Ding Zhou

Thrilled to share new AI research advances from the FAIR team at Meta. These releases highlight our continued commitment towards state-of-the-art open AI research. Read the blog post for details. https://lnkd.in/eeJk8vwr

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We have released TimesFM-2.5, the new leader in the GIFT-Eval on all accuracy metrics among zero-shot foundation models. This improved foundation model is now available on Hugging Face, with an upcoming release on GCP's BigQuery and Model Garden. This release represents a significant advancement. TimesFM-2.5 outperforms TimesFM 2.0 on leading benchmarks by up to 25%, while using half the number of parameters (200M). It also features a longer maximum context length of 16K. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴? Time-series analysis is the process of studying data points collected over time to make predictions and identify trends. It is a critical tool for a wide range of applications, like: ➡️ Forecasting future product demand. ➡️ Tracking weather and precipitation ➡️ Optimizing supply chains and energy grids. 𝗪𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴? Time-series forecasting is difficult because data patterns are often complex, can change over time, and are influenced by numerous factors. Developing a single model that can perform well across diverse datasets without being explicitly trained on each one has been a major challenge. 𝗧𝗶𝗺𝗲𝘀𝗙𝗠-𝟮.𝟱: 𝗔 𝗡𝗲𝘄 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 TimesFM-2.5 sets a new standard for a decoder-only foundation model trained on a large time-series corpus. 🤗 Leading Performance: TimesFM-2.5 holds the top position on the GIFT-Eval leaderboard for point forecasting accuracy (MASE) and probabilistic forecasting accuracy (CRPS). 🤗 Efficiency: The model's efficiency is a key feature, with a small parameter count that makes it practical for a wide range of production environments. 🤗 Longer Context: The increased context length allows the model to process more historical data, leading to more accurate forecasts. This work reiterates that building a single foundation model for time series forecasting is possible. Google Research keeps pushing forward the frontier of time series forecasting research. We are grateful to our community and customers who have provided feedback and deployed TimesFM in production. We are interested to hear more about how you are using TimesFM. Read more in our repository and see the leaderboard. GiFT-Eval: https://lnkd.in/dAwAcKA7 GitHub: https://lnkd.in/dWXH7BAm  Hugging Face: https://lnkd.in/dtx9iMHE To learn more about the foundational model read the Paper: https://lnkd.in/dRw3zzXT

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I met one AI development agency founder, and they said our 50% RAG apps failed in production last year. Because teams don’t know how to handle: chunking + embeddings + vector DB + reranking + hallucinations + evaluation + monitoring. So I'm sharing a RAG Playbook that shows exactly how to build production-ready RAG systems, with practical examples. ✅ What you’ll learn from this playbook This playbook helps you go from: “RAG demo is working” → “RAG system is reliable in production” You’ll learn how to: - reduce hallucinations using prompting techniques - use advanced chunking methods (not random splits) - select the right embedding model for your use case - choose the best vector database (based on real needs) - add reranking to improve relevance - follow clear steps to build an enterprise-grade RAG system - run evaluation before production using key scenarios - monitor and optimize performance after deployment - track RAG quality using 4 powerful metrics If you’re building a RAG-based feature for your product, this playbook will save you weeks of trial and error. ✅ Save this post (you’ll need it later) 🔁 Repost for your team/network ➕ Follow Shubham Vora to learn more on AI agents.

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Organizations need AI models that deliver both performance and cost-efficiency at scale. Today, we’re adding Claude Haiku 4.5 from Anthropic to Amazon Bedrock, giving customers another powerful option that delivers near-frontier intelligence at low cost and fast speeds. Claude Haiku 4.5 matches Claude Sonnet 4's capabilities on coding, computer use, and agent tasks. It’s perfect for scaling AI across your organization. This means you can now more affordably deploy intelligent agents in customer-facing applications, and in free product offerings without compromising on quality. With Opus 4.1 for the most advanced intelligence, Sonnet 4.5 for advanced coding and agentic tasks, and now Haiku 4.5 for efficient scaling, AWS customers have a wide variety of Claude models to choose from, all with the security, compliance, and capabilities of Amazon Bedrock. https://lnkd.in/gPgrF8jV

