Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This week, we have discussed some of the latest industry innovations and trends like GraphRAG, Agentic chatbots, evolving trends with search engines, and some very interesting project-based collaboration opportunities. Enjoy the read! — Louis-François Bouchard, Towards AI Co-founder & Head of Community Reviews are our adrenaline; everything that we create is for the community, and receiving this message from one of you was the best mid-week motivation: We love to hear from you, not just t ..read more

Author(s): Surya Maddula Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Let’s discuss two popular ML algorithms, KNNs and K-Means. Stick around; I’ll make this densely packed. P.S. I’m trying out a new thing: I draw illustrations of graphs, etc., myself, so we’ll also look at some nice illustrations that help us understand the concept. We will discuss KNNs, also known as K-Nearest Neighbours and K-Means Clustering. They are both ML Algorithms, and we’ll explore them more in detail in a bit. K-Nearest Neighbors (KNN) is a supervised ML algo ..read more

Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie This week, we saw several new LLMs released, including an upgrade to Cohere’s Command R and new Qwen2 models. Outside of LLMs, we also saw HelixFold3 from Baidu, an open-source version of Deepmind’s AlphaFold 3 Biomolecular Structure Prediction model. In the LLM world, our eyes were also on new evidence of adoption acceleration. Amid new fundraising news, OpenAI confirmed more than 200 million weekly ChatGPT users and that API usage has doubled in just 5 weeks since the launch of its ..read more

Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This week, the community and I are answering some recurring questions about RAG, coding assistants, transformers, machine learning, and more. You will also find fun collaboration opportunities and memes. Enjoy the read! What’s AI Weekly Many clients asked us (Towards AI), “But why would I use RAG if Gemini can process millions of tokens as input?” So, is RAG dead? That’s what I investigated in this week’s iteration of What’s AI. I explore the differences between RAG and sending all data in th ..read more

Author(s): Towards AI Editorial Team Originally published on Towards AI. Towards AI, together with our partners at Activeloop and Intel Disruptor Initiative, was one of the first organizations to pioneer high-quality, production-oriented GenAI courses, namely our marquee LangChain & Vector Databases in Production, Training & Fine-Tuning LLMs, as well as Retrieval Augmented Generation for Production with LlamaIndex and LangChain courses. One year, and tens of thousands of professionals educated later, we’ve noticed one pattern: a lot of people call themselves “AI Engineers.” In fact, th ..read more

Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie This was a week for small language models (SLMs) with significant releases from Microsoft and NVIDIA. These new models highlight the growing trend towards creating efficient yet powerful AI that can be deployed in resource-constrained environments without compromising performance. The two companies focused on different strategies for achieving these smaller models — Microsoft via training on high-quality synthetic data and Nvidia via pruning and distillation techniques. Microsoft con ..read more

Author(s): Souradip Pal Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. In the world of machine learning, you’re often knee-deep in datasets. These datasets could be anything — a collection of housing prices, handwritten digits, or even details about the passengers on the Titanic. To make accurate predictions, you rely on features or dimensions within these datasets. But here’s the kicker: sometimes, having too many features can be a real headache. That’s where the “Curse of Dimensionality” comes into play. Photo by Cederic Vandenberghe on ..read more

Author(s): Souradip Pal Originally published on Towards AI. Let’s deep dive into the math and code it up from scratch This member-only story is on us. Upgrade to access all of Medium. Suppose you’re on the hunt for a new apartment in your dream location — be it Thailand, Japan, or London. You’ve got the money (let’s skip the how for now), but how do you decide on the right price? You don’t want to just rely on the seller’s word, right? How about staying a step ahead by using a machine learning model to predict the price, ensuring you negotiate like a pro? To build this model, you’ll need a dat ..read more

Author(s): Souradip Pal Originally published on Towards AI. Fall in Love with Decision Trees with dtreeviz’s Visualization This member-only story is on us. Upgrade to access all of Medium. Decision Trees, also known as CART (Classification and Regression Trees), are undoubtedly one of the most intuitive algorithms in the machine learning space, thanks to their simplicity. Unlike neural networks or SVMs, where you have to invest considerable time to understand the underlying processes, decision trees are essentially a series of “if-else” statements stacked together to guide you toward a possibl ..read more

Author(s): Ori Abramovsky Originally published on Towards AI. Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Many data scientists I’ve spoken with agree that LLMs represent the future, yet they often feel that these models are too complex and detached from the everyday challenges faced in enterprise environments. The idea of using LLMs in daily development can seem like a daunting, moonshot endeavor — too complicated and uncertain to pursue. When I suggest ..read more