Building Apps with OpenAI's Products
Insights on large language models, ChatGPT, and the developer landscape with OpenAI products
Good morning fellow AI enthusiast! This week's iteration focuses on none other than OpenAI! Dive into the required AI vocabulary and insights from my interview with Logan Kilpatrick, developer relations at OpenAI. In this interview Logan demystifies terms like tokens, prompt engineering, alignment, and multimodal models, shedding light on the intricate workings of large language models like GPT-4. He also shares lots of great tips for better using OpenAI's products. I hope you enjoy it!
1️⃣ AI is getting smarter. Are you?
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2️⃣ Building with Large Language Models, chatGPT, and Working at OpenAI - What's AI episode 11
Interview Alert: Logan Kilpatrick, working at OpenAI in developer relations, shares his insights on large language models, ChatGPT, and the developer landscape with OpenAI products. A must-listen for OpenAI enthusiasts or just if you want to build LLM-based products!
🎓From community college to Harvard, Apple, and now OpenAI, Logan's journey highlights his passion for machine learning, programming, and developer support. He also shares thoughts on the relevance of PhDs and "required studies" for working with cutting-edge AI technologies.
💼 Logan's role at OpenAI involves helping developers succeed by improving the core developer experience across the API and ChatGPT plugins. His focus is on making OpenAI’s technology more accessible to developers, regardless of prior machine learning experience. He is basically the best person to talk/listen to regarding using OpenAI products.
🧩 Logan highlights many key insights you need to know, like the difference between ChatGPT and GPT4:
ChatGPT is the user interface through which you interact with OpenAI's machine learning models, while models such as GPT-4 are the underlying machine learning models themselves.
🛠️ Get a peek at OpenAI's interview process! From recruiter calls to take-home assessments and final interviews, Logan shares his experience of landing a job at this cutting-edge company.
💡 Dive into AI vocabulary! Logan demystifies terms like tokens, prompt engineering, alignment, and multimodal models, shedding light on the intricate workings of large language models like GPT-4.
Check out the full interview for more insights from Logan Kilpatrick on OpenAI and the exciting landscape of AI-powered developer solutions! Watch on YouTube or listen on Spotify…
3️⃣ AI Ethics with Auxane
Artificial intelligence (AI) has the power to change the way we live, work and communicate with each other. The fast integration of Chat GPT in society is a good example of this! But, there are also ethical concerns that come with the development and use of AI systems, as you now know! One of these is the issue of transparency, or how much we know about how an AI system works.
Transparency is important because it can help people understand how an AI system works and how its use can benefit society. Imagine you're using an AI-powered translator app on your phone to talk to someone who speaks a different language. If you know how the app works, you can feel more confident that it's translating your words accurately! Transparency can also help us identify problems, like if an AI system is making biassed decisions, so we can fix them and make sure the technology is fair for everyone.
But, there are also risks to transparency. If everyone knows how an AI system works, it can be misused.. We need to find a balance between making AI systems transparent enough to build trust and identify problems, while also keeping them secure enough to prevent misuse. Another challenge with transparency is intellectual property. Companies invest a lot of time and money in developing AI systems, so they have a right to protect their intellectual property. But, if they keep the inner workings of their AI systems secret, it can be hard for experts to evaluate them for ethical concerns like bias. We need to find a way to balance the need for transparency with the need to protect intellectual property.
In short, transparency is important for building trust and identifying ethical concerns in AI systems. However, we need to be careful to balance transparency with security and intellectual property protections. As AI technology continues to evolve, we'll need to keep working on finding this balance to make sure that AI is used ethically and for the good of everyone!
I wish you all a great week! - Auxane Boch (iuvenal research consultant, TUM IEAI research associate).
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We will see you next week with another amazing paper!
Louis