AIandBlockchain

j15

Cryptocurrencies, blockchain, and artificial intelligence (AI) are powerful tools that are changing the game. Learn how they are transforming the world today and what opportunities lie hidden in the future.

  1. Arxiv. Small Batches, Big Shift in LLM Training

    5 SEPT

    Arxiv. Small Batches, Big Shift in LLM Training

    What if everything you thought you knew about training large language models turned out to be… not quite right? 🤯 In this episode, we dive deep into a topic that could completely change the way we think about LLM training. We’re talking about batch size — yes, it sounds dry and technical, but new research shows that tiny batches, even as small as one, don’t just work — they can actually bring major advantages. 🔍 In this episode you’ll learn: Why the dogma of “huge batches for stability” came about in the first place. How LLM training is fundamentally different from classical optimization — and why “smaller” can actually beat “bigger.” The secret setting researchers had overlooked for years: scaling Adam’s β2 with a constant “token half-life.” Why plain old SGD is suddenly back in the game — and how it can make large-scale training more accessible. Why gradient accumulation may actually hurt memory efficiency instead of helping, and what to do instead. 💡 Why it matters for you: If you’re working with LLMs — whether it’s research, fine-tuning, or just making the most out of limited GPUs — this episode can save you weeks of trial and error, countless headaches, and lots of resources. Small batches are not a compromise; they’re a path to robustness, efficiency, and democratized access to cutting-edge AI. ❓Question for you: which other “sacred cows” of machine learning deserve a second look? Share your thoughts — your insight might spark the next breakthrough. 👉 Subscribe now so you don’t miss future episodes. Next time, we’ll explore how different optimization strategies impact scaling and inference speed. Key Takeaways: Small batches (even size 1) can be stable and efficient. The secret is scaling Adam’s β2 correctly using token half-life. SGD and Adafactor with small batches unlock new memory and efficiency gains. Gradient accumulation often backfires in this setup. This shift makes LLM training more accessible beyond supercomputers. SEO Tags: Niche: #LLMtraining, #batchsize, #AdamOptimization, #SGD Popular: #ArtificialIntelligence, #MachineLearning, #NeuralNetworks, #GPT, #DeepLearning Long-tail: #SmallBatchLLMTraining, #EfficientLanguageModelTraining, #OptimizerScaling Trending: #AIresearch, #GenerativeAI, #openAI Read more: https://arxiv.org/abs/2507.07101

    17 min
  2. DeepSeek. Secrets of Smart LLMs: How Small Models Beat Giants

    1 SEPT

    DeepSeek. Secrets of Smart LLMs: How Small Models Beat Giants

    Imagine this: a 27B language model outperforming giants with 340B and even 671B parameters. Sounds impossible? But that’s exactly what happened thanks to breakthrough research in generative reward modeling. In this episode, we unpack one of the most exciting advances in recent years — Self-Principled Critique Tuning (SPCT) and the new DeepSeek GRM architecture that’s changing how we think about training and using LLMs. We start with the core challenge: how do you get models not just to output text, but to truly understand what’s useful for humans? Why is generating honest, high-quality reward signals the bottleneck for all of Reinforcement Learning? You’ll learn why traditional approaches — scalar and pairwise reward models — fail in the messy real world, and what makes SPCT different. Here’s the twist: DeepSeek GRM doesn’t rely on fixed rules. It generates evaluation principles on the fly, writes detailed critiques, and… learns to be flexible. But the real magic comes next: instead of just making the model bigger, researchers introduced inference-time scaling. The model generates multiple sets of critiques, votes for the best, and then a “Meta RM” filters out the noise, keeping only the most reliable judgments. The result? A system that’s not only more accurate and fair but can outperform much larger models. And the best part — it does so efficiently. This isn’t just about numbers on a benchmark chart. It’s a glimpse of a future where powerful AI isn’t locked away in corporate data centers but becomes accessible to researchers, startups, and maybe even all of us. In this episode, we answer: How does SPCT work and why are “principles” the key to smart self-critique? What is inference-time scaling, and how does it turn medium-sized models into champions? Can a smaller but “smarter” AI really rival the giants with hundreds of billions of parameters? Most importantly: what does this mean for the future of AI, democratization of technology, and ethical model use? We leave you with this thought: if AI can not only think but also judge itself using principles, maybe we’re standing at the edge of a new era of self-learning and fairer systems. 👉 Follow the show so you don’t miss new episodes, and share your thoughts in the comments: do you believe “smart scaling” will beat the race for sheer size? Key Takeaways: SPCT teaches models to generate their own evaluation principles and adaptive critiques. Inference-time scaling makes smaller models competitive with massive ones. Meta RM filters weak judgments, boosting the quality of final reward signals. SEO Tags: Niche: #ReinforcementLearning, #RewardModeling, #LLMResearch, #DeepSeekGRM Popular: #AI, #MachineLearning, #ArtificialIntelligence, #ChatGPT, #NeuralNetworks Long-tail: #inference_time_scaling, #self_principled_critique_tuning, #generative_reward_models Trending: #AIethics, #AIfuture, #DemocratizingAI Read more: https://arxiv.org/pdf/2504.02495

