Generative AI Basics Flashcards
What are Common Concerns About Generative AI? (5)
- Hallucinations
- Data Security
- Plagiarism
- User Spoofing
- Sustainability
Hallucinations: Remember that generative AI is really another form of prediction, and sometimes predictions are wrong. Predictions from generative AI that diverge from an expected response, grounded in facts, are known as hallucinations. They happen for a few reasons, like if the training data was incomplete or biased, or if the model was not designed well. So with any AI generated text, take the time to verify the content is factually correct.
Data Security: Businesses can share proprietary data at two points in the generative AI lifecycle. First, when fine-tuning a foundational model. Second, when actually using the model to process a request with sensitive data. Companies that offer AI services must demonstrate that trust is paramount and that data will always be protected.
Plagiarism: LLMs and AI models for image generation are typically trained on publicly available data. There’s the possibility that the model will learn a style and replicate that style. Businesses developing foundational models must take steps to add variation into the generated content. Also, they may need to curate the training data to remove samples at the request of content creators.
User Spoofing
It’s easier than ever to create a believable online profile, complete with an AI generated picture. Fake users like this can interact with real users (and other fake users), in a very realistic way. That makes it hard for businesses to identify bot networks that promote their own bot content.
Sustainability: The computing power required to train and run AI models is immense, and can translate to carbon emissions, water depletion, and other environmental impacts. As models get bigger, so do their carbon footprints. That’s why we’re working to ensure AI is sustainable by developing efficient AI models, leading with transparency, and decarbonizing the power sector through renewable energy and modernized grid infrastructure.
New AI model architecture and availability of extensive training data are two factors in the rapid improvement of generative AI. What’s the third?
Increased parallel computing power
True or false: Developers must create their own large language models in order to add natural language processing to their applications.
False
What are the two most popular generative AI models at this time?
GANS and Transformers
Explain the similarities between Generative AI and Predictive AI (2)
Both generative and predictive AI use use machine learning and advanced algorithms to tackle complicated business and logistical challenges.
Explain the difference between Generative AI and Predictive AI (2)
Generative AI can generate images, texts, video, and even software code based on user input, demonstrating its potential for creative applications.
Predictive AI analyzes large datasets to detect patterns over history. By identifying these patterns, predictive AI may conclude and forecast possible outcomes or future trends.
