Simple considerations for simple people building fancy neural networks
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GPT-Lab
· gpt-lab.eu
Oct 14, 2026
Building trustworthy AI: The role of AI harnesses, intent contracts and evals
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
How artificial neural networks build their own learning machinery
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Q&A: Building accountability behind our words — and AI’s — in civic conversations
Professor Deb Roy contends there should be someone who is “answerable” for every piece of text in public life, no matter its origins.
3 Questions: What is the best path forward for AI in academia?
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
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Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
Making the leap to a software platform strategy: Issues and challenges
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
A framework for systematic analysis of open access journals and its application in software engineering and information systems
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.