#35 Advanced prompting techniques are a myth…it’s all about good communication!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, don’t skip to your favorite sections (we know you guys do that); we have some fun bonuses for you, especially if you are in the ‘learning stage’. For the rest, of course, there are great conceptual articles, practical project tutorials, and a handy tool from the community.

What’s AI Weekly

I recently wrote a piece along with two friends for our weekly High Learning Rate newsletter (which you should follow), and since I had many thoughts and opinions on the subject, I decided to share more in the What’s AI newsletter as well. I think, despite all the hype around “advanced” prompting techniques, it’s really just about telling the model what you want in plain language. Read the short opinion piece here!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

This issue is brought to you by… us!

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Learn AI Together Community section!

Featured Community post from the Discord

Arwmoffat just released Manifest, a tool that lets you write a Python function and have an LLM execute it. Manifest relies on runtime metadata, such as a function’s name, docstring, arguments, and type hints. It uses this metadata to compose a prompt and sends it to an LLM. The LLM “executes” the prompt and returns a JSON-based format that can be parsed into the appropriate object. Check it out on GitHub and support a fellow community member. If you have feedback or questions, reach out in the thread!

AI poll of the week!

We did the same poll a couple of years ago, and the results show an interesting trend after two years. Check it out in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Jbird248 is looking for developers, preferably good with UX/UI, to join an open-sourced AI project. If you are interested in front-end, back-end, or AI, reach out to him in the thread!

2. Ritikashakya is looking to team up with someone interested in contests and eager to learn. Teams can be up to two people, so if you’re curious, connect in the thread!

3. Muhib7486 is new to ML/AI and is looking for a study and/or accountability partner. If you are also a beginner diving into ML, contact him in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Understanding and Explaining Neural Networks: A Mathematical and Python Implementation Guide by Shenggang Li

This post will simplify the complexities of Neural Networks (NNs) by explaining the steps in model training: creating the model, defining loss functions, and optimizing them with gradient descent. You will learn how NNs use the chain rule and backpropagation for complex loss functions. The author starts with the basics of logistic regression to show how forward (prediction) and backward (training) processes work. Then, moves to a more complex NN with one hidden layer, explaining its forward and backward training processes in detail.

Our must-read articles

1. Lightweight YOLO Detection with Object Tracking from Scratch by Tan Pengshi Alvin

This article aims to achieve both object detection using the YOLO framework and object tracking using a custom framework built entirely from scratch. The author will introduce both the codes and architectures of the models in detail. For simplicity and proof-of-concept, the data applied for the object detection and tracking model will be completely simulated as jiggling multi-colored particles using OpenCV.

2. Optimization of Language Models for Efficient Inference and Performance Using Mixed Architectures by Antonello Sale

Did you know that innovative architecture designs, hardware advancements, and optimization schemes can take language models to the next level? By leveraging hardware acceleration, parallel computing frameworks, and sophisticated inference procedures, we can achieve a delicate balance between speed and accuracy. This article will get into the methodologies and techniques that power these improvements and provide a vision of what lies ahead for high-performance, efficient language models.

3. Feedback Loops in Generative AI: How AI May Shoot Itself in the Foot by Anthony Demeusy

Generative AI can enhance creativity, but beware of feedback loops! They may amplify biases and lead to unintended consequences. Continuous monitoring and ethical guidelines are crucial to ensure responsible AI use. Check this article to know all about feedback loops.

4. How I Build an Agent with Long-Term, Personalized Memory by Gao Dalie

To solve the problem of AI models’ lack of long-term memory and personalization capabilities, the author introduces Mem0. It is suitable for AI applications that require long-term memory and context retention, such as chatbots and smart assistants. This article provides an easy-to-understand explanation of Mem0 overview, what makes Mem0 unique, How Mem0 is different from Rag, and even how to build an actual application.

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Published via Towards AI