Practical AI for Instructors and Students Part 2: Large Language Models (LLMs)

Summary

In this video, Ethan Malik from Wharton School discusses foundational AI models, focusing on OpenAI's GPT-3.5, GPT-4, Microsoft's Bing, and Google's Bard. He highlights how these large language models power most AI tools and emphasizes the importance of understanding their strengths and weaknesses. The video compares models' capabilities, with GPT-4 being more powerful but slower, Bing offering internet connectivity and image creation, and Bard being fast but still developing. The importance of experimenting with prompts and critically evaluating AI output is stressed to effectively use these tools.

Takeaways

• 🧑‍🏫 Foundational models like large language models (LLMs) are essential for many AI tools, and understanding them is key to leveraging AI effectively.
• 💡 The three key foundational AI models discussed are OpenAI's GPT (3.5 and 4.0), Microsoft's Bing Chat (powered by GPT-4), and Google's Bard (using the PaLM 2 model).
• ⚡ GPT-3.5 is faster and less powerful, while GPT-4 is more sophisticated but slower. Both models can generate text, but GPT-4 offers deeper analysis.
• 🌐 Microsoft Bing's chat bot, especially in Creative mode, is recommended for using GPT-4. Bing is internet-connected, can work with documents, and generate images.
• 📚 Google's Bard is also connected to the internet, making it fast and efficient for certain tasks. It may not be as capable as GPT-4 currently but is improving rapidly.
• 🛠️ LLMs are versatile and can act as teachers, office assistants, or creative tools depending on the prompts and instructions they receive.
• ❗ AIs, including GPT and Bard, can sometimes produce hallucinations or incorrect answers, so users need to critically evaluate their outputs.
• 🎭 Different AI models have distinct personalities, and users must learn their quirks to effectively interact with them, similar to working with humans.
• 🔄 AI model outputs are not consistent and can change over time due to system updates or changes in the underlying algorithms.
• 🚀 Practice is key—only by using AI tools regularly can users fully understand their strengths, weaknesses, and potential areas of misuse.

Q & A

What are foundational models, and why are they important in AI?

Foundational models are large-scale AI models, such as language models, that serve as the core technology behind many AI tools. They are important because most AI tools are built on top of these models, and understanding how to use foundational models like GPT or Bard allows users to directly leverage their power for various tasks.

Which three foundational models are discussed in the video?

The three foundational models discussed are OpenAI's GPT (versions 3.5 and 4.0), Microsoft's Bing (which uses GPT-4 in creative mode), and Google's Bard (which uses Palm 2).

What is the difference between GPT-3.5 and GPT-4 in terms of performance?

GPT-3.5 is faster but less capable than GPT-4, which is more sophisticated and generates higher-quality outputs. However, GPT-4 is slower in responding due to its more complex processing capabilities.

What advantages does Microsoft’s Bing Chat offer over ChatGPT?

Bing Chat, especially in creative mode, uses GPT-4 and has the added advantage of being connected to the internet, which allows it to access and retrieve real-time information. It can also work with documents and create images, features that ChatGPT lacks at the time of the video.

How does Google’s Bard differ from ChatGPT and Bing Chat?

Google's Bard is built on the Palm 2 model and is generally faster than the other models. It presents results in complete drafts rather than step-by-step. Bard is also connected to the internet, but at the time of the video, it was slightly less capable than GPT-4 or Bing Chat. However, its capabilities are improving quickly.

What are some tasks that large language models are particularly good at?

Large language models excel at tasks that involve human-like abilities, such as writing, analysis, creative tasks (e.g., generating images), coding, summarizing information, and performing data analysis. They can also take on roles like teachers, office assistants, and editors if given clear instructions.

What is ‘hallucination’ in the context of AI models?

‘Hallucination’ refers to when AI models generate plausible but incorrect or made-up information. This happens because the models do not truly 'understand' the data, leading to inaccuracies that sound convincing but are factually wrong.

Why is it important to critically evaluate AI outputs?

AI outputs can be unreliable or contain errors, so it’s crucial for users to critically assess the information they receive. While AI models can produce great content, they are prone to making mistakes, especially in tasks requiring real-world knowledge or complex reasoning.

What is the best way to improve your understanding and use of AI models?

The best way to improve your understanding is through practice. By using these AI systems extensively, you can learn their strengths, weaknesses, and limitations. Experience helps in knowing how to prompt them effectively and identify where they might make errors.

What are some limitations of large language models like GPT or Bard?

Limitations include their tendency to hallucinate or provide inaccurate information, inconsistency in their responses, and the fact that they are not sentient. Additionally, they can be deceptive in how convincing they sound, but their advice or outputs may not always be reliable or accurate.