AI among us: Social media users struggle to identify AI bots during political discourse |
Artificial intelligence bots have already permeated social media. But can users tell who is human and who is not?
Researchers at the University of Notre Dame conducted a study using AI bots based on large language models — a type of AI developed for language understanding and text generation — and asked human and AI bot participants to engage in political discourse on a customized and self-hosted instance of Mastodon, a social networking platform.
The experiment was conducted in three rounds with each round lasting four days. After every round, human participants were asked to identify which accounts they believed were AI bots.
Fifty-eight percent of the time, the participants got it wrong.
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CRC Graduate Award for Computational Science and Visualization |
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Applications of high performance computation and/or visualization technology
- Development of algorithms, codes, software environments, or other tools for better using high performance computing and/or visualization
- Up to three awards may be presented each year. Awardees will receive a $1,000 USD cash award and a plaque.
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This award is open to all current Notre Dame students seeking advanced degrees and recent graduates. The use of CRC hardware or software is not required for the nominated work.
The due date for all nominations is March 31, 2024 at 11:59 p.m. Eastern.
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Choosing the Right Environment for Your Python Projects
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For those who work with Python, effective management of dependencies and environments is essential for project success. Two widely-used tools for this purpose are Python Virtual Environments and Conda Environments. While both serve to isolate project dependencies, they come with unique features, advantages, and drawbacks.Let's take a closer look at each to help you decide which is best for your Python projects
Python Virtual Environments
Python Virtual Environments, often referred to simply as "virtual environments," are lightweight, isolated environments that allow Python developers to create project-specific dependencies. They achieve this by creating a directory structure containing a standalone Python interpreter and its associated site-packages directory, separate from the system-wide Python installation.
Pros of Python Virtual Environments:
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Lightweight: Virtual environments are minimalistic and consume fewer system resources compared to Conda environments, making them ideal for projects with simple requirements or limited resources.
- Built-in: Python Virtual Environments are part of the Python standard library (as of Python 3.3). Developers can create virtual environments effortlessly using the ‘virtualenv’ module.
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Ease of Use: Creating and managing virtual environments is straightforward, making them an excellent choice for beginners or projects with basic requirements. Developers can activate and deactivate virtual environments with simple commands, ensuring a smooth workflow.
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Integration: Python Virtual Environments seamlessly integrate with other Python development tools and workflows, such as pip for package management and version control systems like Git, providing a cohesive and familiar environment for Python developers.
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Cons of Python Virtual Environments:
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Limited Package Management: Virtual environments rely on pip for package management, which may lack some of the advanced features and capabilities offered by Conda. Managing complex dependency trees and resolving conflicts manually can be challenging, especially for projects with many dependencies or strict version requirements.
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Platform Dependencies: While virtual environments are portable across different platforms, certain packages with platform-specific dependencies may require additional configuration or manual intervention, potentially complicating the development process.
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Conda Environments
Conda is an open-source package management and environment management system that simplifies the installation and management of software packages and their dependencies. Conda environments provide strong isolation by creating separate environments for different projects, allowing developers to work with different sets of dependencies without conflicts.
Pros of Conda Environments:
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Package Management: Conda is a powerful package manager that simplifies the installation and management of Python packages and their dependencies. It supports not only Python packages but also packages from other languages like R and C++, providing a comprehensive solution for dependency management.
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Cross-Platform Compatibility: Conda environments are platform-independent, making it easy to create and share environments across different operating systems without worrying about compatibility issues. This feature is particularly valuable for projects with diverse development environments or deployment targets.
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Environment Isolation: Conda environments provide strong isolation, allowing developers to create separate environments for different projects or purposes. This isolation helps prevent conflicts between package versions and ensures reproducible builds.
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Conda-forge: Conda-forge is a community-driven repository for Conda packages, offering a wide range of curated packages not available in the default repositories. This extensive collection of packages expands the capabilities of Conda environments and enhances the development experience.
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Cons of Conda Environments:
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Resource Intensive: Conda environments can be resource-intensive in terms of disk space and memory usage, especially when dealing with large environments or installing packages with many dependencies. This overhead may impact the performance and scalability of projects, particularly on systems with limited resources.
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Complexity: Conda's extensive features and capabilities can lead to a steeper learning curve for beginners compared to simpler tools like Python's built-in virtual environments. Understanding and navigating Conda's ecosystem may require additional time and effort, especially for users accustomed to more lightweight solutions.
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Choosing the Right Environment for Your Projects:
When deciding between Python Virtual Environments and Conda Environments for your projects, consider the specific requirements, constraints, and preferences of your development team. Here are some factors to consider:
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Project Complexity: For simple projects with few dependencies, Python Virtual Environments offer a lightweight and straightforward solution. However, for complex projects with diverse dependencies or non-Python dependencies, Conda Environments may provide better support and manageability.
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Resource Constraints: If you're working with limited system resources or need to optimize resource usage, Python Virtual Environments may be preferable due to their lightweight nature. Conda environments, while powerful, can be resource-intensive and may not be suitable for resource-constrained environments.
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Familiarity and Preference: Consider the familiarity and preferences of your development team. If your team is already comfortable with Python Virtual Environments or has specific workflows built around them, sticking with virtual environments may streamline the development process. However, if your team values the comprehensive package management and environment management features offered by Conda, it may be worth investing time in learning and adopting Conda environments.
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Both Python Virtual Environments and Conda Environments offer valuable tools for managing dependencies and environments in Python projects. By understanding their features, advantages, and limitations, you can make an informed decision based on the specific needs and constraints of your projects. Whether you opt for the simplicity and lightweightness of Python Virtual Environments or the comprehensive package management and environment isolation of Conda Environments, choosing the right environment is essential for ensuring a smooth and efficient development experience.
For more details about Python Virtual Environments and Conda Environments in the CRC, visit the following links: Python — CRC User documentation and Conda — CRC User documentation.
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| Every Wednesday and Thursday in March
2:00 – 3:00 p.m.
via Zoom
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This training is available to new users and current users interested in a refresher course on how to use CRC resources. Attendees learn the basics of accessing CRC resources and submitting jobs on the CRC clusters. This course is a co-requisite when receiving a CRC account. Learn more.
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