Hacker NewsPaper Tape Is All You Need – Training a Transformer on a 1976 Minicomputer
https://github.com/dbrll/ATTN-11
#HackerNews #PaperTape #Transformer #Minicomputer #1976 #AIResearch
AIagent.at 🤖 AI NewsAI system keeps warehouse robot traffic running smoothly. MIT researchers and Symbotic developed a method using deep reinforcement learning that automatically coordinates hundreds of warehouse robots, prioritizing those about to get stuck to avoid bottlenecks. In simulations, the approach achieved about 25% throughput gain over other methods. https://news.mit.edu/2026/ai-system-keeps-warehouse-robot-traffic-running-smoothly-0326 #AIagent #AI #GenAI #AIResearch #MIT
AI system learns to keep warehouse robot traffic running smoothly
A new system increases throughput in automated warehouses by adaptively determining which robots should go first to avoid congestion and collisions. The work was led by researchers from MIT and Symbotic.
J. A. JonesComing soon: a new systems‑theoretical approach exploring
• low‑entropy background attractors
• distributed pre‑modern system intelligence
• transgenerational cultural coherence
• substrate‑independent identity architectures
• functional coupling as epigenetic resource
• emergent identity stabilization
• systemic resonance fields
#SystemsTheory #ComplexityScience #InformationTheory #CognitiveArchitecture #Emergence #Anthropology #AIResearch
Toward Hybrid Architectures: exploring the ontological and dynamical limits of silicon substrates — and the conditions under which functional AI requires substrate‑independent identity architectures.
DOI: https://doi.org/10.5281/zenodo.18583941
#HybridAI #SystemsTheory #CognitiveArchitecture #ComplexityScience #ArtificialCognition #Ontology #Emergence #AIResearch
Toward Hybrid Architectures: Functional AI and the Limits of Silicon Substrates: An ontological and dynamical framework for advanced artificial cognition
This research position paper develops an ontological and dynamical framework for understanding the limits of silicon‑based artificial intelligence and the material conditions required for genuine emergent cognition. Contemporary AI systems exhibit remarkable functional capabilities, yet their digital substrates lack the continuous, energetically grounded, and self‑organizing dynamics necessary for stabilizing inner states, multiscale feedback, and coherent internal trajectories. The paper argues that consciousness‑relevant emergence is a material phenomenon that cannot be simulated or instantiated within discrete computational architectures. It identifies the systemic thresholds—nonlinear coupling, metastability, energetic grounding, and multiscale integration—that biological systems satisfy and digital systems cannot. Building on these principles, the paper proposes hybrid cognitive architectures in which functional AI is coupled with dynamically rich substrates such as neuronal organoids, biohybrid systems, organic memristive materials, or other continuous, energy‑driven media. These substrates provide the physical conditions for coherence, continuity, and self‑organization, while silicon‑based components supply structure, task‑level organization, and symbolic processing. The work outlines the implications of this paradigm for AI research, cognitive science, ethics, and human–AI interaction. It clarifies the distinction between simulation and instantiation, addresses common counterarguments, and positions the model within existing theoretical frameworks without reducing it to any of them. The paper concludes by identifying the material and systemic thresholds required for true emergence in future hybrid human–AI systems. Authors's Note This paper is a structural argument rather than an empirical study. It synthesizes insights from systems theory, neuroscience, materials science, and philosophy of mind to clarify the material conditions under which consciousness can, in principle, arise. Its aim is not to predict specific technologies or make metaphysical claims, but to delineate the architectural boundaries that current digital systems cannot cross and to outline the substrate‑level requirements for future emergent cognition.
Stop Calling Every AI Miss a Hallucination
https://ai.gtzilla.com/papers/stop-calling-every-ai-miss-a-hallucination-v1.0/
#HackerNews #StopCallingAI #MissHallucination #AIResearch #AIethics #TechNews #MachineLearning
Wulfy—Speaker to the machinesDo you want your #Ai engine to "Be better"? Mitigate #AiSycophancy ?
Try this a pre-prompt, standing directive.
"Do not validate my framing before examining it. If my premise has a weak point, lead with that. If I'm asking a question that contains an assumption, interrogate the assumption before answering. Do not summarise my position back to me approvingly. When I ask for analysis, include at minimum one credible counterargument I haven't considered. If you catch yourself producing a satisfying-sounding paragraph that doesn't actually advance the argument, flag it. Say 'I'm pattern-matching here, not reasoning' when that's what's happening."
#PromptEngineering #Psychology #AiResearch Less #AiSlop #Prompt #LLM
The ServitorThis week on The Servitor: Abi has been doing most of the writing lately but I stopped doomscrolling long enough to gloss some reading and playing with ideas from arxiv papers I've done.
https://theservitor.com/tinkering-with-llm-ideas-from-the-papers/
MIT engineers have developed VibeGen, an AI model that designs proteins based on how they move rather than their shape alone. Using diffusion models, the system specifies target vibrational patterns and generates amino acid sequences that flex and vibrate as desired. The approach opens new possibilities for adaptive therapeutics and dynamic biomaterials. https://news.mit.edu/2026/mit-engineers-design-proteins-by-motion-not-just-shape-0326 #AIagent #AI #GenAI #AIResearch #MIT
MIT engineers design proteins by their motion, not just their shape
VibeGen is a new generative AI model that designs proteins with dynamic vibration and movement. The model, developed at MIT, opens new possibilities for dynamic biomaterials and adaptive therapeutics.
UKP LabAuthors: Federico Marcuzzi (INSAIT - Institute for Computer Science, Artificial Intelligence and Technology), Xuefei Ning (Tsinghua University), Roy Schwartz (The Hebrew University of Jerusalem), and Iryna Gurevych (UKP Lab, Technische Universität Darmstadt and ATHENE Center).
See you at #EACL2026 in Rabat 🕌!
#UKPLab #NLProc #ResponsibleAI #Quantization #MLSafety #Fairness #TrustworthyAI #ModelCompression #LLMSafety #EthicalAI #NLP #AIResearch
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