Algorithm & Blues
AI research translated into decisions executives can actually make. One clear argument per issue, published weekly.
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Published every Sunday since May 2025. No hype, no filler, one clear argument per issue.
The Curiosity Engine
A neuroscience preprint trained neural networks on two ingredients drawn from human cognition, a drive to reduce uncertainty and the biophysical constraints a real brain operates under, and reproduced patterns of human synaptic development along with fast learning on unfamiliar recombinations. The authors treat curiosity as a determinant of the network's final architecture rather than a downstream consequence of it, which raises an uncomfortable question for systems that can answer almost anything in seconds: what gives them a reason to keep looking?
Two new papers reason about machine consciousness under deep uncertainty — one scoring a language model against a human, a chicken, and the 1960s chatbot ELIZA across nine theories, the other asking what institutions should do before the evidence is in. Neither claims today's models are conscious, but together they turn a philosophical debate into a governance question about how much uncertainty an organization is willing to ignore.
An AI agent doesn't need a malicious instruction to do damage — just an environment that hands it more authority than the task requires. Hardik Goel's paper on tool-enabled agents names this ambient authority leakage, and argues the control has to sit at the task, not the prompt: narrow credentials, no stray secrets in the runtime, and human review of the path an agent took rather than only its output.
A working agent demo proves task completion, not value — and the two come apart once the agent is inside a real workflow. Drawing on CMU's TheAgentCompany, the METR developer study, and a new valuation framework, a look at why supervision, rework, and cleanup belong in the business case.