Google Just Made AI SMARTER Than Ever Before: CROME AI
DeepMind’s Chrome Breakthrough: Making AI Smarter, Not Just Smoother
AI just got a major upgrade, on every front. Google DeepMind just cracked a major flaw in how chatbots think. Now instead of being fooled by flashy nonsense, they’re finally learning to focus on truth, logic, and what really matters.
Over in China, researchers turned a weak math model into a reasoning machine that can go head-to-head with some of the smartest AI out there. Meanwhile, Meta just launched a billion-dollar AI super team packed with talent from OpenAI, DeepMind, and Anthropic. Microsoft built a medical AI system that diagnoses patients four times better than human doctors.
Seriously. And if that wasn’t enough, Xiaomi just dropped smartglasses that blow Meta’s RayBans out of the water, with voice control, real-time translation, and even pay-by-glance tech. Yeah, it’s been a wild week in AI.
Let’s break it all down. Alright, let’s start with a big fix from Google DeepMind. You know how AI models are trained to give answers we like? They use something called a reward model to figure out what’s good.
But the problem is, these reward models often get it wrong. They might give a high score just because an answer is longer or sounds nice or uses fancy formatting, even if it’s not actually accurate or useful. To fix that, DeepMind teamed up with researchers from McGill University and Mila and built a new system called Chrome.
It stands for Causally Robust Reward Modeling, but what really matters is how it works. Instead of just training the model on random examples, they taught it the difference between what really makes an answer good, like being factual or logical, and what just looks good on the surface, like being polite or long-winded. They did this by creating pairs of answers.
Chrome by DeepMind: Teaching AI to Prioritize Truth Over Style
Some of these pairs changed something important, like whether the facts were right. Those are called causal augmentations. Others changed only the style or tone, without touching the facts.
Those are neutral augmentations. By showing the model both kinds and training it to react only when the quality really changes, the model learns to focus on the important stuff and ignore distractions. They used Gemini 2.0 Flash to generate these examples and a high-quality dataset called Ultra Feedback to guide the process with real human opinions.
To test it all, they tried this on three different language models, Gemma 2.9 billion, Quinn 2.57 billion, and a smaller Gemma 2.2 billion, and ran it through a few challenges, one called RewardBench to see how well it ranks answers, another called ReWordBench that adds sneaky distractions to test focus, and a third called WildGuardTest to check how safe the model is when answering dangerous or harmful prompts. The results? Chrome made the models more accurate, especially in areas like safety, up by about 13%, and reasoning, up by around 7%. Even when the tests tried to fool the model with tricky styling, Chrome still held up better than the usual Reward models.
And when they gave it multiple answers to choose from, Chrome was better at avoiding harmful content without becoming overly cautious. So what’s the big takeaway? Chrome teaches AI to focus on what really matters in an answer and ignore the fluff. And that could make future chatbots a lot more helpful, honest, and safe.
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OctoThinker: China’s New AI Pushes the Limits of Step-by-Step Reasoning
Now, while Google DeepMind focused on making AI answers more aligned with human values, researchers at Shanghai Jiaotong University went after something else, raw thinking power. They wanted models that can handle tough problems, especially math, using proper step-by-step reasoning, or what’s often called chain of thought. We already know that some models, like DeepSeq R10 and SimpleRL, got a boost from reinforcement learning when you train them to think in steps.
That trick worked well on the Quen family of models, but when they tried it on models from the Llama family, things got weird. Instead of getting smarter, Llama’s answers just got super long, up to 4,000 tokens, without actually becoming more accurate. So the team figured the real problem wasn’t the reinforcement learning itself.
It was how the base model had been trained before that. Their fix? A two-phase training plan they call Stable Then Decay. In the first phase, they train the model on 200 billion tokens of high-quality math data from a mix called MegaMath Web Pro.
Once that training levels out, they split the model into three branches and continue training, each with different types of math and reasoning questions, about 20 billion tokens per branch. They called the new model family OctoThinker, since each version focuses on a different kind of thinking. The long branch keeps all the detailed reasoning steps.
The short branch trims it down. And the hybrid finds a middle ground. It’s like giving the model three different study styles and seeing which one performs best.
OctoThinker’s Math Breakthrough and Meta’s Bold Move Toward Superintelligence
They tested all three versions on a bunch of well-known math benchmarks, GSM 8K, Math 500, OlympiaBench, and even the American Math Competition from 2023. And across the board, every OctoThinker version outperformed the original Llama model by at least 10%. The long version even matched Quen’s performance, which is impressive because Quen is already known for being really good at step-by-step reasoning.
The biggest win? OctoThinker didn’t fall into that long and messy answer trap. Its answers stayed clean and focused, while regular Llama just rambled on when put through reinforcement learning. The researchers say this shows that if you train the base model the right way first, reinforcement learning can build on top of that and actually improve performance, instead of just making things noisier.
