5 GitHub repositories to learn quantum machine learning
Image by author # Introduction to Quantum Machine Learning Quantum machine learning combines ideas from quantum computing and machine learning. Many researchers are studying how quantum computers can help with …
AI needs a strong data fabric to deliver business value
Without that context, AI can generate answers quickly but still make the wrong decisions, says Irfan Khan, president and chief product officer of SAP Data & Analytics. “AI is incredibly …
Building Trust Through Cryptographic Proofs
AI system Can now execute arbitrary tasks autonomously – running code, invoking external APIs, and making decisions without direct human oversight. This creates a fundamental trust problem: When an agent …
Anthropic’s most dangerous AI model fell into the wrong hands
Anthropic’s Mythos AI model, a powerful cybersecurity tool that the company said could be dangerous in the wrong hands, has been accessed by “a small group of unauthorized users.” bloomberg …
Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets in Specific AI Domains
Training powerful AI models depends on a resource that is quietly running out: specialized data. While the Internet provides an unlimited supply of text and images to train today’s generalist …
AI response to elections is coming
Ask Americans how they feel about AI most say They are worried. Communities have protested and halted data center projects across the US. On social media, anger at AI companies …
Chinese workers left terrified as bosses instructed them to train their AI replacements
Sign up today to see the future Cutting-edge innovations in science and technology cannot be forgotten For years, a busy Silicon Valley startup called Mercor has been hiring an army …
A coding implementation on Microsoft’s Phi-4-Mini for use with the Quantized Inference Logic tool for fine-tuning RAG and LoRA
import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run((sys.executable, “-m”, “pip”, “install”, “-q”, *args), check=True) pip_install((“huggingface_hub>=0.26,<1.0”)) pip_install(( “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, )) for p in glob.glob(os.path.expanduser( …