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Building on top of large language models is fun, but getting consistent performance and reliability is extremely challenging. I love this post from Kimberly Patron about how Sierra uses health and performance-based traffic routing and request hedging to get meaningfully better performance and tail latency from high scale LLMs https://lnkd.in/g4qWRCb5

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Yesterday I shared 25 standout examples of how customers are already generating real value with AI. This VentureBeat article by Matt Marshall underscores a lot of what we’re seeing: ⚡ AI Agents are moving into production at an incredible speed ⚙️ Multi-cloud, multi-model continues to reign - something Google Cloud has always stood for 💡 AI is no longer just a cost or time saving play, it’s solving real, complex problems and driving business growth This shift from experimentation to execution is accelerating. Next week we’ll be sharing new data that quantifies exactly what an impact generative AI has across industries. Stay tuned! #GenerativeAI #AI #GoogleCloud #ROI

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In the era of LLMs, delivering reliable, robust, and trustworthy conversational systems means more than picking a model — it means embedding rigorous evaluation throughout the entire development lifecycle. 🔍 What we cover in the blueprint: - From dataset curation (public & internal data) to synthetic query generation - To designing metrics and rubrics that go beyond BLEU / ROUGE - To building an evaluation platform to manage versioning, experiments, and traceability - To layered gates and automated checks at pull-request, staging, and production levels - To continuous improvement loops, mining real-world failures to guide the next iteration 💡 Key insight: Evaluation is not a “post-hoc step” — it must be treated with the same rigor as production code. Every prompt tweak, retriever update, or model swap must pass through reproducible, automated checks. Tiny changes can cascade, and without guardrails, regressions bleed into production. If you’re building conversational AI — whether retrieval-augmented generation systems, QA assistants, or multimodal agents — we hope this blueprint helps you construct a robust evaluation-first pipeline that scales. Check out the full write-up here: https://lnkd.in/eAkpm2yg We’d love to hear your thoughts or experiences — what evaluation practices have worked (or failed) for you? #AI #MachineLearning #ConversationalAI #LLMs #Evaluation #MLOps

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I sat down with Databricks CTO and Cofounder Matei Zaharia to cut through the noise in AI, looking at what’s working, what’s not, and what business leaders should be thinking about next. We covered a broad range of practical questions: 👉 AI readiness and ROI – Are current LLMs good enough to deliver ROI? – What happens if no new models are released? – Can AI replace employees entirely, or just augment? – What can AI reliably do today? 👉 Organizational and strategic alignment – What are the biggest non-technical reasons AI efforts fail? – How can a CEO or CTO tell if AI is a value driver or a cost center? – When should companies avoid using AI, even if they technically can? 👉 Workflow design and applied AI – Which workflows are best suited for Agentic AI? – How are teams overcoming LLM flaws in production? – How does Databricks’ Agent Bricks contribute to building trustworthy AI? 👉 Mindset shifts and next steps – The shift from deterministic to probabilistic thinking – How to reason about ROI in uncertain systems – How to get skeptical teams moving – What to prioritize over the next 12 months I really do hope you find this 40-minute video practical and helpful. Link: https://lnkd.in/eKnkg4f9 PS: Special thank you to Matei for the conversation, but also Denny Lee for working behind the scenes to get us a good room at Databricks' office for this!

