We live on the cusp of a revolution that is changing how we think about intelligence, that is transforming the architecture of global agency, and that is challenging our assumptions of who gets to build the world we will live in. The AI revolution is being promoted as a tremendous opportunity, but also being portrayed as a grave danger. But what does it mean for education in general, and educators, specifically?

As these societal and potentially global transformations are gaining momentum, my question for us is: What will the educators do?

It is said that we are “training” AI, that machines are “learning” by themselves and that they are becoming “intelligent.” These three words – training, learning, and intelligence – aren’t they the purview of education?

The danger here at the dawn of the AI revolution is that the technocrats are redefining what these words mean. They are slowly but surely isolating them from real human experience and defining them as what machines are good at. (What we are doing is trading in machine-like metaphors for human intelligence — predictive processing, Bayesian computation and the like— and trading in human-like metaphors for what machines are doing.)

This has happened before with the industrialization of work and the urbanization of former farm families. Factories were designed around the needs of the machines, and workers were forced to accommodate them by performing tedious, repetitive actions at a fast but monotonous pace along an assembly line.  As a result, work became a kind of drudgery, and schools followed suit, by training students to rigid clock time and seating them in linear rows.

As modern nations moved away from industrial production and into the knowledge economy, schools again followed suit. Knowledge, which is properly participatory knowing how, became redefined as “information” — propositionally knowing about. Huge amounts of information propagated through the knowledge-economy creating an entirely new consumer class seeking higher degrees by “downloading more and more information.” Education focused on training students to store highly standardized information and to retrieve it in highly standardized ways. As a result, school became another form of drudgery, preparing students to endure endless hours in front of screens along another kind of assembly line. The economy became a closed loop which continuously compounds the information that flows through the system. People became both the producers of information and the consumers of it. Unlike the production of real goods and services, this system was almost frictionless. It led to the development of social media platforms and the world of competitive mimetics. The irony here is that what had been completely displaced from learning and work was the social field itself. And so the algorithms merely capitalized on this by producing a false sense of the social—one that was built on the continuous flow of “information” that had been gutted of real social context.

The standardization and routinization of information opened the doors for AI and its Orwellian distortions. AI cannot be trained on any skills, cannot learn, and is in no sense “intelligent.” These terms have all been distorted to fit what AI is actually good at – storing, sorting, statistically computing and weighing information. The illusion of “intelligence” is particularly effective in the absence of real-life social context. As educators, as experts in training, learning, and intelligence, we need to out the illusion. Training is a word that applies to skills, and machines cannot master skills.  Learning is a word that applies to affect-laden values that are meaningful to the organism and serve several functions:

(1) assessing situations in the context of experience; (2) expanding perception of the environment to search for affordances; (3) reasoning from available affordances to actual possibilities within the context of choice and action that depend upon the degrees of freedom in the agent’s perception and skill-set; (4) fine-tuning goals in real time in relation to changing circumstances (context plus environment) and evaluative reflexivity.

“Intelligence” therefore, is the successful outcome of learning in these terms. The net result of intelligence is that both the agent and the environment i.e., other participating agents, learn from each other.

The illusion of machine “intelligence” depends upon the implementation of significant constraints that satisfy the machine’s needs and accommodates for their substantial weaknesses.

If AI is not intelligent, can’t be trained, and doesn’t learn, then why does it pose such a threat to humans? According to the philosopher of digital information, Luciano Floridi, AI represents a new kind of agency that is decoupled from intelligence. It is the kind of agency that the markets have— the agency of enforced compulsory social protocols. Let me explain.

Enforced compulsory social protocols, or ECSP for short, are social mechanisms which direct human agency in some directions and not others by two means:
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‍1) lowering the action threshold toward the designated direction and
‍2) raising the barriers for action in alternative directions.

For instance, each newly created financial instrument— from currency to credit, to credit cards, to Paypal and Venmo — lowers the threshold of purchasing; while central banks and regulatory systems (enforced by international law) erect barriers against alternative means. The system is obviously subject to failure and perturbations, but for most of us this means that we can never opt-out of the system in order to live.

We experience the lower thresholds for action as easier and more convenient, but are unaware of the costs that are  built into the system. With financial instruments, there is always a delta between the real owners of the instrument (the central banks) and the users. For example, the delta in using the dollar is the interest the banks assign to it — a debt obligation that grows with every dollar used. The same is true when banks transfer money to digital accounts— the thresholds are lower, but the delta only grows.

AI represents a more insidious ECSP on both fronts. It lowers the threshold for action to simple verbal commands. At his Stanford talk, Schmidt was actually giddy about these prospects. “Imagine just telling the AI to write all the code you need … no more complaining, snivelling coders to deal with!” This sounds to many like the power of God —”say it and thy will be done.” The machines will be designed to talk to us as if we were Gods, only to prop up the delusion. But, in the event of AI domination by a single party, the delta will also be huge — everything we think, do and say will have to be run through AI as a governing constraint, enforcing all human agency to machine protocols that are thereby made compulsory for people to live.  The magic trick is to deceive us that AI increases our agency, when in fact it steals it, because there is a secret agent in between our commands and their outcomes — the people who own and control the machines. Everyone else becomes a compulsory user. The real reason why people like Schmidt are so excited about AI domination, is because they can see that it will enable them to make the delta very, very, VERY large— gargantuan.

The bottom line is: AI has an alignment problem. It is not aligned with intelligence — biological or otherwise. It is not aligned with the geological and ecological terrain of the living planet. It is not aligned with the complex richness of the natural world. It is not aligned with human flourishing, freedom and equitable opportunities. In the face of these dystopic events, I am proposing that education be restored by a global council of educators who are not merely concerned that people are equitably schooled according to the needs and constraints of the economy of machines (aka, the Internet of Things), but insistent on the rights of persons to be educated toward the futures they would choose to live in, given sufficient degrees of freedom and adequate opportunities for participation.  To that end, I propose a countervailing revolution that insists on equal global investment — both in terms of financial and human resources — in studying and developing unexplored possibilities for biological intelligence that are naturally aligned with the values of the living world.  It would be a travesty of great proportions if these demands are not made.

~ Bonnitta Roy, Academic Director, The Divinity School

Our capacities for large-scale collective processes that 1) cultivate contextualized insight, 2) efficiently distribute insights within the collective, 3) upgrade shared values while developing individual adaptability across value systems, and 4) lead to agreed-upon civic activities that humans create together are maturing in the background while AI grows increasingly capable of controlling participants without their awareness. The ability to build collective intelligence and deploy it to shape the worlds we inhabit will either 1) require appropriate education that keeps human creativity, emotions, and contextualized knowledge upstream of AI and machine use, or 2) be informed by machines, leading to a loss of diversity and reducing us to mere means, creating worlds and skills that ultimately prioritize the accommodation of machine needs.

~ Corey Cleland, Executive Director, Endemic & The Divinity School