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27 May

Linking In-context Learning in Transformers to Human Episodic Memory

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arXiv:2405.14992v1 Announce Type: new Abstract: Understanding the connections between artificial and biological intelligent systems can reveal fundamental principles underlying general intelligence. While many artificial intelligence (AI) models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we examine the relationship between attention heads and human episodic memory. We focus on the induction heads, which contribute to the in-context learning capabilities of Transformer-based large language models (LLMs). We demonstrate that induction heads are behaviorally, functionally, and mechanistically similar to the contextual maintenance and retrieval (CMR) model of human episodic memory. Our analyses of…

27 May

Data Machina #236

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Mix, Bind & Merge OS Small AI Models FTW! Y2024: The year that open-source, small AI model-combos beat the big boys? The open source AI community and small AI startups are releasing a plethora of open-source, small AI models that are matching or -in some instances- even outperforming AI Titans’ huge models. I’m rooting for the open-source AI community!These new os small models are leveraging supper efficient techniques like quantisation and fine-tuning with QLoRA, and fine-tuning with DPO. There’s a whole new range of open-source tools like LLamaFactory designed to easily, efficiently fine-tune these os models.Using e.g. the free LM…

27 May

Accelerate GenAI App Development with New Updates to Databricks Model Serving

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Last year, we launched foundation model support in Databricks Model Serving to enable enterprises to build secure and custom GenAI apps on a unified data and AI platform. Since then, thousands of organizations have used Model Serving to deploy GenAI apps customized to their unique datasets.Today, we're excited to announce new updates that make it easier to experiment, customize, and deploy GenAI apps. These updates include access to new large language models (LLMs), easier discovery, simpler customization options, and improved monitoring. Together, these improvements help you develop and scale GenAI apps more quickly and at a lower cost. Databricks Model…

27 May

YUI: Day-ahead Electricity Price Forecasting Using Invariance Simplified Supply and Demand Curve

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arXiv:2405.14893v1 Announce Type: new Abstract: In day-ahead electricity market, it is crucial for all market participants to have access to reliable and accurate price forecasts for their decision-making processes. Forecasting methods currently utilized in industrial applications frequently neglect the underlying mechanisms of price formation, while economic research from the perspective of supply and demand have stringent data collection requirements, making it difficult to apply in actual markets. Observing the characteristics of the day-ahead electricity market, we introduce two invariance assumptions to simplify the modeling of supply and demand curves. Upon incorporating the time invariance assumption, we can forecast the supply…

27 May

Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments

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[Submitted on 2 Apr 2024] View a PDF of the paper titled Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments, by Ibne Farabi Shihab and 3 other authors View PDF Abstract:This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate sidewalk detection is crucial in improving road safety and urban planning. The study evaluated the model's performance on Cityscapes, Ade20k, and the Boston Dataset. The results showed that the ensemble model performed better than the individual models,…

27 May

Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction

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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

27 May

Data Machina #237

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AI Code Generation: A New Paradigm. Several developer surveys indicate that devs -especially Sr. devs- who use AI tools are “more productive.” Today you can use lots of AI tools for code completion, pair programming, data generation, an even having a team of AI coding agents to complete tedious tasks for you. Back in October, the team at DeepSense wrote an excellent blogpost on the state of the art in AI coding agents, detailing the pros & cos, and workflows of each AI coding agent. Worth mentioning that -fundamentally- all these AI coding agents are mostly based on prompt engineering,…

27 May

Six Use Cases of Conversational AI in Public Sector Governance

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Today's citizens expect seamless, convenient interactions with government entities, akin to the experiences they have with consumer-facing businesses. However, public sector organizations often struggle to meet these rising expectations due to limited resources, complex bureaucracies, and outdated technological infrastructures. This is where conversational AI – including technologies like chatbots and virtual assistants – emerges as a powerful tool for enhancing public services. It uses the power of natural language processing (NLP) and machine learning to transform how citizens interact with government services. This allows public sector organizations to streamline processes, enhance citizen engagement, and deliver more efficient and responsive governance.…

27 May

Disrupting the Status Quo Through Data and AI: Celebrating the 2024 Data Team Disruptor Award Nominees

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The annual Data Team Awards highlight how diverse enterprise data teams are tackling some of the most prevalent and complex issues facing the business world.This year, more than 200 nominations reflect a variety of industries from around the world. Spread across six distinct categories, these finalists highlight the exceptional creativity and ingenuity that organizations apply in their data and AI endeavors. We're excited to share this groundbreaking work with our community of data experts and enthusiasts. The Data Team Disruptor Award recognizes the enterprise data teams fundamentally changing how their field approaches data management and activation. Honoring those who challenge the…

27 May

Reservoir Computing with Generalized Readout based on Generalized Synchronization

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[Submitted on 3 May 2024] View a PDF of the paper titled Reservoir Computing with Generalized Readout based on Generalized Synchronization, by Akane Ookubo and Masanobu Inubushi View PDF HTML (experimental) Abstract:Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of reservoir computing is its low cost and straightforward training algorithm, i.e. only the readout, given by a linear combination of reservoir variables, is trained. Inspired by recent mathematical studies based on dynamical system theory, in particular generalized synchronization, we propose a novel reservoir computing framework with generalized readout,…