AI & Machine Learning

TL;DR An AI agent is not dependable simply because it can reason, call tools, and retry. It needs a feedback architecture that connects an approved goal to trustworthy observations, bounded actions, outcome verification, and correction. Cybernetics and control theory provide the vocabulary for designing that architecture. The practical priorities are stable correction, sufficient visibility, appropriate […]

TL;DR Bounded autonomous operations connects trustworthy telemetry to policy, scoped execution, and verified service outcomes. Start with approved, reversible workflows that have named owners, explicit limits, and tested fallback paths. In a VCF environment, coordinate observability, lifecycle, workload, security, and resilience responsibilities while preserving each system’s authority boundary. Progress from evidence collection to approval-gated remediation,

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,

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,

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

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.

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

Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most.

Newly unsealed court documents show Microsoft employees discussing whether OpenAI was involved in the “largest theft of labor in human history.”

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