Late interaction seems like it could be the sweet spot between full cross-attention and bi-encoders. Curious how it scales with really large document collections in production, and if we really want that in any case vs. optimizing compaction methods 🙂
MACHINE LEARNING
-
FutureX: Evaluating AI Reasoning and Anticipation in Production
By
–
FutureX tests what most benchmarks miss: Can AI reason, use tools, and anticipate outcomes that haven't happened yet? AI in production isn't about generating content. It's about making decisions in dynamic, uncertain environments. This is where we're seeing strong performance
-
Apollo: Humanoid Robot from Apptronik and NASA Valkyrie
By
–
Apollo: The Humanoid #Robot Built on Apptronik’s Legacy and NASA’s Valkyrie Expertise
— Ronald van Loon (@Ronald_vanLoon) 24 mars 2026
by @Apptronik#Robotics #MachineLearning #ArtificialIntelligence #ML #Innovation pic.twitter.com/8gVtUWzkLTApollo: The Humanoid #Robot Built on Apptronik’s Legacy and NASA’s Valkyrie Expertise
by @Apptronik #Robotics #MachineLearning #ArtificialIntelligence #ML #Innovation -
KNOWIN’s AI-Powered Home Humanoid Robot for Real Homes
By
–
Meet KNOWIN’s #AI-Powered Home Humanoid #Robot Built for Real-Life Messy Homes
— Ronald van Loon (@Ronald_vanLoon) 24 mars 2026
by @CyberRobooo#Robotics #Engineering #ArtificialIntelligence #Innovation #Technology pic.twitter.com/4q1SSfRuipMeet KNOWIN’s #AI-Powered Home Humanoid #Robot Built for Real-Life Messy Homes
by @CyberRobooo #Robotics #Engineering #ArtificialIntelligence #Innovation #Technology -
Viral Workshop on Codex and AI Tips from @aiDotEngineer in London
By
–
My opinion about these things has been pretty consistent I wasn't doing black BLM or rainbow logo or woke stuff on http://
Nomads.com 10 years ago either when everyone else was I didn't like either the radical left or extreme right My foundation has been pretty simple: -
Machine Learning Loss Functions: Comprehensive Guide and Review
By
–
Machine Learning loss functions – infographic Loss Functions in ML Explained: https://
datacamp.com/tutorial/loss-
function-in-machine-learning
… Comprehensive Review of Loss Functions in Deep Learning: https://
arxiv.org/pdf/2504.04242
v1
…
————————
#AI #DataScience #DataScientist #Mathematics -
JEPA Is an Architecture Class, Not a Single Method
By
–
JEPA is a class of architectures, not a method. There were lots of JEPAs before I proposed the name as a unifying concept. For example, Siamese nets (NIPS 1993) are a special case of JEPA (where the predictor is the identity, and the training loss is contrastive as in SimCLR).
-
Multi-Agent Modeling and Geographic Information Systems Practice
By
–
Agent-Based Modeling and Geographical Information Systems: A Practical Primer (GeoSpatial Analytics and GIS) https://amzn.to/3b26CK9 #DataScience #AI #ComputationalScience #SocialScience #NetworkScience #SpatialAnalysis #Simulation
-
Anthropic’s Auto Mode for Claude Code CLI
By
–
Anthropic released Auto Mode for Claude Code CLI, which allows Claude to make its own decisions on which permissions to accept. It is only available on the Team plan in research preview for now. On the desktop app, it is not yet available, but it is in the works.
-
Graph Neural Networks: Essential Guide for Machine Learning Engineers
By
–
Introduction to Graph Neural Networks — A Starting Point for Machine Learning Engineers: http://
arxiv.org/abs/2412.19419 [49-page PDF] #AI #ML #DeepLearning #DataScience #DataScientist