In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure …
The best way to learn AI is by building. From finding quick ways to deploy open models to building complex, multi-agentic systems, it’s easy to feel overwhelmed by the sheer volume of resources out there. To that end, we’ve compiled a living, curated collection of our 25+ favorite how-to guides for Google Cloud. This collection …
Our advanced model officially achieved a gold-medal level performance on problems from the International Mathematical Olympiad (IMO), the world’s most prestigious competition for young mathematicians. It earned a total of 35 points by perfectly solving five out of the six problems.
The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this perspective highlights the core challenge: …
Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions, resulting in not only productivity …
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that …
Read more “Language Models Improve When Pretraining Data Matches Target Tasks”
Generative AI is transforming how businesses deliver personalized experiences across industries, including travel and hospitality. Travel agents are enhancing their services by offering personalized holiday packages, carefully curated for customer’s unique preferences, including accessibility needs, dietary restrictions, and activity interests. Meeting these expectations requires a solution that combines comprehensive travel knowledge with real-time pricing and …
Read more “Build real-time travel recommendations using AI agents on Amazon Bedrock”
As organizations race to deploy powerful GPU-accelerated workloads, they might overlook a foundational step: ensuring the integrity of the system from the very moment it turns on. Threat actors, however, have not overlooked this. They increasingly target the boot process with sophisticated malware like bootkits, which seize control before any traditional security software can load …
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: (i) a ∼3B-parameter on-device model optimized for Apple silicon through architectural innovations such as KV-cache sharing and 2-bit quantization-aware training; and (ii) a scalable server model built on a novel Parallel-Track Mixture-of-Experts (PT-MoE) transformer that combines track …
Read more “Apple Intelligence Foundation Language Models Tech Report 2025”
Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, …
Read more “Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI”