A recursive intelligence-amplifier

Ashbyan Cybernetics

Google's authors reach for the Ashbyan sense of governance because the problem they have encountered was anticipated by three British Cyberneticians, who started working on it as early as 1948: - Ashby's "intelligence-amplifier" (1956), - Beer's Viable System Model (VSM, 1972), and - Pask's Conversation Theory (CT, 1975). Combined, they architect a recursive intelligence-amplification prosthesis that enhances the firm at every level: upward to the industry's regulator, statutory or its own, since most industries regulate themselves to some degree, even where government does not formally; and downward from the board, through senior management and staff, to each agent and its sub-agents, as many levels down as the work goes.

W. Ross Ashby, 1903–1972

An Introduction to Cybernetics, 1956. The architecture: a regulator can only match what it can see, and an intelligence amplifier is a selector working against a goal held outside it.

Stafford Beer, 1926–2002

Brain of the Firm, 1972. The organisation: five systems any viable firm must have, from operations to policy, each needing the others, and the same five again inside every part.

Gordon Pask, 1928–1996

Conversation Theory, 1975. The interaction: understanding is an agreement between two actors, each able to teach the concept back, and the agreement is where learning lives.

A recursive intelligence-amplification prosthesis

Ashby defines intelligence: appropriate selection from available information to keep essential variables within target bounds against the firm's goal. Beer identified a diagnostic model of the firm: five systems in a recursion, so the industry regulator's rule, the board's decision, a manager's brief, a member of staff's check, an agent's task and a sub-agent's sub-task are one problem at every level, coordinated downward through as many levels of agents as the work needs and upward to the regulator, whether a statutory one or the industry's own. Pask says how it is used: as a conversation between people and agents at every level, in which the people can teach back what the agent proposes. That capacity to teach back is the evaluative capacity a firm has to build, and it has to exist at every level, because the prosthesis fails at whichever level it is missing.

IA, not AI: the firm's own intelligence amplified, and measured in the outcomes it is already judged on. The three turn the firm's own context into the thing its agents are selected against, and that is where the return on an AI deployment comes from.