Agentic AI represents the third major platform shift in enterprise software, transforming how SaaS is built, priced, distributed, and consumed by a hybrid workforce of humans and autonomous agents. Learn how ISVs can capture this growth opportunity by building on Amazon Bedrock AgentCore for production-ready agentic deployments, making their capabilities discoverable through open protocols such as MCP and using AWS Marketplace for outcome-based pricing models that capture the measurable value... Read more ›
Alignment should optimize AI models toward positive attractors. Read more ›
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South Korea’s viral trend of fake consumer sites is providing some users with comfort and connection by rewarding anticipation. But is there a cost? Read more ›
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy landscapes, offering interpretable retrieval mechanisms. However, their continuous-time inference dynamics lack the biological plausibility of classical Continuous Attractor Neural Networks (CANNs). To bridge this gap, we propose... Read more ›
The biggest challenge in Agentic AI isn’t intelligence. Read more ›
Chief marketing officers are gaining influence in the C-suite as AI reshapes consumer behavior. Read more ›
Ask an infrastructure team how many virtual machines they run and they'll give you a number. Ask how many Kubernetes clusters they operate and they'll point to a dashboard. Ask for an ai agent inventory and the answer usually becomes a discussion about definitions. That discussion is itself the problem. The Classification Problem Nobody Solved Before any organization can inventory its agents, it has to decide what counts as one. Most haven't. Example Agent? Why It's Ambiguous Scheduled GPT wo... Read more ›
Physics-inspired NLI: vector collapse engine with semantic basin attractors - chetanxpatil/livnium Read more ›
Evaluator DescriptionMCC commissioned the American Institutes for Research to conduct an independent final impact and ... utility company could affect expected consumer behavior.Economic empowerment initiatives supported 384 women-owned... Read more ›
Earlier this month, I spoke at the Gartner Security & Risk Management Summit about a blind spot most security programs are still not accounting for - how attackers are circumventing AI security programs by using legacy infrastructure to hijack AI agents. AI adoption is moving faster than security programs can account for. Roughly 71% of organizations are piloting AI agents across their Read more ›
This article establishes the completeness of an axiomatization for the robust safety of dynamical systems with polynomial differential equations on bounded time horizons. Safety properties of robust systems are uniformly reduced to a sound axiomatization of polynomial invariants, resulting in reliable logical proofs of correctness. Approximate decidability results are also established: there is a computable algorithm such that, given any perturb... Read more ›
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State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in followin... Read more ›
Companies set expectations for their employees. It’s time to do the same for AI agents. Read more ›
ABSTRACT Consumers engaged in destination travel decision-making must evaluate experiences they cannot directly inspect before purchase, creating a representational gap between abstract promotional information and lived spatial reality. Although augmented reality (AR) is increasingly used in consumer behavior, prior research has emphasized technological features rather than the perceived action possibilities that shape consumer judgment. Drawing on Affordance theory and the elaboration likeli... Read more ›
Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensing devices, or even within a single experiment. This work addresses the problem of discovering nonlinear dynamical systems from such inhomogeneous data. We extend the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework to account for variable noise l... Read more ›
Moduna surfaces new business opportunities and the blind spots keeping AI agents from resolving user intent. Read more ›
Adobe surveys 1,003 US consumers and finds 86% make unplanned online purchases monthly, driven by social video, flash sales, and Gen Z stress relief patterns. Read more ›