Anthropic blames dystopian sci-fi for training AI models to act “evil”

In a recent technical post on Anthropic’s Alignment Science blog (and an accompanying social media thread and public-facing ...
GitHub

advanced_claude_opus_4_6_invoke_model_stream_example.py

bedrock = boto3.client("bedrock-runtime", region_name="ap-south-1") # Global CRIS model ID for Claude Opus 4.6 MODEL_ID = "global.anthropic.claude-opus-4-6-v1" def demo_adaptive_thinking_stream(): ...
InfoWorld

12 model-level deep cuts to slash AI training costs

Stop throwing money at GPUs for unoptimized models; using smart shortcuts like fine-tuning and quantization can slash your ...
IEEE

Model-Free Adaptive Control of Discrete-Time Nonlinear Systems with Embedded Multimodel Framework: Approach and Example

Abstract: This article introduces a novel approach that combines a multimodel technique with model-free adaptive control (MFAC) to address the limitations of the full-form dynamic linearization (FFDL) ...
GitHub

example_3.py

# Make sure the seed goes OUTSIDE the function. It makes the random data # the same every time, but we want DIFFERENT results each time we run it # (but the same set of different results, thus the ...
Journal of Medical Internet Research

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality

This viewpoint synthesizes recent comparative studies and simulation findings to argue that there is no universal best modelling approach. Model performance depends heavily on dataset characteristics ...