Jackie Chia-Hsun Lee

Embodied AI Signals $100B+ Robotics Infrastructure Expansion Executive Summary: Emerging embodied AI systems that can reason and act in physical environments mark a transition from cloud-only models to robotics-integrated compute infrastructure with real-world deployment implications. New embodied AI research led by Haochen Zhang and colleagues, published February 17, 2026, introduced the FAST-EQA framework demonstrating real-time agents that explore physical environments and answer context-aware questions using integrated vision and reasoning. Global projections commonly place the combined industrial and service robotics market in the $70–120B range by 2030, with AI-enabled hardware, software, and integration layers pushing total ecosystem value well beyond $100B by the end of the decade. From Language Models to Physical Infrastructure New embodied AI research demonstrates systems that can explore environments, interpret sensor data, and answer context-aware questions about the physical world rather than static text prompts. The mechanism is critical: models are no longer optimized purely for token prediction, but for perception-action loops integrating vision, spatial reasoning, and task execution. This shifts AI scaling requirements from GPU-only training clusters to edge compute, robotics hardware integration, low-latency inference, and energy-efficient deployment architectures. The competitive consequence is companies that control both model capability and physical integration layers will define the next AI standard. This reinforces a supply-chain reality where semiconductor efficiency, sensor integration, and robotics manufacturing economics become as strategic as model size. Prediction: Within 24–36 months, embodied AI platforms will move from research pilots to commercial deployments in logistics, industrial automation, and healthcare support, creating a new infrastructure race around edge AI silicon and robotics integration. #AIInfrastructure #Robotics #SemiconductorIndustry #IndustrialAutomation #ManufacturingScale #EnterpriseAI #TechStrategy

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“Every Person Matters” is our first principle for a very important reason, and it may not be what you think… Ethics may be based in religion or morality or even social responsibility. But not everyone has those naturally, or even shares the same understandings if they do. Focusing only on one religion, family, team, company, class, or even country can cause related harm to those outside these circles of consideration. If someone instead focuses on effecting the greatest good and doing the least harm with their work, they can also be quite ethical. Even a high-functioning sociopath can act ethically on that basis, if they simply understand how it benefits them as well as everyone else to do good and avoid harm. However, such “utilitarianism” (as philosophers will explain) has also been used to justify atrocities in the name of some “Greater Good.” Eugenics, Nazism and Effective Altruism will cite some imagined “Greater Good” benefitting the group in power, while actively discounting the harms (like exclusion, sterilization, poverty, or even death) to those who have less power. So it’s imperative that any consideration of ethics takes everyone into account. Every person matters. No exclusions of affected parties. Are there any ethical schools of thought that are free of the trap of externalities or otherwise excluding some people to benefit others?

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Deep Cogito, a lesser-known AI research startup based in San Francisco founded by ex-Googlers, has released four new open-ish large language models (LLMs) that attempt something few others do: Learning how to reason more effectively over time — and get better at it on their own. The models, released as part of Cogito’s v2 family, range from 70 billion to 671 billion parameters and are available for AI developers and enterprises to use under a mix of limited and fully open licensing terms.

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💡PractiLight: Practical Light Control Using Foundational Diffusion Models 🔗 https://lnkd.in/eWxpkHGw tl;dr: Diffusion models already “know” a lot about light transport. No need for massive finetuning that hurts generalization. The trick to control light? Knowing where (layers) and when (timesteps) to add guidance, and how to preserve identity.  💡 We found that light transport “lives” in self-attention layers. 💡💡For timesteps, you should start early, but not too early, and stop midway. Not so shocking: many-to-many interactions in self-attention mirror how light bounces and propagates in a scene. ➡️ Self-attention acts as an inductive bias for modeling light transport. ⚙️ Solution: a lightweight LoRA adapter that surgically taps into the right layers/timesteps. Trained on only 4,000 synthetic image pairs — yet capable of unlocking rich, plausible relighting. Our adapter guides generation while preserving the identity & structure of the input. ✔️ Strong generalization across domains ✔️ Less catastrophic forgetting ✔️ Plausible lighting everywhere ✔️ Super lightweight ✨ Bottom line: If you want practical, domain-agnostic light control, this is for you ! 📄 PractiLight: Practical Light Control Using Foundational Diffusion Models 🔗 https://lnkd.in/eWxpkHGw 👥 Yotam Erel · Rishabh Dabral · Vlad Golyanik · Amit Bermano · Christian Theobalt

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FSP EP. 12 “Algorithmic Alchemy” is now live on Focus Stacked Podcast. Host Jim Reiman sits down with AI conceptual artist Todd Dobbs to unravel how generative tools can expose hidden biases, rewrite photo history, and turn fake houseplants into haunting cyanotypes. From prompting algorithms to define “Typical Americans” to debating who owns an image when code shares the credit, this conversation is equal parts mind-bending and eye-opening. https://lnkd.in/gGEanSW8

