machine learning
Transcript Summary: Vivek Gupta on AI Leadership, Engineering Growth & MAIDAP Program Vivek Gupta Director of the AI Rotational Program at Microsoft Over 30 years of experience in consulting, software development, product work, customer engagement, and 12+ years focusing on AI and machine learning. Worked initially at Nokia with maps
ICML 2026
# November 6, 2025 – 13:26 (Beijing) --- ## 🚫 Direct Rejection for Non-Compliance  Failure to meet ICML 2026 submission requirements will lead to **immediate rejection** without review. --- ## 📢 ICML 2026 Call for Papers – Key Policy Changes 
Artificial Life
Artificial Life Research Report --- Introduction For years, Artificial Life (ALife) research has revolved around a central question: Can the complexity of life naturally emerge within computational systems? At the core of this inquiry is open-ended complexification — the idea that artificial systems, like biological organisms, can spontaneously generate new structures
Apple AI
Pico-Banana-400K: High-Quality Dataset for Text-Guided Image Editing Pico-Banana-400K is a curated dataset of 400,000 images developed by Apple researchers to advance text-guided image editing models. The dataset was generated through the following pipeline: * Source Images: Real photographs from the Open Images collection. * Image Editing: Google's Nano-Banana applied
AI Models
The Third Scaling Paradigm in AI Emu3.5 Ushers in a Native Multimodal World New Intelligence Report Editor: Peach Sleepy --- Executive Summary The third scaling paradigm in AI is here — Emu3.5. With 34 billion parameters, trained on the equivalent of 790 years of long-form video, this native multimodal
AI course
# Datawhale Course **Course:** AI Research Foundations **Compiled by:** Datawhale --- ## Learn Artificial Intelligence from Top Experts Google DeepMind, in collaboration with **University College London (UCL)**, has launched a free course — **AI Research Foundations**. This course is designed to help learners: - Gain a **deep understanding of Transformers**. - Develop **hands-on
AI peer review
New Intelligence Report: AI and the Future of Peer Review --- When AI Decides Which AI Papers Get Accepted In 2026, an AAAI conference reviewer described on Reddit what they called “the strangest review process ever”. They claimed strong papers were rejected, weak papers advanced, and suspected “connections” were at
LLM training
What It Really Takes to Train a High-Performance LLM Today Published research looks neat and logical: A carefully designed architecture, curated datasets, and adequate compute yield ideal results. Papers present perfect experiments, clean ablation studies, and “obvious” decisions — viewed with hindsight. Reality is messier: They rarely capture 2:00 a.
AI alignment
MLNLP Academic Talk — November 1, 2025 Location: Jilin Time: 09:00–10:00 Speaker: Jia Ming Ji, Peking University Topic: The Two Sides of Intelligent Alignment: From Alignment Resistance to Deceptive Alignment --- About MLNLP MLNLP is a renowned machine learning and natural language processing community. Its audience includes: * Domestic
AI agents
# Understanding AI Agents: From Basics to Real-World Applications Imagine asking a digital assistant to help plan a weekend trip. Instead of simply searching for flights when you type **"flights to Paris"**, it understands your broader goal. It checks flight options, compares hotel prices, looks up the weather, and
AI training
Interpreting Thinking Machines Lab’s Latest Research: On‑Policy Distillation --- Introduction: Rethinking How Machines Learn Imagine you’re teaching a student to write an essay. * Traditional way: Give them ten sample essays and tell them to imitate. * → This is imitation learning. * Problem: Faced with a new topic, they struggle.
LLM
2025-10-28 00:01 — Jilin The system reviewed the overall evolution of large language models (LLMs) in data analysis. --- Large Model Intelligence｜Sharing Source: Machine Heart --- Background Traditional data analysis typically requires manual workflows—writing SQL, running Python scripts, and manually interpreting results. Limitations of this traditional approach include: