When AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
More from the blog
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.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
GenEye in a Box: Making Machine Vision Something You Can Just Ask For
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Using AI to mitigate the growing environmental threat of data centers
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Discovering the value of humanistic inquiry
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Related papers
Token-Mol 1.0: tokenized drug design with large language models
Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...
Computational and AI-Driven Ecosystem for Structure-Based Covalent Drug Discovery.
This Account describes a computational and AI-driven ecosystem for structure-based covalent drug discovery and dives into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization.
AI-Powered Anomaly Detection in Cloud-Based Applications
The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.
Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach
A generalized Multimodal Subspace Support Vector Data Description model with graph-embedded regularization is proposed, illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.