**9. Concept space of deep meta-learning**

**Figure 5** shows the concept space of deep meta-learning. Eq. (4) represents the meta-learning process [8].

$$\min\_{\theta, \theta\_{\mathcal{O}}, \theta\_{\mathcal{O}}} \mathbb{E}\_{T \sim \mathcal{P}(T), \{\mathbf{x}, \mathbf{y}\} \sim \mathbb{D}} \left[ \mathbf{J} \Big( \mathcal{L}\_{T}(\theta\_{\mathcal{M}} \theta\_{\mathcal{G}}), \mathcal{L}\_{\{\mathbf{x}, \mathbf{y}\}}(\theta\_{\mathcal{D}}, \theta\_{\mathcal{G}}) \Big) \right], \tag{4}$$

where *θ*G, *θ*<sup>M</sup> and *θ*<sup>D</sup> are the parameters of deep meta-learning. We assume that the top level mental (neural) energy is available for C–H–C theory of intelligence and a crude approximation of C–H–C theory to mimic human intelligence can be achieved through deep-meta-learning approach. In deep-metal learning approach we crudely approximate to integrate crystalized intelligence ð Þ *Gc* into fluid intelligence *G <sup>f</sup>* � �*:*

**Figure 5.** *Concept space of deep meta-learning.*