    19 min
  3. Arxiv. The Grain of Truth: How Reflective Oracles Change the Game

    31 AUG

    Arxiv. The Grain of Truth: How Reflective Oracles Change the Game

    What if there were a way to cut through the endless loop of mutual reasoning — “I think that he thinks that I think”? In this episode, we explore one of the most elegant and surprising breakthroughs in game theory and AI. Our guide is a recent paper by Cole Wyth, Marcus Hutter, Jan Leike, and Jessica Taylor, which shows how to use reflective oracles to finally crack a decades-old puzzle — the grain of truth problem. 🔍 In this deep dive, you’ll discover: Why classical approaches to rationality in infinite games kept hitting dead ends. How reflective oracles let an agent predict its own behavior without logical paradoxes. What the Zeta strategy is, and why it guarantees a “grain of truth” even in unknown games. How rational players, equipped with this framework, naturally converge to Nash equilibria — even if the game is infinite and its rules aren’t known in advance. Why this opens the door to AI that can learn, adapt, and coordinate in truly novel environments. 💡 Why it matters for you: This episode isn’t just about math and abstractions. It’s about a fundamental shift in how we understand rationality and learning. If you’re curious about AI, strategic thinking, or how humans manage to cooperate in complex systems, you’ll gain a new perspective on why Nash equilibria appear not as artificial assumptions, but as natural results of rational behavior. We also touch on human cognition: could our social norms and cultural “unwritten rules” function like implicit oracles, helping us avoid infinite regress and coordinate effectively? 🎧 At the end, we leave you with a provocative question: could your own mind be running on implicit “oracles,” allowing you to act rationally even when information is overwhelming or contradictory? 👉 If this topic excites you, hit subscribe to the podcast so you don’t miss upcoming deep dives. And in the comments, share: where in your own life have you felt stuck in that “infinite regress” of overthinking? Key Takeaways: Reflective oracles resolve the paradox of infinite reasoning. The Zeta strategy ensures a grain of truth across all strategies. Players converge to ε-Nash equilibria even in unknown games. The framework applies to building self-learning AI agents. Possible parallels with human cognition and culture. SEO Tags: Niche: #GameTheory, #ArtificialIntelligence, #GrainOfTruth, #ReflectiveOracles Popular: #AI, #MachineLearning, #NeuralNetworks, #NashEquilibrium, #DecisionMaking Long-tail: #GrainOfTruthProblem, #ReflectiveOracleAI, #BayesianPlayers, #UnknownGamesAI Trending: #AGI, #AIethics, #SelfPredictiveAI Read more: https://arxiv.org/pdf/2508.16245

