Business Physics AI Lab

Tag: GenAI

  • Staying Current with AI: Habits of a Lifelong Learner

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    Hichem Benzair
    in

    Editor’s Note: This article is based on an interview with Stéphane Paquet, AI Project Lead at Champlain College Saint-Lambert and AI Certificate Coordinator. The responses have been summarized for clarity and brevity and are not word-for-word reproductions of the original conversation. The ideas reflect the speaker’s perspective as of the time of the interview. Question:…

  • The Illusion of Conversation: Rethinking AI Interfaces in Education

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    by

    Hichem Benzair
    in

    Editor’s Note: This article is based on an interview with Stéphane Paquet, AI Project Lead at Champlain College Saint-Lambert and AI Certificate Coordinator. The responses have been summarized for clarity and brevity and are not word-for-word reproductions of the original conversation. The ideas reflect the speaker’s perspective as of the time of the interview. Question:…

  • Writing for AI: The New Literacy We All Need

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    by

    Hichem Benzair
    in

    Editor’s Note: This article is based on an interview with Stéphane Paquet, AI Project Lead at Champlain College Saint-Lambert and AI Certificate Coordinator. The responses have been summarized for clarity and brevity and are not word-for-word reproductions of the original conversation. The ideas reflect the speaker’s perspective as of the time of the interview. Question:…

  • Understanding AI Hallucinations

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    Thomas Bedard
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    AI hallucinations are a modern artificial intelligence problem that makes AI models generate incorrect, fabricated, or nonsensical information. Let’s find out why this happens and why they matter. What are AI Hallucinations? AI hallucinations happen when AI systems produce content that has no basis in their training data or doesn’t align with reality. These errors aren’t random…

  • What Are Vector Databases?

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    Hichem Benzair
    in

    Regular Databases = Rows and Columns Imagine a spreadsheet that keeps track of all the student clubs at Champlain College: id name category room 1 Anime Club Media C-102 2 Art Club Creative C-103 3 Robotics Team Tech C-108 4 Champlain Music Society Creative C-109 With this kind of relational database, you can basically ask:…

  • Understanding Context Window in AI

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    Hichem Benzair
    in

    The context window in AI refers to the maximum amount of text or data an AI model can process in a single interaction. It defines how much information AI can “remember” at once to generate meaningful responses. Analogy: Imagine having a short-term memory limit while reading a book.If you can only remember the last 2…

  • Bias Mitigation in AI

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    Hichem Benzair
    in

    AI models can unintentionally develop biases based on the data they are trained on. Bias mitigation ensures AI treats all users fairly by identifying, correcting, and testing for biases. Identifying Biases in AI Bias in AI happens when models favor certain groups over others due to imbalanced or flawed training data. Examples: How to Identify…

  • Optimization and Specialization in AI

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    Hichem Benzair
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    AI models can be optimized and specialized for specific tasks using Fine-Tuning, Transfer Learning, and Prompt Engineering. These techniques help improve AI performance, efficiency, and accuracy. Fine-Tuning What Is Fine-Tuning? Fine-tuning customizes a pre-trained AI model for a specific task or industry by training it on specialized datasets. Examples: -A general AI chatbot is fine-tuned…

  • Chain-of-thought reasoning (Simply Explained)

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    Thomas Bedard
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    What is Chain-of-thought reasoning (CoT)? “Chain of thought (CoT) mirrors human reasoning, facilitating systematic problem-solving through a coherent series of logical deductions”. IBM: https://www.ibm.com/think/topics/chain-of-thoughts Essentially, CoT encourages an AI model to take intermediate steps before getting to the final answer. Instead of going straight to the solution, the model will explain its thought process in…

  • What is RAG & How Does it Work?

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    Thomas Bedard
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    Retrieval-Augmented Generation (RAG) is an advanced technique in Generative AI that blends text generation with real-time information retrieval. Simply put, RAG allows AI models to access external databases or knowledge bases to fetch accurate and up-to-date information when generating responses. This ensures that the content produced is not only coherent but also factually correct and relevant to the…

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