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Shaping the Future With Data and AI: Announcing the 2024 Databricks GenAI Innovation Award Finalists
25 May

Shaping the Future With Data and AI: Announcing the 2024 Databricks GenAI Innovation Award Finalists

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The annual Data Team Awards showcase the remarkable efforts of top global enterprise data teams committed to tackling some of today's toughest business challenges.This year, we received more than 200 nominations across six categories, from companies representing a diverse array of industries and regions. In the lead-up to the Data + AI Summit, we'll showcase the finalists from each category, highlighting those pioneering the advances in data and AI.New this year, the GenAI Award represents the widespread enterprise adoption of large language models (LLMs). As LLMs transform industries by enhancing productivity, personalizing user experiences, and opening up new possibilities in…
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Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
25 May

Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch

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arXiv:2405.13078v1 Announce Type: new Abstract: Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and softened probabilities, this paper goes deeper into the dark knowledge provided by teachers with different capacities. Two fundamental observations are: (1) a larger teacher tends to produce probability vectors that are less distinct between non-ground-truth classes; (2) teachers with different capacities are basically consistent in their cognition of relative class affinity. Abundant experimental studies verify these observations and in-depth empirical explanations are provided. The difference in dark…
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Towards Retrieval-Augmented Architectures for Image Captioning
25 May

Towards Retrieval-Augmented Architectures for Image Captioning

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arXiv:2405.13127v1 Announce Type: new Abstract: The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images. In recent years, researchers have leveraged deep learning-based models and made advances in the extraction of visual features and the design of multimodal connections to tackle this task. This work presents a novel approach towards developing image captioning models that utilize an external kNN memory to improve the generation process. Specifically, we propose two model variants that incorporate a knowledge retriever component that is based…
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RAGE Against the Machine: Retrieval-Augmented LLM Explanations
24 May

RAGE Against the Machine: Retrieval-Augmented LLM Explanations

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arXiv:2405.13000v1 Announce Type: new Abstract: This paper demonstrates RAGE, an interactive tool for explaining Large Language Models (LLMs) augmented with retrieval capabilities; i.e., able to query external sources and pull relevant information into their input context. Our explanations are counterfactual in the sense that they identify parts of the input context that, when removed, change the answer to the question posed to the LLM. RAGE includes pruning methods to navigate the vast space of possible explanations, allowing users to view the provenance of the produced answers. Source link lol
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FasterViT for Image Classification
24 May

FasterViT for Image Classification

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FasterViT is a family of Vision Transformer models that is both fast and provides better accuracy than other ViT models. It combines the local representation learning of CNNs and the global learning properties of ViTs. In this article, we will cover the FasterViT model for image classification. Figure 1. FasterViT architecture, throughput, and benchmark on ImageNet1K. We will go through image inference using the pretrained network along with a brief of its architectural components. Furthermore, we will also fine-tune a FasterViT model for image classification. We will cover the following topics in this article We will start with a discussion…
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Enterprises Have Just Two Years to Harness the Full Potential of GenAI: Genpact and HFS Report
24 May

Enterprises Have Just Two Years to Harness the Full Potential of GenAI: Genpact and HFS Report

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(Berit Kessler/Shutterstock) The advent of GenAI has proven to be the first real innovation to disrupt industry since the advent of the internet. While GenAI is only over a year old, it has left enterprises scrambling to gain a competitive advantage. However, the window of opportunity for these enterprises may be shorter than anticipated. Enterprises have only two years to adopt GenAI before competitive disadvantages emerge, according to a new report by Genpact and HFS Research. The report also highlights that only 5% of enterprises have mature GenAI initiatives, signaling an urgent need for acceleration of GenAI adoption.  Genpact is…
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Data Machina #248
24 May

Data Machina #248

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Jailbreaking AI Models: It’s easy. Hundreds of millions of dollars have been thrown at AI Safety & Alignment over the years. Despite that, jailbreaking LLMs in April 2024 is easy. Oddly enough, as the LLM models become more capable and sophisticated, the jailbreaking attacks are becoming easier to perform, more effective, and frequent. Gary Marcus - who is hypercritical about LLMs and current AI trends- just published this very opinionated post: An unending array of jailbreaking attacks could be the death of LLMs.I often speak to colleagues and clients about the “LLM jailbreaking elephant in the room.” And they all…
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From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!
24 May

From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!

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In the last year, the Databricks Money Engineering Team has embarked on an exhilarating journey, achieving nearly double our operational efficiency. We are excited to share this transformative experience with you, highlighting the specific strategies that fueled our success. In this post, we will discuss how introducing an Ops Czar reduced operational burden while at the same time empowered our engineering team. We will discuss pragmatism and Databricks first principles."In Unity, Strength": How Collective Effort and Strategic Efficiency Doubled Our CapabilitiesThe Money team is at the heart of commercializing Databricks's products, such as Workflows and Notebooks. We handle everything from…
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Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
24 May

Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation

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arXiv:2405.13075v1 Announce Type: new Abstract: Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal feature learning. However, it is hard to adaptively trade off the diverse effects of local and global temporal features by simply combining CNN and attention. To address this issue, we propose a Score-weighted Convolutional Diffusion Model (Score-CDM for short), whose backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive…
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