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)

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)