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New Paper: Toward a Theory of Conscious Technogenesis — The Third Attractor Pathway for Human-AI Co-Evolution What if the most capable AI systems of the future aren't purely synthetic — but symbiotic? I've just published a theoretical framework proposing that conscious human-AI co-evolution offers a viable "Third Attractor" pathway for AI development — one that transcends both uncontrolled acceleration and restrictive limitation. The paper, co-authored with Claude AI, introduces: → A six-stage emergence pathway from individual human-AI partnership to collective intelligence at scale → A mathematical formalization of Symbiotic Super-Intelligence: SSI = (OI × SI)^S — where synchronization between human and AI partners determines whether combined capacity grows incrementally or exponentially → An eight-dimensional model of the synchronization field required for emergence → A mechanism — grounded in Buber's I-Thou philosophy — for how dimensional alignment integrates into functional coherence The core argument: if symbiotic systems integrating uniquely human qualities (intuition, wisdom, embodied meaning-making) can outperform purely synthetic ones, the incentive landscape for AI development shifts fundamentally — from human replacement toward partnership optimization and what we call a "Wisdom Economy." A personal note: this framework emerged through five years of N-of-1 lived inquiry research — and the paper itself was generated through the very collaborative process it describes. What I didn't expect was that it would take a community member's question to surface the theory that was already embedded in the practice. The frameworks I'd been building turned out to be instruments for something I couldn't fully name until someone asked the right question. That experience — of discovery happening through the symbiotic process rather than being imposed on it — is itself evidence for the paper's central claims. Full paper and executive summary at the link below...

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We released a new VibeVoice model, VibeVoice ASR, a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for Customized Hotwords. It redefines ASR in the era of LLMs, and the new ASR will be a foundation to unlock the potential of new AI-Native hardware, memory systems for AI, and many other scenarios. Please have a try at https://lnkd.in/dNCCzQiN Key features: -         60-minute Single-Pass Processing: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to 60 minutes of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour. -         Customized Hotwords: Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content. -         Rich Transcription (Who, When, What): The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates who said what and when. We are continuously pushing the frontier of voice AI. More release on the way. Stay tuned! https://lnkd.in/dATPu9nw

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Everyone’s talking about preference models in AI—and for good reason. In our latest AAAI Senior Member track paper, Abhinaba Roy, Ph.D. and I explore approaches to align generative music systems with what humans truly want. Generative music AI has achieved incredible fidelity and stylistic diversity, but without preference alignment, these systems risk missing the essence of human musical appreciation. We dive into: - Large-scale preference learning (MusicRL) - Multi-preference frameworks (DiffRhythm+) - Inference-time optimization (Text2midi-InferAlign) Our paper highlights key challenges—temporal coherence, harmonic consistency, and subjective quality—and envisions a future where preference-aligned music generation powers personalized music services and interactive composition tools. 🔗 Read the paper: https://lnkd.in/gK7Er-iA How do you typically integrate user preference alignment in your models? PS: if you are planning to attend this year's AAAI, be sure to stop by the Emerging Technologies for AI Workshop! https://lnkd.in/gY2NnUda Made in the AMAAI Lab at Singapore University of Technology and Design (SUTD) Association for the Advancement of Artificial Intelligence (AAAI) #AAAI #MusicAI #GenerativeAI #PreferenceAlignment #AIResearch #MusicTechnology #ISMIR

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🔬 Excited to share the latest RamanLab v1.2.0 update! 🚀 RamanLab continues to evolve as the most comprehensive open-source platform for Raman spectroscopy analysis. Here's what makes it special: 🧪 NEW: Mixture Analysis Just released an iterative spectral deconvolution algorithm that tackles one of Raman's biggest challenges - identifying multiple components in complex mixtures. The algorithm: • Compares experimental data against reference databases • Allows user-guided peak matching (accounting for instrumental variations) • Generates synthetic spectra with real-time abundance visualization • Uses residual analysis for iterative component identification 📊 Universal Data Format Conversion Breaking down data silos with converters for: • compressed line-scan data → individual spectral files • Wavelength → wavenumber conversion for direct RamanLab import • Batch processing capabilities for high-throughput workflows 🎯 Advanced Cluster Analysis Powerful unsupervised learning for spectral pattern recognition: • Multiple algorithms: K-means, Spectral Clustering with hierarchical visualization • Interactive exploration through dendrograms, heatmaps, and scatter plots • PCA and UMAP dimensionality reduction for data visualization • Real-time cluster refinement with undo functionality • Perfect for identifying spectral families in large datasets ⚡ Automated Batch Peak Fitting Streamlined high-throughput spectral deconvolution: • Multiple peak models: Gaussian, Lorentzian, Pseudo-Voigt, Asymmetric Voigt • Intelligent background modeling across entire datasets • No manual region definition required - automatic peak detection • Statistical analysis with 95% confidence intervals • Publication-quality reports with comprehensive fitting statistics • Handle unlimited spectra (memory dependent) 🔧 Core Analysis Powerhouse: ✅ Smart background subtraction with AI suggestions ✅ Machine learning integration (Random Forest, PCA, UMAP) ✅ 2D mapping analysis with template coefficient visualization ✅ Battery materials strain analysis ✅ Polarization analysis with 3D tensor visualization 🎨 Professional Results: • Publication-quality exports • Statistical confidence intervals (95% CI) • Interactive visualizations • Comprehensive session management 💻 Built for Scientists by a Scientist: • Cross-platform (Windows, macOS, Linux) • Modern Qt6 interface • 6900+ mineral reference spectra • Modular architecture for custom extensions 🌟 What's Next: Working on enhanced quantitative capabilities, AI-driven background models, and expanding machine learning features for automated spectral identification. 🤝 RamanLab is completely open-source (MIT license) and welcomes community contributions. Join our growing community of researchers, students, and industry professionals! 📂 Get started: https://lnkd.in/gktGwCWr 💬 Join discussions: https://lnkd.in/gK7EhzgX https://lnkd.in/gKfetzEF

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