colin f lane

Kissinger, Schmidt, Huttenlocher on Generative AI: WSJ

Kissinger, Henry, et al. 'ChatGPT Heralds an Intellectual Revolution'. The Wall Street Journal, Saturday/Sunday, February 25-26, 2023.

Religious Faith Undermined

Generative artificial intelligence presents a philosophical and practical challenge on a scale not experienced since the beginning of the Enlightenment. The printing press enabled scholars to replicate each other’s findings quickly and share them. An unprecedented consolidation and spread of information generated the scientific method. What had been impenetrable became the starting point of accelerating query. The medieval interpretation of the world based on religious faith was progressively undermined. The depths of the universe could be explored until new limits of human understanding were reached.

Black Box

Sophisticated AI methods produce results without explaining why or how their process works. The GPT computer is prompted by a query from a human. The learning machine answers in literate text within seconds. It is able to do so because it has pregenerated representations of the vast data on which it was trained. Because the process by which it created those representations was developed by machine learning that reflects patterns and connections across vast amounts of text, the precise sources and reasons for any one representation’s particular features remain unknown. By what process the learning machine stores its knowledge, distills it and retrieves it remains similarly unknown. Whether that process will ever be discovered, the mystery associated with machine learning will challenge human cognition for the indefinite future.


AI’s capacities are not static but expand exponentially as the technology advances. Recently, the complexity of AI models has been doubling every few months. Therefore generative AI systems have capabilities that remain undisclosed even to their inventors. With each new AI system, they are building new capacities without understanding their origin or destination. As a result, our future now holds an entirely novel element of mystery, risk and surprise.

The biggest of these models are expensive to train—north of $1 billion per model. Once trained, thousands of computers work 24 hours a day to operate them. Operating a pretrained model is cheap compared with the training itself, and it requires only capital, rather than capital and computing skill. Still, paying for exclusive use of a large language model remains outside the bounds of most enterprises. These models’ developers are likely to sell subscriptions, so that a single model will serve the needs of many thousands of individuals and businesses. As a result, the number of very large language models in the next decade may be relatively constrained. Design and control of these models will be highly concentrated, even as their power to amplify human efforts and thought becomes much more diffuse.

Dialectic, Metacognition, Hermeneutics

It is urgent that we develop a sophisticated dialectic that empowers people to challenge the interactivity of generative AI, not merely to justify or explain AI’s answers but to interrogate them. With concerted skepticism, we should learn to probe the AI methodically and assess whether and to what degree its answers are worthy of confidence. This will require conscious mitigation of our unconscious biases, rigorous training and copious practice.

The question remains: Can we learn, quickly enough, to challenge rather than obey? Or will we in the end be obliged to submit? Are what we consider mistakes part of the deliberate design? What if an element of malice emerges in the AI?

Another key task is to reflect on which questions must be reserved for human thought and which may be risked on automated systems. Yet even with the development of enhanced skepticism and interrogatory skill, ChatGPT proves that the genie of generative technology is out of the bottle. We must be thoughtful in what we ask it.

Computers are needed to harness growing volumes of data. But cognitive limitations may keep humans from uncovering truths buried in the world’s information. ChatGPT possesses a capacity for analysis that is qualitatively different from that of the human mind. The future therefore implies a collaboration not only with a different kind of technical entity but with a different kind of reasoning—which may be rational without being reasonable, trustworthy in one sense but not in another. That dependency itself is likely to precipitate a transformation in metacognition and hermeneutics—the understanding of understanding—and in human perceptions of our role and function.

Mystic Religiosity, Power Concentration

The arrival of an unknowable and apparently omniscient instrument, capable of altering reality, may trigger a resurgence in mystic religiosity. The potential for group obedience to an authority whose reasoning is largely inaccessible to its subjects has been seen from time to time in the history of man, perhaps most dramatically and recently in the 20th-century subjugation of whole masses of humanity under the slogan of ideologies on both sides of the political spectrum. A third way of knowing the world may emerge, one that is neither human reason nor faith. What becomes of democracy in such a world?

Leadership is likely to concentrate in hands of the fewer people and institutions who control access to the limited number of machines capable of high-quality synthesis of reality. Because of the enormous cost of their processing power, the most effective machines within society may stay in the hands of a small subgroup domestically and in the control of a few superpowers internationally. After the transitional stage, older models will grow cheaper, and a diffusion of power through society and among states may commence.

A reinvigorated moral and strategic leadership will be essential. Without guiding principles, humanity runs the risk of domination or anarchy, unconstrained authority or nihilistic freedom. The need for relating major societal change to ethical justifications and novel visions for the future will appear in a new form. If the maxims put forth by ChatGPT are not translated into a cognizably human endeavor, alienation of society and even revolution may become likely.

Learning New Paradigms

Humans will have to learn new restraint. Problems we pose to an AI system need to be understood at a responsible level of generality and conclusiveness. Strong cultural norms, rather than legal enforcement, will be necessary to contain our societal reliance on machines as arbiters of reality. We will reassert our humanity by ensuring that machines remain objects.

Education in particular will need to adapt. A dialectical pedagogy that uses generative AI may enable speedier and more-individualized learning than has been possible in the past. Teachers should teach new skills, including responsible modes of human-machine interlocution. Fundamentally, our educational and professional systems must preserve a vision of humans as moral, psychological and strategic creatures uniquely capable of rendering holistic judgments.

Machines will evolve far faster than our genes will, causing domestic dislocation and international divergence. We must respond with commensurate alacrity, particularly in philosophy and conceptualism, nationally and globally. Global harmonization will need to emerge either by perception or by catastrophe, as Immanuel Kant predicted three centuries ago.