Julien Valentin

You may already be amazed by diffusion models for generating images and videos—but did you know they can also help wildlife conservation🐘?  We propose DiffOracle, combining diffusion models & game theory to stop poaching! Arxiv: https://lnkd.in/ehkwF6YJ We use a conditional diffusion model to forecast the number of snares in each grid cell of the national park, capturing complex high-dimensional distributions. On poaching data from Murchison Falls National Park (Uganda), our model outperforms baselines in forecasting accuracy. To account for imperfections in the learned diffusion model, we formulate a robust patrol optimization problem as a two-player zero-sum game between a ranger defender and a nature adversary that selects the worst-case poaching activity within a constrained space. Empirically, we find that our game-theoretic approach yields patrol strategies with significantly lower regret than baseline methods, resulting in more effective wildlife protection in national parks. Thanks for all my wonderful collaborators including Haichuan Wang, Yuqi Pan,  (Adam) Cheol Woo Kim, Mingxiao Song, Alayna Nguyen, Tonghan Wang, Haifeng Xu and Milind Tambe #DiffusionModel #GenerativeAI #Conservation #Sustainability #ClimateChange

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AI built for society, not for profit. 💡 ETH Zurich and EPFL are about to make a bold move. They are launching a language model without chasing profit. Built for the public, researchers, and education. It’s designed with transparency, data privacy, and long-term public interest in mind. We’re used to seeing Big Tech race ahead with closed systems. Now we have something in Europe which is more open. Some interesting facts: It’s hosted on Swiss infrastructure for data sovereignty. 🇨🇭 It’s trained on publicly available, licensed data, not user content. ETH wants to invite universities, authorities, and civil society to co-develop and explore it together. They want to understand these models instead of just using them. I love that. More of this mindset, please. What do you think? Do we have a chance to lead in ethical, public-focused AI? -J Content: Jonas and AI

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New paper from Yann LeCun. It's a great read as always and it comes out at the right time when I think thousands [of software engineers] are really getting into their coding agents and discovering glimpses of their static nature (forget to save a session?). I think part of the art of writing a high-impact paper is generating a reaction. I can't help but feel like the threat of a biologically inspired engineered optimization algorithm is more for the dramatic story telling. https://lnkd.in/eZAuqWkN

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After years of building and refining #MyFoodRepo 📱 for our own nutritional studies at EPFL's Digital Epidemiology Lab, we're making it available to researchers everywhere. If you've ever run a study with dietary data, you know the pain: food frequency questionnaires that rely on faulty memory, manual food diaries that cause dropout, and research-grade apps that work in a pilot but frustrate participants at scale. Commercial apps might seem tempting, but they're often privacy-invasive, and you never really know if you'll get the data you need, or under what conditions. We built something different. Participants snap a photo, scan a barcode, or type what they ate in plain language. Cutting-edge AI, i.e. the same vision and language models powering today's leading AI applications, identifies foods, estimates portions, and maps to nutritional databases in real time. No endless dropdowns. No clunky search interfaces. And you get full access to all the raw, timestamped data. Privacy is built into the architecture, not bolted on as an afterthought. We collect zero personal information from participants, no names, no emails, no phone numbers, no location data. We don't know who your participants are, because we neither want to nor need to. Over a dozen research groups across multiple countries already use MyFoodRepo, from clinical trials at university hospitals to large-scale digital cohorts with over 1,000 participants. The platform has generated hundreds of thousands of annotated meal entries and contributed to peer-reviewed publications. It works because we made it easy enough that people actually stick with it. Our own studies depend on it. Now you can use it too. You get your own isolated cohort, real-time data monitoring, automatic nutritional mapping to official databases or your own custom food lists, and full data export via API or CSV. For academic research, it's typically free through our collaboration model. If you're planning a clinical trial, digital cohort, or any study involving dietary data, this might be what you've been looking for. 👉 https://lnkd.in/eraiPNf3

