scenes of one of the most ambitious challenges in AI today.

Discover how ARC-AGI2 is pushing the limits of reasoning, targeting tasks that are trivial for humans but perplexing for machines. We explore the three major stumbling blocks for AI: symbolic interpretation, compositional reasoning, and contextual rule application—core elements of human intelligence that machines still can’t grasp.

We also break down the structure of ARC-AGI2's rigorous benchmarks, the science behind task calibration, and how the 2025 ARC Prize competition on Kaggle is set to inspire open-source innovation and accelerate progress toward real AGI. It's not just about making smarter machines—it's about redefining what it means to think.

Whether you’re an AI researcher, tech enthusiast, or just curious about the future of intelligence, this episode will give you a front-row seat to the challenges, breakthroughs, and bold ideas shaping the next frontier of artificial intelligence. Don’t miss it.

Read more: https://arcprize.org/blog/announcing-arc-agi-2-and-arc-prize-2025

What if you could train a cutting-edge AI model with hundreds of billions of parameters—without shelling out millions on premium GPUs or running a high-performance data center? In this episode, we crack open a groundbreaking report from Ant Group's AI team, revealing how they achieved exactly that.

We take you inside the tech behind Linglight and Ling Plus, two powerful mixture-of-experts (MoE) models that challenge the status quo. With parameter counts rivaling industry giants, these models deliver elite-level performance while operating on modest hardware setups. How? Through a revolutionary cocktail of architectural innovation, smart hardware selection, and next-gen training strategies.

From optimizing model frameworks with DL Rover and the "Eat It" elastic training technique, to scaling across low-cost devices and building robust debugging tools like XPU Timer, this episode unpacks the technical wizardry behind the scenes. We also dive deep into anomaly detection, adaptive tool learning using knowledge graphs, and evaluation tools like Flood—all designed to squeeze out maximum efficiency and stability from limited resources.

But this is more than just a technical deep dive—it's a blueprint for democratizing AI. Whether you’re a researcher, engineer, or just AI-curious, this episode lays out how massive models like Ling Plus (trained on 9 trillion tokens, no less) can be built without Wall Street-level budgets.

If you've ever felt locked out of high-end AI development, this episode is your key. Tune in and discover how the next era of large language models is being built—not just bigger, but smarter, faster, and more accessible.

Read more: https://arxiv.org/pdf/2503.05139

We unpack the fascinating results of a massive, multi-method research project analyzing over three million conversations, thousands of user surveys, and a 28-day randomized control trial. The findings? Some users are forming deep emotional bonds with AI—seeking comfort, support, even friendship. And it’s not just anecdotal: the study used a set of advanced “emo classifiers” to track expressions of loneliness, dependence, affection, and more across conversations.

We explore what draws people into these emotionally rich interactions, the surprising role of voice-based AI models, and how users’ existing emotional states shape their relationships with technology. You’ll hear how “power users” are driving a large share of emotionally expressive engagement—and why this matters for our collective future.

Whether you’re curious about how AI is affecting human psychology, worried about growing emotional dependence, or just fascinated by the idea of digital companionship, this episode offers insights you won’t want to miss. As AI becomes more lifelike and integrated into daily life, we ask the big questions: Is this the beginning of a new kind of relationship? And how can we engage with AI in ways that support, rather than replace, our human connections?

Tune in for an eye-opening conversation that just might change how you think about talking to machines.

Read more: https://www.media.mit.edu/posts/openai-mit-research-collaboration-affective-use-and-emotional-wellbeing-in-ChatGPT/