One thing that might be missing is, simply, knowledge. We have many questions about how real biological systems work that remain unanswered. Of course, complete knowledge of natural systems is not a prerequisite for building nearly functionally equivalent systems (see e.g., flight). However, I believe our understanding of natural cognitive systems will continue to play an important role in deciding what kinds of algorithms are worth pursuing as we build more sophisticated artificial agents.
Fortunately, on this front there have been two announcements of significant resources dedicated to improving our knowledge of the brain, which I mentioned in the introduction. One is from the EU and the other from the US. Each are investing over $1 billion in generating the kind of data needed to fill gaps in our understanding of how brains function. The EU’s Human Brain Project (HBP) includes two central subprojects aimed at gathering mouse and human brain data to complement the large-scale models being built within the project. These subprojects will focus on genetic, cellular, vascular, and overall organizational data to complement the large-scale projects of this type already available (such as the Allen Brain Atlas, http://www.brain-map.org/). One central goal of these subprojects is to clarify the relationship between the mouse (which is highly experimentally accessible) and human subjects.
The American “brain research through advancing innovative neurotechnologies” (BRAIN) initiative is even more directly focused on large-scale gathering of neural data. Its purpose is to accelerate technologies to provide large-scale dynamic information about the brain that demonstrates how both single runs and larger neural circuits operate. Its explicit goal is to “fill major gaps in our current knowledge” (http://www.nih.gov/science/brain/). It is a natural complement to the human connectome project, which has been mapping the structure of the human brain on a large-scale (http://www.humanconnectomeproject.org/). Even though it is not yet clear exactly what information will be provided by the BRAIN intiative, it is clear that significant resources are being put into developing technologies that draw on nanoscience, informatics, engineering, and other fields to measure the brain at a level of detail and scale not previously possible.
While both of these projects are just over a year old, they have both garnered international attention and been rewarded with sufficient funding to ensure a good measure of success. Consequently, it is likely that as we build more sophisticated models of brain function, and as we discover where our greatest areas of ignorance lay, we will be able to turn to the methods developed by these projects to rapidly gain critical information and continue improving our models. In short, I believe that there is a confluence of technological, theoretical, and empirical developments that will allow for bootstrapping detailed functional models of the brain. It is precisely these kinds of models that I expect will lead to the most convincing embodiments of artificial cognition that we have ever seen—I am even willing to suggest that their sophistication will rival those of natural cognitive systems.