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Excited to share my conversation with Marc Baselga and Ben Erez on the Supra Insider podcast. We went deep on one of the biggest shifts happening in product leadership right now: why many experienced PMs and product execs are returning to individual contributor (IC) roles in the AI era, and why that’s not a step back, but a strategic leap forward. Other topics we discussed: -- Why the leverage equation has changed—and how managing AI agents now rivals managing people -- The critical importance of hands-on credibility in AI-native companies -- Why great PMs in the future will blend product, design, analytics, and frontend execution -- What it takes to deliver real customer outcomes (not just launch features) -- How to evaluate breakout companies—and what founders often get wrong when hiring product leaders I also shared my thoughts on: -- Joining the right company: mission, trajectory, talent density, and founder courage -- Crushing it as a “super IC”: the power of speed, ownership, and infectious energy -- Transitioning from product leader to GM or founder with the right mindset If you’re navigating your next chapter in product (or just trying to stay sharp in the AI-native world), I hope you’ll find this useful. Episode links: Spotify: https://lnkd.in/gK48PFWK Apple: https://lnkd.in/gmT265BY YouTube: https://lnkd.in/gtr4RAKd Substack: https://lnkd.in/gM38FSWc

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Given Turing's role as a strategic data partner to all the leading AI labs, lots of folks have reached out following Meta's ~$15B investment in Scale AI Turing's goals and stance remain unchanged: we aim to deliver world-class data, talent, and technology, enabling all our clients to advance their AGI roadmaps And we're fully neutral, remaining "Switzerland" in the space, working to advance all the models, proprietary or open source We have extensive experience across Coding, Reasoning, and STEM, as well as Audio, Video, RL Gyms, and Industry-specific solutions. We're also now investing heavily in our Robotics/EmbodiedAI and 3D capabilities And we're bringing this expertise to not only advancing AI via the leading labs, but also to deploying AI at Fortune 500 companies through Turing Intelligence Another exciting week in AI world.

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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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If you’re building with or evaluating LLMs, I am sure, you’re already thinking about benchmarks. But with so many options- MMLU, GSM8K, HumanEval, SWE-bench, MMMU, and dozens more, it’s easy to get overwhelmed. Each benchmark measures something different: → reasoning breadth → math accuracy → code correctness → multimodal understanding → scientific reasoning, and more. This one-pager is a quick reference to help you navigate that landscape. 🧠 You can use the one-pager to understand: → What each benchmark is testing → Which domain it applies to (code, math, vision, science, language) → Where it fits in your evaluation pipeline 📌 For example: → Need a code assistant? Start with HumanEval, MBPP, and LiveCodeBench → Building tutor bots? Look at MMLU, GSM8K, and MathVista → Multimodal agents? Test with SEED-Bench, MMMU, TextVQA, and MathVista → Debugging or auto-fix agents? Use SWE-bench Verified and compare fix times 🧪 Don’t stop at out-of-the-box scores. → Think about what you want the model to do → Select benchmarks aligned with your use case → Build a custom eval set that mirrors your task distribution → Run side-by-side comparisons with human evaluators for qualitative checks Benchmarks aren’t just numbers on a leaderboard, they’re tools for making informed model decisions, so use them intentionally. PS: If you want a cheat sheet that maps benchmarks to common GenAI use cases (e.g. RAG agents, code assistants, AI tutors), let me know in the comments- happy to put them together. Happy building ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

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The best part of being at #Dreamforce is talking to many (MANY!) customers and executives about their biggest challenges in adopting AI agents. The same themes keep surfacing: figuring out where agents actually add value, integrating them into existing workflows, and making sure teams trust the output. My take on it (which has been true for most AI applications for years): 1️⃣ Start with a contained workflow. Don’t “launch an AI agent” . Instead, automate one painful, repetitive process, like summarizing customer calls or drafting support emails. 2️⃣ Focus on clean, connected data before fancy models. Invest in data context. Give your agents access to the right internal docs, CRM notes, and policies and make sure your data is clean, high-quality, and connected. That’s where the real power of agents comes from. 3️⃣ Build trust loops. Let humans review early outputs, collect feedback, and refine prompts and policies before scaling. Focus on the handoff between agents and humans, not replacement. It’s been fascinating to see what’s working (and what’s not) for our customers across many industries. How are you approaching AI agent pilots in your organization? how can we/I help? #salesforce #dreamforce #aileadership #agenticenterprise

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