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Building a Strong AI Foundation: The Critical Role of High-Quality Data
29 May

Building a Strong AI Foundation: The Critical Role of High-Quality Data

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Whether it's manufacturing and supply chain management or the healthcare industry, Artificial Intelligence (AI) has the power to revolutionize operations. AI holds the power to boost efficiency, personalize customer experiences and spark innovation.  That said, getting reliable, actionable results from any AI process hinges on the quality of data it is fed. Let's take a closer look at what's needed to prepare your data for AI-driven success. How Does Data Quality Impact AI Systems? Using poor quality data can result in expensive, embarrassing mistakes like the time Air Canada‘s chatbot gave a grieving customer incorrect information. In areas like healthcare, using…
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CataLM: Empowering Catalyst Design Through Large Language Models
29 May

CataLM: Empowering Catalyst Design Through Large Language Models

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arXiv:2405.17440v1 Announce Type: new Abstract: The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) in catalyst design. Presently, the fine-tuning of open-source large language models (LLMs) has yielded significant breakthroughs across various domains such as biology and healthcare. Drawing inspiration from these advancements, we introduce CataLM Cata}lytic Language Model), a large language model tailored to the domain of electrocatalytic materials. Our findings demonstrate that CataLM exhibits remarkable potential for facilitating human-AI collaboration in catalyst knowledge exploration and design. To the best of our knowledge, CataLM stands…
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Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network
29 May

Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network

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arXiv:2405.17444v1 Announce Type: new Abstract: In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos. Source link lol
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Evaluating the Adversarial Robustness of Retrieval-Based In-Context Learning for Large Language Models
29 May

Evaluating the Adversarial Robustness of Retrieval-Based In-Context Learning for Large Language Models

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arXiv:2405.15984v1 Announce Type: new Abstract: With the emergence of large language models, such as LLaMA and OpenAI GPT-3, In-Context Learning (ICL) gained significant attention due to its effectiveness and efficiency. However, ICL is very sensitive to the choice, order, and verbaliser used to encode the demonstrations in the prompt. Retrieval-Augmented ICL methods try to address this problem by leveraging retrievers to extract semantically related examples as demonstrations. While this approach yields more accurate results, its robustness against various types of adversarial attacks, including perturbations on test samples, demonstrations, and retrieved data, remains under-explored. Our study reveals that retrieval-augmented models can…
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Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
29 May

Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications

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arXiv:2405.15877v1 Announce Type: new Abstract: Large language models (LLMs) significantly enhance the performance of various applications, but they are computationally intensive and energy-demanding. This makes it challenging to deploy them on devices with limited resources, such as personal computers and mobile/wearable devices, and results in substantial inference costs in resource-rich environments like cloud servers. To extend the use of LLMs, we introduce a low-rank decomposition approach to effectively compress these models, tailored to the requirements of specific applications. We observe that LLMs pretrained on general datasets contain many redundant components not needed for particular applications. Our method focuses on identifying…
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Efficient Point Transformer with Dynamic Token Aggregating for Point Cloud Processing
29 May

Efficient Point Transformer with Dynamic Token Aggregating for Point Cloud Processing

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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A hierarchical Bayesian model for syntactic priming
29 May

A hierarchical Bayesian model for syntactic priming

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arXiv:2405.15964v1 Announce Type: new Abstract: The effect of syntactic priming exhibits three well-documented empirical properties: the lexical boost, the inverse frequency effect, and the asymmetrical decay. We aim to show how these three empirical phenomena can be reconciled in a general learning framework, the hierarchical Bayesian model (HBM). The model represents syntactic knowledge in a hierarchical structure of syntactic statistics, where a lower level represents the verb-specific biases of syntactic decisions, and a higher level represents the abstract bias as an aggregation of verb-specific biases. This knowledge is updated in response to experience by Bayesian inference. In simulations, we show…
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J.P. Morgan Launches ‘Containerized Data’ Solution in the Cloud
29 May

J.P. Morgan Launches ‘Containerized Data’ Solution in the Cloud

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(Tee11/Shutterstock) Getting access to consistent, high-quality data ranks as one of the toughest challenges in big data, advanced analytics, and AI. It’s a challenge that is being taken up by Fusion by J.P. Morgan with its new Containerized Data offering, which provides institutional investors with consistent, enriched data that’s been standardized with a common semantic layer. The worst-kept secret in big data is that data prep consumes the vast majority of time in analytics, machine learning, and AI projects. Raw data does contain signals that data scientists so desperately want to leverage for competitive gain, but that data must be…
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5 Ways to Overcome Headwinds in Supply Chain Efficiency
29 May

5 Ways to Overcome Headwinds in Supply Chain Efficiency

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The post-pandemic recovery was a major shock to the supply chain landscape. The emergence of varied and powerful headwinds saw many lingering inefficiencies exposed as firms scrambled to maintain inventory levels against the backdrop of an uneven recovery from the health crisis, geopolitical unrest, environmental concerns, and staffing shortages to name a few. Legacy processes have been adversely impacted by changing consumer sentiment, and it's becoming increasingly clear that digital transformation is essential in helping businesses at all ends of the chain overcome mounting pressures. Pressure Amid Mounting Headwinds Supply chain issues can be varied. The lockdowns driven by the…
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