The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...) the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

The current assumptions of knowledge acquisition brought about the crisis in the reproducibility of experiments. A complementary perspective should account for the specific causality characteristic of life by integrating past, present, and future. A “second Cartesian revolution,” informed by and in awareness of anticipatory processes, should result in scientific methods that transcend the theology of determinism and reductionism. In our days, science, itself an expression of anticipatory activity, makes possible alternative understandings of reality and its dynamics. For this purpose, the (...) study advances G-complexity for defining and comparing decidable and undecidable knowledge. AI and related computational expressions of knowledge could benefit from the awareness of what distinguishes the dynamics of life from any other expressions of change. (shrink)

In his article “Issues of Pragmaticism” published in 1905, in The Monist, Charles S. Peirce complains that “Logicians have been at fault in giving Vagueness the go-by, so far as not even to analyze it.” That same year, occupying himself with the consequences of “Critical commonsensism,” he affirmed, “I have worked out the logic of vagueness with something like completeness,” a statement that causes the majority of the commentators on his work, including the editors of the Collected Papers to ask (...) where this logic is to be found. The fever for finding Peirce's manuscripts is fed by the hope of some researchers of discovering the logic of vagueness, a hope that has grown since Carolyn Eisele's publication of his mathematical works. Others - and I count myself among them - believe that in reality this is a matter of something already known. That is, they interpret the affirmation ending the paragraph of reproach addressed to logicians, “The present writer has done his best to work out the Stechiology, Critic, and Methodeutik of the subject,” as a tripartite semiotic of the vague, still limited, according to Peirce's older works, to symbols, that is, to the signs of natural language examined from the perspective of logic. (shrink)

Justified by spectacular achievements facilitated through applied deep learning methodology, the “Everything is possible” view dominates this new hour in the “boom and bust” curve of AI performance. The optimistic view collides head on with the “It is not possible”—ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI—now (...) or at some moment in the future? AI or not, medicine should incorporate the anticipation perspective in providing care. (shrink)

The global scale of contemporary human life and activity places us in a generic conflict between our identity as individuals and our awareness of the individual's global responsibilities. We face a conflict between the reassuring condition of living according to tradition and the unsettling condition of living in the "new continent" where human self-constitution has global implications. The cohesive set of shared traditional values and ideals is effectively overwritten by the possibility and necessity of innovation in response to global demands. (...) Many paths between the ideal of universahsm and the individual's instantiation in tradition have been contemplated by philosophers and social activists. Given the current dynamics of change, most of these projects, while of good intendon, are nevertheless profoundly naïve in their fundamental assumptions. We are at a crossroads in which we experience a condition of instability that precedes any fundamental bifurcation. But in contrast to the dominant view that instability is by definition negative, we are witnessing an extraordinary explosion of creativity. (shrink)

According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial “brains.” Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General (...) Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance. (shrink)