Ioannis (Yanni) Antonellis

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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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'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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🚀 Thrilled to share our latest research! Our paper, "Unimodal Strategies in Density-Based Clustering", has been accepted to ECML-PKDD 2025! 🎓🎉 This work, in collaboration with the talented Jay Tenenbaum and Prof. Ariel Shamir, was inspired by real-world ML challenges we faced at Microsoft Azure Video Indexer. 🔍 What’s it about? We explore a key property in density-based clustering (like DBSCAN): ➡️ The relationship between the number of clusters and the epsilon radius is nearly unimodal. ➡️ We leverage this to introduce a Ternary Search-based method for efficient hyperparameter tuning. ➡️ Our approach significantly reduces computational overhead in high-dimensional, large-scale datasets. 🧪 Validated across diverse NLP, Audio, and Computer Vision tasks, our method shows strong robustness and practical value. 📄 Read the paper: arXiv:2506.21695v1 💻 Explore the code: https://lnkd.in/dqBugkTT #MachineLearning #DataScience #Clustering #DBSCAN #ECMLPKDD2025 #AzureVideoIndexer #HighDimensionalData

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AI deteriorates to the mean, which is why it will never (in the LLM form) take over creative activities. "The result is a "JPEG of thought" – visually coherent but stripped of its original data density through semantic ablation." https://lnkd.in/euWS8nEv

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In the AI era, this is the only skill that won’t expire — and it’s not coding. Geoffrey Hinton’s career advice for the AI era: don’t just learn skills — learn how to think 🧠 In a world of skills that expire, thinking is the skill that compounds 📈 Nicholas Thompson asked Hinton what advice he’d give to a 17-year-old thinking about college. His answer: “The future is incredibly uncertain. We’re at a point in history where we’ve got no experience of what’s to come — dealing with things smarter than us. We just don’t know what’s going to happen. Getting a good education that makes you good at thinking, rather than a particular skill, seems like the best bet.” That hit home for me. When I chose to study philosophy at Duke University 🎓, it wasn’t because I thought Aristotle would help me code (spoiler: he didn’t). It was because I wanted to learn how to think — to deconstruct an argument, rebuild it, and see if it still stood. Few moments in life give you the space to practice metacognition that deeply. In a world where AI is rewriting the rules faster than we can read them, the ability to think clearly — not just know things — is the edge. And when the future is unknowable, your mind is the only compass you can trust 🧭 #ArtificialIntelligence #CriticalThinking #ThoughtLeadership #FutureOfWork #LifelongLearning #GeoffreyHinton #AI #AIforGood 🎥 Full highlight reel from Nick Thompson’s conversations at the United Nations AI for Good Summit, featuring Hinton, Marc Benioff, Yann LeCun, and Will.i.am: 

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