Machine Learning

Image by Author   # Introduction  When you solve enough interview-style data problems, you start noticing a funny effect: the dataset “shape” quietly dictates your coding style. A time-series table nudges you toward window functions. A star schema pushes you into JOIN chains and GROUP BY. A pandas task with two DataFrames almost begs for .merge() …

Visualizing Patterns in Solutions: How Data Structure Affects Coding Style Read More »

In the rapidly evolving landscape of large language models (LLMs), the spotlight has largely focused on the decoder-only architecture. While these models have shown impressive capabilities across a wide range of generation tasks, the classic encoder-decoder architecture, such as T5 (The Text-to-Text Transfer Transformer), remains a popular choice for many real-world applications. Encoder-decoder models often …

T5Gemma: A new collection of encoder-decoder Gemma models Read More »

is not a data quality problem. It is not a training problem. It is not a problem you can solve with more RLHF, better filtering, or a larger context window. It is a structural property of what these systems are optimized to do. I have held this position for months, and the reaction is predictable: researchers working …

Hallucinations in LLMs Are Not a Bug in the Data Read More »

Image by Editor   # Introduction  Data pipelines in data science and machine learning projects are a very practical and versatile way to automate data processing workflows. But sometimes our code may add extra complexity to the core logic. Python decorators can overcome this common challenge. This article presents five useful and effective Python decorators to …

5 Powerful Python Decorators for High-Performance Data Pipelines Read More »

has handled 2.3 million customer conversations in a single month. That’s the workload of 700 full-time human agents. Resolution time dropped from 11 minutes to under 2. Repeat inquiries fell 25%. Customer satisfaction scores climbed 47%. Cost per service transaction: $0.32 down to $0.19. Total savings through late 2025: roughly $60 million. The system runs on a …

The Multi-Agent Trap | Towards Data Science Read More »

Image by Author   # Introduction   All tutorials on data science make detecting outliers appear to be quite easy. Remove all values greater than three standard deviations; that’s all there is to it. But once you start working with an actual dataset where the distribution is skewed and a stakeholder asks, “Why did you remove …

We Used 5 Outlier Detection Methods on a Real Dataset: They Disagreed on 96% of Flagged Samples Read More »

Catalyzing breakthroughs in science By proving it could navigate the massive search space of a Go board, AlphaGo demonstrated the potential for AI to help us better understand the vast complexities of the physical world. We started by attempting to solve the protein folding problem, a 50-year grand challenge of predicting the 3D structure of …

How AI Innovation Is Paving the Path to AGI — Google DeepMind Read More »

Bayesian statistics you’ve likely encountered MCMC. While the rest of the world is fixated on the latest LLM hype, Markov Chain Monte Carlo remains the quiet workhorse of high-end quantitative finance and risk management. It is the tool of choice when “guessing” isn’t enough and you need to rigorously map out uncertainty. Despite the intimidating …

An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm Read More »

Image by Author   # Introduction   BitNet b1.58, developed by Microsoft researchers, is a native low-bit language model. It is trained from scratch using ternary weights with values of (-1), (0), and (+1). Instead of shrinking a large pretrained model, BitNet is designed from the beginning to run efficiently at very low precision. This reduces …

Run Tiny AI Models Locally Using BitNet A Beginner Guide Read More »

Acknowledgements This research was co-developed by Google DeepMind and Google Research. Google DeepMind: Andrea Burns, Anton Raichuk, Arianna Manzini, Bart van Merrienboer, Burcu Karagol Ayan, Dominic Masters, Drew Purves, Jenny Hamer, Julia Haas, Keith Anderson, Matt Overlan, Maxim Neumann, Melanie Rey, Mustafa Chasmai, Petar Veličković, Ravi Rajakumar, Tom Denton, Vincent Dumoulin Google Research and Google …

Three ways Google scientists use AI to better understand nature — Google DeepMind Read More »