“Databricks 基盤モデルAPIは多種多様なLLM APIを提供しており、Llamaのようなオープンウェイトモデルもあれば、GPT-5.2やClaude Sonnetなどのプロプライエタリモデルをネイティブ提供しています。”

“The article discusses fine-tuning LLMs and the use of methods like LoRA.”

“Google is releasing TranslateGemma.”

“The AI partnerships allow companies to access the org's content, like Wikipedia, at scale.”

“The Wikimedia Foundation says Microsoft, Meta, Amazon, Perplexity, and Mistral joined Wikimedia Enterprise to get “tuned” API access; Google is already a member.”

“Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.”

“MedGemma 1.5, small multimodal model for real clinical data MedGemma […]”

“Collective Communication (CC) is at the core of data exchange between multiple accelerators.”

“The core of the problem is the resource strain and the lack of ethical considerations when scraping data at scale.”

“The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions.”

“Apple and Google have embarked on a non-exclusive, multi-year partnership that will involve Apple using Gemini models and Google cloud technology for future foundational models.”

“"Physical AIのChatGPTモーメントが到来した"”

“A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep.”

“Liquid AI has introduced LFM2.5, a new generation of small foundation models built on the LFM2 architecture and focused at on device and edge deployments.”

“It’s built to power reliable on-device agentic applications: higher quality, lower latency, and broader modality support in the ~1B parameter class.”

“AEF-based models generally exhibit strong performance on all tasks and are competitive with purpose-built RS-ba”

“By unifying these diverse AI components into a single, easy-to-adapt platform”

“Current audio evaluation faces three major challenges: (1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources, hindering fair and efficient cross-model comparison”

“N/A (Source is a Reddit post with no direct quotes)”

“N/A - Article content not directly provided.”

“Unable to extract a direct quote from the provided context.”

“MNIST（エムニスト）は、0から9までの手書き数字の画像データセットです。”

“I'm looking for resources to study the following: -statistics and probability -calculus (for applications like optimization, gradients, and understanding models) ... I don't want to study the entire math courses, just what is necessary for AI/ML.”

“The South Korean government funded the Sovereign AI Foundation Model Project, and the five selected teams released their initial models and presented on December 30, 2025. ... all 5 teams "presented robust open-source policies so that foundation models they develop and release can also be used commercially by other companies, thereby contributing in many ways to expansion of the domestic AI ecosystem, to the acceleration of diverse AI services, and to improved public access to AI."”

“DLMs augmented with polynomial-length chain-of-thought (CoT) can simulate any parallel sampling algorithm using an optimal number of sequential steps.”

“FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS.”

“The paper introduces the semi-overlapping multi-(multi-armed) bandit (SOMMAB), in which a single evaluation provides distinct feedback to multiple bandits due to structural overlap among their arms.”

“mHC restores the identity mapping property while incorporating rigorous infrastructure optimization to ensure efficiency.”

“Learning curves can better capture the effects of multi-task learning and their multi-task extensions can delineate pairwise and contextual transfer effects in foundation models.”

“The model achieves 12% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38% error).”

“OneRec Foundation (1.7B and 8B), a family of models establishing new state-of-the-art (SOTA) results across all tasks in RecIF-Bench.”

“The article quotes Xue Guirong, the technical director of the scientific model overall team at Zhejiang Lab, who points out that LLMs are limited by the 'boundaries of language' and cannot truly understand high-dimensional, multi-type scientific data, nor can they independently complete verifiable scientific discoveries. The article also highlights the 'AI Manhattan Project' as a major initiative in the application of AI in science.”

“The article quotes the founder, Su Wen, emphasizing the importance of building their own models and the unique approach of AutoCoder.cc, which doesn't provide code directly, focusing instead on deployment.”

“BandiK employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits.”

“The discussion revolves around the future of "world models," with researchers offering predictions on breakthroughs in areas like geometric foundation models, self-supervised learning, and the development of 4D/5D/6D AIGC.”

“The paper establishes error bounds for the reconstructed solution and the source term without requiring classical source conditions, and derives an expected convergence rate for the source error in a weaker topology.”

“claims that by doing so it will become “the world’s first publicly listed foundation model company.””

“Memory representation plays a central role in consolidating spatial experience, with structured memories particularly sequential and graph-based representations, substantially improving performance on structure-intensive tasks such as path planning.”

“The central construction is the transport horn: a configuration where a term and a path both cohere, but transport along the path is witnessed as gapped.”

“USF-MAE achieved the highest performance across all evaluation metrics, with 90.57% accuracy, 91.15% precision, 90.57% recall, and 90.71% F1-score.”

“HOLOGRAPH provides rigorous mathematical foundations while achieving competitive performance on causal discovery tasks.”

“The paper introduces an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM).”

“The paper introduces "Semantic Lookout", a camera-only, candidate-constrained vision-language model (VLM) fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority.”

“The paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot.”

“Foundation Models trained on collider data can help improve the prediction of cosmological parameters and to predict halo and galaxy velocities in different datasets from CosmoBench.”

“Alibaba Tongyi Lab have released MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld.”

“I’m trying to build a strong foundation in AI/ML and I’m particularly interested in NLP. I understand that unsupervised learning plays a big role in tasks like topic modeling, word embeddings, and clustering text data. My question: Which unsupervised learning algorithms should I focus on first if my goal is to specialize in NLP?”

“SeedFold outperforms AlphaFold3 on most protein-related tasks.”

“Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112).”

“PGMP framework outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability.”