#42 Teaching AI to "Think", Fine-Tuning to SQL, Encoder-only models, and more!
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And is AGI achievable?
Good morning, AI enthusiasts! This is another resource-heavy issue, with articles focusing on everything from early AI architectures to the latest developments in AI reasoning abilities. Enjoy the read!
What’s AI Weekly
One of the key issues with our current approach to AI reasoning can be summarized by the quote: "We teach the machines how we think we think." It reflects a deeper flaw in training models based on human intuition, which isn’t necessarily how reasoning truly works (nobody knows). This opens up a broader discussion about how machines can independently develop reasoning skills rather than merely mimicking human approaches.
Building on that foundation, this week, in the High Learning Rate newsletter, we are sharing some exciting developments reshaping how AI models might learn to reason. These advancements center around self-taught reasoning, where AI models enhance their capabilities by learning from their own reasoning processes. Read the complete article here!
— Louis-François Bouchard, Towards AI Co-founder & Head of Community
Learn AI Together Community section!
Featured Community post from the Discord
Mahvin_ built a chatbot using ChatGPT. The code imports various libraries like TensorFlow, PyTorch, Transformers, Tkinter, and CLIP to handle tasks related to neural networks, text classification, and image processing. You can try it on GitHub and share your feedback in the Discord thread!
AI poll of the week!
At the beginning of this year, AGI might have seemed a far-fetched idea, but it is surprising how much closer we have come to it. Is it the only obvious progression for AI? We think otherwise, but we would love to hear your thoughts!
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. Samyog_dhital is researching and exploring ways to enhance reasoning capabilities in LLMs. The goal is to solve this challenge, enabling LLMs to solve complex problems with logical, step-by-step planning similar to human reasoning. They are looking for someone to work on this and a potential co-founder. If you are interested, connect with them in the thread!
2. Dykyi_vladk is working on reimplementing and enhancing the PaLM model. If you are interested in NLP, contact him in the thread!
3. Knytfury is looking to work with someone on a new research paper or an existing paper’s implementation. If you are working on something and need some human resources to work on the paper, reach out in the thread!
Meme of the week!
Meme shared by ghost_in_the_machine
TAI Curated section
Article of the week
Solving Complex Business Problems with Mixed-Integer Linear Programming by Shenggang Li
This article provides a clear overview of MILP techniques, showcasing how they can be applied to tackle real-world challenges in various industries. With practical examples and step-by-step explanations, this resource is ideal for business analysts, data scientists, and decision-makers looking to enhance their problem-solving toolkit.
Our must-read articles
1. Fine-tuning LLMs for Natural Language to SQL Query Generation Using Synthetic Data: A Comprehensive Guide for Beginners by Anoop Maurya
This article explores how to fine-tune LLMs to generate SQL queries from natural language inputs. It breaks down each step of the process, explaining key concepts and providing detailed instructions to help you understand and implement your own NL2SQL system. This process, known as Natural Language to SQL (NL2SQL), is a powerful tool that allows non-technical users to interact with databases using everyday language.
2. Dealing With Encoder Language Model Tasks Using Pytorch by Fabio Yáñez Romero
This article provides a clear and practical approach to implementing encoder models, complete with code examples and expert tips. When using an encoder-only language model such as Bert or RoBERTa, if we start from a pre-trained model, the main tasks that can be performed are classification and regression. This post discusses how to perform these tasks using Pytorch.
3. Unlocking the Power of Efficient Vector Search in RAG Applications by Chirag Agrawal
This guide explores the techniques and strategies for optimizing vector search, enabling you to enhance the performance of your AI models. It also shares several indexing methods, their pros and cons, and how to fine-tune them for your specific needs.
4. Meta Learners: Measuring Treatment Effects with Causal Machine Learning by Hajime Takeda
This article aims to explain Meta Learners, discuss its underlying algorithms, and demonstrate a case study using EconML with data from social experiments.
5. Are Diffusion Models Really Superior to GANs on Image Super Resolution? by Valerii Startsev
This thought-provoking article dives into the debate: Are diffusion models truly superior to GANs? It explores the strengths and weaknesses of both approaches and analyzes their performance in enhancing image quality. With detailed comparisons and expert insights, this resource is perfect for researchers, practitioners, and anyone interested in the evolving landscape of image processing.
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