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The Butterfly Effect

for the idea in all of us

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Fluid Intelligence

TEDx Talk

If you didn’t get a chance to see my TED talk live, the video has just been produced and uploaded onto the TEDx channel on Youtube (below).

The talk is about some of my work in artificial intelligence: specifically the results we’ve observed in our research in synthetic neurointerfaces. Our goal was to functionally and synthetically model the human neocortical columns in an artificial intelligence to give a more differentiable insight into the cognitive behaviors we, as humans, exhibit on a daily basis.

If you would like to know more, I have published the working paper here.

Please let me know what you all think in the comments section below or on Youtube, I would love all the feedback I can get!

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Coming Soon: Synthetic Neurointerfaces

I’m getting ready to release my work in persisting synthetic neurointerfaces in unbounded spatial networks. I truly believe that the use of computational tools such as this can be used to study the structure of intelligent computation in high-dimensional neural systems. What I tried to emulate in this project was a neuron by neuron representation of some basic cognitive functions by persisting a memory field in which self organizing neocortical hypercolumns could be functionally represented. The project was inspired by biological neural dynamical systems and foundationally rooted in some of the brilliant work Google’s Deep Mind project has been doing.  Before I publish any results, I would like to give a special thanks to my mentor and long time friend, Dr. Celia Rhodes Davis. Also, I would like to especially thank the Stanford Department of Computational Neuroscience  (Center for Brain, Mind & Computation) for functioning as an advisory board throughout my independent research and functioning as a sound logic board for general guidance.

Below is a problem definition, goals, and a small sneak peek regarding the immediate potential, and execution of my project:

Introduction

The interface between the neuroanatomical activation of neocortical hypercolumns and their expressive function is a realm largely unobserved, due to the inability to efficiently and ethically study causational relationships between previously exclusively observed phenomenon. The field of general neuroscience explores the anatomical significance of cortical portions of the brain, extending anatomy as a means to explain the persistence of various nervous and physically expressive systems. Psychological approaches focus purely on \textit{expressive} behaviors as means to extend, with greater fidelity, the existence and constancy of the brain-mind interface. The interface between the anatomical realms of the mind and their expressive behaviors is a field widely unexplored, with surgeries such as the lobotomy and other controversial, experimental, and life-threatening procedures at the forefront of such study. However, the understanding of these neurological interfaces has potential to function as a window into the neural circuitry of mental illnesses, opening the door for cures and an ultimately more complete understanding of our brain.

Goals

We propose a method to simulate unbounded memory fields upon which recall functions can be parameterized. This model will be able to simulate cortical functions of the amygdala in its reaction to various, unfiltered stimuli. An observer network will be parallely created to analyze geometric anomalies in the neuroanatomical interface in memory recall functions, and extend equivalences between recall function parameters and memory recall gradients. This enables it to extend hypothesis to neuroanatomical functions.

Fluid Intelligence: Introduction

 

Fluid intelligence: the capacity to think logically and solve problems in novel situations, independent of acquired knowledge

Psychology has found the basis of fluid intelligence in the juxtaposition of layered memory and application as means to essentially “connect two fluid ideas with an an abstractly analogous property”. Such a mathematical design would have to be able to therefore derive temporal relationships with weighted bonds between two coherently disparate concepts through the means of similar properties. These properties within node types will have to be self-defined and self-propagated within idea types.

Why?

In a pursuit towards a truly dynamic artificial intelligence, it is necessary to establish a recurrent method to decipher the presence of concrete yet abstract entities (“ideas”) independent of a related and coherent topic set.
A considerable amount of work venturing into this field has culminated in the prevalence of statistical methods to extract probabilistic models dependent on large amounts of unstructured data. These Bayesian data analytic techniques often result in an understanding superficial in the context of a true relational understanding. Furthermore, this “bag-of-words” approach when looking at amounts of unstructured data (quantifiable by correct relationships derived between the idea nodes) often relate to a single dimensional understanding of the topics at hand. Traditionally, when these topics are transformed, it is difficult to extract hierarchy and queryable relations using matrix transformations from a derived data set.

The project that I will be describing in the subsequent posts is an effort to change the approach from which dynamic fluid intelligence is derived, finding a backbone in streaming big data. Ideally, this model would be able to take a layered, multi-dimensional approach to autonomous identification of properties of dynamically changing ideas from portions of said data set. It would also be able to find types of relationships, ultimately deriving a set of previously undefined relational schemas through unsupervised machine learning techniques that would ultimately allow for a queryable graph with properties and nodes initially undefined.

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