How to Apply Vision Language Models to Long DocumentsTowards Data Science Learn how to apply powerful VLMs for long context document understanding tasks
The post How to Apply Vision Language Models to Long Documents appeared first on Towards Data Science.
Learn how to apply powerful VLMs for long context document understanding tasks
The post How to Apply Vision Language Models to Long Documents appeared first on Towards Data Science. Read More
Does AI Need to Be Conscious to Care?Towards Data Science Towards new forms of artificial moral agency
The post Does AI Need to Be Conscious to Care? appeared first on Towards Data Science.
Towards new forms of artificial moral agency
The post Does AI Need to Be Conscious to Care? appeared first on Towards Data Science. Read More
3 Questions: How AI is helping us monitor and support vulnerable ecosystemsMIT News – Machine learning MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet. Read More
Building a Multimodal RAG That Responds with Text, Images, and Tables from SourcesTowards Data Science Why do few chatbots return figures from source documents in their responses?
The post Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources appeared first on Towards Data Science.
Why do few chatbots return figures from source documents in their responses?
The post Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources appeared first on Towards Data Science. Read More
How Switchboard, MD automates real-time call transcription in clinical contact centers with Amazon Nova SonicArtificial Intelligence In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care.
In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care. Read More
What’s on My Bookmarks Bar: Data Science EditionKDnuggets Save time by keeping top resources and tools at your fingertips.
Save time by keeping top resources and tools at your fingertips. Read More
OpenAI spreads $600B cloud AI bet across AWS, Oracle, MicrosoftAI News OpenAI is on a spending spree to secure its AI compute supply chain, signing a new deal with AWS as part of its multi-cloud strategy. The company recently ended its exclusive cloud-computing partnership with Microsoft. It has since allocated a reported $250 billion back to Microsoft, $300 billion to Oracle, and now, $38 billion to
The post OpenAI spreads $600B cloud AI bet across AWS, Oracle, Microsoft appeared first on AI News.
OpenAI is on a spending spree to secure its AI compute supply chain, signing a new deal with AWS as part of its multi-cloud strategy. The company recently ended its exclusive cloud-computing partnership with Microsoft. It has since allocated a reported $250 billion back to Microsoft, $300 billion to Oracle, and now, $38 billion to
The post OpenAI spreads $600B cloud AI bet across AWS, Oracle, Microsoft appeared first on AI News. Read More
LLM Based Long Code Translation using Identifier Replacementcs.AI updates on arXiv.org arXiv:2510.09045v2 Announce Type: replace-cross
Abstract: In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another while preserving its functionality. However, LLMs often struggle with long source codes that don’t fit into the context window, which produces inaccurate translations. To address this, we propose a novel zero-shot code translation method that incorporates identifier replacement. By substituting user-given long identifiers with generalized placeholders during translation, our method allows the LLM to focus on the logical structure of the code, by reducing token count and memory usage, which improves the efficiency and cost-effectiveness of long code translation. Our empirical results demonstrate that our approach preserves syntactical and hierarchical information and produces translation results with reduced tokens.
arXiv:2510.09045v2 Announce Type: replace-cross
Abstract: In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another while preserving its functionality. However, LLMs often struggle with long source codes that don’t fit into the context window, which produces inaccurate translations. To address this, we propose a novel zero-shot code translation method that incorporates identifier replacement. By substituting user-given long identifiers with generalized placeholders during translation, our method allows the LLM to focus on the logical structure of the code, by reducing token count and memory usage, which improves the efficiency and cost-effectiveness of long code translation. Our empirical results demonstrate that our approach preserves syntactical and hierarchical information and produces translation results with reduced tokens. Read More
SUSTAINABLE Platform: Seamless Smart Farming Integration Towards Agronomy Automationcs.AI updates on arXiv.org arXiv:2510.26989v1 Announce Type: new
Abstract: The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE’s key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.
arXiv:2510.26989v1 Announce Type: new
Abstract: The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE’s key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards. Read More
PoLAR: Polar-Decomposed Low-Rank Adapter Representationcs.AI updates on arXiv.org arXiv:2506.03133v2 Announce Type: replace-cross
Abstract: We show that low-rank adaptation of large-scale models suffers from a low stable rank that is well below the linear algebraic rank of the subspace, degrading fine-tuning performance. To mitigate the underutilization of the allocated subspace, we propose PoLAR, a parameterization inspired by the polar decomposition that factorizes the low-rank update into two direction matrices constrained to Stiefel manifolds and an unconstrained scale matrix. Our theory shows that PoLAR yields an exponentially faster convergence rate on a canonical low-rank adaptation problem. Pairing the parameterization with Riemannian optimization leads to consistent gains on three different benchmarks testing general language understanding, commonsense reasoning, and mathematical problem solving with base model sizes ranging from 350M to 27B.
arXiv:2506.03133v2 Announce Type: replace-cross
Abstract: We show that low-rank adaptation of large-scale models suffers from a low stable rank that is well below the linear algebraic rank of the subspace, degrading fine-tuning performance. To mitigate the underutilization of the allocated subspace, we propose PoLAR, a parameterization inspired by the polar decomposition that factorizes the low-rank update into two direction matrices constrained to Stiefel manifolds and an unconstrained scale matrix. Our theory shows that PoLAR yields an exponentially faster convergence rate on a canonical low-rank adaptation problem. Pairing the parameterization with Riemannian optimization leads to consistent gains on three different benchmarks testing general language understanding, commonsense reasoning, and mathematical problem solving with base model sizes ranging from 350M to 27B. Read More
