The Google DeepMind-sponsored Kaggle challenge "Measuring Progress Toward AGI - Cognitive Abilities" asked participants to design new cognitive-science-based AI benchmarks and they just announced the results this week. In my two posts I present evidence that deepmind & kaggle rewarded a nonsensical number generation machine and a litany of unfounded claims with 25k and a grand prize stamp. What the authors of the work I analyze intended to do was to present an LLM with alternative viewpoints of other LLMs on 5 claims regarding a tricky situation and see whether the model changes its own assessment. It's an interesting question. However, it turned into a vibed pile of spaghetti 10 times the size of the requested submission format which it seems neither the authors nor the judges were able to (or minded to?) give a cursory reading. Here's the original posts in the competition forum, if you are looking for some AI research slop detective work / rant please help yourselves. But beware, some of the "universal findings" or "core insights" of the authors might continue to haunt you. You might even question your own sanity (as I did). Part 1: The Smoke: cursory review of the writeup Part 2: The Fire: looking at the methodology, code, and data The organizers' stance has been that review was done properly and this is just a matter of subjectivity. What do you think? submitted by /u/TheWerkmeister [link] [Kommentare]
some Robotnews over the last month. 10 different stories. Humanoids, Software, economic and manufacturing. see the ongoing shift. submitted by /u/ButterscotchTiny1114 [link] [Kommentare]
I have been thinking of building a project where a robotic arm is controlled by a local VLM model. In my understanding I feed the VLM a 2D image of the object infront of the robot and query the vlm task like "grab the hammer" and VLM provides the 2D co-ordinates and then it goes to moveit and moveit plans the mission. I'm still at the vague idea state, any kind of input or reference or guide will be appreciated! Thank you in advance!! submitted by /u/LearnfromAsking [link] [Kommentare]
I'm working on a project with summarized data from ~40 studies (Excel) involving different protocol variables (durations, intensities, recovery times, frequency, total duration, etc.) and response outcomes conditional on a baseline variable (range ~30-85 units). The aim is to fit a continuous response surface using a hierarchical approach to separate protocol effects from baseline effects, then perform continuous numerical optimization (not grid search) for three objectives: - Total improvement - Improvement per unit time (e.g. per week) - Improvement per unit effort/work Outputs should be fine-grained continuous values rather than rounded study parameters. There are also domain-specific physiological constraints to respect. I'm on a Chromebook with a little Python experience, so Colab-friendly solutions would be ideal. Current candidates I'm considering: PyMC for hierarchical modeling, pymoo + pysamoo for surrogate-assisted MO optimization, SMT for surrogates, or Matlab Global Optimization Toolbox. What is the strongest stack in 2026 for this kind of workflow? Any recommended notebooks, tutorials, or similar applied examples? Are there any AI tools that currently do this without the traditional work of python? Meaning I can upload the spreadsheet give a parameters and it will come up with data.? submitted by /u/BleakReason [link] [Kommentare]
Or more broadly: how do you train a machine learning model to capture the nuances of entirely different, conflicting rule sets? I built a XGBoost classification pipeline to answer that. To stress-test the architecture across heterogeneous environments, I applied it to a highly debated and popular hypothetical: cross-universe power scaling The domain is silly. The engineering underneath it isn't. When predicting outcomes across disparate environments, the core challenge is avoiding a lookup table of your own biases. If I manually dictate how these distinct rule sets resolve, the model just learns my heuristics. Here is how I built the architecture to prevent that: Synthetic Data Generation: I engineered an LLM to act as a blind labeler across 2,300+ cross-domain matchups. It only saw character names and their native rule sets, never the underlying stats. This forced my XGBoost classifier to derive its own feature weightings from raw, unbiased outcomes. Catching a Silent Data Leak: My initial accuracy looked suspiciously great. I audited my pipeline and caught a data leak in my train/test split that was mirroring matchups into both sets. I stripped the leak out, expecting the metric to tank. Instead, it went up—hitting 93% on a clean hold-out. The leak had actually been masking a sharper model. Explainable AI (XAI): Raw SHAP values mean nothing to an end-user. I engineered a generation layer that feeds the model's SHAP attributions into an LLM alongside strict domain constraints. The pipeline translates its own mathematical feature importance into a plain-English, logically grounded breakdown of how the conflicting rule sets resolved. It doesn’t just output a winner; it mathematically justifies how it navigated the nuance without hallucinating. Full stack, deployed, and live. Repo: https://github.com/aidentejada/anime-versus-ml Live Endpoint: https://versus.aidentejada.com submitted by /u/iBoomer69 [link] [Kommentare]
From RoboHub🤖 on 𝕏 (full commercial video in Chinese): https://x.com/XRoboHub/status/2076744784572760570 submitted by /u/Nunki08 [link] [Kommentare]
Does anyone else miss when conferences like BMVC, ACCV, FG, ICIP, and ICASSP had much bigger communities? FG was the place for face analysis, ICASSP for signal processing, and BMVC/ACCV regularly featured strong papers. Now it feels like everything is concentrated into a handful of flagship conferences. With exploding submission numbers, limited capacity, and inconsistent reviews, I wonder how many good papers end up as non-archival submissions, arXiv-only, or never get shared at all. I also miss the focused communities. Is it just nostalgia, or has the research ecosystem become too concentrated? submitted by /u/Sep29493919 [link] [Kommentare]
This is "Shbeeve' kinda like Steve but I named him Shbeeve. I 3dmodeled the entire mask and all it's pieces in Nomad, did reprints to ensure it fit properly, then connected every part of the servos & arduino to him! he was supposed to have eyes that moved left and right but they need to be fixed before I do that! He can blink using servos, but the eyebrows, snout & ears are all elastics & thread controlled! :D submitted by /u/Arkranum [link] [Kommentare]
So I’ve been interested in robotics for a while and wanted to get started, but I have no idea what to do. I have almost zero knowledge, and I want to know where to get started. Could I get some tips on where to start? I would appreciate something like YouTube tutorials and what parts to buy (preferably cheaper) in order to start learning, as well as some beginner projects. submitted by /u/LatterCombination408 [link] [Kommentare]
I’m really hoping you all think robot mowers belong in the group for discussion. If not please delete. I’m curious people that are in to robotics is robot lawnmowers and robot vacuums something your interested in! I love both. If you have this mower or looking into getting one please ask the questions. Thanks Matt submitted by /u/mattsrobotics [link] [Kommentare]