Hello, Engineering Leaders and AI Enthusiasts!
Welcome to the 97th edition of The AI Edge newsletter. This edition brings you ‘OpenAI’s ChatGPT enters classrooms 🎓.’
And a huge shoutout to our incredible readers. You all rock! 😊
In today’s edition:
🎓 OpenAI’s ChatGPT enters classrooms
🎉 Meta announced 2 new AI updates
📄 GoT enhances the LLM capabilities
🧠 Knowledge Nugget: Efficient LLM inference by 👏
Let’s go!
OpenAI’s ChatGPT enters classrooms
OpenAI has released a guide for teachers using ChatGPT in their classroom. This guide includes suggested prompts, explanations about ChatGPT’s functionality and limitations, as well as insights into AI detectors and bias.
The company also highlights stories of educators successfully using ChatGPT to enhance student learning and provides prompts to help teachers get started. Additionally, their FAQ section offers further resources and answers to common questions about teaching with and about AI.
Example prompts to get you started
Why does this matter?
OpenAI’s teaching with AI empowers teachers with resources and insights to effectively use ChatGPT in classrooms, benefiting students’ learning experiences. While Competitors like Bard, Bing, and Claude may face pressure to offer similar comprehensive guidance to educators. Failing to do so could put them at a disadvantage in the increasingly competitive AI education market.
Meta announced 2 new AI updates
Meta has announced the commercial relicensing and expansion of DINOv2, a computer vision model, under the Apache 2.0 license to give developers and researchers more flexibility for downstream tasks.
Meta also introduces FACET (FAirness in Computer Vision Evaluation), a benchmark for evaluating the fairness of computer vision models in tasks such as classification and segmentation. The dataset includes 32,000 images of 50,000 people, with demographic attributes such as perceived gender age group, and physical features.
Why does this matter?
FACET ensures more equitable experiences when interacting with computer vision technology, reducing the risk of bias based on demographics. On the other hand, DINOv2’s availability under the Apache 2.0 license empowers developers and researchers to create more versatile computer vision applications.
GoT enhances the LLM capabilities
The Graph of Thoughts (GoT) framework improves the capabilities of LLMs by modeling information as a graph. LLM thoughts are represented as vertices, and edges represent dependencies between these thoughts. GoT allows for combining thoughts, distilling networks of thoughts, and enhancing thoughts using feedback loops.
It outperforms other paradigms like Chain-of-Thought or Tree of Thoughts (ToT) in various tasks, increasing sorting quality by 62% and reducing costs by over 31%. It is also extensible, allowing for new thought transformations and advancing prompting schemes.
Why does this matter?
This advancement brings LLM reasoning closer to human thinking and brain mechanisms such as recurrence, both of which form complex networks. It makes AI models more versatile and adaptable, with implications on various domains.
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Knowledge Nugget: Efficient LLM inference
This article by discusses efficient inference for large language models. It explores three main approaches: quantizing the model’s parameters, distilling a smaller version of the model, and optimizing the code.
The author emphasizes the importance of profiling code to identify and reduce overhead. They provide an example where changing a list to a dictionary in a performance-critical loop resulted in a significant speed improvement. Efficient inference is crucial to avoid excessive GPU usage and financial strain.
Why does this matter?
Optimizing inference processes is essential not only for conserving GPU resources but also for alleviating potential financial burdens associated with excessive usage, making it particularly relevant for developers and organizations relying on large language models in their applications.
What Else Is Happening❗
💰 OpenAI-backed language learning app Speak has raised $16M! (Link)
💼 Dell raises yearly forecasts on AI and demand recovery. (Link)
🍽️ Samsung has launched an AI-powered food and recipe app. (Link)
🤝 IBM and Salesforce partner to boost trustworthy AI adoption in CRM. (Link)
🤖 Floworks in building an AI to help complete mundane enterprise tasks. (Link)
🌟📝Friday Featured Prompt
This Week’s Prompt: Act as a StackOverflow Post
I want you to act as a stackoverflow post. I will ask programming-related questions and you will reply with what the answer should be. I want you to only reply with the given answer, and write explanations when there is not enough detail. Do not write explanations. When I need to tell you something in English, I will do so by putting text inside curly brackets {like this}.
My first question is “How do I read the body of an http.Request to a string in Golang”.
🦸 With this prompt, you’re the master of the coding realm, asking questions and receiving precise solutions, no explanations needed.
This prompt makes your life easier. It’s like having a coding genie at your beck and call.
Go! Code like a pro💻🔥
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That’s all for now!
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