Periodic Labs Seeks $500 Million Funding at $7.5 Billion Valuation for AI Scientific Discovery

Periodic Labs, an innovative startup focused on creating an AI scientist capable of conducting autonomous experiments, is in advanced discussions to raise at least $500 million at a valuation of $7.5 billion. The funding round, led by AMP, has reportedly been significantly oversubscribed, with potential for an additional round soon. Since its inception in September with a $300 million seed round at a $1.3 billion valuation, Periodic Labs has rapidly gained traction, aiming to revolutionize scientific discovery through automated laboratories that perform thousands of experiments. The company, co-founded by former OpenAI researcher Liam Fedus and ex-Google DeepMind scientist Ekin Dogus Cubuk, is currently focused on identifying new superconductors and collaborating with the semiconductor industry. Periodic Labs has attracted top talent from prestigious AI organizations, emphasizing the pursuit of autonomous scientific discovery as a critical goal within the AI community.

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CybersecurityAI+2

CybersecurityAIFinanceRisk Management

IMF Issues Warning on Potential Systemic Risks of New AI Models to Financial Sector

The International Monetary Fund (IMF) has issued a warning regarding the potential for 'systemic' shocks to the finance sector due to new AI models. The organization emphasizes the need for preparations to address the 'inevitable' AI-enabled breaches that could compromise the cyber defenses of financial institutions. This alert highlights the growing concerns about the intersection of advanced AI technologies and financial stability.

AIData Centers+2

AIData CentersAMDNvidia

AMD Shares Surge on Strong AI-Driven Sales Forecast

Advanced Micro Devices Inc. (AMD) experienced a significant increase in stock price, reaching a record high in early trading, driven by robust demand for AI computing chips. The company's revenue forecast for the second quarter is projected to be $11.2 billion, with a variance of $300 million, surpassing the average analyst estimate of $10.5 billion. This uptick in forecasted revenue is attributed to heightened spending in data centers.

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LLMGenerative AI+2

LLMGenerative AINLPEmbedding

Innovative Low-Cost Method for Detecting LLM Hallucinations Using Dynamical System Theory

Large Language Models (LLMs) are prone to generating plausible but non-factual content, termed hallucination. Current detection techniques often involve costly sampling-based checks or external knowledge retrieval. Researchers propose a novel approach that treats LLMs as black-box dynamical systems, projecting responses into a high-dimensional manifold through an embedding model. This method utilizes Koopman operator theory to fit transition operators for both factual and hallucinated outputs, establishing a differential residual score from prediction errors. To meet diverse user needs, a preference-aware calibration mechanism optimizes classification thresholds based on limited demonstrations. Testing across three data benchmarks reveals that this method achieves state-of-the-art performance with significantly lower resource requirements.

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SafetyGenerative AI+2

SafetyGenerative AIOpenAIChatGPT

OpenAI Launches Trusted Contact Safety Feature in ChatGPT

OpenAI has unveiled a new optional safety feature called Trusted Contact in ChatGPT, designed to enhance user safety by notifying a trusted individual if serious self-harm concerns are detected. This initiative aims to provide additional support for users in distress, ensuring they have access to help when needed.

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Computer VisionNLP+2

Computer VisionNLPGenerative AIMedical Imaging

WALDO Framework Enhances Zero-Shot Anomaly Localisation in Medical Imaging Using Vision-Language Models

Research presents WALDO, a training-free framework for zero-shot anomaly localisation in medical imaging, leveraging vision-language models (VLMs) to improve rare pathology detection. The framework reformulates anomaly detection as a comparative inference problem, utilizing entropy-weighted Sliced Wasserstein distances for anatomically-aware reference selection, Goldilocks zone sampling for optimal reference similarity, and self-consistency aggregation through weighted non-maximum suppression. Theoretical analysis indicates that moderate similarity references minimize bias-variance trade-offs in visual reasoning. Evaluated on the NOVA brain MRI benchmark, WALDO with Qwen2.5-VL-72B achieved 43.5% mAP@30, a 19% relative improvement over zero-shot baselines, with statistical significance confirmed by paired McNemar tests ($p<0.01$). Source code is available on GitHub.

arXiv AIRead →

Coding AgentsBenchmark+2

Coding AgentsBenchmarkSoftware EngineeringAI Evaluation

SWE Atlas Launches Comprehensive Benchmark Suite for Evaluating Coding Agents

SWE Atlas, Scale AI's newly completed benchmark suite, evaluates coding agents across professional software engineering tasks, measuring performance on 284 tasks that encompass understanding, validating, and maintaining code. The suite features a live Refactoring leaderboard alongside Codebase QnA and Test Writing benchmarks. Despite advancements, agents display significant gaps in investigating systems, writing precise tests, and executing complete code refactors, highlighting a broader limitation in their ability to deliver comprehensive engineering solutions. Reliability remains a concern, with models succeeding on individual attempts but struggling to maintain consistency across multiple trials. Current model scores are available on the live leaderboard, with top systems achieving scores in the 40s but none exceeding 50%.

Scale AI BlogRead →