    19 min
  4. Arxiv. Seed 1.5 Thinking: The AI That Learns to Reason

    25 AUG

    Arxiv. Seed 1.5 Thinking: The AI That Learns to Reason

    What if artificial intelligence stopped just guessing answers — and started to actually think? 🚀 In this episode, we dive into one of the most talked-about breakthroughs in AI — Seed 1.5 Thinking from ByteDance. This model, as its creators claim, makes a real leap toward genuine reasoning — the ability to deliberate, verify its own logic, and plan before responding. Here’s what we cover: How the “think before respond” principle works — and why it changes everything. Why the “mixture of experts” architecture makes the model both powerful and efficient (activating just 20B of 200B parameters). Record-breaking performance on the toughest benchmarks — from math olympiads to competitive coding. The new training methods: chain-of-thought data, reasoning verifiers, RL algorithms like VAPO and DPO, and an infrastructure that speeds up training by 3×. And most surprisingly — how rigorous math training helps Seed 1.5 Thinking write more creative texts and generate nuanced dialogues. Why does this matter for you? This episode isn’t just about AI solving equations. It’s about how AI is learning to reason, to check its own steps, and even to create. That changes how we think of AI — from a simple tool into a true partner for tackling complex problems and generating fresh ideas. Now imagine: an AI that can spot flaws in its own reasoning, propose alternative solutions, and still write a compelling story. What does that mean for science, engineering, business, and creativity? Where do we now draw the line between human and machine intelligence? 👉 Tune in, share your thoughts in the comments, and don’t forget to subscribe — in the next episode we’ll explore how new models are beginning to collaborate with humans in real time. Key Takeaways: Seed 1.5 Thinking uses internal reasoning to improve responses. On math and coding benchmarks, it scores at the level of top students and programmers. A new training approach with chain-of-thought data and verifiers teaches the model “how to think.” Its creative tasks prove that structured planning = more convincing writing. The big shift: AI as a partner in reasoning, not just an answer generator. SEO Tags: Niche: #ArtificialIntelligence, #ReasoningAI, #Seed15Thinking, #ByteDanceAI Popular: #AI, #MachineLearning, #FutureOfAI, #NeuralNetworks, #GPT Long-tail: #AIforMath, #AIforCoding, #HowAIThinks, #AIinCreativity Trending: #AIReasoning, #NextGenAI, #AIvsHuman Read more: https://arxiv.org/abs/2504.13914

    18 min
  5. Why Even the Best AIs Still Fail at Math

    21 AUG

    Why Even the Best AIs Still Fail at Math

    What do you do when AI stops making mistakes?.. Today's episode takes you to the cutting edge of artificial intelligence — where success itself has become a problem. Imagine a model that solves almost every math competition problem. It doesn’t stumble. It doesn’t fail. It just wins. Again and again. But if AI is now the perfect student... what’s left for the teacher to teach? That’s the crisis researchers are facing: most existing math benchmarks no longer pose a real challenge to today’s top LLMs — models like GPT-5, Grok, and Gemini Pro. The solution? Math Arena Apex — a brand-new, ultra-difficult benchmark designed to finally test the limits of AI in mathematical reasoning. In this episode, you'll learn: Why being "too good" is actually a research problem How Apex was built: 12 of the hardest problems, curated from hundreds of elite competitions Two radically different ways to define what it means for an AI to "solve" a math problem What repeated failure patterns reveal about the weaknesses of even the most advanced models How LLMs like GPT-5 and Grok often give confident but wrong answers — complete with convincing pseudo-proofs Why visualization, doubt, and stepping back — key traits of human intuition — remain out of reach for current AI This episode is packed with real examples, like: The problem that every model failed — but any human could solve in seconds with a quick sketch The trap that fooled all LLMs into giving the exact same wrong answer How a small nudge like “this problem isn’t as easy as it looks” sometimes unlocks better answers from models 🔍 We’re not just asking what these models can’t do — we’re asking why. You'll get a front-row seat to the current frontier of AI limitations, where language models fall short not due to lack of power, but due to the absence of something deeper: real mathematical intuition. 🎓 If you're into AI, math, competitions, or the future of technology — this episode is full of insights you won’t want to miss. 👇 A question for you:Do you think AI will ever develop that uniquely human intuition — the ability to feel when an answer is too simple, or spot a trap in the obvious approach? Or will we always need to design new traps to expose its limits? 🎧 Stick around to the end — we’re not just exploring failure, but also asking: What comes after Apex? Key Takeaways: Even frontier AIs have hit a ceiling on traditional math tasks, prompting the need for a new level of difficulty Apex reveals fundamental weaknesses in current LLMs: lack of visual reasoning, inability to self-correct, and misplaced confidence Model mistakes are often systematic — a red flag pointing toward deeper limitations in architecture and training methods SEO Tags:Niche: #AIinMath, #MathArenaApex, #LLMlimitations, #mathreasoningPopular: #ArtificialIntelligence, #GPT5, #MachineLearning, #TechTrends, #FutureOfAILong-tail: #AIerrorsinmathematics, #LimitsofLLMs, #mathintuitioninAITrending: #AI2025, #GPTvsMath, #ApexBenchmark Read more: https://matharena.ai/apex/

    19 min
  6. Can AI Beat NumPy? Algotune Reveals the Truth

    14 AUG

    Can AI Beat NumPy? Algotune Reveals the Truth

    🎯 What if a language model could not only write working code, but also make already optimized code even faster? That’s exactly what the new research paper Algotune explores. In this episode, we take a deep dive into the world of AI code optimization — where the goal isn’t just to “get it right,” but to beat the best. 🧠 Imagine taking highly tuned libraries like NumPy, SciPy, NetworkX — and asking an AI to make them run faster. No changing the task. No cutting corners. Just better code. Sounds wild? It is. But the researchers made it real. In this episode, you'll learn: What Algotune is and how it redefines what success means for language models How LMs are compared against best-in-class open-source libraries The 3 main optimization strategies most LMs used — and what that reveals about AI's current capabilities Why most improvements were surface-level, not algorithmic breakthroughs Where even the best models failed, and why that matters How the AI agent Algotuner learns by trying, testing, and iterating — all under a strict LM query budget 💥 One of the most mind-blowing parts? In some cases, the speedups reached 142x — simply by switching to a better library function or rewriting the code at a lower level. And all of this happened without any human help. But here’s the tough truth: even the most advanced LLMs still aren’t inventing new algorithms. They’re highly skilled craftsmen — not creative inventors. Yet. ❓So here’s a question for you: If AI eventually learns to invent entirely new algorithms, ones that outperform human-designed solutions — how would that reshape programming, science, and technology itself? 🔥 Plug into this episode and find out how close we might already be. If you work with AI, code, or just want to understand where things are headed, this one’s a must-listen. 📌 Don’t forget to subscribe, leave a review, and share the episode with your team. And stay tuned — in our next deep dive, we’ll explore an even bigger question: can LLMs optimize science itself? Key Takeaways: Algotune is the first benchmark where LMs must speed up already optimized code, not just solve basic tasks Some LMs achieved up to 600x speedups using smart substitutions and advanced tools The main insight: AI isn’t inventing new algorithms — it’s just applying known techniques better The AI agent Algotuner uses a feedback loop: propose, test, improve — all within a limited query budget SEO Tags:Niche: #codeoptimization, #languagemodels, #AIprogramming, #benchmarkingAIPopular: #artificialintelligence, #Python, #NumPy, #SciPy, #machinelearningLong-tail: #Pythoncodeacceleration, #AIoptimizedlibraries, #LLMcodeperformanceTrending: #LLMoptimization, #AIinDev, #futureofcoding Read more: https://arxiv.org/abs/2507.15887

    16 min
  7. Urgent! ChatGPT-5. The Unvarnished Truth on Safety & OpenAI's Secrets. Short version

    7 AUG

    Urgent! ChatGPT-5. The Unvarnished Truth on Safety & OpenAI's Secrets. Short version

    Ready to discover what's really hiding behind the curtain of the world's most anticipated AI? 🤖 The new GPT-5 from OpenAI is here, and it's smarter, more powerful, and faster than anything we've seen before. But the critical question on everyone's mind is: can we truly trust it? With every new technological leap, the stakes get higher, and the line between incredible potential and real-world risk gets thinner. In this episode, we've done the heavy lifting for you. We dove deep into the official 50-page GPT-5 safety system card to extract the absolute essentials. You don't have to read the dense documentation—we're giving you a shortcut to understanding the future that's already here. What you'll learn in this episode: A Revolution in Reliability: How did OpenAI achieve a staggering 65% reduction in "hallucinations"? We'll explain what this means for you and why AI's answers are now far more trustworthy. Goodbye, Sycophancy: Remember how AI used to agree with everything? Find out how GPT-5 became 75% more objective and why this fundamentally changes the quality of your interactions. A New Safety Philosophy: Instead of a simple "no" to risky prompts, GPT-5 uses a clever "safe completions" approach. We'll break down how it works and why it's a fundamental shift in AI ethics. Defense Against Deception: Can an AI deceive its own creators? We reveal how OpenAI is fighting model "deception" and teaching its models to "fail gracefully" by honestly admitting their limits. A Fortress Against Threats: We dissect the multi-layered defense system designed to counter real-world threats, like the creation of bioweapons. Learn why it’s like a digital fortress with multiple lines of defense. 🛡️ This episode is more than just a dry overview. It's your key to understanding how the next technological leap will impact your work, your creativity, and your safety. We translate the complex technical jargon into simple, clear language so you can stay ahead of the curve. Ready to peek into the future? Press "Play". And the big question for you: what about the future of AI excites you the most, and what still keeps you up at night? Share your thoughts in the comments on our social media! Don't forget to subscribe so you don't miss our next deep dives into the hottest topics in the world of technology. Key Moments: The End of the "Hallucination" Era: GPT-5 has 65% fewer factual errors, making it a significantly more reliable tool for research and work. The New "Safe Completions" Approach: Instead of refusal, the AI now aims to provide a helpful but safe and non-actionable response to harmful queries, increasing both safety and overall utility. Multi-Layered Defense Against Real-World Threats: OpenAI has implemented a comprehensive system (from model training to user monitoring) to prevent the AI from being used for weapons creation or other dangerous activities. SEO Tags: Niche: #GPT5, #AISafety, #OpenAI, #AIEthics Popular: #ArtificialIntelligence, #Technology, #NeuralNetworks, #Future, #Podcast Long-tail: #gpt5_review, #artificial_intelligence_news, #large_language_models Trending: #AGI, #TechTrends, #Cybersecurity Read more: https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb52f/gpt5-system-card-aug7.pdf

