Chronological feed of everything captured from Google Brain.
blog / googlebrain / 2d ago
Machine Learning (ML) approaches are being developed to accelerate chemical property prediction, traditionally limited by the computational cost of methods like Density Functional Theory (DFT). Google Brain and collaborators have developed Message Passing Neural Networks (MPNNs) that significantly outperform baseline ML models on the QM9 benchmark. This advancement enables faster and potentially more efficient exploration of chemical space for drug discovery and materials science, though further research is needed for broader applicability beyond the QM9 dataset.
machine-learningquantum-chemistrygraph-neural-networksdrug-discoverygoogle-brain
“Machine learning models can predict chemical properties using DFT-generated training data.”
blog / googlebrain / 2d ago
Tensor2Tensor (T2T) is an open-source TensorFlow-based library designed to standardize and accelerate deep learning research. It provides a flexible, modular architecture, enabling rapid experimentation with various models and datasets. T2T includes state-of-the-art models and best practices, aiming to lower the barrier to entry for deep learning research and foster community contributions.
deep-learningtensorflowopen-source-softwaremachine-learning-frameworksnatural-language-processing
“Deep learning research is hindered by unique, problem-specific system setups requiring significant engineering effort.”
blog / googlebrain / 2d ago
The Google Brain team prioritizes both fundamental advancements in machine learning theory and application-driven research to integrate AI into Google products. This dual strategy involves fostering an environment where researchers pursue broad explorations of new ideas, often publishing in top-tier conferences, while also collaborating externally and developing open-source tools to disseminate knowledge and cultivate future talent. This approach ensures significant real-world impact through a balance of curiosity-driven and application-driven initiatives.
google-brainmachine-learning-researchai-applicationsdeep-learningacademic-collaborationml-education
“The Google Brain team's mission is to make machines intelligent and improve people's lives.”
blog / googlebrain / 2d ago
Self-supervised contrastive learning, particularly with the novel Multi-Instance Contrastive Learning (MICLe) approach, significantly improves medical image classification accuracy and robustness to distribution shifts. This method outperforms traditional supervised pre-training by leveraging unlabeled natural and medical images, addressing the scarcity of labeled medical data. The three-step approach involves self-supervised pre-training on natural images, followed by additional self-supervised pre-training on unlabeled medical data (using SimCLR or MICLe), and finally, task-specific supervised fine-tuning on labeled medical data. This leads to more label-efficient models suitable for clinical deployment.
medical-imagingdeep-learningself-supervised-learningcontrastive-learningmic-lemedical-diagnosticscomputer-vision
“Self-supervised pre-training significantly improves medical image classification accuracy compared to supervised pre-training.”
blog / googlebrain / 2d ago
EfficientDet is a family of object detectors that achieve state-of-the-art accuracy with significantly reduced model size and computational cost. This is achieved through the integration of an EfficientNet backbone, a novel bi-directional feature network (BiFPN), and a new compound scaling method. These optimizations allow EfficientDet to adapt to diverse resource constraints, making high-accuracy object detection feasible for real-world applications in robotics and autonomous driving.
object-detectionefficientdetcomputer-visionneural-networksdeep-learningmodel-optimizationresource-constrained-ai
“EfficientDet achieves state-of-the-art accuracy in object detection while being up to 9x smaller and using significantly less computation than previous models.”
blog / googlebrain / 2d ago
The ReAct paradigm enables language models to combine verbal reasoning with text actions, leading to improved performance in various tasks. This approach allows LMs to dynamically interact with external environments, integrating observations into their reasoning processes. ReAct outperforms models that rely solely on reasoning or acting, and it facilitates human-in-the-loop collaboration by allowing intervention in reasoning traces. The method has been successfully applied to question answering, fact verification, and interactive decision-making tasks.
language-modelsreasoningactingpalm-540bchain-of-thoughtai-agentsinteractive-environments
“The ReAct paradigm integrates reasoning and acting in language models.”
blog / googlebrain / 2d ago
CoCa (Contrastive Captioner) is a novel encoder-decoder model that unifies contrastive and captioning losses, enabling state-of-the-art performance across diverse vision and vision-language tasks. This architecture effectively merges single-encoder, dual-encoder, and encoder-decoder paradigms, providing flexibility and efficiency. CoCa demonstrates strong performance in zero-shot learning and with frozen encoders, often surpassing fine-tuned specialized models.
image-text-modelsfoundation-modelscomputer-visionnatural-language-processingzero-shot-learningcontrastive-learninggoogle-brain
“CoCa achieves state-of-the-art results across various vision and vision-language tasks.”
blog / googlebrain / 2d ago
Google Research and collaborators demonstrate a significant alignment between the internal contextual embeddings of LLMs and human brain activity during natural language processing. This suggests LLMs can serve as a computational framework for understanding neural language processing. The research highlights both shared computational principles and fundamental architectural differences between LLMs and the human brain.
llmsneurosciencenatural-language-processingcognitive-scienceembeddingsspeech-recognition
“Neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within LLMs during everyday conversations.”
blog / googlebrain / 2d ago
A new AI system, powered by Gemini, automates the creation and optimization of empirical software for scientific hypothesis evaluation. This system generates research ideas, implements them as executable code, and iteratively validates performance using tree search to explore thousands of code variants. It has demonstrated expert-level results across diverse scientific domains, significantly accelerating discovery by reducing development time from months to hours or days.
ai-for-scienceempirical-softwarelarge-language-modelscode-generationscientific-discoverymulti-disciplinary-researchgemini-api
“The AI system achieves expert-level performance on diverse scientific problems.”
blog / googlebrain / 2d ago
Nested Learning is a novel machine learning paradigm that reframes models as interconnected, multi-level optimization problems. This approach unifies model architecture and optimization algorithms, which traditionally have been treated separately, into a single system. This unified perspective allows for the creation of more capable AI by mitigating catastrophic forgetting and enabling more effective continual learning, as evidenced by the performance of the "Hope" architecture.
nested-learningcontinual-learningcatastrophic-forgettingllm-architecturedeep-optimizersassociative-memorygoogle-brain-blog
“Nested Learning views ML models as nested optimization problems to combat catastrophic forgetting.”