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05 Jun

NIST Q&A: Getting Ready for the Post Quantum Cryptography Threat? You Should Be

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(Dave Hoeek/Shutterstock) With the National Institute of Standards and Technology (NIST) set to publish the first Post Quantum Cryptography (PQC) Standards in a few weeks, attention is shifting to how to put the new quantum-resistant algorithms into practice. Indeed, the number of companies with practices to help others implement PQC is mushrooming and contains familiar (IBM, Deloitte, et al.) and unfamiliar names (QuSecure, SandboxAQ, etc.). The Migration to Post-Quantum Cryptography project, being run out of NIST’s National Cybersecurity Center of Excellence (NCCoE), is running at full-tilt and includes on the order of 40 commercial participants. In its own words, “The project will engage industry…

05 Jun

Databricks Named a Leader in The Forrester Wave™: AI Foundation Models for Language, Q2 2024

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We are excited to announce that Forrester has recognized Databricks as a Leader in The Forrester Wave™: AI Foundation Models for Language, Q2 2024. A leader is a model provider that has both a strong product offering and strategy. Forrester looked at a 21-criterion evaluation of AI foundation models providers to make their assessment and final results. They concluded that enterprise buyers must look beyond incremental improvements to model benchmarks, but to focus on Foundation Model Language providers that have a clearly articulated roadmap fine-tuned to enterprise needs, the ability to configure and govern models to reduce hallucinations and align…

05 Jun

Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems

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[Submitted on 3 Jun 2024] View a PDF of the paper titled Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems, by Sravan Reddy Chintareddy and 3 other authors View PDF HTML (experimental) Abstract:In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users (SUs) to opportunistically utilize detected "spectrum holes". Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and training a machine learning (ML) model…

05 Jun

The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry

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arXiv:2406.01797v1 Announce Type: new Abstract: As robotics continues to advance, the need for adaptive and continuously-learning embodied agents increases, particularly in the realm of assistance robotics. Quick adaptability and long-term information retention are essential to operate in dynamic environments typical of humans' everyday lives. A lifelong learning paradigm is thus required, but it is scarcely addressed by current robotics literature. This study empirically investigates the impact of catastrophic forgetting and the effectiveness of knowledge transfer in neural networks trained continuously in an embodied setting. We focus on the task of visual odometry, which holds primary importance for embodied agents in…

05 Jun

TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

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arXiv:2406.01855v1 Announce Type: new Abstract: Large Language Model (LLM) evaluation is currently one of the most important areas of research, with existing benchmarks proving to be insufficient and not completely representative of LLMs' various capabilities. We present a curated collection of challenging statements on sensitive topics for LLM benchmarking called TruthEval. These statements were curated by hand and contain known truth values. The categories were chosen to distinguish LLMs' abilities from their stochastic nature. We perform some initial analyses using this dataset and find several instances of LLMs failing in simple tasks showing their inability to understand simple questions. Source…

05 Jun

IoT Time Series Analysis

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IntroductionThe Internet of Things (IoT) is generating an unprecedented amount of data. IBM estimates that annual IoT data volume will reach approximately 175 zettabytes by 2025. That’s hundreds of trillions of Gigabytes! According to Cisco, if each Gigabyte in a Zettabyte were a brick, 258 Great Walls of China could be built.Real time processing of IoT data unlocks its true value by enabling businesses to make timely, data-driven decisions. However, the massive and dynamic nature of IoT data poses significant challenges for many organizations. At Databricks, we recognize these obstacles and provide a comprehensive data intelligence platform to help manufacturing…

05 Jun

A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization

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[Submitted on 3 Jun 2024] View a PDF of the paper titled A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization, by Sebastian Sanokowski and 2 other authors View PDF HTML (experimental) Abstract:Learning to sample from intractable distributions over discrete sets without relying on corresponding training data is a central problem in a wide range of fields, including Combinatorial Optimization. Currently, popular deep learning-based approaches rely primarily on generative models that yield exact sample likelihoods. This work introduces a method that lifts this restriction and opens the possibility to employ highly expressive latent variable models like diffusion models. Our approach…

05 Jun

Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels

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arXiv:2406.01791v1 Announce Type: new Abstract: Video moment retrieval (VMR) is to search for a visual temporal moment in an untrimmed raw video by a given text query description (sentence). Existing studies either start from collecting exhaustive frame-wise annotations on the temporal boundary of target moments (fully-supervised), or learn with only the video-level video-text pairing labels (weakly-supervised). The former is poor in generalisation to unknown concepts and/or novel scenes due to restricted dataset scale and diversity under expensive annotation costs; the latter is subject to visual-textual mis-correlations from incomplete labels. In this work, we introduce a new approach called hybrid-learning video…

05 Jun

An Open Multilingual System for Scoring Readability of Wikipedia

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[Submitted on 3 Jun 2024] View a PDF of the paper titled An Open Multilingual System for Scoring Readability of Wikipedia, by Mykola Trokhymovych and 2 other authors View PDF HTML (experimental) Abstract:With over 60M articles, Wikipedia has become the largest platform for open and freely accessible knowledge. While it has more than 15B monthly visits, its content is believed to be inaccessible to many readers due to the lack of readability of its text. However, previous investigations of the readability of Wikipedia have been restricted to English only, and there are currently no systems supporting the automatic readability assessment…

05 Jun

Who’s Watching Your GenAI Bot?

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(Andrey-Suslov/Shutterstock) In January, a UK delivery service called DPD made headlines for the worst reasons. A customer shared an incredible exchange with DPD’s customer service chatbot, which varied in its replies from, “F**k yeah!” to “DPD is a useless customer chatbot that can’t help you.” This all took place in one very memorable but very brand-damaging exchange. Chatbots and other GenAI tools, whether internally or externally facing, are seeing rapid adoption today. Notions like the “AI arms race” as Time Magazine put it, reflect the pressure on companies to roll out these tools as quickly as possible, or risk falling…