I tend to disagree with the idea that we don't have the computational power equivalent to the brain. The reasoning is that, yes, all the things you say are true about having billions of slow neurons, but that makes the assumption that their only fault is that they are slow. I don't necessarily agree with that assumption. The evolutionary process doesn't find true maxima, it finds local maxima. The key is that the neuron is good enough for the task of intelligence and planning and pattern detection. However, the architecture is most likely highly inefficient. The reason why they are so good comparatively is because we just kept adding neurons, either by increasing the size of the frontal cortex and by the folding, so as to jam more in there.

That is not an architectural design per se, and I highly doubt that it is optimal. Rather, it is good enough. Now, the Von Neumann computer architecture is of course lacking. Parallel computing as we know it works, but it's a total nightmare, and hardly competes with the incredible parallelism in nature. But I do think that there is sufficient power now to really feasibly compete with the brain, intelligence wise.

All this with a grain of salt. I have been reading stuff in the weird corners of AI research. I think this article is fundamentally a straw man. (I believe that...) Deep learning and probabilistic machine learning are fundamentally flawed for strong AI. Jeff Hawkins is a well respected AI researcher that I think seems to agree. Another problem is of course that these are all trained in a supervised fashion. Google does well because they have huge tagged data sets. However, the brain doesn't work by training on tagged data sets, it can learn on it's own, unsupervised. So long as we keep pointing mainstream AI down the supervised statistical machine learning path, we will always be far away from strong AI.