::: info ::: :construction: :construction: :construction: :construction: :construction: Content General Information Room Reservations: Sci & Eng Library Room 212. Tue 1-3pm, Thu 12-3pm (Confirmed) Unique Zoom Link for all DataLab Workshops: https://arizona.zoom.us/j/89667081542

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UA Data Lab Project: Knowledge Transfer for Computational Linguistics Collaboration between Prof Mike Hammond (Department of Linguistics) and the UA DataLab. The Masters in Human Language Technology offers an industry-oriented degree in computational linguistics, which focuses on offering linguistics training to engineers and programmers, and programming skills to linguists with low/no background in software application. This creates an overlap with the objectives of the UA DataLab, particularly for NLP and AI applications. Objectives: Provide small knowledge modules for onboarding students beginning their journey into NLP Provide front-end support for building working models for NLP projects Offer dedicated office hours in order to assist students with developing and debugging their codebase

UA DSI LLM Chatbots: Chatur project Space for project development :bookmark_tabs: :point_right: Mithun's Google Doc "Chatur" project description Goals Products Description Tutor - Bots

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Project Support: Banner Health Medical Informatics, UACOM, and UA Data Science Institute Email: ajay.perumbeti@bannerhealth.com; aperumbe@arizona.edu Program: UACOM-P/Banner Health Systems Clinical informatics; UA Data Science Institute Project Title: Preparing Physicians for Artificial Intelligence Tools in Medical Practice. Preparing Banner Physicians for Artificial Intelligence Product Deployment in Medical Practice Background The Artificial Intelligence (AI) products in the healthcare market are forecasted to have the capability of reducing US healthcare spending by 5-10% (200-360 billion dollars), driving accelerated interest and adoption (1). This has been turbocharged with generative AI applications, such as ChatGPT, astonishing ability to summarize and generate appropriate and human like responses when interacting with people. The acceleration of development of healthcare AI have forced the U.S. government, and regulatory agencies worldwide to respond with guidance to ensure medical safety and ethical use (2,3). Although automation of healthcare processes and decision-making with AI is anticipated to increase both efficiency and improve patient outcomes, it can also be error-prone, susceptible to sudden failure, and biased (2-5). These issues make it critical to arm healthcare providers with knowledge of how to best use AI products and ensure transparency, realistic expectations, and patient safety. The challenge of AI education for healthcare providers, particularly physicians, is they are overburdened with clinical and administrative duties. Introducing additional general AI education such as AI concepts e-learning may not be digestible or relevant and worsen physician burnout and risk AI adoption failures. More engaging methods for beginning physician AI education, include in-context methods such as just-in-time learning, case-based instruction embedded into physician practice, and peer-led learning by focusing on physicians that exhibit an affinity for technology. These physicians then go on to be AI champions and super-users for a healthcare system.

UA DataLab Projects Development Project: Image Segmentation in Material Sciences Description This project aims to utilize Meta's Segment Anything Model (SAM) to automatically detect, classify, and catalogue grain information from micrograph image data collected by materials scientists. Here's an example of a Scanning Electron Microscope (SEM) micrograph taken at the Kuiper Imaging Facility at UArizona: Segmenteverygrain is a SAM-based model for performing image segmentation of grains that relies on a CNN to create a first-pass segmentation for subsequent SAM segmentation. However, most images will still require some sort of pre-processing, such as contrast enhancement. Here's that same SEM micrograph with overlaid SAM masks generated from segmenteverygrain: While most grains are correctly identified, there are masks that cover multiple grains, skewing the population statistics. There are also examples of small masks that are misclassified.

Entrar con su cuenta Gmail a este sitio: https://tinyurl.com/FOSS-CICESE Taller: Introducción a la Ciencia Abierta Biotecnología Marina, CICESE 17 de agosto de 2023 Referencias: Materiales FOSS Recursos de Ciencia Abierta Recursos de Ciencia de Datos