> To date computers (and AI in general) operate prevalently in an input-output mode. This is strikingly not the case for the human brain which works in a projec

> To date computers (and AI in general) operate prevalently in an input-output mode. This is strikingly not the case for the human brain which works in a projective – or predictive - mode constantly testing hypotheses (or pre-representations) on the world including itself (J.-P. Changeux, 1986; Friston et al., 2016; Pezzulo, Parr, Cisek, Clark, & Friston, 2024). This projective/predictive mode relies on the fact that the brain possesses an intrinsic - spontaneous – activity (Dehaene & Changeux, 2005). The earliest forms of animals do exhibit a nervous system where spontaneously active neurons can be recorded (e.g., jelly fish, hydra). Such spontaneous oscillators [...] > Last in agreement with our views, the active inference theory formalizes how autonomous learning agents (whether artificial or natural) shall be endowed with a spontaneous active motivation to explore and learn (Friston et al., 2016), which other studies confirmed to be sufficient for the emergence of complex behavior without the need for immediate rewards (https://arxiv.org/abs/2205.10316 https://arxiv.org/abs/2205.10316). ... What I take from this is that there needs to be something of a [negative] feedback loop in the AI for it to get to consciousness, and if we think about how that works in nature, that means we need several negative feedback loops, including the AI equivalent of various hormones and signaling agents. Think dopamine. Now, AI already has feedback. But I'm talking at a different layer. The AI's interaction with the world has to help drive and modulate what the AI seeks to do through that interaction. The AI needs motivation in the form of pain, pleasure, and instincts.