AI NEWS.ccDatabricks and SAP unleash end-to-end data & AI capabilities to level up enterprise AI transformation
Today's AI models are more capable than ever. What they still lack is the business...
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Wealth Management Client Onboarding Needs More Than AI
Wealth Management client onboarding can stall even when every system has done its job. The document is collected...
What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine
arXiv:2610.37867v1 Announce Type: new Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation. We ask what happens to food similarity beyond identity: how different visual representations organize foods into relational neighborhoods, what culinary relationships these neighborhoods reveal or obscure, and how consistently these structures emerge across representations. Indian cuisine provides a challenging setting in which visually similar foods may have different culinary identities, while foods within a culinary family can vary substantially in appearance. Using an expanded Khana dataset and seven pretrained visual representations spanning convolutional, transforme
DouBao’s PR team responds to the “150,000 extras fed into a model” rumor: The claim is seriously inaccurate.
Liu Xing, head of DouBao’s PR department, issued a statement debunking the rumor, stating that the claim “Hengdian has partnered with ByteDance to feed the portraits and voices of 150,000 extras into ByteDance’s large AI model” is seriously false. He explained that the real “Hengdian Film & TV AI Service Platform” aims to build public digital infrastructure, organizing on-site assets such as film studio locations into high-quality datasets. This helps production crews efficiently scout locations, prepare for shoots, and streamline filming, while also lowering the barrier for small and medium-sized film and TV companies to adopt AI, enabling more creators to produce quality work.
Structured Maps for Landmark-Based Route Instructions with LLMs
arXiv:2610.36582v1 Announce Type: new Abstract: To generate landmark-based route instructions, models must understand both map geometry and human landmark preferences. Although large language models (LLMs) show great abilities in language tasks, the best way to structure map data for these models is not yet established. This paper proposes a structured text representation for landmark selection tasks in route navigation, utilizing SVG format to encode spatial features. The structured text maintains a geometric connection with rendered map images, allowing language models to directly access spatial and semantic data. Using a new dataset created from human-written route descriptions and OpenStreetMap data, we evaluate models on two tasks: landmark selection at turns and around destinations.
How to put generative AI directly into a dbt pipeline with plain SQL
Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.
How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.
Why an AI agent's revenue query was $3.5 million wrong, and how AWS fixed it with dbt Semantic Layer
A plausible AI revenue query was $3.5 million wrong. AWS explains how business context helps prevent those errors.
Why most of your dbt models aren't in a hurry, and what that's worth in Snowflake credits
Most models in a dbt DAG aren't on the critical path. Espresso AI and Authentic Insurance on turning that slack into real savings.
How Ecolab used an AI migration agent to shrink 84,000 lines of legacy SQL by 6x
Ecolab and Datafold used an AI migration agent to rebuild a decade of tangled Snowflake logic as clean, documented dbt models.
How Checkr cut an hours-long metrics investigation to minutes by engineering AI context on dbt and Omni
Checkr gave its AI agent a structured dbt context layer, cutting a support-metric investigation from hours to minutes.
Ship AI agents like tables: dbt materializations for Snowflake CoWork
Under Armour turned Snowflake AI agents into dbt models, giving every agent the same review and CI process as every table.
Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
Why conversational analytics stalls and the data fundamentals that make it reliable.
With great context comes great autonomy
AI agents need governed context, not just more data, to be trusted.
Why dbt Labs thinks AI agents will finally make the semantic layer stick
Why AI agents can make semantic layers stick, if teams can solve the trust problem.
How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.
OpenAI has rewritten the history of human science! “The King’s Conjecture” has been disproven on all fronts, and the 30-year-old ironclad rule governing ETH has collapsed.
New智元 reports: Honestly, the history of human science has been completely rewritten tonight! Just now, OpenAI dropped three major announcements in quick succession, officially revealing that its internal AI independently achieved three groundbreaking discoveries—each a true “milestone”: In mathematics, it proved the “local smoothness conjecture” in all dimensions, finally putting an end to the “Kakeya conjecture,” which had puzzled the mathematical community for a century; in computer science, it refuted the “Exponential Time Hypothesis” (ETH), which has dominated the field for nearly 30 years; and in theoretical physics, it provided insights into four-dimensional quantum chromodynamics (QCD) and pure...
Wang’s conjecture was solved by AI overnight.
AI has already begun turning in its answers, but humans may not even have time to grade them. #Follow the iFanr official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered straight to you.
CFBrainAug: Modularity-Induced Counterfactual Brain Network Augmentation for Interpretable fMRI-based Autism Diagnosis
arXiv:2610.32184v1 Announce Type: new Abstract: Machine learning on resting-state functional magnetic resonance imaging (rs-fMRI) brain networks is promising for diagnosing Autism Spectrum Disorder (ASD), but existing methods are prone to overfitting on limited training data, and their post-hoc statistical attribution offers limited clinical interpretability. We propose CFBrainAug, a counterfactual brain network learning framework that unifies data augmentation and counterfactual explanation by learning label-altered brain networks. Unlike conventional label-preserving augmentation, CFBrainAug first retrieves a label-opposite yet topologically similar candidate and then refines it under a module-number constraint. Jointly training on factual and counterfactual networks enriches the trainin
What Okta learned governing context for AI agents at scale
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.