Looking ahead, they want to expand OctoThinker with even more advanced abilities, like plugging in scratchpad tools or math checkers. And they’re pushing for larger, cleaner math datasets since most of the current ones are still under 100 billion tokens. The goal is to build models that are ready for smart reasoning right out of the box without needing extra patchwork training later.
Now, news of smarter models naturally turns into a hiring frenzy, and Meta is racing hard to catch up. Mark Zuckerberg just announced Meta Superintelligence Labs in an internal memo made public by reporters. The freshly minted group folds all of Meta’s frontier AI work under one roof, and the company spent a multi-billion dollar package to lure Alexander Wang away from scale AI and hand him the chief AI officer badge.
Meta Assembles AI Dream Team as Microsoft Unveils Medical Superintelligence Breakthrough
Former GitHub chief executive Nat Friedman joins as a partner, and 11 other senior researchers from Anthropic, Google DeepMind, OpenAI, and similar shops signed on after receiving offers rumored to sit well inside the eight-figure range. Meta also sniffed around acquisitions of Mira Murati’s Thinking Machines Lab, the search engine startup Perplexity, and Ilya Tsutskever’s Safe Superintelligence venture, though none of those talks reached the binding offer stage. Inside the memo, Zuckerberg says the lab will start research on our next generation of models to get to the frontier in the next year or so, signaling that Meta wants to leapfrog rather than merely match the state of the art.
Given the talent arms race, a lab stocked with ex-Anthropic and ex-DeepMind minds makes Meta’s comeback bid look serious. Microsoft, for its part, chose medicine as the proving ground for multi-agent coordination. Mustafa Suleyman’s AI division unveiled the MAI Diagnostic Orchestrator, nicknamed MAI-DXO, and called it a tangible step toward medical superintelligence.
The orchestrator works like a debate panel. It queries several foundation models, OpenAI-GPT, Google-Gemini, Anthropic-Claude, Meta-Lama, and XAI-Grok, then blends their answers into a single plan. The team pulled 304 real case studies from the New England Journal of Medicine and built the Sequential Diagnosis Benchmark, which walks through the classic clinical sequence.
Note symptoms, decide which test to order, evaluate results, repeat until a diagnosis clicks. On those cases, MAI-DXO reached roughly 80% diagnostic accuracy, four times better than a panel of human doctors barred from consulting external references. Better yet, the orchestrator trimmed costs by about one-fifth because it tended to pick cheaper scans and blood panels when possible.
Xiaomi Unveils Feature-Packed Smart Glasses to Challenge Meta’s Wearable Vision
MIT’s David Sontag and Scripps Research’s Eric Topol praised the study’s rigor but warned that true proof needs a live clinical trial where physicians and AI tackle real patients head-to-head. Microsoft has not set commercialization plans in stone, yet insiders hint at Bing integrations for consumer self-triage and professional tools that could automate parts of patient workup. To build the system, the company quietly poached several high-profile researchers from Google, underscoring how hot the talent market has become.
Hardware is not sitting still either. At the Human Carhome Showcase in Beijing, Xiaomi entered the smart glasses arena with a pair that leans on an in-house assistant called Super Xiao AI. Under the hood sits a Qualcomm AR1 chip plus a Hengsuan 2700 coprocessor, a 12-megapixel ultrawide camera lets wearers snap photos or shoot first-person video, and the glasses can translate text or identify objects in real-time.
You can even confirm an Alipay purchase with nothing more than a glance and a voice confirmation. The battery measures 263 milliamp-hours and promises 8.6 hours of use. Dwarfing the Ray-Ban meta-collaboration that tops out near 4 hours on a much smaller cell, sound comes through open-ear stereo speakers and five microphones handle voice commands.
The frames weigh 40 grams and carry an IP54 splash resistance rating. Arms pivot 12 degrees outward and tilt 5 degrees forward, a geometry tuned for typical Asian facial contour. Electrochromic lenses darken in about two-tenths of a second when you double-tap the temple, and color choices span classic black, parrot green, and translucent tortoiseshell brown.
Price lands at roughly 1,999 yuan, around 280 USD for the base model, rising to 3,000 yuan or about $420 if you want color-shifting lenses. Compared with Meta’s stylish but short-lived Ray-Bans, Xiaomi offers twice the endurance, voice-first controls, and a pay-by-glance party trick, though the company hints that the first release is aimed at the domestic market rather than an immediate global rollout. So now the question is, will all of this actually make AI more useful or just more powerful? Let me know what you think in the comments.
Don’t forget to subscribe and drop a like if you found this interesting. Thanks for reading and I will catch you in the next one.
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
- Google Just Made AI SMARTER Than Ever Before: CROME AI
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