AI & ML

Auto Added by WPeMatico

Behaviorism and AI: How Rewards Shape Model Behavior

TL;DR Behaviorism and operant conditioning offer a useful way to understand how consequences shape AI behavior. Reinforcement learning turns this relationship into an optimization process, while reinforcement learning from human feedback, or RLHF, uses human preferences to help shape model responses. Neither mechanism guarantees that the rewarded behavior achieves the intended outcome. For enterprise teams, […]

Behaviorism and AI: How Rewards Shape Model Behavior Read More »

Implementing AI Sovereignty on VCF: Boundaries, Controls, and Ownership

TL;DR A sovereign AI platform is not created simply by installing GPUs inside a private data center. Sovereignty requires enforceable control over data placement, model provenance, workload identity, network paths, tool access, administrative authority, audit evidence, and recovery behavior. VMware Cloud Foundation 9.1 can provide a strong foundation for that operating model by combining vSphere,

Implementing AI Sovereignty on VCF: Boundaries, Controls, and Ownership Read More »

AI Confidence Is Not Evidence: Building an Evidence Contract

TL;DR AI confidence calibration and evidence qualification solve different problems. Calibration asks whether predicted probabilities align with observed outcomes across comparable cases. Evidence qualification asks whether a particular observation is authentic, relevant, current, and correctly scoped. Agents that report probabilities need both. An evidence contract should preserve observation metadata, expose missing or duplicated information, and

AI Confidence Is Not Evidence: Building an Evidence Contract Read More »

Connectionism in AI: How Neural Networks Learn Relationships

TL;DR Connectionism explains how useful capabilities can develop in networks of interconnected processing units through changes to their connections. It is a central intellectual foundation of neural-network-based AI, including deep learning and large language models. The practical distinction is that training changes model parameters, while ordinary inference uses those parameters to process the current context.

Connectionism in AI: How Neural Networks Learn Relationships Read More »

The City That Rebuilds Itself: VMware Cloud Foundation Lifecycle Management Explained

TL;DR VMware Cloud Foundation lifecycle management is best understood as a controlled operating loop, not as a patch button. The platform observes health and inventory, plans dependencies, stages software, executes changes in the correct scope and sequence, validates service recovery, and records the new baseline. In VCF 9.1, lifecycle and operational capabilities are brought closer

The City That Rebuilds Itself: VMware Cloud Foundation Lifecycle Management Explained Read More »

Small AI models let drones autonomously identify and attack battlefield targets

As European militaries adapt to the use of AI and drones in modern warfare, a NATO-backed startup is helping to deploy AI-driven target detection and selection that can run on small drones for surveillance and attack missions. The company Scaleout Systems was originally founded by researchers from Uppsala University in Sweden in 2018, and initially

Small AI models let drones autonomously identify and attack battlefield targets Read More »

Google announces new experimental “CC” AI agent for families

Google’s AI models may not be in the lead by most measures right now, but the company does have one key advantage: your data. If you’re deep in the Google ecosystem, Gemini models have a lot of context on you already. Google’s latest Google Labs experiment, known as CC, aims to expand that kind of

Google announces new experimental “CC” AI agent for families Read More »