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Retrieval-Augmented Personalization for Multimodal Large Language Models
21 Nov

Retrieval-Augmented Personalization for Multimodal Large Language Models

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[Submitted on 17 Oct 2024 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled Retrieval-Augmented Personalization for Multimodal Large Language Models, by Haoran Hao and 4 other authors View PDF HTML (experimental) Abstract:The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. Starting from a general MLLM, we turn it into a personalized assistant in three steps.…
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Four Steps to Go from Experimentation to Embedding AI Across the Enterprise
20 Nov

Four Steps to Go from Experimentation to Embedding AI Across the Enterprise

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AI is everywhere. In just a couple of years, this technology has evolved significantly and is transforming the way most of us do business. And yet, many organizations continue to grapple with how they can really integrate AI into their daily operations. It’s critical that this changes soon. To thrive in the age of AI, companies must do more than simply adopt AI. They must embrace an iterative approach, continuously learning and adapting as the technology evolves. In this article, I’ll share four commitments that companies should make to transition to full AI adopters. Understand Your Business Challenges AI for…
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Introducing Predictive Optimization for Statistics
20 Nov

Introducing Predictive Optimization for Statistics

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We are excited to introduce the gated Public Preview of Predictive Optimization for statistics. Announced at the Data + AI Summit, Predictive Optimization is now generally available as an AI-driven approach to streamlining optimization processes. This feature currently supports essential data layout and cleanup tasks, and early feedback from users highlights its effectiveness in vastly simplifying routine data maintenance. With the addition of automatic statistics management, Predictive Optimization delivers customer value and simplifies operation through the following advancements:Intelligent selection of data-skipping statistics, eliminating the need for column order managementAutomatic collection of query optimization statistics, removing the necessity to run ANALYZE after…
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Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
20 Nov

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning

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[Submitted on 21 Jul 2024 (v1), last revised 19 Nov 2024 (this version, v3)] View a PDF of the paper titled Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning, by Luis Mandl and 3 other authors View PDF HTML (experimental) Abstract:The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks to map between infinite-dimensional function spaces. In the absence of labeled datasets, we utilize the PDE residual loss to learn the physical system, an approach known as physics-informed DeepONet. This method…
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LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation
20 Nov

LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation

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[Submitted on 9 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation, by Weijie Ma and 4 other authors View PDF HTML (experimental) Abstract:With the attention gained by camera-only 3D object detection in autonomous driving, methods based on Bird-Eye-View (BEV) representation especially derived from the forward view transformation paradigm, i.e., lift-splat-shoot (LSS), have recently seen significant progress. The BEV representation formulated by the frustum based on depth distribution prediction is ideal for learning the road structure and scene layout from…
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20 Nov

Neurosymbolic Graph Enrichment for Grounded World Models

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arXiv:2411.12671v1 Announce Type: cross Abstract: The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for creating a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. Our method begins with an image input, utilizing state-of-the-art large language models to generate a natural language description. This description is then transformed…
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Google Kubernetes Engine Now Supports Trillion-Parameter AI Models
20 Nov

Google Kubernetes Engine Now Supports Trillion-Parameter AI Models

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(Image source: Pepperdata) The exponential growth in large language model (LLM) size and the resulting need for high-performance computing (HPC) infrastructure is reshaping the AI landscape. Some of the newer GenAI models have grown to well over a billion parameters, with some approaching 2 trillion.  Google Cloud announced that in anticipation of even larger models, it has upgraded its Kubernetes Engine’s capacity to support 65,000-node clusters, up from 15,000-node clusters. This enhancement enables Google Kubernetes Engine (GKE) to operate at a 10x scale compared to two other major cloud providers, according to Google Cloud. While Google Cloud did not specify…
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Game Development and Cloud Computing: Benefits of Cloud-Native Game Servers
20 Nov

Game Development and Cloud Computing: Benefits of Cloud-Native Game Servers

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Cloud computing is transforming game development, allowing studios to create, launch, and manage games more efficiently than ever. One of the most significant advancements is the use of cloud-native game servers, which are specially designed to operate within cloud environments. Such servers provide game developers with great benefits, including the ability to cope with high player traffic, lower latency easily, and efficient use of resources for better costs. As a result of using cloud-native servers, game developers are able to develop games that are more robust, ubiquitous, and appealing to gamers irrespective of geographical boundaries. What are Cloud-Native Game Servers?…
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Celebrating Innovation: Announcing the Finalists of the Databricks Generative AI Startup Challenge
20 Nov

Celebrating Innovation: Announcing the Finalists of the Databricks Generative AI Startup Challenge

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We are thrilled to unveil the finalists for the Databricks Generative AI Startup Challenge, a competition designed to spotlight innovative early-stage startups harnessing the power of Generative AI on the Databricks Data Intelligence Platform. In collaboration with AWS, this challenge has attracted an impressive array of participants, all striving to push the boundaries of technology and solve real-world problems. With over $1 million in prizes and potential Databricks Ventures funding available for the three winners, the stakes are high.Meet Our FinalistsAfter a rigorous selection process, we are announcing the following four startups as finalists:ChipStackChipStack is tackling one of the most…
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20 Nov

Learning the Simplicity of Scattering Amplitudes

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arXiv:2408.04720v2 Announce Type: replace-cross Abstract: The simplification and reorganization of complex expressions lies at the core of scientific progress, particularly in theoretical high-energy physics. This work explores the application of machine learning to a particular facet of this challenge: the task of simplifying scattering amplitudes expressed in terms of spinor-helicity variables. We demonstrate that an encoder-decoder transformer architecture achieves impressive simplification capabilities for expressions composed of handfuls of terms. Lengthier expressions are implemented in an additional embedding network, trained using contrastive learning, which isolates subexpressions that are more likely to simplify. The resulting framework is capable of reducing expressions with…
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