Background: Major depressive disorder (MDD) is increasingly viewed through a neuroplasticity lens, with developmental synaptic pruning emerging as a potential core liability. Genetic evidence implicates pruning pathways, while rapid-acting antidepressants like ketamine promote synaptogenesis, suggesting that excessive early elimination leaves circuits vulnerable to later stress. Few computational models, however, capture the specific MDD pattern of latent fragility collapsing under perturbation, followed by recovery via limited plasticity enhancement.Methods: An overparameterized feed-forward neural network (∼396,000 parameters) was trained on a […]

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Today, OpenAI released a new result.  We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros. This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far. The agents we train consistently outperform two-week old agents with […]

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As artificial intelligence (AI) technologies, particularly generative and collaborative learning models— are increasingly integrated into healthcare and other sensitive domains, data privacy, security, and fairness concerns have grown significantly. This paper focuses on a thorough examination of current privacy-preserving AI models, including federated learning (FL), differential privacy (DP), homomorphic encryption, and generative adversarial networks (GANs). Key contributions are reviewed across recent works that explore privacy-preserving mechanisms within domains such as clinical diagnostics, drug discovery, Internet of Medical Things […]

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Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over […]

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There are two things from our announcement today I wanted to highlight. First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that.  Our initial conception when we started OpenAI was that we’d create AI and use it to […]

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arXiv:2601.16406v1 Announce Type: new Abstract: Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, customer support, aviation safety, where positive outcomes are infrequent yet potentially catastrophic. Extreme class imbalance biases conventional models toward majority-class predictions, limiting recall, calibration, and operational usefulness. We propose LPCORP (Low-Prevalence CORrector for Prediction)*, a two-stage framework that combines reasoningenhanced prediction with confidence-based outcome correction. A reasoning model first produces enriched predictions from narrative inputs, after which a lightweight logistic-regression classifier […]

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