Weather Data That AI Depends On Is Becoming a Target for Tampering
MIT Technology Review has warned that the risk of tampering with weather forecast data is growing. Airline dispatchers, power grid operators, and farmers make critical decisions every morning based on weather forecasts. But as AI/ML-powered forecasting systems spread, attacks on forecast data are becoming a real threat.
At first glance, a weather forecast looks like "glance at it for two seconds and move on" information, but the backbone of entire industries depends on it: crop variety selection in agriculture, flight planning in aviation, and demand forecasting in power grids. If malicious actors can manipulate observation data or the data fed into models, the economic impact would be enormous.
The more AI forecasting accuracy improves, the more urgent it becomes to secure the integrity of the data supply chain.
San Francisco Demands Apple and Google Remove AI "Nudify" Apps
The San Francisco City Attorney's office has sent cease-and-desist letters to Apple and Google, targeting 13 apps that use AI-powered face-swapping technology to generate images of women.
These apps have been flagged as "overwhelmingly used to target women and girls." The city also warned that the app stores are profiting from these apps.
The abuse of AI deepfake technology has long been a concern as an area where regulation simply can't keep up. This legal action could set a precedent for holding platform operators accountable.
LongStraw — Long-Context RL Beyond 2 Million Tokens on a Fixed GPU Budget
Featured on Hugging Face Daily Papers, "LongStraw" is an architecture-aware execution stack for running reinforcement learning (RL) post-training with million-token-scale contexts.
LongStraw's key trick is to evaluate the shared prompt once without automatic differentiation, keep only the model-specific state needed for subsequent tokens, and replay short response branches one at a time. This shrinks the live training graph from "entire prompt + response" down to "a single response branch," dramatically cutting GPU memory usage.
It has been implemented and validated on two different model families — Qwen3.6-27B (hybrid regression + full attention) and GLM-5.2 (compressed-attention MoE) — demonstrating the generality of the approach. Long-context RL has long faced a high GPU memory wall, and this method offers a trade-off: extra replay time in exchange for reduced GPU memory.
New Developments in the US-China AI Race: 60% Reliance on Chinese Models and Xi Jinping's Supremacy Bid
Two news items have thrown a new phase of the US-China battle for AI supremacy into sharp relief.
First, according to Briefs.co, 60% of AI usage by US companies is powered by Chinese-made models. The details of the survey methodology are unclear, but it reveals the reality that Chinese models, with their balance of cost-effectiveness and capability, are driving a large share of practical enterprise AI adoption.
At the same time, President Xi Jinping has reaffirmed China's goal of becoming the world's AI leader (reported by the FT). Both in policy and in technology, China's AI presence has reached a level that can no longer be ignored.
As US export controls and China's open-source strategy advance in parallel, the choice of "which country's models to use" is beginning to carry meaning that goes beyond mere technology selection.
The "Schema" Harness Achieves 99% on ARC-AGI-3
"Schema," which has been drawing attention on Reddit's r/MachineLearning, is a new harness that claims to achieve 99% on the ARC-AGI-3 public set by combining Claude Opus 4.8 and Fable 5. It reportedly reached 95.35% even when using GPT-5.6 Sol.
What's interesting is that Schema doesn't touch model weights at all — instead, it changes the process layer: "how to convert observations into a working model of the game," "how to test predictions against the interaction history," and "how to execute and revise plans."
With benchmark scores plateauing, the suggestion that performance hinges far more on "how you operate the model" than on the model's own capabilities aligns with recent research findings that context quality determines agent reliability.
QLoRA's "2e-4" Learning Rate Is Wrong for Small Datasets
Practical insights were shared on r/MachineLearning. The learning rate of 2e-4, widely recommended in QoRA tutorials, was originally a value optimized for Alpaca's 52k samples, and it causes overfitting on datasets in the 5–10k sample range.
The poster pointed out that with fewer samples, overfitting begins as early as epoch 1 — training loss keeps dropping steadily while eval loss plateaus. They warn of the danger of applying the "magic number" 2e-4 without understanding where it came from.
This discussion symbolizes a broader problem in the machine learning community: blind faith in default values. It's essential to verify whether hyperparameters recommended in papers or tutorials actually fit your data scale and task.
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