#64 Here’s how you keep up with AI!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, we’re diving into a challenge many of us face: keeping up with the rapid pace of AI and answering some extremely thought-provoking questions, such as: Is AI helping us think better, or is it leading to cognitive decline?

We’ll also explore DeepSeek’s impact on machine learning engineering, the latest on preference alignment, vector databases, and more.

Let’s break it all down together!

What’s AI Weekly

This week in What’s AI, I’m exploring a challenge many of us face with LLMs: staying updated on the latest developments. With new tools, methods, and research emerging almost daily, it’s easy to feel overwhelmed or unsure about what truly matters.

I’ve also noticed this issue extends to resources like courses and books — you complete one, and suddenly, there’s another skill you’re missing. But what if you could break that cycle? What if you learned how to teach yourself using the most powerful AI tools instead of relying on courses forever? That’s exactly what we’re aiming to solve with our newest offering, coming next week (so stay tuned!).

But for now, I’ll be sharing how to stay updated with LLM industry developments — where to find the most meaningful resources and how to curate them to stay informed at a level that’s both useful and manageable.

Read the full article for a curated list of resources!

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

Learn AI Together Community section!

AI poll of the week!

I ran a similar poll a couple of years ago, and back then, ChatGPT was leading by a wide margin. Today, despite the many new options available, it still holds the top spot by a large margin. Why do you continue using ChatGPT? Does this also apply to specialized tasks? For instance, Claude’s latest release is optimized for coding — would you use it for that? Let’s discuss 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. Bavoyager is looking for a partner to collaborate on a side hustle. To know more about the project, reach out in the thread!

2. Lisz.a is working on identifying novel biomarkers for different disorders with the help of informatics and is looking for people to help him with his ethical AI research. If this sounds interesting, connect with him in the thread!

3. Ayanb1827 is working on a fully open-source personal study app/time management project and is looking for individuals with experience in AI agents, LangChain, agentic reasoning, RAG, and similar technologies within a React application. If you have experience in these areas and want to share some insights or chat, contact them in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Building AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI By Shenggang Li

This article provides a comprehensive guide on configuring Google Vertex AI and utilizing the Gemini API to create intelligent chatbots. It begins with an overview of Vertex AI as a platform for developing AI models, emphasizing its integration with Gemini for tasks like chat and content generation. The guide details the step-by-step process of setting up a Google Cloud account, enabling necessary APIs, and exploring Vertex AI Studio. It also explains how to build a chatbot using LangChain and RAG, focusing on connecting a knowledge base for efficient information retrieval. It concludes with insights on session management and the dynamic nature of the chatbot, highlighting its potential for real-time, context-aware interactions.

Our must-read articles

1. Preference Alignment By Sarvesh Khetan

This article discusses preference alignment in language models through Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). It begins by framing language models as reinforcement learning agents, where human feedback is used to assign rewards based on alignment with human preferences. It details the development of a reward model using supervised learning, emphasizing the challenges of noisy human scoring and the shift to relative comparisons. DPO is presented as a more efficient alternative, directly fine-tuning the model using preference data without the complexities of traditional RLHF. The advantages of DPO include its simplicity, computational efficiency, and improved performance in aligning model outputs with user preferences.

2. Vector Databases 101: A Beginner’s Guide to Vector Search and Indexing By Afaque Umer

This beginner-friendly guide on vector databases emphasizes their role in efficiently storing and retrieving high-dimensional data for applications like recommendation systems and image recognition. It contrasts traditional databases, which rely on exact keyword matches, with vector databases that utilize numerical representations to capture deeper relationships between data points. The piece explains the mechanics of vector databases, including vectorization, indexing techniques, and querying processes, highlighting methods like Approximate Nearest Neighbors for fast searches. It also reviews popular vector databases such as Pinecone and FAISS and discusses real-world applications, including semantic search and fraud detection. It concludes with a hands-on implementation guide using FAISS, encouraging readers to explore further.

3. Is Artificial Intelligence Ushering Cognitive Decline? By Cezary Gesikowski

This article examines the potential cognitive impacts of generative AI and automation, focusing on their influence on critical thinking, memory, and problem-solving skills. It highlights concerns about overreliance on AI tools leading to cognitive offloading, reduced mental effort, and skill decay. Drawing on recent studies, it discusses how AI use may alter brain activity, shorten attention spans, and weaken memory retention. It also explores strategies to mitigate these effects, emphasizing the importance of AI literacy, active engagement, and balanced integration of AI to preserve and enhance human cognitive abilities.

4. DeepSeek in My Engineer’s Eyes By Kelvin Lu

This article discusses the transformative impact of DeepSeek on machine learning engineering, highlighting its efficiency and innovation. DeepSeek challenges traditional AI development by achieving high performance with optimized algorithms and reduced computational resources, countering the reliance on massive datasets and expensive hardware. Key advancements include mixed precision training, RL-based fine-tuning, and a focus on data quality over volume. It also emphasizes DeepSeek’s agile team structure, fostering creativity and rapid innovation. It concludes by reflecting on how DeepSeek sets a new standard for AI development and inspires engineers to embrace emerging techniques.

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Published via Towards AI