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 Beyond Transformers: What’s Next for LLMs in the Deep Learning Era? In just a few years, large language models (LLMs) have gone from research curiosities to powering everyday tools — from coding assistants and marketing copywriters to legal advisors and medical copilots. Under the hood, deep learning has driven this revolution — particularly the Transformer architecture. But what if we’ve only scratched the surface? The Transformer Era: Brilliant but Limited? Transformers brought us the superpowers of: Parallel training over large datasets, Attention mechanisms that let models weigh context efficiently, And scaling laws showing that “bigger is better” — at least until recently. Yet, we’re also seeing diminishing returns: GPT-4’s leap over GPT-3 is real, but not revolutionary. Training and inference costs are skyrocketing. And many LLMs still hallucinate, lack reasoning, and struggle with real-time constraints. It’s time to ask: What comes after Transformers? Neurosymbolic LLMs: Logic Meets Language A new wave is brewing — hybrid architectures that blend deep learning with symbolic reasoning. Imagine LLMs that: Not only generate fluent text, but also verify facts logically, Can explain their decisions, not just output tokens And act like reasoning agents rather than black boxes. Think OpenCog, DeepMind’s Gato, or Meta’s Toolformer as early signals. Memory-Augmented LLMs: From Static to Stateful The next generation of LLMs may be stateful and evolving, not just stateless text generators. New research shows promise in: External memory modules (like RETRO, RAG, or MemGPT), Long-context reasoning across multiple sessions, And lifelong learning — updating models without retraining from scratch. This makes LLMs more like digital companions than just chatbots. LLMs as Cognitive Engines, Not Just Models Here’s a new perspective: LLMs are not just NLP models anymore. They’re cognitive frameworks — bridging perception, memory, reasoning, and action. What if we start designing LLMs as: Multimodal agents (text, image, speech, video), World simulators (for gaming, robotics, strategy), Or even scientific assistants that explore hypotheses and automate literature review? Call to Builders: Don’t Just Use LLMs — Reinvent Them As engineers, researchers, and entrepreneurs, we’re entering a rare moment: A new “post-transformer” paradigm is forming. Ask yourself: What if we mix symbolic AI with LLMs? What if models verify instead of just generate? What if context is not just tokens, but structured memories? Final Thought LLMs have brought us far, but the next leap won’t come from just adding more parameters. It’ll come from rethinking the architecture, memory, reasoning, and embodiment. Let’s not wait for someone else to define what’s next. Let’s build it. #DeepLearning #LLMs #AIResearch #SymbolicAl #NeuroSymbolic #CognitiveAl #MLEngineering #FutureOfAl #LinkedInAl

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Great discussion in our lab today, thanks to Alhusain Abdalla , on Recursive Language Models (RLMs) and how they challenge our assumptions about long-context reasoning. A common belief in the LLM community is that long-horizon tasks are primarily a context window problem: if models could simply “see more,” they would reason better. In practice, we increasingly observe the opposite—context rot—where performance degrades as information density grows, even in frontier models. 🧠 RLMs propose a different framing Rather than forcing models to ingest ever-larger contexts, RLMs treat context as something to interact with programmatically. Conceptually, this represents an evolution: • RAG is efficient but inherently lossy—if retrieval misses the right evidence, the model fails. • Agentic loops improve flexibility, but remain linear and constrained by short-term memory. • Recursive Language Models take a more radical step: the model writes code, recursively decomposes the data, spawns sub-calls, and aggregates results—without ever needing to load the full corpus into its own context window. 🔑 The key insight is that RLMs decouple reasoning capability from context length. The root model never sees the entire dataset—only structured outputs from recursive sub-tasks—enabling scalable reasoning over effectively unbounded data while keeping inference focused and efficient. We’re currently experimenting with their code on several clinical trial–related use cases, and we’re excited to share more results soon 🔥 This feels like an important step toward reasoning systems that scale not by reading more, but by thinking more structurally.

