Generative AI and Academic Integrity

Generative AI, or Generative Artificial Intelligence, refers to a class of artificial intelligence techniques and models that are designed to generate new content, such as text, images, audio, or even video, that is similar to what might be created by humans. These AI systems use complex algorithms and neural networks to produce data rather than just processing or analyzing it.

Generative AI has a wide range of applications, including:

• Text Generation: Creating human-like text, such as in chatbots, content generation, or language translation.

• Image Generation: Producing realistic images or modifying existing ones, as seen in style transfer or generating artwork.

• Music and Audio Generation: Composing music or generating speech.

• Data Augmentation: Expanding datasets for machine learning by generating synthetic data.

• Anomaly Detection: Identifying outliers in data by generating what's considered normal and flagging deviations.

• Drug Discovery: Generating molecular structures and predicting their properties.

• Content Creation: Generating content for video games, stories, and other creative media.

Generative AI has made significant advancements in recent years, but it also raises ethical and societal concerns, particularly regarding the generation of fake content, misinformation, and the potential for deepfakes. As a result, researchers and developers are working on ways to mitigate these risks and promote responsible use of generative AI technology.

(Note: The above text was created using ChatGPT. Why do you think the author chose to create the text this way? How does it make you feel about the authority of the information?)

Generative AI tools have gained popularity and have been applied in various domains. Here are some examples of generative AI tools and applications:

(Please note: These tools have not been reviewed or vetted for student use by the library or any other academic authority. Inclusion on this list does not imply endorsement. Check with your professor before using any generative AI tool as part of your academic work.)

Text Generation

• GPT-4 (Generative Pre-trained Transformer 4): Developed by OpenAI, GPT-4 is an advanced language model that surpasses GPT-3 in terms of performance and capability. It’s widely used in applications like chatbots, content generation, coding assistance, and natural language understanding.

Image Generation and Manipulation

• DeepDream: A Google project that uses neural networks to generate surreal and artistic images from existing pictures.
• StyleGAN (Style Generative Adversarial Network): Used for generating and manipulating images, particularly in the creation of deepfake images.
• MidJourney: An AI image generator that creates artistic and high-quality images from text prompts, becoming widely popular in creative and professional industries.
• Stable Diffusion: An open-source text-to-image model that allows users to generate images from textual descriptions, with a focus on generating high-resolution images.

Music and Audio Generation

• Magenta: Developed by Google, Magenta is a research project focused on music and art generation. It includes tools for generating music and art using neural networks.
• OpenAI’s Jukebox: A neural network-based music generation model that can create music in various genres and styles from raw audio data.

Data Augmentation

• imgaug: A Python library that uses generative techniques to augment image datasets for machine learning.
• AugLy: A newer library developed by Facebook AI that provides a comprehensive set of data augmentation tools for images, audio, video, and text.

Anomaly Detection

• One-Class SVM (Support Vector Machine): Although not a generative model per se, it's used in anomaly detection by learning the characteristics of normal data and flagging deviations as anomalies.
• Autoencoders for Anomaly Detection: Autoencoders, which are neural networks trained to reconstruct their input, can be used to detect anomalies by identifying inputs that cannot be well-reconstructed.

Content Creation

• Artbreeder: An online platform that allows users to create and manipulate images by blending existing ones using GAN technology.
• DALL-E: Another creation from OpenAI, DALL-E is a text-to-image generator that can produce images from textual descriptions.
• Canva’s AI Tools: Canva now offers AI-powered design tools that generate images, logos, and other visual content based on user input, making it accessible for non-designers.

Drug Discovery

• Generative Adversarial Networks for Drug Discovery: These models are used to generate molecular structures and predict their properties, aiding in drug discovery processes.
• Molecule Generation Models (e.g., MolGPT): Newer generative models like MolGPT are being used for drug discovery, focusing on generating potential drug candidates with desirable properties.

Video Game Content

• Procedural Content Generation (PCG): Generative AI is used to create game environments, characters, and levels in video games, reducing the need for manual content creation.
• NVIDIA’s GameGAN: A model capable of generating entire game environments and dynamics, allowing for the creation of playable levels with minimal human intervention.

Storytelling and Narrative Generation

• Various AI tools, such as ChatGPT and similar models, are employed to assist in generating narratives, plotlines, and interactive storytelling in applications and games.
• NovelAI: A platform that uses AI to help users generate stories, write novels, and create immersive narratives with customizable elements.

These are just a few examples, and the field of generative AI is continually evolving with new tools and applications emerging regularly. As AI technologies advance, generative AI is likely to become even more versatile and widespread in various industries.

(Note: Except for the note in parentheses, the above text was created using ChatGPT with few changes. Why do you think the author chose to create the text this way? How does it make you feel about the authority of the information?)