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What happens after a town de-Flocks
Demonstrators hold a banner against Flock security cameras during a protest outside the G20 Innovation Ministerial in Chapel Hill, North Carolina, US, on Wednesday, Sept. 2, 2026. Photographer: Cornell Watson/Bloomberg via Getty Images | Bloomberg via Getty Images From Florida to Washington state, local governments across the country are canceling their contracts with Flock, the company behind the controversial AI-enabled license plate readers, in response to public outcry. But ending the contracts doesn't necessarily mean the cameras will come down immediately - or that the information they've gathered will be deleted anytime soon. Even as state and local governments decide to rein in their surveillance capabilities, uncertainty remains about what Flock can and will do with data that has already been collected. Each jurisdiction's contract is different, but many of them let Flock retain data long af … Read the full story at The Verge.
Top Digital Transformation Companies in 2026
Digital transformation has moved from a buzzword to a business necessity, with organizations across every industry rethinking how Continue reading on Medium »
Beyond Prompt Engineering: What Actually Makes an LLM Agent Reliable?
I compared a prompt-only LLM agent with a tool-grounded transactional agent across 20 controlled tests. The results were more nuanced than Continue reading on Medium »
Robots in society, business and culture: August 2026
Credit: Baumis Robots (Karlsruhe Institute of Technology). By Emmet Cole In our new monthly series, we showcase a selection of robotics stories from society, business, research, and culture, as we track the field’s ongoing journey from specialized industrial machines to an increasingly visible social phenomenon. Reshoring reimagined? In early August, The Information published an article [ ]
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My First Step into Data Science
Exploring the basics of data, Data Science, AI, and the roles that turn raw data into meaningful insights.Continue reading on Medium »
AI Safety Is Becoming a Business Priority — Here’s Why It Matters
Artificial Intelligence is developing much faster than the majority of companies can keep up. Safety has become one of the major issues Continue reading on Medium »
Welcome to the AGI Era — OpenAI’s Boldest AI Announcement Yet
The long-awaited next chapter in artificial intelligence may have arrived.Continue reading on Towards AI »
Getting Fired by a Machine in California Will Soon Cost Your Boss $500.
A bill sitting on Gavin Newsom s desk until 30 September doesn t stop the robot from firing you. It makes the company write down that the Continue reading on Medium »
Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving
arXiv:2609.11947v1 Announce Type: new Abstract: Increasing safety is the primary objective of automated vehicles. Achieving this goal requires reliable safety metrics that incorporate safety-relevant factors such as object type, velocity, and criticality. A key capability of such metrics is the distinction between critical and non-critical objects, which is addressed through criticality or relevance estimation. Existing criticality metrics are typically designed for specific scenarios and primarily focus on vehicle-to-vehicle interactions. In this paper, we therefore propose a novel criticality metric tailored to vulnerable road users (VRUs), which require special consideration due to their less predictable motion behavior. Furthermore, to avoid the complexity introduced by scenario-specific metrics, we introduce a scenario-independent criticality prediction framework applicable to all traffic participant classes. The effectiveness of both the proposed VRU-centric criticality metric an
Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators
arXiv:2609.11935v1 Announce Type: new Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs). However, providing rigorous uncertainty estimates remains an open challenge. We propose Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into the nonconformity score of split conformal prediction, producing prediction intervals that are (i) distribution-free with provable coverage guarantees, and (ii) spatially adaptive when the PDE residual correlates with prediction error -- tighter where physics is well-satisfied, wider where it is violated. Additionally, we prove that FNO's translation equivariance creates a fundamental approximation barrier for PDEs with Dirichlet boundary conditions, and show that coordinate channels resolve this with up to 63x error reduction. We validate PI-CP across six physics scenarios -- heat conduction (2D/3D), structura
Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence
arXiv:2609.12036v1 Announce Type: new Abstract: In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation: a 28-dimensional action value space covering most mainstream embodiments, keeping one model valid across heterogeneous devices. (2) Action-visual injection: URDF- and camera-rendered action videos bridge actions and pixels, giving markedly better controllability across embodiments, scenes, and tasks (PSNR +0.904 over alternative fusion baselines). (3) Sparse mixture-of-experts (MoE): sparse MoE layers add capacity for heterogeneous dynamics and absorb the action modality while reducing inter-modality conflict (FVD -6.530 vs. the dense backbone). (4) Efficient rollout generation: causal adaptation and few-step distillation yi