State-of-the-art video and image generation with Veo 2 and Imagen 3
We’re rolling out a new, state-of-the-art video model, Veo 2, and updates to Imagen 3. Plus, check out our new experiment, Whisk.
The Baseline Team and Forward-Deployed Infrastructure Engineering at Palantir
Inside Look: The Baseline Team and Forward-Deployed Infrastructure Engineering at Palantir At Palantir, our customers rely on our applications operating seamlessly across a variety of cloud providers, on-premises hardware, and both commercial and government networks. They need our platforms to function reliably in these diverse environments. This is where we, the Forward Deployed Infrastructure Engineering team — known …
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Llama 3.3 70B now available in Amazon SageMaker JumpStart
Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on …
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AI Playground: Where learning and innovation converge in the heart of London
AI is rapidly transforming industries and redefining the future of work. However, many organizations face a significant hurdle: bridging the knowledge gap and acquiring the necessary skills to effectively harness the power of AI. Recognizing this challenge, Google Cloud is set to launch the AI Playground in Shoreditch, Central London, in the first quarter of …
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AI Pioneers Win Nobel Prizes for Physics and Chemistry
Artificial intelligence, once the realm of science fiction, claimed its place at the pinnacle of scientific achievement Monday in Sweden. In a historic ceremony at Stockholm’s iconic Konserthuset, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for their pioneering work on neural networks — systems that mimic the brain’s architecture and form …
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o1’s Thoughts on LNMs and LMMs
TL;DR We asked o1 to share its thoughts on our recent LNM/LMM post. https://www.artificial-intelligence.show/the-ai-podcast/o1s-thoughts-on-lnms-and-lmms What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“? Thought about large numerical and mathematics models for a few seconds.Confirming Additional BreakthroughsOK, I’m confirming if LNMs/LMMs need more than Transformer models to match …
Leading Federal IT Innovation
Palantir and Grafana Labs’ Strategic Partnership Introduction In today’s rapidly evolving technological landscape, government agencies face the pressing challenge of managing increasingly complex IT infrastructures. Effective software observability and monitoring are critical for ensuring operational efficiency and security. Recognizing this need, Palantir and Grafana Labs have formed a strategic partnership through the FedStart program aimed …
How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows …
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Orchestrating GPU-based distributed training workloads on AI Hypercomputer
When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs and TPUs) across multiple compute hosts to train their models efficiently. This can lead to orchestration, resource …
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BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale
Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting …