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March Madness is infamously unpredictable, a perfect storm where favorites tumble and underdogs rise to do the impossible. Every March, 64 men’s and 64 women’s College Basketball teams battle for glory, while millions of fans, analysts, and betting markets scramble to predict the outcomes. But the odds of picking a perfect bracket? 1 in 9.2 […]

Introduction It’s no news that artificial intelligence has made huge strides in recent years, particularly with the advent of multimodal models that can process and create both text and images, and some very new ones that also process and produce audio and video. I think that these Ai Systems have the potential to revolutionize data

As the race to move AI agents from prototype to production heats up, the need for a standardized way for agents to call tools across different providers is pressing. This transition to a standardized approach to agent tool calling is similar to what we saw with REST APIs. Before they existed, developers had to deal

0. Introduction Least Squares is used almost everywhere when it comes to numerical optimization and regression tasks in machine learning. It aims at minimizing the Mean Squared Error (MSE) of a given model. Both L1 (sum of absolute values) and L2 (sum of squares) norms offer an intuitive way to sum signed errors while preventing

Jupyter AI brings generative AI capabilities right into the Jupyter interface. Having a local AI assistant ensures privacy, reduces latency, and provides offline functionality, making it a powerful tool for developers. In this article, we’ll learn how to set up a local AI coding assistant in JupyterLab using Jupyter AI, Ollama and Hugging Face. By

In a previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of outcomes, outsourcing of staff, and the geographical proximity of team members — can yield a variety of organizational archetypes for implementing strategic AI initiatives, each implying a different twist to the product operating

In today’s world, the reliability of data solutions is everything. When we build dashboards and reports, one expects that the numbers reflected there are correct and up-to-date. Based on these numbers, insights are drawn and actions are taken. For any unforeseen reason, if the dashboards are broken or if the numbers are incorrect — then it becomes

€1,600 per second. That’s how much interest Germany has to pay for its debts. In total, the German state has debts ranging into the trillions — more than a thousand billion Euros. And the government is planning to make even more, up to one trillion additional debt is rumored to follow over the next 10

On March 3rd, Google officially rolled out its Data Science Agent to most Colab users for free. This is not something brand new — it was first announced in December last year, but it is now integrated into Colab and made widely accessible. Google says it is “The future of data analysis with Gemini”, stating:

With the new age of problem-solving augmented by Large Language Models (LLMs), only a handful of problems remain that have subpar solutions. Most classification problems (at a PoC level) can be solved by leveraging LLMs at 70–90% Precision/F1 with just good prompt engineering techniques, as well as adaptive in-context-learning (ICL) examples. What happens when you