MACHINE LEARNING - AI Dynamics

AI Framework Creates Shadow Art from Object Scans

By

–

11 June 2026 18h53

Framework generates 'shadow art' from scan of any object
by Tom Fleischman @TechXplore_com Learn more: https://
bit.ly/4uYO5VB #GenerativeAI #ArtificialIntelligence #MachineLearning #ML

→ View original post on X — @ronald_vanloon

11 June 2026

@clementdelangue — 2026-06-11

By

@clementdelangue

–

11 June 2026 18h51

HF est devenu la meilleure plateforme de stockage pour les modèles et les jeux de données PRIVÉS et PUBLICS, qu'ils soient intermédiaires ou finaux ! Excellent exemple de @heyjasperai qui a utilisé les buckets HF pour stocker leur jeu de données Monet et entraîner des modèles

→ View original post on X — @clementdelangue

11 June 2026

Deep AI, Questioning the Status Quo, Human Purpose

By

@nvidia

–

11 June 2026 18h43

Our CEO Jensen Huang on the opportunity ahead: use AI deeply, challenge the status quo, and stay focused on the human purpose behind the work.

Every job has tasks. People give those tasks purpose, and AI can help turn that purpose into greater impact. pic.twitter.com/al0ghqrqvL
— NVIDIA (@nvidia) 11 juin 2026

Our CEO Jensen Huang on the coming opportunity: using deep AI, questioning the status quo, and staying focused on the human purpose behind the work. Every job has tasks. People give meaning to those tasks, and AI can help transform

→ View original post on X — @nvidia

11 June 2026

Speculative decoding makes LLMs 8.5x faster without accuracy loss

By

@akshay_pachaar

–

11 June 2026 18h38

Researchers found a way to make LLMs 8.5x faster!

(without compromising accuracy)

Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference.

A small "draft" model first generates the next several tokens, then the large… https://t.co/JCdqjCKcKU pic.twitter.com/HbKmRqdF5P
— Akshay 🚀 (@akshay_pachaar) 11 juin 2026

Researchers found a way to make LLMs 8.5x faster! (without compromising accuracy) Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference. A small "draft" model first generates the next several tokens, then the large

→ View original post on X — @akshay_pachaar

11 June 2026

Standardization of inter-paradigm evaluation for tabular encoders

By

@_akhaliq

–

11 June 2026 18h13

TRL-Bench Standardization of representation-level inter-paradigm evaluation for tabular encoders

→ View original post on X — @_akhaliq

11 June 2026

Anthropic’s Fable 5 surpasses competitors in visual tasks

By

@bamitav

–

11 June 2026 18h06

🤖 Anthropic's Claude Fable 5 Outperforms Peers

Real tests show #Claude #Fable5 beating Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5 on tough visual tasks like 3D hydrodynamics and complex physics. The gap is significant.

Fable 5 is Anthropic's most powerful model open to all… pic.twitter.com/VMf5HPnkuu
— Amitav Bhattacharjee (@bamitav) 11 juin 2026

Real tests show that #Claude #Fable5 beats Claude Opus 4.8, Gemini 3.1 Pro and GPT 5.5 on difficult visual tasks such as 3D hydrodynamics and complex physics. The gap is significant. Fable 5 is the

→ View original post on X — @bamitav

11 June 2026

Duration of work sessions and cost comparison between Codex and Fable

By

@skirano

–

11 June 2026 18h06

It really depends on the duration of your work sessions. I run loops on Codex on average for 5/10 hours. This would be incredibly expensive with Fable.

→ View original post on X — @skirano

11 June 2026