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Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
arXiv:2607.26121v1 Announce Type: new Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, tra
Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning
arXiv:2607.26059v1 Announce Type: new Abstract: We report a striking phenomenon: deep reinforcement learning agents trained with frozen, randomly initialized CNN feature extractors spontaneously develop extremely sparse fully-connected representations, without any sparsity-inducing objective. In the first fully-connected layer (FC1, $3{,}136 \to 64$), agents compress task-relevant information through as few as 1-3 neurons out of 64 for deterministic Pong (5-11 for stochastic Pong), while trainable CNNs activate 55-64 neurons under matched conditions. We establish four principal findings. First, FC1 sparsity scales with task complexity: 1-11 for Pong, 19-26 for Breakout, and $\sim$42 for Space Invaders. Width-scaling confirms this reflects task structure rather than a fixed capacity fraction. Second, within-game scaling emerges: three identical Pong seeds produce 5, 7, and 11 active neurons. The 5-neuron seed plateaus at $+14$ reward, while the others reach expert performance ($+18.4$,
Sim2Win: A Team-Agnostic, Event-Based Pre-Match Outcome Prediction and Tactical Profiling System for Football
arXiv:2607.26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams. This paper presents Sim2Win, a team-agnostic, event-based pre-match tactical recommendation framework that reframes match outcome prediction as a tactical decision-support problem. Using StatsBomb open event data from eleven competitions spanning 178 teams and 1,411 team-match records, Sim2Win constructs five-match rolling tactical profiles, engineers four interpretable tactical feature ratios, clusters team behaviors into eight playstyles via K-Means, and trains thirteen classifiers to estimate win, draw, and loss probabilities from tactical matchup representations. The system operates without team names or identity features, enabling generalization to teams never seen during training. A rigorous Leave-One-Competition-Out (LOCO) evaluation demonstrates that Si
I Made a 100$ Premium Chocolate Commercial in 10 Minutes With Seedance 2.0
No After Effects. No DaVinci Resolve.Continue reading on Artificial Intelligence in Plain English »
How to ACTUALLY Pass Data/ML Behavioural Interviews
Frameworks and guides to nail your next behavioural interviewContinue reading on Medium »
Production-grade LLM summarization: Three engineering decisions that determine whether it scales
Authors: Priyank Srivastava divas vermaContinue reading on Medium »
How Teaching Salsa Gave Me a Framework I Didn’t Know I Needed for Working With AI
A story about a Monday-night dance class, a very confidently wrong AI agent, and a framework I was able to cross-reference.Continue reading on Medium »
Microsoft is openly competing with OpenAI, Anthropic more than ever
Microsoft pitched its own homegrown AI models, harnesses, and even a Mythos competitor on Wednesday, telling Wall Street it plans for continued growth.
Show HN: A local merge queue for parallel Claude Code agents
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What Is a GPU Server? A Beginner’s Guide to AI Cloud Computing
Artificial intelligence has made GPU servers a hot topic, but many people still aren t sure what they actually are or whether they need Continue reading on Medium »
The Verification Economy Is Bigger Than AI
Artificial Intelligence is transforming how we create, analyze, and automate information. Every week brings a new model, a new Continue reading on Medium »
Mark Zuckerberg predicts that billions of people will have personal AI agents in five years
As Meta pours billions into AI infrastructure and agents, Zuckerberg is working to convince investors that the payoff will be worth the price.