Haley Townsend, PMP, SSM

📣 Check out the upcoming tutorial by Darren Vengroff, "Working with U.S. Census Data in Python: Discovery, Analysis, and Visualization", at #SciPy2024! 👀

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#OSS in #HigherEd Check out Johns Hopkins', "#OSPO Explainer Videos," a collection of bite-sized tutorials tackling a variety of #OpenSource essentials. New topics added monthly – drop them a line if there’s a topic you’re interested in that’s not on their list yet. New videos coming Soon: README Basics, Citing Research Software, and Digital Object Identifiers! #OpenScience #EdTech #OpenResearch #OSS https://lnkd.in/eNcWAx_u

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Stay up to date with the latest research in Measurement, Evaluation, Statistics, & Assessment with the latest ESM MAD Blog! #ELPS Check out our latest blog by Austin Boyd on how to critically consume quantitative research here: https://lnkd.in/gh8NaDVW

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📣 Check out the upcoming talk by Isabel Zimmerman, "From Code to Clarity: Using Quarto for Python Documentation", at #SciPy2024! 👀

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With one of our public data challenges, we’re inviting citizen scientists to help us unravel the mysteries of high cholesterol, and determine the role of genetics, diet, and lifestyle. These challenges are open to anyone and everyone who wants to build an algorithm to solve complex biomedical problems. Learn more about this new approach to science, and how you can participate, by joining us for our free Breakfast & Benchmarking event at our Seattle office, July 30, during Seattle Tech Week. Spots are limited! Register here: https://lu.ma/uo7xumku #seattletechweek #techweek #citizenscience #datachallenges #AI #ML

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A quick guide to using Riza as a Python code interpreter with Anthropic's Claude models. https://lnkd.in/gFiHQTyD

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In case you missed it, the first After Your PhD newsletter went out this morning about insights I learned from looking at 25 Data Scientists job postings. 📬 If you want to catch these special newsletters you can sign up here: https://lnkd.in/gZjv-n2G If you missed this last one on Data Scientists, no worries, sign up and email afteryourphd@gmail.com and I can forward you the recent newsletter. (: #datascience #datascientist #newsletter #phds #phd #academia #leavingacademia #afteryourphd #data #datajobs

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I'm exploring how Splink can be used for address matching. Does anyone know of any labelled datasets of messy address data that could be used to measure performance? (i.e. labelled to indicate which addresses really match) FWIW there are some rough demos of matching some open address data (EPC to price paid data, and food hygiene score to price paid data) here: https://lnkd.in/e_wHRDcm But without labels, difficult to quantify how good it is

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⬇ A recent paper by researchers from several European universities unveiled the HPLT (High Performance Language Technologies) dataset for language modeling and machine translation. This dataset incorporates monolingual and bilingual corpora sourced from web crawls by the Internet Archive and CommonCrawl, a first at this scale. According to the team, these resources are among the largest open text corpora ever released, providing invaluable material for language modeling and machine translation training. They detailed their acquisition and processing methods, leveraging open-source software tools and high-performance computing, and have made everything publicly available on GitHub as a model for others. Covering 75 languages, including many low- to medium-resourced ones, the monolingual collection comprises a whopping 5.25 billion documents. Additionally, the parallel corpus, mainly focused on English, offers 18 language pairs with over 96 million aligned sentence pairs, with a specific emphasis on low-resource languages to bolster MT development. Moreover, the researchers created a synthetic dataset by pivoting existing parallel datasets through English, totaling 171 language pairs and 157 million sentence pairs. All datasets include metadata for user filtering. In addition to the datasets, the researchers have released initial MT models and large language models (LLMs) along with their training pipelines. This development highlights the changing world of translation. Machine learning is taking center stage, boosting translation services and paving the way for the future of this art form. Other news are available at https://lnkd.in/gz4-TSpA #translation #FrontPageIP #MachineTranslation

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Over this wide-ranging discussion, Noah Giansiracusa delves into the benefits and drawbacks of AI as it relates to the creation and dissemination of news. #AI #datascience #artificialintelligence https://hubs.li/Q02C13H50

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Webinar alert - April 30, 1PM EDT. George Anadiotis from LinkedDataOrchestration and George Demarest of #Aerospike will guide you through selecting the right graph database and essential selection criteria tailored to your organizational needs. https://lnkd.in/eDV-QQuB #GraphDatabase #DataManagement

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What's the difference between model explainability, interpretability, and observability? Jason Zhong's debut TDS article outlines the nuanced distinctions you should be aware of.

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Stay up to date with the latest research in Measurement, Evaluation, Statistics, & Assessment with the latest ESM MAD Blog! #ELPS Check out our latest blog by Andrew Young titled "To Evaluate, Or To Be Evaluated? That is the Question" here: https://lnkd.in/eFdj5CVT

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How well do language models perform in spatial-reasoning tasks? Nathan Bos offers a comprehensive overview of the good, the terrible, and the improving, as of right now.

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🗽 Join us at Semantic Data 2024: Taxonomy, Ontology, and Knowledge Graphs in New York City on October 23, 2024! 🗽 Top Reasons to Join: 1️⃣ Master the Semantic Layer: Build a robust semantic foundation for better AI and decision-making. 2️⃣ Navigate the AI Revolution: Learn how semantics shape intelligent systems. 3️⃣ Develop a Semantic Mindset: Align data semantics with business objectives. 4️⃣ Mitigate Bias in AI: Create unbiased semantic models for fairer machine learning. 5️⃣ Leverage AI as a Taxonomy Assistant: Integrate AI into taxonomy development. 6️⃣ Bridge Taxonomies and Ontologies: Connect and extend taxonomies for better data analysis. 7️⃣ Prepare for the Technology Supercycle: Get expert predictions on AI, Web3, and bioengineering. 8️⃣ Network with Industry Leaders: Engage with practitioners sharing real-world solutions. 🌟 SAVE $200 - Super Early Bird Offer Expires August 9 🌟 Plus a 20% discount for additional delegates registering at the same time Register Today - https://lnkd.in/djG27Ud2 Sponsored by: Factor Firm LLC and Datavid  🚀 Sponsorship Opportunities: Enhance your brand presence and connect with industry leaders. Contact Rebecca at Rebeccac@henrystewart.co.uk for customized packages. Don't miss this chance to future-proof your data strategies and stay ahead in the world of data management and AI. #SemanticData2024 #SemanticDataNY #AI #KnowledgeGraphs #Taxonomy #Ontology #SemanticData #Ontologies #Taxonomies #Taxonomist #Ontologist 

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"In this article, we won’t be focusing on sampling methods themselves per se, but rather on using these concepts to split the dataset used for machine learning approaches into Train-Test-Validation sets." Amarpreet Singh explains how to ensure distributional integrity when splitting small datasets.

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💡 Interesting observations (part 1) on LLM output safety evaluations by BreezeML’s Founding Engineer, Hunter Zhang, Ph.D.! #artificialintelligence #ai #ml #governance #aigovernance #responsibleai #grc #compliance #aicompliance #airegulation #law #regulation #machinelearning #technology #breezeml #evaluations #llm #safety #aisafety

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