Peter Norvig

Super excited to announce the GA of Nova Sonic foundation model. Nova Sonic unifies speech understanding and speech generation into a single model, to enable more human-like voice conversations in AI applications. Nova Sonic model is now available via a new API in Amazon Bedrock, and it simplifies the development of voice applications, such as customer service call automation and AI agents across a broad range of industries, including travel, education, health care, entertainment, and more. https://lnkd.in/g3uQXfRm

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Among other things, my team has been focusing on AI research for agents. We just blogged about a recent project: the Test-Time Diffusion Deep Researcher (TTD-DR). This framework uses a Deep Research agent to draft and revise its own drafts using high-quality retrieved information. This approach achieves new state-of-the-art results in writing long-form research reports and completing complex reasoning tasks. The work achieves better results than any other deep research agent we compared to. It also shows that self-evaluation and self-improvement are must-have components of a successful agent. https://lnkd.in/gBZpVMQy

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In NVIDIA’s Q2 earnings this week, Jensen Huang highlighted physical AI—alongside reasoning AI—as a key growth driver. Analysts echoed this, noting breakthroughs in robotics and autonomous systems as the next stage of AI adoption. It’s a welcome acknowledgment that AI’s impact won’t be confined to digital spaces. The real opportunity lies in the physical world—transportation, logistics, energy, construction—where safety, efficiency, and sustainability gains can be profound. That’s the frontier many of us are working on: bringing AI to the systems and workers that keep the real economy moving.

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I'm excited to share three research papers from our talented team at Together AI that are advancing the frontiers of efficient and effective AI systems: ## 🚀 Accelerating Large Model Inference Our researchers have developed "Ladder-residual," a novel parallelism-aware architecture that significantly accelerates large model inference through communication overlapping. This innovation addresses one of the key bottlenecks in deploying large language models at scale, making AI systems faster and more cost-effective. 📄 Read more: [Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping](https://lnkd.in/g825uRRz) ## 🤝 Enhancing Model Alignment Through Collective Intelligence By leveraging the collective intelligence of open-source models, we've developed new methods to improve AI alignment without the computational expense of traditional alignment techniques. This research demonstrates how cooperation between diverse models can lead to superior performance and better alignment with human values. 📄 Read more: [Improving Model Alignment Through Collective Intelligence of Open-Source Models](https://lnkd.in/g9FZ83P8) ## 📱 Optimizing On-device and Cloud Collaboration Our collaboration with researchers from Stanford introduces cost-efficient methods for on-device and cloud language model cooperation. This work enables more powerful AI capabilities on edge devices while intelligently leveraging cloud resources, resulting in better user experiences and reduced costs. 📄 Read more: [Cost-efficient Collaboration between On-device and Cloud Language Models](https://lnkd.in/ggM6v3R3) These papers represent our commitment to developing AI that is more efficient, aligned, and accessible. I'd love to hear your thoughts on these research directions or discuss potential collaborations! #TogetherAI #MachineLearning #AI #ResearchAndDevelopment #LLM #EdgeComputing #AIAlignment

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The emergent multi-agent applications rely on sophisticated language models and often need to interact with other systems, built on other frameworks and crossing organizational boundaries. Two distinct protocols play critical roles in enabling these capabilities: Anthropic’s Model Context Protocol (MCP) and our recently announced Google Agent-to-Agent (A2A) interoperability protocol. Both relate to agentic AI, but, they address different levels and aspects of agentic system design. During our Google NEXT 2025 conference, after we annoucned this, as an author of this protocol, I was asked many questions by our customers and partners and academics. One of the most salient questions were around how these protocols compare and contrast. This article aims to clarify their specific functions, differences, and how they can complement each other. Let me know if you have any questions. https://lnkd.in/gKbQa8sM #A2A #AgenticAI #Agents #MultiAgentSystems #EnterpriseAI Google Cloud #Agent2Agent #Interoperability #GenAI

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Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?

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