⚙️ Google Dreams, OpenAI Sweats

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Google’s new AI just learned to dream - and it’s already beating models trained on 100× more data. Meanwhile, OpenAI’s billion-dollar hardware project is stuck in reality, and Oracle’s founder wants to record everyone to make society “behave better.”

What are on FIRE 🔥

• 🧠 Dreamer 4: DeepMind’s AI Learns in Imagination

• 🌟 AI Highlights

• 📚 AI Sources From AI Fire

• 🏅 AI Tools

• ⚡ 3 AI Quick Hits

• 📊 AI Chart

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AI INSIGHTS

🧠 Dreamer 4: DeepMind’s AI Learns in Imagination

Google DeepMind just dropped Dreamer 4, a world-model AI that learns entirely inside its own simulation. It’s the first agent to mine diamonds in Minecraft - using 100× less data than OpenAI’s VPT.

🚀 The Breakthrough

Dreamer 4 trains by imagining the world instead of playing it.

• 2.5K hrs of data vs VPT’s 270K hrs

• 21 FPS on a single H100 GPU

• Finishes 14/16 tasks, beating Lucid v1 and MineWorld

⚙️ How It Works

Two steps:

1. Learn the world from video.

2. Practice inside that model.

Key upgrades:

• Shortcut Forcing → 16× faster frame prediction

• Ramp loss weighting for sharper visuals

• GQA tokens for long-term memory

Why It Matters: Dreamer 4 moves from reactive AI to imaginative AI. It generalizes to unseen worlds and runs in real time. DeepMind calls it a foundation for agentic AI - future systems that can learn from YouTube-scale video data without human help.

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TODAY IN AI

AI HIGHLIGHTS

⚙️ OpenAI and Jony Ive are struggling with major technical issues on their palm-sized AI device. It’s meant to be “always-on” and context-aware - but compute limits, privacy design, and personality tuning are slowing the launch. The full story on their $500B hardware gamble is here.

🧠 Oracle founder Larry Ellison claims AI will make “citizens behave better” - because “everything will be recorded.” Critics call it Orwellian, but Ellison’s plan to merge TikTok data with Oracle’s cloud empire could redefine what “privacy” means. Read about the AI dystopia vision here.

💡 Why do coding AIs like GPT-5 and Gemini 2.5 evolve faster than writing bots? It’s the reinforcement learning gap - skills with clear test results (like code) improve quicker than subjective ones (like writing). A must-read on AI’s uneven progress here.

💥 Jeff Bezos says today’s AI rush is like the dot-com era - messy, overfunded, but worth it. He believes most startups will fail, yet society will gain massive productivity from the survivors.

🚀 Sam Altman and Jony Ive headline OpenAI’s biggest event ever in San Francisco. Expect new AI device demos, GPT Store updates, and maybe the long-rumored AI browser reveal. See what’s happening at DevDay 2025 here.

💰 AI Daily Fundraising: Google is bringing the Atlantic Quantum team from MIT into its Quantum AI unit. It’s unclear if this is a full acquisition, but the move strengthens Google’s quantum hardware push. The startup previously raised $9M and signed a $1.8M U.S. Air Force contract for its fluxonium qubit tech.

AI SOURCES FROM AI FIRE

NEW EMPOWERED AI TOOLS

1. 💻 Dropstone is an AI IDE that learns and improves itself as you code - built for autonomous programming

2. 📊 Plus AI Presentations API generates native PowerPoint decks with charts, images & custom templates

3. 🗣️ LangLime makes language learning fun again, using real-world snippets instead of textbook phrases

4. 📢 Scaloom lets you multi-post on Reddit in one click with AI scheduling & rule compliance

AI QUICK HITS

1. 🇪🇺 EU launches “Apply AI” strategy to boost European-made AI and reduce reliance on the U.S. and China. €1B funding aims to power local startups and defence tech.

2. ⚠️ John Chambers warns that 50% of Fortune 500 could vanish as AI moves 5× faster than the internet era - only adaptable leaders will survive.

3. 🧠 California passes SB 53, the first AI safety law forcing transparency from major labs. Backed by Encode AI, it shows regulation and innovation can work together.

AI CHART

📊 RLPT: Reinforcement Learning Without Human Labels

Tencent HunYuan and CUHK introduced RLPT (Reinforcement Learning on Pre-Training Data) - a new way to train large models without human labels.

Instead of learning from instructions or scores, the model rewards itself using unlabeled data from Wikipedia, arXiv, and STEM forums.

It runs two reasoning tasks:

• ASR: predict the next sentence from context.

• MSR: fill missing sentences between two parts.
  A reward model checks if the prediction logically fits - no humans needed.

🧠 Results
On Qwen3 (4B/8B) and Llama-3.2 (3B), RLPT adds +3–8 points on benchmarks like MMLU, GPQA, and AIME.
When combined with RLVR, results climb even higher (AIME24 Pass@1 → 29.9).

⚙️ How It Works

• Uses MinHash, PII masking, and GRPO optimization.

• Segment = sentence; 8 samples per prompt.

Why It Matters: RLPT sits between SFT and RLHF, letting models learn from raw text at scale. It links to ideas behind DeepSeek-R1 and Quiet-STaR, showing how future AIs might teach themselves to reason - cheaper, faster, and smarter.

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The AI Fire Team