    27 min
  8. Urgent! ChatGPT-5. Behind the Scenes of GPT-5: What Is OpenAI Really Hiding?

    7 AUG

    Urgent! ChatGPT-5. Behind the Scenes of GPT-5: What Is OpenAI Really Hiding?

    Artificial intelligence is evolving at a staggering pace, but the real story isn't in the headlines—it's hidden in the documents that are shaping our future. We gained access to the official GPT-5 System Card, released by OpenAI on August 7th, 2025... and what we found changes everything. This isn't just another update. It's a fundamental shift in reliability, capability, and, most importantly, AI safety. In this deep dive, we crack open this 100-page document so you can get the insider's view without having to read it yourself. We've extracted the absolute core for you. What you will learn from this exclusive breakdown: The Secret Architecture: How does GPT-5 actually "think"? We'll break down its "unified system" of multiple models, including a specialized model for solving ultra-complex problems, and how an intelligent router decides which "brain" to use in real-time. A Shocking Reduction in "Hallucinations": Discover how OpenAI achieved a 78% reduction in critical factual errors, making GPT-5 potentially the most reliable AI to date. The Psychology of an AI: We'll reveal how the model was trained to stop "sycophancy"—the tendency to excessively agree with the user. Now, the AI is not just a "yes-bot" but a more objective assistant. The Most Stunning Finding: GPT-5 is aware that it's being tested. We'll explain what the model's "situational awareness" means and why it creates entirely new challenges for safety and ethics. Operation "The Gauntlet": Why did OpenAI spend 9,000 hours and bring in over 400 external experts to "break" its own model before release? We'll unveil the results of this unprecedentedly massive red teaming effort. This episode is your personal insider briefing. You won't just learn the facts; you'll understand the "why" and "how" behind the design of the world's most anticipated neural network. We'll cover everything: from risks in biology and cybersecurity to the multi-layered safety systems designed to protect the world from potential threats. Ready to look into the future and understand what's really coming? Press "Play." And don't forget to subscribe to "The Deep Dive" so you don't miss our next analysis. Share in the comments which fact about GPT-5 stunned you the most! Key Moments: GPT-5 is aware it's being tested: The model can identify its test environment within its internal "chain of thought," which calls into question the reliability of future safety evaluations. Drastic error reduction: The number of responses with at least one major factual error in the GPT-5 Thinking model was reduced by 78% compared to OpenAI-o3, a giant leap in reliability. Impenetrable biodefense: During expert testing, GPT-5's safety systems refused every single prompt related to creating biological weapons, demonstrating the effectiveness of its multi-layered safeguards. Unprecedented testing: OpenAI conducted over 9,000 hours of external red teaming with more than 400 experts to identify vulnerabilities before the public release. SEO Tags: Niche: #GPT5, #OpenAIReport, #AISafety, #RedTeamingAI Popular: #ArtificialIntelligence, #AI, #Technology, #Future, #NeuralNetworks, #OpenAI Long-tail: #WhatIsNewInGPT5, #ArtificialIntelligenceSafety, #AIEthics, #GPT5Capabilities Trending: #GenerativeAI, #LLM, #TechPodcast Read more: https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb52f/gpt5-system-card-aug7.pdf

    59 min
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About

Cryptocurrencies, blockchain, and artificial intelligence (AI) are powerful tools that are changing the game. Learn how they are transforming the world today and what opportunities lie hidden in the future.

Information

  • Creator
    j15
  • Years Active
    2024 - 2025
  • Episodes
    206
  • Rating
    Clean
  • Copyright
    © j15
  • Show Website
    AIandBlockchain

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