Machine Learning

Discover how to set up an efficient MLflow environment to track your experiments, compare and choose the best model for deployment Training and fine-tuning various models is a basic task for every computer vision researcher. Even for easy ones, we do a hyper-parameter search to find the optimal way of training the model over our …

Track Computer Vision Experiments with MLflow | by Yağmur Çiğdem Aktaş | Dec, 2024 Read More »

Introducing SIMA, a Scalable Instructable Multiworld Agent

During the Interview — How to Prepare? Once you start the application process and start to get interviews, there are a few things you need to search and prepare for: Interview format Referrals, networking LinkedIn or Portfolio Interview resources and materials Strategies: planning, tracking, evolving, prompting, estimating your level, wearing your “lens of career,” getting …

2024 Survival Guide for Machine Learning Engineer Interviews | by Mengliu Zhao | Dec, 2024 Read More »

Research Published 19 March 2024 Authors By Zhe Wang and Petar Veličković As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks ‘Corner taken quickly… Origi!’ Liverpool FC made a historic comeback in the 2019 UEFA Champions League semi-finals. One of the most …

TacticAI: an AI assistant for football tactics Read More »

Tip 2: Use structured outputs Using structured outputs means forcing the LLM to output valid JSON or YAML text. This will allow you to reduce the useless ramblings and get “straight-to-the-point” answers about what you need from the LLM. It also will help with the next tips as it makes the LLM responses easier to …

An Agentic Approach to Reducing LLM Hallucinations | by Youness Mansar | Dec, 2024 Read More »

Responsibility & Safety Published 19 April 2024 Authors Iason Gabriel and Arianna Manzini Exploring the promise and risks of a future with more capable AI Imagine a future where we interact regularly with a range of advanced artificial intelligence (AI) assistants — and where millions of assistants interact with each other on our behalf. These …

The ethics of advanced AI assistants Read More »

LEC surpasses best in class models, like GPT-4o, by combining the efficiency of a ML classifier with the language understanding of an LLM Imagine sitting in a boardroom, discussing the most transformative technology of our time — artificial intelligence — and realizing we’re riding a rocket with no reliable safety belt. The Bletchley Declaration, unveiled …

A New Approach to AI Safety: Layer Enhanced Classification (LEC) | by Sandi Besen | Dec, 2024 Read More »

While video models often “hallucinate” unwanted details — extra fingers or unexpected objects, for example — Veo 2 produces these less frequently, making outputs more realistic. Our commitment to safety and responsible development has guided Veo 2. We have been intentionally measured in growing Veo’s availability, so we can help identify, understand and improve the …

Updates to Veo, Imagen and VideoFX, plus introducing Whisk in Google Labs Read More »

Learn what the Functional API is, and how to build complex keras models using it Photo by Hunter Harritt on Unsplash TensorFlow’s Sequential API helps the user to stack layers one on top of another, easily creating linear models, where the input of each layer is always the output of the previous one. But what …

Introduction to TensorFlow’s Functional API | by Javier Martínez Ojeda | Dec, 2024 Read More »

Responsibility & Safety Published 17 December 2024 Authors FACTS team Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations Large language models (LLMs) are transforming how we access information, yet their grip on factual accuracy remains imperfect. They can “hallucinate” …

FACTS Grounding: A new benchmark for evaluating the factuality of large language models Read More »