ContentsCatalyzing breakthroughs in scienceFuture of intelligence 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 …

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   Contents# Introduction# Step 1: Installing The Required Tools On Linux# Step 2: Cloning And Building BitNet From Source# Step 3: Downloading A Lightweight BitNet Model# Step 4: Running BitNet In Interactive Chat Mode On Your CPU# Step 5: Starting A Local BitNet Inference Server# Step 6: Connecting To Your BitNet Server Using OpenAI Python SDK# Concluding Remarks # Introduction   …

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 »

an interesting library the other day that I hadn’t heard of before.  PythoC is a Domain-Specific Language (DSL) compiler that allows developers to write C programs using standard Python syntax. It takes a statically-typed subset of Python code and compiles it directly down to native machine code via LLVM IR (Low Level Virtual Machine Intermediate …

Write C Code Without Learning C: The Magic of PythoC Read More »

Image by Editor   Contents# Introduction# 1. In-memory Caching# 2. Caching On Persistent Disk# 3. Network-resilient Apps# 4. Client-side Throttling# 5. Structured Output Binding# Wrapping Up # Introduction  Python decorators are tailor-made solutions that are designed to help simplify complex software logic in a variety of applications, including LLM-based ones. Dealing with LLMs often involves coping with unpredictable, slow—and frequently expensive—third-party APIs, and …

5 Powerful Python Decorators to Optimize LLM Applications Read More »

Embracing the opportunity ahead, responsibly We believe the promise of AI in education can only be achieved by developing it responsibly. One essential facet of this is doing so in close partnership with the entire education ecosystem. When my team and I met with Damian Harvey, Interim Head of C2k, he emphasized this point. He …

How AI is giving Northern Ireland teachers time back Read More »

of a series about distributed AI across multiple GPUs: ContentsIntroductionThe Memory Problem in DDPZeRO-1: Optimizer State PartitioningZeRO-2: Gradient PartitioningZeRO-3: Parameter PartitioningUsing ZeRO in PyTorchConclusionReferences Introduction In the previous post, we saw how Distributed Data Parallelism (DDP) speeds up training by splitting batches across GPUs. DDP solves the throughput problem, but it introduces a new challenge: memory …

AI in Multiple GPUs: ZeRO & FSDP Read More »

Image by Editor   Contents# Introduction# Getting Started With Kedro# Exploring the Core Elements of Kedro# Wrapping Up # Introduction  Data science projects usually begin as exploratory Python notebooks but need to be moved to production settings at some stage, which might be tricky if not planned carefully. QuantumBlack’s framework, Kedro, is an open-source tool that bridges the gap between …

A Guide to Kedro: Your Production-Ready Data Science Toolbox Read More »

Today, we’re introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Starting today, 3.1 Flash-Lite is rolling out in preview to developers via the Gemini API in Google AI Studio and for enterprises …

Our most cost-effective AI model yet Read More »