Forwarded from DAF
New publication: "The Ant Stack"
https://zenodo.org/records/16782757
We present the Ant Stack: a compact, modular framework that emulates an ant from physics to cognition. It comprises three interoperable layers—
https://zenodo.org/records/16782757
We present the Ant Stack: a compact, modular framework that emulates an ant from physics to cognition. It comprises three interoperable layers—
AntBody (morphology, actuation, sensors), AntBrain (functional neural circuits templated from conserved insect architectures), and AntMind (active inference and stigmergy for individual and collective cognition). The stack uses explicit I/O contracts, sparse/low‑power compute, and species‑parameterized configurations aligned with current myrmecology.
DX space
Есть гипотеза, что в Долине заканчивается разделение людей на два типа: - олдскульных Кремниевых, оставшиеся тру-инженерные-инженеры, работающие в очень разных ролях и должностях - новая волна Соевых Силиконовых, в ряды которых вливаются и ветераноы индустрии…
Реальность скоро сильно удивит всех силиконовых.
Остальным пора готовиться к событиям масштаба краха доткомов 2000-х
https://qz.com/ai-boom-bubble-fears
Остальным пора готовиться к событиям масштаба краха доткомов 2000-х
https://qz.com/ai-boom-bubble-fears
Quartz
AI bubble fears get worse. Wall Street plays it cool
Wall Street finds itself caught between optimism and anxiety, deploying different vocabularies depending on which side of the ledger it's addressing
Forwarded from Active Inference Institute
5th Applied Active Inference Symposium
12-14 November, 2025
https://symposium.activeinference.institute/
12-14 November, 2025
https://symposium.activeinference.institute/
DX space
Реальность скоро сильно удивит всех силиконовых. Остальным пора готовиться к событиям масштаба краха доткомов 2000-х https://qz.com/ai-boom-bubble-fears
mint
Mark Zuckerberg agrees with Sam Altman on possible AI bubble, explains reason behind Meta's aggressive investments | Mint
Meta CEO Mark Zuckerberg acknowledges the possibility of an AI bubble, likening it to past bubbles. However, he emphasized the need for heavy investment in AI to avoid missing superintelligence advancements.
Blossom by blossom the Active Inference spring begins.
https://resiliencemedia.co/stanhope-ai-raises-8m-for-new-approach-of-ai-for-physical-applications/
https://resiliencemedia.co/stanhope-ai-raises-8m-for-new-approach-of-ai-for-physical-applications/
Resilience Media
Stanhope AI raises $8M for new approach of AI for physical applications
A startup spun out of UCL research into how the brain works is building a new kind of AI model
DX space
"It's a time bomb, and I want to short it", The Big Short
Premium-grade cash evaporation
DX space
Premium-grade cash evaporation
Borrowing money at a 17.5% interest rate to run a business that’s making losses is the chef’s kiss, truly iconic
https://www.reuters.com/business/openai-sweetens-private-equity-pitch-amid-enterprise-turf-war-with-anthropic-2026-03-23/
https://www.reuters.com/business/openai-sweetens-private-equity-pitch-amid-enterprise-turf-war-with-anthropic-2026-03-23/
Active Inference for Physical AI Agents - An Engineering Perspective
Bert de Vries, 21 Mar 2026
"Physical AI agents, such as robots and other embodied systems operating under tight and fluctuating resource constraints, remain far less capable than biological agents in open-ended real-world environments. This paper argues that Active Inference (AIF), grounded in the Free Energy Principle, offers a principled foundation for closing that gap. We develop this argument from first principles, following a chain from probability theory through Bayesian machine learning and variational inference to active inference and reactive message passing. From the FEP perspective, systems that maintain their structural and functional integrity over time can, under suitable assumptions, be described as minimizing variational free energy (VFE), and AIF operationalizes this by unifying perception, learning, planning, and control within a single computational objective. We show that VFE minimization is naturally realized by reactive message passing on factor graphs, where inference emerges from local, parallel computations. This realization is well matched to the constraints of physical operation, including hard deadlines, asynchronous data, fluctuating power budgets, and changing environments. Because reactive message passing is event-driven, interruptible, and locally adaptable, performance degrades gracefully under reduced resources while model structure can adjust online. We further show that, under suitable coupling and coarse-graining conditions, coupled AIF agents can be described as higher-level AIF agents, yielding a homogeneous architecture based on the same message-passing primitive across scales. Our contribution is not empirical benchmarking, but a clear theoretical and architectural case for the engineering community."
https://arxiv.org/abs/2603.20927
Bert de Vries, 21 Mar 2026
"Physical AI agents, such as robots and other embodied systems operating under tight and fluctuating resource constraints, remain far less capable than biological agents in open-ended real-world environments. This paper argues that Active Inference (AIF), grounded in the Free Energy Principle, offers a principled foundation for closing that gap. We develop this argument from first principles, following a chain from probability theory through Bayesian machine learning and variational inference to active inference and reactive message passing. From the FEP perspective, systems that maintain their structural and functional integrity over time can, under suitable assumptions, be described as minimizing variational free energy (VFE), and AIF operationalizes this by unifying perception, learning, planning, and control within a single computational objective. We show that VFE minimization is naturally realized by reactive message passing on factor graphs, where inference emerges from local, parallel computations. This realization is well matched to the constraints of physical operation, including hard deadlines, asynchronous data, fluctuating power budgets, and changing environments. Because reactive message passing is event-driven, interruptible, and locally adaptable, performance degrades gracefully under reduced resources while model structure can adjust online. We further show that, under suitable coupling and coarse-graining conditions, coupled AIF agents can be described as higher-level AIF agents, yielding a homogeneous architecture based on the same message-passing primitive across scales. Our contribution is not empirical benchmarking, but a clear theoretical and architectural case for the engineering community."
https://arxiv.org/abs/2603.20927