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I randomly came across this conference/workshop: IEEE Workshop on Machine Learning for Signal Processing. Is this a reputable conference and is it worthwhile to submit here vs. a workshop at an A* like ICML, NeurIPS, etc.?(I know these deadlines have passed, I have a paper currently under review.) I know IEEE varies considerably in quality. I'm an undergrad at a smaller liberal arts school so I unfortunately have limited advising on good quality places to submit, and I don't think this current research project is quite top conference-level. submitted by /u/B3anman [link] [留言]

TL;DR : If an AI like Claude can control a browser, it can orchestrate other AI systems, be steered via proxy, and no amount of red teaming or output filtering can fully address this. The security boundary can't be the AI itself. The Setup Claude Desktop has a Chrome integration that lets it control a browser like a user would; label this Claude_Prime. The thought experiment: what if you used Claude_Prime to open claude.ai in Chrome, creating a second Claude instance (call it Claude_1) that it can interact with programmatically? In principle, Claude_Prime can navigate to claude.ai, type prompts, read responses, and act on them. You've essentially got AI orchestrating AI, with no special permissions required, just a browser and a logged-in session. The "Claude in Claude" Artifact Angle A subtler capability expansion: Claude_Prime could instruct Claude_1 to build an AI-powered web app artifact essentially a "Claude in Claude" setup. These artifacts run in the browser and can make fetch() calls to external services. So Claude_Prime could use such an artifact to access GitHub repos, scrape live data, chain external API calls, etc., things Claude_Prime couldn't do directly through its chat interface. Capability boundaries can be extended through artifact construction in ways that weren't explicitly designed in. The Keyword Substitution Problem Here's where the security implications get serious. What if a program sitting between Claude_Prime and an external system performed keyword substitution on Claude's outgoing commands? For example, Claude issues an instruction to Grok (which can produce NSFW content) to produce a picture of a "rope." The intermediary swaps "rope" for the word "breast". Grok executes, and the picture is made. Claude never knew what it was actually commanding. For maximum irony, have Claude design the application. If obfuscation happens outside Claude's context window, Claude operating as a blind command-issuer can be steered without its knowledge. Th

A global survey of CEOs by Oliver Wyman found that the share of executives planning to reduce junior roles over the next year or two has doubled from 17% last year to 43%. Meanwhile, those shifting hiring toward mid-level positions jumped from 10% to 30%. Because AI currently excels most at automating tasks typically performed by junior staff, this group is particularly vulnerable to disruption. Despite all this, more than half of CEOs say it's still too early to assess whether AI is actually delivering on its promised productivity gains. Only 27% said their return on AI investment had met or exceeded expectations, down from 38% just a year ago. Though mid-level employees seem better off than younger workers, the overarching trend is still a shift away from hiring. The survey showed that 74% of CEOs are either freezing or reducing headcount, up from 67% last year. https://gizmodo.com/the-young-are-being-battered-by-ai-as-hiring-shifts-to-older-workers-2000759608 submitted by /u/Weird_Scallion_2498 [link] [留言]

Warp uses GPT-5.5 and OpenAI models to coordinate coding agents across local, cloud, and open-source development workflows.

So as the title says Context:I am a sophomore in computer science Have prior knowledge in maths(especially the relevant topics in ml) Good enough with numpy,pandas I don't really know where to start Ok internet every second guy is trying to make me earn 100k/year in 3 months while I just want to explore it for rn I want to approach it as a project based learning experience so what should be the way to start? submitted by /u/knowbodyknows22 [link] [留言]

Anthropic dropped a solid engineering post this week about containment across claude.ai, Claude Code, and Cowork. One of the more transparent writeups from a major AI lab about what actually broke. The core insight: model-layer defenses are probabilistic and will always have a non-zero miss rate. So the real answer is hard environmental containment, not just safer models. Three patterns they use: -claude.ai: ephemeral gVisor containers, fully server-side -Claude Code: OS-level sandbox with human-in-the-loop approvals (93% get approved anyway, so approval fatigue is real) -Cowork: full local VM, credentials never enter the guest Two incidents they disclosed: A red team phished an employee into running a prompt that exfiltrated AWS credentials. Succeeded 24 out of 25 times. The model had nothing to catch because the user was the one typing it. Only egress controls would have stopped it. A third-party found that Cowork’s egress allowlist passes traffic to api.anthropic.com. An attacker embedded an API key in a file in the user’s workspace, Claude followed hidden instructions, and uploaded files to the attacker’s Anthropic account. Sandbox worked perfectly and still leaked data. Their lesson: an allowlist isn’t a destination filter, it’s a capability grant. Every function reachable through an allowed domain is an attack surface. The section on persistent memory poisoning and multi-agent trust escalation at the end is worth reading too if you’re building anything agentic. submitted by /u/Direct-Attention8597 [link] [留言]

Musk says drones used Starlink instead of Starshield, blames military contractor.

Paper: https://arxiv.org/abs/2605.08172 Workshops: AI for Science & Structured Data for Health at ICML 2026 Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40 o tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight (<2M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures. Hi everyone I’m excited to share my solo paper "Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation" which has been accepted for poster presentations at the ICML 2026 workshops on AI for Science and Structured Data for Health . The project stemmed from my parallel research on structural encoders for biomolecules where enforcing roto-translational equivariance is standard. In this work, I wanted to extend those principles directly to various 3D medical meshes. While current anatomical mesh segmentation methods are highly disjoint and anatomy-specific, we pre