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Hello readers,
Welcome to the AI For All newsletter! Today, we’re talking about a new application for AI in making sense of brain scans, news from around the web, and more!
AI in Action: Reading Brain MRIs
Researchers at Mass General Brigham and Harvard Medical School have developed a new AI foundation model called BrainIAC (Brain Imaging Adaptive Core) that can extract multiple disease risk signals from routine brain MRIs — all without the need for large, labeled datasets. Described in Nature Neuroscience, BrainIAC was trained on nearly 49,000 MRI scans and demonstrated strong performance across seven diverse clinical tasks, including estimating brain age, predicting dementia risk, detecting brain tumor mutations, and forecasting survival in brain cancer patients.
What sets BrainIAC apart is its self-supervised learning approach, which allows the model to learn rich features from unlabeled MRI images before adapting to specific medical tasks. This is a major leap beyond conventional task-specific AI models, which typically require curated, annotated datasets and struggle with image variability across institutions and clinical use cases (e.g., neurology vs. oncology). BrainIAC showed especially strong generalization across healthy and pathological cases and across MRI types, adapting flexibly even when training data was limited.
In benchmarking tests, BrainIAC outperformed three task-specific AI models and rivaled or exceeded human expert performance on complex evaluations, including IDH mutation classification and MGMT promoter methylation prediction in brain tumors (as shown in Figures 3 and 4 of the study). Its robustness across institutions and scan protocols (see Figure 2) suggests it can be widely deployed with minimal fine-tuning.
The research team sees BrainIAC as a promising tool for accelerating biomarker discovery, improving diagnostic accuracy, and helping personalize neurological and oncological care. Future directions include testing the model on larger, longitudinal datasets and other imaging modalities to validate its scalability in real-world clinical settings.
🔥 Rapid Fire
Analysis: The AI Data Center Financial Crisis
Commentary: No, something big isn’t coming
80% of firms report AI had no impact on employment or productivity
CIOs’ AI budgets could be cut or frozen if there’s no ROI by mid-2026
Blue Owl permanently restricts investors from withdrawing their cash 🤔
AMD agrees to rent its own chips from Crusoe in $300 million loan 🙃
Federal Reserve injects $18.4 billion in liquidity—surpassing dot-com bubble
Anthropic raises only $30 billion from 37 investors including Qatar
Meta starts lobbying against state AI regulation so it can burn more money
Neuro-symbolic AI system solves IMO geometry problems with limitations
Frontier LLMs still fail at reasoning and are ‘not the path to AGI’
The consequences of ‘AI psychosis’ and sycophantic chatbots
I hacked ChatGPT and Google's AI and it only took 20 minutes
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📖 What We’re Reading
Artificial intelligence (AI) is silently reshaping how site reliability engineering (SRE) operates. Instead of people reacting after something breaks, AI-powered systems now detect issues before they occur. As applications become increasingly intertwined, machines handle what humans can’t, such as continuously monitoring countless metrics simultaneously. When problems arise, intelligent software responds faster and adapts more effectively than any single expert could.
