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20 Nov

Tips on Building a Winning Data and AI Strategy from JPMC

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(Lewis-Tse/Shutterstock) With $274 billion in revenue last year and $3.3 trillion in assets under management, JPMorgan Chase has more resources than most to devote to building a winning data and AI strategy. But as James Massa, JPMorgan Chase’s senior executive director of software engineering and architecture, explained during his SolixEmpower keynote last week, even the biggest companies in the world must pay close attention to the data and AI details in order to succeed. In his Solix Empower 2024 keynote address, titled “Data Quality and Data Strategy for AI, Measuring AI Value, Testing LLMs, and AI Use Cases,” Massa provided…

20 Nov

4+3 Phases of Compute-Optimal Neural Scaling Laws

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[Submitted on 23 May 2024 (v1), last revised 17 Nov 2024 (this version, v2)] View a PDF of the paper titled 4+3 Phases of Compute-Optimal Neural Scaling Laws, by Elliot Paquette and 3 other authors View PDF Abstract:We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count. We use this neural scaling model to derive new predictions about the compute-limited, infinite-data scaling law regime. To train the neural scaling model, we run one-pass stochastic gradient descent on a mean-squared loss. We derive a representation of the loss curves which holds over all iteration counts…

20 Nov

The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods

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arXiv:2411.10546v1 Announce Type: new Abstract: This paper introduces a large-scale multi-modal dataset captured in and around well-known landmarks in Oxford using a custom-built multi-sensor perception unit as well as a millimetre-accurate map from a Terrestrial LiDAR Scanner (TLS). The perception unit includes three synchronised global shutter colour cameras, an automotive 3D LiDAR scanner, and an inertial sensor - all precisely calibrated. We also establish benchmarks for tasks involving localisation, reconstruction, and novel-view synthesis, which enable the evaluation of Simultaneous Localisation and Mapping (SLAM) methods, Structure-from-Motion (SfM) and Multi-view Stereo (MVS) methods as well as radiance field methods such as Neural…

20 Nov

A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification

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[Submitted on 17 May 2024 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification, by D. Subhalingam and 3 other authors View PDF HTML (experimental) Abstract:In the e-commerce domain, the accurate extraction of attribute-value pairs (e.g., Brand: Apple) from product titles and user search queries is crucial for enhancing search and recommendation systems. A major challenge with neural models for this task is the lack of high-quality training data, as the annotations for attribute-value pairs in the available datasets are often incomplete. To…

20 Nov

Without Data Context, There is No AI

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Sponsored Content by Precisely Approximately 80% of data has a location attribute associated with it – and that location data provides a connection with the physical world. For Generali Real Estate*, the addition of alternative forms of data, such as spatial data, created greater context for its data and helped to power highly accurate AI-driven insights for data-driven decision-making. Let’s take a closer look at their journey. Generali Real Estate is one of the world’s leading real estate asset managers. Headquartered in Italy and with operations across Europe, the company has €36.9 billion assets under management (Q2 2024). When Generali…

20 Nov

Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications

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arXiv:2411.10544v1 Announce Type: new Abstract: Artificial intelligence based predictive models trained on the clinical notes can be demographically biased. This could lead to adverse healthcare disparities in predicting outcomes like length of stay of the patients. Thus, it is necessary to mitigate the demographic biases within these models. We proposed an implicit in-processing debiasing method to combat disparate treatment which occurs when the machine learning model predict different outcomes for individuals based on the sensitive attributes like gender, ethnicity, race, and likewise. For this purpose, we used clinical notes of heart failure patients and used diagnostic codes, procedure reports and…

20 Nov

MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection

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arXiv:2411.10888v1 Announce Type: cross Abstract: In the aftermath of the COVID-19 pandemic and amid accelerating climate change, emerging infectious diseases, particularly those arising from zoonotic spillover, remain a global threat. Mpox (caused by the monkeypox virus) is a notable example of a zoonotic infection that often goes undiagnosed, especially as its rash progresses through stages, complicating detection across diverse populations with different presentations. In August 2024, the WHO Director-General declared the mpox outbreak a public health emergency of international concern for a second time. Despite the deployment of deep learning techniques for detecting diseases from skin lesion images, a robust…

20 Nov

Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English

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arXiv:2411.10730v1 Announce Type: new Abstract: Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful news by leveraging multilingual satire detection methods in English and Arabic. We explore both zero-shot and chain-of-thought (CoT) prompting using two language models, Jais-chat(13B) and LLaMA-2-chat(7B). Our results show that CoT prompting offers a significant advantage for the Jais-chat model over the LLaMA-2-chat model.…

20 Nov

DBHawk Flies with Text-to-SQL, SOC 2 Compliance

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Customers will be able to interact with their database using natural language thanks to the new text-to-SQL function in DBHawk, a database access tool developed by Datasparc. The company also announced its SOC 2 Type II compliance and an expansion of its partnership with IBM. DBHawk is Datasparc’s handy database tool that lets different users accomplish a range of different database tasks. For instance, data analysts can use it to write and execute SQL queries against dozens of databases, including relational and NoSQL databases. Data engineers can use it to perform joins and schedule SQL queries. Administrators can also use…

19 Nov

Unmasking Parkinson’s Disease with Smile: An AI-enabled Screening Framework

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[Submitted on 3 Aug 2023 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework, by Tariq Adnan and 8 other authors View PDF HTML (experimental) Abstract:We present an efficient and accessible PD screening method by leveraging AI-driven models enabled by the largest video dataset of facial expressions from 1,059 unique participants. This dataset includes 256 individuals with PD, 165 clinically diagnosed, and 91 self-reported. Participants used webcams to record themselves mimicking three facial expressions (smile, disgust, and surprise) from diverse sources encompassing their homes…