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𝗦𝗜𝗡𝗤 🇨🇭 𝗔𝗽𝗲𝗿𝘁𝘂𝘀: Open Source AI, Made in Switzerland. We’re proud to announce a 𝗻𝗲𝘄 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗲𝗱 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗽𝗲𝗿𝘁𝘂𝘀-𝟴𝗕 model using our novel 𝗦𝗜𝗡𝗤 method! This release combines Apertus, an open-source large language model born from EPFL, Eidgenössische Technische Hochschule Zürich, and the Swiss National Supercomputing Centre (CSCS), with 𝗦𝗜𝗡𝗤, our Zurich-born quantization method released under the Apache 2.0 license. Thanks to the integration with fast kernels, you can now experience 𝗵𝗶𝗴𝗵-𝘀𝗽𝗲𝗲𝗱 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 using our 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝟰-𝗯𝗶𝘁 𝗺𝗼𝗱𝗲𝗹𝘀. I am personally excited to continue contributing to the Swiss open-source AI ecosystem and this is just the beginning. New quantized versions, including for the 70B model, are coming soon! 👉 New SINQ-quantized Apertus model: https://lnkd.in/d5hvFVDC 👉 SINQ GitHub Repo: https://lnkd.in/dctt5jK2 ⭐ Don’t forget to star our repo and join the journey toward fast, open, and collaborative AI made in Switzerland.

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FunctionGemma, a new 270M parameter language model from Google, designed specifically for function calling and tool execution — not general chat.   Why is FunctionGemma special? 🔹 Extremely lightweight FunctionGemma can run in ~550MB RAM, making it possible to deploy on laptops, edge devices, and even phones — all without large GPUs. 🔹 Purpose-built for function calling Unlike general LLMs, this model is trained for text-only tool calling, making it ideal for automation, agents, and API-driven workflows. 🔹 Easy local deployment The model is released in GGUF format, so it works seamlessly with llama.cpp and Ollama using quantized versions (Q4/Q5/Q8). 🔹 Fine-tuning ready from day one Thanks to Unsloth’s day-zero support, you can: Run FunctionGemma on CPU or GPU Fine-tune it locally using Colab notebooks Choose full fine-tuning or LoRA   What can you fine-tune it for? FunctionGemma is meant to be customized for specific tasks, such as: Multi-turn function-calling agents Mobile actions (calendar events, reminders, flashlight, timers) Task-specific automation and workflows Lightweight, offline AI assistants This model clearly shows a shift toward small, focused, and efficient AI — where models are trained to do one thing well and can be adapted quickly to real-world use cases.   Model (GGUF): https://lnkd.in/gxVuBhZA Docs & fine-tuning guides: https://lnkd.in/gchpAjUF Run and Deploy LLMs on your iOS or AndroidPhone: https://lnkd.in/grk72Bey

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Why "On-Device" is the most ethical AI strategy. We talk a lot about the size of models. Parameters. Trillions of tokens. The race for AGI. But in my academic life at EPFL and my work in Speech, I’ve always been obsessed with the opposite end of the spectrum: Accessibility. When we published our research in Nature Communications on AI for social projects, one conclusion was clear: If AI requires a massive data center and a high-speed fiber connection to run, it excludes half the world. Cloud models are undeniably impressive and they are pushing the boundaries of what is possible. But they shouldn't be the only way we deploy intelligence. I'm seeing a resurgence of interest in on-device solutions, not to replace the cloud, but to handle the sensitive, immediate, and personal tasks where the edge shines: - Privacy: Processing data right where it’s created. This is crucial for multimodal data (voice, video) that is highly identifiable and shouldn't leave the user's hand. - Latency: Real-time processing that feels like a conversation, not a transaction. - Energy Efficiency: Running a small, optimized model locally is often far more sustainable than round-tripping every query to a power-hungry data center. - Reliability: True accessibility means working offline. AI should be helpful even when the signal drops or the fiber hasn't been laid yet. Better locally-run alternatives make these powerful tools accessible to everyone, even for problems and data that are highly sensitive. I’m planning to dive deeper into this myself by experimenting with some of the latest open-weights models specifically for local deployment. If you’ve been testing small language models (SLMs) or edge-optimized architectures recently, I’d love to hear about your experience. What frameworks or models are you finding most promising for mobile/edge/small server hardware right now? #AIforGood #OnDeviceML #NLP #EthicalAI #TechForGood #EdgeAI #SustainableTech #OpenWeights

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