Happy Birthday, Geoffrey Hinton! Hinton received the 2018 #ACMTuringAward for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. In 2024, he won the Nobel Prize in Physics: https://t.co/L0st1qEMuG pic.twitter.com/fE982T0XTc
— Association for Computing Machinery (@TheOfficialACM) December 6, 2025
AI concern
Google CEO Sundar Pichai just told Fox News the one AI nightmare that actually keeps him awake: “When deepfakes get so good that we literally won’t be able to tell what’s real anymore… and bad actors get their hands on it.” His exact words after Shannon Bream pressed him: “That’s the kind of thing you sit and think about.” He still believes humanity can steer it toward curing cancer — but the clock is ticking. This 64 second clip is chilling.
Samsung Recursive Model
A tiny 7 Million parameter model just beat DeepSeek R1, Gemini 2.5 pro, and o3 mini at reasoning on both ARG AGI 1 and ARC AGI 2. It's called Tiny Recursive Model (TRM) from Samsung. How can a model 10,000x smaller be smarter? Here's how it works: 1. Draft an Initial Answer: Unlike an LLM that writes word by word, TRM first generates a quick, complete "draft" of the solution. Think of this as its first rough guess. 2. Create a "Scratchpad": It then creates a separate space for its internal thoughts, a latent reasoning "scratchpad." This is where the real magic happens. 3. Intensely Self Critique: The model enters an intense inner loop. It compares its draft answer to the original problem and refines its reasoning on the scratchpad over and over (6 times in a row), asking itself, "Does my logic hold up? Where are the errors?" 4. Revise the Answer: After this focused "thinking," it uses the improved logic from its scratchpad to create a brand new, much better draft of the final answer. 5. Repeat until Confident: The entire process, draft, think, revise, is repeated up to 16 times. Each cycle pushes the model closer to a correct, logically sound solution. Why this matters: Business Leaders: This is what algorithmic advantage looks like. While competitors are paying massive inference costs for brute force scale, a smarter, more efficient model can deliver superior performance for a tiny fraction of the cost. Researchers: This is a major validation for neuro symbolic ideas. The model's ability to recursively "think" before "acting" demonstrates that architecture, not just scale, can be a primary driver of reasoning ability. Practitioners: SOTA reasoning is no longer gated behind billion dollar GPU clusters. This paper provides a highly efficient, parameter light blueprint for building specialized reasoners that can run anywhere. This isn't just scaling down; it's a completely different, more deliberate way of solving problems.
Kobe 81 pts
Kobe Bryant 81 pts 28/46 FG
The Sad Reality of Rust Adoption
🦀 The Sad Reality of Rust Adoption If you’ve been hanging around developer socials, then you’ve obviously heard the heated debate recently about Rust’s adoption by established software, especially in Coreutils and Git. Some devs are thrilled, while others are not so happy about the move especially in battle tested projects. The internet has, naturally, turned it into a meme war.
「Google is sepcial case」
Google is a special case and took the time to highlight them specifically He says that being a "China Hawk" has been seen as some kind of badge of honour but in fact it is a "badge of shame" He dispels the notion that China is decades or years behind and in fact states they are "nanoseconds behind". I have been very doubtful about OpenAi's progress in coming years and seen bubbly but Jensen is convinced they are going to be a hyperscale company, $1T in time.