Browsing: AI

SIMA 2 represents a significant advancement in AI agents, evolving from an instruction-follower into an interactive gaming companion powered by Gemini models. It can now reason about goals, converse with users, and self-improve, marking a crucial step towards Artificial General Intelligence in virtual 3D worlds.

Indian AI lab Sarvam has launched a new generation of large language models, aiming to challenge larger rivals with efficient, open-source solutions tailored for local languages and use cases. This move aligns with India’s strategy to reduce reliance on foreign AI platforms.

Using advanced AI to fix critical software vulnerabilities

With $2 million in funding from Google.org, the Sundance Institute, in collaboration with The Gotham and Film Independent, will build a community-led ecosystem for AI education and empowerment in the world of film, to support creatives.

Research explores the use of transformer-based language models for automated theorem proving, aiming to overcome the challenge of generating novel mathematical terms. A system called GPT-f, designed for the Metamath language, successfully generated new, short proofs that were accepted into the Metamath library, marking a significant milestone for deep-learning in formal mathematics.

Working together to create open-source and private datasets for AI training.

Chinese open-source AI models are rapidly gaining global traction, impacting the AI landscape from development platforms to major tech hubs. This article explores the significance of their rise and what lies ahead.

This article explores the deployment of open-source Large Language Models (LLMs) using Hugging Face Inference Endpoints. It details the setup process for models like Falcon 40B, explains how to test the deployed endpoints, and demonstrates streaming responses with Python and JavaScript. Key benefits such as easy deployment, cost efficiency, and enterprise-grade security are also highlighted.

7 Python tricks applicable to your early exploratory data analyses (EDA) to identify and deal with various data quality issues.

Google has introduced PaliGemma 2, a new series of vision language models. This iteration combines the SigLIP vision encoder with the latest Gemma 2 text decoder. Available in 3B, 10B, and 28B parameter sizes, these models support various input resolutions (224×224, 448×448, 896×896), offering flexibility for different applications. Pre-trained models are designed for easy fine-tuning, with Google also releasing fine-tuned variants on the DOCCI dataset for robust captioning.