Project physics
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This document contains a self-evaluation and goals from a student named Allen Jay Filipino for their physics class. In the self-evaluation, the student indicates that they were highly engaged in class, contributing ideas and answering questions. They were attentive in lessons and followed rules. For their goals in the second grading period, the student promises to improve their studies in physics by focusing more and preparing for difficult lessons. They aim to achieve higher than the first grading period and get a 100% in the second grading period through sharing their knowledge and helping classmates.
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際際滷 deck related to the GitHub Copilot Bootcamp in Melbourne on 17 June 2025GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash
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GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing NowOpenACC and Open Hackathons Monthly Highlights June 2025
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC
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The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open
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Presented at All Things Open RTP Meetup
Presented by William Hill - Developer Advocate, NVIDIA
Title: Agentic AI for Developers and Data Scientists
Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code
Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIAs developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprisesAI Agents and FME: A How-to Guide on Generating Synthetic Metadata
AI Agents and FME: A How-to Guide on Generating Synthetic MetadataSafe Software
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In the world of AI agents, semantics is king. Good metadata is thus essential in an organization's AI readiness checklist. But how do we keep up with the massive influx of new data? In this talk we go over the tips and tricks in generating synthetic metadata for the consumption of human users and AI agents alike.CapCut Pro Crack For PC Latest Version {Fully Unlocked} 2025
CapCut Pro Crack For PC Latest Version {Fully Unlocked} 2025pcprocore
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CapCut Pro Crack is a powerful tool that has taken the digital world by storm, offering users a fully unlocked experience that unleashes their creativity. With its user-friendly interface and advanced features, its no wonder why aspiring videographers are turning to this software for their projects.Lessons Learned from Developing Secure AI Workflows.pdf
Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash
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Lessons Learned from Developing Secure AI Workflows9-1-1 Addressing: End-to-End Automation Using FME
9-1-1 Addressing: End-to-End Automation Using FMESafe Software
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This session will cover a common use case for local and state/provincial governments who create and/or maintain their 9-1-1 addressing data, particularly address points and road centerlines. In this session, you'll learn how FME has helped Shelby County 9-1-1 (TN) automate the 9-1-1 addressing process; including automatically assigning attributes from disparate sources, on-the-fly QAQC of said data, and reporting. The FME logic that this presentation will cover includes: Table joins using attributes and geometry, Looping in custom transformers, Working with lists and Change detection.10 Key Challenges for AI within the EU Data Protection Framework.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash
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10 Key Challenges for AI within the EU Data Protection FrameworkMastering AI Workflows with FME by Mark Doring
Mastering AI Workflows with FME by Mark DoringSafe Software
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Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FMEs powerful tools for training, optimization, and seamless integrationQuantum AI: Where Impossible Becomes Probable
Quantum AI: Where Impossible Becomes ProbableSaikat Basu
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Imagine combining the "brains" of Artificial Intelligence (AI) with the "super muscles" of Quantum Computing. That's Quantum AI!
It's a new field that uses the mind-bending rules of quantum physics to make AI even more powerful.Connecting Data and Intelligence: The Role of FME in Machine Learning
Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software
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In this presentation, we want to explore powerful data integration and preparation for Machine Learning. FME is known for its ability to manipulate and transform geospatial data, connecting diverse data sources into efficient and automated workflows. By integrating FME with Machine Learning techniques, it is possible to transform raw data into valuable insights faster and more accurately, enabling intelligent analysis and data-driven decision making.Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdf
Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdfcaoyixuan2019
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A presentation at Internetware 2025.Using the SQLExecutor for Data Quality Management: aka One man's love for the...
Using the SQLExecutor for Data Quality Management: aka One man's love for the...Safe Software
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The SQLExecutor is one of FMEs most powerful and flexible transformers. Pivvot maintains a robust internal metadata hierarchy used to support ingestion and curation of thousands of external data sources that must be managed for quality before entering our platform. By using the SQLExecutor, Pivvot can efficiently detect problems and perform analysis before data is extracted from our staging environment, removing the need for rollbacks or cycles waisted on a failed job. This presentation will walk through three distinct examples of how Pivvot uses the SQLExecutor to engage its metadata hierarchy and integrate with its Data Quality Management workflows efficiently and within the source postgres database. Spatial Validation Validating spatial prerequisites before entering a production environment. Reference Data Validation - Dynamically validate domain-ed columns across any table and multiple columns per table. Practical De-duplication - Removing identical or near-identical well point locations from two distinct source datasets in the same table.The Future of Product Management in AI ERA.pdf
The Future of Product Management in AI ERA.pdfAlyona Owens
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Hi, Im Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around.
Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as Ive transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs Well talk about it shortly.
But before that, I want to take you back to 1997. One of my favorite movies is Fifth Element. It wowed me with futuristic predictions when I was a kid and Im impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves:
Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink).
Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldnt be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future.
So, we as humans, when we dont know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didnt steal jobsthey transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
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