Nice work. Couple questions:
1. Tensor logic already incorporates probability and graphical models (Section 4.4. of paper). Is your goal to support nonstandard uncertainty calculi?
2. Efficient GPU implementation via tensor factorizations is part of tensor logic’s design (Section
MACHINE LEARNING
-
Tensor Logic Probability Models GPU Implementation Advances
By
–
-
L1 Penalty Limitations: Overshooting Zero with Standard Optimizers
By
–
L1 penalty doesn’t actually make truly sparse features with standard optimizers either — it overshoots zero back and forth.
-
AI Predictive Analytics Reduces Pediatric Hospital No-Show Rates
By
–
SAS + Pinnacle Solutions = improved pediatric care.🤝
— SAS Software (@SASsoftware) 16 décembre 2025
Children’s Specialized Hospital reduced missed appointments using Pinnacle’s Predictive Health Solutions Patient No-Show Predictor, powered by SAS Viya and hosted on AWS. https://t.co/litERGcYIE pic.twitter.com/tWsfmiH9ObSAS + Pinnacle Solutions = improved pediatric care. Children’s Specialized Hospital reduced missed appointments using Pinnacle’s Predictive Health Solutions Patient No-Show Predictor, powered by SAS Viya and hosted on AWS. http://
2.sas.com/6017CMkmx -
AI Implementation at Major For-Profit Hospital Chain
By
–
How AI is already playing a role at the country's largest for-profit hospital chain
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @HaroldSinnott @fogoros @iainljbrown @NandoDF @katecrawford @drhassanrashidi @YuHelenYu -
Don’t Trust Verify: Research on AI Model Reliability
By
–
[1] Don't Trust: Verify (ICLR 2024) https://
proceedings.iclr.cc/paper_files/pa
per/2024/file/0a79ecda13603817de4cdfc68b417e89-Paper-Conference.pdf
… -
DTV Math Model: CPU-First Approach vs Symbolic Specialization
By
–
The DTV paper[1] from 2024 uses a 62B model specialized in math and reasoning, building up from 48.6% baseline by putting more emphasis on the symbolic side. What if we did the opposite, matching the baseline with specialized system on CPU and adding more parameters to improve?
-
AI recognition problem, not seeing, fixable with more training
By
–
Because it is time for everyone else to catch up. This was NOT a "seeing" problem. Camera saw it. It's an AI problem. It didn't recognize it properly, or make the right moves. This will be fixed with more training. AI is an exponentially learning system. Train it on one
-
Emotionally partitioned AI agents switch personas for different audiences
By
–
You compartmentalize your emotions, why not your AI?
Emotionally partitioned AI agents are single AI systems that switch between different personas for regulators, customers, and internal teams, adjusting tone, priorities, and emotional style to match each audience. This -
SAM Audio: Unified Model for Sound Isolation from Complex Mixtures
By
–
🔉 Introducing SAM Audio, the first unified model that isolates any sound from complex audio mixtures using text, visual, or span prompts.
— AI at Meta (@AIatMeta) 16 décembre 2025
We’re sharing SAM Audio with the community, along with a perception encoder model, benchmarks and research papers, to empower others to… pic.twitter.com/FuMJyULmJRIntroducing SAM Audio, the first unified model that isolates any sound from complex audio mixtures using text, visual, or span prompts. We’re sharing SAM Audio with the community, along with a perception encoder model, benchmarks and research papers, to empower others to
-
SAM Audio: Advanced Audio Separation Technology Outperforms Previous Models
By
–
SAM Audio represents a significant advancement in audio separation technology, outperforming previous models across a wide range of benchmarks and tasks.