AGI

A factory line does not need a chatbot with feelings. It needs a control system that can tell the difference between a harmless deviation, a costly delay, and a situation that deserves to interrupt a human operator before the machine becomes expensive sculpture. That is the useful way to read Computational Concept of the Psyche by Anton Kolonin and Vladimir Krykov.1 The paper’s title sounds as if we are about to attach a synthetic soul to a machine, perhaps with a dashboard of emotions and a tasteful blue glow. Fortunately, the core argument is more operational than theatrical: an intelligent agent should not only predict the next state of the world; it should manage its own state of needs while acting under uncertainty, risk, and resource limits. ...

Every AI vendor now wants to sell autonomy. Not “software that helps your team,” which sounds quaintly 2023, but agents that plan, act, recover, learn, orchestrate, and perhaps one day replace half the org chart while politely generating meeting notes about it. The problem is not that autonomy is meaningless. The problem is that it is usually measured like a perfume ad: evocative language, dramatic lighting, very little instrumentation. ...

TL;DR for operators The paper is not really about whether a model can answer exam questions. Given the right context, the frontier models do very well. The hard part is whether an agent can notice what must be preserved, store it in a useful form, retrieve it at the right time, and act without being explicitly prodded. That is the difference between an assistant that sounds competent and an assistant that can actually carry operational state across days, weeks, and dependent workflows. ...

TL;DR for operators Dashboards are good at telling you where performance is today. They are worse at telling you whether the rate of improvement is itself accelerating. That is the useful business translation of David Orban’s paper on “jolting” AI capabilities: do not only monitor model scores; monitor the shape of improvement. ...

TL;DR for operators Most companies do not have an “AI agent” problem. They have an agent zoo problem. One bot answers customer questions. Another writes code. Another searches documents. Another runs workflows. Another tries to sound friendly and occasionally performs the emotional equivalent of wearing a fake moustache. The paper behind this article argues that this fragmentation is not the end state. It proposes NGENT: a next-generation AI agent that integrates multiple specialist abilities into one broadly capable system.1 ...