Babies are born with a fully functioning image recognition stack complete with a segmentation model, facial recognition, gaze estimator, motion tracker and more. Likewise, most of the language model is pre-trained and language acquisition is in large part a pruning process to coalesce unused phonemes, specialize general syntax rules etc. Compare with other animals that lack such a pre-trained model - no matter how much you fine-tune a dog, it's not going to recite Shakespeare. Several other subsystems come online in the first few years with or without training; one example that humans share with other great apes is universal gesture production and recognition models. You can stretch out your arm towards just about any human or chimpanzee on the planet and motion your hand towards your chest and they will understand that you want them to come over. Babies also ship with a highly sophisticated stereophonic audio source segmentation model that can easily isolate speaking voices from background noise. Even when you limit yourself to just I/O related functions, the list goes on from reflexively blinking in response to rapidly approaching objects to complicated balance sensor fusion.
Can we do better than evolution? Probably; evolution is a fairly brute force search approach and we are pretty clever monkeys. After all, we have made multiple orders of magnitude improvements in the state of the art of computations per watt in just a few decades. Can we do MUCH better than evolution at finding efficient intelligences? Maybe, maybe not.
So six billion bits since two bits can represent four values. Base pairs and bases are effectively the same because (from the link) "the identity of one of the bases in the pair determines the other member of the pair."