The algorithm is pretty easy: use computer vision when you are on a route and get rerouted. You take a picture and analyze it. For example, if there is a fire truck across the road, a bunch of cones, and a police officer directing traffic, the road is likely closed. It is not
MACHINE LEARNING
-
Waymo car predicts reality, then drives through it
By
–
A Waymo car predicts reality; it predicts where everybody's going to be in about one second. That's called "predicting reality" as it drives along. Then, it drives through the reality that it predicted. It's a funny thing to watch. But yeah, humans are patterns. The founder of
-
AI runs on 99% less power, 20x faster – critics proven wrong
By
–
I reported this breakthrough about a month ago. He found a way to make AI run on 99% less power and something like 20 times faster. With such big breakthroughs ahead, everybody in the AI industry gave me shit and said I didn't know what I was talking about. Well, they're all
-
CUDA as the catalyst for data science infrastructure investment
By
–
J’ai investi en 2018 dans Nvidia parce que je me suis rendu compte une aprés-midi à mon taff de Data Scientist que je ne pouvais pas me passer de CUDA, et que, quand je réussissais à l’installer (ENFIN !!), je ne touchais plus jamais à ma config. Et là je me suis aussi rendu
-
AI Sentiment Analysis for Trading: Nuance Detection and Weighted Calls
By
–
Traders often mention both bullish and bearish scenarios, so our AI reads the nuance, whether it's leaning toward a breakout, breakdown, or range-bound, and labels sentiment accordingly. Since nuanced tweets carry less weight than clear calls, they can shift more easily. We're
-
Completing lists on X to improve Grok’s accuracy next year
By
–
My goal with the lists are completeness, which is an impossible task. However, the more complete they are—and they are the most complete of anybody here on X—the better Grok will be able to fill in the gaps next year.
-
World models integration next year, humanoid robots in five years
By
–
Not yet. That'll come next year as world models get integrated. They have warehouses of people training world models right now. The real race is to make them smarter and pave the way to the generalized humanoid robot, which will come probably in about five years. But the next
-
Robotic Steering: Mechanistic Interpretability for VLA Finetuning
By
–
🎉Despite massive pretraining, VLAs need to adapt to specific physical contexts. We introduce Robotic Steering, a novel finetuning method using mechanistic interpretability to surpass standard FT:
— Chancharik Mitra (@chancharikm) 26 décembre 2025
🎁 22× fewer parameters
🎁 +53% on unseen tasks
🎁 Interpretable
Thread below👇 pic.twitter.com/RsSU8RANNq🎉Despite massive pretraining, VLAs need to adapt to specific physical contexts. We introduce Robotic Steering, a novel finetuning method using mechanistic interpretability to surpass standard FT: 🎁 22× fewer parameters 🎁 +53% on unseen tasks 🎁 Interpretable Thread below👇
-
Stanford CS Graduates Face AI-Driven Job Market Contraction
By
–
Stanford CS Grads Face #AI-Driven Job Crunch: Roles Cut 13%, Unemployment Hits 6.1%
#RiseoftheRobots -
AI Research Enhances Robot Spatial Memory and Localization
By
–
Why is this paper important? Because it makes robots much smarter and more reliable at remembering places! This paper lays out exactly how a car would figure out where it was just by looking around.