Information Management and AI
Overview
Key topics of information management and AI are:
Information Management
In today’s digital world, effectively managing information involves collecting, organizing, analyzing, and presenting data to make informed decisions and solve problems.
The Role of AI
Artificial Intelligence (AI) helps process large amounts of data quickly, organize it efficiently, and extract relevant information, which is essential for both academic and professional purposes.
Challenges of AI
While AI offers many advantages, it can also introduce biases or inaccuracies. It’s crucial to verify AI-generated information and understand its limitations.
Future of AI in Information Management
AI tools are continuously improving and are expected to become more precise and accessible, helping individuals make better decisions and learn more efficiently, but ethical and responsible use is essential.
Check Your Understanding
What else should I know?
LLMs learn from real-world data, absorbing biases from their sources. Internet text often contains stereotypes or biased perspectives, which models unintentionally reproduce. For example, frequent negative portrayals of a group in training data can lead to the model repeating these biases. Recognizing and mitigating these biases is crucial. Developers and researchers are continually working on strategies to:
- Curate data more carefully.
- Use bias detection tools.
- Implement techniques to reduce biased outputs.
Still, it’s important to remember current LLMs contain biases that could impact their responses and take responsibility for potential issues in content generated through your chatbot interactio
Models like ChatGPT also learn from actual user interactions (prompts, corrections, follow-ups), enabling them to adapt and better handle real-world tasks. However, it’s important to be cautious, as user-submitted data could potentially reappear in future responses.
Your Data in LLMs
When you use services like ChatGPT, your interactions can be used to help train and improve the model. This means your shared information, like personal details or assignments, or private information, might shape future responses. For example, if you submit your own essay or original work, the model might generate similar text in the future, potentially sharing your ideas with others.
To keep your data safe and maintain privacy:
- Think twice before sharing sensitive or personal details.
- Avoid inputting private information like your full name, student ID, passwords, or confidential research data.
- Always check how a service handles your data to make informed decisions.
Remember, being cautious about what you share online helps protect your privacy and ensures responsible use of technology.
For additional guidance visit the University of Toronto Information Security Guidelines on use of GenAI.
During training, LLMs repeatedly see text sequences and attempt to predict the next word based on the context. The model has billions, or even trillions of internal parameters that are adjusted each time the prediction differs from the actual next word. Over time, the model gradually becomes more adept at correctly predicting the next word based on language structure, grammar and more.
This training process helps to explain why these models can provide coherent and contextually relevant responses. However, they don’t “understand” language the way we do – they simply predict words based on highly complex patterns in the data. This is also why models can generate plausible but incorrect information, as their predictions are based on statistical modelling rather than a
