Figuring Out the Algorithms of Intelligence

Nathan R. Wilson, Ph.D., CTO and Co-Founder of Nara Logics, KDnuggets News, June 2016

Data science, and knowledge discovery, are among the most “brain-like” operations that a company does, and its practitioners have a unique vantage point into the utility of artificial intelligence. With the emergence of deep learning now upending AI, it is worth exploring how this powerful class of techniques relates to knowledge and understanding, using our own brain as a gold standard for how information is stored for synthesis and insight.

In Search of the Master Algorithm

Is there a general “process” by which data can be turned into knowledge, or a “rule” for learning rules? Most neuroscientists think so, and so do deep learning researchers. They comprise two search parties, looking for the self-organizing logic that is the magic key for turning data into knowledge. But both agree that there is something special about the nature of information, passed through a general structure intended to dynamically filter for veracity and novelty. Such a possibility makes it feasible to envision a true “brain” for our data, and thus knowledge at the organizational level. What will our data brain look like?

Inspired by Biology – Data Storage Will Start to Reflect the Natural World

The way we store and interact with data is already changing, and becoming more like the “connectionist” models that, after decades of falling in and out of favor in machine learning, may at last be here to stay, thus converging with neuroscience and other dominant models of information processing in the natural world (genetics, ecology and systematics, immunology, etc). Data in our machine systems are still stored in rows and columns but it is their relations to other data (which increasingly comes with weights), that define the value of each quantum. New tools, storage and programming methodologies are arising that make it possible for data to be readily connected both through better curation and recirculating automation. The dynamism that results looks less like a fixed circuit and more like an organic system.

From an evolutionary perspective, the relational structure of databases discovered in the 1970s became the early scaffold for structuring and connecting data, a true breakthrough that now underpins data storage in every industry, and whose value is only now starting to be truly appreciated. The difference between now and the future is that these connections are still binary (pointers to other tables) whereas brain structures, including those produced by deep learning, are “associational” – learning the strength of relatedness between stored concepts.

In the future, growing data trees of associations will be increasingly fused (like the unified “data lakes” that are evolving in advanced organizations). Data records will not be duplicated into many different places and fragmented, but rather different places will connect to the same record in different ways. This seems to be how biology has mastered information, with your brain maintaining a master record for each concept (such as the “Halle Berry” cells in one famous study), and this record is accessed and retrieved in many different ways from completely different brain areas (for example, seeing a picture of Halle Berry through the eyes excites the same cells as hearing the spoken name through the ears). Unified representations of course, once realized, have advantages for efficacy and maintainability.

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